CN114201328A - Fault processing method and device based on artificial intelligence, electronic equipment and medium - Google Patents
Fault processing method and device based on artificial intelligence, electronic equipment and medium Download PDFInfo
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
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Abstract
The application relates to the technical field of artificial intelligence, and provides a fault processing method, a fault processing device, electronic equipment and a medium based on artificial intelligence, wherein the method comprises the following steps: acquiring error report sentences corresponding to a target system based on the buried points; analyzing the error-reporting statement, and determining the error-reporting items corresponding to the error-reporting statement; extracting a plurality of fault processing methods from a fault map based on the fault reporting items; determining a target user corresponding to the error item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error item based on the fault record; inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method; and determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method, and performing fault processing according to the target fault processing method. The method and the device improve the efficiency of fault handling.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a fault handling method and device based on artificial intelligence, electronic equipment and a medium.
Background
After the system runs for a long time or performs some irregular operations, various problems are easy to occur, the alarm amount for the current system operation and maintenance is very huge, and the problems occurring each time may be different and the most are alarm processing of the same type. However, each time a system problem occurs, a system administrator or operation and maintenance personnel is required to manually repair the system, and the processing efficiency of the system problem is low due to shortage of human resources. Therefore, how to realize the automatic processing of the system problem and improve the processing efficiency of the system problem becomes a technical problem to be solved urgently.
Disclosure of Invention
In view of the above, it is necessary to provide a fault handling method, device, electronic device and medium based on artificial intelligence, so as to improve the efficiency of handling system faults.
In a first aspect, the present application provides a fault handling method based on artificial intelligence, the method including:
acquiring an error report corresponding to a target system based on a pre-implanted buried point;
analyzing the error-reporting statement, and determining an error-reporting item corresponding to the error-reporting statement;
extracting a plurality of fault processing methods from a fault map constructed in advance based on the error items;
determining a target user corresponding to the error item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error item based on the fault record;
inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method;
and determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method, and performing fault processing according to the target fault processing method.
According to an optional embodiment of the present application, the determining the target user corresponding to the error event and the fault record corresponding to the target user includes:
positioning and monitoring a target moment corresponding to the error report;
acquiring a user identifier corresponding to the target system at the target moment, and determining a target user according to the user identifier;
and acquiring a fault record corresponding to the target user from a fault processing log library based on the target user.
According to an optional embodiment of the present application, the training process of the first random forest model comprises:
obtaining characteristic values corresponding to a plurality of historical error reporting items and a plurality of historical fault processing methods as a training data set;
randomly extracting M training sample sets from the training data set;
learning the M training sample sets to generate M decision trees, randomly extracting N characteristic variables from the characteristic variables of the M decision trees in the generation process of the M decision trees, branching each decision tree by utilizing an optimal splitting mode on the N characteristic variables, and setting an N value as a constant in the formation process of a random forest model;
and when the branch rules are met, stopping the generation process of the M decision trees to obtain the first random forest model.
According to an optional embodiment of the present application, the generating process of the M decision trees includes:
randomly adding noise interference to each of the characteristic variables;
determining the importance of each characteristic variable according to the degree of accuracy reduction;
and optimizing the generation process of the M decision trees according to the importance.
According to an optional embodiment of the present application, the constructing of the fault map comprises:
acquiring a plurality of historical fault processing records;
extracting fault events in the plurality of historical fault handling records;
clustering the fault events to obtain at least one fault category and a clustering event corresponding to each fault category;
calculating the incidence relation between the clustering events;
and constructing a fault map according to the clustering events and the incidence relation.
According to an optional embodiment of the present application, the extracting the fault events in the plurality of historical fault handling records comprises:
performing word segmentation processing on each historical fault processing record to obtain information word segmentation;
determining a target part of speech of the information participle in an information sentence where the information participle is located, and determining the information participle with the target part of speech as a preset part of speech as a participle to be selected;
matching the to-be-selected participles with template faults in a preset fault template library, and determining the to-be-selected participles successfully matched with the template faults as fault events.
According to an optional embodiment of the application, the method further comprises:
setting a site-embedded Service (SDK) in the determined target system;
exposing a data buried point service interface to the outside through the buried point service SDK;
and when the error reporting signal of the target system is detected, calling the data buried point service interface to acquire an error reporting statement corresponding to the error reporting signal.
In a second aspect, the present application provides an artificial intelligence based fault handling apparatus, the apparatus comprising:
the error reporting acquisition module is used for acquiring an error reporting statement corresponding to the target system based on the pre-implanted buried point;
the statement analysis module is used for analyzing the error-reporting statement and determining an error-reporting item corresponding to the error-reporting statement;
the map query module is used for extracting a plurality of fault processing methods from a fault map constructed in advance based on the error report items;
the characteristic determining module is used for determining a target user corresponding to the error item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error item based on the fault record;
the score prediction module is used for inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method;
and the method determining module is used for determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method and performing fault processing according to the target fault processing method.
In a third aspect, the present application provides an electronic device comprising a processor and a memory, the processor being configured to implement the artificial intelligence based fault handling method when executing a computer program stored in the memory.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the artificial intelligence based fault handling method.
To sum up, the fault handling method, the fault handling device, the electronic device and the medium based on artificial intelligence according to the present application can obtain an error report statement appearing in a target system in time through a buried point, determine an error report item currently corresponding to the target system based on the error report statement, and extract a plurality of fault handling methods in a fault map constructed in advance based on the error report item, wherein the plurality of fault handling methods are associated with the error report item and can be used for solving a fault corresponding to the error report item; determining a target user corresponding to the error-reporting item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error-reporting item based on the fault record, wherein the characteristic is used for indicating the occurrence frequency of the error-reporting item, and according to the characteristic value corresponding to the error-reporting item, a fault processing method using the current error-reporting item can be determined, so that the accuracy of determining the fault processing method is improved; and then inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method, wherein the matching values are used for determining a target fault processing method in the fault processing methods, so that the accuracy of determining the fault processing method is further improved, and the fault processing is performed based on the target fault processing method, so that the active processing of the fault is realized, and the efficiency of processing the system fault is improved.
Drawings
Fig. 1 is a flowchart of a fault handling method based on artificial intelligence according to an embodiment of the present application.
Fig. 2 is a block diagram of an artificial intelligence based fault handling apparatus according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present application can be more clearly understood, a detailed description of the present application will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing an example in an alternative implementation and is not intended to be limiting of the present application.
The fault processing method based on the artificial intelligence is executed by the electronic equipment, and accordingly the fault processing device based on the artificial intelligence runs in the electronic equipment. The electronic device may include a mobile phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, a wearable device, and the like.
According to the method and the device, the error report sentences corresponding to the target system can be processed based on the artificial intelligence technology, the fault processing method corresponding to the error report sentences is obtained, and the system fault processing efficiency is improved. 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.
Example one
Fig. 1 is a flowchart of a fault handling method based on artificial intelligence according to an embodiment of the present application. The fault handling method based on artificial intelligence specifically comprises the following steps, and the sequence of the steps in the flowchart can be changed and some steps can be omitted according to different requirements.
And S11, acquiring an error report corresponding to the target system based on the pre-implanted buried points.
Wherein the target system may be a company business system, a company platform system, a documentation system, an internal book repository system, and so on. The error report is used for determining a fault occurring when a user accesses a target system, and may include an SQL statement corresponding to an error report signal occurring when the user accesses the target system through an electronic device, where the error report signal may be displayed on a screen panel of the electronic device accessing the target system.
The method of embedding points may be adopted in advance with the help of an SDK (software development kit) of a third party, for example, a probe or a bytecode-enhanced method of embedding points is implanted in the target system in advance, behavior data of a user accessing the target system is collected, and then an error report corresponding to the target system is obtained.
In an optional embodiment, the method further comprises:
setting a site-embedded Service (SDK) in the determined target system;
exposing a data buried point service interface to the outside through the buried point service SDK;
and when the error reporting signal of the target system is detected, calling the data buried point service interface to acquire an error reporting statement corresponding to the error reporting signal.
Monitoring the access behavior of the user through the target system, acquiring behavior data of the user in the access process, for example, monitoring that an error signal is triggered when the user accesses the target system, calling a data embedded point service interface, and returning an error statement corresponding to the error signal to the electronic equipment.
In an optional implementation manner, a point burying service SDK is set in the background application service of the electronic device to capture an error report occurring in the process of accessing the target system by the user, so that compared with embedding a point in the target system, a risk of behavior data leakage can be avoided, and when a third-party SDK needs to be upgraded, the corresponding application needs to be upgraded.
And S12, analyzing the error statement and determining the error item corresponding to the error statement.
An Error statement is a regular expression composed of a plurality of character strings, the Error statement is analyzed, preset keywords such as Warn, Error, Fatal and the like in the Error statement are extracted, and then the Error statement is segmented according to the preset keywords, so that one or more Error keywords contained in the Error statement can be determined. Alternatively, the error statement may be analyzed based on a mapping relationship between the error statement and the error item in a preset statement item mapping table, so as to determine the error item corresponding to the error statement.
And S13, extracting a plurality of fault processing methods from a fault map constructed in advance based on the error report.
In an alternative embodiment, the constructing of the fault map comprises:
acquiring a plurality of historical fault processing records;
extracting fault events in the plurality of historical fault handling records;
clustering the fault events to obtain at least one fault category and a clustering event corresponding to each fault category;
calculating the incidence relation between the clustering events;
and constructing a fault map according to the clustering events and the incidence relation.
The fault event is a time corresponding to a fault solved in the historical fault handling record, and may include, for example, an access failure, a failure to jump to a target page, a data loading exception, and the like.
The fault event may be clustered based on preset clustering conditions, and at least one fault category is determined, where a fault category refers to a category corresponding to a similar fault, such as access exception. The clustering conditions are used for dividing the fault events, the fault events meeting the same clustering conditions belong to the same cluster, namely belong to the same fault category, and the clustering conditions can be set according to actual conditions without any limitation.
The clustering events refer to similar faults, and if a plurality of fault events correspond to the same fault category, the plurality of fault events are clustering events corresponding to the fault category. A fault map corresponding to a fault category. And the map nodes in the fault map represent clustering events (fault events), one map node corresponds to one clustering event, and the fault processing method of the clustering event corresponding to the map node can be called through the map nodes. Connecting lines in the fault map represent a logical relationship between two clustering events, and different connecting lines represent that the two nodes have different relevance sizes, for example, a longer connecting line represents a smaller relevance, and a shorter connecting line represents a larger relevance.
In an optional embodiment, the extracting, based on the error event, a plurality of fault processing methods in a pre-constructed fault map includes: determining a fault type corresponding to the fault reporting item, determining a fault map corresponding to the fault type, determining map nodes corresponding to the fault reporting item in the fault map, acquiring at least one associated node corresponding to the map nodes, and acquiring a plurality of fault processing methods based on the map nodes and the at least one associated node corresponding to the map nodes.
The map node corresponds to a fault processing method, and each associated node in the at least one associated node corresponds to a fault processing method.
In an alternative embodiment, said extracting fault events from said plurality of historical fault handling records comprises:
performing word segmentation processing on each historical fault processing record to obtain information word segmentation;
determining a target part of speech of the information participle in an information sentence where the information participle is located, and determining the information participle with the target part of speech as a preset part of speech as a participle to be selected;
matching the to-be-selected participles with template faults in a preset fault template library, and determining the to-be-selected participles successfully matched with the template faults as fault events.
And performing word segmentation processing on each sentence of information sentence in each historical fault processing record to obtain information word segmentation. The target part of speech refers to a role that the information participle plays in the information sentence, for example, the target part of speech is an object, and the target part of speech is a subject. The preset part of speech is determined after being analyzed according to big data, the preset part of speech can be set as a subject, and the specific determination mode of the preset part of speech is not explained. For example, the preset part of speech may be set as an object, the information participle is abnormal for data loading, and the information sentence corresponding to the information participle is: the fault is data loading abnormity, and the data loading abnormity is a preset part of speech, so that the data loading abnormity is determined as a word to be selected.
The preset fault template library stores a plurality of template faults, a user can perform operations such as template addition, template deletion, template modification and the like in the preset fault template library according to experience, and the template faults in the preset fault template library can be faults which can be automatically processed.
And S14, determining a target user corresponding to the error event and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error event based on the fault record.
One error item can correspond to a plurality of different fault processing methods, and the fault processing method can be determined according to the occurrence frequency of the error item, for example, if the occurrence frequency of the error item is more, a mode of more thorough processing but longer time consumption can be used; if the number of times of error reporting is less, a mode with simpler processing and shorter time consumption can be used. The times of error items reported by different users are different, so that the times of error items reported by the user can be determined according to the target user and the fault record corresponding to the target user, and the accuracy of determining the times of error items is improved. The feature value corresponding to the error item can be determined according to the number of times of occurrence of the error item, for example, the more the number of times of occurrence of an error item is, the larger the feature value corresponding to the error item is; the less the number of occurrences of an error event, the smaller the eigenvalue corresponding to the error event. The mapping relationship between the occurrence frequency of the error item and the characteristic value can be set according to the actual situation.
In an optional embodiment, the determining the target user corresponding to the error event and the fault record corresponding to the target user includes:
positioning and monitoring a target moment corresponding to the error report;
acquiring a user identifier corresponding to the target system at the target moment, and determining a target user according to the user identifier;
and acquiring a fault record corresponding to the target user from a fault processing log library based on the target user.
The target time is the time corresponding to the error-reporting statement when the error-reporting statement appears. The target user is a target system user with an error report sentence at present. The fault processing log library stores relevant log information in the fault processing process, wherein the relevant log information comprises fault records corresponding to each target system user.
The log information can be accurately acquired through the target time and the task identifier, so that the response result can be accurately acquired.
And S15, inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method.
The first random forest model is a machine learning model which is trained in advance, the characteristic value corresponding to the error reporting item and the plurality of fault processing methods are used as input of the first random forest model, the matching value corresponding to each fault processing method is predicted and output through the first random forest model, the matching value is used for representing the suitability degree of the fault processing method and the error reporting implementation, and if the matching value of one fault processing method is higher, the fault processing method is more suitable for processing the error reporting item.
In an optional embodiment, the training process of the first random forest model comprises:
obtaining characteristic values corresponding to a plurality of historical error reporting items and a plurality of historical fault processing methods as a training data set;
randomly extracting M training sample sets from the training data set;
learning the M training sample sets to generate M decision trees, randomly extracting N characteristic variables from the characteristic variables of the M decision trees in the generation process of the M decision trees, branching each decision tree by utilizing an optimal splitting mode on the N characteristic variables, and setting an N value as a constant in the formation process of a random forest model;
and when the branch rules are met, stopping the generation process of the M decision trees to obtain the first random forest model.
The characteristic value corresponding to the historical error reporting event is the characteristic value corresponding to the historical error reporting event corresponding to the time period before the preset time period. The characteristic value corresponding to the historical error event in the preset time period can be determined based on the fault record.
The historical fault handling method may query for fault record determinations over a preset time period.
Random sampling can be carried out by adopting a self-service method or a Bootstrap method, random sampling is carried out in a replacement mode, a training sample set is formed by extracted training data sets (M), a verification sample set is formed by training data sets which are not extracted (except for M parts in the training data sets), then decision trees are constructed for all training samples in the training sample set one by one, and M decision trees are constructed by M training sample sets.
In the process of growing the decision tree, each node randomly extracts N characteristic variables from all the characteristic variables to serve as the to-be-selected characteristics of the current node splitting, and branch growing is carried out on the to-be-selected characteristics without pruning. Repeating the steps to enable the decision tree to continue branching and growing until the branching rule is met and the growth is stopped, and storing M decision trees. And establishing a random forest model according to the stored M decision trees, and calculating an average value of the results of the M decision trees to obtain a final prediction result, wherein the values of M and N can be determined according to actual conditions, and are not limited herein.
In an optional embodiment, the generating of the M decision trees includes:
randomly adding noise interference to each of the characteristic variables;
determining the importance of each characteristic variable according to the degree of accuracy reduction;
and optimizing the generation process of the M decision trees according to the importance.
Different noise interferences can be added to different characteristic variables, or the same noise interference can be added to all the characteristic variables, for example, gaussian noise is added.
Firstly, a verification sample set is used for verifying a first prediction accuracy of the random forest model, then a characteristic variable is randomly selected from N characteristic variables each time to add noise interference, and then a verification sample set is used for verifying a second prediction accuracy of the random forest model, so that the accuracy reduction degree is obtained according to the difference value of the first prediction accuracy and the second prediction accuracy. After noise interference is added to each of the N characteristic variables in sequence, N first prediction accuracies can be obtained, thereby obtaining N accuracy reduction degrees. And sequencing the N accuracy reduction degrees from large to small or from small to large, and determining the importance of each characteristic variable according to a sequencing result.
When noise interference is added into a certain characteristic variable, the greater the accuracy of the random forest model is reduced, which indicates that the greater the influence degree of the characteristic variable on the random forest model is, the higher the importance of the characteristic variable is. When noise interference is added into a certain characteristic variable, the smaller the accuracy of the random forest model is reduced, which shows that the smaller the influence degree of the characteristic variable on the random forest model is, the lower the importance degree of the characteristic variable is.
After the importance of the feature variables is determined, importance weights can be added according to the importance in the process of splitting the feature variables, so that the optimization of the generation process of the M decision trees is realized.
According to the optional implementation mode, the importance of the variable characteristics is found out, and the decision tree generation process is optimized according to the importance, so that the prediction accuracy of the random forest model is improved.
And S16, determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method, and performing fault processing according to the target fault processing method.
For example, a fault handling method with the largest matching value among the plurality of fault handling methods may be determined as a target fault handling method, and the control electronic device may solve a fault corresponding to a current fault report item of the target system according to the target fault handling method.
According to the fault processing method based on artificial intelligence, error-reporting sentences appearing in a target system can be obtained in time through embedded points, error-reporting items corresponding to the target system at present are determined based on the error-reporting sentences, and a plurality of fault processing methods are extracted from a fault map constructed in advance based on the error-reporting items, are associated with the error-reporting items and can be used for solving faults corresponding to the error-reporting items; determining a target user corresponding to the error-reporting item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error-reporting item based on the fault record, wherein the characteristic is used for indicating the occurrence frequency of the error-reporting item, and according to the characteristic value corresponding to the error-reporting item, a fault processing method using the current error-reporting item can be determined, so that the accuracy of determining the fault processing method is improved; and then inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method, wherein the matching values are used for determining a target fault processing method in the fault processing methods, so that the accuracy of determining the fault processing method is further improved, and the fault processing is performed based on the target fault processing method, so that the active processing of the fault is realized, and the efficiency of processing the system fault is improved.
Example two
Fig. 2 is a block diagram of an artificial intelligence based fault handling apparatus according to a second embodiment of the present invention.
In some embodiments, the artificial intelligence based fault handling apparatus 20 may include a plurality of functional modules comprised of computer program segments. The computer programs of the respective program segments in the artificial intelligence based fault handling apparatus 20 may be stored in a memory of an electronic device and executed by at least one processor to perform (see detailed description of fig. 1) the functions of the artificial intelligence based fault handling method.
In this embodiment, the artificial intelligence based fault handling apparatus 20 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: the system comprises an error reporting acquisition module 201, a statement analysis module 202, a map query module 203, a feature determination module 204, a score prediction module 205 and a method determination module 206. A module as referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in a memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
An error reporting acquiring module 201, configured to acquire an error reporting statement corresponding to the target system based on a pre-implanted embedded point.
Wherein the target system may be a company business system, a company platform system, a documentation system, an internal book repository system, and so on. The error report is used for determining a fault occurring when a user accesses a target system, and may include an SQL statement corresponding to an error report signal occurring when the user accesses the target system through an electronic device, where the error report signal may be displayed on a screen panel of the electronic device accessing the target system.
The method of embedding points may be adopted in advance with the help of an SDK (software development kit) of a third party, for example, a probe or a bytecode-enhanced method of embedding points is implanted in the target system in advance, behavior data of a user accessing the target system is collected, and then an error report corresponding to the target system is obtained.
In an optional implementation, the error reporting module 201 is further configured to:
setting a site-embedded Service (SDK) in the determined target system;
exposing a data buried point service interface to the outside through the buried point service SDK;
and when the error reporting signal of the target system is detected, calling the data buried point service interface to acquire an error reporting statement corresponding to the error reporting signal.
Monitoring the access behavior of the user through the target system, acquiring behavior data of the user in the access process, for example, monitoring that an error signal is triggered when the user accesses the target system, calling a data embedded point service interface, and returning an error statement corresponding to the error signal to the electronic equipment.
In an optional implementation manner, the error reporting acquisition module 201 captures error reporting statements occurring in the process of accessing the target system by the user by setting the embedded point service SDK in the background application service of the electronic device, and compared with embedding a embedded point in the target system, the error reporting acquisition module can avoid a risk of behavior data leakage, and can avoid that when a third-party SDK needs to be upgraded, the corresponding application needs to be upgraded.
The statement parsing module 202 is configured to parse the error report statement, and determine an error item corresponding to the error report statement.
An Error statement is a regular expression composed of a plurality of character strings, the Error statement is analyzed, preset keywords such as Warn, Error, Fatal and the like in the Error statement are extracted, and then the Error statement is segmented according to the preset keywords, so that one or more Error keywords contained in the Error statement can be determined. Alternatively, the error statement may be analyzed based on a mapping relationship between the error statement and the error item in a preset statement item mapping table, so as to determine the error item corresponding to the error statement.
And the map query module 203 is configured to extract a plurality of fault processing methods from a fault map that is constructed in advance based on the error report.
In an optional embodiment, the graph query module 203 is further configured to:
acquiring a plurality of historical fault processing records;
extracting fault events in the plurality of historical fault handling records;
clustering the fault events to obtain at least one fault category and a clustering event corresponding to each fault category;
calculating the incidence relation between the clustering events;
and constructing a fault map according to the clustering events and the incidence relation.
The fault event is a time corresponding to a fault solved in the historical fault handling record, and may include, for example, an access failure, a failure to jump to a target page, a data loading exception, and the like.
The fault event may be clustered based on preset clustering conditions, and at least one fault category is determined, where a fault category refers to a category corresponding to a similar fault, such as access exception. The clustering conditions are used for dividing the fault events, the fault events meeting the same clustering conditions belong to the same cluster, namely belong to the same fault category, and the clustering conditions can be set according to actual conditions without any limitation.
The clustering events refer to similar faults, and if a plurality of fault events correspond to the same fault category, the plurality of fault events are clustering events corresponding to the fault category. A fault map corresponding to a fault category. And the map nodes in the fault map represent clustering events (fault events), one map node corresponds to one clustering event, and the fault processing method of the clustering event corresponding to the map node can be called through the map nodes. Connecting lines in the fault map represent a logical relationship between two clustering events, and different connecting lines represent that the two nodes have different relevance sizes, for example, a longer connecting line represents a smaller relevance, and a shorter connecting line represents a larger relevance.
In an optional embodiment, the graph query module 203 extracts a plurality of fault processing methods in the pre-constructed fault graph based on the error report item, including: determining a fault type corresponding to the fault reporting item, determining a fault map corresponding to the fault type, determining map nodes corresponding to the fault reporting item in the fault map, acquiring at least one associated node corresponding to the map nodes, and acquiring a plurality of fault processing methods based on the map nodes and the at least one associated node corresponding to the map nodes.
The map node corresponds to a fault processing method, and each associated node in the at least one associated node corresponds to a fault processing method.
In an alternative embodiment, the map query module 203 extracting fault events in the plurality of historical fault handling records comprises:
performing word segmentation processing on each historical fault processing record to obtain information word segmentation;
determining a target part of speech of the information participle in an information sentence where the information participle is located, and determining the information participle with the target part of speech as a preset part of speech as a participle to be selected;
matching the to-be-selected participles with template faults in a preset fault template library, and determining the to-be-selected participles successfully matched with the template faults as fault events.
And performing word segmentation processing on each sentence of information sentence in each historical fault processing record to obtain information word segmentation. The target part of speech refers to a role that the information participle plays in the information sentence, for example, the target part of speech is an object, and the target part of speech is a subject. The preset part of speech is determined after being analyzed according to big data, the preset part of speech can be set as a subject, and the specific determination mode of the preset part of speech is not explained. For example, the preset part of speech may be set as an object, the information participle is abnormal for data loading, and the information sentence corresponding to the information participle is: the fault is data loading abnormity, and the data loading abnormity is a preset part of speech, so that the data loading abnormity is determined as a word to be selected.
The preset fault template library stores a plurality of template faults, a user can perform operations such as template addition, template deletion, template modification and the like in the preset fault template library according to experience, and the template faults in the preset fault template library can be faults which can be automatically processed.
The characteristic determining module 204 is configured to determine a target user corresponding to the error event and a fault record corresponding to the target user, and determine a characteristic value corresponding to the error event based on the fault record.
One error item can correspond to a plurality of different fault processing methods, and the fault processing method can be determined according to the occurrence frequency of the error item, for example, if the occurrence frequency of the error item is more, a mode of more thorough processing but longer time consumption can be used; if the number of times of error reporting is less, a mode with simpler processing and shorter time consumption can be used. The times of error items reported by different users are different, so that the times of error items reported by the user can be determined according to the target user and the fault record corresponding to the target user, and the accuracy of determining the times of error items is improved. The feature value corresponding to the error item can be determined according to the number of times of occurrence of the error item, for example, the more the number of times of occurrence of an error item is, the larger the feature value corresponding to the error item is; the less the number of occurrences of an error event, the smaller the eigenvalue corresponding to the error event. The mapping relationship between the occurrence frequency of the error item and the characteristic value can be set according to the actual situation.
In an optional embodiment, the determining, by the feature determining module 204, a target user corresponding to the error event and a fault record corresponding to the target user includes:
positioning and monitoring a target moment corresponding to the error report;
acquiring a user identifier corresponding to the target system at the target moment, and determining a target user according to the user identifier;
and acquiring a fault record corresponding to the target user from a fault processing log library based on the target user.
The target time is the time corresponding to the error-reporting statement when the error-reporting statement appears. The target user is a target system user with an error report sentence at present. The fault processing log library stores relevant log information in the fault processing process, wherein the relevant log information comprises fault records corresponding to each target system user.
The log information can be accurately acquired through the target time and the task identifier, so that the response result can be accurately acquired.
And the score prediction module 205 is configured to input the feature values and the plurality of fault processing methods into a first random forest model for prediction, so as to obtain a matching value corresponding to each fault processing method.
The first random forest model is a machine learning model which is trained in advance, the characteristic value corresponding to the error reporting item and the plurality of fault processing methods are used as input of the first random forest model, the matching value corresponding to each fault processing method is predicted and output through the first random forest model, the matching value is used for representing the suitability degree of the fault processing method and the error reporting implementation, and if the matching value of one fault processing method is higher, the fault processing method is more suitable for processing the error reporting item.
In an alternative embodiment, the score prediction module 205 is further configured to:
obtaining characteristic values corresponding to a plurality of historical error reporting items and a plurality of historical fault processing methods as a training data set;
randomly extracting M training sample sets from the training data set;
learning the M training sample sets to generate M decision trees, randomly extracting N characteristic variables from the characteristic variables of the M decision trees in the generation process of the M decision trees, branching each decision tree by utilizing an optimal splitting mode on the N characteristic variables, and setting an N value as a constant in the formation process of a random forest model;
and when the branch rules are met, stopping the generation process of the M decision trees to obtain the first random forest model.
The characteristic value corresponding to the historical error reporting event is the characteristic value corresponding to the historical error reporting event corresponding to the time period before the preset time period. The characteristic value corresponding to the historical error event in the preset time period can be determined based on the fault record.
The historical fault handling method may query for fault record determinations over a preset time period.
Random sampling can be carried out by adopting a self-service method or a Bootstrap method, random sampling is carried out in a replacement mode, a training sample set is formed by extracted training data sets (M), a verification sample set is formed by training data sets which are not extracted (except for M parts in the training data sets), then decision trees are constructed for all training samples in the training sample set one by one, and M decision trees are constructed by M training sample sets.
In the process of growing the decision tree, each node randomly extracts N characteristic variables from all the characteristic variables to serve as the to-be-selected characteristics of the current node splitting, and branch growing is carried out on the to-be-selected characteristics without pruning. Repeating the steps to enable the decision tree to continue branching and growing until the branching rule is met and the growth is stopped, and storing M decision trees. And establishing a random forest model according to the stored M decision trees, and calculating an average value of the results of the M decision trees to obtain a final prediction result, wherein the values of M and N can be determined according to actual conditions, and are not limited herein.
In an alternative embodiment, the score prediction module 205 is further configured to:
randomly adding noise interference to each of the characteristic variables;
determining the importance of each characteristic variable according to the degree of accuracy reduction;
and optimizing the generation process of the M decision trees according to the importance.
Different noise interferences can be added to different characteristic variables, or the same noise interference can be added to all the characteristic variables, for example, gaussian noise is added.
Firstly, a verification sample set is used for verifying a first prediction accuracy of the random forest model, then a characteristic variable is randomly selected from N characteristic variables each time to add noise interference, and then a verification sample set is used for verifying a second prediction accuracy of the random forest model, so that the accuracy reduction degree is obtained according to the difference value of the first prediction accuracy and the second prediction accuracy. After noise interference is added to each of the N characteristic variables in sequence, N first prediction accuracies can be obtained, thereby obtaining N accuracy reduction degrees. And sequencing the N accuracy reduction degrees from large to small or from small to large, and determining the importance of each characteristic variable according to a sequencing result.
When noise interference is added into a certain characteristic variable, the greater the accuracy of the random forest model is reduced, which indicates that the greater the influence degree of the characteristic variable on the random forest model is, the higher the importance of the characteristic variable is. When noise interference is added into a certain characteristic variable, the smaller the accuracy of the random forest model is reduced, which shows that the smaller the influence degree of the characteristic variable on the random forest model is, the lower the importance degree of the characteristic variable is.
After the importance of the feature variables is determined, importance weights can be added according to the importance in the process of splitting the feature variables, so that the optimization of the generation process of the M decision trees is realized.
According to the optional implementation mode, the importance of the variable characteristics is found out, and the decision tree generation process is optimized according to the importance, so that the prediction accuracy of the random forest model is improved.
A method determining module 206, configured to determine a target fault handling method among the multiple fault handling methods based on the matching value corresponding to each fault handling method, and perform fault handling according to the target fault handling method.
For example, a fault handling method with the largest matching value among the plurality of fault handling methods may be determined as a target fault handling method, and the control electronic device may solve a fault corresponding to a current fault report item of the target system according to the target fault handling method.
According to the fault processing device based on artificial intelligence, error-reporting sentences appearing in a target system can be timely acquired through embedded points, error-reporting items corresponding to the target system at present are determined based on the error-reporting sentences, and a plurality of fault processing methods are extracted from a fault map constructed in advance based on the error-reporting items, are associated with the error-reporting items and can be used for solving faults corresponding to the error-reporting items; determining a target user corresponding to the error-reporting item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error-reporting item based on the fault record, wherein the characteristic is used for indicating the occurrence frequency of the error-reporting item, and according to the characteristic value corresponding to the error-reporting item, a fault processing method using the current error-reporting item can be determined, so that the accuracy of determining the fault processing method is improved; and then inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method, wherein the matching values are used for determining a target fault processing method in the fault processing methods, so that the accuracy of determining the fault processing method is further improved, and the fault processing is performed based on the target fault processing method, so that the active processing of the fault is realized, and the efficiency of processing the system fault is improved.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, which stores thereon a computer program, which when executed by a processor implements the steps in the artificial intelligence based fault handling method embodiments described above, such as S11-S16 shown in fig. 1:
s11, acquiring error-reporting sentences corresponding to the target system based on the pre-implanted buried points;
s12, analyzing the error statement, and determining the error item corresponding to the error statement;
s13, extracting a plurality of fault processing methods from a fault map constructed in advance based on the error items;
s14, determining a target user corresponding to the error item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error item based on the fault record;
s15, inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method;
and S16, determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method, and performing fault processing according to the target fault processing method.
Alternatively, the computer program, when executed by the processor, implements the functions of the modules/units in the above-mentioned device embodiments, for example, the module 201 and 206 in fig. 2:
an error reporting acquisition module 201, configured to acquire an error reporting statement corresponding to a target system based on a pre-implanted embedded point;
a statement parsing module 202, configured to parse the error report statement, and determine an error item corresponding to the error report statement;
the map query module 203 is used for extracting a plurality of fault processing methods from a fault map which is constructed in advance based on the error items;
a feature determining module 204, configured to determine a target user corresponding to the error event and a fault record corresponding to the target user, and determine a feature value corresponding to the error event based on the fault record;
the score prediction module 205 is configured to input the feature values and the plurality of fault processing methods into a first random forest model for prediction, so as to obtain a matching value corresponding to each fault processing method;
a method determining module 206, configured to determine a target fault handling method among the multiple fault handling methods based on the matching value corresponding to each fault handling method, and perform fault handling according to the target fault handling method.
Example four
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application. In the preferred embodiment of the present application, the electronic device 3 comprises a memory 31, at least one processor 32, a transceiver 33, and at least one communication bus 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiments of the present application, and may be a bus-type configuration or a star-type configuration, and that the electronic device 3 may include more or less hardware or software than those shown, or a different arrangement of components.
In some embodiments, the electronic device 3 is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.
It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present application, should also be included in the scope of protection of the present application, and are included by reference.
In some embodiments, the memory 31 has stored therein a computer program that, when executed by the at least one processor 32, performs all or part of the steps of the artificial intelligence based fault handling method as described. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or part of the steps of the artificial intelligence based fault handling method described in the embodiments of the present application; or implement all or part of the functions of the fault handling device based on artificial intelligence. The at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips.
In some embodiments, the at least one communication bus 34 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules 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 are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the specification may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.
Claims (10)
1. An artificial intelligence based fault handling method, characterized in that the method comprises:
acquiring an error report corresponding to a target system based on a pre-implanted buried point;
analyzing the error-reporting statement, and determining an error-reporting item corresponding to the error-reporting statement;
extracting a plurality of fault processing methods from a fault map constructed in advance based on the error items;
determining a target user corresponding to the error item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error item based on the fault record;
inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method;
and determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method, and performing fault processing according to the target fault processing method.
2. The artificial intelligence based fault handling method of claim 1, wherein the determining the target user corresponding to the error event and the fault record corresponding to the target user comprises:
positioning and monitoring a target moment corresponding to the error report;
acquiring a user identifier corresponding to the target system at the target moment, and determining a target user according to the user identifier;
and acquiring a fault record corresponding to the target user from a fault processing log library based on the target user.
3. The artificial intelligence based fault handling method of claim 1, wherein the training process of the first random forest model comprises:
obtaining characteristic values corresponding to a plurality of historical error reporting items and a plurality of historical fault processing methods as a training data set;
randomly extracting M training sample sets from the training data set;
learning the M training sample sets to generate M decision trees, randomly extracting N characteristic variables from the characteristic variables of the M decision trees in the generation process of the M decision trees, branching each decision tree by utilizing an optimal splitting mode on the N characteristic variables, and setting an N value as a constant in the formation process of a random forest model;
and when the branch rules are met, stopping the generation process of the M decision trees to obtain the first random forest model.
4. The artificial intelligence based fault handling method of claim 3 wherein the generating of the M decision trees comprises:
randomly adding noise interference to each of the characteristic variables;
determining the importance of each characteristic variable according to the degree of accuracy reduction;
and optimizing the generation process of the M decision trees according to the importance.
5. An artificial intelligence based fault handling method according to any one of claims 1 to 4, wherein the construction of the fault map comprises:
acquiring a plurality of historical fault processing records;
extracting fault events in the plurality of historical fault handling records;
clustering the fault events to obtain at least one fault category and a clustering event corresponding to each fault category;
calculating the incidence relation between the clustering events;
and constructing a fault map according to the clustering events and the incidence relation.
6. The artificial intelligence based fault handling method of claim 5, wherein said extracting fault events in said plurality of historical fault handling records comprises:
performing word segmentation processing on each historical fault processing record to obtain information word segmentation;
determining a target part of speech of the information participle in an information sentence where the information participle is located, and determining the information participle with the target part of speech as a preset part of speech as a participle to be selected;
matching the to-be-selected participles with template faults in a preset fault template library, and determining the to-be-selected participles successfully matched with the template faults as fault events.
7. An artificial intelligence based fault handling method according to any of claims 1-4, characterised in that the method further comprises:
setting a site-embedded Service (SDK) in the determined target system;
exposing a data buried point service interface to the outside through the buried point service SDK;
and when the error reporting signal of the target system is detected, calling the data buried point service interface to acquire an error reporting statement corresponding to the error reporting signal.
8. An artificial intelligence based fault handling apparatus, the apparatus comprising:
the error reporting acquisition module is used for acquiring an error reporting statement corresponding to the target system based on the pre-implanted buried point;
the statement analysis module is used for analyzing the error-reporting statement and determining an error-reporting item corresponding to the error-reporting statement;
the map query module is used for extracting a plurality of fault processing methods from a fault map constructed in advance based on the error report items;
the characteristic determining module is used for determining a target user corresponding to the error item and a fault record corresponding to the target user, and determining a characteristic value corresponding to the error item based on the fault record;
the score prediction module is used for inputting the characteristic values and the fault processing methods into a first random forest model for prediction to obtain a matching value corresponding to each fault processing method;
and the method determining module is used for determining a target fault processing method in the plurality of fault processing methods based on the matching value corresponding to each fault processing method and performing fault processing according to the target fault processing method.
9. An electronic device, comprising a processor and a memory, wherein the processor is configured to implement the artificial intelligence based fault handling method according to any one of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the artificial intelligence based fault handling method according to any one of claims 1 to 7.
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| CN117234776A (en) * | 2023-09-18 | 2023-12-15 | 厦门国际银行股份有限公司 | Intelligent judging method, device and equipment for batch processing error reporting operation |
| CN117785542A (en) * | 2024-02-28 | 2024-03-29 | 深圳耀德数据服务有限公司 | Intelligent operation and maintenance method, system, equipment and storage medium for data center |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117234776A (en) * | 2023-09-18 | 2023-12-15 | 厦门国际银行股份有限公司 | Intelligent judging method, device and equipment for batch processing error reporting operation |
| CN117785542A (en) * | 2024-02-28 | 2024-03-29 | 深圳耀德数据服务有限公司 | Intelligent operation and maintenance method, system, equipment and storage medium for data center |
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