CN117520127A - Abnormality information processing method, abnormality information processing device, electronic device, and storage medium - Google Patents

Abnormality information processing method, abnormality information processing device, electronic device, and storage medium Download PDF

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Publication number
CN117520127A
CN117520127A CN202311535653.5A CN202311535653A CN117520127A CN 117520127 A CN117520127 A CN 117520127A CN 202311535653 A CN202311535653 A CN 202311535653A CN 117520127 A CN117520127 A CN 117520127A
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China
Prior art keywords
information
vector
fault
target
service system
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Inventor
余海涛
刘波
谢亚南
扈文成
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Beijing Softong Intelligent Technology Co ltd
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Beijing Softong Intelligent Technology Co ltd
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Priority to CN202311535653.5A priority Critical patent/CN117520127A/en
Publication of CN117520127A publication Critical patent/CN117520127A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3466—Performance evaluation by tracing or monitoring
    • G06F11/3476—Data logging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention discloses an anomaly information processing method, an anomaly information processing device, electronic equipment and a storage medium. The method comprises the following steps: determining an operation log of a target service system, wherein the operation log is information for representing the current operation state of the target service system, and comprises first information and second information, and the second information is fault classification of the target service system; information identification is carried out on the operation log of the target service system, and the identification result is converted into a first vector; and determining target fault information according to the first processing model and the first vector, wherein the fault information is information capable of solving the fault of the target service system. The method can shorten the processing time of the running log of the target service system and reduce the resource waste caused by manual monitoring.

Description

Abnormality information processing method, abnormality information processing device, electronic device, and storage medium
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and apparatus for processing abnormal information, an electronic device, and a storage medium.
Background
In a practical operating environment, a system is often required to handle a large number of data flow tasks, which may have a variety of operating results. Among these operation results, it is particularly necessary to pay attention to the abnormality information, and the processing of the abnormality information is not only concerned with the operation efficiency but also with the quality of the final data processing effect. The existing system task running log processing and analyzing system cannot effectively conduct automatic exception classification and solution recommendation, and additional manual operation and analysis are needed, so that the efficiency is low, and the problem cannot be solved in real time.
Disclosure of Invention
The invention provides an abnormal information processing method, an abnormal information processing device, electronic equipment and a storage medium, which are used for solving the problem of low efficiency caused by the fact that abnormal information in an operation log needs to be manually processed.
According to an aspect of the present invention, there is provided an abnormality information processing method including:
determining an operation log of a target service system, wherein the operation log is information for representing the current operation state of the target service system, and comprises first information and second information, and the second information is fault classification of the target service system;
information identification is carried out on the operation log of the target service system, and the identification result is converted into a first vector;
and determining target fault information according to the first processing model and the first vector, wherein the fault information is information capable of solving the fault of the target service system.
According to another aspect of the present invention, there is provided an abnormality information processing apparatus including:
the system comprises an operation log determining module, a fault classifying module and a fault classifying module, wherein the operation log determining module is used for determining an operation log of a target service system, the operation log is information used for representing the current operation state of the target service system, and comprises first information and second information, and the second information is fault classification of the target service system;
the first vector determining module is used for identifying information of the operation log of the target service system and converting an identification result into a first vector;
and the target fault information determining module is used for determining target fault information according to the first processing model and the first vector, wherein the fault information is information capable of solving the fault of the target service system.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the anomaly information processing method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the abnormality information processing method according to any one of the embodiments of the present invention.
According to the technical scheme, the operation log of the target service system is identified and classified, the target fault information matched with the first information is matched according to the classification result, and the target fault information is processed. In addition, the method has universality and can be applied to different task operation logs.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an anomaly information processing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an abnormality information processing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device embodying an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of an anomaly information processing method according to an embodiment of the present invention, where the method may be performed by an anomaly information processing apparatus, and the anomaly information processing apparatus may be implemented in hardware and/or software, and the anomaly information processing apparatus may be configured in any electronic device having a network communication function. As shown in fig. 1, the method includes:
s110, determining an operation log of the target service system.
The operation log is information used for representing the current operation state of the target service system, and comprises first information and second information, wherein the second information is fault classification of the target service system. The first information is information generated when the target service system operates normally in the operation process.
Further, the running log of the target business system may be a data center task running log.
Further, the fault classification of the target service system is error information generated in the running process of the target service system, for example, the fault classification can be timeout, typeError and the like.
S120, carrying out information identification on the operation log of the target service system, and converting the identification result into a first vector.
The first vector is a vector obtained by converting abnormal information in an operation log of the target service system through a vector conversion model.
And identifying the second information in the target service system from the operation log of the target service system, and converting the second information obtained by identification into a first vector.
Optionally, information identification is performed on the running log of the target service system, and the identification result is converted into a first vector, including steps A1-A2:
and A1, determining second information according to the operation log of the target service system and a second processing model.
The second processing model is a model for extracting second information in the operation log of the target service system according to the text information.
Further, the second processing model can realize processing such as text cleaning of text information and irrelevant information rejection.
And performing text cleaning on the operation log of the target service system according to the second processing model, and removing the first information in the operation log of the target service system to obtain second information.
And A2, converting the second information into a first vector by adopting a third processing model.
The third processing model is a pre-established vector conversion model.
Further, the vector transformation model may employ a BGE vector model (BAAI General Embedding), which is a semantic space model that encodes words, phrases, or documents using Bounded Encoding of the Aggregate Language Environment methods, representing them as vectors, capturing their semantic relationships and contextual information in a language environment.
Wherein Bounded Encoding of the Aggregate Language Environment is a pre-trained language characterization model. It employs a new Masked Language Model (MLM) to enable the generation of deep bi-directional language representations.
And encoding the second information by adopting a third processing model, and converting the encoding result into a first vector.
S130, determining target fault information according to the first processing model and the first vector.
The fault information is information capable of solving the fault of the target service system. The target fault information is information that can solve the first vector that the first vector matches.
The first processing model may be a model that matches the target fault information matched with the first vector from a preset knowledge base according to the content of the first vector. For example, the first process model may be GPT-4, etc.
Further, if the environment of the target service system does not have a large amount of computing power required by the GPT-4 model, the first processing model may use a more traditional machine learning model with smaller resource requirements, such as an SVM (support vector machine, support vector machines), a decision tree, or a random forest.
The preset knowledge base is a knowledge base which is pre-established and used for storing abnormal information and an abnormal information processing method.
Furthermore, the preset knowledge base can adopt PG (PostgreSQL), and the postgreSQL is an object-relational database management system with very complete characteristics and free software, and can convert complex objects (such as texts, pictures and the like) into vectors for storage, searching and comparison, so that the method has higher efficiency in processing complex object queries compared with the traditional database mode.
Optionally, determining the target fault information according to the first processing model and the first vector includes steps B1-B2:
and B1, matching a second vector matched with the first vector from a preset knowledge base according to a fourth processing model.
The fourth processing model is used for comparing the similarity of the first vector and the second vector in the preset knowledge base. The preset knowledge base comprises a second vector and fault information of the second vector.
The second vector may be historical abnormal information of the target service system, and the fault information of the second vector is a solution corresponding to the historical abnormal information of the target service system.
The fourth processing model matches the information contained in the first vector with a second vector similar to the first vector from a preset knowledge base.
Further, a vector having the same size as the first vector is determined based on the size and direction of the first vector, and a vector having a similar direction to the first vector is determined as the second vector from among the obtained vectors having the same size.
Further, the vector close to the first vector may be a vector having an included angle with the first vector within a predetermined angle range, for example, if the predetermined angle range is 30 °, the vector having an included angle with the first vector less than or equal to 30 ° may be regarded as the second vector.
When new second information appears, the new second information is converted into vectors by using a BGE model, similarity comparison is then carried out on the vectors stored in a preset knowledge base, the second information corresponding to the most similar vectors is found, the second information is classified, and the second information and the classification are stored.
And B2, determining target fault information according to the second vector and the first processing model.
And matching the target fault information from a preset knowledge base according to the second vector according to the first processing model.
Optionally, determining the target fault information according to the second vector and the first processing model includes steps C1-C2:
and step C1, matching corresponding candidate fault information from a preset knowledge base according to the second vector.
The candidate fault information is fault information matched with the second information, and the candidate fault information can be one or a plurality of fault information.
And matching the fault information corresponding to the second vector from a preset knowledge base according to the acquired second vector, and taking the acquired fault information as candidate fault information.
And C2, determining target fault information according to the first processing model and the candidate fault information.
The first processing model acquires a vector closest to the first vector from the acquired second vector, and takes candidate fault information corresponding to the vector as target fault information.
Further, the vector closest to the first vector may be the vector having the smallest angle with the first vector.
For each type of anomaly, for example, a solution library may be pre-established, each solution library being combined together to obtain a pre-set knowledge base. Once some type of anomaly is detected, the corresponding solution strategy can be extracted from the library, and the optimal solution can be found out through large model processing.
The models in the steps are all trained by adopting the running logs of the historical target service system, so that the established models are more fit with actual demands.
Optionally, after determining the target fault information according to the first processing model and the candidate fault information, the method further includes steps D1-D3:
and D1, determining whether the target fault information can solve the second information according to the fed-back second information processing result.
The processing result of the second information may be that the target fault information can solve the second information or that the target fault information cannot solve the second information.
And determining whether the target fault information can effectively solve the second information according to the processing result of the second information fed back by the terminal.
And D2, if yes, storing second information and target fault information.
And if the target fault information can solve the second information, storing the second information and the target fault information into a preset knowledge base.
And D3, if not, updating the preset knowledge base.
If the target fault information can not solve the second information, the fault information corresponding to the second information is inquired, and the preset knowledge base is updated according to the inquired result.
Optionally, updating the preset knowledge base includes steps E1-E2:
and E1, acquiring fault information of second information input by the terminal.
And if the target fault information can not solve the second information, inquiring the terminal input module, and when the fault information with the second information is inquired, sending the acquired fault information of the second information to the first processing model.
And E2, updating the preset knowledge base by the first processing model according to the fault information of the second information input by the terminal and the fault information of the second information retrieved by the first processing model.
The first processing model searches the fault information related to the second information from the external database through the query, and updates the preset knowledge base according to the fault information input by the terminal and the fault information obtained through the query.
According to the technical scheme, the operation log of the target service system is identified and classified, the target fault information matched with the first information is matched according to the classification result, and the target fault information is processed. In addition, the method has universality and can be applied to different task operation logs.
Fig. 2 is a schematic structural diagram of an abnormality information processing apparatus according to an embodiment of the present invention. The abnormality information processing apparatus may be implemented in the form of hardware and/or software, and may be configured in any electronic device having a network communication function. As shown in fig. 2, the apparatus includes: a log determination module 210, a first vector determination module 220, and a target fault information determination module 230, wherein:
the operation log determination module 210: the method comprises the steps that an operation log of a target service system is determined, wherein the operation log is information used for representing the current operation state of the target service system, the operation log comprises first information and second information, and the second information is fault classification of the target service system;
the first vector determination module 220: the method comprises the steps of carrying out information identification on an operation log of a target service system, and converting an identification result into a first vector;
the target fault information determination module 230: and determining target fault information according to the first processing model and the first vector, wherein the fault information is information capable of solving the fault of the target service system.
Optionally, the first vector determination module 220 includes:
a second information determination unit: the second processing model is used for extracting the second information in the operation log of the target service system according to the text information;
a first vector determination unit: the method is used for converting the second information into the first vector by adopting a third processing model, and the third processing model is a pre-established vector conversion model.
Optionally, the target fault information determining module 230 includes:
a second vector determination unit: the processing system comprises a first processing model, a second processing model and a third processing model, wherein the first processing model is used for matching a first vector in a preset knowledge base according to the first processing model, the second processing model is used for comparing the similarity of the first vector with the second vector in the preset knowledge base, and the preset knowledge base comprises the second vector and fault information of the second vector;
target fault information determination unit: and the target fault information is determined according to the second vector and the first processing model.
Optionally, the target fault information determining unit includes:
the candidate fault information determining subunit is used for matching corresponding candidate fault information from a preset knowledge base according to the second vector;
and the target fault information determining subunit is used for determining the target fault information according to the first processing model and the candidate fault information.
Optionally, the target fault information determining unit further includes:
a processing result judging subunit: the target fault information processing unit is used for determining whether the target fault information can solve the second information according to the fed-back second information processing result;
a storage subunit: if yes, storing second information and target fault information;
knowledge base update subunit: if the user cannot, updating the preset knowledge base.
Optionally, the knowledge base updating subunit is specifically configured to:
acquiring fault information of second information input by a terminal;
the first processing model updates the preset knowledge base according to the fault information of the second information input by the terminal and the fault information of the second information retrieved by the first processing model.
The exception information processing device provided in the embodiment of the present invention can execute the exception information processing method provided in any embodiment of the present invention, and has the corresponding functions and beneficial effects of executing the exception information processing method, and the detailed process refers to the related operation of the exception information processing method in the foregoing embodiment.
Fig. 3 is a schematic structural diagram of an electronic device embodying an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as an abnormality information processing method.
In some embodiments, the anomaly information processing method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the abnormality information processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the exception information processing method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. An abnormality information processing method, comprising:
determining an operation log of a target service system, wherein the operation log is information for representing the current operation state of the target service system, and comprises first information and second information, and the second information is fault classification of the target service system;
information identification is carried out on the operation log of the target service system, and the identification result is converted into a first vector;
and determining target fault information according to the first processing model and the first vector, wherein the fault information is information capable of solving the fault of the target service system.
2. The method of claim 1, wherein identifying the information on the log of the target business system, and converting the identification result to the first vector, comprises:
determining second information according to the operation log of the target service system and a second processing model, wherein the second processing model is a model for extracting the second information in the operation log of the target service system according to text information;
and converting the second information into the first vector by adopting a third processing model, wherein the third processing model is a pre-established vector conversion model.
3. The method of claim 1, wherein determining target fault information from a first process model and the first vector comprises:
matching a second vector matched with the first vector from a preset knowledge base according to a fourth processing model, wherein the fourth processing model is used for comparing the similarity of the first vector and the second vector in the preset knowledge base, and the preset knowledge base comprises the second vector and fault information of the second vector;
and determining target fault information according to the second vector and the first processing model.
4. The method of claim 3, wherein determining target fault information from the second vector and the first process model comprises:
matching corresponding candidate fault information from a preset knowledge base according to the second vector;
and determining the target fault information according to the first processing model and the candidate fault information.
5. The method of claim 4, wherein after determining the target fault information based on the first process model and the candidate fault information, further comprising:
determining whether the target fault information can solve the second information according to the fed-back second information processing result;
if yes, storing the second information and the target fault information;
if not, updating the preset knowledge base.
6. The method of claim 5, wherein updating the preset knowledge base comprises:
acquiring fault information of second information input by a terminal;
the first processing model updates the preset knowledge base according to the fault information of the second information input by the terminal and the fault information of the second information retrieved by the first processing model.
7. An abnormality information processing apparatus, comprising:
the system comprises an operation log determining module, a fault classifying module and a fault classifying module, wherein the operation log determining module is used for determining an operation log of a target service system, the operation log is information used for representing the current operation state of the target service system, and comprises first information and second information, and the second information is fault classification of the target service system;
the first vector determining module is used for identifying information of the operation log of the target service system and converting an identification result into a first vector;
and the target fault information determining module is used for determining target fault information according to the first processing model and the first vector, wherein the fault information is information capable of solving the fault of the target service system.
8. The apparatus of claim 7, wherein the target fault information determination module comprises:
the second vector determining unit is used for matching a second vector matched with the first vector from a preset knowledge base according to a fourth processing model, the fourth processing model is used for comparing the similarity of the first vector and the second vector in the preset knowledge base, and the preset knowledge base comprises the second vector and fault information of the second vector;
and the target fault information determining unit is used for determining target fault information according to the second vector and the first processing model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the anomaly information processing method of any one of claims 1-6.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the anomaly information processing method of any one of claims 1 to 6 when executed.
CN202311535653.5A 2023-11-17 2023-11-17 Abnormality information processing method, abnormality information processing device, electronic device, and storage medium Pending CN117520127A (en)

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