Operation and maintenance training method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of artificial intelligence and research and development, and relates to an operation and maintenance practical training method and device, computer equipment and a storage medium, and the method comprises the steps: employing a pre-constructed operation and maintenance knowledge graph, and generating an operation and maintenance practical training scene according to training demand information inputted by a user; when the user carries out practical training operation in the operation and maintenance practical training scene, obtaining operation index data of the user in the practical training operation process; performing fault diagnosis on the operation index data by adopting an operation and maintenance knowledge graph, and analyzing according to a fault diagnosis result to obtain a practical training operation problem; and evaluating a training learning result of the user according to the training operation problem, and generating an operation and maintenance skill improvement scheme according to the training learning result. In addition, the invention also relates to a block chain technology, and the training demand information, the operation index data, the operation and maintenance knowledge graph and other data of the user can be stored in a block chain. According to the invention, an operation and maintenance practical training scheme meeting the learning requirements and individualized differences can be provided for the user, and the learning effect of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and research and development technology, and in particular to an operation and maintenance training method, device, computer equipment and storage medium. Background Art

[0002] In the field of Internet technology (IT), with the rapid development of technology, IT operation and maintenance has become an indispensable key link in enterprise operations. The importance of IT operation and maintenance talents is becoming increasingly prominent. For the sustainable development of enterprises and organizations, it is crucial to improve the comprehensive skills of operation and maintenance personnel, discover and cultivate technical talents, and ensure the continuity of the talent team and the diversity of skills. In order to meet the urgent needs of enterprises for IT operation and maintenance talents, a variety of IT operation and maintenance training systems have emerged on the market. However, these operation and maintenance training systems often have a single training content, over-emphasize theoretical knowledge and lack the necessary practical training links, making it difficult for trainees to effectively apply the theoretical knowledge they have learned to actual work; and the training content fails to provide customized learning paths based on the learning needs and individual differences of trainees, resulting in poor learning effects of operation and maintenance knowledge.

[0003] In view of this, it is necessary to propose an operation and maintenance training program that can provide students with an operation and maintenance training program that meets their learning needs and individual differences. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose an operation and maintenance training method, device, computer equipment and storage medium, which can provide students with an operation and maintenance training plan that meets their learning needs and individual differences, thereby improving the learning effect of students.

[0005] In order to solve the above technical problems, the present application embodiment provides an operation and maintenance training method, which adopts the following technical solutions:

[0006] Obtain training demand information input by users;

[0007] Using a pre-built operation and maintenance knowledge graph, an operation and maintenance training scenario is generated according to the training demand information;

[0008] When the user performs a training operation in the operation and maintenance training scenario, obtaining operation indicator data of the user during the training operation;

[0009] The operation and maintenance knowledge graph is used to perform fault diagnosis on the operation indicator data, and practical operation problems are obtained according to the fault diagnosis result analysis;

[0010] The practical training learning outcomes of the user are evaluated based on the practical training operation problems, and an operation and maintenance skill improvement plan is generated based on the practical training learning outcomes.

[0011] In order to solve the above technical problems, the embodiment of the present application also provides an operation and maintenance training device, which adopts the following technical solution:

[0012] Demand acquisition module, used to obtain training demand information input by users;

[0013] A scenario generation module, used to generate an operation and maintenance training scenario based on the training demand information using a pre-built operation and maintenance knowledge graph;

[0014] An indicator acquisition module, used to acquire the operation indicator data of the user during the training operation when the user performs the training operation in the operation and maintenance training scenario;

[0015] A fault diagnosis module, used to perform fault diagnosis on the operation indicator data using the operation and maintenance knowledge graph, and obtain practical operation problems according to the fault diagnosis result analysis;

[0016] The practical training evaluation module is used to evaluate the practical training learning outcomes of the user based on the practical training operation problems, and generate an operation and maintenance skill improvement plan based on the practical training learning outcomes.

[0017] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, which adopts the following technical solution: the computer device includes a memory and a processor, the memory stores computer-readable instructions, and the processor implements the steps of the operation and maintenance training method described in any one of the above items when executing the computer-readable instructions.

[0018] In order to solve the above technical problems, an embodiment of the present application also provides a computer-readable storage medium, which adopts the following technical solution: the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the operation and maintenance training method described in any one of the above items are implemented.

[0019] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0020] The embodiment of the present application adopts a pre-built operation and maintenance knowledge graph to generate operation and maintenance training scenarios according to the training demand information input by the user, so as to accurately understand the user's training needs and generate personalized operation and maintenance training scenarios according to the training demand information, so that the generated scenarios are close to the actual working environment, providing a richer and more realistic training operation experience; by obtaining the user's operation indicator data during the user's training process, and using the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data, it can automatically identify and analyze the training operation problems encountered by trainees during the training process; by evaluating the user's training learning outcomes according to the training operation problems, and generating a personalized operation and maintenance skills improvement plan based on the evaluation results, it not only takes into account the user's current skill level, but also combines the user's learning progress and interests, so as to provide users with learning paths and resources that are more in line with their own needs, and significantly improve the quality of training and user learning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0023] Figure 2 It is a flowchart of an embodiment of the operation and maintenance training method of the present application;

[0024] Figure 3 It is a structural schematic diagram of an embodiment of the operation and maintenance training device of the present application;

[0025] Figure 4 It is a structural diagram of an embodiment of a computer device of the present application. DETAILED DESCRIPTION

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0027] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0029] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0030] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0031] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0032] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0033] It should be noted that the operation and maintenance training method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the operation and maintenance training device is generally set in the server / terminal device.

[0034] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0035] Continue to refer Figure 2 , showing a flow chart of an embodiment of the question-answering method according to the present application. The operation and maintenance training method comprises the following steps:

[0036] Step S201, obtaining training demand information input by a user;

[0037] In this embodiment, the operation and maintenance training method is run on the electronic device (for example Figure 1 The server / terminal device shown in the figure) can obtain the training demand information input by the user through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 2G / 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0038] Specifically, the training needs information input by the user is obtained. The training needs information refers to the user's specific needs and expectations regarding operation and maintenance training, which may include but is not limited to the user's current operation and maintenance skill level, the operation and maintenance skill areas that are expected to be improved (such as network security, database management, system operation and maintenance, etc.), the expected training duration, and the learning method preference (such as theory practice, practical training consolidation, etc.).

[0039] In some optional implementations, before obtaining the training demand information input by the user, the following steps may also be included:

[0040] Acquire pre-collected operation and maintenance knowledge data in the field of Internet technology operation and maintenance, wherein the operation and maintenance knowledge data includes operation and maintenance scenario data, operation and maintenance failure data, and operation and maintenance success data;

[0041] Specifically, the pre-collected operation and maintenance knowledge data in the field of Internet technology (IT) operation and maintenance is obtained, and the operation and maintenance knowledge data includes operation and maintenance scenario data, operation and maintenance failure data, and operation and maintenance success data. Among them, the operation and maintenance scenario data covers key information such as various environmental parameters, equipment data, system status, etc. in the operation and maintenance process, which is used to describe the specific situation and changes of the real operation and maintenance scenario; the operation and maintenance failure data is the recorded data describing the main characteristics of the failure event, including key information such as failure mode, cause, location, impact and consequences, and occurrence time; the operation and maintenance success data refers to the cases and data of successful problem solving in the operation and maintenance process, which records the successful operation steps taken by the operation and maintenance personnel in dealing with various historical operation and maintenance problems.

[0042] A deep learning model is used to extract operation and maintenance knowledge from the operation and maintenance knowledge data to obtain a scenario architecture knowledge set, a fault diagnosis knowledge set, and a successful step knowledge set;

[0043] Specifically, the acquired operation and maintenance knowledge data is input into the deep learning model, and the deep learning model is used to extract operation and maintenance knowledge from the operation and maintenance knowledge data. For example, scenario architecture knowledge is extracted from the operation and maintenance scenario data to form a scenario architecture knowledge set; fault diagnosis knowledge is extracted from the operation and maintenance fault data to form a fault diagnosis knowledge set; and success step knowledge is extracted from the operation and maintenance success data to form a success step knowledge set.

[0044] Semantically associating each scene architecture knowledge in the scene architecture knowledge set to obtain scene architecture association structures of several operation and maintenance scene types;

[0045] Specifically, natural language processing (NLP) technology is used to perform entity recognition on each scenario architecture knowledge in the scenario architecture knowledge set, extract the relationship between entities and construct the association structure between entities; semantic analysis is performed on the association structure between entities, and according to the semantic results of each association structure, each association structure is divided into the corresponding operation and maintenance scenario type in turn, so as to obtain the scenario architecture association structure of several operation and maintenance scenario types.

[0046] Semantically associating each fault diagnosis knowledge in the fault diagnosis knowledge set to obtain a fault diagnosis association structure of several fault modes;

[0047] Specifically, natural language processing (NLP) technology is used to perform entity recognition on each fault diagnosis knowledge in the fault diagnosis knowledge set, extract the relationship between entities and construct the association structure between entities; semantic analysis is performed on the association structure between entities, and according to the semantic results of each association structure, each association structure is divided into the corresponding fault mode in turn, and the fault diagnosis association structures of several fault modes are obtained.

[0048] Semantically associating each success step knowledge in the success step knowledge set to obtain a success step association structure of several operation and maintenance success cases;

[0049] Specifically, natural language processing (NLP) technology is used to perform entity recognition on each successful step knowledge in the successful step knowledge set, extract the relationship between entities and construct the association structure between entities; semantic analysis is performed on the association structure between entities, and according to the semantic results of each association structure, each association structure is divided into the corresponding operation and maintenance success case in turn, and the successful step association structure of several operation and maintenance success cases is obtained.

[0050] An operation and maintenance knowledge graph is constructed based on the scenario architecture association structure, the fault diagnosis association structure and the successful step association structure.

[0051] Specifically, by using graph databases (such as Neo4j) and other technologies, the scenario architecture association structure, fault diagnosis association structure, and successful step association structure are merged to form an operation and maintenance knowledge graph. The operation and maintenance knowledge graph has visualization functions and supports knowledge query.

[0052] The embodiment of the present application obtains pre-collected operation and maintenance knowledge data and divides it into operation and maintenance scenario data, operation and maintenance fault data and operation and maintenance success data, and uses a deep learning model for knowledge extraction and semantic association, which can organize scattered knowledge to form a structured knowledge set and association structure, facilitating the efficient construction of an operation and maintenance knowledge graph. Through the scenario architecture association structure, fault diagnosis association structure and successful step association structure in the operation and maintenance knowledge graph, the system is provided with a basis for virtualization scenario construction, fault location and fault analysis.

[0053] Step S202, using a pre-built operation and maintenance knowledge graph to generate an operation and maintenance training scenario according to the training demand information;

[0054] In this embodiment, a pre-built operation and maintenance knowledge graph is obtained. The operation and maintenance knowledge graph refers to a graph covering various operation and maintenance knowledge (such as concepts, relationships, rules, etc.) in the field of IT operation and maintenance. It is a tool for the system to understand the knowledge structure of the operation and maintenance field, generate operation and maintenance training scenarios, and analyze the training operation status. According to the acquired training demand information, the operation and maintenance knowledge related to the construction of the operation and maintenance training scenario is selected from the pre-built operation and maintenance knowledge graph to generate the corresponding operation and maintenance training scenario. Among them, the operation and maintenance training scenario is a virtual operation and maintenance environment that supports users to perform actual operation and maintenance operations and exercises.

[0055] In some optional implementations, the above-mentioned generating an operation and maintenance training scenario according to the training demand information may include the following steps:

[0056] Acquire a target operation and maintenance scenario type from the training demand information;

[0057] Matching a target scenario architecture association structure from the operation and maintenance knowledge graph according to the target operation and maintenance scenario type;

[0058] Generate an operation and maintenance training scenario based on the target scenario architecture association structure.

[0059] Specifically, the target operation and maintenance scenario type is identified and extracted from the training demand information input by the user. The target operation and maintenance scenario type can be a database management operation and maintenance scenario, a network security operation and maintenance scenario, a system performance optimization operation and maintenance scenario, etc. The pre-built operation and maintenance knowledge base is used to match the corresponding operation and maintenance scenario type from the operation and maintenance knowledge graph according to the target operation and maintenance scenario type, and the corresponding scenario architecture association structure is obtained according to the matched operation and maintenance scenario type as the target scenario architecture association structure. The operation and maintenance training scenario is generated according to the target scenario architecture association structure. For example, virtualization technology is used to generate a simulated operation and maintenance training scenario according to the key components, device configuration, network configuration, environmental parameter configuration and other information in the target scenario architecture association structure.

[0060] Step S203, when the user performs a training operation in the operation and maintenance training scenario, obtaining operation index data of the user during the training operation;

[0061] In this embodiment, the user's operating behavior in the operation and maintenance training scenario is monitored. When the user performs training operations in the operation and maintenance training scenario, the operation indicator data generated by the user during the training operation is obtained. The operation indicator data may include but is not limited to operation time, operation sequence, operation content, etc.

[0062] Step S204, using the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data, and obtaining practical operation problems according to the fault diagnosis result analysis;

[0063] In this embodiment, a pre-built operation and maintenance knowledge graph is used to perform fault diagnosis on the operation indicator data. For example, the operation indicator data is matched with the fault mode in the operation and maintenance knowledge graph. If there is a matching fault mode, it indicates that the operation indicator data has a fault. At this time, a corresponding fault diagnosis result is generated according to the matching fault mode. The fault diagnosis result may include information such as the fault mode, possible cause, and impact range. The fault mode may include operating system crash, database anomaly, application error, network delay, packet loss, connection interruption, data leakage, etc. According to the analysis of the fault diagnosis result, the practical operation problem is obtained. For example, when the fault mode in the fault diagnosis result is an operating system crash, the operation content that causes the operating system crash is checked one by one according to the operation indicator data, and the cause of the operating system crash is analyzed to be a configuration error, improper command use, etc.

[0064] In some optional implementations, the above-mentioned use of the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data may include the following steps:

[0065] Acquire a fault diagnosis association structure of a fault mode from the operation and maintenance knowledge graph;

[0066] The operation indicator data is compared and analyzed with the fault diagnosis association structure to obtain a fault diagnosis result.

[0067] Specifically, the fault diagnosis association structure of each fault mode is obtained from the pre-built operation and maintenance knowledge graph, and the fault diagnosis association structure may include fault symptoms, fault causes, and repair suggestions. The user's operation indicator data is compared and analyzed with the fault diagnosis association structure. For example, for the comparison of fault symptoms, it is determined whether the abnormal values ​​in the operation indicator data match the abnormal values ​​of the fault symptoms in the fault diagnosis association structure. If they match, the corresponding fault diagnosis result is generated; for the comparison of fault causes, it is determined whether the change trend in the operation indicator data is associated with the fault cause in the fault diagnosis association structure. If they are associated, the corresponding fault diagnosis result is generated.

[0068] The embodiment of the present application can efficiently utilize existing operation and maintenance knowledge and experience, quickly identify potential faults, and generate corresponding fault diagnosis results by quickly extracting fault modes and their related fault diagnosis association structures from the operation and maintenance knowledge graph, and comparing and analyzing the user's operation indicator data with the fault diagnosis association structure, thereby avoiding the tedious process of analyzing faults from scratch.

[0069] In some optional implementations, the above-mentioned obtaining the practical operation problem according to the fault diagnosis result analysis may include the following steps:

[0070] If the fault diagnosis result is that the operation index data is faulty, the operation steps are traced according to the operation index data to obtain the training operation steps;

[0071] The successful step association structure of the successful operation and maintenance case is obtained from the operation and maintenance knowledge graph, and the successful step association structure is compared and analyzed with the practical training operation steps to obtain the practical training operation problems.

[0072] Specifically, when the fault diagnosis result indicates that the user's operation indicator data has a fault, the operation log, system log or other monitoring data generated by the user during the practical operation is obtained according to the user's operation indicator data, and the operation steps are traced to obtain the user's practical operation steps during the practical operation. From the pre-built operation and maintenance knowledge graph, the successful step association structure of the operation and maintenance success case related to the current fault is obtained, and the successful step association structure can include the operation and maintenance operation steps of the successful operation and maintenance case. Compare and analyze the user's practical operation steps during the practical operation with the successful step association structure. For example, compare the order of the practical operation steps with the order of the operation and maintenance operation steps of the successful operation and maintenance case to find out the differences between the step sequences; compare the operation content of the practical operation steps with the operation content of the operation and maintenance operation steps of the successful operation and maintenance case to find out the differences between the operation contents; compare the parameter configuration of the practical operation steps with the parameter configuration of the operation and maintenance operation steps of the successful operation and maintenance case to find out the differences between the parameter configurations, etc., to obtain the practical operation problems.

[0073] The embodiments of the present application can accurately restore the practical operation steps of the user during the practical operation by tracing the operation steps according to the operation indicator data; by obtaining the successful step association structure of the operation and maintenance success case from the operation and maintenance knowledge graph, the successful step association structure is compared and analyzed with the practical operation steps to obtain the practical operation problems, and the operation and maintenance experience and successful cases stored in the operation and maintenance knowledge graph can be fully utilized to quickly discover the deficiencies and deviations in the practical operation, and automatically identify the practical operation problems, which not only improves the accuracy of the analysis, but also reduces the need for manual intervention and reduces the analysis cost.

[0074] Step S205: evaluating the user's practical training learning outcomes according to the practical training operation problems, and generating an operation and maintenance skill improvement plan according to the practical training learning outcomes.

[0075] In this embodiment, the user's mastery of each skill point (such as proficient mastery, basic mastery, and non-mastery) is evaluated based on the user's practical training operation problems, and the user's practical training learning results are determined based on the mastery of the skill points. A personalized operation and maintenance skill improvement plan is generated based on the user's practical training learning results. For example, for skill points that are not mastered in the practical training learning results, a higher proportion of practical training plans are set in the operation and maintenance skill improvement plan; for skill points that are basically mastered in the practical training learning results, a medium proportion of practical training plans are set in the operation and maintenance skill improvement plan; for skill points that are proficiently mastered in the practical training learning results, a lower proportion of practical training plans are set in the operation and maintenance skill improvement plan.

[0076] In some optional implementations, the above-mentioned evaluating the user's practical training learning outcomes according to the practical training operation questions may include the following steps:

[0077] Get the preset question scoring rules;

[0078] The practical training operation questions are scored according to the question scoring rules to obtain a practical training score as the practical training learning outcome of the user.

[0079] Specifically, a preset problem scoring rule is obtained, and the problem scoring rule sets corresponding scoring weights according to factors such as the severity, impact range, difficulty of solving, and skill points involved in different practical training operation problems. The problem scoring rule is used to score each practical training operation problem one by one, and the scoring results of each skill point are summarized according to each skill point involved in the practical training operation problem to obtain a practical training score, and the practical training learning outcome of the user is determined based on the practical training score. The practical training learning outcome shows the user's mastery of each skill point in the current practical training process.

[0080] The embodiment of the present application scores the user's practical training operation questions according to the question scoring rules, obtains the user's practical training learning results, and realizes an accurate assessment of the user's mastery level of each skill point.

[0081] In some optional implementations, the above-mentioned generating an operation and maintenance skill improvement plan according to the practical training learning results may include the following steps:

[0082] Determining the user's skill weaknesses based on the practical training learning outcomes;

[0083] The operation and maintenance skill knowledge related to the skill weaknesses is obtained from the operation and maintenance knowledge graph, and an operation and maintenance skill improvement plan is generated based on the operation and maintenance skill knowledge.

[0084] Specifically, the user's skill weaknesses are determined based on the practical training learning results. For example, the scoring results of each skill point in the practical training learning results are sorted in order from low to high, and the top N skill points are selected as the user's skill weaknesses. A pre-built operation and maintenance knowledge graph is obtained, which also integrates the operation and maintenance skill knowledge corresponding to each skill point; based on the user's skill weaknesses, the operation and maintenance skill knowledge related to the skill weaknesses is obtained from the operation and maintenance knowledge graph, and based on the operation and maintenance skill knowledge related to the skill weaknesses, an operation and maintenance skill improvement plan for the user is generated. The operation and maintenance skill improvement plan includes a practical training plan for the user's skill weaknesses.

[0085] The embodiment of the present application determines the user's skill weaknesses based on the practical training learning results, generates a personalized operation and maintenance skill improvement plan for the skill weaknesses, and can provide users with more targeted operation and maintenance skill improvement plans based on the user's learning needs and current skill level, which helps to improve the quality of practical training and user learning effects.

[0086] The embodiment of the present application adopts the above-mentioned scheme, specifically by adopting a pre-built operation and maintenance knowledge graph, and generating an operation and maintenance training scenario according to the training demand information input by the user, so as to accurately understand the user's training needs, and generate personalized operation and maintenance training scenarios according to the training demand information, so that the generated scenarios are close to the actual working environment, and provide a richer and more realistic training operation experience; by obtaining the user's operation indicator data during the user's training process, and using the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data, it is possible to automatically identify and analyze the training operation problems encountered by trainees during the training process; by evaluating the user's training learning outcomes based on the training operation problems, and generating a personalized operation and maintenance skills improvement plan based on the evaluation results, not only the user's current skill level is taken into account, but also the user's learning progress and interests are combined, so as to provide users with learning paths and resources that are more in line with their own needs, and significantly improve the quality of training and user learning effects.

[0087] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned users' training needs information, operation indicator data, operation and maintenance knowledge graph and other information as well as other relevant data, the above-mentioned users' training needs information, operation indicator data, operation and maintenance knowledge graph and other information as well as other relevant data can also be stored in a blockchain node.

[0088] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0090] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0091] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of an operation and maintenance training device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0092] like Figure 3 As shown, the operation and maintenance training device 400 described in this embodiment includes: a demand acquisition module 401, a scenario generation module 402, an indicator acquisition module 403, a fault diagnosis module 404 and a training evaluation module 405. Among them:

[0093] The demand acquisition module 401 is used to acquire the training demand information input by the user;

[0094] A scenario generation module 402 is used to generate an operation and maintenance training scenario according to the training demand information using a pre-built operation and maintenance knowledge graph;

[0095] The indicator acquisition module 403 is used to acquire the operation indicator data of the user during the training operation when the user performs the training operation in the operation and maintenance training scenario;

[0096] A fault diagnosis module 404 is used to perform fault diagnosis on the operation indicator data using the operation and maintenance knowledge graph, and obtain training operation problems according to the fault diagnosis result analysis;

[0097] The practical training evaluation module 405 is used to evaluate the practical training learning results of the user according to the practical training operation problems, and generate an operation and maintenance skill improvement plan according to the practical training learning results.

[0098] In this embodiment, the demand acquisition module 401 is used to obtain training demand information input by the user. The training demand information refers to the user's specific needs and expectations regarding operation and maintenance training, which may include but is not limited to the user's current operation and maintenance skill level, the operation and maintenance skill areas that are expected to be improved (such as network security, database management, system operation and maintenance, etc.), the expected training duration, and the learning method preference (such as theory practice, practical training consolidation, etc.).

[0099] Specifically, the scenario generation module 402 is used to obtain a pre-built operation and maintenance knowledge graph, which refers to a graph covering various operation and maintenance knowledge (such as concepts, relationships, rules, etc.) in the field of IT operation and maintenance. It is a tool for the system to understand the knowledge structure of the operation and maintenance field, generate operation and maintenance training scenarios, and analyze the training operation status. According to the acquired training demand information, the operation and maintenance knowledge related to the construction of the operation and maintenance training scenario is selected from the pre-built operation and maintenance knowledge graph to generate the corresponding operation and maintenance training scenario. Among them, the operation and maintenance training scenario is a virtual operation and maintenance environment that supports users to perform actual operation and maintenance operations and exercises.

[0100] Specifically, the indicator acquisition module 403 is used to monitor the user's operating behavior in the operation and maintenance training scenario. When the user performs training operations in the operation and maintenance training scenario, the operation indicator data generated by the user during the training operation is obtained. The operation indicator data may include but is not limited to operation time, operation sequence, operation content, etc.

[0101] Specifically, the fault diagnosis module 404 is used to perform fault diagnosis on the operation indicator data using the pre-built operation and maintenance knowledge graph. For example, the operation indicator data is matched with the fault mode in the operation and maintenance knowledge graph. If there is a matching fault mode, it indicates that the operation indicator data is faulty. At this time, a corresponding fault diagnosis result is generated according to the matching fault mode. The fault diagnosis result may include information such as the fault mode, possible cause, and impact range. The fault mode may include operating system crash, database anomaly, application error, network delay, packet loss, connection interruption, data leakage, etc. According to the analysis of the fault diagnosis result, the practical operation problem is obtained. For example, when the fault mode in the fault diagnosis result is operating system crash, the operation content that causes the operating system crash is checked one by one according to the operation indicator data, and the cause of the operating system crash is analyzed to be a configuration error, improper command use, etc.

[0102] Specifically, the training evaluation module 405 is used to evaluate the user's mastery of each skill point (such as proficient mastery, basic mastery, and non-mastery) according to the user's training operation problems, and determine the user's training learning results based on the mastery of the skill points. Generate a personalized operation and maintenance skill improvement plan based on the user's training learning results. For example, for the skill points that are not mastered in the training learning results, set a higher proportion of training plans in the operation and maintenance skill improvement plan; for the skill points that are basically mastered in the training learning results, set a medium proportion of training plans in the operation and maintenance skill improvement plan; for the skill points that are proficiently mastered in the training learning results, set a lower proportion of training plans in the operation and maintenance skill improvement plan.

[0103] The operation and maintenance training device 400 of the embodiment of the present application, by adopting a pre-built operation and maintenance knowledge graph, generates an operation and maintenance training scenario according to the training demand information input by the user, can accurately understand the user's training needs, and generate personalized operation and maintenance training scenarios according to the training demand information, so that the generated scenarios are close to the actual working environment, providing a richer and more realistic training operation experience; by obtaining the user's operation indicator data during the user training process, and using the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data, it can automatically identify and analyze the training operation problems encountered by trainees during the training process; by evaluating the user's training learning outcomes based on the training operation problems, and generating a personalized operation and maintenance skill improvement plan based on the evaluation results, it not only takes into account the user's current skill level, but also combines the user's learning progress and interests, to provide users with learning paths and resources that are more in line with their own needs, and significantly improves the quality of training and user learning effects.

[0104] In some optional implementations of this embodiment, the operation and maintenance training device 400 further includes a graph construction module.

[0105] A graph construction module, which is used to obtain pre-collected operation and maintenance knowledge data in the field of Internet technology operation and maintenance, wherein the operation and maintenance knowledge data includes operation and maintenance scenario data, operation and maintenance failure data, and operation and maintenance success data;

[0106] A deep learning model is used to extract operation and maintenance knowledge from the operation and maintenance knowledge data to obtain a scenario architecture knowledge set, a fault diagnosis knowledge set, and a successful step knowledge set;

[0107] Semantically associating each scene architecture knowledge in the scene architecture knowledge set to obtain scene architecture association structures of several operation and maintenance scene types;

[0108] Semantically associating each fault diagnosis knowledge in the fault diagnosis knowledge set to obtain a fault diagnosis association structure of several fault modes;

[0109] Semantically associating each success step knowledge in the success step knowledge set to obtain a success step association structure of several operation and maintenance success cases;

[0110] An operation and maintenance knowledge graph is constructed based on the scenario architecture association structure, the fault diagnosis association structure and the successful step association structure.

[0111] Specifically, the graph construction module is used to obtain pre-collected operation and maintenance knowledge data in the field of Internet technology (IT) operation and maintenance, which includes operation and maintenance scenario data, operation and maintenance failure data, and operation and maintenance success data. Among them, the operation and maintenance scenario data covers key information such as various environmental parameters, equipment data, system status, etc. in the operation and maintenance process, and is used to describe the specific situation and changes of the real operation and maintenance scenario; the operation and maintenance failure data is the recorded data describing the main characteristics of the failure event, including key information such as failure mode, cause, location, impact and consequences, and occurrence time; the operation and maintenance success data refers to the cases and data of successful problem solving in the operation and maintenance process, and records the successful operation steps taken by the operation and maintenance personnel in dealing with various historical operation and maintenance problems.

[0112] Specifically, the acquired operation and maintenance knowledge data is input into the deep learning model, and the deep learning model is used to extract operation and maintenance knowledge from the operation and maintenance knowledge data. For example, scenario architecture knowledge is extracted from the operation and maintenance scenario data to form a scenario architecture knowledge set; fault diagnosis knowledge is extracted from the operation and maintenance fault data to form a fault diagnosis knowledge set; and success step knowledge is extracted from the operation and maintenance success data to form a success step knowledge set.

[0113] Specifically, natural language processing (NLP) technology is used to perform entity recognition on each scenario architecture knowledge in the scenario architecture knowledge set, extract the relationship between entities and construct the association structure between entities; semantic analysis is performed on the association structure between entities, and according to the semantic results of each association structure, each association structure is divided into the corresponding operation and maintenance scenario type in turn, so as to obtain the scenario architecture association structure of several operation and maintenance scenario types.

[0114] Specifically, natural language processing (NLP) technology is used to perform entity recognition on each fault diagnosis knowledge in the fault diagnosis knowledge set, extract the relationship between entities and construct the association structure between entities; semantic analysis is performed on the association structure between entities, and according to the semantic results of each association structure, each association structure is divided into the corresponding fault mode in turn, and the fault diagnosis association structures of several fault modes are obtained.

[0115] Specifically, natural language processing (NLP) technology is used to perform entity recognition on each successful step knowledge in the successful step knowledge set, extract the relationship between entities and construct the association structure between entities; semantic analysis is performed on the association structure between entities, and according to the semantic results of each association structure, each association structure is divided into the corresponding operation and maintenance success case in turn, and the successful step association structure of several operation and maintenance success cases is obtained.

[0116] Specifically, by using graph databases (such as Neo4j) and other technologies, the scenario architecture association structure, fault diagnosis association structure, and successful step association structure are merged to form an operation and maintenance knowledge graph. The operation and maintenance knowledge graph has visualization functions and supports knowledge query.

[0117] The graph construction module of the embodiment of the present application obtains the pre-collected operation and maintenance knowledge data and divides it into operation and maintenance scenario data, operation and maintenance fault data and operation and maintenance success data. It uses a deep learning model for knowledge extraction and semantic association, and can organize the scattered knowledge to form a structured knowledge set and association structure, which is convenient for efficiently constructing the operation and maintenance knowledge graph. Through the scenario architecture association structure, fault diagnosis association structure and successful step association structure in the operation and maintenance knowledge graph, the system is provided with a basis for virtualization scenario construction, fault location and fault analysis.

[0118] In some optional implementations of this embodiment, the scene generation module 402 may include a type acquisition submodule, a scene matching submodule and a scene generation submodule.

[0119] A type acquisition submodule is used to acquire the target operation and maintenance scenario type from the training demand information;

[0120] A scenario matching submodule, used to match the target scenario architecture association structure from the operation and maintenance knowledge graph according to the target operation and maintenance scenario type;

[0121] The scenario generation submodule is used to generate an operation and maintenance training scenario according to the target scenario architecture association structure.

[0122] Specifically, the type acquisition submodule is used to identify and extract the target operation and maintenance scenario type from the training demand information input by the user. The target operation and maintenance scenario type can be a database management operation and maintenance scenario, a network security operation and maintenance scenario, a system performance optimization operation and maintenance scenario, etc. The scenario matching submodule is used to use the pre-built operation and maintenance knowledge base to match the corresponding operation and maintenance scenario type from the operation and maintenance knowledge graph according to the target operation and maintenance scenario type, and obtain the corresponding scenario architecture association structure as the target scenario architecture association structure according to the matched operation and maintenance scenario type. The scenario generation submodule is used to generate an operation and maintenance training scenario according to the target scenario architecture association structure. For example, using virtualization technology, a simulated operation and maintenance training scenario is generated according to the key components, device configuration, network configuration, environmental parameter configuration and other information in the target scenario architecture association structure.

[0123] In some optional implementations of this embodiment, the fault diagnosis module 404 may include a mode acquisition submodule and a fault comparison submodule.

[0124] A pattern acquisition submodule, used to acquire a fault diagnosis association structure of a fault pattern from the operation and maintenance knowledge graph;

[0125] The fault comparison submodule is used to compare and analyze the operation indicator data with the fault diagnosis association structure to obtain a fault diagnosis result.

[0126] Specifically, the mode acquisition submodule is used to obtain the fault diagnosis association structure of each fault mode from the pre-built operation and maintenance knowledge graph, and the fault diagnosis association structure may include fault symptoms, fault causes, and repair suggestions. The fault comparison submodule is used to compare and analyze the user's operation indicator data with the fault diagnosis association structure. For example, for the comparison of fault symptoms, it is determined whether the abnormal values ​​in the operation indicator data match the abnormal values ​​of the fault symptoms in the fault diagnosis association structure. If they match, the corresponding fault diagnosis results are generated; for the comparison of fault causes, it is determined whether the change trend in the operation indicator data is associated with the fault cause in the fault diagnosis association structure. If they are associated, the corresponding fault diagnosis results are generated.

[0127] The fault diagnosis module 404 of the embodiment of the present application can efficiently utilize the existing operation and maintenance knowledge and experience, quickly identify potential faults, and generate corresponding fault diagnosis results by quickly extracting fault modes and their related fault diagnosis association structures from the operation and maintenance knowledge graph, and comparing and analyzing the user's operation indicator data with the fault diagnosis association structure, thereby avoiding the tedious process of analyzing faults from scratch.

[0128] In some optional implementations of this embodiment, the fault diagnosis module 404 may include a step tracing submodule and a step comparison submodule, wherein:

[0129] A step tracing submodule is used to, if the fault diagnosis result is that the operation index data is faulty, trace the operation steps according to the operation index data to obtain the training operation steps;

[0130] The step comparison submodule is used to obtain the successful step association structure of the operation and maintenance success case from the operation and maintenance knowledge graph, compare and analyze the successful step association structure with the practical training operation steps, and obtain the practical training operation problems.

[0131] Specifically, the step tracing submodule is used to obtain the operation log, system log or other monitoring data generated by the user during the practical training operation according to the user's operation indicator data when the fault diagnosis result indicates that the user's operation indicator data has a fault, and to trace the operation steps to obtain the user's practical training operation steps during the practical training operation. The step comparison submodule is used to obtain the successful step association structure of the operation and maintenance success case related to the current fault from the pre-built operation and maintenance knowledge graph, and the successful step association structure can include the operation and maintenance operation steps of the successful operation and maintenance case; compare and analyze the user's practical training operation steps during the practical training operation with the successful step association structure, for example, compare the order of the practical training operation steps with the order of the operation and maintenance operation steps of the successful operation and maintenance case to find out the difference between the step orders; compare the operation content of the practical training operation steps with the operation content of the operation and maintenance operation steps of the successful operation and maintenance case to find out the difference between the operation contents; compare the parameter configuration of the practical training operation steps with the parameter configuration of the operation and maintenance operation steps of the successful operation and maintenance case to find out the difference between the parameter configurations, etc., to obtain the practical training operation problems.

[0132] The fault diagnosis module 404 of the embodiment of the present application can accurately restore the practical operation steps of the user during the practical operation by tracing the operation steps according to the operation indicator data; by obtaining the successful step association structure of the operation and maintenance success case from the operation and maintenance knowledge graph, the successful step association structure is compared and analyzed with the practical operation steps to obtain the practical operation problems, and the operation and maintenance experience and successful cases stored in the operation and maintenance knowledge graph can be fully utilized to quickly discover the deficiencies and deviations in the practical operation, and automatically identify the practical operation problems, which not only improves the accuracy of the analysis, but also reduces the need for manual intervention and reduces the analysis cost.

[0133] In some optional implementations of this embodiment, the above-mentioned training evaluation module 405 may include a rule acquisition submodule and a question scoring submodule. Among them:

[0134] A rule acquisition submodule is used to obtain preset question scoring rules;

[0135] The question scoring submodule is used to score the practical training operation questions according to the question scoring rules to obtain the practical training scores as the practical training learning outcomes of the users.

[0136] Specifically, the rule acquisition submodule is used to obtain the preset problem scoring rules, which set corresponding scoring weights according to factors such as the severity, impact range, difficulty of solving, and skill points involved in different practical training operation problems. The problem scoring submodule is used to use the problem scoring rules to score each practical training operation problem one by one, and summarize the scoring results of each skill point according to the skill points involved in the practical training operation problem to obtain the practical training score, and determine the user's practical training learning results based on the practical training score. The practical training learning results show the user's mastery of each skill point in the current practical training process.

[0137] The practical training evaluation module 405 of the embodiment of the present application scores the practical training operation questions of the user according to the problem scoring rules, obtains the practical training learning results of the user, and realizes an accurate evaluation of the mastery level of each skill point of the user.

[0138] In some optional implementations of this embodiment, the practical training assessment module 405 may include a skill determination submodule and a solution generation submodule.

[0139] A skill determination submodule, used to determine the skill weaknesses of the user according to the practical training learning results;

[0140] The solution generation submodule is used to obtain the operation and maintenance skill knowledge related to the skill weaknesses from the operation and maintenance knowledge graph, and generate an operation and maintenance skill improvement plan based on the operation and maintenance skill knowledge.

[0141] Specifically, the user's skill weaknesses are determined based on the practical training learning results. For example, the scoring results of each skill point in the practical training learning results are sorted in order from low to high, and the top N skill points are selected as the user's skill weaknesses. A pre-built operation and maintenance knowledge graph is obtained, which also integrates the operation and maintenance skill knowledge corresponding to each skill point; based on the user's skill weaknesses, the operation and maintenance skill knowledge related to the skill weaknesses is obtained from the operation and maintenance knowledge graph, and based on the operation and maintenance skill knowledge related to the skill weaknesses, an operation and maintenance skill improvement plan for the user is generated. The operation and maintenance skill improvement plan includes a practical training plan for the user's skill weaknesses.

[0142] The practical training evaluation module 405 of the embodiment of the present application determines the user's skill weaknesses based on the practical training learning outcomes, generates a personalized operation and maintenance skill improvement plan for the skill weaknesses, and can combine the user's learning needs and current skill level to provide the user with a more targeted operation and maintenance skill improvement plan, which helps to improve the quality of practical training and user learning effects.

[0143] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0144] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 6 with a memory 61, a processor 62, and a network interface 63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0145] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0146] The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions of the operation and maintenance training method, etc. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0147] The processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip in some embodiments. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run computer-readable instructions stored in the memory 61 or process data, such as computer-readable instructions for running the operation and maintenance training method.

[0148] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0149] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the operation and maintenance training method as described above.

[0150] The computer device, computer-readable storage medium and computer-readable instructions thereof provided in the embodiments of the present application use a pre-built operation and maintenance knowledge graph to generate an operation and maintenance training scenario according to the training demand information input by the user through the processor execution, and can accurately understand the user's training needs, and generate personalized operation and maintenance training scenarios according to the training demand information, so that the generated scenarios are close to the actual working environment, providing a richer and more realistic training operation experience; by obtaining the user's operation indicator data during the user's training process, and using the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data, it is possible to automatically identify and analyze the training operation problems encountered by trainees during the training process; by evaluating the user's training learning outcomes based on the training operation problems, and generating a personalized operation and maintenance skills improvement plan based on the evaluation results, it not only takes into account the user's current skill level, but also combines the user's learning progress and interests, to provide users with learning paths and resources that are more in line with their own needs, and significantly improves the quality of training and user learning effects.

[0151] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0152] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

[0153] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

Claims

1. An operation and maintenance training method, characterized in that: The steps include: Obtain training demand information input by users; Using a pre-built operation and maintenance knowledge graph, an operation and maintenance training scenario is generated according to the training demand information; When the user performs a training operation in the operation and maintenance training scenario, obtaining operation indicator data of the user during the training operation; The operation and maintenance knowledge graph is used to perform fault diagnosis on the operation indicator data, and practical operation problems are obtained according to the fault diagnosis result analysis; The practical training learning outcomes of the user are evaluated based on the practical training operation problems, and an operation and maintenance skill improvement plan is generated based on the practical training learning outcomes.

2. The operation and maintenance training method according to claim 1, characterized in that: Before the step of obtaining the training demand information input by the user, the method further includes: Acquire pre-collected operation and maintenance knowledge data in the field of Internet technology operation and maintenance, wherein the operation and maintenance knowledge data includes operation and maintenance scenario data, operation and maintenance failure data, and operation and maintenance success data; A deep learning model is used to extract operation and maintenance knowledge from the operation and maintenance knowledge data to obtain a scenario architecture knowledge set, a fault diagnosis knowledge set, and a successful step knowledge set; Semantically associating each scene architecture knowledge in the scene architecture knowledge set to obtain scene architecture association structures of several operation and maintenance scene types; Semantically associating each fault diagnosis knowledge in the fault diagnosis knowledge set to obtain a fault diagnosis association structure of several fault modes; Semantically associating each success step knowledge in the success step knowledge set to obtain a success step association structure of several operation and maintenance success cases; An operation and maintenance knowledge graph is constructed based on the scenario architecture association structure, the fault diagnosis association structure and the successful step association structure.

3. The operation and maintenance training method according to claim 2, characterized in that: The step of generating an operation and maintenance training scenario according to the training demand information includes: Acquire a target operation and maintenance scenario type from the training demand information; Matching a target scenario architecture association structure from the operation and maintenance knowledge graph according to the target operation and maintenance scenario type; Generate an operation and maintenance training scenario based on the target scenario architecture association structure.

4. The operation and maintenance training method according to claim 2, characterized in that: The step of using the operation and maintenance knowledge graph to perform fault diagnosis on the operation indicator data includes: Acquire a fault diagnosis association structure of a fault mode from the operation and maintenance knowledge graph; The operation indicator data is compared and analyzed with the fault diagnosis association structure to obtain a fault diagnosis result.

5. The operation and maintenance training method according to claim 2, characterized in that: The step of analyzing the practical training operation problem according to the fault diagnosis result includes: If the fault diagnosis result is that the operation index data is faulty, the operation steps are traced according to the operation index data to obtain the training operation steps; The successful step association structure of the successful operation and maintenance case is obtained from the operation and maintenance knowledge graph, and the successful step association structure is compared and analyzed with the practical training operation steps to obtain the practical training operation problems.

6. The operation and maintenance training method according to claim 1, characterized in that: The step of evaluating the user's practical training learning outcomes according to the practical training operation questions includes: Get the preset question scoring rules; The practical training operation questions are scored according to the question scoring rules to obtain a practical training score as the practical training learning outcome of the user.

7. The operation and maintenance training method according to claim 1, characterized in that: The step of generating an operation and maintenance skills improvement plan based on the practical training learning results includes: Determining the user's skill weaknesses based on the practical training learning outcomes; The operation and maintenance skill knowledge related to the skill weaknesses is obtained from the operation and maintenance knowledge graph, and an operation and maintenance skill improvement plan is generated based on the operation and maintenance skill knowledge.

8. An operation and maintenance training device, characterized in that: The device comprises: Demand acquisition module, used to obtain training demand information input by users; A scenario generation module, used to generate an operation and maintenance training scenario based on the training demand information using a pre-built operation and maintenance knowledge graph; An indicator acquisition module, used for acquiring the operation indicator data of the user during the training operation when the user performs the training operation in the operation and maintenance training scenario; A fault diagnosis module, used to perform fault diagnosis on the operation indicator data using the operation and maintenance knowledge graph, and obtain practical operation problems according to the fault diagnosis result analysis; The practical training evaluation module is used to evaluate the practical training learning outcomes of the user based on the practical training operation problems, and generate an operation and maintenance skill improvement plan based on the practical training learning outcomes.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the operation and maintenance training method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the operation and maintenance training method according to any one of claims 1 to 7 are implemented.

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