Intelligent configuration method and device, electronic equipment and storage medium

Abstract

The invention relates to an artificial intelligence technology, in particular to an intelligent configuration method and device, electronic equipment and a storage medium. Acquiring a configuration request of a user; determining a target configuration intention according to the configuration request through an intention understanding model; and according to the historical configuration data and the current operation environment information, generating target configuration information of the target configuration intention to perform configuration management. According to the embodiment of the invention, the intelligence of configuration is improved, the labor cost is reduced, and the configuration efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to artificial intelligence technology, and more particularly to an intelligent configuration method, device, electronic device and storage medium. Background Art

[0002] Distributed systems have gradually become an important part of modern information technology infrastructure. In distributed systems, configuration management is particularly critical to the performance, reliability and security of the system.

[0003] In the prior art, configuration is usually performed by manually configuring static files or inputting commands through a command line interface.

[0004] However, manual configuration requires the configuration personnel to have corresponding professional knowledge, which places high demands on the professional level of the configuration personnel, high labor costs, low efficiency, and manual configuration cannot quickly adapt to new configuration requirements and has poor intelligence. Summary of the invention

[0005] The present application provides an intelligent configuration method, device, electronic device and storage medium to improve the intelligence of configuration, reduce labor costs and improve configuration efficiency.

[0006] In a first aspect, an embodiment of the present application provides an intelligent configuration method, the intelligent configuration method comprising:

[0007] Get the user's configuration request;

[0008] Determine the target configuration intent based on the configuration request through the intent understanding model;

[0009] Based on historical configuration data and current operating environment information, target configuration information of target configuration intent is generated for configuration management.

[0010] In a second aspect, an embodiment of the present application further provides an intelligent configuration device, the intelligent configuration device comprising:

[0011] A configuration request acquisition module is used to obtain the user's configuration request;

[0012] A target configuration intention determination module is used to determine the target configuration intention according to the configuration request through the intention understanding model;

[0013] The target configuration information generation module is used to generate target configuration information of the target configuration intention based on historical configuration data and current operating environment information for configuration management.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0015] one or more processors;

[0016] A storage device for storing one or more programs;

[0017] When one or more programs are executed by one or more processors, the one or more processors implement any one of the intelligent configuration methods provided in the embodiments of the present application.

[0018] In a fourth aspect, an embodiment of the present application further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute any one of the intelligent configuration methods provided in the embodiments of the present application.

[0019] This application obtains the user's configuration request; determines the target configuration intent according to the configuration request through the intent understanding model, accurately understands the user's intent, and improves the accuracy of subsequent configuration; generates target configuration information of the target configuration intent based on historical configuration data and current operating environment information for configuration management, which can adapt to the configuration environment and improve the intelligence of configuration, and reduce labor costs and improve configuration efficiency by automatically generating target configuration information. Therefore, the technical solution of this application solves the problem that manual configuration requires high professionalism of configuration personnel, has high labor costs, low efficiency, cannot quickly adapt to new configuration requirements, and has poor intelligence, and achieves the effect of improving the intelligence of configuration, reducing labor costs, and improving configuration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of an intelligent configuration method in Embodiment 1 of the present application;

[0021] Figure 2 is a flow chart of an intelligent configuration method in Embodiment 2 of the present application;

[0022] Figure 3 It is a flow chart of an intelligent configuration method in Embodiment 3 of the present application;

[0023] Figure 4 It is a structural diagram of an intelligent configuration device in Embodiment 4 of the present application;

[0024] Figure 5 It is a structural diagram of an electronic device in Example 5 of the present application. DETAILED DESCRIPTION

[0025] 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 drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first" and "second" etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 This is a flowchart of an intelligent configuration method provided in Example 1 of the present application. This embodiment can be applied to the situation of intelligently configuring a distributed system. The method can be executed by an intelligent configuration device, which can be implemented in software and / or hardware and specifically configured in a configuration management system, for example, a current limiting center platform.

[0029] See also Figure 1 The intelligent configuration method shown specifically includes the following steps:

[0030] S110: Obtain a configuration request from a user.

[0031] The configuration request may be a configuration request sent by a user, and may be used to implement the current limiting function. Exemplarily, the configuration request may include relevant information of the target system to be configured and relevant information of the current limiting, etc. The configuration request may be a configuration request described in text, which reduces the requirements for the professional knowledge and proficiency of the configuration personnel and reduces labor costs. The user may send a configuration request to the current limiting center platform through the client, and the current limiting center platform obtains the user's configuration request.

[0032] The flow control center platform can be used to achieve comprehensive flow protection. The flow protection function requires the cooperation of the client and the server. The client can access the software development kit (SDK), send the user's configuration request, and configure static flow control rules to achieve flow control capabilities; at the same time, when the application node is in the runtime state, the server's dynamic delivery capability can be used to cover or add flow control rules.

[0033] S120. Determine the target configuration intent according to the configuration request through the intent understanding model.

[0034] The intent understanding model can be a trained deep learning model, which is used to understand the user's configuration intent in the obtained configuration request. Exemplarily, the intent understanding model can be a deep learning model such as a large model, a convolutional neural network or a decision tree, which is not specifically limited in this application. The target configuration intent can be a configuration intent obtained by understanding the user's configuration request through the intent understanding model, which is used to obtain computer-understandable configuration requirements to generate target configuration information. The configuration request is input into the intent understanding model, and the output obtained is the target configuration intent.

[0035] S130. Generate target configuration information of target configuration intent according to historical configuration data and current operating environment information to perform configuration management.

[0036] The historical configuration data may be static configuration data of the target system corresponding to the configuration request before the configuration request is obtained, and may be used to predict the best practices or potential problems of the target configuration intent. The current operating environment information may include the operating environment parameters and external environment parameters of the system. Exemplarily, the system operating environment parameters may include memory capacity, CPU usage, link relationships, and network traffic, etc., which are not specifically limited in this application. Exemplarily, the external environment parameters may include load and resource utilization, etc., which are not specifically limited in this application. The target configuration information may be an optimal configuration script or command set automatically generated based on the historical configuration data and the current operating environment information, which may be directly applied to the target system.

[0037] The target configuration information of the target configuration intention can be automatically generated through the preset model according to the historical configuration data and the current operating environment information. Exemplarily, the preset model can be a deep learning model or an expert model, which is not specifically limited in this application. For example, the deep learning model can be a large model or a convolutional neural network model, etc. The target configuration intention, historical configuration data and current operating environment information are input into the preset model, and the output is the target configuration information, which is sent to the corresponding server to implement configuration management.

[0038] With the widespread application of cloud computing, microservice architecture and containerization technology, distributed systems have gradually become an important part of modern information technology infrastructure. In distributed systems, configuration management is particularly critical because it directly affects the performance, reliability and security of distributed systems.

[0039] Traditional configuration management usually relies on static files or command line interfaces. Static configuration files store configuration parameters in text files or database tables, and administrators need to manually edit these files or use specific tools to update them. The command line interface provides a series of commands for administrators to operate directly, which is suitable for users with certain professional knowledge. Centralized configuration management systems can centrally manage and distribute configuration information, support version control and health checks, etc., and remotely manage and change configurations through RESTful APIs (professional terms, a communication protocol) or other protocols.

[0040] Traditional configuration management methods can be adapted to small-scale deployments, but cannot adapt to complex distributed environments. Traditional configuration management methods have the following disadvantages:

[0041] Most solutions of traditional configuration management are based on fixed rules or patterns, cannot automatically adapt to new situations or optimize existing settings, and lack intelligence;

[0042] Many tools and technologies of traditional configuration management require users to have certain professional knowledge, which limits their scope of application, increases the risk of misoperation, and leads to poor user experience.

[0043] Traditional configuration management methods, whether manually editing configuration files or entering commands through command line interfaces, are time-consuming, labor-intensive, and inefficient, especially when dealing with large-scale or complex configurations.

[0044] In a distributed system, since configurations are scattered and frequently change, it is difficult to ensure that all instances can synchronize the latest configurations in a timely manner, which can also easily introduce security risks.

[0045] When faced with emergencies or urgent changes, traditional methods are often unable to respond quickly, have slow response speeds, and are inefficient, affecting business continuity.

[0046] The technical solution of this embodiment obtains the configuration request of the user; determines the target configuration intent according to the configuration request through the intent understanding model, accurately understands the user's intent, and improves the accuracy of subsequent configuration; generates the target configuration information of the target configuration intent according to the historical configuration data and the current operating environment information for configuration management, which can adapt to the configuration environment and improve the intelligence of the configuration, and reduce labor costs and improve the efficiency of configuration by automatically generating the target configuration information. Therefore, the technical solution of this application solves the problem that manual configuration requires a high level of professionalism of the configuration personnel, has high labor costs, low efficiency, cannot quickly adapt to new configuration requirements, and has poor intelligence, and achieves the effect of improving the intelligence of the configuration, reducing labor costs, and improving configuration efficiency.

[0047] Embodiment 2

[0048] Figure 2 This is a flow chart of an intelligent configuration method provided in Example 2 of the present application. The technical solution of this embodiment is further refined on the basis of the above technical solution.

[0049] Furthermore, the "generate target configuration information of target configuration intent based on historical configuration data and current operating environment information for configuration management" is refined as: "generate target configuration information of target configuration intent based on historical configuration data and current operating environment information through the pre-trained large model" to automatically generate target configuration information of target configuration intent.

[0050] See also Figure 2 An intelligent configuration method is shown, comprising:

[0051] S210: Obtain a configuration request from a user.

[0052] S220. Determine the target configuration intent according to the configuration request through the intent understanding model.

[0053] S230. Generate target configuration information of target configuration intent through the pre-trained large model according to historical configuration data and current operating environment information for configuration management.

[0054] A large model refers to a "large parameter" model trained using large-scale data and powerful computing power. Large models are usually highly versatile and generalizable and can be applied to natural language processing. The large model can be pre-trained by converting configuration intent into relevant sample data of the configuration script to obtain a large model that has the ability to convert the target configuration intent into the target configuration information, that is, the pre-trained large model. The sample data in the training process includes the current operating environment information and historical configuration data corresponding to each sample data.

[0055] Generate prompt words based on the target configuration intent, combine historical configuration data and current operating environment information, and input them into the pre-trained large model to obtain the output target configuration information. Implement configuration management through target configuration information. Utilize the processing power of the pre-trained large model to convert the target configuration intent described in natural language into specific instructions or parameter settings that can be executed by the computer in the target configuration information, ensuring that even users who are not familiar with technology can easily express their configuration requirements through natural language and implement configuration management.

[0056] Optionally, natural language annotations can be added to the target configuration information and displayed to the user to facilitate the user's understanding of the target configuration information. This lowers the user's knowledge threshold. Optionally, after configuring according to the target configuration information, the target system's operating parameters and user feedback can be monitored in real time, the target configuration information can be adjusted, and the adjusted target configuration information can be configured into the target system to implement a dynamic adjustment mechanism.

[0057] In an optional embodiment, after generating target configuration information of the target configuration intention based on historical configuration data and current operating environment information for configuration management, it also includes: monitoring the operating parameters of the target system after the target configuration information is configured, and generating operating feedback parameters based on the operating parameters; based on a preset update cycle, incrementally training the pre-trained large model according to the operating feedback parameters within the preset update cycle.

[0058] The target system may be a system that needs to configure the target configuration information. The operation parameter may be an operation parameter related to the configuration item, which is used to evaluate the accuracy of the target configuration information. For example, the operation parameter may be network traffic and response speed, etc. The operation feedback parameter may be a parameter for providing feedback to the pre-trained large model based on the operation parameter. The operation feedback parameter may include an evaluation of the operation result of the operation parameter. For example, the operation parameter is a response rate, and the operation feedback parameter may be a low response rate.

[0059] The preset update cycle may be a preset update cycle of the large model. It may be set by professional technicians based on the application scenario, experience or data analysis, and this application does not specifically limit this. For example, the preset update cycle may be 10 days, a week or a month. Incremental training may be to train the pre-trained large model according to the operation feedback parameters and adjust the parameters of the pre-trained large model. Through incremental training, the large model after incremental training can be able to sense and adapt to changes in the operating environment in a timely manner.

[0060] Optionally, during a preset update cycle, if a new business or technology is detected, relevant knowledge about the new business can be collected, and the knowledge base and algorithm framework of the large model can be updated regularly to maintain its advancement and adaptability.

[0061] After the target configuration information is configured to the target system, the operating parameters of the target system are monitored, and the operating feedback parameters are obtained based on the operating parameters. After the preset update cycle, the large model is incrementally trained based on the operating feedback parameters collected within the preset update cycle. Through the feedback learning mechanism, a continuous learning mechanism is established, which can regularly update the parameters of the large model and continuously optimize the large model.

[0062] After generating the target configuration information of the target configuration intention according to the historical configuration data and the current operating environment information for configuration management, the operating parameters of the target system after the target configuration information is configured are monitored, and operating feedback parameters are generated according to the operating parameters; based on the preset update cycle, the pre-trained large model is incrementally trained according to the operating feedback parameters within the preset update cycle, and through the feedback learning mechanism, the pre-trained large model is incrementally trained based on the preset update cycle, and a continuous learning mechanism is established, which can regularly update the parameters of the large model, continuously optimize the large model, improve the adaptability of the large model output after incremental training, and ensure the accuracy of the large model output.

[0063] In an optional embodiment, after generating target configuration information of the target configuration intention based on historical configuration data and current operating environment information for configuration management, it also includes: predicting configuration risks for the target configuration information based on the current operating environment information; and when the prediction result shows that there is a configuration risk, determining a correction strategy for the configuration risk.

[0064] Risk prediction can be used to conduct risk assessment on the target configuration information to be implemented, so as to warn of possible risk points in advance and prepare countermeasures. Optionally, when the prediction result shows that there is a configuration risk, a risk point warning can be performed. The warning method can include at least one of text, voice, and light, and this application does not specifically limit this.

[0065] When the prediction result shows that there is a configuration risk, the correction strategy for the configuration risk can be determined through the big model, so that when the risk occurs, it can be corrected in time according to the correction strategy to ensure the security and stability of the target system. The historical configuration data of risk accidents and the relevant data for responding to risk accidents can be collected, and the correction data can be used to train the big model, so that the big model has the ability to predict and correct risks.

[0066] After performing configuration management by generating target configuration information of target configuration intent based on historical configuration data and current operating environment information, configuration risk prediction is performed on the target configuration information based on the current operating environment information to identify risk points in advance and prepare response measures. When the prediction result shows that there is a configuration risk, a correction strategy for the configuration risk is determined to improve the ability to respond to risks and ensure the security and stability of the target system.

[0067] The technical solution of this embodiment generates target configuration information of target configuration intent based on historical configuration data and current operating environment information through a pre-trained big model. Based on the powerful natural language processing capability of the big model, the target configuration information of the target configuration intent is automatically generated, thereby improving the intelligence of the configuration and reducing labor costs. In addition, the trained big model has high accuracy and fast response speed, which can improve the efficiency and accuracy of the configuration and improve the user experience.

[0068] Embodiment 3

[0069] Figure 3 This is a flow chart of an intelligent configuration method provided in Example 3 of the present application. The technical solution of this embodiment is further refined on the basis of the above technical solution.

[0070] Furthermore, "determine the target configuration intent according to the configuration request through the intent understanding model" is refined as: "When the intent understanding model parses the user intent, if it is found that the configuration request intent is unclear, then generate a query statement for the content with unclear intent; obtain the user's query reply to the query statement, and continue to parse the user intent based on the query reply; if it is found that the query reply intent is unclear, then continue to generate query statements for the content with unclear intent until there is no content with unclear intent; determine the target configuration intent based on the configuration request and at least one query reply" to determine the target configuration intent through multiple rounds of inquiries.

[0071] See also Figure 3 An intelligent configuration method is shown, comprising:

[0072] S310: Obtain a configuration request from a user.

[0073] S320. When the intent understanding model analyzes the user intent, if it is found that the configuration request intent is unclear, a query statement is generated for the unclear intent content.

[0074] Unclear intent may mean that the user's intent cannot be understood, and may include missing configuration parameters and missing data. The query statement may be a query statement to the user generated for the content with unclear intent. For example, if the content with unclear intent is that configuration parameter A is missing, the query statement may be "Please supplement configuration parameter A".

[0075] Optionally, before inputting the configuration request into the intent understanding model, data preprocessing may be performed on the configuration request, and the preprocessing may include data formatting, data cleaning, and scene labeling.

[0076] Data formatting can be used to standardize the data of configuration requests, standardizing the data in all configuration requests into a unified text format or structured data form to facilitate subsequent processing and avoid reducing the accuracy of the processing results of the intent understanding model due to non-standard data.

[0077] Data cleaning can remove and configure irrelevant data, reduce information redundancy, reduce interference from irrelevant information, and improve the output efficiency and accuracy of the intent understanding model.

[0078] Scenario annotation can be used to annotate the application scenarios of configuration requests so that the intent understanding model can understand the configuration requirements in different scenarios and improve the accuracy of the target configuration intent.

[0079] In an optional embodiment, before determining the target configuration intent based on the configuration request, it also includes: performing anomaly detection on the configuration request through an intent understanding model; if the detection result is that an anomaly exists, determining a correction suggestion, and feeding back the correction suggestion to the user; obtaining user correction feedback on the correction suggestion, and correcting the abnormal content in the configuration request based on the user correction feedback.

[0080] Anomaly detection can be used to detect anomalies such as description contradictions in the configuration request. For example, the anomaly can be increasing or decreasing parameter A. Correction suggestions can be correction suggestions for the anomaly to prompt the user to modify the method. For example, the anomaly is increasing or decreasing parameter A, and the correction suggestion can be "It is recommended to correct it by increasing parameter A or decreasing parameter A".

[0081] Optionally, anomaly detection may include consistency detection. When a configuration request involves multiple subsystems or services, the dependencies and service links between the multiple subsystems or services are analyzed to ensure the consistency of the overall configuration. When consistency is not met, the detection result is anomaly, and correction suggestions are determined based on the cause of the consistency, and the correction suggestions are fed back to the user.

[0082] After the correction suggestion is fed back to the user, the user's correction feedback on the correction suggestion is obtained, and the abnormal content in the configuration request is replaced based on the user's correction feedback. The user correction feedback can be the user's feedback on the correction suggestion. For example, if the correction suggestion is "it is recommended to correct to increase parameter A or decrease parameter A", the user correction feedback can be "increase parameter A".

[0083] Before determining the target configuration intent based on the configuration request, an anomaly detection is performed on the configuration request through the intent understanding model; if the detection result is an anomaly, a correction suggestion is determined and the correction suggestion is fed back to the user; the user correction feedback on the correction suggestion is obtained, and the abnormal content in the configuration request is corrected based on the user correction feedback, the abnormal content in the configuration request is corrected, and the accuracy of the target configuration intent is improved.

[0084] S330: Obtain the user's reply to the query statement, and continue to analyze the user's intention according to the query reply.

[0085] The query statement is displayed to the user, and the user's reply to the query statement is obtained. The intent understanding model replaces the original unclear content with the query reply, and parses the user's intent based on the configuration request updated by the query reply.

[0086] S340: If it is found that the query reply intention is unclear, continue to generate query statements for the content with unclear intention until there is no content with unclear intention.

[0087] If it is found that the query reply intention is unclear, continue to generate query statements for the unclear intention content, repeat the above-mentioned S320 and S330 operations, and go through multiple rounds of querying until there is no unclear intention content.

[0088] S350: Determine a target configuration intention according to the configuration request and at least one query response.

[0089] The target configuration request is obtained by replacing the original unclear content with the query response. The intent understanding model determines the target configuration intent based on the target configuration request. When there are multiple rounds of queries, the corresponding content is replaced with all the query responses to obtain the target configuration request.

[0090] S360: Generate target configuration information of target configuration intent based on historical configuration data and current operating environment information for configuration management.

[0091] The technical solution of this embodiment is to generate a query statement for the unclear intention content if it is found that the configuration request intention is unclear when parsing the user intention through the intent understanding model; obtain the user's query reply to the query statement, and continue to parse the user intention based on the query reply; if it is found that the query reply intention is unclear, continue to generate query statements for the unclear intention content until there is no unclear intention content; determine the target configuration intention based on the configuration request and at least one query reply, and through multiple rounds of questioning, fully understand the user intention and improve the accuracy of the target configuration intention.

[0092] Embodiment 4

[0093] Figure 4 The figure shows a schematic diagram of the structure of an intelligent configuration device provided in the fourth embodiment of the present application. This embodiment can be applied to the case of intelligent configuration of a distributed system and is configured in a configuration management system. The specific structure of the intelligent configuration device is as follows:

[0094] The configuration request acquisition module 410 is used to acquire the configuration request of the user;

[0095] A target configuration intention determination module 420 is used to determine the target configuration intention according to the configuration request through an intention understanding model;

[0096] The target configuration information generation module 430 is used to generate target configuration information of the target configuration intention according to the historical configuration data and the current operating environment information for configuration management.

[0097] The technical solution of this embodiment obtains the configuration request of the user; determines the target configuration intent according to the configuration request through the intent understanding model, accurately understands the user's intent, and improves the accuracy of subsequent configuration; generates the target configuration information of the target configuration intent according to the historical configuration data and the current operating environment information for configuration management, which can adapt to the configuration environment and improve the intelligence of the configuration, and reduce labor costs and improve the efficiency of configuration by automatically generating the target configuration information. Therefore, the technical solution of this application solves the problem that manual configuration requires a high level of professionalism of the configuration personnel, has high labor costs, low efficiency, cannot quickly adapt to new configuration requirements, and has poor intelligence, and achieves the effect of improving the intelligence of the configuration, reducing labor costs, and improving configuration efficiency.

[0098] Optionally, the target configuration information generating module 430 includes:

[0099] The target configuration information generation unit is used to generate target configuration information of the target configuration intention based on the pre-trained large model, historical configuration data and current operating environment information, so as to perform configuration management.

[0100] Optionally, the intelligent configuration device also includes:

[0101] An operation feedback parameter generation module is used to monitor the operation parameters of the target system after the target configuration information is configured, and to generate operation feedback parameters according to the operation parameters;

[0102] The large model incremental training module is used to perform incremental training on the pre-trained large model based on a preset update cycle and according to the operation feedback parameters within the preset update cycle.

[0103] Optionally, the intelligent configuration device also includes:

[0104] A configuration risk prediction module is used to predict the configuration risk of target configuration information based on the current operating environment information;

[0105] The correction strategy determination module is used to determine the correction strategy for the configuration risk when the prediction result shows that there is a configuration risk.

[0106] Optionally, the target configuration intention determination module 420 includes:

[0107] The query statement generation unit is used for generating a query statement for the unclear intent content when the intent understanding model finds that the configuration request intent is unclear when parsing the user intent;

[0108] The inquiry response parsing unit is used to obtain the user's inquiry response to the inquiry statement, and continue to parse the user's intention according to the inquiry response;

[0109] The inquiry loop unit is used to continue to generate inquiry statements for the unclear intention content if it is found that the inquiry response intention is unclear, until there is no unclear intention content;

[0110] The target configuration intention determination unit is used to determine the target configuration intention based on the configuration request and at least one query response.

[0111] Optionally, the target configuration intention determination module 420 further includes:

[0112] Anomaly detection unit, used to detect anomalies of configuration requests through intent understanding model;

[0113] A correction suggestion feedback unit is used to determine correction suggestions if the detection result shows that an abnormality exists, and to feed back the correction suggestions to the user;

[0114] The configuration request correction unit is used to obtain user correction feedback on the correction suggestion from the user, and correct the abnormal content in the configuration request based on the user correction feedback.

[0115] The intelligent configuration device provided in the embodiments of the present application can execute the intelligent configuration method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects for executing the intelligent configuration method.

[0116] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0117] Embodiment 5

[0118] Figure 5 A schematic diagram of the structure of an electronic device provided in Embodiment 5 of the present application is shown in FIG. Figure 5 As shown, the electronic device includes a processor 510, a memory 520, an input device 530 and an output device 540; the number of the processor 510 in the electronic device can be one or more. Figure 5 A processor 510 is taken as an example; the processor 510, the memory 520, the input device 530 and the output device 540 in the electronic device can be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0119] The memory 520, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the intelligent configuration method in the embodiment of the present application (for example, the configuration request acquisition module 410, the target configuration intention determination module 420 and the target configuration information generation module 430). The processor 510 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 520, that is, realizing the above-mentioned intelligent configuration method.

[0120] The memory 520 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 520 may further include a memory remotely arranged relative to the processor 510, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0121] The input device 530 may be used to receive input character information and generate key signal input related to user settings and function control of the electronic device. The output device 540 may include a display device such as a display screen.

[0122] Embodiment 6

[0123] Embodiment 6 of the present application also provides a storage medium containing computer executable instructions, which, when executed by a computer processor, are used to execute an intelligent configuration method, the method comprising: obtaining a user's configuration request; determining a target configuration intent according to the configuration request through an intent understanding model; and generating target configuration information of the target configuration intent according to historical configuration data and current operating environment information for configuration management.

[0124] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the method operations described above, can also execute related operations in the intelligent configuration method provided in any embodiment of the present application.

[0125] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented with the help of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for an electronic device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0126] It is worth noting that in the embodiment of the above-mentioned intelligent configuration device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0127] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. An intelligent configuration method, characterized in that: include: Get the user's configuration request; Determine the target configuration intent according to the configuration request through the intent understanding model; According to the historical configuration data and the current operating environment information, the target configuration information of the target configuration intention is generated to perform configuration management.

2. The method according to claim 1, characterized in that The step of generating target configuration information of the target configuration intention according to the historical configuration data and the current operating environment information for configuration management includes: Through the pre-trained large model, the target configuration information of the target configuration intention is generated according to the historical configuration data and the current operating environment information to perform configuration management.

3. The method according to claim 2, characterized in that After generating the target configuration information of the target configuration intention according to the historical configuration data and the current operating environment information for configuration management, the method further includes: Monitoring the operating parameters of the target system after the target configuration information is configured, and generating operating feedback parameters according to the operating parameters; Based on a preset update cycle, incremental training is performed on the pre-trained large model according to the operation feedback parameters within the preset update cycle.

4. The method according to claim 1, characterized in that After generating the target configuration information of the target configuration intention according to the historical configuration data and the current operating environment information for configuration management, the method further includes: Performing configuration risk prediction on the target configuration information according to the current operating environment information; When the prediction result shows that there is a configuration risk, a correction strategy for the configuration risk is determined.

5. The method according to claim 1, characterized in that The determining the target configuration intent according to the configuration request through the intent understanding model includes: When the intention understanding model analyzes the user intention, if it is found that the configuration request intention is unclear, a query statement is generated for the unclear intention content; Obtaining a user's response to the query statement, and continuing to analyze the user's intention based on the query response; If it is found that the query reply intention is unclear, continue to generate query statements for the unclear intention content until there is no unclear intention content; The target configuration intention is determined according to the configuration request and at least one query response.

6. The method according to claim 5, characterized in that Before determining the target configuration intention according to the configuration request, the method further includes: Perform anomaly detection on the configuration request through an intent understanding model; If the detection result shows that there is an abnormality, a correction suggestion is determined and the correction suggestion is fed back to the user; Obtain user correction feedback on the correction suggestion, and correct the abnormal content in the configuration request based on the user correction feedback.

7. An intelligent configuration device, characterized in that: include: A configuration request acquisition module is used to obtain the user's configuration request; A target configuration intention determination module, used to determine the target configuration intention according to the configuration request through an intention understanding model; The target configuration information generation module is used to generate the target configuration information of the target configuration intention according to the historical configuration data and the current operating environment information for configuration management.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the intelligent configuration method as described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the intelligent configuration method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the intelligent configuration method according to any one of claims 1 to 6.

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