Method and system for automatically solving cloud desktop exception through AI artificial intelligence

Abstract

The invention relates to a method and system for automatically solving cloud desktop exception through AI artificial intelligence, an exception library is maintained, the library records all problems encountered on a project, the problems occurring in subsequent use can be automatically written into the library, and when a cloud desktop of a certain project is abnormal or a server is abnormal, the cloud desktop of the project is not abnormal or the server is abnormal. When an abnormal condition is detected, the abnormal condition can be automatically reported to an abnormal library of a company, the abnormal library automatically matches a solution according to error information, then a program automatically processes the abnormal condition and notifies a company administrator to timely follow up, and the process does not need manual intervention and is independently completed by the program.

Description

Technical Field

[0001] The present application relates to the field of cloud desktop technology, and in particular, to a method and system for automatically resolving cloud desktop anomalies using AI. Background Art

[0002] The cloud desktop projects currently in use all require implementation personnel to go on-site for on-site implementation and deployment. They are responsible for the on-site installation, configuration, and deployment of the software or system, ensuring that the product can run smoothly in the customer environment as planned, resolving technical problems encountered by customers during on-site use, ensuring stable system operation, and providing product use training to customers so that they can operate and use the system proficiently.

[0003] If problems arise during the use of the program after the project is delivered, and the customer base is weak, the implementation personnel will need to go to the site to solve the problem. Although this can bring advantages such as direct communication and rapid response to on-site problems, it also has some disadvantages, mainly including the following aspects: (1) High cost. Human cost: Sending implementation personnel to the site requires additional costs such as travel expenses, accommodation expenses, and transportation expenses, which increases the total cost of the project. Time cost: On-site implementation may involve long business trips, which will affect the implementation personnel's other work arrangements and may also extend the overall implementation cycle of the project. (2) Limited communication efficiency: Information transmission is delayed. On-site problems may need to be fed back to the remote team via telephone or email, which may lead to delays in information transmission and misunderstandings. (3) Strong dependence: Dependence on implementation personnel: On-site implementation is often highly dependent on the professional ability and experience of implementation personnel. Once the implementation personnel are unable to continue working for some reason, it may have a significant impact on the project progress. Dependence on the site environment: On-site implementation may be affected by factors such as the site environment and equipment conditions, resulting in unforeseen problems during implementation. (4) Increased risks: Data security risks. On-site implementation may involve the processing of sensitive data. If security measures are not in place, data leakage or loss may occur. (5) Difficulty in standardization and scalability: On-site implementation often requires customized operations based on specific circumstances, which may make the implementation process difficult to standardize and scale, increasing the difficulty of subsequent maintenance and upgrades. Summary of the invention

[0004] In order to overcome at least one deficiency in the prior art, the present application provides a method and system for automatically resolving cloud desktop anomalies using AI.

[0005] In a first aspect, a system for automatically solving cloud desktop anomalies using AI is provided, comprising: at least one cloud desktop, a first server and a second server; the first server is used to manage and maintain the cloud desktop; an AI library is provided in the second server; the AI ​​library stores abnormal situations and solutions in project operation;

[0006] During the project operation, when any cloud desktop or the first server has an abnormality, the first server sends the error information and logs to the second server through the message module;

[0007] The second server obtains key information based on the error information and logs, and interacts with the artificial intelligence library based on the key information to determine the abnormal conditions and solutions that match the key information; and sends the solutions to the cloud desktop or the first server where the abnormality occurs to repair the cloud desktop or the first server where the abnormality occurs.

[0008] In one embodiment, the first server and the second server are the same server, or two servers.

[0009] In one embodiment, when an abnormality occurs in the first server, the alarm module of the first server issues an alarm, and the alarm module packages the error information and logs and sends them to the second server through the message module.

[0010] In one embodiment, if there is no abnormal situation and solution matching the key information in the artificial intelligence library, the second server sends the error information and log to the maintenance personnel of the artificial intelligence library, and the maintenance personnel determines the solution to the abnormal situation and repairs the abnormal cloud desktop or first server; and stores the abnormal situation and solution in the artificial intelligence library.

[0011] In one embodiment, after the abnormal cloud desktop or first server is repaired, the abnormal situation is synchronously solved for the cloud desktop of the same model or the first server of other projects according to the abnormal situation and the solution.

[0012] In one embodiment, when an abnormality occurs in the cloud desktop, the cloud desktop packages the error information and logs and sends them to the first server.

[0013] In a second aspect, a method for automatically solving cloud desktop abnormalities using AI is provided, which is applied to a second server and includes:

[0014] During the project operation, when any cloud desktop or the first server encounters an abnormality, receive error information and logs;

[0015] Based on the error information and logs, key information is obtained, and based on the key information, the key information interacts with the artificial intelligence library to determine the abnormal conditions and solutions that match the key information; and the solutions are sent to the cloud desktop or the first server where the abnormality occurs.

[0016] In one embodiment, the method also includes: if there is no abnormal situation and solution matching the key information in the artificial intelligence library, the second server sends the error information and log to the maintenance personnel of the artificial intelligence library, and the maintenance personnel determines the solution to the abnormal situation and repairs the abnormal cloud desktop or first server; and stores the abnormal situation and solution in the artificial intelligence library.

[0017] In one embodiment, the method further includes: synchronously resolving the abnormal situation for the first server of the cloud desktop or other projects of the same model according to the abnormal situation and the solution.

[0018] Compared with the prior art, the present application has the following beneficial effects: the AI ​​artificial intelligence automated method and system for solving cloud desktop anomalies of the present application maintains an exception library, which includes all the problems encountered in the project, and the problems encountered in subsequent use will also be automatically written into the library. When an exception occurs in the code of a project or an exception occurs in a service, it will be automatically reported to the company's exception library. The exception library automatically matches the solution according to the error information, and then the program automatically handles the exception and notifies the company administrator to follow up in time. This process does not require human intervention and is completed independently by the program. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0020] Figure 1 The structural diagram of the system for automatically solving cloud desktop anomalies with AI is shown;

[0021] Figure 2 A flowchart of a method for automatically solving cloud desktop anomalies using AI is shown;

[0022] Figure 3 Shows a structural block diagram of multiple projects. DETAILED DESCRIPTION

[0023] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0024] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure closely related to the scheme according to the present application is shown in the drawings, while other details that are not closely related to the present application are omitted.

[0025] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0026] The present application embodiment provides an AI artificial intelligence system for automatically solving cloud desktop abnormalities. Figure 1 The structure diagram of the system for automatically solving cloud desktop anomalies with AI is shown in Figure 1 , including: at least one cloud desktop, a first server and a second server; the first server is used to manage and maintain the cloud desktop; the second server is provided with an artificial intelligence library; the artificial intelligence library stores abnormal situations and solutions in project operation. Here, the first server and the second server can be the same server or two servers. Establish an artificial intelligence library in the company, similar to a public network server, so that all projects delivered by the company can access it. The artificial intelligence library will include all abnormal situations and solutions encountered in the project.

[0027] During the project operation, that is, during the project implementation or maintenance, when any cloud desktop or the first server has an abnormality, the first server sends the error information and log to the second server through the message module; here, the project includes multiple delivered cloud desktops, and the abnormality may exist in the cloud desktop or in the first server.

[0028] The second server obtains key information based on the error information and logs, and interacts with the artificial intelligence library based on the key information to determine the abnormal conditions and solutions that match the key information; and sends the solutions to the cloud desktop or the first server where the abnormality occurs to repair the cloud desktop or the first server where the abnormality occurs.

[0029] Here, after obtaining the error information and logs, the first server cleans the data, obtains key information, and performs format conversion. The solution can be an executable script. After sending the script to the abnormal cloud desktop or the first server, the script can be remotely executed to repair the abnormal cloud desktop or the first server.

[0030] In one embodiment, if there is no abnormal situation and solution matching the key information in the artificial intelligence library, the second server sends the error information and log to the maintenance personnel of the artificial intelligence library, and the maintenance personnel determines the solution to the abnormal situation and repairs the abnormal cloud desktop or first server; and stores the abnormal situation and solution in the artificial intelligence library.

[0031] In one embodiment, after the abnormal cloud desktop or first server is repaired, the abnormal situation is synchronously solved for the cloud desktop of the same model or the first server of other projects according to the abnormal situation and the solution.

[0032] This embodiment mainly considers that the cloud desktop with the same model as the abnormal cloud desktop may also have the same abnormal situation. In order to prevent it before it happens, when the cloud desktop has an abnormal situation, the abnormal situation and the solution are sent to the cloud desktop of the same model, and the abnormal situation is synchronously solved in advance. Similarly, considering that the first server of other projects may also have the same abnormal situation, in order to prevent it before it happens, when the first server has an abnormal situation, the abnormal situation and the solution are sent to the first server of other projects, and the abnormal situation is synchronously solved in advance.

[0033] In one embodiment, when an abnormality occurs in the first server, the alarm module of the first server issues an alarm, and the alarm module packages the error information and logs and sends them to the second server through the message module.

[0034] In this embodiment, after the abnormal cloud desktop or the first server is repaired, the alarm information of the alarm module is displayed normally.

[0035] In one embodiment, when an abnormality occurs in the cloud desktop, the cloud desktop packages the error information and logs and sends them to the first server. Afterwards, the first server sends the error information and logs to the second server through the message module.

[0036] The embodiment of the present application also provides an AI artificial intelligence automated method for solving cloud desktop anomalies, which is applied to the second server. Figure 2 A flowchart showing the method of using AI to automatically solve cloud desktop anomalies is shown in Figure 2 ,include:

[0037] Step S1, during the project operation, when any cloud desktop or the first server has an abnormality, receiving error information and logs;

[0038] Step S2, obtaining key information based on error information and logs, and interacting with the artificial intelligence library based on the key information to determine abnormal situations and solutions that match the key information; and sending the solutions to the cloud desktop or the first server where the abnormality occurs.

[0039] Furthermore, the method also includes: if there is no abnormal situation and solution matching the key information in the artificial intelligence library, the second server sends the error information and log to the maintenance personnel of the artificial intelligence library, and the maintenance personnel determines the solution to the abnormal situation and repairs the abnormal cloud desktop or first server; and stores the abnormal situation and solution in the artificial intelligence library.

[0040] Furthermore, the method also includes: synchronously resolving the abnormal situation of the cloud desktop of the same model or the first server of other projects according to the abnormal situation and the solution. Figure 3 Shows a structural block diagram of multiple projects.

[0041] The AI ​​artificial intelligence automated method for solving cloud desktop anomalies in this embodiment has the same inventive concept as the AI ​​artificial intelligence automated system for solving cloud desktop anomalies mentioned above. Therefore, the specific implementation method of this method can be seen in the embodiment section of the system in the previous text, and its technical effect corresponds to the technical effect of the above method, which will not be repeated here.

[0042] In summary, this application has the following technical effects:

[0043] 1. Improved efficiency

[0044] Automated detection: AI systems can automatically analyze software codes, quickly locate potential bugs, and significantly improve the efficiency of bug discovery. Compared with traditional manual troubleshooting methods, this can greatly shorten the time it takes to discover and solve problems.

[0045] Instant feedback: Once a bug is detected, the AI ​​system can provide instant feedback to the development team, allowing the development team to respond quickly and take measures to fix the problem, reducing quality issues caused by bugs.

[0046] 2. Improved accuracy

[0047] Intelligent identification: Using machine learning algorithms, AI systems can learn from large amounts of data and identify more hidden and complex bugs, improving the accuracy and coverage of bug discovery. This helps reduce missed and false positives and improves software quality.

[0048] 3. Reduced costs

[0049] Reduced manpower investment: AI automated bug solving reduces the need for manual intervention and reduces the reliance on professionals. As a result, companies can reduce their investment in manpower costs.

[0050] Avoiding human errors: AI systems reduce the possibility of human error and subjectivity, improve the accuracy and reliability of bug resolution, and thus reduce the additional costs caused by bug fixing.

[0051] 4. Automation and intelligent support

[0052] Automatically generate fixes: In some cases, AI systems can not only detect bugs, but also automatically generate fixes or fix codes based on the specific circumstances of the bugs, further reducing the workload of the development team.

[0053] 5. Facilitate remote collaboration

[0054] Breaking geographical restrictions: AI’s remote bug-solving method breaks geographical restrictions, allowing development teams to collaborate to solve problems without face-to-face communication, improving the ability to optimize resource allocation.

[0055] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An AI artificial intelligence automated system for solving cloud desktop anomalies, characterized in that: include: At least one cloud desktop, a first server, and a second server; The first server is used to manage and maintain the cloud desktop; The second server is provided with an artificial intelligence library; The artificial intelligence library stores abnormal situations and solutions in project operation; During the operation of the project, when any cloud desktop or the first server has an abnormality, the first server sends the error information and logs to the second server through the message module; The second server obtains key information based on the error information and log, and interacts with the artificial intelligence library based on the key information to determine the abnormal situation and solution matching the key information; and sends the solution to the cloud desktop or the first server where the abnormality occurs to repair the cloud desktop or the first server where the abnormality occurs.

2. The system according to claim 1, characterized in that The first server and the second server are the same server, or two servers.

3. The system according to claim 1, characterized in that When an abnormality occurs in the first server, the alarm module of the first server issues an alarm, and the alarm module packages the error information and logs and sends them to the second server through the message module.

4. The system according to claim 1, characterized in that If there is no abnormal situation and solution matching the key information in the artificial intelligence library, the second server sends the error information and log to the maintenance personnel of the artificial intelligence library, and the maintenance personnel determines the solution to the abnormal situation and repairs the abnormal cloud desktop or first server; and stores the abnormal situation and solution in the artificial intelligence library.

5. The system according to claim 1 or 4, characterized in that: After the abnormal cloud desktop or first server is repaired, the abnormal situation is synchronously solved for the cloud desktop of the same model or the first server of other projects according to the abnormal situation and solution.

6. The system according to claim 1, wherein: When an abnormality occurs in the cloud desktop, the cloud desktop packages the error information and logs and sends them to the first server.

7. An AI artificial intelligence automated method for solving cloud desktop anomalies, applied to a second server, characterized in that: include: During the project operation, when any cloud desktop or the first server encounters an abnormality, receive error information and logs; Based on the error information and log, key information is obtained, and based on the key information, the key information is interacted with the artificial intelligence library to determine the abnormal situation and solution matching the key information; and the solution is sent to the cloud desktop or the first server where the abnormality occurs.

8. The method according to claim 7, characterized in that The method also includes: if there is no abnormal situation and solution matching the key information in the artificial intelligence library, the second server sends the error information and log to the maintenance personnel of the artificial intelligence library, and the maintenance personnel determines the solution to the abnormal situation and repairs the abnormal cloud desktop or first server; and stores the abnormal situation and solution in the artificial intelligence library.

9. The method according to claim 1 or 7, characterized in that: The method further includes: synchronously resolving the abnormal situation for the cloud desktop of the same model or the first server of other projects according to the abnormal situation and the solution.

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