Software deployment method and device, computer equipment and readable storage medium

Abstract

The invention relates to a software deployment method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a demand prompt text for representing a software deployment demand; inputting the demand prompt text into the intelligent model, and taking the output deployment scheme as a target deployment scheme; in the process of performing software deployment based on the target deployment scheme, obtaining an actual output log; acquiring a preset output log when the target deployment scheme does not have a deployment exception in the software deployment process, and acquiring an exception prompt text for representing the exception when the exception exists in the deployment process under the condition that a difference exists between the preset output log and an actual output log; and inputting the exception prompt text and the demand prompt text into the intelligent model, and taking the output deployment scheme as a target deployment scheme. The deployment scheme can be generated based on the demand prompt text representing the deployment demand, so that the deployment scheme can be matched with actual demands such as environment and operation and maintenance.

Description

Technical Field

[0001] The present application relates to the field of application technology, and in particular to a software deployment method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] At present, traditional software deployment methods face many challenges. The confidence of deployment solutions is often low due to incompatibility between software and system versions or limitations of the deployment system environment. When encountering abnormal logs during software deployment, manual retrieval of abnormal logs is required to find solutions. The whole process is time-consuming and cumbersome, which will affect the progress of software deployment and increase operating costs and risks. Summary of the invention

[0003] Based on this, it is necessary to provide a software deployment method, apparatus, computer device, computer-readable storage medium and computer program product that can improve software deployment efficiency in response to the above technical problems.

[0004] In a first aspect, the present application provides a software deployment method, comprising:

[0005] Obtain requirement prompt text for representing software deployment requirements;

[0006] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0007] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0008] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0009] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0010] In one of the embodiments, the requirement prompt text includes at least one of an environment prompt text for representing a software running environment or an operation and maintenance prompt text for representing a software operation and maintenance requirement.

[0011] In one embodiment, each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log; the difference comparison process between the preset output log and the actual output log includes:

[0012] For each deployment step in the target deployment scheme, the deployment record corresponding to the deployment step in the preset output log is compared with the deployment record corresponding to the actual output log; wherein, in case of inconsistency, it indicates that there is a deployment anomaly in the deployment step.

[0013] In one embodiment, after the abnormal prompt text and the requirement prompt text are input into the intelligent model and the output deployment plan is used as the target deployment plan again, the following steps are further included:

[0014] Returns the step of obtaining the actual output log in the process of software deployment based on the target deployment plan and continues to execute until the iteration end condition of the deployment plan is met.

[0015] In one of the embodiments, the iteration end condition includes that there is no difference between the preset output log and the actual output log.

[0016] In one embodiment, the method further comprises:

[0017] In the case where there is a difference between the preset output log and the actual output log, the input and output of the intelligent model when obtaining the actual output log are used as negative samples;

[0018] When there is no difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are taken as positive samples;

[0019] The intelligent model is trained based on positive samples and negative samples.

[0020] In a second aspect, the present application also provides a software deployment device, including:

[0021] A first acquisition module is used to acquire a requirement prompt text for representing software deployment requirements;

[0022] A processing module is used to input the requirement prompt text into the intelligent model and use the output deployment plan as the target deployment plan;

[0023] The second acquisition module is used to obtain the actual output log during the software deployment based on the target deployment solution;

[0024] The third acquisition module is used to obtain the preset output log of the target deployment solution when no deployment exception occurs during the software deployment process. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and an exception prompt text used to characterize the exception is obtained;

[0025] The processing module is also used to input the exception prompt text and the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan again.

[0026] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0027] Obtain requirement prompt text for representing software deployment requirements;

[0028] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0029] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0030] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0031] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0033] Obtain requirement prompt text for representing software deployment requirements;

[0034] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0035] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0036] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0037] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0038] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0039] Obtain requirement prompt text for representing software deployment requirements;

[0040] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0041] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0042] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0043] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0044] The above-mentioned software deployment method, device, computer equipment, computer-readable storage medium and computer program product generate a deployment plan according to the input demand prompt text used to characterize the software deployment requirements by utilizing the powerful data processing and learning capabilities of the large model. In the process of executing the deployment plan, the actual output log is obtained, and the actual output log is compared with the preset output log, so as to discover the deployment abnormality through the difference. The large model is used again to regenerate the deployment plan according to the demand prompt text and the abnormal prompt text used to characterize the abnormality. Since the deployment plan can be generated based on the demand prompt text characterizing the deployment requirements, the deployment plan can be matched with the actual requirements such as the environment and operation and maintenance. Therefore, it can adapt to the changing server environment, generate highly customized deployment plans, and significantly reduce human configuration errors. In addition, since the abnormal prompt text can be generated based on the captured deployment abnormality, and the deployment plan can be regenerated based on the large model, it is ensured that when encountering actual deployment problems, the deployment strategy can be automatically adjusted, the problem can be quickly responded and solved, and the success rate of deployment and the stability of the system are greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] FIG1 is a schematic diagram of a process of manually deploying software in one embodiment;

[0047] FIG2 is an application environment diagram of a software deployment method in one embodiment;

[0048] FIG3 is a schematic diagram of a flow chart of a software deployment method in one embodiment;

[0049] FIG4 is a schematic diagram of a process of software deployment based on a large model and prompt in one embodiment;

[0050] FIG5 is a structural block diagram of a software deployment device in one embodiment;

[0051] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] At present, when software is deployed, if an abnormal log is encountered, it is usually manually retrieved, and a new deployment plan is obtained based on the search results before software deployment is performed; if an abnormality occurs again, the above manual process needs to be repeated until the deployment is successful. The above process can be specifically referred to in Figure 1. In Figure 1, it is mainly through manual repeated retrieval of abnormal logs, repeated adjustment of deployment plans, and then software deployment on the server. It can be seen that the whole process is time-consuming and cumbersome, which will affect the progress of software deployment and increase operating costs and risks.

[0054] With the development of large model technology, its powerful data processing and learning capabilities provide new possibilities for automated and intelligent software deployment. The software deployment method provided in the embodiment of the present application can be applied to the application environment as shown in Figure 2. Among them, the terminal 202 communicates with the server 204 via the network. The data storage system can store the data that the server 204 needs to process. The data storage system can be integrated on the server 204, or it can be placed on the cloud or other network servers. The terminal 202 can upload a demand prompt text to the server 204 for characterizing the software deployment requirements, so that the server 204 can obtain the demand prompt text.

[0055] The server 204 inputs the demand prompt text into the intelligent model, and uses the output deployment scheme as the target deployment scheme; in the process of software deployment based on the target deployment scheme, the actual output log is obtained; the preset output log when the target deployment scheme does not have a deployment exception during the software deployment process is obtained, and when there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and the abnormal prompt text used to characterize the abnormality is obtained; the abnormal prompt text and the demand prompt text are input into the intelligent model, and the output deployment scheme is used again as the target deployment scheme. After obtaining the target deployment scheme, the server 204 can be deployed according to the target deployment scheme. Among them, the terminal 202 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality devices, smart glasses, etc. The server 204 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0056] In an exemplary embodiment, as shown in FIG3 , a software deployment method is provided, which is described by taking the method applied to the server in FIG2 as an example, and includes the following steps 302 to 306. Among them:

[0057] Step 302: Obtain a requirement prompt text for representing the software deployment requirement.

[0058] Among them, the software deployment requirements may refer to the required configuration and required operating environment of the server on which the software is deployed; they may also refer to the type and version of the service components and the type and version of the servlet container that the engineering personnel need to install, which is not specifically limited in the embodiments of the present application. The requirement prompt text is mainly the text obtained by textualizing the above content. For example, if the required configuration of the server on which the software is deployed is a 6-core CPU, 32G memory, and a 480G solid-state drive, the corresponding requirement prompt text may be "6 cores, 32G memory; 1x 480GB SSD storage". The requirement prompt text may be input by relevant staff through the terminal, which is not specifically limited in the embodiments of the present application.

[0059] Step 304: input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan.

[0060] Specifically, intelligent models can refer to large models LLM (Large Language Model). Large models refer to machine learning models with large-scale parameters and complex computational structures. These models are usually built with deep neural networks and have billions or even hundreds of billions of parameters. Large models are designed to improve the expressiveness and predictive performance of the model and to handle more complex tasks and data. Large models are widely used in various fields, including natural language processing, computer vision, speech recognition, and recommendation systems. Large models learn complex patterns and features by training massive amounts of data, have stronger generalization capabilities, and can make accurate predictions on unseen data.

[0061] The deployment plan refers to the specific steps of software deployment. For example, the deployment plan may include: (1) uploading the archive file of the service program and decompressing it; (2) adding startup parameters; (3) adding a new service file; (4) adding execution permissions; (5) reloading the service program; (6) starting the corresponding service of the service program. By inputting the requirement prompt text into the intelligent model, the deployment plan can be output. It can be understood that the deployment plan output at this time can be used as the initial deployment plan. For the convenience of subsequent description, the output deployment plan is used as the target deployment plan in this step.

[0062] Step 306: in the process of software deployment based on the target deployment solution, obtain the actual output log.

[0063] Specifically, you can use the exception log capture plug-in to monitor the logs generated during the software deployment process in real time as the actual output log. For example, the actual output log of the exception can be as follows:

[0064] script ' / etc / keepalived / script / checkmysql.sh' is not executable foruid:gid 0:0

[0065] The above content indicates that the script checkmysql.sh in the directory ' / etc / keepalived / script / ' does not have execution permission.

[0066] Step 308, obtaining a preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and an exception prompt text used to characterize the exception is obtained.

[0067] As can be seen from the previous content, the deployment plan refers to the specific steps of software deployment. When each step is clear, the normal output log when executing each step without deployment exceptions can also be clear. Therefore, you can first obtain the preset output log of the target deployment plan when no deployment exceptions occur during the software deployment process. Compare the preset output log with the actual output log to determine whether there is a difference between the two. If there is a difference, it means that some steps may have exceptions when the target deployment plan is executed, so the exception prompt text used to characterize the exception can be obtained. For example, the exception prompt text can be:

[0068] "Deployment plan step 20, start the keepalived service, execute the command / etc / init.d / keepalivedstart, and report an error "script ' / etc / keepalived / script / checkmysql.sh' is not executablefor uid:gid 0:0"; keepalived version 2.22; service installation directory / usr / local / keepalived / "

[0069] The above content indicates that when executing step 20 and executing the instruction to start the keepalived service, an error occurs. Among them, the version of the keepalived service component is 2.22 and the installation directory of the service component is given. It should be noted that the number of exceptions corresponding to the abnormal prompt text can be more than one, and the prompt text corresponding to each abnormality can be integrated into one abnormal prompt text, or each abnormality can correspond to one abnormal prompt text, which is not specifically limited in the embodiment of the present application.

[0070] Step 310: Input the abnormal prompt text and the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan again.

[0071] After incorporating the exception prompt text, the deployment plan can be output again through the intelligent model based on the requirement prompt text. After the deployment plan is obtained, it can be used as the target deployment plan and executed.

[0072] In the above software deployment method, by utilizing the powerful data processing and learning capabilities of the big model, a deployment plan is generated according to the input demand prompt text used to characterize the software deployment requirements. In the process of executing the deployment plan, the actual output log is obtained, and the actual output log is compared with the preset output log, so that the deployment abnormality is discovered through the difference. The big model is used again to regenerate the deployment plan according to the demand prompt text and the abnormal prompt text used to characterize the abnormality. Since the deployment plan can be generated based on the demand prompt text that characterizes the deployment requirements, the deployment plan can be matched with the actual requirements such as the environment and operation and maintenance. Therefore, it can adapt to the changing server environment, generate highly customized deployment plans, and significantly reduce human configuration errors. In addition, since the abnormal prompt text can be generated based on the captured deployment abnormality, and the deployment plan can be regenerated based on the big model, it is ensured that when encountering actual deployment problems, the deployment strategy can be automatically adjusted, and the problem can be quickly responded and solved, which greatly improves the success rate of deployment and the stability of the system.

[0073] In an exemplary embodiment, the requirement prompt text includes at least one of an environment prompt text for characterizing a software running environment or an operation and maintenance prompt text for characterizing a software operation and maintenance requirement.

[0074] Among them, both the environment prompt text and the operation and maintenance prompt text can be expressed by prompt. For example, the prompt corresponding to the environment prompt text can be:

[0075] "The operating system is Linux 7.6; the number of cores is 6 and the memory is 32G; the storage is 1x 480GB SSD; the network can connect to the external network; the bandwidth is 100Mbps."

[0076] The corresponding prompt for the operation and maintenance prompt text can be:

[0077] "Build a highly available load balancing system with two servers; use nginx version 1.16.1; use keepalived version adaptation; use tomcat as the web server; use dual-master configuration deployment."

[0078] In this embodiment, since the powerful data processing and learning capabilities of the large model can be used to generate a deployment plan based on the environment prompt text and operation and maintenance prompt text, the deployment plan can be matched with the actual needs of the environment, operation and maintenance, etc. Therefore, it can adapt to the changing server environment, generate highly customized deployment plans, and significantly reduce human configuration errors.

[0079] In an exemplary embodiment, each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log; the difference comparison process between the preset output log and the actual output log includes:

[0080] For each deployment step in the target deployment scheme, the deployment record corresponding to the deployment step in the preset output log is compared with the deployment record corresponding to the actual output log; wherein, in case of inconsistency, it indicates that there is a deployment anomaly in the deployment step.

[0081] Specifically, as can be seen from the previous content, the deployment plan refers to the specific steps of software deployment; when each step is clear, the normal output log when executing each step without deployment exceptions can also be clear, and each deployment step will have a corresponding deployment record in the output log. Therefore, the corresponding deployment records of each deployment step in the preset output log and the actual output log can be compared. If they are inconsistent, it can be considered that there is a deployment exception in the corresponding deployment step.

[0082] In this embodiment, since deployment exceptions can be captured to generate exception prompt text, and the deployment plan is regenerated based on the large model, it is ensured that when actual deployment problems are encountered, the deployment strategy can be automatically adjusted to quickly respond and solve the problems, greatly improving the deployment success rate and system stability.

[0083] In an exemplary embodiment, after the abnormal prompt text and the requirement prompt text are input into the intelligent model and the output deployment scheme is used again as the target deployment scheme, the following further includes:

[0084] Returns the step of obtaining the actual output log in the process of software deployment based on the target deployment plan and continues to execute until the iteration end condition of the deployment plan is met.

[0085] Specifically, after the exception prompt text and the requirement prompt text are input into the intelligent model, the intelligent model can output a deployment plan. In actual implementation, the deployment plan can be directly used as the final target deployment plan and executed. It is also possible to return to the previous step of "obtaining the actual output log during software deployment based on the target deployment plan" and continue to execute, thereby iteratively updating the target deployment plan. Among them, the iteration end condition can be that the number of iterations reaches a preset number, which is not specifically limited in the embodiments of the present application.

[0086] In this embodiment, since an exception prompt text can be generated based on the captured deployment exception situation, and the exception prompt text can be input into the large model to trigger the iterative update of the deployment plan, it is ensured that when encountering actual deployment problems, the deployment strategy can be automatically adjusted to respond and solve the problem quickly, which greatly improves the deployment success rate and system stability.

[0087] In an exemplary embodiment, the iteration end condition includes that there is no difference between the preset output log and the actual output log.

[0088] Among them, there is no difference between the preset output log and the actual output log, which can indicate that there is no abnormality when the corresponding target deployment plan is executed.

[0089] In this embodiment, since an exception prompt text can be generated based on the captured deployment exception situation, the exception prompt text can be input into the large model to trigger the iterative update of the deployment plan, and the iterative update is stopped when there is no execution exception in the deployment plan, it is ensured that when encountering actual deployment problems, the deployment strategy can be automatically adjusted to respond and solve the problem quickly, greatly improving the deployment success rate and system stability.

[0090] In an exemplary embodiment, the method further comprises:

[0091] In the case where there is a difference between the preset output log and the actual output log, the input and output of the intelligent model when obtaining the actual output log are used as negative samples;

[0092] When there is no difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are taken as positive samples;

[0093] The intelligent model is trained based on positive samples and negative samples.

[0094] Specifically, for a specific execution round of the iterative update process, if there is a difference between the actual output log obtained in the execution round and the preset output log, the input of the intelligent model in the execution round (that is, the demand prompt text and the abnormal prompt text) and the output of the intelligent model (that is, the target deployment plan obtained according to the input) can be used as negative samples together. Similarly, if there is no difference between the actual output log obtained in the execution round and the preset output log, the input and output of the intelligent model in the execution round can be used as positive samples together. Based on positive samples and negative samples, the intelligent model can be trained again.

[0095] In this embodiment, based on the captured deployment anomalies, the input and output of the large model in abnormal situations can be used as negative samples, and the input and output in normal situations can be used as positive samples to train the large model. Therefore, the knowledge base of the large model can be expanded, and the prediction and adaptability of the large model can be improved, so as to enhance the accuracy and adaptability of the large model when generating deployment solutions.

[0096] For ease of understanding, the complete method flow provided by the embodiment of the present application is now described. Take the intelligent model as a large model and the prompt text as prompt as an example. Referring to FIG. 4 , the specific implementation can be shown in the following process steps:

[0097] (1) Based on the environment prompt and operation prompt, obtain the initial prompt.

[0098] (2) Input the initial prompt into the large model and output the initial deployment plan.

[0099] (3) Deploy the software according to the initial deployment plan and capture exceptions through the log exception plug-in.

[0100] (4) When a deployment exception occurs, obtain the exception log and update the prompt based on the exception log.

[0101] The exception log may correspond to the "actual output log when there is a difference between the preset output log and the actual output log" in the previous content. When the prompt is updated based on the exception log, the exception prompt (corresponding to the exception prompt text in the previous content) may be obtained based on the exception log, that is, the prompt is updated.

[0102] (5) Input the updated prompt into the large model and re-output the deployment plan. If the deployment plan does not meet the end condition, the previous process can be iteratively executed until the iteration ends.

[0103] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0104] Based on the same inventive concept, the embodiment of the present application also provides a software deployment device for implementing the software deployment method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more software deployment device embodiments provided below can refer to the limitations on the software deployment method above, and will not be repeated here.

[0105] In an exemplary embodiment, as shown in FIG5 , a software deployment device is provided, including: a first acquisition module 502 , a processing module 504 , a second acquisition module 506 and a third acquisition module 508 , wherein:

[0106] A first acquisition module 502 is used to acquire a requirement prompt text for representing a software deployment requirement;

[0107] Processing module 504, used to input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0108] The second acquisition module 506 is used to acquire the actual output log during the software deployment based on the target deployment solution;

[0109] The third acquisition module 508 is used to obtain the preset output log when no deployment exception occurs in the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and an exception prompt text used to characterize the exception is obtained;

[0110] The processing module 504 is further used to input the abnormal prompt text and the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan again.

[0111] In an exemplary embodiment, the requirement prompt text includes at least one of an environment prompt text for characterizing a software running environment or an operation and maintenance prompt text for characterizing a software operation and maintenance requirement.

[0112] In an exemplary embodiment, each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log respectively; the processing module 504 is also used to compare the corresponding deployment record of the deployment step in the preset output log with the corresponding deployment record in the actual output log for each deployment step in the target deployment scheme; wherein, in the case of inconsistency, it is characterized that there is a deployment anomaly in the deployment step.

[0113] In an exemplary embodiment, the processing module 504 is further used to return to the step of obtaining the actual output log in the process of software deployment based on the target deployment scheme and continue to execute until the iteration end condition of the deployment scheme is met.

[0114] In an exemplary embodiment, the iteration end condition includes that there is no difference between the preset output log and the actual output log.

[0115] In an exemplary embodiment, the processing module 504 is also used to, when there is a difference between the preset output log and the actual output log, use the input and output of the intelligent model when obtaining the actual output log as negative samples; when there is no difference between the preset output log and the actual output log, use the input and output of the intelligent model when obtaining the actual output log as positive samples; and train the intelligent model based on the positive samples and the negative samples.

[0116] Each module in the above software deployment device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0117] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store intelligent models. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a software deployment method is implemented.

[0118] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0120] Obtain requirement prompt text for representing software deployment requirements;

[0121] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0122] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0123] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0124] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0125] In one embodiment, when the processor executes the computer program, the requirement prompt text includes at least one of an environment prompt text for representing the software running environment or an operation and maintenance prompt text for representing the software operation and maintenance requirement.

[0126] In one embodiment, each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log; when the processor executes the computer program, the following steps are also implemented:

[0127] For each deployment step in the target deployment scheme, the deployment record corresponding to the deployment step in the preset output log is compared with the deployment record corresponding to the actual output log; wherein, in case of inconsistency, it indicates that there is a deployment anomaly in the deployment step.

[0128] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0129] Returns the step of obtaining the actual output log in the process of software deployment based on the target deployment plan and continues to execute until the iteration end condition of the deployment plan is met.

[0130] In one embodiment, when the processor executes the computer program, the iteration end condition includes that there is no difference between the preset output log and the actual output log.

[0131] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0132] In the case where there is a difference between the preset output log and the actual output log, the input and output of the intelligent model when obtaining the actual output log are used as negative samples;

[0133] When there is no difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are taken as positive samples;

[0134] The intelligent model is trained based on positive samples and negative samples.

[0135] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0136] Obtain requirement prompt text for representing software deployment requirements;

[0137] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0138] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0139] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0140] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0141] In one embodiment, when the computer program is executed by a processor, the requirement prompt text includes at least one of an environment prompt text for representing the software running environment or an operation and maintenance prompt text for representing the software operation and maintenance requirement.

[0142] In one embodiment, each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log; when the computer program is executed by the processor, the following steps are also implemented:

[0143] For each deployment step in the target deployment scheme, the deployment record corresponding to the deployment step in the preset output log is compared with the deployment record corresponding to the actual output log; wherein, in case of inconsistency, it indicates that there is a deployment anomaly in the deployment step.

[0144] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0145] Returns the step of obtaining the actual output log in the process of software deployment based on the target deployment plan and continues to execute until the iteration end condition of the deployment plan is met.

[0146] In one embodiment, when the computer program is executed by a processor, the iteration end condition includes that there is no difference between the preset output log and the actual output log.

[0147] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0148] In the case where there is a difference between the preset output log and the actual output log, the input and output of the intelligent model when obtaining the actual output log are used as negative samples;

[0149] When there is no difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are taken as positive samples;

[0150] The intelligent model is trained based on positive samples and negative samples.

[0151] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0152] Obtain requirement prompt text for representing software deployment requirements;

[0153] Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan;

[0154] During the software deployment process based on the target deployment plan, obtain the actual output log;

[0155] Obtain the preset output log when no deployment exception occurs during the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtain the exception prompt text used to characterize the exception.

[0156] The exception prompt text and the requirement prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

[0157] In one embodiment, when the computer program is executed by a processor, the requirement prompt text includes at least one of an environment prompt text for representing the software running environment or an operation and maintenance prompt text for representing the software operation and maintenance requirement.

[0158] In one embodiment, each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log; when the computer program is executed by the processor, the following steps are also implemented:

[0159] For each deployment step in the target deployment scheme, the deployment record corresponding to the deployment step in the preset output log is compared with the deployment record corresponding to the actual output log; wherein, in case of inconsistency, it indicates that there is a deployment anomaly in the deployment step.

[0160] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0161] Returns the step of obtaining the actual output log in the process of software deployment based on the target deployment plan and continues to execute until the iteration end condition of the deployment plan is met.

[0162] In one embodiment, when the computer program is executed by a processor, the iteration end condition includes that there is no difference between the preset output log and the actual output log.

[0163] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0164] In the case where there is a difference between the preset output log and the actual output log, the input and output of the intelligent model when obtaining the actual output log are used as negative samples;

[0165] When there is no difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are taken as positive samples;

[0166] The intelligent model is trained based on positive samples and negative samples.

[0167] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0168] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0169] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A software deployment method, characterized in that: The method comprises: Obtain requirement prompt text for representing software deployment requirements; Input the requirement prompt text into the intelligent model, and use the output deployment plan as the target deployment plan; In the process of software deployment based on the target deployment scheme, obtaining actual output logs; Obtaining a preset output log when no deployment exception occurs in the software deployment process of the target deployment solution; if there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and obtaining an exception prompt text for characterizing the exception; The abnormal prompt text and the demand prompt text are input into the intelligent model, and the output deployment plan is used again as the target deployment plan.

2. The method according to claim 1, characterized in that The requirement prompt text includes at least one of an environment prompt text for representing a software running environment or an operation and maintenance prompt text for representing a software operation and maintenance requirement.

3. The method according to claim 1, characterized in that Each deployment step in the target deployment scheme has a corresponding deployment record in the preset output log and the actual output log respectively; The process of comparing the difference between the preset output log and the actual output log includes: For each deployment step in the target deployment scheme, the deployment record corresponding to the deployment step in the preset output log is compared with the deployment record corresponding to the actual output log; wherein, in case of inconsistency, it indicates that there is a deployment anomaly in the deployment step.

4. The method according to claim 1, characterized in that: After the abnormal prompt text and the demand prompt text are input into the intelligent model and the output deployment scheme is used as the target deployment scheme again, the method further includes: Return to the step of obtaining the actual output log in the process of software deployment based on the target deployment scheme and continue to execute until the iteration end condition of the deployment scheme is met.

5. The method according to claim 4, characterized in that The iteration end condition includes that there is no difference between the preset output log and the actual output log.

6. The method according to claim 4, characterized in that The method further comprises: In the case where there is a difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are used as negative samples; In the case where there is no difference between the preset output log and the actual output log, the input and output of the intelligent model when the actual output log is obtained are taken as positive samples; The intelligent model is trained based on the positive samples and the negative samples.

7. A software deployment device, characterized in that: The device comprises: A first acquisition module is used to acquire a requirement prompt text for representing software deployment requirements; A processing module, used for inputting the requirement prompt text into the intelligent model, and taking the output deployment plan as the target deployment plan; A second acquisition module is used to acquire actual output logs during the process of software deployment based on the target deployment solution; The third acquisition module is used to obtain the preset output log when no deployment exception occurs in the software deployment process of the target deployment solution. If there is a difference between the preset output log and the actual output log, there is an exception in the deployment process, and an exception prompt text for characterizing the exception is obtained; The processing module is also used to input the abnormal prompt text and the demand prompt text into the intelligent model, and use the output deployment plan as the target deployment plan again.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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