Operating system deployment method and device, equipment, storage medium and program product

Abstract

The invention provides an operating system deployment method which can be applied to the field of cloud computing. The operating system deployment method comprises the steps that obtained attribute information of a target server in a target environment is input into a system recommendation model running in a system deployment engine, a system recommendation type is obtained, and the system recommendation model is obtained by training with the attribute information of a server with an operating system deployed in the target environment as a reference sample; according to historical deployment information of a system deployment engine, a target partitioning result corresponding to the system recommendation type is determined, and the target partitioning result is dynamically generated by using a partitioning algorithm called by the system deployment engine on the basis of system operation information of a server; and generating a system deployment scheme based on the system recommendation type and the target partition result, so that the target server completes installation of an operating system and initialization partition configuration based on the system deployment scheme. The invention further provides an operating system deployment device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing, and in particular to an operating system deployment method, device, equipment, medium and program product. Background Art

[0002] With the continuous development of server hardware technology, the hardware environment has become more and more complex. When selecting an operating system suitable for a server, it is usually necessary to make targeted recommendations based on the server's hardware information. During the deployment process, the operating system resources are manually partitioned according to the server's business needs to complete the deployment of the operating system.

[0003] In the process of implementing the concept of the present invention, the inventors found that there are at least the following problems in the related art: when faced with complex hardware environments and diverse business scenarios, it is easy for the recommended operating system to not match the business scenario. In addition, manual partitioning may cause the deployment of the operating system to have a low degree of match with the server hardware configuration, requiring repeated adjustments, and the efficiency of system deployment is low. Summary of the invention

[0004] In view of the above problems, the present invention provides an operating system deployment method, apparatus, device, medium and program product.

[0005] According to a first aspect of the present invention, there is provided an operating system deployment method, comprising: inputting the acquired attribute information of a target server in a target environment into a system recommendation model running in a system deployment engine to obtain a system recommendation type, wherein the target environment is an environment for deploying an operating system to a server, the system recommendation model is trained using the attribute information of a server in the target environment on which an operating system has been deployed as a reference sample, and the target server is connected to a computer on which the system deployment engine is deployed; determining a target partition result corresponding to the system recommendation type based on historical deployment information of the system deployment engine, wherein the target partition result is dynamically generated based on system operation information of the server using a partitioning algorithm called by the system deployment engine, and the target partition result is used to divide storage resources of the server; generating a system deployment plan based on the system recommendation type and the target partition result, so that the target server completes the installation of the operating system and initializes the partition configuration based on the system deployment plan.

[0006] The second aspect of the present invention provides an operating system deployment device, including: a system recommendation module, which is used to input the acquired attribute information of the target server in the target environment into the system recommendation model running in the system deployment engine to obtain a system recommendation type, wherein the above-mentioned target environment is an environment for deploying an operating system to the server, and the above-mentioned system recommendation model is trained using the attribute information of the server in the above-mentioned target environment on which the operating system has been deployed as a reference sample, and the above-mentioned target server is connected to a computer deployed with the above-mentioned system deployment engine; a partition determination module, which is used to determine the target partition result corresponding to the above-mentioned system recommendation type according to the historical deployment information of the above-mentioned system deployment engine, wherein the above-mentioned target partition result is dynamically generated based on the system operation information of the server using the partition algorithm called by the above-mentioned system deployment engine, and the above-mentioned target partition result is used to divide the storage resources of the server; and a system deployment module, which is used to generate a system deployment plan based on the above-mentioned system recommendation type and the above-mentioned target partition result, so that the above-mentioned target server completes the installation of the operating system and initializes the partition configuration based on the above-mentioned system deployment plan.

[0007] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0008] The fourth aspect of the present invention further provides a computer-readable storage medium on which a computer program or instruction is stored, and the steps of the above method are implemented when the above computer program or instruction is executed by a processor.

[0009] The fifth aspect of the present invention also provides a computer program product, including a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0010] According to an embodiment of the present invention, the attribute information of the target server is analyzed using the system recommendation model running in the system deployment engine to determine the system recommendation type that is adapted thereto. During the recommendation process, since the system recommendation model is trained based on the server attribute information of the operating system deployed in the target environment, the recommendation result can be better coordinated with the configuration of other servers in the target environment to achieve personalized recommendations. After determining the system recommendation type, the target partition result corresponding to the recommendation type is extracted from the historical deployment information. Since the target partition result is dynamically generated based on the system operation information of the server, during the deployment process, resource division is performed based on the target partition result, which can ensure that the deployment of the operating system is more closely matched with the attribute information of the server. At the same time, predetermining the target partition result effectively reduces the repeated adjustment of the initialization partition, significantly improving the efficiency and accuracy of system deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings.

[0012] Figure 1 An application scenario diagram of an operating system deployment method, apparatus, device, medium, and program product according to an embodiment of the present invention is shown.

[0013] Figure 2 A flow chart of an operating system deployment method according to an embodiment of the present invention is shown.

[0014] Figure 3 A schematic diagram of a decision tree in an operating system deployment method according to an embodiment of the present invention is shown.

[0015] Figure 4 A flowchart of model training and verification in an operating system deployment method according to an embodiment of the present invention is shown.

[0016] Figure 5 A data flow diagram showing the interaction between a target server and a computer in an operating system deployment method according to an embodiment of the present invention is shown.

[0017] Figure 6 A flow chart of an operating system deployment method according to another embodiment of the present invention is shown.

[0018] Figure 7 A structural block diagram of an operating system deployment device according to an embodiment of the present invention is shown.

[0019] Figure 8 A block diagram of an electronic device suitable for implementing an operating system deployment method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0021] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0022] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0023] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0024] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0025] An embodiment of the present invention provides an operating system deployment method, which includes: inputting the acquired attribute information of a target server in a target environment into a system recommendation model running in a system deployment engine to obtain a system recommendation type, wherein the target environment is an environment for deploying an operating system to a server, the system recommendation model is trained using the attribute information of a server in the target environment on which an operating system has been deployed as a reference sample, and the target server is connected to a computer on which a system deployment engine is deployed; according to historical deployment information of the system deployment engine, determining a target partition result corresponding to the system recommendation type, wherein the target partition result is dynamically generated based on system operation information of the server using a partition algorithm called by the system deployment engine, and the target partition result is used to divide storage resources of the server; based on the system recommendation type and the target partition result, generating a system deployment plan so that the target server completes the installation of the operating system and initializes the partition configuration based on the system deployment plan.

[0026] Figure 1 An application scenario diagram of an operating system deployment method, apparatus, device, medium, and program product according to an embodiment of the present invention is shown.

[0027] like Figure 1As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0028] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0029] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0030] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0031] It should be noted that the operating system deployment method provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the operating system deployment device provided in the embodiment of the present invention can generally be set in the server 105. The operating system deployment method provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the operating system deployment device provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0032] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0033] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The operating system deployment method according to the embodiment of the present invention is described in detail.

[0034] Figure 2 A flow chart of an operating system deployment method according to an embodiment of the present invention is shown.

[0035] like Figure 2 As shown, this embodiment includes operations S210 to S230.

[0036] In operation S210, the acquired attribute information of the target server in the target environment is input into the system recommendation model running in the system deployment engine to obtain a system recommendation type, wherein the target environment is an environment for deploying an operating system on the server, and the system recommendation model is trained using the attribute information of the server in the target environment on which the operating system has been deployed as a reference sample, and the target server is connected to a computer on which the system deployment engine is deployed.

[0037] In operation S220, a target partition result corresponding to the system recommended type is determined based on historical deployment information of the system deployment engine, wherein the target partition result is dynamically generated based on the system operation information of the server using a partitioning algorithm called by the system deployment engine, and the target partition result is used to divide the storage resources of the server.

[0038] In operation S230, a system deployment plan is generated based on the system recommendation type and the target partition result, so that the target server completes the installation of the operating system and the initialization partition configuration based on the system deployment plan.

[0039] According to an embodiment of the present invention, when deploying an operating system to a server in a target environment, since the hardware configurations of multiple servers in the environment vary greatly, and the servers that have deployed operating systems are all manually selected rather than completed by a system deployment engine, in order to ensure that the operating system recommended for the target server is better compatible with other servers in the environment, it is necessary to refer to the attribute information of the deployed servers in order to select the most suitable operating system for the target server.

[0040] According to an embodiment of the present invention, the training of the system recommendation model not only refers to the server attribute information of the deployed operating system in the target environment, but also integrates the historical data accumulated when the system was recommended by the system deployment engine in the past, so as to ensure the accuracy and adaptability of the recommendation results. Specifically, the attribute information obtained during the training process is different from the attribute information obtained during the system deployment. The attribute information in the training process also includes the operating system type, version, and partition information. This additional information provides a more comprehensive context for the model, enabling it to more accurately understand the relationship between the server configuration and the operating system, thereby improving the reliability and adaptability of the recommendation results.

[0041] According to an embodiment of the present invention, the acquisition of server attribute information in the target environment is completed through the collection tool in the system deployment engine, using the server's IPMI (Intelligent Platform Management Interface) and Redfish protocol to collect hardware information, and the SSH protocol (Secure Shell) to collect the operating system type, version and partition information of the installed server. The collected information is stored in the local collection library and used for the training and optimization of the system recommendation model, thereby improving the recommendation accuracy and adaptability of the model.

[0042] According to an embodiment of the present invention, the training of the system recommendation model not only refers to the server attribute information of the deployed operating system in the target environment, but also integrates the historical data accumulated in the past when the system was recommended through the system deployment engine, so as to ensure the accuracy and adaptability of the recommendation results. Specifically, the attribute information obtained during the training process is different from the attribute information obtained during the system deployment. The attribute information in the training process also includes the operating system type, version and partition information. The acquisition of server attribute information in the target environment is carried out through the collection tool in the system deployment engine. The server's IPMI (Intelligent Platform Management Interface) and Redfish protocol are used to collect the server's hardware information, and the SSH protocol (Secure Shell) is used to collect the system type, version and partition information of the installed server. The collected information falls into the collection library of this device for model training.

[0043] According to an embodiment of the present invention, during the operating system deployment process, storage resources need to be initialized and divided. Therefore, after obtaining the system recommended type, the target partition result corresponding to the recommended type can be retrieved from the historical deployment information of the system deployment engine. The partition results in these historical deployment information are dynamically generated by the system deployment engine based on the system operation information of the server by calling the partitioning algorithm. The final target partition result is a dynamically optimized partitioning scheme that can better meet the storage requirements and performance optimization goals of the server.

[0044] According to an embodiment of the present invention, the version and configuration requirements of the operating system are determined according to the system recommendation type, and the storage resource allocation strategy is planned in combination with the target partition result, including the partition size, file system type and mount point, so as to generate a system deployment plan. Based on the deployment plan, the target server automatically completes the installation of the operating system and initializes the partition configuration, ensuring the efficiency and accuracy of the deployment process.

[0045] According to an embodiment of the present invention, the attribute information of the target server is analyzed using the system recommendation model running in the system deployment engine to determine the system recommendation type that is adapted thereto. During the recommendation process, since the system recommendation model is trained based on the server attribute information of the operating system deployed in the target environment, the recommendation result can be better coordinated with the configuration of other servers in the target environment to achieve personalized recommendations. After determining the system recommendation type, the target partition result corresponding to the recommendation type is extracted from the historical deployment information. Since the target partition result is dynamically generated based on the system operation information of the server, during the deployment process, resource division is performed based on the target partition result, which can ensure that the deployment of the operating system is more closely matched with the attribute information of the server. At the same time, predetermining the target partition result effectively reduces the repeated adjustment of the initialization partition, significantly improving the efficiency and accuracy of system deployment.

[0046] According to an embodiment of the present invention, a system recommendation model includes a decision layer and an output layer; the acquired attribute information of the target server in the target environment is input into the system recommendation model running in the system deployment engine to obtain a system recommendation type, including: inputting the attribute information of the target server into the decision layer for analysis to determine the matching degree between the sub-attributes in the attribute information and the decision nodes in the decision layer, and traversing multiple decision nodes in the decision layer according to the matching degree to determine the decision result corresponding to the attribute information; and outputting the system recommendation type corresponding to the decision result through the output layer.

[0047] According to an embodiment of the present invention, the attribute information of the target server is input into the decision layer, and the attribute information is matched with multiple decision nodes in sequence through the tree structure of the decision tree in the decision layer. During the matching process, the system gradually traverses the nodes in the decision tree by calculating the matching degree between the attribute information and each decision node. In addition, since each decision node in the decision tree corresponds to a value range of a sub-attribute, the system can determine the value range to which it belongs based on the specific value of each sub-attribute in the attribute information, thereby selecting the corresponding decision node for matching. This method not only realizes the accurate traversal of the decision nodes, but also ensures the logic and efficiency of the matching process, providing a reliable basis for subsequent decision-making.

[0048] According to an embodiment of the present invention, after traversing the decision tree based on the attribute information, the corresponding leaf node in the decision tree is determined, and the leaf node is used as the final decision result. The system recommendation type corresponding to the leaf node is determined, and the system recommendation type is output using the output layer of the system recommendation model, thereby completing the recommendation of the target server operating system. The system displays the recommended operating system type and version for each server in the target environment that does not have an operating system installed to the user in a visual manner for the user to select and confirm. This process ensures the accuracy and logic of the system recommendation type, provides a reliable basis for the selection of the operating system of the target server, and improves the user experience and deployment efficiency.

[0049] According to an embodiment of the present invention, the system recommendation model is trained in the following manner: when it is determined that the attribute information of the external server different from the target environment obtained from the system deployment engine meets the predetermined conditions, the attribute information of the reference sample and the external server is divided equally to obtain multiple sample data sets; the following operations are repeated until multiple sample data sets are selected as verification sets: one is selected from multiple sample data sets as the verification set, and the rest are selected as training sets; the initial system recommendation model is trained using the training set to obtain a first prediction model; the prediction accuracy of the first prediction model is verified using the verification set to obtain a verification result; when it is determined that multiple sample data sets are selected as verification sets, the first prediction model whose prediction accuracy reaches a preset value in the multiple verification results is determined as the system recommendation model.

[0050] According to an embodiment of the present invention, it is determined whether the attribute information of the external server obtained from the system deployment engine meets the predetermined conditions. If the preset conditions are met, it indicates that the user agrees to use the current data for decision tree training. The attribute information of the reference sample and the external server is divided into multiple sample data sets in equal proportion. For example, if the collected external server samples are 80 and the server samples in the target environment are 20, the samples are divided into 5 data sets at a ratio of 1:4, and each data set contains 20 samples. Among them, each sample data set randomly selects 16 samples from the samples of the external server and 4 samples from the samples of the target environment server, and finally generates 5 non-intersecting sample data sets D1, D2, D3, D4, and D5. This division method ensures the balance of data distribution and provides diversified data support for model training.

[0051] According to an embodiment of the present invention, after completing the division of the sample data set, one of the data sets is selected as a validation set, and the remaining data sets are selected as training sets for training the initial system recommendation model. For example, D1 is selected as the validation set, and D2, D3, D4, and D5 are used as training sets for model training to generate four decision trees, namely the first prediction model. Subsequently, the generated first prediction model is predicted using the D1 sample data set, and its prediction accuracy is calculated. For example, if 15 of the 20 samples in D1 are correctly predicted, the prediction accuracy of the first prediction model is 0.75. This process evaluates the performance of the model by cross-validation, providing a reliable basis for subsequent model optimization.

[0052] According to an embodiment of the present invention, multiple sample data sets are iteratively trained, D2 is used as a validation set, and the remaining data sets (D1, D3, D4, D5) are used as training sets, and the above steps are repeated to generate a second first prediction model and calculate its prediction accuracy. By analogy, D3, D4, and D5 are used as validation sets, and the remaining data sets are used as training sets to generate the third, fourth, and fifth first prediction models, and their prediction accuracy is calculated respectively. Finally, from the five generated first prediction models, the model with a prediction accuracy of 1 is selected as the system recommendation model. If there is no model with a prediction accuracy of 1, the first prediction model with the highest prediction accuracy can be selected as the system recommendation model. This iterative training and selection process ensures the robustness of the model and the reliability of the recommendation results.

[0053] According to an embodiment of the present invention, the operating system deployment method also includes: displaying attribute information of an external server that is different from a target environment and obtained from a system deployment engine on an interactive interface of a computer; responding to a determination instruction for the attribute information of the external server, determining that the attribute information of the external server meets a predetermined condition; responding to a modification instruction for the attribute information of the external server, reacquiring the attribute information of the external server from the system deployment engine according to the modification information input through the interactive interface, until the acquired attribute information of the external server meets the predetermined condition.

[0054] According to an embodiment of the present invention, the attribute information of the external server obtained is displayed on the interactive interface of the computer on which the system deployment engine is deployed, and the user can view all selected samples through the interactive interface. If the system receives a confirmation instruction sent by the user through the interactive interface, it indicates that the user recognizes the current sample and believes that it meets the requirements of the target environment. At this time, the system determines that the attribute information of the external server meets the predetermined conditions, and performs subsequent model training based on this information. This interactive mechanism ensures the accuracy of the training data and the user's participation in data selection, thereby improving the reliability and practicality of model training.

[0055] According to an embodiment of the present invention, if a modification instruction is received from a user through an interactive interface, it indicates that the user wishes to make targeted adjustments to the current sample. According to the requirements input by the user through the interactive interface, the system re-searches for qualified samples from the system deployment engine, and displays the re-acquired external server attribute information on the interactive interface for further confirmation by the user. This process will be repeated until the user sends a confirmation instruction. After receiving the user's final confirmation, the system will start model training based on the adjusted sample data. This interactive mechanism ensures that users can flexibly adjust sample data, thereby improving the accuracy and applicability of model training.

[0056] According to an embodiment of the present invention, an initial system recommendation model is trained using a training set to obtain a first prediction model, including: determining information gain of the training set according to the type of sub-attribute of each attribute information in the training set to obtain a gain value of the sub-attribute; sorting multiple gain values ​​to determine the splitting attribute of the root node in the initial decision tree; dividing the training set into multiple sub-data sets according to multiple attribute value ranges of the splitting attribute, wherein the sub-nodes in the decision tree represent the sub-data sets; for each sub-node, repeating the following operations until multiple sub-attributes are traversed: determining the gain value of the remaining sub-attributes; and redetermining the splitting attribute; further dividing the sub-data sets according to the redetermined splitting attribute; when it is determined that multiple sub-attributes have been traversed, using the obtained target decision tree as a decision layer; connecting the decision layer to the output layer to obtain the first prediction model.

[0057] According to an embodiment of the present invention, in the process of training the initial system recommendation model, the information gain of each training set is calculated according to the operating system type of each attribute information in the training set. Specifically, the calculation method of the training set information gain is defined as shown in formula (1):

[0058]

[0059] Among them, p i represents the proportion of samples of the i-th category (i.e., different operating system types) in the training set D, j represents the total number of operating system types, and H(D) represents the information gain. For example, assuming that the training set D contains 200 samples, of which 80 samples have an operating system type of system a and 120 samples have an operating system type of system b, then the information gain H(D) of the training set D is: H(D)= =0.971. By calculating the information gain, we can quantify the distribution of different operating system types in the training set and provide data support for subsequent model training.

[0060] According to an embodiment of the present invention, based on the information gain of the training set, the gain value of each sub-attribute in the attribute information is calculated. The multiple gain values ​​calculated are sorted, and the sub-attribute with the largest gain value is selected as the splitting attribute of the root node. For example, assuming that the gain value of the graphics processing unit (GPU) video memory is the largest, the graphics processing unit video memory is used as the splitting attribute of the root node, that is, the first-level node. In this way, the decision tree can give priority to the attributes that contribute the most to the classification results for division, thereby improving the classification performance and efficiency of the model.

[0061] According to an embodiment of the present invention, the training set is divided into multiple sub-data sets according to different attribute value ranges of the splitting attributes, and corresponding child nodes are generated. For example, after the graphics processor video memory is used as the splitting attribute, two sub-nodes of "less than 16G" and "greater than or equal to 16G" are formed, and the training set is divided into sub-data sets corresponding to the sub-nodes. On each child node, the gain values ​​of the remaining sub-attributes are recalculated based on the sub-data set, and the sub-attribute with the largest gain value is selected as the new splitting attribute to construct the child nodes of the next layer. This process is performed recursively until the stopping condition is met. Through this recursive partitioning method, a complete decision tree structure is gradually constructed to provide efficient decision logic for the system recommendation model.

[0062] like Figure 3 As shown, the target decision tree uses the graphics processor memory as the splitting attribute of the root node 301, and its value range is divided into graphics processor memory less than 16G, graphics processor memory 16G-32G and graphics processor memory greater than 32G, which correspond to the three first child nodes 302 under the root node. In each child node, by calculating the sub-attribute gain value of the sub-data set, it is determined that the next layer of splitting attribute is the number of graph processor cores.

[0063] The value range of the number of graph processor cores is divided into graph processor cores less than 8, graph processor cores 8-16, and graph processor cores greater than 16, which respectively correspond to the three second child nodes 303. After further calculating the sub-attribute gain value, it is determined that the split attribute is the central processing unit (CPU) architecture.

[0064] The value range of the CPU architecture is divided into CPU architecture A and CPU architecture B, which correspond to two third child nodes 304 respectively. After continuing to calculate the sub-attribute gain value, the split attribute is determined to be the number of CPU cores, and its value range is divided into CPU cores less than 8, CPU cores 8-16, and CPU cores greater than 16, which correspond to three fourth child nodes 305 respectively. Through this recursive partitioning method, the target decision tree is finally constructed. Figure 3 As shown, the three leaf nodes 306 of the target decision tree respectively recommend system a, system b and system c.

[0065] According to an embodiment of the present invention, when constructing a target decision tree, the recursive stopping condition is that all sub-attributes have been traversed, or the gain values ​​are all 0 (i.e., no further division is possible). Through this recursive division method, a target decision tree is finally generated, and it is used as a decision layer for the decision process of the system recommendation model. Finally, the generated decision layer is connected to the output layer to construct a first prediction model, thereby completing the training of the initial system recommendation model. This process ensures the logic and accuracy of the model, and provides reliable support for the operating system recommendation of the target server.

[0066] According to an embodiment of the present invention, information gain is determined for a training set according to the type of sub-attribute of each attribute information in the training set to obtain a gain value of the sub-attribute, including: dividing the training set according to the type of operating system of each attribute information in the training set to obtain multiple sample sets; for each sample set, determining the intermediate gain values ​​of multiple sub-attributes in the attribute information respectively; and summing the intermediate gain values ​​of the same sub-attribute in multiple sample sets to obtain the gain value of the sub-attribute.

[0067] According to an embodiment of the present invention, when calculating the gain value of each sub-attribute, it is necessary to perform calculations for different operating system types. The training set is divided according to the operating system type of each attribute information in the training set to obtain multiple sample sets. For each sample set, the intermediate gain values ​​of multiple sub-attributes in the attribute information are calculated respectively. The intermediate gain value includes the gain value of the same sub-attribute under different operating system types and in different attribute value ranges. The intermediate gain values ​​of the same sub-attribute in multiple sample sets are summed to obtain the final gain value of the sub-attribute. The calculation method of the sub-attribute gain value is shown in formula (2):

[0068]

[0069] Where n represents the nth attribute value range corresponding to sub-attribute A in the sample set, v represents the total number of attribute value ranges corresponding to sub-attribute A, It represents the intermediate gain value of sub-attribute A, and IG(D,A) represents the gain value of sub-attribute A.

[0070] For example, the number of CPU cores (sub-attribute A) has three attribute value ranges: less than 8, 8-16, and greater than 16. Taking 200 samples as an example, there are 50 samples with less than 8 cores, of which 20 recommend system A and 30 recommend system B. There are 100 samples with 8-16 cores, of which 40 recommend system A and 60 recommend system B. There are 50 samples with more than 16 cores, of which 20 recommend system A and 30 recommend system B.

[0071] According to an embodiment of the present invention, based on the above formula (1), the gain value of less than 8 cores is =0.971. The gain value for 8-16 cores is =0.971The gain value is greater than 16 cores =0.971. Based on the above formula (2), the gain value of the sub-attribute of CPU core number is .

[0072] According to an embodiment of the present invention, the same method is used to calculate the gain value of the graphics processor video memory. Assume that the graphics processor video memory (sub-attribute A) has two values, less than 16G and greater than or equal to 16G. There are 80 samples for the value of less than 16G video memory, of which 30 recommend system a and 50 recommend system b. There are 120 samples for the value of greater than or equal to 16G video memory, of which 50 recommend system a and 70 recommend system b. Based on the above formula (1), the information entropy of less than 16G video memory is calculated as follows: =0.954, information entropy greater than or equal to 16G =0.985. Based on the above formula (2), we can calculate that the information gain IG(D,A) of the sub-attribute of the graphics processor memory is IG(D,A)= =0.003. By calculating the gain value of each sub-attribute, its contribution to the classification result can be quantified, thus providing a basis for the model to select the optimal partitioning attribute.

[0073] According to an embodiment of the present invention, the operating system deployment method also includes: when it is determined that the prediction accuracy rates of multiple first prediction models are all less than a preset value, re-dividing the attribute information of the reference sample and the external server in equal proportion to obtain multiple updated data sets; using the multiple updated data sets to train and verify the initial system recommendation model until the number of verifications reaches a preset number of times, or the prediction accuracy rate of the trained second prediction model reaches a preset value; when it is determined that the number of verifications reaches a preset number of times and the prediction accuracy rates of the multiple second prediction models obtained by training are all less than the preset value, sorting the prediction accuracy rates of the multiple second prediction models to determine the system recommendation model from the multiple second prediction models according to the sorting result.

[0074] According to an embodiment of the present invention, after detecting the prediction accuracy of multiple first prediction models, if it is found that the prediction accuracy of all first prediction models is less than a preset value of 1, the attribute information of the reference sample and the external server is re-divided in equal proportion to generate multiple updated data sets. Specifically, the sample data is randomly shuffled and re-divided into five updated data sets D1, D2, D3, D4, and D5. Based on these updated data sets, continue to train and calculate the decision tree with the highest prediction accuracy to construct a second prediction model.

[0075] According to an embodiment of the present invention, if after verifying the generated multiple second prediction models, it is found that the prediction accuracy has reached 1, the prediction accuracy of the multiple second prediction models is sorted, and the system recommendation model is determined according to the sorting result. If the prediction accuracy of the multiple second prediction models is less than 1, the iterative training continues. After the preset number of verifications, if the prediction accuracy still does not reach 1, it is necessary to adjust the preset number of times to further optimize the iterative process. Since the model training adopts the k-fold cross-validation algorithm, the preset number of times can be updated by adjusting the k value to 9, thereby increasing the diversity of data partitioning and the generalization ability of the model, and ultimately improving the prediction accuracy.

[0076] According to an embodiment of the present invention, after the preset number of times is updated, the cross-validation process is repeated until the number of validations reaches the updated preset number of times. Because in the k-fold cross-validation algorithm, when the k value is 9, after completing 9 iterations, the selection of the decision tree will no longer be significant. If the second prediction model with a prediction accuracy of 1 is still not found in this process, the second prediction model with the highest prediction accuracy is selected from the second prediction models of historical training as the system recommendation model. This method ensures that even if the ideal accuracy cannot be achieved after multiple iterations, the optimal model can still be selected for system recommendation, thereby ensuring the reliability and practicality of the recommendation results.

[0077] like Figure 4As shown, the process of model training and verification in the embodiment of the present invention includes operations S411 to S424.

[0078] In operation S411, obtain attribute information of reference samples and external servers different from the target environment. In operation S412, divide the attribute information of reference samples and external servers in equal proportion to obtain multiple sample data sets. In operation S413, select one from the multiple sample data sets as a validation set, and the rest as training sets. In operation S414, determine the gain value of each sub-attribute in the training set. In operation S415, use the sub-attribute with the largest gain value as the splitting attribute. In operation S416, determine whether all sub-attributes have been traversed. In operation S417, further divide the training set according to the multiple attribute value ranges of the splitting attribute. In operation S418, generate a first prediction model.

[0079] In operation S419, the prediction accuracy of the first prediction model is calculated using the validation set. In operation S420, it is determined whether the prediction accuracy is equal to 1. In operation S421, it is determined whether the number of verifications reaches a preset number. In operation S422, it is determined whether the preset number is a target value. In operation S423, the preset number is updated. In operation S424, the system recommendation model is output.

[0080] According to an embodiment of the present invention, by obtaining the attribute information of reference samples and external servers, and combining proportional division to generate multiple sample data sets, the diversity and representativeness of the training data are ensured. By calculating the gain value of the sub-attribute and selecting the optimal split attribute, a decision tree model is gradually constructed to generate a first prediction model. The prediction accuracy of the model is evaluated using a validation set, and the accuracy and stability of the model are ensured through iterative optimization. Finally, the system recommendation model is output to provide a reliable basis for the operating system recommendation of the target server. This process not only improves the accuracy and adaptability of the system recommendation, but also reduces manual intervention through automation, significantly improving the efficiency and reliability of system deployment.

[0081] According to an embodiment of the present invention, the operating system deployment method also includes: testing the prediction accuracy of multiple second prediction models based on preset standard values ​​to obtain detection results; when the detection results indicate that the prediction accuracy of multiple second prediction models are all less than the preset standard values, outputting prompt information, wherein the prompt information is used to prompt the target object to check the reference sample.

[0082] According to an embodiment of the present invention, the prediction accuracy of multiple second prediction models is detected based on a preset standard value, where the preset standard value can be set to 0.9. If the prediction accuracy of multiple second prediction models is lower than the preset standard value, it indicates that the data collected by the user in the target environment may be unreasonable. At this time, the system will output a prompt message to prompt the user to check and improve the reference sample to ensure the accuracy and rationality of the data, thereby providing a more reliable basis for subsequent model training and optimization.

[0083] According to an embodiment of the present invention, a target partition result corresponding to a system recommended type is determined based on historical deployment information of a system deployment engine, including: determining a plurality of candidate partition results corresponding to the system recommended type based on a mapping relationship between an operating system type and a partition result in the historical deployment information; analyzing partition information of a server in a target environment on which an operating system has been deployed to determine a partition matching degree between the partition information and each candidate partition result; and determining a target partition result from a plurality of candidate partition results based on a size relationship between a plurality of partition matching degrees.

[0084] According to an embodiment of the present invention, after determining the system recommended type of the target server, the mapping relationship between the operating system type and the partition result is extracted from the historical deployment information. Specifically, a dictionary or a hash table can be used to store this mapping relationship, where the key is the operating system type and the value is the corresponding partition result. According to the operating system type recommended by the system, the corresponding partition result is searched from the mapping relationship table. If the recommended operating system type corresponds to multiple possible partition results, these partition results will be used as candidate partition results for further screening and optimization. This method ensures the accuracy and flexibility of the partition results and provides reliable support for the deployment of the operating system.

[0085] According to an embodiment of the present invention, the partition information of the server in the target environment where the operating system has been deployed is obtained, and the partition information is analyzed to extract the key attributes such as the name, size, and type of the partition. For each candidate partition result, the degree of matching with the partition information of the deployed server is calculated. The matching degree can be calculated by comparing the attributes such as the partition name, size, and type. A simple string matching method or a more complex similarity algorithm (such as cosine similarity or edit distance) can be used. In this way, the degree of fit between the candidate partition results and the target environment can be quantified, thereby providing data support for selecting the optimal partitioning scheme.

[0086] According to an embodiment of the present invention, based on the size relationship of the matching degrees, the candidate partition result with the highest matching degree is selected as the target partition result. If there are multiple candidate partition results with the same matching degree, they can be further screened according to other factors (such as partition size, type, etc.). If the calculation of the partition matching degree requires higher precision, you can consider using a machine learning model to predict the partition matching degree. In addition, if there is a lot of partition information in the target environment, parallel computing technology can be used to speed up the calculation process of the matching degree. Through the above method, the system can automatically combine historical data and the partition information of the target environment to determine the most appropriate target partition result, thereby improving deployment efficiency and the adaptability of the partition scheme.

[0087] According to an embodiment of the present invention, partition information of servers with deployed operating systems in a target environment is analyzed to determine the partition matching degree between the partition information and each candidate partitioning result, including: obtaining business operation requirements of multiple servers in the target environment, wherein the business operation requirements include at least one of resource requirements, performance requirements and fault tolerance requirements; determining the partition matching degree according to the business matching degree between multiple candidate partitioning results and the business operation requirements and the similarity between the partition information and each candidate partitioning result.

[0088] According to an embodiment of the present invention, the business operation requirements of each server are collected from the target environment, wherein the business operation requirements include at least one of resource requirements, performance requirements, and fault tolerance requirements. Specifically, resource requirements cover central processing units, memory, disk space, etc., performance requirements include throughput, latency, etc., and fault tolerance requirements involve backup strategies, high availability requirements, etc. The calculation rules for business matching are defined, and the matching degree of each dimension is calculated according to resource requirements, performance requirements, and fault tolerance requirements. Finally, the matching degree of each dimension is weighted and summed to obtain the total business matching degree. This method can comprehensively evaluate the degree of fit between the server and business requirements, and provide a more accurate basis for system recommendation and deployment.

[0089] According to an embodiment of the present invention, a calculation rule for partition similarity is defined, and the degree of matching is quantified by comparing the similarity between the partition information of the target server and the candidate partition results. Specifically, string matching, set similarity or other similarity algorithms (such as cosine similarity) can be used. The business matching degree and the partition similarity are weighted and summed to obtain the final partition matching degree. The weights can be adjusted according to actual needs, for example, the business matching degree accounts for 70% and the partition similarity accounts for 30%. Finally, the candidate partition result with the highest partition matching degree is selected as the target partition result. This method comprehensively considers the matching degree between business needs and partition characteristics, ensuring that the selected partitioning scheme not only meets the business operation needs, but also is highly consistent with the partition configuration of the target environment.

[0090] According to an embodiment of the present invention, the operating system deployment method also includes: sending a generated system installation package corresponding to the system deployment plan to a target server, wherein a system management component for information acquisition and information processing is installed in the target server; sending an operating system installation instruction to the system management component so that the target server installs an operating system corresponding to the system recommended type according to the system installation package; when it is determined that the operating system installation is complete, setting the target partition result in the system deployment plan as the initialization partition plan of the operating system; sending a partition configuration instruction to the system management component so that the target server performs an initialization partition configuration operation on the installed operating system based on the initialization partition plan.

[0091] like Figure 5 As shown, the address information of the target server 510 is determined, and the system installation package is transferred from the computer 520 deployed with the system deployment engine to the target server using the file transfer protocol. After ensuring that the system management component of the target server 510 successfully receives and stores the system installation package, the computer 520 sends the operating system installation instruction through the API (Application Programming Interface) or command line interface of the system management component. The system installation instruction should include the path and installation parameters (such as installation directory, language settings, etc.) of the system installation package. Subsequently, the status interface of the system management component is polled regularly to check the progress and status of the operating system installation. Specifically, it can be determined whether the installation is successfully completed based on the returned status code or log information. This process ensures the automation and controllability of the operating system installation, while improving the deployment efficiency and reliability.

[0092] According to an embodiment of the present invention, after the operating system is installed, the computer 520 sends a partition configuration instruction to the target server 510 through the API or command line interface of the system management component. The partition configuration instruction includes an instruction to set the target partition result as the initialization partition scheme of the operating system, and includes the path or specific content of the initialization partition scheme. By sending the partition configuration instruction, the target partition result is written into the configuration file of the target server or passed to the system management component to ensure that the target partition scheme can be correctly identified and applied by the operating system. This process ensures the accuracy and automation of the partition configuration and provides reliable support for the storage resource allocation of the target server.

[0093] According to an embodiment of the present invention, the operating system deployment method also includes: when it is determined that the deployment of the target server system is complete, repeatedly performing the following operations until the gain change of the added value reaches a preset threshold, or the number of partition adjustments reaches a preset number of partitions, and outputting a dynamic partitioning result: according to the preset adjustment ratios of multiple partitions in the operating system, performing a proportional adjustment operation on each partition to obtain a dynamic partitioning result, wherein the proportional adjustment operation includes an increase operation, a maintenance operation, or a decrease operation; based on the target operation information of the target server, using a partitioning algorithm to determine the added value corresponding to the proportional adjustment operation; determining the gain change of the added value relative to the partition adjustment under the proportional adjustment operation.

[0094] According to an embodiment of the present invention, after the operating system of the target server is installed, storage resources are divided into system partitions, data storage partitions, Web (network) service partitions, and file storage partitions according to the initialization partitioning scheme. After the partitioning is completed, wait for the system to run stably and obtain the target operation information of the target server, including resource usage, performance indicators, and system status.

[0095] According to an embodiment of the present invention, according to the preset adjustment ratios of multiple partitions in the operating system, a ratio adjustment operation is performed on each partition to generate a dynamic partition result. Among them, the preset adjustment ratio of the system partition is 5%, the preset adjustment ratio of the database partition is 3%, the preset adjustment ratio of the Web service partition is 2%, and the preset adjustment ratio of the file storage partition is 1%. Through this dynamic adjustment method, the allocation of storage resources can be optimized according to actual needs and operating conditions, thereby improving the performance and resource utilization of the system.

[0096] According to an embodiment of the present invention, the preset adjustment ratio of each type of partition has a fixed value, and these ratios are set based on experience and comprehensive consideration of the sensitivity of server resource adjustment. If the adjustment step size is set too large, for example, adjusted to 0.1 (i.e., 10%), the change in server resource allocation will be more drastic for each adjustment, which may cause excessive fluctuations in the system state and is not conducive to finding a stable optimal solution. In particular, for partitions of a storage nature, an excessively large adjustment step size may cause serious problems such as system downtime. Therefore, using a smaller adjustment ratio can gradually optimize resource allocation while ensuring system stability, avoiding unnecessary interference with system operation.

[0097] According to an embodiment of the present invention, if the step size is set too small, for example 0.01 (i.e. 1%), although the adjustment process is more refined, it will significantly increase the time and computational cost of exploring all possible combinations. After practice and trade-offs, the preset adjustment ratio set for each type of partition can explore more weight combinations within a reasonable time, while avoiding excessive impact on the system for each adjustment. This setting enables the system to explore the optimal partitioning results relatively efficiently and stably. In addition, the preset adjustment ratio of each type of partition supports adjustment through the customer page, allowing modifications within the range of -0.1 to 0.1, thereby providing users with flexibility and controllability to meet different business needs and system operating environments.

[0098] According to an embodiment of the present invention, when performing a proportion adjustment operation on each partition, first obtain the current value of the current partition and its proportion. For example, the system partition weight is: sys , the file partition is W file , the web partition is W web , the database partition is W db etc., and satisfy W sys +W file +W web +W db =1. Define a series of operations to adjust the weight of each partition. , where each The value range of is [-0.1, 0.1], which means that the maximum adjustment range of each weight is 10%. In this way, the system can gradually optimize the partition weights while ensuring a reasonable adjustment range, thereby improving the efficiency and stability of resource allocation.

[0099] According to an embodiment of the present invention, after executing the proportional adjustment operation, the target operation information of the target server is obtained, and the partition algorithm is used to determine the added value corresponding to the proportional adjustment operation. The added value is divided into three dimensions: the first dimension is the resource balance reward, which is used to evaluate the balance of resource allocation; the second dimension is the system response time, which is used to measure the performance of the system; the third dimension is the partition usage rationality reward, which is used to evaluate the rationality of the partition configuration. By calculating the gain change of the added value relative to the gain before the adjustment under the proportional adjustment operation, the impact of the adjustment operation on the system operation state is quantified. This method can provide data support for the optimization of partition weights and ensure that the adjusted partition configuration is optimal in terms of resource balance, system performance and partition rationality.

[0100] According to an embodiment of the present invention, the above-mentioned proportional adjustment operation is repeatedly performed, and the operating status of the target server is observed at the same time. Since the execution of each type of action requires a certain amount of time, the system will record the change in the added value gain after each adjustment. If it is detected that the gain change exceeds the preset threshold of 10%, the exploration is stopped, and the current partition result is used as the optimal dynamic partition result. If the gain change does not exceed the preset threshold, all possible actions will continue to be explored until the number of partition adjustments reaches the preset maximum number of times. Finally, the system will select the action with the largest added value as the execution action this time, thereby determining the optimal dynamic partition result. This method ensures that the partition configuration can reach the optimal state in terms of resource balance, system response time, and rationality of partition use through dynamic adjustment and real-time monitoring, while avoiding the impact of excessive adjustment on system stability.

[0101] According to an embodiment of the present invention, a ratio adjustment operation is performed on each partition according to the preset adjustment ratios of each of the multiple partitions in the operating system to obtain a dynamic partitioning result, including: randomly combining the preset adjustment ratios of each of the multiple partitions with the preset adjustment type to generate an operation vector, wherein the preset adjustment type includes an increase type, a maintenance type or a decrease type; based on the operation vector, a corresponding ratio adjustment operation is performed on each partition to obtain a dynamic partitioning result.

[0102] According to an embodiment of the present invention, when performing a proportional adjustment operation on each partition, there are currently four partitions W sys +W file +W web +W db Each partition has three preset adjustment types: increase, unchanged, or decrease. Since an increase in one partition requires a corresponding decrease in another partition, in the case of four partitions, there are a total of = 81 possible action combinations. The preset adjustment ratios and preset adjustment types of multiple partitions are randomly combined to generate an operation vector, such as [0.05, -0.03, 0.02, 0]. Based on the generated operation vector, the corresponding ratio adjustment operation is performed on each partition to obtain the dynamic partition result. This method ensures that the partition configuration can achieve the optimal state in terms of resource balance, system performance, and partition rationality by exploring multiple possible adjustment combinations.

[0103] like Figure 6 As shown, another embodiment includes operations S601 to S609.

[0104] In operation S601, attribute information of multiple servers in the target environment is collected. In operation S602, a list of servers to be installed is generated based on target servers that do not have an operating system installed. In operation S603, the system recommendation type of each target server is determined using the system recommendation model running in the system deployment engine. In operation S604, the target partition results of each target server are determined based on the system recommendation type.

[0105] In operation S605, the installation of the operating system is completed according to the system recommended type, and the initialization partition configuration is completed according to the target partition result. In operation S606, the current partition result is dynamically adjusted. In operation S607, the added value of the current partition result is calculated. In operation S608, it is determined whether the gain change of the added value reaches a preset threshold, or whether the number of partition adjustments reaches a preset maximum number. In operation S609, the dynamic partition result is output.

[0106] According to an embodiment of the present invention, the attribute information of the servers in the target environment is collected and a list of servers to be installed is generated, ensuring that the hardware configuration and business requirements of the target server are accurately identified. The system recommendation model is used to recommend an adaptive operating system type for the target server, and the installation of the operating system and the initialization of the partition configuration are completed in combination with the target partition result, ensuring the efficiency and accuracy of the system deployment. By dynamically adjusting the partition configuration and calculating the added value, the system can optimize resource allocation, improve resource balance, system response time and rationality of partition use. The system determines the optimal dynamic partitioning result based on the gain change or adjustment number of the added value, thereby ensuring that the target server reaches the best state in terms of resource utilization and performance. This process not only improves the automation level of system deployment, but also enhances the stability and adaptability of the system, providing reliable support for business operations.

[0107] According to an embodiment of the present invention, based on the target operation information, a partitioning algorithm is used to determine an additional value corresponding to a proportional adjustment operation, including: performing a multi-dimensional analysis on the utilization of system resources in the target operation information to generate a system operation vector; determining a first additional value based on the standard deviation of multiple component values ​​in the system operation vector; analyzing the component value representing the response time in the system operation vector to determine a second additional value; determining a third additional value corresponding to the component value representing the partition resource utilization in the system operation vector based on a preset mapping relationship between the partition resource utilization and the third additional value; and performing a weighted summation on the first additional value, the second additional value, and the third additional value to obtain an additional value corresponding to the proportional adjustment operation.

[0108] According to an embodiment of the present invention, after the system runs stably, the CPU utilization, memory utilization, hard disk load, network bandwidth utilization and usage of each partition of the target server are collected.CPU = Number of CPU cores used / Total number of CPU cores, memory utilization U mem =Used memory / total memory size, hard disk load D iops = Current hard disk read / write rate / maximum hard disk read / write rate, network bandwidth utilization U net = Used network bandwidth / total network bandwidth, usage of each partition S parti = Partition usage space / partition total space. Based on these indicators, a set of vectors S=[U CPU ,U mem ,D iops ,U net ,S part1 ,S part2 ,...,S partn ] and uses this vector to calculate the additional value corresponding to the current scaling operation.

[0109] According to an embodiment of the present invention, the first dimension is resource balance reward, which aims to reward balanced utilization of various resources to avoid excessive use of a certain resource while other resources are idle. Resource balance is measured by calculating the inverse of the standard deviation of each resource utilization rate. The smaller the standard deviation, the more balanced the resource utilization and the higher the reward value. The calculation method is shown in formula (3):

[0110]

[0111] Where m represents the number of component values ​​(i.e., CPU, memory, disk I / O, network), U q represents the qth component value, U p Represents the average value of each component, R b Indicates the first additional value.

[0112] According to an embodiment of the present invention, the second dimension is the system response time. The response time of a certain logic segment is calculated, and the maximum acceptable response time is 500 milliseconds. The shorter the system response time, the better the system performance and the higher the response efficiency. The calculation method is shown in formula (4):

[0113]

[0114] Among them, R r Indicates the second additional value, T max represents the maximum acceptable response time, T now Indicates the component value that characterizes the response time.

[0115] According to an embodiment of the present invention, the third dimension is the partition usage rationality reward, which aims to ensure that the usage of each partition is within a reasonable range and avoid insufficient or wasted partition space. parti, set the reasonable range of partition resource utilization to [min, max], where min=0.1, max=0.85. If the current partition resource utilization is within this range, the third additional value is determined to be R according to the preset mapping relationship. p =1; if it exceeds this range, the third additional value is determined to be R according to the preset mapping relationship p =-1. After batch data analysis, it is found that when the partition utilization rate reaches 0.85, the system performance will drop significantly. Therefore, the reward and penalty mechanism can effectively guide the optimization of partition resource utilization, thereby improving the overall system performance and resource utilization efficiency.

[0116] According to an embodiment of the present invention, after the calculation of the added values ​​of the three dimensions is completed, the added value corresponding to the proportional adjustment operation is calculated. The calculation method is shown in formula (5):

[0117]

[0118] Among them, P b , P r , P p Represents the weight of the added value of the three dimensions, P b +P r +P p = 1. Through weighted summation, the impact of the ratio adjustment operation on the system resource balance, response time and partition usage rationality can be comprehensively evaluated, thus providing a quantitative basis for optimizing partition configuration.

[0119] According to an embodiment of the present invention, the weights of the first additional value, the second additional value, and the third additional value are dynamically adjusted in the following manner: when it is determined that multiple component values ​​in the system operation vector are all greater than their respective preset performance standards, the weight of the first additional value is set to the average value of the multiple component values, and the weights of the second additional value and the third additional value are the average value obtained by subtracting the weight of the first additional value from the target total weight; when it is determined that only multiple component values ​​representing the utilization of computing resources in the system operation vector are greater than the corresponding preset performance standards, the weight of the second additional value is set to the average value of the multiple component values ​​representing the utilization of computing resources, and the weights of the first additional value and the third additional value are the average value obtained by subtracting the weight of the second additional value from the target total weight; when it is determined that the difference between the multiple component values ​​representing the utilization of partition resources in the system operation vector is less than the preset difference, the weight of the third additional value is set to the target weight, and the weights of the first additional value and the second additional value are the average value obtained by subtracting the weight of the third additional value from the target total weight; when it is determined that the system operation vector meets the target condition, the weights of the first additional value, the second additional value, and the third additional value are all set to the average value of the first additional value, the second additional value, and the third additional value.

[0120] According to an embodiment of the present invention, the weights of the first additional value, the second additional value, and the third additional value are dynamically adjusted, and will be continuously optimized and adjusted according to the real-time operating status of the target server, the dynamic changes in the business load, and the long-term performance monitoring data. If the target server mainly runs big data analysis business, involving a large amount of data reading, calculation, and storage operations, resulting in a continuous high demand for central processing unit, memory, and disk I / O resources, and multiple component values ​​in the system operation vector exceed their respective preset performance standards, then the weight of the first additional value will be dynamically adjusted based on the average value of the central processing unit utilization, memory utilization, hard disk load, and network bandwidth utilization. Specifically, the average value of multiple component values ​​is taken as the weight of the first additional value, and the weights of the other two additional values ​​are half of this weight minus one half.

[0121] According to an embodiment of the present invention, if the utilization rates of the CPU and the Graph Processor of the server are continuously at a high level, and the multiple component values ​​characterizing the utilization rate of computing resources all exceed the corresponding preset performance standards, indicating that the server has a high requirement for the computing response time, the weight of the second additional value is increased. Specifically, the weight of the second additional value is set to the average value of the utilization rates of the CPU and the Graph Processor, and the weights of the other two additional values ​​are half of this weight value minus.

[0122] According to an embodiment of the present invention, if the utilization rate of each type of partition of the server is very balanced, and the difference between the multiple component values ​​representing the utilization rate of the partition resources is less than the preset difference, and different partitions store different types of data, the weight of the third additional value is increased and set to 0.4. This adjustment can ensure that the rationality of partition usage has a higher priority in resource allocation, thereby optimizing the utilization efficiency of storage resources and avoiding waste or insufficient partition space. At the same time, the weights of the other two additional values ​​are half of this weight value.

[0123] According to an embodiment of the present invention, if none of the above conditions are met, it means that the system has reached the target condition, and the weights of the first additional value, the second additional value, and the third additional value are all set to the average of the three. This dynamic weight adjustment mechanism can ensure that in different operating scenarios, the system can flexibly balance resource balance, response time, and partition usage rationality, thereby adapting to changing business needs and system loads, and further improving the overall performance and resource utilization efficiency of the system.

[0124] According to an embodiment of the present invention, the operating system deployment method also includes: in response to a weight adjustment request received from a target object, performing a preset range detection on the weight adjustment value in the weight adjustment request; when it is determined that the weight adjustment value satisfies the preset range, updating the current dynamically adjusted weight using the weight adjustment value; and ending the dynamic adjustment operation of the weight.

[0125] According to an embodiment of the present invention, the system supports customers to manually maintain weight values, and the weight values ​​set by customers after manual maintenance are given priority. In response to the weight adjustment request received from the target object, the system will perform a preset range detection on the weight adjustment value in the weight adjustment request. The weight ratio of each category shall not be less than 0.1 and shall not be higher than 0.8 to ensure the rationality of the weight distribution and the stability of the system. When it is determined that the weight adjustment value meets the preset range, the system will use the weight adjustment value to update the current dynamically adjusted weight and end the dynamic adjustment operation of the weight. This mechanism not only ensures the flexibility of automatic adjustment, but also provides customers with room for manual optimization, while avoiding situations where the weight distribution is too extreme, thereby ensuring that the system can remain efficient and stable in different operating scenarios.

[0126] Based on the above operating system deployment method, the present invention also provides an operating system deployment device. Figure 7 The device is described in detail.

[0127] Figure 7 A structural block diagram of an operating system deployment device according to an embodiment of the present invention is shown.

[0128] like Figure 7 As shown, the operating system deployment device 700 of this embodiment includes a system recommendation module 710 , a partition determination module 720 and a system deployment module 730 .

[0129] The system recommendation module 710 is used to input the acquired attribute information of the target server in the target environment into the system recommendation model running in the system deployment engine to obtain the system recommendation type, wherein the target environment is an environment for deploying an operating system to the server, the system recommendation model is trained using the attribute information of the server in the target environment where the operating system has been deployed as a reference sample, and the target server is connected to a computer where the system deployment engine is deployed. In one embodiment, the system recommendation module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0130] The partition determination module 720 is used to determine the target partition result corresponding to the system recommended type according to the historical deployment information of the system deployment engine, wherein the target partition result is dynamically generated based on the system operation information of the server using the partition algorithm called by the system deployment engine, and the target partition result is used to divide the storage resources of the server. In one embodiment, the partition determination module 720 can be used to perform the operation S220 described above, which will not be repeated here.

[0131] The system deployment module 730 is used to generate a system deployment plan based on the system recommendation type and the target partition result, so that the target server completes the installation of the operating system and the initialization partition configuration based on the system deployment plan. In one embodiment, the system deployment module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0132] According to an embodiment of the present invention, any multiple modules of the system recommendation module 710, the partition determination module 720 and the system deployment module 730 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the system recommendation module 710, the partition determination module 720 and the system deployment module 730 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the system recommendation module 710, the partition determination module 720 and the system deployment module 730 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0133] It should be noted that the operating system deployment device part in the embodiment of the present invention corresponds to the operating system deployment method part in the embodiment of the present invention. The description of the operating system deployment device part specifically refers to the operating system deployment method part, which will not be repeated here.

[0134] Figure 8 A block diagram of an electronic device suitable for implementing an operating system deployment method according to an embodiment of the present invention is shown.

[0135] like Figure 8As shown, the electronic device 800 according to an embodiment of the present invention includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a central processing unit), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0136] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present invention by executing the programs stored in one or more memories.

[0137] According to an embodiment of the present invention, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the input / output (I / O) interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that the computer program read therefrom is installed into the storage portion 808 as needed.

[0138] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0139] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0140] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the operating system deployment method provided by the embodiment of the present invention.

[0141] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when the processor 801 executes the computer program. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0142] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0143] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0144] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0145] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0146] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention may be combined and / or combined in various ways. All of these combinations and / or combinations fall within the scope of the present invention.

[0147] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. An operating system deployment method, characterized in that: The method comprises: Input the acquired attribute information of the target server in the target environment into the system recommendation model running in the system deployment engine to obtain the system recommendation type, wherein the target environment is an environment for deploying an operating system on the server, the system recommendation model is trained using the attribute information of the server in the target environment where the operating system has been deployed as a reference sample, and the target server is connected to a computer where the system deployment engine is deployed; Determine, according to the historical deployment information of the system deployment engine, a target partition result corresponding to the system recommended type, wherein the target partition result is dynamically generated based on the system operation information of the server by using the partition algorithm called by the system deployment engine, and the target partition result is used to divide the storage resources of the server; Based on the system recommendation type and the target partition result, a system deployment plan is generated, so that the target server completes the installation of the operating system and initializes the partition configuration based on the system deployment plan.

2. The method according to claim 1, characterized in that: The system recommendation model includes a decision layer and an output layer; the attribute information of the target server in the target environment is input into the system recommendation model running in the system deployment engine to obtain the system recommendation type, including: Inputting the attribute information of the target server into the decision layer for analysis to determine the matching degree between the sub-attributes in the attribute information and the decision nodes in the decision layer, According to the matching degree, traversing a plurality of the decision nodes in the decision layer to determine a decision result corresponding to the attribute information; The system recommendation type corresponding to the decision result is outputted through the output layer.

3. The method according to claim 2, characterized in that The system recommendation model is trained in the following way: When it is determined that the attribute information of the external server different from the target environment obtained from the system deployment engine meets a predetermined condition, the reference sample and the attribute information of the external server are divided in equal proportion to obtain a plurality of sample data sets; Repeat the following steps until multiple sample data sets are selected as validation sets: Select one of the multiple sample data sets as a validation set, and the rest as training sets; Using the training set to train the initial system recommendation model to obtain a first prediction model; Using the verification set to verify the prediction accuracy of the first prediction model to obtain a verification result; When it is determined that the plurality of sample data sets are all selected as verification sets, the first prediction model whose prediction accuracy reaches a preset value among the plurality of verification results is determined as the system recommended model.

4. The method according to claim 3, characterized in that The method further comprises: Displaying, on an interactive interface of the computer, attribute information of an external server different from the target environment obtained from the system deployment engine; In response to a determination instruction for the attribute information of the external server, determining that the attribute information of the external server satisfies the predetermined condition; In response to a modification instruction for the attribute information of the external server, the attribute information of the external server is reacquired from the system deployment engine according to the modification information input through the interactive interface until the acquired attribute information of the external server meets the predetermined condition.

5. The method according to claim 3, characterized in that: The step of training the initial system recommendation model using the training set to obtain a first prediction model includes: According to the type of each sub-attribute of the attribute information in the training set, determining the information gain of the training set to obtain the gain value of the sub-attribute; Sorting the plurality of gain values ​​to determine a splitting attribute of a root node in an initial decision tree; According to multiple attribute value ranges of the split attribute, the training set is divided into multiple sub-data sets, wherein the sub-nodes in the decision tree represent the sub-data sets; For each of the child nodes, repeatedly perform the following operations until the multiple child attributes are traversed: Determine the gain values ​​of the remaining sub-attributes; and redetermine the splitting attributes; and further divide the sub-datasets according to the redetermined splitting attributes; When it is determined that a plurality of the sub-attributes have been traversed, the obtained target decision tree is used as the decision layer; The decision layer is connected to the output layer to obtain the first prediction model.

6. The method according to claim 5, characterized in that The determining of information gain of the training set according to the type of each sub-attribute of the attribute information in the training set to obtain the gain value of the sub-attribute includes: According to the operating system type of each attribute information in the training set, the training set is divided to obtain multiple sample sets; For each of the sample sets, determining intermediate gain values ​​of a plurality of sub-attributes in the attribute information respectively; The intermediate gain values ​​of the same sub-attribute in the plurality of sample sets are summed to obtain the gain value of the sub-attribute.

7. The method according to claim 3, characterized in that The method further comprises: When it is determined that the prediction accuracy rates of the plurality of first prediction models are all less than the preset value, the attribute information of the reference sample and the external server is re-divided in equal proportion to obtain a plurality of updated data sets; The initial system recommendation model is trained and verified using the multiple updated data sets until the number of verifications reaches a preset number, or the prediction accuracy of the trained second prediction model reaches the preset value; When it is determined that the number of verifications reaches the preset number of times and the prediction accuracy rates of the multiple second prediction models obtained through training are all less than the preset value, the prediction accuracy rates of the multiple second prediction models are sorted to determine the system recommended model from the multiple second prediction models based on the sorting results.

8. The method according to claim 7, characterized in that The method further comprises: Testing the prediction accuracy of the plurality of second prediction models based on a preset standard value to obtain a test result; When the detection result indicates that the prediction accuracy of the plurality of second prediction models are all less than the preset standard value, a prompt message is output, wherein the prompt message is used to prompt the target object to check the reference sample.

9. The method according to claim 1, characterized in that: The determining, according to the historical deployment information of the system deployment engine, a target partition result corresponding to the system recommended type includes: Determine, according to the mapping relationship between the operating system type and the partition result in the historical deployment information, a plurality of candidate partition results corresponding to the system recommended type; Analyzing the partition information of the server in the target environment where the operating system has been deployed to determine the partition matching degree between the partition information and each of the candidate partition results; The target partition result is determined from the multiple candidate partition results according to the size relationship between the multiple partition matching degrees.

10. The method according to claim 9, characterized in that The analyzing the partition information of the server in the target environment where the operating system has been deployed to determine the partition matching degree between the partition information and each of the candidate partition results includes: Acquire business operation requirements of multiple servers in the target environment, wherein the business operation requirements include at least one of resource requirements, performance requirements, and fault tolerance requirements; The partition matching degree is determined according to the business matching degree between the plurality of candidate partitioning results and the business operation requirement and the similarity between the partitioning information and each of the candidate partitioning results.

11. The method according to claim 1, characterized in that: The method further comprises: Sending the generated system installation package corresponding to the system deployment solution to the target server, wherein the target server is installed with a system management component for information acquisition and information processing; Sending an operating system installation instruction to the system management component so that the target server installs the operating system corresponding to the system recommended type according to the system installation package; When it is determined that the installation of the operating system is complete, setting the target partition result in the system deployment plan as the initialization partition plan of the operating system; A partition configuration instruction is sent to the system management component, so that the target server performs an initialization partition configuration operation on the installed operating system based on the initialization partition scheme.

12. The method according to claim 1, characterized in that The method further comprises: When it is determined that the target server system has been deployed, the following operations are repeated until the gain change of the added value reaches a preset threshold, or the number of partition adjustments reaches a preset number of partitions, and the dynamic partition result is output: According to the preset adjustment ratios of the multiple partitions in the operating system, a ratio adjustment operation is performed on each of the partitions to obtain a dynamic partition result, wherein the ratio adjustment operation includes an increase operation, a maintenance operation or a decrease operation; Based on the target operation information of the target server, determining an additional value corresponding to the proportional adjustment operation using the partitioning algorithm; A gain change of the additional value relative to that before the partition adjustment under the proportional adjustment operation is determined.

13. The method according to claim 12, characterized in that The step of performing a ratio adjustment operation on each of the partitions according to the preset adjustment ratios of the partitions in the operating system to obtain a dynamic partition result includes: Randomly combining the preset adjustment ratios and preset adjustment types of the respective partitions to generate an operation vector, wherein the preset adjustment type includes an increase type, a maintenance type, or a decrease type; Based on the operation vector, a corresponding proportional adjustment operation is performed on each of the partitions to obtain the dynamic partition result.

14. The method according to claim 12, characterized in that The determining, based on the target operation information, an additional value corresponding to the proportional adjustment operation by using the partition algorithm includes: Performing a multi-dimensional analysis on the utilization of system resources in the target operation information to generate a system operation vector; Determining a first additional value according to a standard deviation of a plurality of component values ​​in the system operation vector; Analyzing the component value representing the response time in the system operation vector to determine the second additional value; Determine, according to a preset mapping relationship between the partition resource utilization and the third additional value, a third additional value corresponding to the component value representing the partition resource utilization in the system operation vector; A weighted sum is performed on the first additional value, the second additional value, and the third additional value to obtain an additional value corresponding to the proportion adjustment operation.

15. The method according to claim 14, characterized in that The weights of the first additional value, the second additional value and the third additional value are dynamically adjusted in the following manner: When it is determined that the values ​​of the plurality of components in the system operation vector are all greater than the respective preset performance standards, the weight of the first additional value is set to the average value of the plurality of component values, and the weights of the second additional value and the third additional value are the average values ​​obtained by deducting the weight of the first additional value from the total target weight; When it is determined that only the multiple component values ​​representing the computing resource utilization in the system operation vector are greater than the corresponding preset performance standard, the weight of the second additional value is set to the average value of the multiple component values ​​representing the computing resource utilization, and the weights of the first additional value and the third additional value are the average value obtained by deducting the weight of the second additional value from the total target weight; When it is determined that the difference between the multiple component values ​​representing the partition resource utilization in the system operation vector is less than the preset difference, the weight of the third additional value is set to the target weight, and the weights of the first additional value and the second additional value are the average value obtained by deducting the weight of the third additional value from the total target weight; When it is determined that the system operation vector meets the target condition, the weights of the first additional value, the second additional value and the third additional value are all set to the average value of the first additional value, the second additional value and the third additional value.

16. The method according to claim 15, characterized in that Also includes: In response to a received weight adjustment request sent by a target object, performing a preset range detection on a weight adjustment value in the weight adjustment request; When it is determined that the weight adjustment value satisfies the preset range, the currently dynamically adjusted weight is updated using the weight adjustment value; and the dynamic adjustment operation of the weight is ended.

17. An operating system deployment device, characterized in that: The device comprises: A system recommendation module, used to input the acquired attribute information of the target server in the target environment into the system recommendation model running in the system deployment engine to obtain the system recommendation type, wherein the target environment is an environment for deploying an operating system to the server, the system recommendation model is trained using the attribute information of the server in the target environment where the operating system has been deployed as a reference sample, and the target server is connected to a computer where the system deployment engine is deployed; a partition determination module, configured to determine a target partition result corresponding to the system recommendation type according to the historical deployment information of the system deployment engine, wherein the target partition result is dynamically generated based on the system operation information of the server by using the partition algorithm called by the system deployment engine, and the target partition result is used to divide the storage resources of the server; and The system deployment module is used to generate a system deployment plan based on the system recommendation type and the target partition result, so that the target server completes the installation of the operating system and initializes the partition configuration based on the system deployment plan.

18. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 16.

19. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 16 are implemented.

20. A computer program product, characterized in that The method comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 16.

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