Operating System Deployment Method, Device, Equipment, Storage Medium and Program Product

Abstract

The present invention provides an operating system deployment method, which can be applied to the field of cloud computing. The operating system deployment method includes: inputting the attribute information of the target server in the target environment obtained into the system recommendation model running in the system deployment engine to obtain the system recommendation type, where the system recommendation model is trained with the attribute information of the servers with the operating system already deployed in the target environment as the reference samples; determining the target partition result corresponding to the system recommendation type according to the historical deployment information of the system deployment engine, where the target partition result is dynamically generated based on the system running information of the server by using the partition algorithm called by the system deployment engine; 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 the initialization partition configuration based on the system deployment plan. The present invention also provides an operating system deployment device, equipment, storage medium and program product.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing, and particularly to a method, apparatus, device, medium and program product for operating system deployment. Background Art

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

[0003] In the process of implementing the inventive concept, the inventors found that there are at least the following problems in the related art. In the face of a complex hardware environment and diverse business scenarios, it is easy to have a situation where the recommended operating system does not match the business scenario. In addition, manual partitioning may result in a low degree of matching between the deployment of the operating system and the hardware configuration of the server, requiring repeated adjustment and resulting in low efficiency of system deployment. Summary of the Invention

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

[0005] According to a first aspect of the present invention, there is provided a method for operating system deployment, including: inputting the 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, where the target environment is an environment for operating system deployment of a server, the system recommendation model is trained with the attribute information of the servers with operating systems already deployed in the target environment as reference samples, and the target server is connected to a computer with the system deployment engine deployed thereon; determining a target partitioning result corresponding to the system recommendation type according to the historical deployment information of the system deployment engine, where the target partitioning result is dynamically generated based on the system running information of the server by using a partitioning algorithm called by the system deployment engine, and the target partitioning result is used to divide the storage resources of the server; generating a system deployment plan based on the system recommendation type and the target partitioning result, so that the target server completes the installation and initialization partitioning configuration of the operating system 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, configured to input the obtained 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, where the above target environment is an environment for deploying an operating system to a server, the above system recommendation model is trained using the attribute information of the servers with the operating system already deployed in the above target environment as reference samples, and the above target server is connected to the computer on which the above system deployment engine is deployed; a partition determination module, configured to determine a target partition result corresponding to the above system recommendation type according to the historical deployment information of the above system deployment engine, where the above target partition result is dynamically generated based on the system operation information of the server by using the partition algorithm called by the above system deployment engine, and the above target partition result is used to divide the storage resources of the server; and a system deployment module, configured to generate a system deployment plan based on the above system recommendation type and the above target partition result, so that the above target server completes the installation of the operating system and the initialization partition configuration based on the above system deployment plan.

[0007] The third aspect of the present invention provides an electronic device, including: one or more processors; a memory, configured to store one or more computer programs, where the above one or more processors execute the above 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 when the above computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0009] The fifth aspect of the present invention further provides a computer program product, including a computer program or instruction, and when the above computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0010] According to the embodiments of the present invention, the attribute information of the target server is analyzed by using the system recommendation model running in the system deployment engine, so as to determine the system recommendation type adapted thereto. During the recommendation process, since the system recommendation model is trained based on the attribute information of the servers with the operating system already deployed in the target environment, the recommendation result can better coordinate with the configurations of other servers in the target environment, realizing personalized recommendation. After determining the system recommendation type, the target partition result corresponding to this recommendation type is extracted from the historical deployment information. Since the target partition result is dynamically generated according to the system operation information of the server, during the deployment process, resource division is carried out according to the target partition result, which can ensure that the deployment of the operating system better matches the attribute information of the server. At the same time, determining the target partition result in advance effectively reduces the repeated adjustment of the initialization partition, significantly improving the efficiency and accuracy of system deployment. Description of the Drawings

[0011] Through the following description of the embodiments of the present invention with reference to the accompanying drawings, the above content and other objects, features, and advantages of the present invention will become clearer.

[0012] Figure 1 The application scenario diagram of the operating system deployment method, device, equipment, medium, and program product according to the embodiments of the present invention is shown.

[0013] Figure 2 The flowchart of the operating system deployment method according to the embodiments of the present invention is shown.

[0014] Figure 3 The schematic diagram of the decision tree in the operating system deployment method according to the embodiments of the present invention is shown.

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

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

[0017] Figure 6 The flowchart of the operating system deployment method according to another embodiment of the present invention is shown.

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

[0019] Figure 8 The block diagram of the electronic device suitable for implementing the operating system deployment method according to the embodiments of the present invention is shown. Detailed Embodiments

[0020] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

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

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

[0023] In cases where expressions similar to "at least one of A, B, and C, etc." are used, generally, it should be interpreted according to the meaning commonly understood by those of ordinary skill in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0024] In the technical solution of the present invention, the user information involved (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 for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties. Moreover, the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure, and application, all comply with relevant laws, regulations, and standards, adopt necessary confidentiality measures, do not violate public order and good customs, 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 attribute information of the target server in the target environment obtained into the system recommendation model running in the system deployment engine to obtain a system recommendation type, where the target environment is an environment for deploying an operating system to the server, the system recommendation model is trained with the attribute information of the servers with the operating system already deployed in the target environment as reference samples, and the target server is connected to the computer with the system deployment engine deployed; determining a target partition result corresponding to the system recommendation type according to the historical deployment information of the system deployment engine, where the target partition result is dynamically generated based on the system running 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; 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 the initialization partition configuration based on the system deployment plan.

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

[0027] As 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 communication links 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 fiber optic cables, etc.

[0028] Users 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 may 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 with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc.

[0030] The server 105 may be a server that provides various services, such as a background management server that supports the websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (only for example). The background management server may analyze and process data such as received user requests, etc., and feedback 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 by the embodiments of the present invention can generally be executed by the server 105. Correspondingly, the operating system deployment device provided by the embodiments of the present invention can generally be set in the server 105. The operating system deployment method provided by the embodiments of the present invention can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the operating system deployment device provided by the embodiments of the present invention can also be set in a server or a server cluster different from the server 105 and capable of communicating 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, Figure 1The numbers of the terminal devices, networks, and servers in it are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers.

[0033] Based on the Figure 1 described scenario, through Figures 2 to 6 the operating system deployment method of the embodiments of the present invention will be described in detail.

[0034] Figure 2 It shows a flowchart of the operating system deployment method according to the embodiments of the present invention.

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

[0036] In operation S210, the attribute information of the target server in the obtained target environment is input into the system recommendation model running in the system deployment engine to obtain the system recommendation type. Here, the target environment is the environment for operating system deployment on the server, the system recommendation model is trained with the attribute information of the servers with the operating system already deployed in the target environment as the reference samples, and the target server is connected to the computer with the system deployment engine deployed.

[0037] In operation S220, according to the historical deployment information of the system deployment engine, the target partition result corresponding to the system recommendation type is determined. Here, 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.

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

[0039] According to the embodiments of the present invention, when deploying the operating system for the servers in the target environment, since the hardware configurations of multiple servers in this environment vary greatly, and the servers with the operating system already deployed all adopt the manual selection method instead of being completed through the system deployment engine. Therefore, in order to ensure that the operating system recommended for the target server can be better compatible with other servers in the environment, it is necessary to refer to the attribute information of the deployed servers so as 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 operating system already deployed in the target environment, but also incorporates the historical data accumulated during previous system recommendations through the system deployment engine, thereby ensuring 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 system deployment. The attribute information during the training process also includes the operating system type, version, and partition information, etc. These additional information provide a more comprehensive context for the model, enabling it to more accurately understand the association between server configurations and operating systems, thereby enhancing 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 by the collection tool in the system deployment engine. The IPMI (Intelligent Platform Management Interface) and Redfish protocol of the server are used to collect hardware information, and at the same time, the operating system type, version, and partition information of the installed server are collected through the SSH protocol (Secure Shell). The collected information is stored in the local collection library 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 operating system already deployed in the target environment, but also incorporates the historical data accumulated during previous system recommendations through the system deployment engine, thereby ensuring the accuracy and adaptability of the recommendation results. Specifically, the difference between the attribute information obtained during the training process and the attribute information obtained during system deployment is that the attribute information during the training process also additionally 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 IPMI (Intelligent Platform Management Interface) and Redfish protocol of the server are used to collect the hardware information of the server, and the SSH protocol (Secure Shell) is used to collect the system type, version, and partition information of the installed server. The collected information is collected 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, it is necessary to initialize and partition storage resources. Therefore, after obtaining the system recommended type, the target partition result corresponding to this 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 by calling a partitioning algorithm based on the system running information of the server. And the finally determined target partition result is a partition scheme after dynamic optimization, which can better meet the storage requirements and performance optimization goals of the server.

[0044] According to an embodiment of the present invention, determine the version and configuration requirements of the operating system according to the system recommended type, and combine the target partition result to plan the allocation strategy of storage resources, including partition size, file system type, mount point, etc., so as to generate a system deployment plan. Based on this deployment plan, the target server automatically completes the installation of the operating system and the initialization partition configuration, ensuring the efficiency and accuracy of the deployment process.

[0045] According to an embodiment of the present invention, use the system recommendation model running in the system deployment engine to analyze the attribute information of the target server, so as to determine the system recommended type suitable for it. During the recommendation process, since the system recommendation model is trained based on the attribute information of the servers with the operating system already deployed in the target environment, the recommendation result can better coordinate with the configurations of other servers in the target environment to achieve personalized recommendation. After determining the system recommended type, extract the target partition result corresponding to this recommended type from the historical deployment information. Since the target partition result is dynamically generated according to the system running information of the server, during the deployment process, partitioning resources according to the target partition result can ensure that the deployment of the operating system matches the attribute information of the server more. At the same time, determining the target partition result in advance effectively reduces the repeated adjustment of the initialization partition and significantly improves the efficiency and accuracy of system deployment.

[0046] According to an embodiment of the present invention, the system recommendation model includes a decision-making layer and an output layer; input the obtained 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 recommended type, including: input the attribute information of the target server into the decision-making layer for analysis to determine the matching degree between the sub-attributes in the attribute information and the decision-making nodes in the decision-making layer, and according to the matching degree, traverse multiple decision-making nodes in the decision-making layer to determine the decision result corresponding to the attribute information; output the system recommended 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-making layer. Through the tree structure of the decision tree in the decision-making layer, the attribute information is sequentially matched with multiple decision nodes. 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 according to the specific value of each sub-attribute in the attribute information, so as to select 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 this leaf node is used as the final decision result. By determining the system recommendation type corresponding to the leaf node and using the output layer of the system recommendation model to output the system recommendation type, the recommendation of the operating system of the target server is completed. The system visually displays to the user each server in the target environment that does not have an operating system installed, the type and version of the operating system recommended for installation, 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 at the same time.

[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 obtained from the system deployment engine and different from the target environment meets the predetermined conditions, the reference sample and the attribute information of the external server are divided proportionally to obtain multiple sample data sets; the following operations are repeatedly executed until multiple sample data sets are all selected as the validation set: select one of the multiple sample data sets as the validation set, and the rest as the training set; use the training set to train the initial system recommendation model to obtain the first prediction model; use the validation set to verify the prediction accuracy of the first prediction model to obtain the verification result; when it is determined that multiple sample data sets are all selected as the validation set, the first prediction model with the prediction accuracy reaching the preset value among 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 a predetermined condition. If the preset condition is met, it indicates that the user agrees to use the current data for the training of the decision tree. The reference samples and the attribute information of the external server are divided into multiple sample data sets in equal proportion. For example, if 80 external server samples are collected and 20 server samples are in the target environment, the samples are divided into 5 data sets according to a ratio of 1:4, and each data set contains 20 samples. Among them, 16 samples are randomly selected from the samples of the external server and 4 samples are randomly selected from the samples of the target environment server in each sample data set, and finally 5 non-overlapping sample data sets D 1 、D 2 、D 3 、D 4 、D 5 are generated. This division method ensures the balance of data distribution and provides diverse data support for model training.

[0051] According to an embodiment of the present invention, after the division of the sample data sets is completed, one of the data sets is selected as the validation set, and the remaining data sets are used as the training set to train the initial system recommendation model. For example, D 1 is selected as the validation set, and D 2 、D 3 、D 4 、D 5 are used as the training set for model training to generate four decision trees, that is, the first prediction model. Subsequently, the generated first prediction model is predicted using the D 1 sample data set, and its prediction accuracy is calculated. For example, if 15 out of 20 samples in D 1 are correctly predicted, the prediction accuracy of this first prediction model is 0.75. This process evaluates the performance of the model through cross-validation and provides a reliable basis for subsequent model optimization.

[0052] According to an embodiment of the present invention, iterative training is performed on multiple sample data sets. D 2 is used as the validation set, and the remaining data sets (D 1 、D 3 、D 4 、D 5 ) are used as the training set, and the above steps are repeated to generate the second first prediction model and calculate its prediction accuracy. And so on, D 3 、D 4 、D 5Using the remaining data sets as the training set and the validation set, the third, fourth, and fifth first prediction models are generated, and their prediction accuracies are 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 further includes: displaying, on an interaction interface of a computer, attribute information of an external server obtained from a system deployment engine that is different from a target environment; in response 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; in response to a modification instruction for the attribute information of the external server, according to the modification information input through the interaction interface, re-obtaining the attribute information of the external server from the system deployment engine until the obtained attribute information of the external server meets the predetermined condition.

[0054] According to an embodiment of the present invention, on the interaction interface of a computer on which a system deployment engine is deployed, the obtained attribute information of the external server is displayed, and the user can view all selected samples through the interaction interface. If the system receives a determination instruction sent by the user through the interaction interface, it indicates that the user approves of 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 condition and performs subsequent model training based on this information. This interaction 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 sent by the user through the interaction interface is received, it indicates that the user hopes to make targeted adjustments to the current sample. According to the requirements input by the user through the interaction interface, the system re-searches for eligible samples from the system deployment engine and displays the re-obtained attribute information of the external server on the interaction interface for the user to further confirm. This process will be repeated until the user sends a determination instruction. After receiving the user's final confirmation, the system will start training the model based on the adjusted sample data. This interaction mechanism ensures that the user can flexibly adjust the sample data, thereby improving the accuracy and applicability of model training.

[0056] According to an embodiment of the present invention, training an initial system recommendation model using a training set to obtain a first prediction model includes: determining the information gain of the training set according to the types of sub-attributes of each attribute information in the training set to obtain the gain values of the sub-attributes; sorting a plurality of gain values to determine the splitting attribute of the root node in the initial decision tree; dividing the training set into a plurality of sub-datasets according to the multiple attribute value ranges of the splitting attribute, wherein the sub-nodes in the decision tree represent the sub-datasets; for each sub-node, repeating the following operations until multiple sub-attributes are traversed: determining the gain values of the remaining sub-attributes; and re-determining the splitting attribute; further dividing the sub-dataset according to the re-determined splitting attribute; in the case where it is determined that multiple sub-attributes have been traversed, using the obtained target decision tree as the 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, during the training of 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] where p i represents the proportion of the i-th type of samples (i.e., different operating system types) in the training set D, j represents the total number of categories of the operating system type, and H(D) represents the information gain. For example, assuming that the training set D contains 200 samples, among which 80 samples have the operating system type of system a and 120 samples have the 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, the distribution of different operating system types in the training set can be quantified, providing 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 values of each sub-attribute in the attribute information are calculated. The calculated multiple gain values 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, then 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 preferentially select the attribute that contributes the most to the classification result for division, thereby improving the classification performance and efficiency of the model.

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

[0062] As Figure 3 shown, the target decision tree takes the graphics processor video memory as the splitting attribute of the root node 301, and its value range is divided into less than 16G of the graphics processor video memory, 16G - 32G of the graphics processor video memory, and greater than 32G of the graphics processor video memory, corresponding to three first child nodes 302 under the root node respectively. In each child node, by calculating the gain values of the sub-attributes of the sub-dataset, the splitting attribute of the next layer is determined to be the number of graphics processor cores.

[0063] The value range of the number of graphics processor cores is divided into less than 8 of the graphics processor cores, 8 - 16 of the graphics processor cores, and greater than 16 of the graphics processor cores, corresponding to three second child nodes 303 respectively. After further calculating the gain values of the sub-attributes, the splitting attribute is determined to be the central processing unit (CPU) architecture.

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

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

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

[0067] According to an embodiment of the present invention, when calculating the gain values of each sub-attribute, it is necessary to calculate according to different operating system types. The training set is divided according to the operating system types of each attribute information in the training set to obtain a plurality of sample sets. For each sample set, the intermediate gain values of a plurality of sub-attributes in the attribute information are respectively calculated. The intermediate gain values include the gain values of the same sub-attribute under different operating system types and within different attribute value ranges. The intermediate gain values of the same sub-attribute in the plurality of 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, represents the intermediate gain value of sub-attribute A, and IG(D,A) represents the gain value of sub-attribute A.

[0070] For example, for the number of central processing unit cores (sub-attribute A), there are 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, among which 20 are recommended for system a and 30 are recommended for system b. There are 100 samples with 8 - 16 cores, among which 40 are recommended for system a and 60 are recommended for system b. There are 50 samples with more than 16 cores, among which 20 are recommended for system a and 30 are recommended for system b.

[0071] According to an embodiment of the present invention, calculated based on the above formula (1): the gain value for less than 8 cores is = 0.971. The gain value for 8 - 16 cores is = 0.971. The gain value for more than 16 cores is = 0.971. Calculated based on the above formula (2): the gain value of the sub-attribute of the number of central processing unit cores is .

[0072] According to an embodiment of the present invention, the gain value of the graphics processor video memory is calculated in the same way. Assume that the graphics processor video memory (sub-attribute A) has two values, less than 16G and greater than or equal to 16G. Among the 80 samples with a video memory less than 16G, 30 are recommended system a and 50 are recommended system b. Among the 120 samples with a video memory greater than or equal to 16G, 50 are recommended system a and 70 are recommended system b. Then, based on the above formula (1), it is calculated that the information entropy of the video memory less than 16G = 0.954, and the information entropy of the video memory greater than or equal to 16G = 0.985. Based on the above formula (2), it is calculated that the information gain IG(D,A) of the sub-attribute of the graphics processor video 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, so as to provide 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 further includes: when it is determined that the prediction accuracies of multiple first prediction models are all less than a preset value, re-performing an equal-proportion partitioning on the reference samples and the attribute information of the external server to obtain multiple updated data sets; using the multiple updated data sets to train and verify the initial system recommendation model until the verification times reach the preset times, or the prediction accuracy of the trained second prediction model reaches the preset value; when it is determined that the verification times reach the preset times and the prediction accuracies of the multiple trained second prediction models are all less than the preset value, sorting the prediction accuracies of the multiple second prediction models, so as 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 accuracies of multiple first prediction models, if it is found that the prediction accuracies of all first prediction models are less than the preset value 1, re-perform an equal-proportion partitioning on the reference samples and the attribute information of the external server to generate multiple updated data sets. Specifically, randomly shuffle the sample data and re-partition it into D 1 、D 2 、D 3 、D 4 、D 5 five updated data sets. 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 validating multiple generated second prediction models, it is found that the prediction accuracy rate has reached 1, then sort the prediction accuracy rates of the multiple second prediction models, and determine a system recommendation model therefrom according to the sorting result. If the prediction accuracy rates of the multiple second prediction models are all less than 1, continue iterative training. After undergoing a preset number of validations, if the prediction accuracy rate still has not reached 1, then it is necessary to adjust the preset number to further optimize the iterative process. Since the model training uses the k-fold cross-validation algorithm, the preset number 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 rate.

[0076] According to an embodiment of the present invention, after updating the preset number, continue to repeat the cross-validation process until the number of validations reaches the updated preset number. Since in the k-fold cross-validation algorithm, when the k value is 9, after 9 iterations, the selection of the decision tree will no longer be of significant significance. If a second prediction model with a prediction accuracy rate of 1 is still not found during this process, then select the second prediction model with the highest prediction accuracy rate from the historically trained second prediction models as the system recommendation model. This method ensures that even if the ideal accuracy rate is not reached after multiple iterations, the optimal model can still be selected for system recommendation, thereby ensuring the reliability and practicality of the recommendation results.

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

[0078] In operation S411, obtain a reference sample and the attribute information of an external server different from the target environment. In operation S412, perform an equal-proportion partitioning on the reference sample and the attribute information of the external server to obtain multiple sample data sets. In operation S413, select one of the multiple sample data sets as the validation set, and the rest as the training set. 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 partition 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, calculate the prediction accuracy rate of the first prediction model using the validation set. In operation S420, determine whether the prediction accuracy rate is equal to 1. In operation S421, determine whether the number of validations has reached the preset number. In operation S422, determine whether the preset number is the target value. In operation S423, update the preset number. In operation S424, output the system recommendation model.

[0080] According to an embodiment of the present invention, by obtaining the attribute information of the reference sample and the external server, and generating multiple sample data sets through equal-proportion division, the diversity and representativeness of the training data are ensured. By calculating the gain value of the sub-attributes and selecting the optimal splitting attribute, a decision tree model is gradually constructed to generate the first prediction model. The prediction accuracy of the model is evaluated using the validation set, and the accuracy and stability of the model are ensured through iterative optimization. Finally, a system recommendation model is output, providing 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 enhancing the efficiency and reliability of system deployment.

[0081] According to an embodiment of the present invention, the operating system deployment method further includes: detecting the prediction accuracy of multiple second prediction models based on a preset standard value to obtain a detection result; and outputting a prompt message when the detection result indicates that the prediction accuracy of multiple second prediction models is less than the preset standard value, where the prompt message 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, and 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 reasonableness of the data, thereby providing a more reliable basis for subsequent model training and optimization.

[0083] According to an embodiment of the present invention, determining a target partition result corresponding to the system recommendation type according to the historical deployment information of the system deployment engine includes: determining multiple candidate partition results corresponding to the system recommendation type according to the mapping relationship between the operating system type and the partition result in the historical deployment information; analyzing the partition information of the servers with the operating system already deployed in the target environment to determine the partition matching degree between the partition information and each candidate partition result; and determining the target partition result from multiple candidate partition results according to the magnitude relationship between multiple partition matching degrees.

[0084] According to an embodiment of the present invention, after determining the system recommendation type of the target server, the mapping relationship between the operating system type and the partitioning 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 partitioning result. According to the operating system type recommended by the system, the corresponding partitioning result is looked up in the mapping relationship table. If the recommended operating system type corresponds to multiple possible partitioning results, these partitioning results will be used as candidate partitioning results for further screening and optimization in the follow-up. This method ensures the accuracy and flexibility of the partitioning result, providing reliable support for the deployment of the operating system.

[0085] According to an embodiment of the present invention, the partitioning information of the servers with the operating system already deployed in the target environment is obtained, and these partitioning information are analyzed to extract key attributes such as the name, size, and type of the partitions. For each candidate partitioning result, the matching degree between it and the partitioning information of the deployed servers is calculated. The calculation of the matching degree can be achieved by comparing attributes such as the partition name, size, and type. Either 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 partitioning result and the target environment can be quantified, providing data support for selecting the optimal partitioning scheme.

[0086] According to an embodiment of the present invention, based on the magnitude relationship of the matching degrees, the candidate partitioning result with the highest matching degree is selected as the target partitioning result. If there are multiple candidate partitioning results with the same matching degree, further screening can be performed according to other factors (such as partition size, type, etc.). If a higher precision is required for the calculation of the partition matching degree, a machine learning model can be considered to predict the partition matching degree. In addition, if there is a large amount of partitioning information in the target environment, parallel computing technology can be adopted to accelerate the calculation process of the matching degree. Through the above methods, the system can automatically combine historical data and the partitioning information of the target environment to determine the most suitable target partitioning result, thereby improving the deployment efficiency and the adaptability of the partitioning scheme.

[0087] According to an embodiment of the present invention, the partitioning information of the servers with the operating system already deployed in the target environment is analyzed to determine the partition matching degree between the partitioning information and each candidate partitioning result, including: obtaining the business operation requirements of multiple servers in the target environment, where 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 the multiple candidate partitioning results and the business operation requirements and the similarity between the partitioning 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, where the business operation requirements include at least one of resource requirements, performance requirements, and fault tolerance requirements. Specifically, the resource requirements cover the central processing unit, memory, disk space, etc., the performance requirements include throughput, latency, etc., and the fault tolerance requirements involve backup strategies, high availability requirements, etc. Define the calculation rules for business matching degree, and calculate the matching degree of each dimension according to the resource requirements, performance requirements, and fault tolerance requirements respectively. Finally, perform a weighted sum of the matching degrees of each dimension to obtain the total business matching degree. This method can comprehensively evaluate the degree of fit between the server and the business requirements, providing a more accurate basis for system recommendation and deployment.

[0089] According to an embodiment of the present invention, define the calculation rules for partition similarity, and quantify the matching degree by comparing the partition information of the target server with the similarity of the candidate partition results. Specifically, string matching, set similarity, or other similarity algorithms (such as cosine similarity) can be used. Perform a weighted sum of the business matching degree and the partition similarity 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, select the candidate partition result with the highest partition matching degree as the target partition result. This method comprehensively considers the matching degree of business requirements and partition characteristics, ensuring that the selected partition scheme not only meets the business operation requirements but also highly fits the partition configuration of the target environment.

[0090] According to an embodiment of the present invention, the operating system deployment method further includes: sending the generated system installation package corresponding to the system deployment plan to the target server, where 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 the operating system corresponding to the system recommendation type according to the system installation package; in the case of determining that the operating system installation is completed, 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] As Figure 5As shown, the address information of the target server 510 is determined, and the system installation package is transferred from the computer 520 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 an 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 of the system installation package and installation parameters (such as installation directory, language settings, etc.). 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 according to 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 installation of the operating system is completed, 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 recognized and applied by the operating system. This process ensures the accuracy and automation of the partition configuration, providing 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 further includes: when it is determined that the system deployment of the target server is completed, the following operations are repeatedly executed 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 ratio of each partition in the operating system, a ratio adjustment operation is performed on each partition to obtain a dynamic partition result, where 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, a partition algorithm is used to determine the added value corresponding to the ratio adjustment operation; and it is determined the gain change of the added value relative to before the partition adjustment under the ratio adjustment operation.

[0094] According to an embodiment of the present invention, after the installation of the operating system of the target server is completed, according to the initialization partition scheme, the storage resources are divided into a system partition, a data storage partition, a Web (network) service partition, and a file storage partition. 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 metrics, and system status, etc.

[0095] According to an embodiment of the present invention, a proportional adjustment operation is performed on each partition according to the preset adjustment ratios of multiple partitions in the operating system 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 rate 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 a comprehensive consideration of experience and the sensitivity of server resource adjustment. If the adjustment step size is set too large, for example, adjusted to 0.1 (i.e., 10%), then each adjustment will cause a relatively large change in the server resource allocation, which may lead to excessive fluctuations in the system state and is not conducive to finding a stable optimal solution. Especially for storage-type partitions, too large an 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 and avoid unnecessary interference with system operation.

[0097] According to an embodiment of the present invention, if the step size is set too small, such as 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. Through practice and trade-offs, the preset adjustment ratios set for each type of partition can explore more weight combinations within a reasonable time while avoiding too much impact on the system for each adjustment. This setting enables the system to explore the optimal partition result relatively efficiently and stably. In addition, the preset adjustment ratio of each type of partition supports adjustment through the customer page, allowing modification within the range of -0.1 to 0.1, thereby providing flexibility and controllability for users to meet different business requirements and system operating environments.

[0098] According to an embodiment of the present invention, when performing a proportional adjustment operation on each partition, first obtain the current value and its proportion of the current partition. For example, the system partition weight is: W 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 weights of each partition, operation , where each The value range of is [-0.1, 0.1], indicating that the maximum amplitude of each weight adjustment is 10%. In this way, the system can gradually optimize the partition weights while ensuring a reasonable adjustment amplitude, thereby improving the efficiency and stability of resource allocation.

[0099] According to an embodiment of the present invention, after performing the ratio adjustment operation, obtain the target running information of the target server, and use the partition algorithm to determine the additional value corresponding to the ratio adjustment operation. The additional 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 additional value relative to that before the ratio adjustment operation under the ratio adjustment operation, the impact of the adjustment operation on the system running state is quantified. This method can provide data support for the optimization of partition weights, ensuring that the adjusted partition configuration reaches the optimal in terms of resource balance, system performance, and partition rationality.

[0100] According to an embodiment of the present invention, repeatedly perform the above ratio adjustment operation while observing the running state of the target server. Since the execution of each type of action requires a certain amount of time, the system records the gain change of the additional value after each adjustment. If it is detected that the gain change exceeds the preset threshold of 10%, stop the exploration and use the current partition result as the optimal dynamic partition result. If the gain change does not exceed the preset threshold, continue to explore all possible actions until the number of partition adjustments reaches the preset maximum number. Finally, the system selects the action with the largest additional value as the execution action for 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 partition usage rationality 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, perform the ratio adjustment operation on each partition according to the preset adjustment ratios of multiple partitions in the operating system to obtain a dynamic partition result, including: randomly combining the preset adjustment ratios of multiple partitions with the preset adjustment types to generate an operation vector, where the preset adjustment types include an increase type, a maintenance type, or a decrease type; based on the operation vector, perform the corresponding ratio adjustment operation on each partition to obtain a dynamic partition result.

[0102] According to an embodiment of the present invention, when performing the ratio adjustment operation on each partition, currently there are four partitions W sys +W file +W web +W db, each partition has three preset adjustment types: increase, remain unchanged, or decrease. Since an increase in one partition requires a corresponding decrease in another partition, in the case of four partitions, there are = 81 possible combinations of actions. Randomly combine the preset adjustment ratios and preset adjustment types of multiple partitions to generate an operation vector, such as [0.05, -0.03, 0.02, 0]. Based on the generated operation vector, perform corresponding proportional adjustment operations on each partition to obtain the dynamic partition result. This method explores various possible adjustment combinations to ensure that the partition configuration can reach the optimal state in terms of resource balance, system performance, and partition rationality.

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

[0104] In operation S601, collect the attribute information of multiple servers in the target environment. In operation S602, generate a list of servers to be installed based on the target servers without an operating system installed. In operation S603, use the system recommendation model running in the system deployment engine to determine the system recommendation type for each target server. In operation S604, based on the system recommendation type, determine the target partition result for each target server.

[0105] In operation S605, complete the installation of the operating system according to the system recommendation type and complete the initial partition configuration according to the target partition result. In operation S606, dynamically adjust the current partition result. In operation S607, calculate the additional value of the current partition result. In operation S608, determine whether the gain change of the additional value reaches a preset threshold or whether the number of partition adjustments reaches a preset maximum number. In operation S609, output the dynamic partition result.

[0106] According to the embodiments of the present invention, collecting the attribute information of servers in the target environment and generating a list of servers to be installed ensures that the hardware configuration and business requirements of the target servers are accurately identified. Using the system recommendation model to recommend a suitable operating system type for the target servers and completing the installation of the operating system and the initial partition configuration in combination with the target partition result ensure the efficiency and accuracy of system deployment. By dynamically adjusting the partition configuration and calculating the additional value, the system can optimize resource allocation, improve resource balance, system response time, and partition usage rationality. The system determines the optimal dynamic partition result based on the gain change of the additional value or the number of adjustments, so as to ensure that the target servers reach 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 target operation information, using a partitioning algorithm to determine an additional value corresponding to a proportional adjustment operation includes: performing a multi-dimensional analysis on the utilization rate of system resources in the target operation information to generate a system operation vector; determining a first additional value according to 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 rate in the system operation vector according to a preset mapping relationship between the partition resource utilization rate and the third additional value; performing a weighted sum 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, collect the central processor utilization rate, memory utilization rate, hard disk load, network bandwidth utilization rate of the target server, and the usage situation of each partition. Specifically, the central processor utilization rate U CPU = number of used central processor cores / total number of central processor cores, the memory utilization rate U mem = used memory / total memory size, the hard disk load D iops = current hard disk read / write rate / maximum hard disk read / write rate, the network bandwidth utilization rate U net = used network bandwidth / total network bandwidth, the usage situation S of each partition parti = partition used space / partition total space. Based on these metrics, generate a vector S = [U CPU , U mem , D iops , U net , S part1 , S part2 ,..., S partn of the current system operation, and use this vector to calculate the additional value corresponding to the current proportional adjustment operation.

[0109] According to an embodiment of the present invention, the first dimension is the resource balance reward, and its purpose is to reward the balanced utilization of each resource and avoid overuse of a certain resource while other resources are idle. The resource balance is measured by calculating the reciprocal 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 as shown in formula (3):

[0110]

[0111] where m represents the number of types of component values (i.e., central processor, memory, disk I / O, network), U q represents the qth component value, U p represents the average value of each component value, and R b represents 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 piece of logic 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] where R r represents the second additional value, T max represents the maximum acceptable response time, and T now represents the component value characterizing the response time.

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

[0116] According to an embodiment of the present invention, after calculating the additional values of the three dimensions, calculate the additional value corresponding to the ratio adjustment operation. The calculation method is shown in formula (5):

[0117]

[0118] where P b , P r , P p represent the weights of the additional values of the three dimensions, and 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 reasonable partition usage can be comprehensively evaluated, thereby providing a quantitative basis for optimizing the partition configuration.

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

[0120] According to an embodiment of the present invention, the weights of the first added value, the second added value, and the third added value are dynamically adjusted and will be continuously optimized and adjusted according to the real-time operation 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 services, involving a large amount of data reading, computing, and storage operations, resulting in a continuously high demand for central processing unit, memory, and disk I / O resources, and multiple component values in the system operation vector all exceed their respective preset performance criteria, then the weight of the first added value will be dynamically adjusted according to the average value of the central processing unit utilization rate, memory utilization rate, hard disk load, and network bandwidth utilization rate. Specifically, the average value of the multiple component values is taken as the weight of the first added value, and the weights of the other two added values are one-half of the target total weight minus this weight.

[0121] According to an embodiment of the present invention, if the utilization rates of the central processing unit and the graphics processing unit of the server are continuously at a high level, and multiple component values representing the computing resource utilization rate all exceed the corresponding preset performance criteria, indicating that the server has a high requirement for computing response time, then the weight of the second added value is increased. Specifically, the weight of the second added value is set to the average value of the utilization rates of the central processing unit and the graphics processing unit, and the weights of the other two added values are one-half of the target total weight minus this weight value.

[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 multiple component values representing the partition resource utilization rate is less than a preset difference, and different partitions store different types of data, then the weight of the third added value is increased and set to 0.4. This adjustment can ensure that the rationality of partition usage takes a higher priority in resource allocation, thereby optimizing the utilization efficiency of storage resources and avoiding the situation of wasted or insufficient partition space. At the same time, the weights of the other two added values are one-half of the value obtained by subtracting this weight value.

[0123] According to an embodiment of the present invention, if the above conditions are not all satisfied, it indicates that the system has reached the target conditions, and the weights of the first added value, the second added value, and the third added value are all set to the average value 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, so as to adapt to changing business requirements and system loads, and further improve the overall performance and resource utilization efficiency of the system.

[0124] According to an embodiment of the present invention, the operating system deployment method further includes: detecting a preset range for the weight adjustment value in the weight adjustment request in response to the weight adjustment request sent by the target object; updating the currently dynamically adjusted weight with the weight adjustment value when it is determined that the weight adjustment value meets the preset range; 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 the weight value, and after the customer manually maintains it, the weight value set by the customer takes precedence. In response to the weight adjustment request sent by the target object, the system will detect a preset range for the weight adjustment value in the weight adjustment request. The weight ratio of each category shall not be lower than 0.1 and shall not be higher than 0.8 to ensure the rationality of weight distribution and the stability of the system. When it is determined that the weight adjustment value meets the preset range, the system will update the currently dynamically adjusted weight with the weight adjustment value and end the dynamic adjustment operation of the weight. This mechanism not only ensures the flexibility of automatic adjustment but also provides space for customers to manually optimize, while avoiding the situation of overly extreme weight distribution, thus ensuring that the system can maintain high efficiency and stability in different operating scenarios.

[0126] Based on the above operating system deployment method, the present invention also provides an operating system deployment device. The following will be combined with Figure 7 Describe this device in detail.

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

[0128] As Figure 7As 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 configured to input the attribute information of the target server in the target environment obtained into the system recommendation model running in the system deployment engine to obtain the system recommendation type. Herein, the target environment is the environment for operating system deployment on the server, the system recommendation model is trained with the attribute information of the servers with the operating system already deployed in the target environment as the reference samples, and the target server is connected to the computer with the system deployment engine deployed. In one embodiment, the system recommendation module 710 can be used to perform the operation S210 described above, which will not be elaborated herein.

[0130] The partition determination module 720 is configured to determine the target partition result corresponding to the system recommendation type according to the historical deployment information of the system deployment engine. Herein, 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. In one embodiment, the partition determination module 720 can be used to perform the operation S220 described above, which will not be elaborated herein.

[0131] The system deployment module 730 is configured 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 elaborated herein.

[0132] According to an embodiment of the present invention, any multiple of the system recommendation module 710, the partition determination module 720, and the system deployment module 730 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may 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 may 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 chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or any other reasonable manner of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the system recommendation module 710, the partition determination module 720, and the system deployment module 730 may be at least partially implemented as a computer program module, which can execute corresponding functions when the computer program module is run.

[0133] It should be noted that the operating system deployment device part in the embodiments of the present invention corresponds to the operating system deployment method part in the embodiments of the present invention. For the description of the operating system deployment device part, please refer to the operating system deployment method part specifically, and details will not be repeated here.

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

[0135] As Figure 8 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 the program stored in the read only memory (ROM) 802 or the program loaded from the storage part 808 into the random access memory (RAM) 803. The processor 801 may include, for example, a general microprocessor (such as a central processing unit), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 801 may also include on board 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 the RAM 803, various programs and data required for the operation of the electronic device 800 are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other via the bus 804. The processor 801 performs various operations of the method flow according to the embodiments of the present invention by executing the programs in the ROM 802 and / or the RAM 803. It should be noted that the programs can also be stored in one or more memories other than the ROM 802 and the RAM 803. The processor 801 can also perform various operations of the method flow according to the embodiments 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, and the input / output (I / O) interface 805 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, for example, 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 a computer program read from it can be 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 embodiments; or may exist separately 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 embodiments 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: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include one or more memories other than the above-described ROM 802 and / or RAM 803 and / or ROM 802 and RAM 803.

[0140] An embodiment of the present invention also includes a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs 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] When the computer program is executed by the processor 801, it executes the above functions defined in the system / apparatus of the embodiment of the present invention. According to an embodiment of the present invention, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0142] In one embodiment, the computer program can rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program can also be transmitted and distributed in the form of a signal on a network medium, and is downloaded and installed through the communication part 809, and / or installed from the removable medium 811. The program code contained in the computer program can be transmitted by any suitable 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, it executes the above functions defined in the system of the embodiment of the present invention. According to an embodiment of the present invention, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0144] In accordance with embodiments of the present invention, program code for executing the computer programs provided by the embodiments 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 procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or alternatively, can be connected to an external computing device (e.g., by connecting through the Internet using an Internet service provider).

[0145] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0146] Those skilled in the art can understand that the features described in the various embodiments of the present invention can 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 can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0147] The above describes the embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments are described separately above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should 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 the initialization partition configuration based on the system deployment plan; When it is determined that the target server system has been deployed, the current partition result is dynamically adjusted; the dynamic partition result is output according to the gain change of the adjusted added value or the number of partition adjustments, wherein the added value is determined based on the target operation information of the target server.

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, re-dividing the attribute information of the reference sample and the external server 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 A system deployment module, 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; The device is also used to dynamically adjust the current partition result when it is determined that the target server system has been deployed; output the dynamic partition result according to the gain change of the adjusted additional value or the number of partition adjustments, wherein the additional value is determined based on the target operation information of the target server.

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.

Images