Cloud-based business deployment methods and devices, electronic equipment and storage media

Abstract

This application provides a cloud computing-based business deployment method, apparatus, electronic device, and storage medium. Through this application, the system can automatically receive the executable file of the business system and runtime environment parameters including operating system type, middleware type, performance indicators, and security requirements. Based on the performance indicators and security requirements, it determines the appropriate container or virtual machine environment, automatically matches cloud computing resource components that meet the performance indicators based on the runtime environment parameters, and completes the entire process of environment creation, component deployment initialization, and business system startup. Therefore, it can solve the problems of low deployment efficiency and error-proneness caused by relying on a large amount of manual operation in the prior art, and the difficulty in accurately matching suitable cloud computing resources and runtime environments according to the performance and security requirements of the business system. This achieves the technical effects of reducing manual intervention, improving deployment efficiency, reducing human error, and simultaneously realizing accurate matching of cloud computing resources and business needs.

Description

Technical Field

[0001] This application relates to the field of cloud computing business system technology, and in particular to a cloud computing-based business deployment method and apparatus, electronic device and storage medium. Background Technology

[0002] With the rapid development of cloud computing technology, it has become a crucial support platform for modern IT architecture and has been widely applied in areas such as enterprise application deployment, elastic resource scheduling, and rapid business iteration. Among related technologies, the collaborative operation of modules such as parameter reception, runtime environment assessment, resource matching and deployment, and business lifecycle management has constructed the basic framework of an automated deployment system. Specifically, this technology system covers the entire process from business requirement input, intelligent runtime environment decision-making, dynamic matching of resource components, infrastructure creation to business system deployment and management, including key aspects such as operating system image selection, middleware configuration, container and virtual machine resource scheduling, and load balancing strategies, forming a cloud resource adaptation mechanism oriented towards performance and security requirements.

[0003] However, existing business deployment methods rely on manual configuration or static rules for environment selection and resource matching, lacking a dynamic evaluation model based on multi-dimensional parameters. This can lead to problems such as low resource utilization, insufficient security isolation, or performance bottlenecks. Specifically, traditional deployment processes typically depend on operations personnel manually selecting the operating system, middleware version, and runtime environment, lacking systematic analysis of performance indicators such as business concurrency, response time, and data processing capabilities. They also fail to effectively integrate security requirements such as data encryption and access control for environment adaptation. Furthermore, in hybrid container and virtual machine deployment scenarios, existing technologies lack a unified management interface, requiring separate handling of scaling, shutdown, and startup operations for different platforms. This increases operational complexity and limits the rapid response and flexible expansion capabilities of business systems. Summary of the Invention

[0004] This application provides a cloud computing-based business deployment method, apparatus, electronic device, and storage medium to at least solve the problem in related technologies where the lack of a unified management interface in a mixed deployment scenario of containers and virtual machines leads to high operational complexity due to the need to handle expansion, shutdown, and other operations separately for different platforms.

[0005] This application provides a cloud computing-based business deployment method, including:

[0006] The system receives uploaded executable files of the business system and the runtime environment parameters required for their operation. These runtime environment parameters include operating system type, middleware type, performance indicators, and security requirements.

[0007] Based on the performance metrics and security requirements, it is determined that the business system is suitable for running in a container environment or a virtual machine environment.

[0008] Based on the aforementioned operating environment parameters, match cloud computing resource components that meet the aforementioned performance indicators in the cloud resource library;

[0009] Based on the judgment result, create the corresponding container or virtual machine environment, deploy the matching cloud computing resource components to the container or virtual machine environment, and perform initial configuration.

[0010] Deploy the executable file of the business system to the configured container or virtual machine environment and start the business system.

[0011] This application also provides a cloud computing-based business deployment apparatus, including:

[0012] The receiving unit is used to receive the uploaded executable file of the business system and the runtime environment parameters required for its operation. The runtime environment parameters include operating system type, middleware type, performance indicators and security requirements.

[0013] The judgment unit is used to determine, based on the performance indicators and the security requirements, whether the business system is suitable for running in a container environment or a virtual machine environment.

[0014] The matching unit is used to match cloud computing resource components that meet the performance indicators in the cloud resource library based on the operating environment parameters.

[0015] The first deployment unit is used to create a corresponding container or virtual machine environment based on the judgment result, deploy the matching cloud computing resource components to the container or virtual machine environment, and perform initial configuration.

[0016] The second deployment unit is used to deploy the executable file of the business system to a configured container or virtual machine environment and start the business system.

[0017] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the cloud computing-based business deployment methods described above.

[0018] This application also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of any of the cloud computing-based business deployment methods described above.

[0019] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described cloud computing-based business deployment methods.

[0020] This application provides a cloud computing-based business deployment method, apparatus, electronic device, and storage medium. Through this application, the system can automatically receive the executable file of the business system and runtime environment parameters including operating system type, middleware type, performance indicators, and security requirements. Based on the performance indicators and security requirements, it determines the appropriate container or virtual machine environment, automatically matches cloud computing resource components that meet the performance indicators based on the runtime environment parameters, and completes the entire process of environment creation, component deployment initialization, and business system startup. Therefore, it can solve the problems of low deployment efficiency and error-proneness caused by relying on a large amount of manual operation in the prior art, and the difficulty in accurately matching suitable cloud computing resources and runtime environments according to the performance and security requirements of the business system. This achieves the technical effects of reducing manual intervention, improving deployment efficiency, reducing human error, and simultaneously realizing accurate matching of cloud computing resources with business needs and ensuring the stable operation of the business system.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0023] Figure 1 A flowchart illustrating a cloud computing-based business deployment method provided in this application embodiment;

[0024] Figure 2 A schematic diagram of a cloud computing-based business deployment device provided in this application embodiment;

[0025] Figure 3 This is a schematic diagram of another cloud computing-based business deployment device provided in an embodiment of this application. Detailed Implementation

[0026] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0027] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] The specific application environment architecture or specific hardware architecture on which the execution of cloud-based business deployment methods depends is described here.

[0029] The embodiments of this application provide a cloud computing-based business deployment method. Figure 1 This is a flowchart illustrating a cloud computing-based business deployment method provided in an embodiment of this application.

[0030] like Figure 1 As shown, the method includes the following steps:

[0031] Step 101: Receive the uploaded executable file of the business system and the runtime environment parameters required for its operation. The runtime environment parameters include operating system type, middleware type, performance indicators and security requirements.

[0032] In this embodiment, the core of step 101 lies in receiving key information uploaded by the business system implementation personnel. This information is the fundamental prerequisite for subsequent business deployment. Among them, the business system executable file is the core carrier for the business to run in the cloud computing environment. It is a program file that can be directly executed after being compiled and packaged in the early stage. It contains the core functional code and running logic of the business system, which can ensure that the business can achieve the established business service capabilities in the target environment.

[0033] The runtime environment parameters are key configuration information that ensures the normal operation of executable files in the business system and meets business performance and security requirements. Specifically, they cover four main categories: operating system type, middleware type, performance metrics, and security requirements. Operating system type refers to the underlying operating system required for the business system to run. Different operating systems vary in their support for hardware resources, software compatibility, and operational stability. Accurate operating system type information ensures that the subsequently matched cloud computing resources are compatible with the underlying operational requirements of the business system. Middleware type includes database middleware, web middleware, such as MySQL database middleware and Nginx. Web middleware, and similar middleware, acts as a bridge connecting business systems with underlying hardware resources. It provides fundamental support services such as data storage, network communication, and transaction processing. Clearly defining the middleware type is crucial for ensuring the normal operation of business systems. Performance metrics are key parameters for measuring the operational capabilities of business systems, typically including expected concurrent access, data processing speed, and response time. For example, an online business might require an expected concurrent access of 5000 users, a data processing speed of 1000 records per second, and a response time of no more than 2 seconds. These metrics directly determine the performance specifications of the cloud computing resources required. Security requirements involve the security needs of business data and the operating environment, including data encryption levels, access control policies, and security isolation levels. Examples include end-to-end encrypted data transmission, strict user authentication, and access control. These requirements are essential for ensuring data security and preventing unauthorized access during business operations.

[0034] By accurately receiving the executable files and runtime environment parameters of the aforementioned business systems, precise data support can be provided for subsequent operations such as determining the runtime environment suitable for the business systems and matching appropriate cloud computing resources. This reduces deployment errors caused by missing or inaccurate information and lays the foundation for the smooth deployment of the business systems.

[0035] Step 102: Based on performance metrics and security requirements, determine whether the business system is suitable to run in a container environment or a virtual machine environment.

[0036] In this embodiment, based on previously obtained performance metrics and security requirements, the appropriate environment type for the business system is scientifically determined, namely a container environment or a virtual machine environment. Performance metrics, as one of the criteria for judgment, cover the key performance requirements during the operation of the business system, such as expected concurrent access volume, data processing speed, and response time. These metrics directly reflect the business system's requirements for computing resource capacity and operational efficiency. For example, if the business system has low expected concurrent access volume, high sensitivity to resource consumption, and requires rapid deployment and flexible expansion to cope with short-term business fluctuations, these lightweight performance requirements are more in line with the technical characteristics of a container environment. Conversely, when the business system has extremely high expected concurrent access volume, complex data processing tasks, and stringent requirements for operational performance stability, requiring long-term stable resource support, its performance requirements are more aligned with the advantages of a virtual machine environment.

[0037] Security requirements are also a core consideration in environment assessment, primarily including data encryption level, access control policies, and security isolation level. Data encryption level determines the strength of security protection for business data during transmission and storage. Access control policies clarify the access permissions of different users or roles to business system resources. Security isolation level affects the degree of logical isolation between the business system and other systems or resources. If the business system involves sensitive data processing, requiring extremely high data encryption levels, strict access control, and strong security isolation to prevent data leakage or unauthorized access, such high security requirements are more suitable for virtual machine environments. This is because virtual machines provide stronger security protection through hardware-level resource isolation. For business systems with relatively moderate security requirements that do not require extreme isolation protection, container environments, with their lightweight nature, can meet basic security needs.

[0038] In the actual judgment process, it is necessary to comprehensively weigh the priority and specific parameters of performance indicators and security requirements, and combine the technical differences between container environments (lightweight, flexible, and fast deployment) and virtual machine environments (strong isolation and high stability) to form a clear judgment logic. This will ensure that the final selected operating environment can not only meet the performance requirements of the business system, but also ensure its operational security, and provide the correct direction for subsequent resource matching and business deployment.

[0039] This step, by accurately matching business needs with the operating environment, effectively avoids performance waste or security risks caused by improper environment selection, providing a fundamental guarantee for the stable and efficient operation of business systems.

[0040] Step 103: Based on the runtime environment parameters, match cloud computing resource components that meet the performance indicators in the cloud resource library.

[0041] In this embodiment, based on previously acquired runtime environment parameters, cloud computing resource components that meet business performance indicators are precisely selected from the cloud resource repository, providing suitable resource support for subsequent business system deployment. The runtime environment parameters here cover operating system type, middleware type, performance indicators, and security requirements. The operating system type clearly defines the underlying system environment required for business operation; the middleware type points to the key component categories supporting business functions, including database middleware (such as MySQL, Oracle), web middleware (such as Nginx, Apache), etc. These parameters together constitute the basic screening conditions for resource matching. The cloud resource repository, as a collection storing various cloud computing resources, includes operating system images of different versions and performance specifications, middleware installation packages, and supporting components, providing diverse resource options for different business needs.

[0042] During the matching process, the first step is to filter resources from the cloud resource library based on the operating system type and middleware type in the runtime environment parameters. This initial screening identifies resources that match the underlying environment requirements of the business system. For example, if the business requires a Linux operating system and MySQL database middleware, resources that do not meet these requirements are excluded. Subsequently, a second screening is conducted, focusing on performance metrics such as expected concurrent access volume, data processing speed, and response time. These performance metrics become the core criteria for determining resource suitability. For instance, if the business system expects high concurrent access volume (e.g., 5000 simultaneous accesses) and a data processing speed requirement of 1000 records per second, priority will be given to selecting optimized versions of MySQL database middleware with high concurrency processing capabilities, high-throughput Linux operating system images, and high-performance web components from the candidate set. This ensures that the selected cloud computing resource components meet the business's operational efficiency needs and avoids issues such as business lag and response delays due to insufficient resource performance. The entire matching process, through explicit parameter mapping and performance verification logic, achieves precise alignment between "requirements" and "resources," ensuring that each selected cloud computing resource component is highly compatible with the operational and performance requirements of the business system. This precise matching of cloud computing resource components effectively avoids performance waste or business operation limitations caused by resource mismatches, providing reliable resource guarantees for the stable and efficient operation of the business system in the target environment.

[0043] Step 104: Based on the judgment result, create the corresponding container or virtual machine environment, deploy the matching cloud computing resource components to the container or virtual machine environment, and perform initial configuration.

[0044] In this embodiment of the application, the core execution step of implementing the preliminary environment judgment and resource matching results into the actual operating environment is to strictly follow the container or virtual machine environment judgment results obtained in step 102 and complete the environment creation, resource deployment and initial configuration operations in stages.

[0045] The environment creation process relies on the resource creation interface provided by the cloud computing platform for automated operation. If the determination result is a virtual machine environment, the system will automatically call the cloud platform's virtual machine creation interface to generate a virtual machine instance that meets the requirements based on the potential resource needs of the business system (such as the number of CPU cores, memory capacity, storage specifications, etc., which can be indirectly derived from performance indicators, for example, high-concurrency businesses correspond to higher CPU and memory configurations). If the determination result is a container environment, the system will call the cloud platform's container orchestration interface (such as Docker API, Kubernetes interface, etc.) to create a lightweight container instance. The resource allocation of the container instance will also take performance indicators into account to ensure that it can support the business operation load.

[0046] Next is the cloud computing resource component deployment stage. Here, the resource components are the PASS components matched in step 103, such as the operating system image, database middleware, and web components. For virtual machine environments, the system uses the cloud platform's image deployment tool to import the matched operating system image into the created virtual machine. Then, an automated script sequentially installs the database middleware, web components, etc., ensuring that the component versions match business requirements. For container environments, a container image is built based on the matched operating system base image, and then the database middleware, web components, etc., are integrated into the container instance using a containerized deployment method, leveraging the lightweight nature of containers to achieve rapid component deployment.

[0047] Finally, there's the initialization configuration, which involves configuring the deployed resource components and runtime environment. For example, database middleware is initialized, including creating the database instances required by the business, configuring database connection parameters, and setting data storage paths and backup strategies. Web components are configured, such as setting port numbers, configuring reverse proxy rules, and optimizing concurrent connections. Basic operating system configurations are also performed, such as disabling unnecessary services, configuring firewall rules, and setting system timezones and log storage strategies. These initialization configuration operations are all executed through automated scripts, avoiding errors from manual configuration and ensuring that the runtime environment and resource components directly meet the operational requirements of the business system. By automating environment creation, component deployment, and initialization configuration, the cycle from resource preparation to a runnable state for the business system is significantly shortened, while ensuring the accuracy of the runtime environment and resource component configurations, laying a stable foundation for subsequent business system deployment and startup.

[0048] Step 105: Deploy the business system executable file to the configured container or virtual machine environment and start the business system.

[0049] In this embodiment, the executable file of the business system serves as the carrier of business functions, containing business logic code, functional modules, and runtime dependencies. Its deployment process requires adaptation operations based on the characteristics of the target environment (container or virtual machine). If the target environment is a virtual machine, the system will transfer the executable file to a preset business deployment directory (such as / usr / local / business / directory) within the virtual machine via a remote file transfer protocol (such as SSH file transfer protocol). This directory has been initialized and configured in the early stage and already has the permissions and dependent environment required for the executable file to run. If the target environment is a container, the executable file will be integrated into a container image that already contains matching cloud computing resource components through a container image building tool, or the executable file will be mapped to a specified path inside the container directly through container mounting directory to ensure that the environment inside the container can recognize and call the file.

[0050] After the file deployment is complete, the startup operation will execute the corresponding startup command based on the environment type. In a virtual machine environment, the system uses a remote command execution tool to call a preset startup script (such as start.sh). This script checks the integrity of the executable file and the running status of dependent components (such as whether the database middleware is running normally and whether the web component port is occupied). After confirming that everything is correct, it starts the business process and records the process ID to a specified log file for easy subsequent process management. In a container environment, a container instance containing the business executable file is started using container management commands (such as docker start, kubectl apply). The containerized startup method automatically associates with the previously configured component dependencies, ensuring that the business process runs in conjunction with the middleware and operating system.

[0051] During startup, the system also monitors the initialization status of the business system in real time. It confirms successful startup by checking if the business ports are listening normally and if they return preset health check responses (such as an HTTP 200 status code). If an anomaly occurs during startup (such as failed connection of dependent components or insufficient file permissions), error logs are promptly output and the startup process is paused, providing a basis for subsequent troubleshooting. If startup is successful, the business system enters normal operation and can begin receiving and processing user requests. Automated file deployment and startup verification ensure the business system is quickly and stably put into operation, avoiding startup delays or configuration errors caused by manual operation. Furthermore, startup status monitoring provides initial assurance for the reliable operation of the business system.

[0052] This application provides a cloud computing-based business deployment method. Since the system can automatically receive the executable file of the business system and runtime environment parameters including operating system type, middleware type, performance indicators, and security requirements, and determine the appropriate container or virtual machine environment based on the performance indicators and security requirements, it automatically matches cloud computing resource components that meet the performance indicators based on the runtime environment parameters, and completes the entire process of environment creation, component deployment initialization, and business system startup. Therefore, it can solve the problems of low deployment efficiency and error-proneness caused by relying on a large amount of manual operation in the prior art, and the difficulty in accurately matching suitable cloud computing resources and runtime environments according to the performance and security requirements of the business system. This achieves the technical effects of reducing manual intervention, improving deployment efficiency, reducing human error, and simultaneously realizing accurate matching of cloud computing resources with business needs and ensuring the stable operation of the business system.

[0053] In this application embodiment, the specific implementation of the operation of "determining whether the business system is suitable to run in a container environment or a virtual machine environment based on performance indicators and security requirements" is diverse. For clarity, some exemplary implementation methods are described below: a preset decision model is used to perform a weighted evaluation of security requirements and performance indicators, and output a running environment type suggestion; the final running environment is determined to be a container or a virtual machine based on the running environment type suggestion.

[0054] Specifically, using a pre-defined decision-making model for weighted evaluation is a concrete and feasible implementation method. First, the pre-defined decision-making model needs to be developed in advance, taking into account the technical characteristics and business scenario requirements of container and virtual machine environments in the cloud computing field, to construct a multi-dimensional evaluation index system. This system uses performance indicators and security requirements as the core evaluation dimensions. Performance indicators can be refined into quantifiable parameters such as expected concurrent access volume, data processing speed, and response time. Security requirements can be broken down into tiered indicators such as data encryption level, access control strictness, and security isolation requirements.

[0055] During the model building phase, historical business deployment data and industry best practice cases are used to assign corresponding weight values ​​to each detailed indicator. For example, for business scenarios with extremely high data security requirements, such as finance and healthcare, the weight of data encryption level and security isolation requirements will be significantly higher than other indicators. In the actual evaluation process, the system first extracts the specific values ​​of the business system performance indicators and security requirement levels obtained in step 101, and scores each indicator according to the evaluation scale. Then, based on the preset weights, it calculates the comprehensive score of performance indicators and security requirements. Subsequently, the model outputs a suggested operating environment type based on the comprehensive score and preset environment judgment rules. Finally, the system performs a secondary verification based on the operating environment type suggestion output by the decision model, combined with the current resource load of the cloud platform. If the resources of the suggested environment can meet the business needs, the final operating environment is directly determined to be a container or virtual machine. If the suggested environment resources are tight, and the business has room for adjustment of non-core indicators, the system will select the second-best but resource-sufficient environment type after confirmation with the business system implementation personnel, ensuring that the environment judgment meets both the core business needs and is practically feasible.

[0056] Furthermore, in this embodiment, the decision model is a machine learning-based classification model, and the training data includes the performance data, security configuration, and corresponding operating environment type of the historical business system.

[0057] Specifically, the training data must closely align with the key elements of the business deployment scenario. This includes performance data, security configurations, and corresponding operating environment types from historical business systems. Performance data covers quantifiable metrics such as expected concurrent access volume, actual data processing speed, and average response time in the actual operation of each historical business system. These data directly reflect the business's load requirements for computing resources. Security configurations include configuration information such as data encryption levels, access control policies, and security isolation schemes adopted by historical business systems, reflecting the business's security protection requirements for the operating environment. The corresponding operating environment type is the container or virtual machine environment finalized during the historical business deployment, serving as label data for model training and providing a basis for the model to learn the "requirement-environment" mapping relationship.

[0058] During the model training phase, the training data must first be preprocessed: outliers in the performance data are corrected using interpolation or deletion methods, and non-quantitative information in the security configuration is encoded and converted into numerical features that the model can recognize. Subsequently, an appropriate classification algorithm is selected based on the characteristics of the business scenario, such as logistic regression, random forest, gradient boosting tree (XGBoost), etc. Considering the interactive impact of performance and security indicators in business deployment, ensemble learning algorithms that can capture complex relationships between features are preferred. By constructing multiple decision trees, the relationship between "performance-security-environment" is learned, reducing the bias of a single model.

[0059] During the model application phase, upon receiving the performance metrics and security requirements of the current business system, the system will perform feature transformation on this data according to the preprocessing rules of the training phase and input it into the trained machine learning classification model. The model will then calculate the correlation probability between each feature and the environment type and output a prediction result indicating whether the current business system is suitable for a container environment or a virtual machine environment.

[0060] Furthermore, in this embodiment of the application, the operating environment parameters also include at least one of the following: expected concurrent access volume of the business system, data processing speed, response time, data encryption level, access control policy, and security isolation level.

[0061] Specifically, the runtime environment parameters serve as the core requirements for deploying business systems. In addition to operating system type and middleware type, they also explicitly cover at least one of the following: expected concurrent access volume, data processing speed, response time, data encryption level, access control policy, and security isolation level. These parameters further refine the specific requirements of business systems for the cloud computing environment from the two key dimensions of performance and security, providing a more accurate basis for subsequent environment assessment and resource matching.

[0062] Among them, expected concurrent access volume refers to the number of users or requests that the business system is expected to access simultaneously during normal operation; data processing speed is the number of data records that the business system can process per unit time, which is an important indicator for screening high-performance database middleware and server components; response time is the delay from when the user initiates a request to when the system returns a result, which is usually required to be controlled within 2 seconds. This parameter is closely related to network bandwidth and resource scheduling efficiency, and is a key performance standard to ensure user experience.

[0063] From a security perspective, data encryption level clarifies the encryption strength of business data during transmission and storage. For example, SSL / TLS protocols are used for transmission encryption, and AES-256 algorithms are used for storage encryption. Different encryption levels correspond to different security protection capabilities and also affect the selection of cloud platform encryption components and key management services. Access control policies specify the access permissions of different user roles to business system resources. Security isolation level reflects the degree of isolation between the business system and other systems or resources, such as whether an independent network partition or dedicated computing node is required. High isolation level usually corresponds to virtual machine environment, while low isolation level can be adapted to container environment. This parameter is an important security basis for judging the type of operating environment.

[0064] In this application embodiment, the specific implementation of the operation of "matching cloud computing resource components that meet performance indicators in the cloud resource library based on runtime environment parameters" is diverse. For clarity, some exemplary implementation methods are described below: filtering compatible operating system images in the cloud resource library according to the operating system type; matching database middleware and Web service components that support the corresponding performance level according to the middleware type and performance indicators.

[0065] Specifically, compatible images are selected based on the operating system type specified in the runtime environment parameters. The cloud resource library stores operating system images of various versions and adapted to different scenarios, and each image is clearly labeled with its system type, kernel version, supported hardware architecture, and adapted business scenarios. During the selection process, the system first extracts the operating system type specified in the runtime environment parameters, and then uses the cloud resource library's built-in search engine to match candidate images whose labeling information matches the specified system type. Next, the candidate set undergoes compatibility verification, excluding images with known vulnerabilities, those no longer maintained, or those incompatible with the current version of the cloud platform. Finally, the system determines the operating system images that can be used for business deployment, ensuring that the underlying system environment is fully compatible with business requirements and avoiding subsequent component deployment failures due to system incompatibility.

[0066] Subsequently, the system matches corresponding components based on middleware type and performance metrics. The middleware type in the runtime environment parameters clarifies the component categories required by the business, while the performance metrics specify the operational capabilities the components must meet. For database middleware, the system first filters components from the cloud resource repository that match the specified type, and then performs a secondary filter based on performance metrics. For web service components, the system similarly filters candidate components by type first, and then matches them based on performance metrics—if the business expects high concurrent access volume, it prioritizes Nginx components that support event-driven models and can achieve high concurrency processing; if the business has high requirements for static resource loading speed, it selects web service component versions that integrate Gzip compression and caching acceleration functions to ensure that the web service response time meets the performance metric requirements. Throughout the matching process, the system retrieves the component performance test reports from the cloud resource repository in real time and compares them with the business performance metrics. Only components whose actual test data meets or exceeds the metric requirements are included in the final matching results, ensuring that component performance fully supports the business operation needs.

[0067] Furthermore, in this embodiment of the application, the operation of "matching cloud computing resource components that meet performance indicators in the cloud resource library based on runtime environment parameters" further includes: performing compatibility verification on the selected components to verify compatibility with the selected operating system image and business system executable file.

[0068] Specifically, after completing the selection of operating system images and matching of database middleware and web service components, adding a compatibility verification step for the selected components is a crucial supplementary step to ensure the stable deployment and operation of subsequent business systems. Compatibility verification needs to focus on two core dimensions: "selected components and operating system images" and "selected components and business system executable files." On the one hand, it verifies the compatibility between the selected components and the operating system image: different operating system versions have differences in component dependencies, runtime permissions, and kernel interfaces. Simultaneously, the system automatically simulates the component installation process in the target operating system image, checking for issues such as missing dependencies and permission conflicts during installation. If any anomalies are found, an incompatibility result is returned, and other component versions adapted to the operating system image are matched again from the cloud resource library.

[0069] On the other hand, the system verifies the compatibility between the selected components and the business system executable file. During development, the business system executable file is written based on specific versions of middleware interfaces and protocol specifications. Therefore, during compatibility verification, the system extracts the configuration information of the business system executable file and compares it with the version information and supported interface types of the selected components. Simultaneously, by launching a lightweight test environment, the system deploys the business system executable file and the selected components in the test environment to perform basic functional tests and check for issues such as connection failures, data format incompatibility, and abnormal interface responses. If the test passes, the component is deemed compatible with the business system executable file; if the test fails, the cause of incompatibility is analyzed, and the system returns to the cloud resource library to re-match components that meet the requirements of the business system executable file.

[0070] Throughout the compatibility verification process, the system automatically records the verification results and anomaly information, generating a compatibility report. If all selected components pass the verification in both dimensions, the batch of cloud computing resource components is confirmed to be usable for subsequent deployment. If incompatible components exist, based on the problem type indicated in the report, priority is given to upgrading component versions or installing missing dependency packages to resolve the issue. If these methods fail, the resource matching process is restarted until fully compatible components are selected.

[0071] In this application embodiment, the specific implementation of the operation of "creating a corresponding container or virtual machine environment according to the judgment result and deploying the matching cloud computing resource components to the container or virtual machine environment" is diverse. For clarity, some exemplary implementation methods are described below: calling the application interface provided by the cloud computing platform to create a virtual machine or container instance; installing and configuring the matching cloud computing resource components in the virtual machine or container instance; deploying the business system executable file and starting it to run.

[0072] Specifically, the system calls the application programming interface (API) of the cloud computing platform to create virtual machines or container instances. The cloud computing platform provides standardized resource creation APIs. For example, the virtual machine creation API supports specifying parameters such as the number of CPU cores, memory capacity, storage type (e.g., SSD, HDD), and network configuration (e.g., private IP, security group rules). The container instance creation API allows specifying container image version, resource quotas, and mounted storage volumes. During execution, the system first reads the environment judgment result from step 102. If it is a virtual machine environment, it extracts the resource requirements derived from performance metrics (e.g., 4 cores, 8GB CPU, 100GB SSD storage for high-concurrency services), encapsulates these parameters into a request format that conforms to the platform API requirements, and calls the virtual machine creation interface. If it is a container environment, it sets parameters such as container CPU utilization limit and memory limit based on the matching operating system base image information and business resource requirements, and calls the container creation API (e.g., Docker's create interface, Kubernetes' create pod interface). After the API call, the system will monitor the creation status returned by the platform in real time. If it returns "Creating", it will continue to wait. If it returns "Creation successful", it will obtain the unique identifier of the instance (such as virtual machine ID, container ID). If it returns "Creation failed" (such as insufficient resources, incorrect parameters), it will adjust the parameters according to the error message (such as reducing the configuration of non-core resources, changing the availability zone) and call the API again to ensure that the instance is created successfully.

[0073] Next, the matching cloud computing resource components are installed and configured in the instance. For virtual machine instances, the system connects to the instance via a remote execution tool (such as SSH), first installing basic dependency libraries based on the matching operating system image type by calling the corresponding package management tool (such as the yum and apt commands in Linux systems), then transferring the installation packages of the matching database middleware (such as MySQL) and web service components (such as Nginx) from the cloud resource repository to the specified directory of the instance, and executing an automated installation script—for example, when installing MySQL, the script automatically sets the installation path, initializes the database service, and configures the listening port; when installing Nginx, the script automatically configures the service startup items and sets the default site directory. For container instances, the system adopts a containerized deployment approach, integrating the matching components into the container image: first, using the selected operating system base image as the underlying layer, component installation instructions are written in a Dockerfile (such as "RUN apt install mysql-server") to build a custom container image containing database middleware and web service components, then pushing the image to the cloud platform image repository, and finally starting the container instance by specifying the custom image through the container creation API, achieving integrated deployment of components and containers. Regardless of the instance type, basic configuration checks will be performed after the component is installed, such as checking whether the database service is running normally and whether the web component port can listen normally, to ensure that the component is in a usable state.

[0074] In addition to the foregoing content, this application embodiment also includes other specific implementation steps. To clearly present these components, the relevant specific implementation methods are described in detail below: receiving management instructions issued by the business system, including expansion, startup, or shutdown; automatically executing the corresponding resource adjustment operation according to the instruction type, and updating the system status after the operation is completed.

[0075] Specifically, the system provides a unified command interaction interface for business system implementation personnel. This interface can be integrated into the business management interface or made available via an API, allowing implementation personnel to initiate expansion, activation, or deactivation commands based on actual business needs. Expansion commands typically require basic requirement parameters (such as the expected number of new users and the type of resources to be expanded), activation commands require a clear target business instance identifier, and deactivation commands require confirmation of whether configuration data should be retained. Upon receiving a command, the system first verifies the command format and parameter completeness. For example, it verifies whether the expansion command includes the "expected concurrent access volume after expansion" to prevent subsequent operations from failing due to missing parameters. After successful verification, the command is stored in a task queue and processed sequentially according to the order of receipt.

[0076] Subsequently, the system executes corresponding resource adjustment operations based on the instruction type: For expansion instructions, the system first combines the current operating status of the service (such as the CPU utilization, memory usage, and current concurrent access of existing virtual machines / containers) with the requirement parameters in the instruction to automatically assess the amount of resources required for expansion. If the service is running in a virtual machine environment, and the current virtual machine CPU utilization has reached 80%, and it needs to support an additional 2000 concurrent users, the assessment indicates that two more virtual machines with the same configuration are needed. If it is running in a container environment, the assessment indicates that three container instances need to be added. After the assessment is completed, the system calls the cloud computing platform resource creation interface to create the corresponding number of virtual machine / container instances, synchronously deploys the matching cloud computing resource components to the new instances and completes initialization, and finally distributes the new user requests to the new instances through load balancing configuration, thus realizing the expansion operation.

[0077] For shutdown / start commands, the system first locates the virtual machine / container instance where the target business resides, pauses the operation of the business system processes in the instance (such as stopping the application service process or disconnecting the database connection), but retains the instance's resource configuration and stored business data—disk partitions and component configuration files are retained in the virtual machine environment, and mounted storage volume data is retained in the container environment, to avoid data loss due to shutdown / start operations. At the same time, the instance status is marked as "paused" to facilitate rapid recovery later.

[0078] For shutdown commands, after stopping the business process, the system further releases the resources occupied by the instance: in a virtual machine environment, it will deregister the virtual machine instance, release the bound CPU, memory and storage resources, and delete the temporary configuration file; in a container environment, it will delete the container instance, clean up non-persistent storage data, retain only the business data that needs to be backed up for a long time to the cloud storage service, and update the resource usage record so that the released resources can be redistributed to other businesses.

[0079] During the execution of all resource adjustment operations, the system will record operation logs in real time (such as instruction reception time, number of resources adjusted, and execution results). After the operation is completed, the system will automatically update the business system status information (such as updating "current number of instances" and "maximum concurrent capacity" after expansion, updating "running status" to paused after shutdown and restart, and updating "resource occupation status" to released after shutdown). The status will be synchronized to the business management interface so that the implementation personnel can view the operation results in real time and ensure that the execution of management instructions is transparent and traceable.

[0080] Furthermore, upon receiving a scaling instruction from the business system, the system executes the scaling operation based on the instruction type, including: monitoring the current operating status and resource usage of the business system; automatically calculating the required amount of additional resources according to preset strategies; calling the scaling interface of the cloud computing platform to increase the number of container instances or virtual machines; and adjusting the load balancing configuration to ensure the stable operation of the business system.

[0081] Specifically, the system uses the monitoring interface provided by the cloud computing platform to collect core operational data in real time from the containers or virtual machine instances where the business system resides. This operational status data includes key indicators such as the survival status of business processes, request processing success rate, average response time, and the number of concurrent requests. For example, it may detect that the current number of concurrent requests has reached 8,000 per second, approaching the system's preset concurrency limit of 10,000 per second, and the response time has increased from the normal 1.2 seconds to 2.5 seconds. Resource usage data covers CPU utilization, memory usage, disk I / O read / write speed, and network bandwidth utilization. For instance, it may detect that the average CPU utilization of the three existing virtual machines has reached 85%, memory usage exceeds 70%, and disk I / O is under high load. Simultaneously, the system compares the collected data with historical operational baselines (such as the average resource utilization and average response time over the same period in the past 7 days) to identify current resource bottlenecks, providing data support for subsequent resource calculations and avoiding blind expansion.

[0082] Next, the required increase in resources is automatically calculated based on preset strategies. These preset strategies are formulated in conjunction with business performance requirements, resource utilization efficiency, and cost control goals. For example, the strategy might specify that "when CPU utilization exceeds 80% for 5 consecutive minutes or concurrent requests reach 80% of the upper limit, expand by 30%-50% of the current total resources" or "if the response time exceeds the preset threshold by 1.5 times, prioritize response speed, and the expansion ratio is the upper limit." Based on the monitoring data mentioned earlier, with current CPU utilization at 85%, concurrent requests reaching the upper limit of 80%, and response time exceeding the threshold, and calculating a 50% expansion ratio according to the preset strategy, 1-2 new virtual machines are needed for the existing 3 virtual machines. Further consideration is given to future business growth forecasts for the next hour (e.g., based on historical data predicting concurrent requests will increase to 9500 times / second), ultimately determining to add 2 more virtual machines with the same configuration. If the business runs in a container environment with 10 existing container instances, a 50% expansion ratio would require adding 5 more container instances, ensuring that the new resources fully cover the current and short-term predicted resource gaps while avoiding excessive expansion and resource waste.

[0083] The system then calls the scaling interface of the cloud computing platform to increase the number of container instances or virtual machines. Based on the previously determined business operating environment (containers or virtual machines), the system selects the corresponding scaling interface. For virtual machine environments, it calls the platform's virtual machine scaling API, passing in the configuration parameters of the new virtual machine (such as the number of CPU cores, memory capacity, and storage type, ensuring compatibility with existing virtual machines), its availability zone, network security group rules, etc. For container environments, it calls the Horizontal Pod Autoscaler interface of the container orchestration platform (such as Kubernetes) or a custom scaling API, specifying the number of new container instances, image version (consistent with existing container images), resource quotas, etc. After the interface call, the system monitors the instance creation progress in real time, obtaining information such as the IP address and instance ID of the new instance. Once the instance creation is complete and passes health checks (such as checking if the instance starts normally and can establish a connection with the database middleware), the system confirms that the expanded instance is in an available state.

[0084] Finally, the load balancing configuration is adjusted to ensure the stable operation of the business system. The system calls the load balancing service interface of the cloud computing platform to add the newly added container instances or virtual machine instances to the backend server list of the load balancing cluster, and updates the load balancing strategy. If a round-robin strategy is used, it is necessary to ensure that requests are evenly distributed to all instances (including existing and newly added instances). If a weighted round-robin strategy is used, a higher weight can be set according to the differences in instance resource configuration (such as the newly added instance having stronger CPU performance) to improve resource utilization efficiency. For example, the IPs of two newly added virtual machines are added to the load balancing backend, and the round-robin weights are adjusted to ensure that the proportion of requests received by each instance is consistent, avoiding performance issues caused by request overload on a single instance. At the same time, the system will continuously monitor the request distribution and running status of each instance after adjustment. If it finds that the request volume of a certain instance is too high or the response is abnormal, the load balancing weight is fine-tuned in real time to ensure that the load of all instances is even, the overall response time of the business system returns to the normal range, the concurrent processing capacity meets the requirements, and the business is operating stably after the expansion.

[0085] Furthermore, in the embodiments of this application, the start-up operation includes suspending the operation of the business system but retaining its operating environment and configuration information; the shutdown operation includes stopping the operation of the business system and releasing all the cloud computing resources it occupies.

[0086] Specifically, the core of the shutdown and startup operation is to completely preserve the operating environment and configuration information of the business system while pausing its operation, providing a foundation for rapid business recovery later. During execution, the system first locates the container or virtual machine instance where the target business system resides through the business management module and sends a process pause command to the instance. In a virtual machine environment, it stops the application service processes corresponding to the business system (such as the Tomcat process for Java applications and the uwsgi process for Python applications), but does not shut down the virtual machine instance itself. At the same time, it preserves the operating system image, database middleware, web service components, and all configuration files (such as database connection configuration, port mapping configuration, and security policy configuration) deployed within the virtual machine. In a container environment, it calls container management commands (such as docker pause, kubectl pause) to pause the business processes within the container, preserving the container instance and its mounted storage volumes and network configurations, ensuring that the business system's operating environment and personalized configurations remain completely unchanged. After the operation is completed, the system will mark the business status as "paused" and record information such as the shutdown and restart time and the operator. When the business needs to be restored later, it is only necessary to send a process restart command (such as starting the Tomcat process or the Docker unpause command) to quickly restore the business operation without redeploying the environment and configuration, which greatly shortens the business interruption time.

[0087] The key to the shutdown operation is to completely release all cloud computing resources occupied by the business system after it stops running, avoiding resource idleness and waste. In terms of process, the system first stops the business process (with the same process stopping logic as the shutdown / startup operation), and then performs resource release operations for container or virtual machine environments. For virtual machine environments, it calls the cloud computing platform's virtual machine deletion interface to deregister the target virtual machine instance, releasing its occupied CPU, memory, storage resources, and network IP address. Simultaneously, it deletes temporary business data generated within the virtual machine (such as log cache and temporary files), only backing up core business data that needs to be retained long-term (such as user database data) to the cloud storage service. For container environments, it executes container deletion commands (such as `docker rm` or `kubectl delete pod`) to remove the container instance, releasing the computing resources and storage volumes (non-persistent storage volumes) occupied by the container, and cleaning up the network configuration corresponding to the container. In addition, the shutdown operation also simultaneously releases the resources occupied by previously matched cloud computing resource components (such as database middleware and web service components), updates the cloud platform's resource usage ledger, and allows the released resources to be reallocated to other businesses. After the operation is completed, the system will update the business status to "closed" and generate a resource release report, which will specify the type, quantity and release time of the released resources, so that operation and maintenance personnel can track the resource usage.

[0088] In addition to the foregoing, this application embodiment also includes: receiving uploaded files, setting parameters, sending management instructions, and viewing deployment status based on a preset graphical user interface or application programming interface.

[0089] Specifically, from the perspective of the pre-designed graphical user interface (GUI), the system will be designed with a visual interface and clearly defined functional areas to accommodate different operational needs. From the perspective of the application programming interface (API), the system will provide standardized API interface documentation, clearly defining the calling methods, request parameters, return formats, and error codes of each interface, allowing technicians to integrate them into automated deployment scripts or third-party systems through programming. Regardless of whether operations are performed through the GUI or API, the system will uniformly verify and process the received files, parameters, and instructions.

[0090] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0091] Corresponding to the aforementioned cloud-based business deployment method, this disclosure also proposes a cloud-based business deployment apparatus. Since the apparatus embodiments of this disclosure correspond to the aforementioned method embodiments, details not disclosed in the apparatus embodiments can be referred to the aforementioned method embodiments, and will not be repeated here.

[0092] Figure 2 A schematic diagram of the structure of a cloud computing-based service deployment device provided in this disclosure embodiment is shown below. Figure 2 As shown, it includes:

[0093] The receiving unit 21 is used to receive the uploaded executable file of the business system and the runtime environment parameters required for its operation. The runtime environment parameters include operating system type, middleware type, performance indicators and security requirements.

[0094] The judgment unit 22 is used to determine whether the business system is suitable to run in a container environment or a virtual machine environment based on performance indicators and security requirements.

[0095] Matching unit 23 is used to match cloud computing resource components that meet performance indicators in the cloud resource library based on runtime environment parameters;

[0096] The first deployment unit 24 is used to create the corresponding container or virtual machine environment according to the judgment result, deploy the matching cloud computing resource components to the container or virtual machine environment, and perform initial configuration.

[0097] The second deployment unit 25 is used to deploy the business system executable file to the configured container or virtual machine environment and start the business system.

[0098] This application provides a cloud computing-based business deployment device. Because the system can automatically receive the executable file of the business system and runtime environment parameters including operating system type, middleware type, performance indicators, and security requirements, and determine the appropriate container or virtual machine environment based on the performance indicators and security requirements, it automatically matches cloud computing resource components that meet the performance indicators based on the runtime environment parameters, and completes the entire process of environment creation, component deployment initialization, and business system startup. Therefore, it can solve the problems of low deployment efficiency and error-proneness caused by relying on a large amount of manual operation in the prior art, and the difficulty in accurately matching suitable cloud computing resources and runtime environments according to the performance and security requirements of the business system. This achieves the technical effects of reducing manual intervention, improving deployment efficiency, reducing human error, and simultaneously realizing accurate matching of cloud computing resources with business needs and ensuring the stable operation of the business system.

[0099] Furthermore, in one possible implementation of this embodiment, such as Figure 3 As shown, the judgment unit 22 includes:

[0100] The evaluation module 221 is used to perform a weighted evaluation of security requirements and performance indicators using a preset decision model, and output a recommendation on the type of operating environment.

[0101] Module 222 is used to recommend whether the final runtime environment is a container or a virtual machine based on the runtime environment type.

[0102] Furthermore, in one possible implementation of this embodiment, the decision model is a machine learning-based classification model, and the training data includes the performance data, security configuration, and corresponding operating environment type of the historical business system.

[0103] Furthermore, in one possible implementation of this embodiment, the operating environment parameters also include at least one of the following: expected concurrent access volume of the business system, data processing speed, response time, data encryption level, access control policy, and security isolation level.

[0104] Furthermore, in one possible implementation of this embodiment, such as Figure 3 As shown, the matching unit 23 includes:

[0105] The filtering module 231 is used to filter compatible operating system images in the cloud resource library according to the operating system type;

[0106] Matching module 232 is used to match database middleware and web service components that support the corresponding performance level based on middleware type and performance metrics.

[0107] Furthermore, in one possible implementation of this embodiment, such as Figure 3 As shown, the matching unit 23 further includes:

[0108] Verification module 233 is used to perform compatibility verification on the selected components to verify compatibility with the selected operating system image and business system executable file.

[0109] Furthermore, in one possible implementation of this embodiment, such as Figure 3 As shown, the first deployment unit 24 includes:

[0110] Create module 241, which is used to call the application programming interface provided by the cloud computing platform to create virtual machine or container instances;

[0111] Configuration module 242 is used to install and configure matching cloud computing resource components in virtual machines or container instances;

[0112] Deployment module 243 is used to deploy the executable file of the business system and start it running.

[0113] Furthermore, in one possible implementation of this embodiment, such as Figure 3 As shown, it also includes:

[0114] The first receiving unit 26 is used to receive management instructions issued by the business system, including expansion, start-up, or shutdown.

[0115] The execution unit 27 is used to automatically perform the corresponding resource adjustment operation according to the instruction type, and update the system status after the operation is completed.

[0116] Furthermore, in one possible implementation of this embodiment, the expansion operation includes:

[0117] Monitor the current operational status and resource usage of business systems;

[0118] The required increase in resources is automatically calculated based on a preset strategy;

[0119] Call the scaling interface of the cloud computing platform to increase the number of container instances or virtual machines;

[0120] Adjust the load balancing configuration to ensure the stable operation of the business system.

[0121] Furthermore, in one possible implementation of this embodiment, the start-up operation includes pausing the operation of the business system while retaining its operating environment and configuration information; the shutdown operation includes stopping the operation of the business system and releasing all the cloud computing resources it occupies.

[0122] Furthermore, in one possible implementation of this embodiment, such as Figure 3As shown, it also includes:

[0123] The second receiving unit 28 is used to receive uploaded files, set parameters, send management instructions, and view deployment status based on a preset graphical user interface or application interface.

[0124] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0125] For a description of the features in the embodiment of the cloud-based business deployment device, please refer to the relevant description of the embodiment of the cloud-based business deployment method, which will not be repeated here.

[0126] Embodiments of this application also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above-described embodiments of the cloud computing-based business deployment method.

[0127] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above-described embodiments of the cloud computing-based business deployment method.

[0128] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0129] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the cloud computing-based business deployment method.

[0130] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above-described cloud computing-based business deployment method embodiments.

[0131] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] The foregoing has provided a detailed description of a cloud computing-based business deployment method, apparatus, electronic device, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A cloud computing-based business deployment method, characterized in that, include: The system receives uploaded executable files of the business system and the runtime environment parameters required for their operation. These runtime environment parameters include operating system type, middleware type, performance indicators, and security requirements. Based on the performance metrics and security requirements, it is determined that the business system is suitable for running in a container environment or a virtual machine environment. Based on the aforementioned operating environment parameters, match cloud computing resource components in the cloud resource library that meet the aforementioned performance indicators and are consistent with the aforementioned operating system type and middleware type. Based on the judgment result, create the corresponding container or virtual machine environment, deploy the matching cloud computing resource components to the container or virtual machine environment, and perform initial configuration. Deploy the executable file of the business system to the configured container or virtual machine environment and start the business system.

2. The cloud computing-based business deployment method according to claim 1, characterized in that, The step of determining whether the business system is suitable for running in a container environment or a virtual machine environment based on the performance indicators and security requirements includes: Using a pre-defined decision model, the security requirements and performance indicators are weighted and evaluated, and a suggested operating environment type is output. Based on the aforementioned runtime environment type, it is recommended to determine the final runtime environment as either a container or a virtual machine.

3. The cloud computing-based business deployment method according to claim 2, characterized in that, The decision model is a machine learning-based classification model, and the training data includes historical business system performance data, security configurations, and their corresponding operating environment types.

4. The method according to claim 1, characterized in that, The operating environment parameters also include at least one of the following: expected concurrent access volume of the business system, data processing speed, response time, data encryption level, access control policy, and security isolation level.

5. The cloud computing-based business deployment method according to claim 1, characterized in that, The process of matching cloud computing resource components in the cloud resource library based on the operating environment parameters, which meet the performance indicators and are consistent with the operating system type and middleware type, includes: Select compatible operating system images from the cloud resource library based on the operating system type; Based on the middleware type and the performance metrics, match database middleware and web service components that support the corresponding performance levels.

6. The cloud computing-based business deployment method according to claim 5, characterized in that, The method of matching cloud computing resource components in the cloud resource library based on the operating environment parameters, which meet the performance indicators and are consistent with the operating system type and middleware type, further includes: Perform compatibility verification on the selected components to verify compatibility with the selected operating system image and business system executable.

7. The cloud computing-based business deployment method according to claim 1, characterized in that, The step of creating a corresponding container or virtual machine environment based on the judgment result and deploying the matching cloud computing resource components to the container or virtual machine environment includes: Use the application programming interfaces (APIs) provided by the cloud computing platform to create virtual machine or container instances; Install and configure the matching cloud computing resource components in the virtual machine or container instance; Deploy the executable file of the business system and start it running.

8. The cloud computing-based business deployment method according to claim 1, characterized in that, Also includes: Receive management instructions from the business system, including expansion, startup, or shutdown; The system will automatically perform the corresponding resource adjustment operation based on the command type and update the system status after the operation is completed.

9. The cloud computing-based business deployment method according to claim 8, characterized in that, The capacity expansion operation includes: Monitor the current operational status and resource usage of business systems; The required increase in resources is automatically calculated based on a preset strategy; Call the scaling interface of the cloud computing platform to increase the number of container instances or virtual machines; Adjust the load balancing configuration to ensure the stable operation of the business system.

10. The cloud computing-based business deployment method according to claim 8, characterized in that, The shutdown / start operation includes pausing the operation of the business system while retaining its operating environment and configuration information; the shutdown operation includes stopping the operation of the business system and releasing all cloud computing resources it occupies.

11. The cloud computing-based business deployment method according to claim 1, characterized in that, Also includes: Based on a preset graphical user interface or application programming interface, it can receive uploaded files, set parameters, send management commands, and view deployment status.

12. A cloud computing-based business deployment device, characterized in that, include: The receiving unit is used to receive the uploaded executable file of the business system and the runtime environment parameters required for its operation. The runtime environment parameters include operating system type, middleware type, performance indicators and security requirements. The judgment unit is used to determine, based on the performance indicators and the security requirements, whether the business system is suitable for running in a container environment or a virtual machine environment. The matching unit is used to match cloud computing resource components in the cloud resource library that meet the performance indicators and are consistent with the operating system type and middleware type, based on the operating environment parameters. The first deployment unit is used to create a corresponding container or virtual machine environment based on the judgment result, deploy the matching cloud computing resource components to the container or virtual machine environment, and perform initial configuration. The second deployment unit is used to deploy the executable file of the business system to the configured container or virtual machine environment and start the business system.

13. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed, enables the at least one processor to perform the cloud-based business deployment method according to any one of claims 1-11.

14. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the cloud-based business deployment method according to any one of claims 1-11.

15. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the cloud-based business deployment method according to any one of claims 1-11.

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