A cross-cloud platform and multi-environment hybrid cloud management and automated deployment method

Abstract

The application provides a cross-cloud platform and multi-environment hybrid cloud management and automated deployment method, device, equipment and storage medium, the method comprises the following steps: in response to the environment application data sent by the user end, the server obtains the private cloud resource state data from the private cloud platform; the server determines the private cloud satisfaction degree according to the environment application data and the private cloud resource state data; when the private cloud satisfaction degree is partial satisfaction, the server generates a split deployment strategy according to the environment application data and the private cloud resource state data; the server determines the cloud deployment instruction set corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and sends them to the corresponding cloud platform respectively. The application solves the problems of rigid cross-cloud deployment strategy and lack of data support for resource scheduling, and the problems of insufficient private cloud hardware elasticity and difficult procurement decision, and realizes fine split scheduling and automated delivery of cross-cloud resources.

Description

Technical Field

[0001] This application relates to the fields of cloud computing and enterprise information technology, specifically to a hybrid cloud management and automated deployment method, apparatus, device, and storage medium that spans cloud platforms and multiple environments. Background Technology

[0002] As enterprises deepen their digital transformation, the construction and management of R&D testing and software runtime environments have become core aspects of IT operations and maintenance. Currently, enterprises typically use public cloud, private cloud, or hybrid cloud architectures to support the parallel R&D testing needs of multiple projects. In existing technologies, the deployment of R&D testing environments largely relies on manual operations and maintenance or traditional work order approval processes. Operations and maintenance personnel need to manually configure virtual machines, containers, middleware, and network resources based on business requests. Meanwhile, for multi-cloud environments, some multi-cloud management platforms are attempting to achieve unified monitoring and command forwarding of heterogeneous resource pools through resource management subsystems and scheduling subsystems, in order to realize the application, creation, and release of cross-cloud resources.

[0003] However, existing cross-cloud deployment solutions typically rely on manual work order processing or simply allocate resources based on basic load balancing strategies. They fail to differentiate between public and private cloud deployment strategies based on the actual needs of enterprise tenants, resulting in insufficient flexibility in cross-cloud deployment strategies and a high barrier to entry for enterprise tenants to configure them themselves. Summary of the Invention

[0004] In view of the aforementioned problems, this application is proposed to provide a method, apparatus, device, and storage medium for hybrid cloud management and automated deployment across cloud platforms and multiple environments that overcomes or at least partially solves the aforementioned problems, including: A hybrid cloud management and automated deployment method that spans multiple cloud platforms and environments, the method involving user terminals, server terminals, private cloud platforms, and public cloud platforms; The method includes the following steps: In response to the environment request data sent by the user terminal, the server obtains private cloud resource status data from the private cloud platform; The server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; When the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform according to the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

[0005] Furthermore, the environment application data includes: service type and quantity parameters; the step of the server determining the private cloud satisfaction level based on the environment application data and the private cloud resource status data includes: The server matches the service type with the corresponding private cloud resource status data to determine the available private cloud quota data. The server compares the quantity parameter with the available quota data of the private cloud to determine the corresponding private cloud satisfaction level.

[0006] Furthermore, the step of generating a split deployment strategy based on the environment application data and the private cloud resource status data when the private cloud satisfaction level is partially satisfied includes: When the private cloud satisfaction level is partially satisfied, the server determines the private cloud deployment list based on the environment application data and the private cloud resource status data. The server compares the environment application data with the private cloud deployment list to determine the public cloud deployment list.

[0007] Furthermore, the method also includes: When the private cloud satisfaction level is fully satisfied, the server maps all the environment application data to the private cloud platform based on the environment application data to determine the private cloud deployment list; wherein, the public cloud deployment list is empty.

[0008] Furthermore, the method also includes: When the private cloud satisfaction level is not met, the server maps all the environment application data to the public cloud platform based on the environment application data to determine the public cloud deployment list; wherein, the private cloud deployment list is empty.

[0009] Further, the environment application data includes user-readable parameters displayed on the user-end front-end interface; the server pre-stores an environment template parameter mapping table corresponding to the private cloud platform and the public cloud platform; the steps of the server determining the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and sending them to the corresponding cloud platforms respectively, include: The server converts the user-readable parameters into IaC operable parameters based on the split deployment strategy and the environment template parameter mapping table. The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform based on the IaC operable parameters, and sends them to the corresponding cloud platforms respectively.

[0010] Furthermore, it also includes the following steps: The server obtains the corresponding platform template structure from the private cloud platform and the public cloud platform; The server compares the environment template parameter mapping table with the platform template structure and updates the environment template parameter mapping table accordingly.

[0011] Furthermore, the server pre-stores or dynamically creates a region lock corresponding to the region identifier in the environment application data; the method also includes the following steps: The server generates trial operation environment data in the regional lock environment based on the cloud deployment instruction set; wherein, the trial operation environment data includes: operation abnormal or operation without abnormality; When the trial operation environment data shows no abnormalities, the server releases the region lock and sends the cloud deployment instruction set to the corresponding cloud platform.

[0012] Furthermore, the server pre-stores resource quota data corresponding to the user terminal; the method also includes the following steps: In response to the project budget data sent by the user, the server determines the verification result based on the project budget data and the resource quota data; wherein, the verification result includes verification passed or verification failed; When the verification result is successful, the server retrieves private cloud resource status data from the private cloud platform.

[0013] Furthermore, it also includes the following steps: The server determines the environment operation status data based on the deployment status data; When the environmental operation status data meets the recycling conditions corresponding to the environmental application data, the server generates a resource release instruction based on the environmental operation status data and sends it to the corresponding cloud platform; the corresponding cloud platform is used to execute the resource release instruction.

[0014] Further, the environment application data includes: lifecycle parameters; the step of the server generating a resource release instruction based on the environment operation status data and sending it to the corresponding cloud platform when the environment operation status data meets the recycling conditions corresponding to the environment application data includes: The server starts a countdown timer based on the lifecycle parameters; When the countdown timer reaches a preset first threshold, the server generates expiration notification data and sends it to the user terminal. When the countdown timer reaches a preset second threshold and no extension instruction is received from the user terminal, the server generates a resource release instruction based on the environmental operation status data and sends the resource release instruction to the corresponding cloud platform; wherein, the second threshold is less than the first threshold.

[0015] A hybrid cloud management and automated deployment device spanning multiple cloud platforms and environments, the device involving a user terminal, a server terminal, a private cloud platform, and a public cloud platform; the device includes: The private cloud resource status data acquisition module is used to acquire private cloud resource status data from the private cloud platform in response to the environment application data sent by the user terminal. The private cloud satisfaction determination module is used to determine the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; The split deployment strategy generation module is used to generate a split deployment strategy based on the environment application data and the private cloud resource status data when the private cloud satisfaction level is partially satisfied; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The cloud deployment instruction set determination module is used to determine the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and send them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

[0016] A computer electronic device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When executed by the processor, the computer program implements the steps of the method described in any embodiment of this application.

[0017] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in any embodiment of this application.

[0018] This application has the following advantages: In the embodiments of this application, addressing the technical problems of insufficient flexibility in existing cross-cloud deployment strategies and high barriers to self-configuration by enterprise tenants, this application provides a solution for dynamically generating split deployment strategies based on private cloud satisfaction and achieving automated cross-cloud deployment. Specifically, in response to environment application data sent by the user, the server obtains private cloud resource status data from the private cloud platform; the server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; when the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; the server determines cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively based on the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server based on the corresponding cloud deployment instruction sets. This application solves the problems of rigid cross-cloud deployment strategies, lack of data support for resource scheduling, insufficient hardware elasticity of private clouds, and difficulty in procurement decisions by dynamically generating split deployment strategies based on the satisfaction of private clouds and executing cross-cloud command issuance, thereby realizing fine-grained split scheduling and automated delivery of cross-cloud resources. Attached Figure Description

[0019] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the steps of a hybrid cloud management and automated deployment method across cloud platforms and multiple environments, as provided in an embodiment of this application. Figure 2 This is a structural block diagram of a server interacting with the user and the cloud platform respectively in one embodiment of this application; Figure 3 This is a flowchart of the nodes of the server-side execution actions in one embodiment of this application; Figure 4 This is a schematic diagram of the data flow during the automatic deployment process of cloud services in one embodiment of this application; Figure 5 This is a schematic diagram illustrating the change process of the front-end interface during the automatic deployment of cloud services in one embodiment of this application; Figure 6 This is a structural block diagram of a hybrid cloud management and automated deployment device that spans multiple cloud platforms and environments, provided in one embodiment of this application. Figure 7 This is a schematic diagram of the structure of a computer electronic device provided in an embodiment of this application; Figure 8 This is a schematic diagram illustrating the types of cloud computing resources in a cloud computing resource server according to one embodiment of this application; The attached figures are labeled as follows: 1. Computer electronic device; 2. External device; 3. Processing unit; 4. Bus; 5. Network adapter; 6. I / O interface; 7. Display; 8. Memory; 9. Random access memory; 10. Cache memory; 11. Storage system; 12. Program / utility; 13. Program module. Detailed Implementation

[0021] To make the objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It's important to note that in the actual use of public and private clouds by enterprises, the process typically involves project funding arrangements and the procurement and deployment of physical computing resources in data centers. Funds for purchasing private cloud racks for enterprises initially come from the project budgets of each project team (tenant). During actual procurement, these funds are usually allocated to a pool of funds by the project team for unified procurement. The purchased resources are the physical computing resources that can be actually purchased after the project team's budget is allocated within the total private cloud resources in the form of quotas. For public clouds, the management of physical computing resources is usually the responsibility of the cloud vendor, and enterprise tenants directly enjoy the flexibility of resource expansion and contraction without worrying about physical implementation. However, for private clouds, the physical computing resources are the responsibility of the enterprise tenant; the resource management process previously handled by the cloud vendor is now handled by the enterprise tenant. Overpurchasing the quota leads to resource waste, while underpurchasing results in unusable resources and delays in project progress. Therefore, in the context of private clouds, features that assist in procurement decisions are particularly important for enterprise tenants.

[0023] The inventors, through analysis of existing technologies, discovered the following shortcomings in practical applications: First, existing cross-cloud deployment solutions mostly adopt a single allocation mode of "full private cloud" or "full public cloud", or only allocate resources based on simple load balancing strategies. They lack dynamic matching calculations of the real-time resource status of the private cloud and the application requirements, resulting in a lack of data support for cross-cloud deployment decisions and low resource scheduling efficiency.

[0024] Second, existing solutions typically fail to link and verify project budgets, resource quotas, and real-time cloud platform inventory during the environment application phase. This leads to a dilemma for enterprise tenants: either "over-purchasing resulting in idle assets" or "under-purchasing delaying project progress." Due to the lack of pre-deployment capacity forecasting and quota verification processes, procurement decisions often rely on manual experience-based estimations, making it difficult to achieve refined resource management and cost optimization.

[0025] Third, when multiple tenants operate concurrently within an enterprise, existing solutions rely heavily on static configurations for resource isolation and access control, making it difficult to dynamically adapt to the isolation strategies of different cloud platforms during deployment. Furthermore, after the test environment is created, existing systems largely rely on manual monitoring or scheduled scripts for resource recycling, lacking automated lifecycle timing and expiration triggering mechanisms based on application parameters (such as the tenant's expected usage period). This prolonged idleness leads to significant resource waste and increased operational costs.

[0026] Fourth, the traditional work order model has a lengthy process, complex parameter configuration, and is prone to errors, making it difficult to meet the needs of agile development for rapid and self-service delivery of testing environments. The existing system's front-end interface is mostly designed for administrators with cloud computing expertise, which raises the usage threshold for ordinary developers or testers, and it lacks the ability to dynamically adapt front-end parameters driven by underlying templates.

[0027] Based on the above analysis, one of the core technical concepts of this application is to generate cross-cloud split deployment strategies by dynamically matching the status of private cloud resources, and combine IaC parameter mapping, budget quota pre-verification and automated lifecycle management to achieve accurate, elastic and self-service automated environment deployment based on user needs.

[0028] The embodiments of this application are applicable to multi-tenant concurrent scenarios for internal enterprise R&D and testing purposes. Specifically, they are applicable to the unified deployment and management of virtual machines, containers, and various cloud services, which helps enterprises plan their internal budget procurement costs and manage resources. In particular, they enable hybrid cloud management and automated deployment across cloud platforms and multiple environments. Specifically, they involve the management and automated deployment of cloud services and hardware resources across cloud platforms and environments. The implementation process includes the communication, transmission, and processing of data elements such as cloud computing resources.

[0029] In particular, due to the special nature of the financial industry, for security reasons, certain scenarios or core data can only be deployed in private clouds. However, private clouds cannot obtain unlimited elasticity like public clouds, and the amount of resources purchased is also a headache for operations and maintenance personnel. Hybrid cloud solutions can solve security issues while enjoying the unlimited resource elasticity of public clouds.

[0030] Reference Figure 1This application illustrates a hybrid cloud management and automated deployment method across cloud platforms and multiple environments, provided by an embodiment of the present application. The method involves a user terminal, a server terminal, a private cloud platform, and a public cloud platform. The method includes the following steps: S110. In response to the environment request data sent by the user terminal, the server obtains private cloud resource status data from the private cloud platform. S120. The server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; S130. When the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; S140. The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

[0031] In the embodiments of this application, addressing the technical problems of insufficient flexibility in existing cross-cloud deployment strategies and high barriers to self-configuration by enterprise tenants, this application provides a solution for dynamically generating split deployment strategies based on private cloud satisfaction and achieving automated cross-cloud deployment. Specifically, in response to environment application data sent by the user, the server obtains private cloud resource status data from the private cloud platform; the server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; when the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; the server determines cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively based on the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server based on the corresponding cloud deployment instruction sets. This application solves the problems of rigid cross-cloud deployment strategies, lack of data support for resource scheduling, insufficient hardware elasticity of private clouds, and difficulty in procurement decisions by dynamically generating split deployment strategies based on the satisfaction of private clouds and executing cross-cloud command issuance, thereby realizing fine-grained split scheduling and automated delivery of cross-cloud resources.

[0032] The following will further describe a hybrid cloud management and automated deployment method across cloud platforms and multiple environments in this exemplary embodiment.

[0033] It should be noted that this embodiment also incorporates private cloud and public cloud into the same management plane, and isolates the two functionally. The private cloud can be used to host core testing tasks and sensitive business data with high requirements for data isolation, network latency, and long-term cost controllability; the public cloud can serve as an elastic supplement to cope with sudden computing power demands or deploy general resources.

[0034] The private cloud resource status data is used to characterize the real-time inventory and load of computing, storage, and network resources currently available for allocation on the private cloud platform. This data can be obtained by querying the inventory table or metering interface provided by the private cloud platform. The private cloud satisfaction level is a quantitative assessment result obtained by the server after matching the private cloud's carrying capacity with the environment application requirements. It includes three states: fully satisfied, partially satisfied, or not satisfied.

[0035] When the conditions are partially met, the server no longer uses the mandatory allocation mode of a single cloud platform. Instead, it generates a split deployment strategy to route and distribute resources. The split deployment strategy is used to clearly define which services are carried by the private cloud and which services are supplemented by the public cloud.

[0036] The cloud deployment instruction set consists of automated execution scripts or configuration code adapted to the underlying interface specifications of various cloud platforms. By issuing instructions to different types of cloud platforms, it enables the parallel construction of heterogeneous resources. The mechanism described in this embodiment enables dynamic adaptation of deployment decisions, meeting the personalized needs of different enterprise tenants.

[0037] As an example, the user end may include different tenants on the enterprise side, which typically have different R&D testing environment requirements due to differences in business, projects, production cycles, participants, and usage duration.

[0038] As an example, situations where the private cloud satisfaction level is partially satisfied may include: the private cloud platform can provide virtual machine instances but cannot provide database services of the specified specifications, or the available network address range of the private cloud platform can only cover half of the application requirements, etc.

[0039] As an example, the cloud deployment instruction set can be a configuration description file based on the Infrastructure as Code (IaC) specification, used to define virtual machine specifications, the number of container replicas, and network routing rules.

[0040] To facilitate understanding of the specific steps in this exemplary embodiment, Figure 2The diagram illustrates the data interaction process between the server, the user, and the cloud platform, where OC refers to the Operations Center. Figure 3 This illustrates the actions and data content involved when the server executes the method. Figure 4 The diagram illustrates the hierarchical structure of the data involved when the server executes the method, as well as the flow and mapping relationships between the data.

[0041] In one embodiment of this application, the environment application data includes: service type and quantity parameters; the specific process of "the server determining the private cloud satisfaction level based on the environment application data and the private cloud resource status data" in step S120 can be further explained in conjunction with the following description.

[0042] The server matches the service type with the corresponding private cloud resource status data to determine the available private cloud quota data. The server compares the quantity parameter with the available quota data of the private cloud to determine the corresponding private cloud satisfaction level.

[0043] It should be noted that the service type is used to identify the specific type of cloud service applied for by the user; the quantity parameter is used to quantify the scale requirement of the service type.

[0044] By establishing a mapping relationship between service types and private cloud resource pools, the server accurately locates the available quota data for the corresponding categories. Then, by comparing the quantity parameters with the available quota data item by item, it can objectively reflect the private cloud's carrying capacity for the current application. This transforms abstract business requirements into specific physical resources, providing an accurate basis for determining whether to trigger split deployment in the future.

[0045] As an example, the service type may include at least one of virtual machine instances, databases, container clusters, object storage, and caching services.

[0046] As an example, the quantity parameter may correspond to the number of vCPU cores, memory capacity (in GB), disk capacity (in TB), or number of instances, etc.

[0047] As an example, the environmental application data may also include: project-related identification information, user-preferred platforms, etc.

[0048] In one specific implementation, the server retrieves the corresponding resource pool entry from the private cloud resource status data based on the service type, and extracts the maximum quota value that is currently available and matches the service type. The quantity parameter is compared with the available quota data. If the available quota data is zero, it is directly determined that the requirement is not met; if the quantity parameter is greater than the available quota data, it is determined that the requirement is partially met; if the quantity parameter is less than or equal to the available quota data, it is determined that the requirement is fully met; and finally, the corresponding private cloud satisfaction level is output.

[0049] In one embodiment of this application, the specific process of step S130, "when the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data," can be further explained in conjunction with the following description.

[0050] When the private cloud satisfaction level is partially satisfied, the server determines the private cloud deployment list based on the environment application data and the private cloud resource status data. The server compares the environment application data with the private cloud deployment list to determine the public cloud deployment list.

[0051] It should be noted that the private cloud deployment list records services and their configuration parameters that the private cloud platform can fully support. The public cloud deployment list records services and their configuration parameters that the private cloud cannot cover and that need to be supplemented by the public cloud platform. Through the differential comparison mechanism, the server automatically completes the boundary division of cross-cloud resources, ensuring that the private cloud prioritizes security isolation requirements, while the public cloud flexibly fills the elasticity gap. This differential calculation process avoids the tediousness and omissions of manual configuration splitting, ensuring that user needs are fully met even after fine-grained splitting.

[0052] In one specific implementation, the server can iterate through the service type and quantity parameters in the environment application data and allocate them to the private cloud deployment list item by item according to the available quota data identified in the private cloud resource status data; when the quota to be allocated exceeds the available quota of the private cloud, the remaining quota to be allocated for the service requirement is allocated to the public cloud deployment list.

[0053] In an additional embodiment of this application, the specific steps of the method can be further described in conjunction with the following description.

[0054] When the private cloud satisfaction level is fully satisfied, the server maps all the environment application data to the private cloud platform based on the environment application data to determine the private cloud deployment list; wherein, the public cloud deployment list is empty.

[0055] It should be noted that the "fully satisfied" state indicates that the private cloud platform has the resource capability to independently handle all application requirements. At this point, the system routes the environment application data entirely to the private cloud platform, maximizing the isolation and cost advantages of the private cloud. Emptying the public cloud deployment list simplifies the subsequent deployment process, avoids unnecessary cross-cloud network configuration and resource scheduling overhead, provides compatibility support for a single cloud scenario in cross-cloud deployment scenarios, and improves the flexibility of deployment strategies.

[0056] As an example, the condition for determining whether all conditions are met could include that the quantity parameters corresponding to all service types are less than or equal to the corresponding private cloud available quota data. An empty public cloud deployment list can be implemented using an empty list or a flag bit in a data structure.

[0057] In one specific implementation, after confirming that the private cloud satisfaction level is fully met, the server can directly configure all environment application data to the private cloud deployment list, initialize the public cloud deployment list as an empty set, directly enter the private cloud deployment instruction set generation stage, and convert the private cloud deployment list into a configuration script executable by the private cloud platform and distribute it.

[0058] In an additional embodiment of this application, the specific steps of the method can be further described in conjunction with the following description.

[0059] When the private cloud satisfaction level is not met, the server maps all the environment application data to the public cloud platform based on the environment application data to determine the public cloud deployment list; wherein, the private cloud deployment list is empty.

[0060] It should be noted that the aforementioned unmet state indicates that the private cloud platform is completely mismatched with user needs. In this case, the server automatically triggers the public cloud fallback mechanism to route all requests to the public cloud platform, maximizing the satisfaction of user needs and ensuring the fault tolerance of cross-cloud scheduling.

[0061] As an example, conditions for determining non-compliance may include zero available private cloud quota data, unsupported service type, or the system being marked as maintenance. An empty private cloud deployment list can be implemented using an empty list or a flag bit in a data structure.

[0062] In one specific implementation, after confirming that the private cloud satisfaction level is not met, the server can directly configure all the environment application data to the public cloud deployment list, initialize the private cloud deployment list to empty, directly enter the public cloud deployment instruction set generation stage, and convert the public cloud deployment list into a configuration script executable by the public cloud platform and distribute it.

[0063] In one embodiment of this application, the environment application data includes user-readable parameters displayed on the user terminal front-end interface; the server pre-stores an environment template parameter mapping table corresponding to the private cloud platform and the public cloud platform; the specific process of step S140, "the server determines the cloud deployment instruction set corresponding to the private cloud platform and the public cloud platform according to the split deployment strategy, and sends them to the corresponding cloud platforms respectively," can be further explained in conjunction with the following description.

[0064] The server converts the user-readable parameters into IaC operable parameters based on the split deployment strategy and the environment template parameter mapping table. The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform based on the IaC operable parameters, and sends them to the corresponding cloud platforms respectively.

[0065] It should be noted that users can configure their business requirements through user-readable parameters on the front-end interface. These user-readable parameters express resource requirements through descriptive fields of business terminology, reducing the configuration threshold for non-professional users.

[0066] The environment template parameter mapping table is used to establish conversion rules between user-readable parameters and the underlying IaC (Infrastructure as Code) specification. Parameter conversion through the mapping table accurately restores front-end abstract requirements to operable IaC parameters that can be parsed by various cloud platforms. This conversion process ensures the syntactic correctness and compatibility of cross-cloud deployment instruction sets and enables self-service configuration on the user end and automatic deployment on the server end.

[0067] As an example, the user-readable parameters may include descriptive fields such as "high-configuration virtual machine" and "storage". The environment template parameter mapping table may be stored in a configuration file or a database table to record the business language description fields that different users may provide, as well as the parameter types, format requirements, dependencies, and optional value ranges corresponding to different cloud platforms.

[0068] As an example, a virtual machine is called ECS on a private cloud platform A, and EC2 on a public cloud platform B. During configuration, parameters such as CPU, memory, and operating system are described in plain language on the user side. After being transformed by the environment template parameter mapping table, the operable parameters of CPU and memory on private cloud platform A are ECS Flavor; the operable parameters of CPU and memory on public cloud platform B are EC2 Instance Type; and the operating system is uniformly abstracted as the base image (Golden Image) as a user option. In the syntax definition of the environment template parameter mapping table, different parameters (such as resource name, data format, type, and architecture parameters) have independent mappings for different cloud platforms. In addition, the environment template parameter mapping table can also integrate parameters such as replica count and high availability (HA) strategy to achieve multi-node isolated deployment.

[0069] In a specific implementation, the cloud environment can be deployed with secure isolation through the following steps: Establish dedicated line connections between the cloud platform and enterprise tenants to achieve network isolation and ensure that communication takes place only in the tenant's dedicated private network; Grant enterprise tenants the minimum necessary permissions, restricting them to performing only the operations authorized within the scope of their permissions; The established resources are encrypted with a public key, and the encrypted resources can only be used after being unlocked by a private key issued within the enterprise tenant. Before resources are packaged into a fixed-version image, they must be authorized for security compliance within the enterprise tenant; once the authorization is granted, they can be deployed to the corresponding cloud environment.

[0070] In an additional embodiment of this application, the specific steps of the method can be further described in conjunction with the following description.

[0071] The server obtains the corresponding platform template structure from the private cloud platform and the public cloud platform; The server compares the environment template parameter mapping table with the platform template structure and updates the environment template parameter mapping table accordingly.

[0072] It should be noted that because the infrastructure templates and interface specifications of various cloud platforms change frequently with version iterations, the server needs to periodically or triggerively pull the platform template structure to update the local environment template parameter mapping table. Specifically, by comparing the differences between the platform template structure and the local environment template parameter mapping table, newly added, obsolete, or modified parameter items can be automatically identified. This dynamic synchronization mechanism ensures that the mapping table is always synchronized with the underlying specifications of the cloud platform vendor, avoiding deployment failures or parameter parsing errors due to template expiration, ensuring the continuity of cross-cloud adaptation services, and improving the configuration flexibility of the local environment template parameter mapping table.

[0073] Reference Figure 5 In one specific implementation, the server may pre-store a front-end dynamic update template and front-end configuration information corresponding to the user's end. After the environment template parameter mapping table is updated, the server updates the front-end configuration information based on the front-end dynamic update template and the updated environment template parameter mapping table, and sends it to the user terminal; the user terminal is used to update the front-end interface based on the updated front-end configuration information.

[0074] It should be noted that the above-mentioned environment template parameter mapping table and front-end interface update mechanism realize the synchronization of underlying cloud vendor specification changes to the user-end interactive interface, ensuring that the user's self-service application process is always adapted to the latest cloud platform architecture.

[0075] The user-side front-end can adopt a hierarchical and modular architecture. After receiving the front-end configuration information, it dynamically renders or replaces the corresponding front-end interface elements based on the field definitions and component mapping relationships.

[0076] In one specific implementation, the front-end reads the back-end configuration file in a hierarchical and modular manner and updates dynamically according to the automatically generated configuration file. Given the user parameter as ECS CPU / Mem, the system executes the replacement of service and parameter options based on the configuration group selected by the service type (ECS) in the configuration file, along with the parameter's prompts, format, validation, and preview, thus achieving dynamic front-end updates.

[0077] In an additional embodiment of this application, the server pre-stores or dynamically creates a region lock corresponding to the region identifier in the environment application data; the specific steps of the method can also be further described in conjunction with the following description.

[0078] The server generates trial operation environment data in the regional lock environment based on the cloud deployment instruction set; wherein, the trial operation environment data includes: operation abnormal or operation without abnormality; When the trial operation environment data shows no abnormalities, the server releases the region lock and sends the cloud deployment instruction set to the corresponding cloud platform.

[0079] It should be noted that the regional lock is used to isolate concurrent deployment requests within the same geographical area, preventing resource conflicts or configuration overwriting. Generating trial operation environment data under the protection of the regional lock allows verification of the syntactic correctness and logical feasibility of the cloud deployment instruction set without actually consuming production resources. This trial operation mechanism acts as a security sandbox before deployment, effectively intercepting potential configuration errors or network conflicts. The lock is only released after confirming that there are no abnormalities in the operation, and real instructions are issued, reducing the failure rate and rollback costs of cross-cloud automated deployment.

[0080] In one specific implementation, the server queries whether a corresponding region lock already exists based on the region identifier in the environment application data. If it does not exist, the corresponding region lock is created. Subsequently, a pre-rehearsal of the cloud deployment instruction set is executed in the region lock environment, and the pre-rehearsal results are collected to generate trial operation environment data. If the trial operation environment data is marked as running without abnormalities, the area lock is released and the cloud deployment instruction set is officially sent to the target cloud platform; If the trial environment data is marked as abnormal, the environment will be rolled back to the state before the cloud deployment instruction set was executed, and an error log will be generated based on the trial environment data.

[0081] It should be noted that after manual intervention and correction, the server can attempt to execute the updated cloud deployment instruction set in the rebuilt trial environment.

[0082] In an additional embodiment of this application, the server pre-stores resource quota data corresponding to the user terminal; the specific steps of the method can also be further described in conjunction with the following description.

[0083] In response to the project budget data sent by the user, the server determines the verification result based on the project budget data and the resource quota data; wherein, the verification result includes verification passed or verification failed; When the verification result is successful, the server retrieves private cloud resource status data from the private cloud platform.

[0084] It should be noted that the resource quota data is used to record the upper limit of physical resources that a tenant can allocate on the private cloud side. The project budget data is used to characterize the financial scale associated with the user's application for the environment. By verifying the project budget data, access control for funds and capacity can be completed before the deployment process begins. This pre-verification mechanism embeds the procurement decision support process into the self-service application chain, ensuring that resource release only occurs under the premise of financial compliance and capacity feasibility, thus avoiding over-budget applications.

[0085] When the verification result is that the verification fails, the server can calculate the expected procurement cost based on the project budget data and the resource quota data, generate a quotation list, and send it to the user to remind the user to purchase additional resources to replenish the resource quota.

[0086] As an example, the verification result can be obtained by comparing the value mapping relationship between project budget data and available resource quota data, or by comparing the quota corresponding to project budget data with available resource quota data.

[0087] In an additional embodiment of this application, the specific steps of the method can be further described in conjunction with the following description.

[0088] The server determines the environment operation status data based on the deployment status data; When the environmental operation status data meets the recycling conditions corresponding to the environmental application data, the server generates a resource release instruction based on the environmental operation status data and sends it to the corresponding cloud platform; the corresponding cloud platform is used to execute the resource release instruction.

[0089] It should be noted that the environmental operation status data is used to reflect the activity level and resource usage of the deployed R&D testing environment in real time. The recycling conditions are used to define the thresholds or event flags that trigger automated resource cleanup.

[0090] By continuously monitoring the environment's operational status data, the server can automatically identify when to release resources after a test task ends or after prolonged idle periods. This closed-loop management mechanism eliminates the lag of manual intervention, achieving automated management and control of cloud resources throughout their entire lifecycle from creation to destruction, and improving the turnover efficiency of cloud resources.

[0091] In one specific implementation, the server may have a pre-set shared resource pool; when a resource release command is executed, or when the actual usage of the user terminal is less than the requested resources, the excess resources can be pooled into the shared resource pool, and the server can allocate flexible scheduling quotas to increase the flexibility of resource scheduling.

[0092] In one embodiment of this application, the environment application data includes: lifecycle parameters; the specific process of "when the environment operation status data meets the recycling conditions corresponding to the environment application data, the server generates a resource release instruction based on the environment operation status data and sends it to the corresponding cloud platform" can be further explained in conjunction with the following description.

[0093] The server starts a countdown timer based on the lifecycle parameters; When the countdown timer reaches a preset first threshold, the server generates expiration notification data and sends it to the user terminal. When the countdown timer reaches a preset second threshold and no extension instruction is received from the user terminal, the server generates a resource release instruction based on the environmental operation status data and sends the resource release instruction to the corresponding cloud platform; wherein, the second threshold is less than the first threshold.

[0094] It should be noted that the lifecycle parameter is used to define the expected usage duration of the test environment. The countdown timer is used to accurately track the environment's lifespan. The first threshold and the second threshold represent the remaining availability of the cloud platform.

[0095] By setting a tiered trigger mechanism with first and second thresholds, a warning notification is pushed to the user's device before the expiration date, providing a buffer period for the user to decide whether to extend the period. Forced release is only executed when the grace period ends and no extension instruction is received. This satisfies the rigid cost requirements of resource reclamation and avoids potential customer complaints arising from resource reclamation without prior notification to the user.

[0096] In one specific embodiment of this application, after a tenant's R&D personnel (user-side) logs into the (server-side) system, they select the "Standard Test Environment Template" and set the environment lifecycle to 14 days. The system verifies the budget available for the tenant's project, checks the tenant's corresponding cloud quota, deploys the application service to a private cloud or public cloud virtual machine, deploys the test database to a private cloud database service, and deploys auxiliary services in a container platform. Before the environment expires, the system notifies the user; if the usage period is not extended, the relevant cloud resources are automatically released.

[0097] As the apparatus embodiment is basically similar to the method embodiment, it is described in a relatively simple manner. For relevant details, please refer to the description of the method embodiment.

[0098] Reference Figure 6 This application illustrates a hybrid cloud management and automated deployment apparatus across cloud platforms and multiple environments, according to an embodiment of the present application. The apparatus involves a user terminal, a server terminal, a private cloud platform, and a public cloud platform. The apparatus includes: The private cloud resource status data acquisition module 610 is used to acquire private cloud resource status data from the private cloud platform in response to the environment application data sent by the user terminal. The private cloud satisfaction determination module 620 is used to determine the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; The split deployment strategy generation module 630 is used to generate a split deployment strategy based on the environment application data and the private cloud resource status data when the private cloud satisfaction level is partially satisfied; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The cloud deployment instruction set determination module 640 is used to determine the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and send them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

[0099] In one embodiment of this application, the environment application data includes: service type and quantity parameters; the private cloud satisfaction determination module 620 includes: The private cloud available quota data determination submodule is used to match the service type with the corresponding private cloud resource status data to determine the private cloud available quota data. The private cloud satisfaction determination submodule is used to compare the quantity parameters with the available private cloud quota data to determine the corresponding private cloud satisfaction level.

[0100] In one embodiment of this application, the split deployment strategy generation module 630 includes: The private cloud deployment list determination submodule is used to determine the private cloud deployment list based on the environment application data and the private cloud resource status data when the private cloud satisfaction level is partially satisfied. The public cloud deployment list determination submodule is used to determine the public cloud deployment list by comparing the difference between the environment application data and the private cloud deployment list.

[0101] In an additional embodiment of this application, the apparatus further includes: The private cloud full deployment module is used to map all the environment application data to the private cloud platform based on the environment application data when the private cloud satisfaction level is fully satisfied, and to determine the private cloud deployment list; wherein the public cloud deployment list is empty.

[0102] In an additional embodiment of this application, the apparatus further includes: The public cloud full deployment module is used to map all the environment application data to the public cloud platform based on the environment application data when the private cloud satisfaction level is not met, and to determine the public cloud deployment list; wherein, the private cloud deployment list is empty.

[0103] In one embodiment of this application, the environment application data includes user-readable parameters displayed on the user-side front-end interface; the server pre-stores an environment template parameter mapping table corresponding to the private cloud platform and the public cloud platform; the cloud deployment instruction set determination module 640 includes: The IaC operable parameter conversion submodule is used to convert the user-readable parameters into IaC operable parameters based on the split deployment strategy and the environment template parameter mapping table. The cloud deployment instruction set determination submodule is used to determine the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively based on the IaC operable parameters, and send them to the corresponding cloud platforms respectively.

[0104] In an additional embodiment of this application, it also includes: The platform template structure acquisition module is used to acquire the corresponding platform template structure from the private cloud platform and the public cloud platform; The environment template parameter mapping table update module is used to compare the environment template parameter mapping table with the platform template structure and update the environment template parameter mapping table.

[0105] In an additional embodiment of this application, the server pre-stores or dynamically creates a region lock corresponding to the region identifier in the environment application data; the device further includes: The cloud deployment instruction set trial operation module is used to generate trial operation environment data in the regional lock environment based on the cloud deployment instruction set; wherein, the trial operation environment data includes: operation abnormal or operation without abnormality; The area lock release module is used to release the area lock when the trial operation environment data shows no abnormalities, and send the cloud deployment instruction set to the corresponding cloud platform respectively.

[0106] In an additional embodiment of this application, the server pre-stores resource quota data corresponding to the user terminal; the device further includes: The verification result determination module is used to respond to the project budget data sent by the user terminal and determine the verification result based on the project budget data and the resource quota data; wherein, the verification result includes verification passed or verification failed; The verification and determination module is used to obtain private cloud resource status data from the private cloud platform when the verification result is that the verification is passed.

[0107] In an additional embodiment of this application, it also includes: The environment operation status data determination module is used to determine the environment operation status data based on the deployment status data. The resource release instruction generation module is used to generate a resource release instruction based on the environment operation status data when the environment operation status data meets the recycling conditions corresponding to the environment application data, and send it to the corresponding cloud platform; the corresponding cloud platform is used to execute the resource release instruction.

[0108] In one embodiment of this application, the environmental application data includes: lifecycle parameters; the resource release instruction generation module includes: The timing submodule is used to start a countdown timer based on the lifecycle parameters; The first threshold determination submodule is used to generate expiration notification data and send it to the user terminal when the countdown timer reaches the preset first threshold. The second threshold determination submodule is used to generate a resource release instruction based on the environmental operation status data and send the resource release instruction to the corresponding cloud platform when the countdown timer reaches a preset second threshold and no extension instruction is received from the user terminal; wherein the second threshold is less than the first threshold.

[0109] Reference Figure 7 The illustration shows a computer electronic device for implementing a hybrid cloud management and automated deployment method across cloud platforms and multiple environments, which may specifically include the following: The aforementioned computer electronic device 1 is manifested in the form of a general-purpose computing device. The components of the computer electronic device 1 may include, but are not limited to: one or more processors or processing units 3, memory 8, and a bus 4 connecting different system components (including memory 8 and processing unit 3).

[0110] Bus 4 represents one or more of several bus architectures, including memory buses or memory controllers, peripheral buses, graphics acceleration ports, processors, or local buses using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Audio / Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0111] Computer electronic device 1 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer electronic device 1, including volatile and non-volatile media, removable and non-removable media.

[0112] Memory 8 may include computer system readable media in the form of volatile memory, such as random access memory 9 and / or cache memory 10. Computer electronic device 1 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 11 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). Although Figure 7 As not shown, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") may be provided. In these cases, each drive may be connected to bus 4 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 13 configured to perform the functions of the embodiments of this application.

[0113] A program / utility 12 having a set (at least one) of program modules 13 may be stored, for example, in memory. Such program modules 13 include—but are not limited to—an operating system, one or more application programs, other program modules 13, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 13 typically perform the functions and / or methods described in the embodiments of this application.

[0114] The computer electronic device 1 can also communicate with one or more external devices 2 (e.g., keyboard, pointing device, display 7, camera, etc.), and with one or more devices that enable an operator to interact with the computer electronic device 1, and / or with any device that enables the computer electronic device 1 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through the I / O interface 6. Furthermore, the computer electronic device 1 can also communicate with one or more networks (e.g., local area network (LAN)), wide area network (WAN), and / or public networks (e.g., the Internet) through the network adapter 5. Figure 7 As shown, network adapter 5 communicates with other modules of computer electronic device 1 via bus 4. It should be understood that, although... Figure 7 Not shown, it can be combined with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing unit 3, external disk drive array, RAID system and data backup storage system 11, etc.

[0115] The processing unit 3 executes various functional applications and data processing by running programs stored in memory 8, such as implementing a hybrid cloud management and automated deployment method across cloud platforms and multiple environments provided in the embodiments of this application.

[0116] That is, when the above-mentioned processing unit 3 executes the above-mentioned program, it achieves the following: In response to the environment request data sent by the user terminal, the server obtains private cloud resource status data from the private cloud platform; The server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; When the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform according to the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

[0117] Reference Figure 8 In a specific embodiment of this application, the computer electronic device 1 is a cloud computing resource server.

[0118] It should be noted that since the cloud computing resources of the cloud computing resource server are stored and executed in the cloud, the cloud computing resource server does not need to be equipped with external devices 2 (such as keyboards, pointing devices, monitors 7, cameras, etc.).

[0119] As an example, Figure 8 The document demonstrates the types of cloud computing resources that the cloud computing resource server can use, such as: instance type (compute unit size, memory unit size), storage unit size (system disk, user disk), storage unit type, operating system image, number of instances, subnet segment, gateway route, static route list, encryption key binding, availability zone selection, custom tags, and custom names.

[0120] In practical use, any combination of one or more of the above-mentioned cloud computing resource types can be used.

[0121] In this document, cloud computing resources can be any, including but not limited to, processors, hard disks, fast storage, object storage, databases, elastic load balancers, container orchestration clusters, container repositories, message queue streaming platforms, gateway routes, encryption keys, certificates, API gateways, etc., and the program can be used by or in conjunction with instruction execution systems, devices, or equipment.

[0122] In this application embodiment, the application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a hybrid cloud management and automated deployment method across cloud platforms and multiple environments as provided in all embodiments of the application.

[0123] That is, when the program is executed by the processor, it implements: In response to the environment request data sent by the user terminal, the server obtains private cloud resource status data from the private cloud platform; The server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; When the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform according to the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

[0124] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0125] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0126] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Python, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the operator's computer, partially on the operator's computer, as a standalone software package, partially on the operator's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the operator's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider). The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0127] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0128] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0129] The foregoing has provided a detailed description of a hybrid cloud management and automated deployment method, apparatus, device, and storage medium for cross-cloud platforms and multiple environments provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A hybrid cloud management and automated deployment method across cloud platforms and multiple environments, characterized in that, The method involves the user end, the server end, a private cloud platform, and a public cloud platform; The method includes the following steps: In response to the environment request data sent by the user terminal, the server obtains private cloud resource status data from the private cloud platform; The server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; When the private cloud satisfaction level is partially satisfied, the server generates a split deployment strategy based on the environment application data and the private cloud resource status data; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform according to the split deployment strategy, and sends them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

2. The method according to claim 1, characterized in that, The environment application data includes: service type and quantity parameters; the steps by which the server determines the private cloud satisfaction level based on the environment application data and the private cloud resource status data include: The server matches the service type with the corresponding private cloud resource status data to determine the available private cloud quota data. The server compares the quantity parameter with the available quota data of the private cloud to determine the corresponding private cloud satisfaction level.

3. The method according to claim 2, characterized in that, When the private cloud satisfaction level is partially satisfied, the step of the server generating a split deployment strategy based on the environment application data and the private cloud resource status data includes: When the private cloud satisfaction level is partially satisfied, the server determines the private cloud deployment list based on the environment application data and the private cloud resource status data. The server compares the environment application data with the private cloud deployment list to determine the public cloud deployment list.

4. The method according to claim 1, characterized in that, The method further includes: When the private cloud satisfaction level is fully satisfied, the server maps all the environment application data to the private cloud platform based on the environment application data to determine the private cloud deployment list; wherein, the public cloud deployment list is empty.

5. The method according to claim 1, characterized in that, The method further includes: When the private cloud satisfaction level is not met, the server maps all the environment application data to the public cloud platform based on the environment application data to determine the public cloud deployment list; wherein, the private cloud deployment list is empty.

6. The method according to claim 1, characterized in that, The environment application data includes user-readable parameters displayed on the user-end front-end interface; the server pre-stores environment template parameter mapping tables corresponding to the private cloud platform and the public cloud platform; the steps of the server determining the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and sending them to the corresponding cloud platforms respectively, include: The server converts the user-readable parameters into IaC operable parameters based on the split deployment strategy and the environment template parameter mapping table. The server determines the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform based on the IaC operable parameters, and sends them to the corresponding cloud platforms respectively.

7. The method according to claim 6, characterized in that, It also includes the following steps: The server obtains the corresponding platform template structure from the private cloud platform and the public cloud platform; The server compares the environment template parameter mapping table with the platform template structure and updates the environment template parameter mapping table accordingly.

8. The method according to claim 1, characterized in that, The server pre-stores or dynamically creates a region lock corresponding to the region identifier in the environment application data; the method further includes the following steps: The server generates trial operation environment data in the regional lock environment based on the cloud deployment instruction set; wherein, the trial operation environment data includes: operation abnormal or operation without abnormality; When the trial operation environment data shows no abnormalities, the server releases the region lock and sends the cloud deployment instruction set to the corresponding cloud platform.

9. The method according to claim 1, characterized in that, The server pre-stores resource quota data corresponding to the user terminal; the method further includes the following steps: In response to the project budget data sent by the user, the server determines the verification result based on the project budget data and the resource quota data; wherein, the verification result includes verification passed or verification failed; When the verification result is successful, the server retrieves private cloud resource status data from the private cloud platform.

10. The method according to claim 1, characterized in that, It also includes the following steps: The server determines the environment operation status data based on the deployment status data; When the environmental operation status data meets the recycling conditions corresponding to the environmental application data, the server generates a resource release instruction based on the environmental operation status data and sends it to the corresponding cloud platform; the corresponding cloud platform is used to execute the resource release instruction.

11. The method according to claim 10, characterized in that, The environment application data includes: lifecycle parameters; the step of the server generating a resource release instruction based on the environment operation status data and sending it to the corresponding cloud platform when the environment operation status data meets the recycling conditions corresponding to the environment application data includes: The server starts a countdown timer based on the lifecycle parameters; When the countdown timer reaches a preset first threshold, the server generates expiration notification data and sends it to the user terminal. When the countdown timer reaches a preset second threshold and no extension instruction is received from the user terminal, the server generates a resource release instruction based on the environmental operation status data and sends the resource release instruction to the corresponding cloud platform; wherein, the second threshold is less than the first threshold.

12. A hybrid cloud management and automated deployment device that spans multiple cloud platforms and environments, characterized in that, The device involves a user terminal, a server terminal, a private cloud platform, and a public cloud platform; the device includes: The private cloud resource status data acquisition module is used to acquire private cloud resource status data from the private cloud platform in response to the environment application data sent by the user terminal. The private cloud satisfaction determination module is used to determine the private cloud satisfaction level based on the environment application data and the private cloud resource status data; wherein, the private cloud satisfaction level includes: fully satisfied, partially satisfied, or not satisfied; The split deployment strategy generation module is used to generate a split deployment strategy based on the environment application data and the private cloud resource status data when the private cloud satisfaction level is partially satisfied; wherein, the split deployment strategy includes a private cloud deployment list and a public cloud deployment list; The cloud deployment instruction set determination module is used to determine the cloud deployment instruction sets corresponding to the private cloud platform and the public cloud platform respectively according to the split deployment strategy, and send them to the corresponding cloud platforms respectively; the corresponding cloud platforms are used to return deployment status data to the server according to the corresponding cloud deployment instruction sets.

13. A computer electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 11.

Images