A method for multi-cloud platform configuration management

Abstract

The application particularly relates to a multi-cloud platform configuration management method. The multi-cloud platform configuration management method constructs a unified resource management platform, automatically configures resources of multiple cloud platforms according to requirements by defining standardized configurations and templated operations, automatically deploys and changes resource configurations of the cloud platforms based on infrastructure as code (IaC) technology, automatically schedules and optimizes resource allocation across the cloud platforms according to business requirements, resource usage and cloud platform pricing strategies, monitors and analyzes usage, performance indicators and cost consumption of resources of each cloud platform in real time, optimizes resources based on data analysis, and performs security and compliance management on resource configurations across the cloud platforms. The multi-cloud platform configuration management method significantly reduces the complexity of multi-cloud configuration management, improves configuration management efficiency and reliability, is suitable for operation and maintenance requirements of enterprises in complex multi-cloud environments, and provides important technical support for modern cloud computing.

Description

Technical Field

[0001] This invention relates to the field of cloud computing technology, and in particular to a method for configuration management of multiple cloud platforms. Background Technology

[0002] With the rapid development of cloud computing technology, more and more enterprises are adopting multi-cloud strategies, leveraging the different advantages offered by multiple cloud service providers (such as AWS, Azure, and Google Cloud) to meet complex business needs. However, significant differences exist in the technical specifications, API interfaces, and resource management methods between different cloud platforms, making configuration management in a multi-cloud environment complex and inefficient. Furthermore, frequent and uncontrollable configuration changes can easily lead to cross-platform inconsistencies, thereby increasing operational risks.

[0003] In addition, traditional single-platform management tools lack support for multi-cloud scenarios, making it difficult to effectively detect and dynamically repair configuration conflicts, resulting in frequent system failures and resource waste.

[0004] To achieve standardized, automated adaptation and consistent maintenance of configurations, and to help enterprises simplify operation and maintenance processes and improve the reliability and management efficiency of multi-cloud environments, this invention proposes a method for multi-cloud platform configuration management. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a simple and efficient method for multi-cloud platform configuration management.

[0006] This invention is achieved through the following technical solution:

[0007] A method for multi-cloud platform configuration management includes the following steps:

[0008] Step S1: Build a unified resource management platform, and automatically configure resources of multiple cloud platforms according to requirements by defining standardized configurations and templated operations;

[0009] Step S2: Based on Infrastructure as Code (IaC) technology, automatically deploy and change the resource configuration of the cloud platform;

[0010] Step S3: Based on business needs, resource usage, and cloud platform pricing strategies, automatically schedule and optimize cross-cloud platform resource allocation;

[0011] Step S4: Monitor and analyze the usage, performance indicators and cost consumption of resources on each cloud platform in real time, and optimize resources based on data analysis;

[0012] Step S5: Perform security and compliance management on cross-cloud platform resource configuration.

[0013] In step S1, the following steps are implemented:

[0014] Step S1.1: Adopt a layered architecture to design the resource management platform, including the management layer, the adaptation layer, and the execution layer;

[0015] The management layer is used to implement centralized resource configuration management, the adaptation layer is responsible for abstracting the APIs of different cloud platforms to form a unified management interface, and the execution layer is the cloud resources (virtual machines, databases, etc.) to be managed and configured.

[0016] Step S1.2 Establish a unified resource naming rule for unified management and querying;

[0017] Step S1.3: Develop a labeling strategy, using labels to identify and classify resources in order to quickly locate and identify them;

[0018] Step S1.4: Use template tools such as Terraform or Pulumi to manage multi-cloud resources, thereby improving configuration reusability, reducing human error, and ensuring the consistency of resources across different cloud platforms.

[0019] In step S1, the resource management platform supports modular configuration management and distributed deployment, and integrates load balancing and disaster recovery mechanisms. The load balancing mechanism is used to ensure the reasonable allocation of resources; when a cloud platform fails, it can quickly switch to a backup cloud platform under the disaster recovery mechanism.

[0020] Employ centralized identity management tools such as Azure AD or Okta to provide a unified authentication mechanism across all cloud platforms, ensuring consistent access control across cloud platforms;

[0021] Define and select sensitive data (such as API keys, database credentials, etc.) and encrypt them. Use Vault or AWS Secrets Manager tools to manage them to ensure data security.

[0022] The resource management platform adopts a zero-trust architecture, requiring authentication of all users and devices. Regardless of whether users and devices are on the intranet or the internet, they must be authenticated before they can access resources.

[0023] Configuration files and related sensitive data requiring user-defined confirmations are stored in encrypted form, and access to configuration files is managed through fine-grained access control.

[0024] All user-defined critical data is backed up across different cloud platforms, and the backup data can be used with cross-cloud backup tools such as Veeam and CloudEndure to automate data synchronization and recovery operations through automated recovery mechanisms.

[0025] Customize scheduled tasks to periodically perform fault injection tests, simulating cloud platform or service failures to verify the system's disaster recovery capabilities and high availability;

[0026] In a multi-cloud environment, use load balancers such as AWS ELB or Google Cloud Load Balancing to customize the distribution of traffic to resources on different cloud platforms and automatically failover in case of failure. This ensures that traffic can be smoothly switched between different cloud platforms to avoid single points of failure.

[0027] To achieve high availability and disaster recovery, a multi-site active-active architecture is designed across regions and clouds. When a region or cloud platform fails, traffic will automatically switch to other healthy regions or cloud platforms to ensure uninterrupted business operations.

[0028] Introduce automated tools (such as Terraform, CloudFormation, etc.) to manage cross-cloud disaster recovery architecture, and combine them with real-time monitoring tools to detect faults in real time and initiate preset recovery processes to ensure that the system can quickly return to normal.

[0029] In step S1, each configuration change must be reviewed and approved to prevent configuration errors.

[0030] Version control tools (such as Git) can be used to manage configuration files in a versioned manner, supporting quick rollback to historical versions, thereby avoiding system failures caused by configuration errors;

[0031] Log all configuration changes and create custom scheduled tasks to generate audit reports periodically;

[0032] Before making configuration changes, use the Terraform Plan tool to simulate the execution results to ensure that the configuration changes will not have an adverse impact on the system.

[0033] In step S2, the following steps are implemented:

[0034] Step S2.1: Use Terraform or Pulumi tools to encode the creation, modification and destruction operations of resources into IaC configuration files to ensure the consistency and reusability of resource configuration;

[0035] Step S2.2: Incorporate the IaC configuration file into version control (such as Git), and automate the deployment and update of resources through the CI / CD pipeline to achieve rapid configuration changes and deployments, avoiding human error.

[0036] Step S2.3: Use Ansible orchestration tool or SaltStack orchestration tool to achieve resource coordination and automated management across multiple cloud platforms to ensure unified scheduling of multi-cloud resources;

[0037] Step S2.4: Use the Terraform tool's plan command to periodically check the matching degree between the actual status of resources and the configuration file, so as to detect and fix resource drift problems in a timely manner.

[0038] In step S3, the following steps are implemented:

[0039] Step S3.1: Utilize third-party tools such as CloudHealth or cost analysis tools provided by various cloud service providers to customize scheduled tasks, regularly analyze resource usage and cost structure, and identify potential waste points;

[0040] Step S3.2: Regularly review resource usage, optimize resource allocation, and shut down idle resources or resources with utilization rates below a custom threshold;

[0041] Step S3.3: Set budgets for each project or team according to requirements, and ensure that relevant personnel are promptly reminded to make adjustments when the budget is overrun through a real-time warning mechanism;

[0042] Step S3.4: Optimize cloud resource costs by selecting reserved instances, spot instances, or long-term contracts; Step S3.5: Regularly audit and clean up resources using automated tools such as AWS Lambda or Google Cloud Functions to reclaim unused resources in a timely manner and prevent resource idleness and waste.

[0043] In step S4, the following steps are implemented:

[0044] Step S4.1 Use cross-cloud monitoring tools such as Prometheus or Datadog to uniformly collect and display performance data and log information from different cloud platforms;

[0045] Step S4.2: Use the visualization tool Grafana to display the monitoring data as a dashboard and monitor key indicators in real time, including CPU utilization, storage utilization and network latency.

[0046] Step S4.3: Automatically adjust resource configuration through the Auto Scaling function to ensure that resources are expanded in a timely manner when the load increases and resources are reclaimed in a timely manner when the load decreases.

[0047] Step S4.4: Set up multi-level alarm rules to promptly notify relevant personnel and activate the emergency response mechanism when system performance is abnormal.

[0048] Step S5 is implemented as follows:

[0049] Step S5: Use Cloud Custodian or AWS Config tools to regularly scan the resource configurations on all cloud platforms to ensure they comply with industry standards.

[0050] Step S5: Sensitive data and resources are encrypted according to industry standards to ensure data security during transmission and storage.

[0051] Step S5: Define a custom scheduled task to generate compliance reports periodically, so that managers can understand the current compliance status of the system in a timely manner and make necessary adjustments based on the reports.

[0052] A multi-cloud platform configuration management system, comprising:

[0053] A unified resource management platform is used to automatically configure resources across multiple cloud platforms through standardized configuration and templated operations;

[0054] The automated deployment and change management module is used to automatically manage the resource configuration and changes of the cloud platform based on Infrastructure as Code (IaC) technology.

[0055] The cross-cloud platform scheduling and optimization module is used to automatically optimize resource allocation across cloud platforms based on business needs and resource usage.

[0056] The real-time monitoring and data analysis module is used to collect and analyze resource usage, performance indicators and cost consumption data in real time, and provides a data visualization interface for managers to make decisions.

[0057] The compliance and security management module is used to manage the security and compliance of cloud platform resource configuration.

[0058] A device for multi-cloud platform configuration management, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to implement the above-described method steps.

[0059] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program, when executed by a processor, implements the above-described method steps.

[0060] The beneficial effects of this invention are: the multi-cloud platform configuration management method significantly reduces the complexity of multi-cloud configuration management and improves configuration management efficiency and reliability through template-based, automated and intelligent means. It is particularly suitable for the operation and maintenance needs of enterprises in complex multi-cloud environments and provides important technical support for modern cloud computing. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Appendix Figure 1 This is a schematic diagram of the multi-cloud platform configuration management system architecture of the present invention.

[0063] Appendix Figure 2 This is a schematic diagram of the multi-cloud platform configuration management method of the present invention. Detailed Implementation

[0064] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0065] The complexity of multi-cloud environments in multi-cloud configuration management leads to differences between different platforms, and the key is to manage these resources in a unified and efficient manner.

[0066] Unified resource management: Enterprises need to manage all resources across multiple cloud platforms in a unified manner, including virtual machines, storage, and networks. A central management platform is needed to coordinate resources from different cloud service providers.

[0067] Configuration changes are traceable: Every configuration change needs to be auditable to ensure that every operation can be recorded and that it can be quickly rolled back to a previous stable state.

[0068] Automation and Scalability: Automated deployment is key to reducing human error, and automated configuration management can improve efficiency and simplify operations. At the same time, as business expands, the platform needs to be elastic, supporting rapid resource scaling up and down.

[0069] Basic security and compliance requirements: Resources in a multi-cloud environment involve different security standards and compliance requirements, and it is essential to ensure that data protection measures, authentication, and access control mechanisms are consistent.

[0070] Cost optimization goals: Managing multi-cloud resources not only needs to meet performance requirements, but also to control costs, avoid unnecessary resource waste, and ensure maximum resource utilization.

[0071] The method for configuring and managing this multi-cloud platform includes the following steps:

[0072] Step S1: Build a unified resource management platform, and automatically configure resources of multiple cloud platforms according to requirements by defining standardized configurations and templated operations;

[0073] Step S2: Based on Infrastructure as Code (IaC) technology, automatically deploy and change the resource configuration of the cloud platform;

[0074] Step S3: Based on business needs, resource usage, and cloud platform pricing strategies, automatically schedule and optimize cross-cloud platform resource allocation;

[0075] Step S4: Monitor and analyze the usage, performance indicators and cost consumption of resources on each cloud platform in real time, and optimize resources based on data analysis;

[0076] Step S5: Perform security and compliance management on cross-cloud platform resource configuration.

[0077] In step S1, the following steps are implemented:

[0078] Step S1.1: Adopt a layered architecture to design the resource management platform, including the management layer, the adaptation layer, and the execution layer;

[0079] The management layer is used to implement centralized resource configuration management, the adaptation layer is responsible for abstracting the APIs of different cloud platforms to form a unified management interface, and the execution layer is the actual cloud resources (virtual machines, databases, etc.) to be managed and configured.

[0080] In multi-cloud environments, the complexity of resource management stems from the differences between different cloud platforms. Therefore, establishing unified configuration standards and resource consistency management is crucial.

[0081] Step S1.2 Establish a unified resource naming rule. All cloud platform resources (such as virtual machines, storage, networks, etc.) should follow the same naming rule. These rules can include information such as business modules, environment types, regions, and instance numbers to facilitate unified management and querying.

[0082] Step S1.3: Develop a labeling strategy, using labels to identify and classify resources. The labeling strategy should cover all cloud platforms and include information such as responsibility attribution, environment type, and business module to quickly locate and identify resources.

[0083] Step S1.4: Use template tools such as Terraform or Pulumi to manage multi-cloud resources, thereby improving configuration reusability, reducing human error, and ensuring the consistency of resources across different cloud platforms.

[0084] In step S1, the resource management platform supports modular configuration management and distributed deployment, and integrates load balancing and disaster recovery mechanisms. The load balancing mechanism is used to ensure the reasonable allocation of resources; when a cloud platform fails, it can quickly switch to a backup cloud platform under the disaster recovery mechanism.

[0085] Employ centralized identity management tools such as Azure AD or Okta to provide a unified authentication mechanism across all cloud platforms, ensuring consistent access control across cloud platforms;

[0086] Define and select sensitive data (such as API keys, database credentials, etc.) and encrypt them. Use Vault or AWS Secrets Manager tools to manage them to ensure data security.

[0087] The resource management platform adopts a zero-trust architecture, requiring authentication of all users and devices. Regardless of whether users and devices are on the intranet or the extranet, they must be authenticated before they can access resources. This mechanism can improve security in multi-cloud environments.

[0088] Configuration files and related sensitive data requiring user-defined confirmations are stored in encrypted form, and access to configuration files is managed through fine-grained access control.

[0089] In a multi-cloud environment, disaster recovery and high availability design are core requirements for ensuring business continuity. Since services on multiple cloud platforms may fail in different regions, it is crucial to design an architecture that can still guarantee the normal operation of the system in the event of a failure on any cloud platform or in any region.

[0090] All user-defined critical data is backed up across different cloud platforms, and the backup data can be used with cross-cloud backup tools such as Veeam and CloudEndure to automate data synchronization and recovery operations through automated recovery mechanisms.

[0091] Customize scheduled tasks to periodically perform fault injection tests, simulating cloud platform or service failures to verify the system's disaster recovery capabilities and high availability, ensuring that the system can quickly switch to the backup system when encountering a failure, minimizing downtime.

[0092] In a multi-cloud environment, use load balancers such as AWS ELB or Google Cloud Load Balancing to customize the distribution of traffic to resources on different cloud platforms and automatically failover in case of failure. This ensures that traffic can be smoothly switched between different cloud platforms to avoid single points of failure.

[0093] To achieve high availability and disaster recovery, a multi-site active-active architecture is designed across regions and clouds. When a region or cloud platform fails, traffic will automatically switch to other healthy regions or cloud platforms to ensure uninterrupted business operations.

[0094] Introduce automated tools (such as Terraform, CloudFormation, etc.) to manage cross-cloud disaster recovery architecture, and combine them with real-time monitoring tools to detect faults in real time and initiate preset recovery processes to ensure that the system can quickly return to normal.

[0095] Management and auditing mechanisms for configuration changes are crucial for ensuring resource stability and traceability.

[0096] In step S1, each configuration change must be reviewed and approved to prevent configuration errors.

[0097] Version control tools (such as Git) can be used to manage configuration files in a versioned manner, supporting quick rollback to historical versions, thereby avoiding system failures caused by configuration errors;

[0098] Log all configuration changes and customize scheduled tasks to generate audit reports periodically; audit reports are crucial for issue tracing, compliance checks, and issue remediation.

[0099] Before making configuration changes, use the Terraform Plan tool to simulate the execution results to ensure that the configuration changes will not have an adverse impact on the system.

[0100] In step S2, the following steps are implemented:

[0101] Step S2.1: Use Terraform or Pulumi tools to encode the creation, modification and destruction operations of resources into IaC configuration files to ensure the consistency and reusability of resource configuration;

[0102] Step S2.2: Incorporate the IaC configuration file into version control (such as Git), and automate the deployment and update of resources through the CI / CD pipeline to achieve rapid configuration changes and deployments, avoiding human error.

[0103] Step S2.3: Use Ansible orchestration tool or SaltStack orchestration tool to achieve resource coordination and automated management across multiple cloud platforms to ensure unified scheduling of multi-cloud resources;

[0104] Step S2.4: Use the Terraform tool's plan command to periodically check the matching degree between the actual status of resources and the configuration file, so as to detect and fix resource drift problems in a timely manner.

[0105] In step S3, the following steps are implemented:

[0106] Step S3.1: Utilize third-party tools such as CloudHealth or cost analysis tools provided by various cloud service providers to customize scheduled tasks, regularly analyze resource usage and cost structure, and identify potential waste points;

[0107] Step S3.2: Regularly review resource usage, optimize resource allocation, and shut down idle resources or resources with utilization rates below a custom threshold to avoid unnecessary cost expenditures;

[0108] Step S3.3: Set budgets for each project or team according to requirements, and use a real-time warning mechanism to ensure that relevant personnel are promptly reminded to make adjustments when the budget is exceeded; this can help ensure that all cloud resource costs are within the budget, thereby achieving more accurate financial management.

[0109] Step S3.4: Optimize cloud resource costs by selecting reserved instances, spot instances, or long-term contracts; these resources are relatively inexpensive and suitable for applications or services with fixed loads.

[0110] Step S3.5: Use automated tools such as AWS Lambda or Google Cloud Functions to regularly audit and clean up resources, and reclaim unused resources in a timely manner to prevent resource idleness and waste.

[0111] In step S4, the following steps are implemented:

[0112] Step S4.1 Use cross-cloud monitoring tools such as Prometheus or Datadog to uniformly collect and display performance data and log information from different cloud platforms;

[0113] Step S4.2: Use the visualization tool Grafana to display the monitoring data as a dashboard and monitor key indicators in real time, including CPU utilization, storage utilization and network latency.

[0114] Step S4.3: Automatically adjust resource configuration through the Auto Scaling function to ensure that resources are expanded in a timely manner when the load increases and resources are reclaimed in a timely manner when the load decreases.

[0115] Step S4.4: Set up multi-level alarm rules to promptly notify relevant personnel and activate the emergency response mechanism when system performance is abnormal.

[0116] Step S5 is implemented as follows:

[0117] Step S5: Use Cloud Custodian or AWS Config tools to regularly scan the resource configurations on all cloud platforms to ensure they comply with industry standards.

[0118] Step S5: Sensitive data and resources are encrypted according to industry standards to ensure data security during transmission and storage.

[0119] Step S5: Define a custom scheduled task to generate compliance reports periodically, so that managers can understand the current compliance status of the system in a timely manner and make necessary adjustments based on the reports.

[0120] This multi-cloud platform configuration management system includes:

[0121] A unified resource management platform is used to automatically configure resources across multiple cloud platforms through standardized configuration and templated operations;

[0122] The automated deployment and change management module is used to automatically manage the resource configuration and changes of the cloud platform based on Infrastructure as Code (IaC) technology.

[0123] The cross-cloud platform scheduling and optimization module is used to automatically optimize resource allocation across cloud platforms based on business needs and resource usage.

[0124] The real-time monitoring and data analysis module is used to collect and analyze resource usage, performance indicators and cost consumption data in real time, and provides a data visualization interface for managers to make decisions.

[0125] The compliance and security management module is used to manage the security and compliance of cloud platform resource configuration.

[0126] The device configured and managed by the multi-cloud platform includes a memory and a processor; the memory is used to store computer programs, and the processor is used to execute the computer programs to implement the above-described method steps.

[0127] The readable storage medium stores a computer program that, when executed by a processor, implements the above-described method steps.

[0128] Compared with existing technologies, this multi-cloud platform configuration management method has the following characteristics:

[0129] First, by defining a unified configuration template and standardizing the description of network, storage, and security rules, the differences between different cloud platforms can be masked.

[0130] At the same time, design a multi-cloud adapter to convert the general template into a native configuration format or API call supported by various cloud platforms (such as AWS, Azure, Google Cloud, etc.);

[0131] Secondly, by combining load conditions and business needs, the optimal cloud platform is dynamically selected and resource allocation is optimized through intelligent scheduling algorithms, thereby reducing expenses.

[0132] Third, by using an event-driven dynamic synchronization mechanism, configuration changes in a multi-platform environment are monitored and updated in real time, ensuring configuration consistency.

[0133] Fourth, it implements cross-cloud platform identity and access management, data encryption, log auditing, and automated compliance checks, which can ensure the security and compliance of resources. At the same time, it has a built-in conflict detection and repair module, which analyzes potential conflicts based on an intelligent rule engine and can provide automated or manual handling solutions.

[0134] Fifth, it provides version management functionality, supports configuration history tracing and quick rollback, ensuring system stability.

[0135] In summary, this multi-cloud platform configuration management method solves the problems of complexity and inefficiency in configuration management caused by platform differences in multi-cloud environments. It realizes standardized, automated, and intelligent management of multi-cloud platform resource configuration, reduces operation and maintenance difficulty, improves system reliability and management efficiency, and provides technical support for enterprises to efficiently deploy and manage resources in complex multi-cloud environments.

[0136] The embodiments described above are merely one specific implementation of the present invention. Ordinary changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for configuration management of a multi-cloud platform, characterized in that: Includes the following steps: Step S1: Build a unified resource management platform, and automatically configure resources of multiple cloud platforms according to requirements by defining standardized configurations and templated operations; In step S1, the following steps are implemented: Step S1.1: Adopt a layered architecture to design the resource management platform, including the management layer, the adaptation layer, and the execution layer; The management layer is used to implement centralized resource configuration management, the adaptation layer is responsible for abstracting the APIs of different cloud platforms to form a unified management interface, and the execution layer is the cloud resources to be managed and configured. The resource management platform supports modular configuration management and distributed deployment, and integrates load balancing and disaster recovery mechanisms. The load balancing mechanism ensures the reasonable allocation of resources; when a cloud platform fails, the disaster recovery mechanism can quickly switch to a backup cloud platform. Employ centralized identity management tools such as Azure AD or Okta to provide a unified authentication mechanism across all cloud platforms, ensuring consistent access control across cloud platforms; Customize and select sensitive data, encrypt it, and manage it using Vault or AWS Secrets Manager tools to ensure data security; The resource management platform adopts a zero-trust architecture, requiring authentication of all users and devices. Regardless of whether users and devices are on the intranet or the internet, they must be authenticated before they can access resources. Configuration files and related sensitive data requiring user-defined confirmations are all stored in encrypted form, and access to configuration files is managed through fine-grained access control. All user-defined critical data is backed up across different cloud platforms, and the backed-up data can be synchronized and restored using cross-cloud backup tools and automated recovery mechanisms. Customize scheduled tasks to periodically perform fault injection tests, simulating cloud platform or service failures to verify the system's disaster recovery capabilities and high availability; In a multi-cloud environment, use load balancers such as AWS ELB or Google Cloud Load Balancing to customize the distribution of traffic to resources on different cloud platforms and automatically failover in the event of a failure. This ensures that traffic can be smoothly switched between different cloud platforms to avoid single points of failure. To achieve high availability and disaster recovery, a multi-site active-active architecture is designed across regions and clouds. When a region or cloud platform fails, traffic will automatically switch to other healthy regions or cloud platforms to ensure uninterrupted business operations. Introduce automated tools to manage cross-cloud disaster recovery architecture, and combine them with real-time monitoring tools to detect faults in real time and initiate preset recovery processes to ensure that the system can quickly return to a normal state. Step S1.2 Establish a unified resource naming rule for unified management and querying; Step S1.3: Develop a labeling strategy, using labels to identify and classify resources in order to quickly locate and identify them; Step S1.4: Use template tools such as Terraform or Pulumi to manage multi-cloud resources to improve configuration reusability, reduce human error, and ensure the consistency of resources across different cloud platforms. Step S2: Based on Infrastructure as Code (IaC) technology, automatically deploy and change the resource configuration of the cloud platform; In step S2, the following steps are implemented: Step S2.1: Use Terraform or Pulumi tools to encode the creation, modification and destruction operations of resources into IaC configuration files to ensure the consistency and reusability of resource configuration; Step S2.2: Incorporate the IaC configuration file into version control and automate the deployment and updates of resources through the CI / CD pipeline to achieve rapid configuration changes and deployments, avoiding human error. Step S2.3: Use Ansible orchestration tool or SaltStack orchestration tool to achieve resource coordination and automated management across multiple cloud platforms to ensure unified scheduling of multi-cloud resources; Step S2.4: Regularly check the matching degree between the actual status of resources and the configuration file using the plan command of the Terraform tool, so as to detect and fix resource drift problems in a timely manner; Step S3: Based on business needs, resource usage, and cloud platform pricing strategies, automatically schedule and optimize cross-cloud platform resource allocation; In step S3, the following steps are implemented: Step S3.1: Utilize third-party tools such as CloudHealth or cost analysis tools provided by various cloud service providers to customize scheduled tasks, regularly analyze resource usage and cost structure, and identify potential waste points; Step S3.2: Regularly review resource usage, optimize resource allocation, and shut down idle resources or resources with utilization rates below a custom threshold; Step S3.3: Set budgets for each project or team according to requirements, and ensure that relevant personnel are promptly reminded to make adjustments when the budget is overrun through a real-time warning mechanism; Step S3.4: Optimize cloud resource costs by selecting reserved instances, spot instances, or long-term contracts; Step S3.5: Regularly audit and clean up resources using automated tools such as AWS Lambda or Google Cloud Functions to reclaim unused resources in a timely manner and prevent resource idleness and waste. Step S4: Monitor and analyze the usage, performance indicators and cost consumption of resources on each cloud platform in real time, and optimize resources based on data analysis; In step S4, the following steps are implemented: Step S4.1 Use cross-cloud monitoring tools such as Prometheus or Datadog to uniformly collect and display performance data and log information from different cloud platforms; Step S4.2: Use the visualization tool Grafana to display the monitoring data as a dashboard and monitor key indicators in real time, including CPU utilization, storage utilization and network latency. Step S4.3: Automatically adjust resource configuration through the automatic expansion function to ensure that resources are expanded in a timely manner when the load increases and resources are reclaimed in a timely manner when the load decreases. Step S4.4: Set up multi-level alarm rules to promptly notify relevant personnel and activate the emergency response mechanism when system performance is abnormal. Step S5: Perform security and compliance management on cross-cloud platform resource configuration.

2. The method for multi-cloud platform configuration management according to claim 1, characterized in that: In step S1, each configuration change must be reviewed and approved to prevent configuration errors. Version control tools can be used to manage configuration files in a versioned manner, supporting quick rollback to historical versions, thereby avoiding system failures caused by configuration errors; Log all configuration changes and create custom scheduled tasks to generate audit reports periodically; Before making configuration changes, use the Terraform Plan tool to simulate the execution results to ensure that the configuration changes will not have an adverse impact on the system.

3. The method for multi-cloud platform configuration management according to claim 1, characterized in that: Step S5 is implemented as follows: Step S5.1: Use Cloud Custodian or AWS Config tools to regularly scan the resource configurations on all cloud platforms to ensure they comply with industry standards. Step S5.2: Sensitive data and resources are encrypted according to industry standards to ensure data security during transmission and storage. Step S5.3: Define a custom scheduled task to generate compliance reports periodically, so that managers can understand the current compliance status of the system in a timely manner and make adjustments based on the reports.

4. A multi-cloud platform configuration management system, characterized in that: For implementing the method as described in any one of claims 1 to 3, comprising: A unified resource management platform is used to automatically configure resources across multiple cloud platforms through standardized configuration and templated operations; The automated deployment and change management module is used to automatically manage the resource configuration and changes of the cloud platform based on Infrastructure as Code (IaC) technology. The cross-cloud platform scheduling and optimization module is used to automatically optimize resource allocation across cloud platforms based on business needs and resource usage. The real-time monitoring and data analysis module is used to collect and analyze resource usage, performance indicators and cost consumption data in real time, and provides a data visualization interface for managers to make decisions. The compliance and security management module is used to manage the security and compliance of cloud platform resource configuration.

5. A device for multi-cloud platform configuration management, characterized in that: It includes a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to implement the method as described in any one of claims 1 to 3.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 3.

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