Life cycle management system and control method for cloud workspace

Abstract

The invention discloses a cloud workspace life cycle management system and a control method, which are used for full-process automatic management of workspace from creation, use to recovery, and comprise a scheduling control module, an access authority management module, a monitoring analysis module, a diagnosis and repair module, a resource management module and a recovery processing module, life cycle management of a cloud working space and system resources thereof is realized, end-to-end process optimization is realized, the intelligent level and predictive ability of the system are improved, and intelligent decision making is performed; the automatic processing capability of the working space is perfected, manual intervention is not needed, the system performance is optimized, the resource utilization rate, the system optimization capability and the response speed are improved, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud service technology, and in particular to a lifecycle management system and a control method for a cloud workspace. Background Art

[0002] With the deepening of digital transformation, enterprise-level cloud office has evolved from an optional option to a must. Under this trend, cloud workspace, as the key infrastructure for enterprise digital office, its management efficiency directly affects the enterprise's operating costs and employee productivity.

[0003] The traditional workspace management method has problems such as long deployment cycle, complex operation and maintenance, and waste of resources, resulting in the lack of a unified lifecycle management framework for the workspace system as a whole, relatively fragmented management of each stage, lack of end-to-end process optimization, and inability to achieve global strategy coordination; the system intelligence level is not high, data analysis is not fully applied, lacks predictive capabilities, and does not support intelligent decision-making; the workspace automation capabilities are imperfect, there is a high demand for manual intervention, and the ability to adapt to scenarios is insufficient; the system performance is insufficiently optimized, resource utilization is low, response speed is slow, and user experience is poor. Summary of the invention

[0004] In view of this, it is necessary to provide an efficient, secure and intelligent lifecycle management system and control method for cloud workspace.

[0005] A cloud workspace lifecycle management system that automates the entire process of workspace creation, use, and recycling, including: The scheduling control module is used to quickly deploy resources in the cloud workspace, establish an image preheating mechanism, intelligently preload data, and realize intelligent scheduling of computing power; Access rights management module, used to implement dynamic management and intelligent analysis of access rights to cloud workspaces; The monitoring and analysis module is used for health monitoring of the cloud workspace. Through multi-dimensional monitoring of the workspace, the system's intelligent alarm function is realized; The diagnosis and repair module is used for fault diagnosis and repair of cloud workspaces. By establishing a distributed diagnosis architecture, the automatic repair function of the workspace is realized. Resource management module, used for imperceptible upgrade and intelligent allocation of system resources in cloud workspaces; The recycling processing module is used for the rapid recycling, release, and intelligent cleanup of system resources in the cloud workspace.

[0006] Preferably, the scheduling control module includes: The image preheating mechanism module uses distributed image caching technology to establish an application image layered storage system to achieve rapid deployment of incremental images; The data intelligent preloading module uses a data prediction algorithm based on user portraits and a multi-level cache data distribution mechanism to achieve parallel transmission of data blocks; The intelligent computing power scheduling module uses deep learning-based load prediction and a multi-dimensional resource scheduling algorithm to achieve elastic computing power pool management.

[0007] Preferably, the access rights management module includes: Dynamic permission control module, establishes a fine-grained permission model based on RBAC (role-based access control mechanism), adopts real-time permission policy update mechanism, and implements a multi-factor authentication system; The intelligent permission analysis module analyzes permission usage behavior, detects abnormal access, and optimizes access permissions.

[0008] Preferably, the monitoring and analysis module includes: Multi-dimensional monitoring module, used to monitor system performance indicators and application health status, and complete user experience data collection; The intelligent alarm module performs intelligent classification of alarms and realizes automatic fault location through anomaly detection based on machine learning.

[0009] Preferably, the diagnosis and repair module includes: Distributed diagnostic architecture module, which achieves cross-regional fault location and intelligent routing optimization through global node status synchronization; The automated repair module improves the system's fault self-healing capabilities, enables automatic switching of services, and ensures data consistency.

[0010] Preferably, the resource management module includes: The resource upgrade management module realizes the imperceptible upgrade of system resources and has a rollback protection mechanism to ensure zero-interruption upgrades and incremental resource configuration updates; The intelligent resource allocation module conducts dynamic resource evaluation based on cost optimization strategies to achieve adaptive expansion and contraction of system resources.

[0011] Preferably, the recycling processing module comprises: The resource recovery module uses a parallel resource recovery strategy to complete the secure erasure of data and realize the rapid recovery of storage space; The resource intelligent cleaning module is used to identify idle resources and process junk data to achieve resource optimization and reuse.

[0012] And, a lifecycle management control method for a cloud workspace, using the above-mentioned lifecycle management system for a cloud workspace to perform full-process automated management of the workspace from creation, use to recycling, including the workspace creation process, the workspace operation and maintenance process, and the workspace recycling process; The specific steps of the workspace creation process include: Step 1.1, receiving a workspace creation request; receiving and parsing the workspace creation request parameters, verifying the legitimacy of the request and the integrity of the parameters, and generating a workspace identifier; Step 1.2, resource evaluation and allocation: plan system resources according to request parameters, select the optimal resource pool and deployment area, reserve and lock the required resources, and generate a resource allocation plan; Step 1.3, image and data preheating: start the distributed image preheating mechanism, pull the required application image layers in parallel, verify the integrity and security of the image, predict and preload high-priority data based on user portraits, and establish multi-level cache to accelerate data access; Step 1.4, environment initialization: Create an infrastructure environment according to the resource allocation plan, deploy and start necessary system services, initialize the storage environment and mount data volumes, and configure system parameters and environment variables; Step 1.5, permission configuration; create access control policies, user roles and permission mappings for the workspace, generate access keys and authentication information, and establish an audit log recording mechanism; Step 1.6, health check: perform system-level health checks, verify service availability, test data access performance, check monitoring alarm configuration, and verify the effectiveness of permission policies; Step 1.7, generate a workspace creation report and send it to the administrator and relevant users, update the resource management database, start regular monitoring tasks, and record the creation completion timestamp.

[0013] Preferably, the specific steps of the operation and maintenance process of the workspace include: Step 2.1, system operation status monitoring: collect system performance indicators, collect user experience data, predict and monitor resource usage trends, record operation logs and audit information, and generate real-time health status scores; Step 2.2, perform anomaly detection on the system operation status; analyze monitoring data, identify performance anomaly patterns, detect security threats, and predict potential problems; Step 2.3, intelligent diagnosis: build a problem diagnosis decision tree, analyze the cause of the fault, assess the scope of impact, generate a diagnosis report, and propose solutions and suggestions; Step 2.4, automatic repair: execute the predefined repair strategy, automatically restart or switch the service, adjust the system configuration parameters, clean up abnormal processes or sessions, and restore data consistency; Step 2.5, performance optimization; analyze system performance bottlenecks, optimize resource allocation, adjust load balancing strategies, optimize cache strategies, and improve application response speed; Step 2.6, resource adjustment; optimize resource allocation, update resource quotas, evaluate resource utilization, predict resource demand trends, and perform dynamic expansion and contraction.

[0014] Preferably, the specific steps of the workspace recycling process include: Step 3.1, detect and verify the recycling trigger conditions, receive the recycling request or detect the idle timeout; notify the relevant users, stop new access requests, and save the on-site snapshot; Step 3.2, data processing: back up important data, clean up temporary files, perform data export, securely erase sensitive information, and verify data integrity; Step 3.3, release resources: stop running services, unbind resources, clean up network configuration, reclaim storage space, and delete access keys; Step 3.4, update the resource pool status and generate a recycling report; archive the operation log, clean up the monitoring configuration, and send a completion notification.

[0015] In the above-mentioned life cycle management system and control method of the cloud workspace, the scheduling control module adopts a mirror preheating mechanism, and realizes the rapid deployment of resources in the cloud workspace through intelligent data preloading, parallel transmission of data blocks and intelligent scheduling of computing power; the access permission management module realizes the real-time effectiveness of permission changes through a role-based access control mechanism; the monitoring and analysis module performs multi-dimensional monitoring of the cloud workspace and intelligent classification of alarms, realizes automatic fault location, and effectively improves the accuracy of fault prediction; the diagnosis and repair module adopts a distributed diagnosis architecture and an automated repair mechanism, which effectively shortens the average fault repair response time; the resource management module realizes the imperceptible automatic upgrade and rollback protection mechanism of system resources, and improves resource utilization; the recycling processing module adopts a parallel resource recovery strategy to achieve rapid resource recovery. The control method realizes the life cycle management of the cloud workspace and its system resources through the creation, operation, maintenance and recycling process of the workspace, realizes end-to-end process optimization, improves the system intelligence level and prediction ability, and makes intelligent decisions; improves the automatic processing ability of the workspace without manual intervention, optimizes system performance, improves resource utilization, system optimization ability and response speed, and enhances user experience. The technical solution of the present invention is simple, easy to implement, low-cost, and easy to promote. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural block diagram of the composition of the lifecycle management system of the cloud workspace according to an embodiment of the present invention.

[0017] Figure 2 It is a flowchart of the workspace creation process of the cloud workspace lifecycle management control method according to an embodiment of the present invention.

[0018] Figure 3 It is a flowchart of the operation and maintenance process of a workspace in the lifecycle management control method of a cloud workspace in an embodiment of the present invention.

[0019] Figure 4 It is a flowchart of a workspace recycling process of a cloud workspace lifecycle management control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] This embodiment takes the lifecycle management system and control method of a cloud workspace as an example, and the present invention will be described in detail below in conjunction with specific embodiments and drawings.

[0021] See also Figure 1 , showing a lifecycle management system for a cloud workspace provided by an embodiment of the present invention, which is used for full-process automated management of the workspace from creation, use to recycling, including: The scheduling control module is used to quickly deploy resources in the cloud workspace, establish an image preheating mechanism, intelligently preload data, and realize intelligent scheduling of computing power; Access rights management module, used to implement dynamic management and intelligent analysis of access rights; The monitoring and analysis module is used for health monitoring of the workspace. Through multi-dimensional monitoring of the workspace, the intelligent alarm function of the system is realized; Diagnosis and repair module, establishes a distributed diagnosis architecture and realizes the automatic repair function of the system; Resource management module, used for imperceptible upgrade and intelligent allocation of system resources; The recycling processing module is used for rapid recycling, release and intelligent cleanup of system resources.

[0022] Preferably, the scheduling control module includes: The image preheating mechanism module uses distributed image caching technology to establish an application image layered storage system to achieve rapid deployment of incremental images; The data intelligent preloading module uses a data prediction algorithm based on user portraits and a multi-level cache data distribution mechanism to achieve parallel transmission of data blocks; The intelligent computing power scheduling module uses deep learning-based load prediction and a multi-dimensional resource scheduling algorithm to achieve elastic computing power pool management.

[0023] Preferably, the access rights management module includes: Dynamic permission control module, establishes a fine-grained permission model based on role-based access control (RBAC), adopts a real-time permission policy update mechanism, and implements a multi-factor authentication system; The intelligent permission analysis module analyzes permission usage behavior, detects abnormal access, and optimizes access permissions.

[0024] Preferably, the monitoring and analysis module includes: Multi-dimensional monitoring module, used to monitor system performance indicators and application health status, and complete user experience data collection; The intelligent alarm module performs intelligent classification of alarms and realizes automatic fault location through anomaly detection based on machine learning.

[0025] Preferably, the diagnosis and repair module includes: Distributed diagnostic architecture module, which achieves cross-regional fault location and intelligent routing optimization through global node status synchronization; The automated repair module improves the system's fault self-healing capabilities, enables automatic switching of services, and ensures data consistency.

[0026] Preferably, the resource management module includes: The resource upgrade management module realizes the imperceptible upgrade of system resources and has a rollback protection mechanism to ensure zero-interruption upgrades and incremental resource configuration updates; The intelligent resource allocation module conducts dynamic resource evaluation based on cost optimization strategies to achieve adaptive expansion and contraction of system resources.

[0027] Preferably, the recycling processing module comprises: The resource recovery module uses a parallel resource recovery strategy to complete the secure erasure of data and realize the rapid recovery of storage space; The resource intelligent cleaning module is used to identify idle resources and process junk data to achieve resource optimization and reuse.

[0028] And, a lifecycle management and control method for a cloud workspace, which uses the lifecycle management system of the cloud workspace as described above to perform full-process automated management of the workspace from creation, use to recycling, including the workspace creation process, the workspace operation and maintenance process, and the workspace recycling process, covering the three main stages in the lifecycle of the workspace.

[0029] Among them, see Figure 2The specific steps of the workspace creation process include: Step 1.1, receiving a workspace creation request; The specific steps include: Step 1.1.1: Receive and parse the workspace creation request parameters.

[0030] Specifically, the creation request is initiated by a user or administrator, where the request parameters include required computing power specifications, storage capacity, application type, data requirements, etc.

[0031] Step 1.1.2: Verify the legitimacy of the request and the integrity of the parameters.

[0032] Step 1.1.3, generate a unique workspace identifier.

[0033] Specifically, after the system confirms that the request is legal and the parameters are complete, it generates a unique workspace identifier, which uniquely identifies a workspace.

[0034] Step 1.2, resource assessment and allocation.

[0035] The specific steps include: Step 1.2.1, evaluate the availability of resources based on the request parameters.

[0036] Step 1.2.2, execute the multi-dimensional resource planning algorithm to plan the system resources.

[0037] Specifically, the system resources include CPU, memory, storage, network bandwidth, etc.

[0038] Step 1.2.3, select the optimal resource pool and deployment area, reserve and lock the required resources.

[0039] Step 1.2.4, generate a resource allocation plan.

[0040] Specifically, a resource request contains the user's request parameters for resources, including the user's current region, the TAG description of the user space, the length of time the space is occupied, etc. The resources in the resource pool are categorized by label according to region, resource type (for example, compute-intensive, memory-intensive, GPU model, etc.), and resource size. The selection of resource pools is usually based on the principle of regional proximity and best resource matching.

[0041] Step 1.3, image and data preheating.

[0042] The specific steps include: Step 1.3.1, start the distributed image preheating mechanism and pull the required application image layers in parallel.

[0043] Specifically, the application image adopts a layered mechanism, in which the base layer includes basic operating system services and basic configurations, and the layers above it are application tools and other contents superimposed on the base layer. In the base layer, multiple images are shared and cached, and multi-level cache can accelerate data access.

[0044] Specifically, the images are distributed to each node in advance for caching, and multiple threads or multiple nodes simultaneously download different application image layers to improve efficiency and reduce latency in large-scale deployment environments.

[0045] Step 1.3.2, verify the integrity and security of the image.

[0046] Step 1.3.3, predict and preload high-priority data based on user profiles, and establish a multi-level cache to accelerate data access.

[0047] Specifically, based on the request parameters created by the workspace, user profiling is implemented, and data is loaded based on the user profiling to achieve fast access to data.

[0048] Step 1.4, environment initialization.

[0049] The specific steps include: Step 1.4.1, create the infrastructure environment according to the resource allocation plan.

[0050] Step 1.4.2, configure the network environment, deploy and start necessary system services.

[0051] Specifically, necessary system services include starting network, remote access and other system services.

[0052] Step 1.4.3, initialize the storage environment and mount the data volume, configure system parameters and environment variables.

[0053] Step 1.5, permission configuration.

[0054] The specific steps include: Step 1.5.1, create access control policies, user roles, and permission mappings for the workspace.

[0055] Step 1.5.2, generate access keys and authentication information.

[0056] Step 1.5.3, set resource usage quotas and establish an audit log recording mechanism.

[0057] Step 1.6, health check.

[0058] The specific steps include: In step 1.6.1, perform system-level health checks to verify service availability.

[0059] Step 1.6.2, test data access performance and check monitoring alarm configuration.

[0060] Step 1.6.3: Verify the effectiveness of the permission policy.

[0061] Step 1.7, the workspace is created.

[0062] The specific steps include: Step 1.7.1, generate a workspace creation report and send it to the administrator and relevant users.

[0063] Step 1.7.2, update the resource management database, start the regular monitoring task, and record the creation completion timestamp.

[0064] Preferably, see Figure 3 The specific steps of the operation and maintenance process of the workspace include: Step 2.1, system operation status monitoring.

[0065] The specific steps include: Step 2.1.1, collect system performance indicators, including CPU usage, memory usage, disk IO status, network traffic, and application response time.

[0066] Step 2.1.2, collect user experience data.

[0067] Specifically, the collected user experience data includes network latency, desktop rendering frame rate, CPU usage, memory usage and other data.

[0068] Step 2.1.3, predict and monitor resource usage trends; Step 2.1.4, record operation logs and audit information, and generate real-time health status scores.

[0069] Step 2.2: Detect abnormality in the system operation status.

[0070] The specific steps include: Step 2.2.1, analyze monitoring data based on machine learning models to identify performance anomaly patterns.

[0071] Step 2.2.2, detect security threats.

[0072] Step 2.2.3: Abnormal resource usage is found.

[0073] Step 2.2.4, predict potential problems.

[0074] Step 2.3, intelligent diagnosis.

[0075] The specific steps include: Step 2.3.1, construct a problem diagnosis decision tree.

[0076] Step 2.3.2: Analyze the cause of the fault, assess the scope of impact, and generate a diagnostic report.

[0077] Step 2.3.3, propose solutions and suggestions.

[0078] Step 2.4, automatic repair.

[0079] The specific steps include: Step 2.4.1, execute the predefined repair strategy.

[0080] Specifically, the predefined repair strategy refers to the platform's built-in repair strategy, including network route switching, application configuration reset, user directory cleanup and other strategies.

[0081] Step 2.4.2, automatically restart or switch the service and adjust the system configuration parameters.

[0082] Step 2.4.3, clean up abnormal processes or sessions.

[0083] Step 2.4.4, restore data consistency.

[0084] Step 2.5, performance optimization.

[0085] The specific steps include: Step 2.5.1, analyze system performance bottlenecks and optimize resource allocation.

[0086] Step 2.5.2, adjust the load balancing strategy, optimize the cache strategy, and improve the application response speed.

[0087] Step 2.6, resource adjustment.

[0088] The specific steps include: Step 2.6.1, evaluate resource utilization.

[0089] Step 2.6.2: Predict resource demand trends and perform dynamic scaling.

[0090] Step 2.6.3, optimize resource allocation and update resource quotas.

[0091] Preferably, see Figure 4 The specific steps of the workspace recycling process include: Step 3.1, recycle trigger.

[0092] The specific steps include: Step 3.1.1, receiving a recycling request or detecting an idle timeout.

[0093] Step 3.1.2, verify the recycling conditions.

[0094] Step 3.1.3, notify relevant users.

[0095] Step 3.1.4, stop new access requests and save the on-site snapshot.

[0096] Specifically, a snapshot of the data and equipment status of the resources to be recycled is retained, and relevant users are notified.

[0097] Step 3.2, data processing.

[0098] The specific steps include: Step 3.2.1, back up important data, clean up temporary files, and perform data export.

[0099] Step 3.2.2, securely erase sensitive information.

[0100] Step 3.2.3, verify data integrity.

[0101] Step 3.3, resource release.

[0102] The specific steps include: Step 3.3.1, stop the running service.

[0103] Step 3.3.2, unbind resources, clean up network configuration, and reclaim storage space.

[0104] Step 3.3.3, delete the access key.

[0105] Specifically, resources that were once allocated to the user's workspace but not used, such as idle CPU resources, memory resources, disk space, etc., will be recycled by the system due to triggering recycling conditions.

[0106] During the system resource recycling process, resources in the workspace that were once triggered by the user but have not been used for a long time will also be marked as user workspace resources that have stopped service due to long-term idleness.

[0107] Step 3.4, complete the recycling.

[0108] The specific steps include: Step 3.4.1, update the resource pool status and generate a recycling report.

[0109] Step 3.4.2, archive the operation log, clean up the monitoring configuration, and send a completion notification.

[0110] Specifically, in this embodiment, the following technical effects can be achieved: 1) Workspace deployment time is reduced from hours to minutes; 2) Permission changes take effect in real time; 3) Fault prediction accuracy reaches 95%; 4) Average fault repair response time is <5 minutes; 5) Resource utilization is increased to more than 85%; 6) Resource recovery time is controlled within seconds.

[0111] In the above-mentioned life cycle management system and control method of the cloud workspace, the scheduling control module adopts a mirror preheating mechanism, and realizes the rapid deployment of resources in the cloud workspace through intelligent data preloading, parallel transmission of data blocks and intelligent scheduling of computing power; the access permission management module realizes the real-time effectiveness of permission changes through a role-based access control mechanism; the monitoring and analysis module performs multi-dimensional monitoring of the cloud workspace and intelligent classification of alarms, realizes automatic fault location, and effectively improves the accuracy of fault prediction; the diagnosis and repair module adopts a distributed diagnosis architecture and an automated repair mechanism, which effectively shortens the average fault repair response time; the resource management module realizes the imperceptible automatic upgrade and rollback protection mechanism of system resources, and improves resource utilization; the recycling processing module adopts a parallel resource recovery strategy to achieve rapid resource recovery. The control method realizes the life cycle management of the cloud workspace and its system resources through the creation, operation, maintenance and recycling process of the workspace, realizes end-to-end process optimization, improves the system intelligence level and prediction ability, and makes intelligent decisions; improves the automatic processing ability of the workspace without manual intervention, optimizes system performance, improves resource utilization, system optimization ability and response speed, and enhances user experience. The technical solution of the present invention is simple, easy to implement, low-cost, and easy to promote.

[0112] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A lifecycle management system for cloud workspaces, characterized in that: include: The scheduling control module is used to quickly deploy resources on the cloud work platform, establish an image preheating mechanism, intelligently preload data, and realize intelligent scheduling of computing power; Access rights management module, used to implement dynamic management and intelligent analysis of access rights; The monitoring and analysis module is used for health monitoring of the workspace. Through multi-dimensional monitoring of the workspace, the intelligent alarm function of the system is realized; Diagnosis and repair module, establishes a distributed diagnosis architecture and realizes the automatic repair function of the system; Resource management module, used for imperceptible upgrade and intelligent allocation of system resources; The recycling processing module is used for rapid recycling, release and intelligent cleanup of system resources.

2. The lifecycle management system for cloud workspace according to claim 1, characterized in that: The dispatch control module includes: The image preheating mechanism module uses distributed image caching technology to establish an application image layered storage system to achieve rapid deployment of incremental images; The data intelligent preloading module uses a data prediction algorithm based on user portraits and a multi-level cache data distribution mechanism to achieve parallel transmission of data blocks; The intelligent computing power scheduling module uses deep learning-based load prediction and a multi-dimensional resource scheduling algorithm to achieve elastic computing power pool management.

3. The lifecycle management system for cloud workspace according to claim 1, characterized in that: The access permission management module includes: Dynamic permission control module, establishes a fine-grained permission model based on RBAC (role-based access control mechanism), adopts real-time permission policy update mechanism, and implements a multi-factor authentication system; The intelligent permission analysis module analyzes permission usage behavior, detects abnormal access, and optimizes access permissions.

4. The lifecycle management system for cloud workspace according to claim 1, characterized in that: The monitoring and analysis module includes: Multi-dimensional monitoring module, used to monitor system performance indicators and application health status, and complete user experience data collection; The intelligent alarm module performs intelligent classification of alarms and realizes automatic fault location through anomaly detection based on machine learning.

5. The lifecycle management system for cloud workspace according to claim 1, characterized in that: The diagnosis and repair module comprises: Distributed diagnostic architecture module, which achieves cross-regional fault location and intelligent routing optimization through global node status synchronization; The automated repair module improves the system's fault self-healing capabilities, enables automatic switching of services, and ensures data consistency.

6. The lifecycle management system for cloud workspace according to claim 1, characterized in that: The resource management module includes: The resource upgrade management module realizes the imperceptible upgrade of system resources and has a rollback protection mechanism to ensure zero-interruption upgrades and incremental resource configuration updates; The intelligent resource allocation module conducts dynamic resource evaluation based on cost optimization strategies to achieve adaptive expansion and contraction of system resources.

7. The lifecycle management system for cloud workspace according to claim 1, characterized in that: The recycling module comprises: The resource recovery module uses a parallel resource recovery strategy to complete the secure erasure of data and realize the rapid recovery of storage space; The resource intelligent cleaning module is used to identify idle resources and process junk data to achieve resource optimization and reuse.

8. A method for lifecycle management and control of a cloud workspace, using the lifecycle management system of a cloud workspace as described in any one of claims 1 to 7 to perform full-process automated management of the workspace from creation, use to recycling, characterized in that: Including the workspace creation process, workspace operation and maintenance process, and workspace recycling process; The specific steps of the workspace creation process include: Step 1.1, receiving a workspace creation request; receiving and parsing the workspace creation request parameters, verifying the legitimacy of the request and the integrity of the parameters, and generating a workspace identifier; Step 1.2, resource evaluation and allocation: plan system resources according to request parameters, select the optimal resource pool and deployment area, reserve and lock the required resources, and generate a resource allocation plan; Step 1.3, image and data preheating: start the distributed image preheating mechanism, pull the required application image layers in parallel, verify the integrity and security of the image, predict and preload high-priority data based on user portraits, and establish multi-level cache to accelerate data access; Step 1.4, environment initialization: Create an infrastructure environment according to the resource allocation plan, deploy and start necessary system services, initialize the storage environment and mount data volumes, and configure system parameters and environment variables; Step 1.5, permission configuration; create access control policies, user roles and permission mappings for the workspace, generate access keys and authentication information, and establish an audit log recording mechanism; Step 1.6, health check: perform system-level health checks, verify service availability, test data access performance, check monitoring alarm configuration, and verify the effectiveness of permission policies; Step 1.7, generate a workspace creation report and send it to the administrator and relevant users, update the resource management database, start regular monitoring tasks, and record the creation completion timestamp.

9. The method for lifecycle management and control of a cloud workspace according to claim 8, characterized in that: The specific steps of the operation and maintenance process of the workspace include: Step 2.1, system operation status monitoring: collect system performance indicators, collect user experience data, predict and monitor resource usage trends, record operation logs and audit information, and generate real-time health status scores; Step 2.2, perform anomaly detection on the system operation status; analyze monitoring data, identify performance anomaly patterns, detect security threats, and predict potential problems; Step 2.3, intelligent diagnosis: build a problem diagnosis decision tree, analyze the cause of the fault, assess the scope of impact, generate a diagnosis report, and propose solutions and suggestions; Step 2.4, automatic repair: execute the predefined repair strategy, automatically restart or switch the service, adjust the system configuration parameters, clean up abnormal processes or sessions, and restore data consistency; Step 2.5, performance optimization; analyze system performance bottlenecks, optimize resource allocation, adjust load balancing strategies, optimize cache strategies, and improve application response speed; Step 2.6, resource adjustment; optimize resource allocation, update resource quotas, evaluate resource utilization, predict resource demand trends, and perform dynamic expansion and contraction.

10. The method for lifecycle management and control of cloud workspace according to claim 8, characterized in that: The specific steps of the workspace recycling process include: Step 3.1, detect and verify the recycling trigger conditions, receive the recycling request or detect the idle timeout; notify the relevant users, stop new access requests, and save the on-site snapshot; Step 3.2, data processing; Back up important data, clean temporary files, perform data export, securely erase sensitive information, and verify data integrity; Step 3.3, release resources: stop running services, unbind resources, clean up network configuration, reclaim storage space, and delete access keys; Step 3.4, update the resource pool status and generate a recycling report; archive the operation log, clean up the monitoring configuration, and send a completion notification.

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