An operation and maintenance method and system for a multi-cloud management platform based on cloud-edge collaboration

Abstract

The present invention discloses an operation and maintenance method and system of a multi-cloud management platform based on cloud-edge collaboration, comprising the following steps: S1, deploying cloud services and edge nodes, initializing a cloud-edge collaborative environment; S2, building an edge autonomous optimization network, adjusting and optimizing task strategies, and predicting the task load of edge nodes; S3, adjusting task priorities and classifying task strategies; S4, building a task feature matrix, a cloud-edge resource matrix, and a task and resource matching model; S5, allocating resources to each task and generating a task resource allocation table; S6, identifying operational anomalies through a time series anomaly detection model, and triggering task migration, resource switching, or task reallocation when an edge node anomaly is detected. The present invention utilizes an edge autonomous optimization network and an intelligent scheduling algorithm to achieve dynamic task allocation and anomaly recovery, and has the advantages of high resource utilization, strong real-time scheduling, and good system stability.

Description

Technical Field

[0001] The present invention relates to the field of cloud management platform operation and maintenance, and in particular to an operation and maintenance method and system for a multi-cloud management platform based on cloud-edge collaboration. Background Art

[0002] With the rapid development of cloud computing and edge computing technologies, multi-cloud collaboration and edge intelligence have become crucial means of meeting complex business needs. Traditional cloud computing relies on centralized data centers. While providing powerful computing power and resource pools, it suffers from limitations in real-time performance, network latency, and local data processing. In scenarios requiring low latency and high reliability, such as the Internet of Things, smart manufacturing, and the Internet of Vehicles, relying solely on cloud resources is insufficient to meet the demands for efficient operations in dynamic environments. To address this issue, edge computing has emerged, distributing some computing tasks to edge nodes close to data sources to enable localized data processing and real-time response. However, the independent operation of cloud computing and edge computing also presents new challenges. The collaborative management of cloud resources and edge nodes within a multi-cloud management platform has become a hot topic in current technical research.

[0003] In existing technologies, many multi-cloud management platforms have introduced the concept of edge computing, achieving cloud-edge collaboration through distributed resource scheduling. However, these platforms typically use static task scheduling and resource allocation methods and lack dynamic optimization capabilities. Specifically, existing resource scheduling schemes are often based on predefined rules or simple weighted algorithms, which are difficult to adapt to changing resource states and task characteristics. This lack of intelligent scheduling mechanisms can lead to low resource utilization, unreasonable task allocation, and degraded system performance. In addition, existing anomaly detection methods are mostly based on fixed thresholds or simple rule matching. These methods are prone to misjudgment or omission in complex dynamic environments, making it difficult to effectively ensure system stability.

[0004] In terms of edge computing, existing technologies typically rely on centralized cloud-based instructions to control the task execution and resource management of edge nodes, and the edge nodes themselves lack autonomous capabilities. This high reliance on the cloud not only increases network communication overhead, but may also cause interruptions in task execution in the event of high latency or network disconnection. Even though some technologies have attempted to introduce the concept of edge autonomous optimization, their optimization methods mostly remain at the level of simple local rule adjustments, making it difficult to achieve adaptive optimization and dynamic adjustments in complex task scenarios. In addition, edge nodes lack the ability to recover in abnormal situations. Existing recovery solutions typically rely on cloud-based instructions for global resource adjustments, lacking a rapid response mechanism for the edge nodes themselves, resulting in weak fault tolerance and robustness of the system.

[0005] Existing technologies have limited capabilities for dynamically displaying resource distribution and task routing within multi-cloud management platforms. They typically employ static configuration files or offline-generated routing tables, which struggle to adapt to dynamically changing resource states and network topologies. This not only impacts task scheduling accuracy but also increases system management complexity. In multi-cloud collaboration scenarios, due to the significant heterogeneity of task and resource characteristics, traditional methods struggle to efficiently establish matching relationships between tasks and resources, resulting in poor efficiency and fairness in resource allocation. Furthermore, the lack of refined processing of task priority and real-time requirements means that high-priority tasks may not be completed in a timely manner due to scheduling delays, severely impacting the quality of service for critical tasks.

[0006] Furthermore, existing anomaly detection modeling approaches often use traditional statistical methods or simple machine learning models, which fail to fully capture the complex temporal characteristics of edge node operations. For example, parameters such as CPU utilization, storage occupancy, and network latency can exhibit nonlinear and temporal dependencies within edge node operations, which are crucial for detecting anomalies. Existing methods exhibit significant limitations in handling these complex relationships, resulting in low detection accuracy and difficulty in promptly identifying potential risks within the system.

[0007] In summary, existing technologies for cloud-edge collaborative O&M on multi-cloud management platforms suffer from the following shortcomings: a lack of intelligent optimization mechanisms for dynamic resource scheduling and task allocation, insufficient autonomy of edge nodes, low accuracy and real-time performance of anomaly detection, and limited dynamic display capabilities for resource distribution and task routing. These issues severely limit the adaptability and stability of multi-cloud collaborative O&M systems in complex and dynamic environments, necessitating an innovative technical solution to address them. Summary of the Invention

[0008] One purpose of the present invention is to propose an operation and maintenance method for a multi-cloud management platform based on cloud-edge collaboration. The present invention realizes dynamic matching and optimal allocation of tasks and resources by introducing an edge autonomous optimization network and an intelligent scheduling mechanism, thereby improving resource utilization and task execution efficiency. At the same time, through the accurate prediction of the task load of edge nodes through a bidirectional long short-term memory network, the task queue can be scientifically sorted and prioritized in dynamic task scenarios, ensuring the timely processing of high-priority tasks and reducing the delay and error rate in the task scheduling process.

[0009] An operation and maintenance method for a multi-cloud management platform based on cloud-edge collaboration according to an embodiment of the present invention includes the following steps:

[0010] S1. Deploy cloud services and edge nodes in the multi-cloud management platform, initialize the cloud-edge collaborative environment, and configure the computing resources, storage resources, and network resource pools of the edge nodes;

[0011] S2. Build an edge autonomous optimization network, use the PPO algorithm to train the edge autonomous optimization network, adjust and optimize the task strategy, and predict the task load of edge nodes through a bidirectional long short-term memory network;

[0012] S3, dynamically adjust task priorities and classify task strategies based on the predicted task load and local resource status of edge nodes;

[0013] S4. Based on the adjusted task priorities, a task feature matrix and a cloud-edge resource matrix are constructed, and a task and resource matching model is established by combining the task feature matrix and the cloud-edge resource matrix.

[0014] S5. Allocate resources to each task according to the task and resource matching model, generate a task resource allocation table, and optimize and adjust the task resource allocation table;

[0015] S6. Monitor the operating status of edge nodes in real time, identify operating anomalies through the time series anomaly detection model, and trigger task migration, resource switching, or task reallocation when edge node anomalies are detected.

[0016] Optionally, the S1 specifically includes:

[0017] S11. Creating a cloud control module and an edge management module in the multi-cloud management platform. The cloud control module manages the configuration, distributed deployment, and resource association of cloud resources with edge nodes. The cloud resources include computing resources, storage resources, and network resources. The edge management module monitors the resource status, task allocation, and operation status of edge nodes.

[0018] S12. Initialize the edge nodes, connect the edge nodes to the multi-cloud management platform, configure basic environment parameters for each edge node, and assign a unique node identifier. The basic environment parameters include network bandwidth, storage capacity, and computing units.

[0019] S13, performing distributed deployment of cloud services, dividing the cloud resources into multiple virtual resource pools, each of which includes multiple computing instances, storage units, and network interfaces;

[0020] S14. Based on preset resource allocation rules, bind the computing instances in the virtual resource pool to the edge nodes and establish a cloud-edge resource association relationship. The resource allocation rules include node location, task delay requirements, and network transmission efficiency.

[0021] S15. Configure monitoring services for edge nodes, monitor the resource usage, network connection status, and task allocation of edge nodes in real time through the cloud control module, and generate node status logs;

[0022] S16. Enable a task distribution interface for the edge node. The task distribution interface supports uploading tasks and returning results, including a receiving interface and a response interface for task data streams.

[0023] S17: Establish a task routing table to map the connection relationship between the cloud and each edge node into node routing information. The task routing table includes node identification, current available bandwidth, task transmission delay, and node status identification.

[0024] S18. After completing the initial configuration of cloud services and edge nodes, a resource distribution map of cloud-edge collaboration is generated, and the resource distribution map is synchronized to the multi-cloud management platform in real time through the cloud control module. The resource distribution map includes the computing power, storage capacity and network connection status of each node.

[0025] Optionally, the S2 specifically includes:

[0026] S21. Build an edge autonomous optimization network and use the PPO algorithm for training. The edge autonomous optimization network consists of the PPO algorithm and a bidirectional long short-term memory network. The PPO algorithm optimizes the task strategy, and the bidirectional long short-term memory network predicts the task load of the edge node.

[0027] S22. Initialize the parameters of the PPO algorithm, where the parameters of the PPO algorithm include a state space, an action space, a reward function, and an objective function. The state space includes resource utilization of edge nodes, task queue length, network delay, and task completion time. The action space includes task execution, task push, resource allocation adjustment, and node sleep.

[0028] S23. Construct reward function:

[0029]

[0030] Among them, r t Represents the reward value for performing a specific action a at time step t t The performance change of the back-edge node, α, γ and δ represent weight parameters, U t Indicates resource utilization, L t Indicates network delay, L threshold represents the delay threshold, β represents the delay sensitivity coefficient, P t It represents the task priority loss rate, which is the ratio of high-priority tasks not processed by edge nodes, E t represents the node energy consumption;

[0031] S24, optimization strategy function π θ , sample the action a of the current strategy t Generate samples and use the objective function to maximize the cumulative reward value r tTo update the mission strategy:

[0032] L PPO (θ)=E t [min(r t (θ)·A t ,clip(r t (θ),1-∈,1+∈)·A t )];

[0033]

[0034] Among them, L PPO (θ) represents the objective function of the PPO algorithm, E t represents the expected value at time step t, r t (θ) represents the importance sampling ratio, which represents the ratio of the current strategy to the old strategy, and clip represents the clipping function, which is used to limit r t The range of (θ), ∈ represents the truncation range, which is used to limit the change range of the strategy update, A t represents the advantage function, which is used to estimate the performance difference between the current action and the benchmark action, γ represents the discount factor, which is used to weigh the importance of current rewards and future rewards, represents the state value at time step t, θ represents the parameters of the current strategy, and π θ Indicates the current strategy, Indicates the old policy, a t Indicates the action of the current strategy, s t Represents the node resource status at time step t;

[0035] S25. Collect load data of historical tasks of edge nodes and pre-process the load data, wherein the load data includes CPU usage, memory usage, storage remaining amount, and task queue length;

[0036] S26. Construct a bidirectional long short-term memory network. Design two forward and backward long short-term memory networks to capture the forward and reverse temporal characteristics of the task load. Use the mean square error as the loss function and the Adam optimization algorithm to iteratively train the parameters of the bidirectional long short-term memory network until the loss function converges.

[0037] S27. Deploy the trained bidirectional long short-term memory network to the task scheduling module of the edge node, receive the real-time task load sequence as input, and output the task load for several time periods in the future.

[0038] Optionally, the S3 specifically includes:

[0039] S31. Obtain the load data output by the bidirectional long short-term memory network and define the resource status indicator function RS tIndicates the resource load at time step t:

[0040]

[0041] Among them, RS t represents the resource status indicator function, w1, w2, w3 and w4 represent weight parameters, k represents the storage space adjustment parameter, U CPU Indicates CPU usage, U MEM Indicates memory usage, S FREE Indicates the remaining storage capacity, Q LEN Indicates the length of the task queue;

[0042] S32. Define a task priority calculation function to dynamically adjust the task priorities in the task queue:

[0043]

[0044] Among them, P represents the task priority calculation function, T RT Indicates the real-time requirements of the task, D SIZE Indicates the amount of data, T LIMIT Indicates the time limit for completion;

[0045] S33. Based on the output of the task priority calculation function, the task strategy is classified, and the classification includes local execution, push to the cloud, and delayed processing. If the priority calculation function value is greater than the high priority threshold, the task is executed preferentially on the local edge node. If the priority calculation function value is less than the low priority threshold, the task is delayed. If the priority calculation function value is between the high and low priority thresholds, the task is pushed to the cloud for processing.

[0046] Optionally, the S4 specifically includes:

[0047] S41. Construct a task feature matrix and a cloud-edge resource matrix. The task features include the real-time nature of the task, computational complexity, data volume, task arrival time, and priority. The cloud-edge resources include computing resources, storage capacity, network bandwidth, and resource availability.

[0048] T={(C i ,T RT,i ,D SIZE,i ,P i ,T ARR,i )|i=1,2,,N};

[0049] R={(E CPU,j ,E MEM,j ,B j ,L j )|j=1,2,,M};

[0050] Among them, T represents the task feature matrix, R represents the cloud-edge resource matrix, and C i represents the computational complexity, T RT,i Indicates the real-time nature of the task, D SIZE,i Indicates the amount of data, P i Indicates the task priority, T ARR,i represents the task arrival time, E CPU,j represents computing resources, E MEM,j Indicates storage capacity, B j Indicates network bandwidth, L j represents resource availability, N represents the total number of tasks, and M represents the total number of edge nodes;

[0051] S42. Establish a task and resource matching model and calculate the matching degree between tasks and resources:

[0052]

[0053] Among them, S(i,j) represents the matching function, w1, w2, w3, w4, w5 and w6 represent the weight coefficients of the matching function, which represent the impact of different task characteristics on the matching degree;

[0054] S43. Based on the value of the matching model, construct the matching relationship matrix S between tasks and resources:

[0055] S={S(i,j)|i=1,2,,N; j=1,2,,M};

[0056] Among them, S(i,j) represents the matching degree between task index i and resource node index j.

[0057] Optionally, the S5 specifically includes:

[0058] S51. Sort each row in the matching relationship matrix S to find the best match between the current task and the resource node, that is, S(i,j) with the largest matching value:

[0059] j * =argmax j S(i,j),j=1,2,…,n;

[0060] Among them, j * Indicates the best resource node index matching the i-th task, argmax j S(i,j) represents the index for finding the maximum value of S(i,j);

[0061] S52. Build a task resource allocation table:

[0062] A={(i,j * )|i=1,2,…,m};

[0063] Among them, A represents the task resource allocation table, (i,j * ) indicates that task i is assigned to resource node j * ;

[0064] S53, for each pair of allocation relations (i, j * ), re-evaluate the rationality of the allocation by optimizing the objective function:

[0065] O(i,j * )=λ1·P(i)+λ2·A r (j * )+λ3·C r (j * )+λ4·B r (j * );

[0066] Among them, O(i,j * ) represents the optimized allocation score, λ1, λ2, λ3 and λ4 represent the weight coefficients of the matching function, and represent the optimization weight coefficients, which are used to balance the influence of priority and resource attributes. r (j * ) represents the jth * The computing power of resource nodes, B r (j * ) represents the jth * The network bandwidth of resource nodes, A r (j * ) represents the jth * The current availability of resource nodes;

[0067] S54, if the assignment relationship of task i (i, j * ) score is low, then rematch the resource nodes and look for feasible alternative nodes that meet:

[0068] O(i,j′)>O(i,j * );

[0069] Where O(i,j′) represents the new assigned score.

[0070] Optionally, the S6 specifically includes:

[0071] S61. Monitor the operating status of edge nodes in real time, collect multi-dimensional monitoring data, including CPU usage, storage occupancy, network latency, and task execution status, and pre-process the multi-dimensional monitoring data;

[0072] S62. Use a gated temporal convolutional network in combination with an autoencoder to construct a time series anomaly detection model. The gated temporal convolutional network includes causal convolution, dilated convolution, and a gating mechanism. The autoencoder consists of an encoder and a decoder.

[0073] S63. Use the gated temporal convolutional network to extract multi-scale temporal features from the pre-processed multi-dimensional monitoring data and output temporal embedding features:

[0074] H t =σ(W f *S t +b f )⊙tanh(W g *S t +b g );

[0075] Among them, H t represents the temporal embedding feature of time step t, W f and W g Represent the weights of the gate and main convolution kernel, b f and b g Represents the corresponding bias term, σ represents the Sigmoid activation function, ⊙ represents the point-by-point product, S t represents the input sequence;

[0076] S64: Input the temporal embedding feature into an autoencoder, compress the high-dimensional temporal embedding feature into a low-dimensional potential representation, and decode the low-dimensional potential representation back into reconstructed data of the original input dimension through a decoder of the autoencoder;

[0077] S65. Compare the input data sequence and the reconstructed data generated by the autoencoder and calculate the reconstruction error:

[0078]

[0079] Where E represents the reconstruction error, t represents the time step, and x i represents the input data sequence, Represents the reconstructed data generated by the autoencoder;

[0080] S66, comparing the reconstruction error with a preset threshold, and determining that it is abnormal when the reconstruction error is greater than the preset threshold;

[0081] S67. When an abnormal edge node is detected, an autonomous recovery mechanism is triggered according to the task queue and resource allocation status. The autonomous recovery mechanism includes task migration, resource switching and task reallocation. The task migration means migrating high-priority tasks on the abnormal node to other normal nodes. The resource switching means enabling redundant resource nodes to take over the tasks of the abnormal node. The task reallocation means recalculating the priority of unfinished tasks and reallocating them to suitable resource nodes.

[0082] An operation and maintenance system of a multi-cloud management platform based on cloud-edge collaboration according to an embodiment of the present invention includes:

[0083] The cloud management module is used to manage the configuration, distributed deployment and resource association of cloud resources with edge nodes;

[0084] Edge management module, used to monitor the resource status, task allocation and operation status of edge nodes;

[0085] The task scheduling module is used to generate matching relationships between tasks and resources based on task characteristics and resource status, and optimize task resource allocation strategies;

[0086] Edge autonomy module, which is used to implement task priority classification and dynamic policy adjustment of edge nodes by optimizing network and load prediction;

[0087] Anomaly detection module, which is used to monitor the status of edge nodes in real time, detect anomalies and make relevant adjustments;

[0088] Resource distribution map generation module, used to generate and synchronize cloud-edge collaborative resource distribution maps and display resource status;

[0089] The task management module is used to record task lifecycle data, update task status, and support scheduling and recovery operations;

[0090] The communication interface module is used to realize task data transmission and status feedback between the cloud and edge nodes.

[0091] The beneficial effects of the present invention are:

[0092] First, the present invention effectively realizes the precise matching and optimal allocation of tasks and resources by adopting the edge autonomous optimization network and dynamic task scheduling mechanism. The introduction of the bidirectional long short-term memory network enables the edge nodes to accurately predict the task load, and combines the task priority dynamic adjustment strategy to ensure the real-time and high efficiency of task scheduling, and reduce task delays and resource waste.

[0093] Secondly, in view of the high task complexity and strong resource heterogeneity in multi-cloud environments, the present invention dynamically calculates the optimal allocation plan for tasks and resources through multi-dimensional modeling of the task feature matrix and the resource status matrix, combined with a matching function. Unlike traditional static rules or simple weighted algorithms, the present invention can adjust the allocation strategy in real time to adapt to changes in task characteristics and resource status, ensuring the efficient operation of the system in complex scenarios. Through the optimized task resource allocation table and scheduling plan, the present invention improves resource utilization and task execution efficiency, providing a new technical path for intelligent task management in multi-cloud environments.

[0094] Again, in terms of anomaly detection, the present invention uses a gated temporal convolutional network combined with an autoencoder to construct a time series anomaly detection model, which can extract deep time series features from multi-dimensional monitoring data and accurately determine anomalies by reconstructing errors. Compared with traditional rule matching or threshold detection methods, the accuracy and robustness of this model in processing complex time series data are significantly improved. When an anomaly occurs, the autonomous recovery mechanism designed by the present invention can quickly initiate task migration, resource switching or task reallocation, ensuring the high fault tolerance and stability of the system in emergencies, and reducing the risk of task interruption and data loss.

[0095] Finally, this invention dynamically generates and synchronizes resource distribution maps in real time, combined with a task routing table update mechanism, to intuitively display the distribution status of cloud-edge resources and the task execution path. The real-time and visual nature of the resource distribution map not only reduces the complexity of operation and maintenance management, but also significantly improves the transparency and decision-making efficiency of the system. Combining the collaborative optimization capabilities of the cloud and edge, this invention provides comprehensive data support and intelligent operation tools for operation and maintenance managers.

[0096] In summary, the present invention has achieved improvements in resource utilization efficiency, task scheduling real-time performance, and system stability through the combination of intelligent scheduling, precise anomaly detection, and efficient recovery mechanisms. It exhibits relatively strong adaptability and efficiency in dynamic and complex environments, and can provide advanced technical support and solutions for operation and maintenance management in multi-cloud collaborative scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0098] Figure 1 This is a flow chart of an operation and maintenance method for a multi-cloud management platform based on cloud-edge collaboration proposed by the present invention;

[0099] Figure 2This is a flowchart of the construction of the task feature matrix and resource status matrix and the optimization of the task resource matching relationship of the operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration proposed by the present invention. DETAILED DESCRIPTION

[0100] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0101] refer to Figure 1 and Figure 2 , an operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration, comprising the following steps:

[0102] S1. Deploy cloud services and edge nodes in the multi-cloud management platform, initialize the cloud-edge collaborative environment, and configure the computing resources, storage resources, and network resource pools of the edge nodes;

[0103] S2. Build an edge autonomous optimization network, use the PPO algorithm to train the edge autonomous optimization network, adjust and optimize the task strategy, and predict the task load of edge nodes through a bidirectional long short-term memory network;

[0104] S3, dynamically adjust task priorities and classify task strategies based on the predicted task load and local resource status of edge nodes;

[0105] S4. Based on the adjusted task priorities, a task feature matrix and a cloud-edge resource matrix are constructed, and a task and resource matching model is established by combining the task feature matrix and the cloud-edge resource matrix.

[0106] S5. Allocate resources to each task according to the task and resource matching model, generate a task resource allocation table, and optimize and adjust the task resource allocation table;

[0107] S6. Monitor the operating status of edge nodes in real time, identify operating anomalies through the time series anomaly detection model, and trigger task migration, resource switching, or task reallocation when edge node anomalies are detected.

[0108] In this embodiment, S1 specifically includes:

[0109] S11. Creating a cloud control module and an edge management module in the multi-cloud management platform. The cloud control module manages the configuration, distributed deployment, and resource association of cloud resources with edge nodes. The cloud resources include computing resources, storage resources, and network resources. The edge management module monitors the resource status, task allocation, and operation status of edge nodes.

[0110] S12. Initialize the edge nodes, connect the edge nodes to the multi-cloud management platform, configure basic environment parameters for each edge node, and assign a unique node identifier. The basic environment parameters include network bandwidth, storage capacity, and computing units.

[0111] S13, performing distributed deployment of cloud services, dividing the cloud resources into multiple virtual resource pools, each of which includes multiple computing instances, storage units, and network interfaces;

[0112] S14. Based on preset resource allocation rules, bind the computing instances in the virtual resource pool to the edge nodes and establish a cloud-edge resource association relationship. The resource allocation rules include node location, task delay requirements, and network transmission efficiency.

[0113] S15. Configure monitoring services for edge nodes, monitor the resource usage, network connection status, and task allocation of edge nodes in real time through the cloud control module, and generate node status logs;

[0114] S16. Enable a task distribution interface for the edge node. The task distribution interface supports uploading tasks and returning results, including a receiving interface and a response interface for task data streams.

[0115] S17: Establish a task routing table to map the connection relationship between the cloud and each edge node into node routing information. The task routing table includes node identification, current available bandwidth, task transmission delay, and node status identification.

[0116] S18. After completing the initial configuration of cloud services and edge nodes, a resource distribution map of cloud-edge collaboration is generated, and the resource distribution map is synchronized to the multi-cloud management platform in real time through the cloud control module. The resource distribution map includes the computing power, storage capacity and network connection status of each node.

[0117] In this embodiment, S2 specifically includes:

[0118] S21. Build an edge autonomous optimization network and use the PPO algorithm for training. The edge autonomous optimization network consists of the PPO algorithm and a bidirectional long short-term memory network. The PPO algorithm optimizes the task strategy, and the bidirectional long short-term memory network predicts the task load of the edge node.

[0119] S22. Initialize the parameters of the PPO algorithm, where the parameters of the PPO algorithm include a state space, an action space, a reward function, and an objective function. The state space includes resource utilization of edge nodes, task queue length, network delay, and task completion time. The action space includes task execution, task push, resource allocation adjustment, and node sleep.

[0120] S23. Construct reward function:

[0121]

[0122] Among them, r t represents the reward value for performing a specific action a at time step t t The performance change of the back-edge node, α, γ and δ represent weight parameters, U t Indicates resource utilization, L t Indicates network delay, L threshold represents the delay threshold, β represents the delay sensitivity coefficient, P t It represents the task priority loss rate, which is the ratio of high-priority tasks not processed by edge nodes, E t represents the node energy consumption;

[0123] S24, optimization strategy function π θ , sample the action a of the current strategy t Generate samples and use the objective function to maximize the cumulative reward value r t To update the mission strategy:

[0124] L PPO (θ)=E t [min(r t (θ)·A t ,clip(r t (θ),1-∈,1+∈)·A t )];

[0125]

[0126] Among them, L PPO (θ) represents the objective function of the PPO algorithm, E t represents the expected value at time step t, r t (θ) represents the importance sampling ratio, which represents the ratio of the current strategy to the old strategy, and clip represents the clipping function, which is used to limit r t The range of (θ), ∈ represents the truncation range, which is used to limit the change range of the strategy update, A t represents the advantage function, which is used to estimate the performance difference between the current action and the benchmark action, γ represents the discount factor, which is used to weigh the importance of current rewards and future rewards, represents the state value at time step t, θ represents the parameters of the current strategy, and π θ Indicates the current strategy, Indicates the old policy, a t Indicates the action of the current strategy, s t Represents the node resource status at time step t;

[0127] S25. Collect load data of historical tasks of edge nodes and pre-process the load data, wherein the load data includes CPU usage, memory usage, storage remaining amount, and task queue length;

[0128] S26. Construct a bidirectional long short-term memory network. Design two forward and backward long short-term memory networks to capture the forward and reverse temporal characteristics of the task load. Use the mean square error as the loss function and the Adam optimization algorithm to iteratively train the parameters of the bidirectional long short-term memory network until the loss function converges.

[0129] S27. Deploy the trained bidirectional long short-term memory network to the task scheduling module of the edge node, receive the real-time task load sequence as input, and output the task load for several time periods in the future.

[0130] In this embodiment, S3 specifically includes:

[0131] S31. Obtain the load data output by the bidirectional long short-term memory network and define the resource status indicator function RS t Indicates the resource load at time step t:

[0132]

[0133] Among them, RS t represents the resource status indicator function, w1, w2, w3 and w4 represent weight parameters, k represents the storage space adjustment parameter, U CPU Indicates CPU usage, U MEM Indicates memory usage, S FREE Indicates the remaining storage capacity, Q LEN Indicates the length of the task queue;

[0134] S32. Define a task priority calculation function to dynamically adjust the task priorities in the task queue:

[0135]

[0136] Among them, P represents the task priority calculation function, T RT Indicates the real-time requirements of the task, D SIZE Indicates the amount of data, T LIMIT Indicates the time limit for completion;

[0137] S33. Based on the output of the task priority calculation function, the task strategy is classified, and the classification includes local execution, push to the cloud, and delayed processing. If the priority calculation function value is greater than the high threshold of the priority, the task is executed preferentially on the local edge node. If the priority calculation function value is less than the low threshold of the priority, the task is delayed. If the priority calculation function value is between the high threshold and the low threshold of the priority, the task is pushed to the cloud for processing.

[0138] In this embodiment, the S4 specifically includes:

[0139] S41. Construct a task feature matrix and a cloud-edge resource matrix. The task features include the real-time nature of the task, computational complexity, data volume, task arrival time, and priority. The cloud-edge resources include computing resources, storage capacity, network bandwidth, and resource availability.

[0140] T={(C i ,T RT,i ,D SIZE,i ,P i ,T ARR,i )|i=1,2,,N};

[0141] R={(E CPU,j ,E MEM,j ,B j ,L j )|j=1,2,,M};

[0142] Among them, T represents the task feature matrix, R represents the cloud-edge resource matrix, and C i represents the computational complexity, T RT,i Indicates the real-time nature of the task, D SIZE,i Indicates the amount of data, P i Indicates the task priority, T ARR,i represents the task arrival time, E CPU,j represents computing resources, E MEM,j Indicates storage capacity, B j Indicates network bandwidth, L j represents resource availability, N represents the total number of tasks, and M represents the total number of edge nodes;

[0143] S42. Establish a task and resource matching model and calculate the matching degree between tasks and resources:

[0144]

[0145] Among them, S(i,j) represents the matching function, w1, w2, w3, w4, w5 and w6 represent the weight coefficients of the matching function, which represent the impact of different task characteristics on the matching degree;

[0146] S43. Based on the value of the matching model, construct the matching relationship matrix S between tasks and resources:

[0147] S={S(i,j)|i=1,2,,N; j=1,2,,M};

[0148] Among them, S(i,j) represents the matching degree between task index i and resource node index j.

[0149] In this embodiment, the S5 specifically includes:

[0150] S51. Sort each row in the matching relationship matrix S to find the best match between the current task and the resource node, that is, S(i,j) with the largest matching value:

[0151] j * =argmax j S(i,j),j=1,2,…,n;

[0152] Among them, j * Indicates the best resource node index matching the i-th task, argmax j S9i,j) represents the index for finding the maximum value of S(i,j);

[0153] S52. Build a task resource allocation table:

[0154] A={(i,j * )|i=1,2,…,m};

[0155] Among them, A represents the task resource allocation table, (i,j * ) indicates that task i is assigned to resource node j * ;

[0156] S53, for each pair of allocation relations (i, j * ), re-evaluate the rationality of the allocation by optimizing the objective function:

[0157] O(i,j * )=λ1·P(i)+λ2·A r (j * )+λ3·C r (j * )+λ4·B r (j * );

[0158] Among them, O(i,j * ) represents the optimized allocation score, λ1, λ2, λ3 and λ4 represent the weight coefficients of the matching function, and represent the optimization weight coefficients, which are used to balance the influence of priority and resource attributes. r (j* ) represents the jth * The computing power of resource nodes, B r (j * ) represents the jth * The network bandwidth of resource nodes, A r (j * ) represents the jth * The current availability of resource nodes;

[0159] S54, if the assignment relationship of task i (i, j * ) score is low, then rematch the resource nodes and look for feasible alternative nodes that meet:

[0160] O(i,j′)>O(i,j * );

[0161] Where O(i,j′) represents the new assigned score.

[0162] In this embodiment, S6 specifically includes:

[0163] S61. Monitor the operating status of edge nodes in real time, collect multi-dimensional monitoring data, including CPU usage, storage occupancy, network latency, and task execution status, and pre-process the multi-dimensional monitoring data;

[0164] S62. Use a gated temporal convolutional network in combination with an autoencoder to construct a time series anomaly detection model. The gated temporal convolutional network includes causal convolution, dilated convolution, and a gating mechanism. The autoencoder consists of an encoder and a decoder.

[0165] S63. Use the gated temporal convolutional network to extract multi-scale temporal features from the pre-processed multi-dimensional monitoring data and output temporal embedding features:

[0166] H t =σ(W f *S t +b f )⊙tanh(W g *S t +b g );

[0167] Among them, H t represents the temporal embedding feature of time step t, W f and W g Represent the weights of the gate and main convolution kernel, b f and b g Represents the corresponding bias term, σ represents the Sigmoid activation function, ⊙ represents the point-by-point product, S t represents the input sequence;

[0168] S64: Input the temporal embedding feature into an autoencoder, compress the high-dimensional temporal embedding feature into a low-dimensional potential representation, and decode the low-dimensional potential representation back into reconstructed data of the original input dimension through a decoder of the autoencoder;

[0169] S65. Compare the input data sequence and the reconstructed data generated by the autoencoder and calculate the reconstruction error:

[0170]

[0171] Where E represents the reconstruction error, t represents the time step, and x i represents the input data sequence, Represents the reconstructed data generated by the autoencoder;

[0172] S66, comparing the reconstruction error with a preset threshold, and determining that it is abnormal when the reconstruction error is greater than the preset threshold;

[0173] S67. When an abnormal edge node is detected, an autonomous recovery mechanism is triggered according to the task queue and resource allocation status. The autonomous recovery mechanism includes task migration, resource switching and task reallocation. The task migration means migrating high-priority tasks on the abnormal node to other normal nodes. The resource switching means enabling redundant resource nodes to take over the tasks of the abnormal node. The task reallocation means recalculating the priority of unfinished tasks and reallocating them to suitable resource nodes.

[0174] An operation and maintenance system for a multi-cloud management platform based on cloud-edge collaboration, including:

[0175] The cloud management module is used to manage the configuration, distributed deployment and resource association of cloud resources with edge nodes;

[0176] Edge management module, used to monitor the resource status, task allocation and operation status of edge nodes;

[0177] The task scheduling module is used to generate matching relationships between tasks and resources based on task characteristics and resource status, and optimize task resource allocation strategies;

[0178] Edge autonomy module, which is used to implement task priority classification and dynamic policy adjustment of edge nodes by optimizing network and load prediction;

[0179] Anomaly detection module, which is used to monitor the status of edge nodes in real time, detect anomalies and make relevant adjustments;

[0180] Resource distribution map generation module, used to generate and synchronize cloud-edge collaborative resource distribution maps and display resource status;

[0181] The task management module is used to record task lifecycle data, update task status, and support scheduling and recovery operations;

[0182] The communication interface module is used to realize task data transmission and status feedback between the cloud and edge nodes.

[0183] Example 1:

[0184] The scenario of this embodiment is in a smart manufacturing factory, where the factory's production process relies on the collaborative work of a large number of distributed devices, such as CNC machine tools, industrial robots, sensor networks, etc. The tasks generated by these devices include quality inspection tasks with high real-time requirements, image processing tasks with large computational loads, and low-priority log archiving tasks. The traditional cloud computing model is difficult to meet the needs of real-time response due to bandwidth limitations and network latency issues, while the model that relies entirely on edge computing cannot efficiently complete high-computation tasks due to limited resources. In addition, the operating status of the equipment is complex and dynamic, and problems such as unreasonable task scheduling, resource overload, and equipment abnormalities may occur, seriously affecting production efficiency and system stability.

[0185] In this scenario, the operation and maintenance method of the multi-cloud management platform based on cloud-edge collaboration of the present invention is applied to the task scheduling and exception management of the production line. First, the factory deployed a multi-cloud management platform, and the cloud service was responsible for the unified management of computing resources, storage resources and network resources. At the same time, edge nodes were deployed on the production line for local real-time task processing. By building an edge autonomous optimization network, the edge nodes can optimize the task execution strategy according to the PPO algorithm and use the bidirectional long short-term memory network to predict the task load. Based on task characteristics and resource status, the system dynamically adjusts task priorities and generates a task resource allocation table in real time. For abnormal situations in the production line, such as equipment overload or network delay exceeding the standard, the system quickly identifies the anomaly through the time series anomaly detection model and triggers the autonomous recovery mechanism, including task migration and resource switching, to ensure the efficient operation of the production line. In the smart manufacturing factory, the system processed 500 tasks respectively. The task types included quality inspection tasks with high real-time requirements (120), image processing tasks with large computational load (250), and low-priority log archiving tasks (130).

[0186] Table 1 Test data comparison table

[0187] Test indicators Traditional methods Method of the present invention Optimization effect Average task scheduling time (seconds) 3.0 2.1 30% reduction Edge node CPU utilization 65％ 85％ 20% increase Cloud resource utilization 50％ 72％ 22% increase Anomaly detection accuracy 90％ 98.5％ 8.5% increase Mission success rate 92％ 96.5％ 4.5% increase Abnormal recovery time (seconds) 3.5 2.0 42.8% reduction Abnormal recovery rate 85％ 95％ 10% increase

[0188] In terms of task scheduling efficiency, the average task scheduling time of the method of the present invention was reduced from 3.0 seconds of the traditional method to 2.1 seconds, an optimization margin of 30%. This result shows that through dynamic task allocation and resource optimization scheduling mechanism, the present invention can quickly complete resource allocation and task scheduling in high-concurrency task scenarios, effectively improving overall operational efficiency.

[0189] In terms of resource utilization, edge node CPU utilization increased from 65% to 85% with traditional methods, and cloud resource utilization increased from 50% to 72%. This significant increase in resource utilization is due to improvements in task characteristics and resource state modeling, as well as the application of a dynamic matching algorithm, which makes resource allocation more rational and efficient. This optimization also avoids the problems of idle resources and task backlogs, providing a better solution for resource management in multi-cloud environments.

[0190] The present invention also demonstrates powerful capabilities in anomaly detection. The accuracy of anomaly detection has increased from 90% of the traditional method to 98.5%, an increase of 8.5 percentage points, indicating that the detection model combining the gated time convolutional network and the autoencoder adopted by the present invention can more accurately capture complex time series features and significantly reduce the occurrence of missed detections and false detections. In terms of anomaly recovery efficiency, the average recovery time is shortened from 3.5 seconds of the traditional method to 2.0 seconds, while the anomaly recovery rate is increased from 85% to 95%. This result proves that the autonomous recovery mechanism of the present invention can respond to sudden abnormal situations quickly and efficiently, ensuring the continuity of tasks and the stability of the system.

[0191] Furthermore, the present invention improved the overall success rate of tasks from 92% for traditional methods to 96.5%, with particularly strong performance for high-priority tasks. This demonstrates that through dynamic adjustment of task priorities and scientific task sequencing, the present invention can ensure the efficient completion of critical tasks, providing strong technical support for time-sensitive tasks.

[0192] Overall, the present invention has achieved optimization in key indicators such as task scheduling efficiency, resource utilization, anomaly detection accuracy, and task success rate compared with traditional methods. These data fully verify the technical advantages and application value of the method of the present invention in complex dynamic environments, and can provide a reliable solution for operation and maintenance management in multi-cloud collaborative scenarios.

[0193] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An operation and maintenance method for a multi-cloud management platform based on cloud-edge collaboration, characterized in that: The steps include: S1. Deploy cloud services and edge nodes in the multi-cloud management platform, initialize the cloud-edge collaborative environment, and configure the computing resources, storage resources, and network resource pools of the edge nodes; S2. Build an edge autonomous optimization network, use the PPO algorithm to train the edge autonomous optimization network, adjust and optimize the task strategy, and predict the task load of edge nodes through a bidirectional long short-term memory network; S3, dynamically adjust task priorities and classify task strategies based on the predicted task load and local resource status of edge nodes; S4. Based on the adjusted task priorities, a task feature matrix and a cloud-edge resource matrix are constructed, and a task and resource matching model is established by combining the task feature matrix and the cloud-edge resource matrix. S5. Allocate resources to each task according to the task and resource matching model, generate a task resource allocation table, and optimize and adjust the task resource allocation table; S6. Monitor the operating status of edge nodes in real time, identify operating anomalies through the time series anomaly detection model, and trigger task migration, resource switching, or task reallocation when edge node anomalies are detected.

2. The operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration according to claim 1 is characterized in that: Said S1 specifically includes: S11. Creating a cloud control module and an edge management module in the multi-cloud management platform. The cloud control module manages the configuration, distributed deployment, and resource association of cloud resources with edge nodes. The cloud resources include computing resources, storage resources, and network resources. The edge management module monitors the resource status, task allocation, and operation status of edge nodes. S12. Initialize the edge nodes, connect the edge nodes to the multi-cloud management platform, configure basic environment parameters for each edge node, and assign a unique node identifier. The basic environment parameters include network bandwidth, storage capacity, and computing units. S13, performing distributed deployment on the cloud service, dividing the cloud resources into multiple virtual resource pools, each of which includes multiple computing instances, storage units, and network interfaces; S14. Based on preset resource allocation rules, bind the computing instances in the virtual resource pool to the edge nodes and establish a cloud-edge resource association relationship. The resource allocation rules include node location, task delay requirements, and network transmission efficiency. S15. Configure monitoring services for edge nodes, monitor the resource usage, network connection status, and task allocation of edge nodes in real time through the cloud control module, and generate node status logs; S16. Enable a task distribution interface for the edge node. The task distribution interface supports uploading tasks and returning results, including a receiving interface and a response interface for task data streams. S17: Establish a task routing table to map the connection relationship between the cloud and each edge node into node routing information. The task routing table includes node identification, current available bandwidth, task transmission delay, and node status identification. S18. After completing the initial configuration of cloud services and edge nodes, a resource distribution map of cloud-edge collaboration is generated, and the resource distribution map is synchronized to the multi-cloud management platform in real time through the cloud control module. The resource distribution map includes the computing power, storage capacity and network connection status of each node.

3. The operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration according to claim 1 is characterized in that: The S2 specifically includes: S21. Build an edge autonomous optimization network and use the PPO algorithm for training. The edge autonomous optimization network consists of the PPO algorithm and a bidirectional long short-term memory network. The PPO algorithm optimizes the task strategy, and the bidirectional long short-term memory network predicts the task load of the edge node. S22. Initialize the parameters of the PPO algorithm, where the parameters of the PPO algorithm include a state space, an action space, a reward function, and an objective function. The state space includes resource utilization of edge nodes, task queue length, network delay, and task completion time. The action space includes task execution, task push, resource allocation adjustment, and node sleep. S23. Construct reward function: Among them, r t Represents the reward value for performing a specific action a at time step t t The performance change of the back-edge node, α, γ and δ represent weight parameters, U t Indicates resource utilization, L t Indicates network delay, L threshold represents the delay threshold, β represents the delay sensitivity coefficient, P t It represents the task priority loss rate, which is the ratio of high-priority tasks not processed by edge nodes, E t represents the energy consumption of the node; S24, optimization strategy function π θ , sample the action a of the current strategy t Generate samples and use the objective function to maximize the cumulative reward value r t To update the mission strategy: L PPO (θ)=E t [min(r t (θ)·A t ,clip(r t (θ),1-∈,1+∈)·A t )]; Among them, L PPO (θ) represents the objective function of the PPO algorithm, E t represents the expected value at time step t, r t (θ) represents the importance sampling ratio, which represents the ratio of the current strategy to the old strategy, and clip represents the clipping function, which is used to limit r t The range of (θ), ∈ represents the truncation range, which is used to limit the change range of the strategy update, A t represents the advantage function, which is used to estimate the performance difference between the current action and the benchmark action, γ represents the discount factor, which is used to weigh the importance of current rewards and future rewards, represents the state value at time step t, θ represents the parameters of the current strategy, and π θ Indicates the current strategy, Indicates the old policy, a t Indicates the action of the current strategy, s t Represents the node resource status at time step t; S25. Collect load data of historical tasks of edge nodes and pre-process the load data, wherein the load data includes CPU usage, memory usage, storage remaining amount, and task queue length; S26. Construct a bidirectional long short-term memory network. Design two forward and backward long short-term memory networks to capture the forward and reverse temporal characteristics of the task load. Use the mean square error as the loss function and the Adam optimization algorithm to iteratively train the parameters of the bidirectional long short-term memory network until the loss function converges. S27. Deploy the trained bidirectional long short-term memory network to the task scheduling module of the edge node, receive the real-time task load sequence as input, and output the task load for several time periods in the future.

4. The operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration according to claim 1 is characterized in that: The S3 specifically includes: S31. Obtain the load data output by the bidirectional long short-term memory network and define the resource status indicator function RS t Indicates the resource load at time step t: Among them, RS t represents the resource status indicator function, w1, w2, w3 and w4 represent weight parameters, k represents the storage space adjustment parameter, U CPU Indicates CPU usage, U MEM Indicates memory usage, S FREE Indicates the remaining storage capacity, Q LEN Indicates the length of the task queue; S32. Define a task priority calculation function to dynamically adjust the task priorities in the task queue: Among them, P represents the task priority calculation function, T RT Indicates the real-time requirements of the task, D SIZE Indicates the amount of data, T LIMIT Indicates the time limit for completion; S33. Based on the output of the task priority calculation function, the task strategy is classified, and the classification includes local execution, push to the cloud, and delayed processing. If the priority calculation function value is greater than the high priority threshold, the task is executed preferentially on the local edge node. If the priority calculation function value is less than the low priority threshold, the task is delayed. If the priority calculation function value is between the high and low priority thresholds, the task is pushed to the cloud for processing.

5. The operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration according to claim 1 is characterized in that: The S4 specifically includes: S41. Construct a task feature matrix and a cloud-edge resource matrix. The task features include the real-time nature of the task, computational complexity, data volume, task arrival time, and priority. The cloud-edge resources include computing resources, storage capacity, network bandwidth, and resource availability. T={(C i ,T RT,i ,D SIZE,i ,P i ,T ARR,i )∣i=1,2,,N}; R={(E CPU,j ,E MEM,j ,B j ,L j )∣j=1,2,,M}; Among them, T represents the task feature matrix, R represents the cloud-edge resource matrix, and C i represents the computational complexity, T RT,i Indicates the real-time nature of the task, D SIZE,i Indicates the amount of data, P i Indicates the task priority, T ARR,i represents the task arrival time, E CPU,j represents computing resources, E MEM,j Indicates storage capacity, B j Indicates network bandwidth, L j represents resource availability, N represents the total number of tasks, and M represents the total number of edge nodes; S42. Establish a task and resource matching model and calculate the matching degree between tasks and resources: Among them, S(i,j) represents the matching function, w1, w2, w3, w4, w5 and w6 represent the weight coefficients of the matching function, which represent the impact of different task characteristics on the matching degree; S43. Based on the value of the matching model, construct the matching relationship matrix S between tasks and resources: S={S(i,j)|i=1,2,,N; j=1,2,,M}; Among them, S(i,j) represents the matching degree between task index i and resource node index j.

6. The operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration according to claim 1 is characterized in that: The S5 specifically includes: S51. Sort each row in the matching relationship matrix S to find the best match between the current task and the resource node, that is, S(i,j) with the largest matching value: j * =argmax j S(i,j),j=1,2,…,n; Among them, j * Indicates the best resource node index matching the i-th task, argmax j S(i,j) represents the index for finding the maximum value of S(i,j); S52. Build a task resource allocation table: A={(i,j * )|i=1,2,…,m}; Among them, A represents the task resource allocation table, (i,j * ) indicates that task i is assigned to resource node j * ; S53, for each pair of allocation relations (i, j * ), re-evaluate the rationality of the allocation by optimizing the objective function: O(i,j * )=λ1·P(i)+λ2·A r (j * )+λ3·C r (j * )+λ4·B r (j * ); Among them, O(i,j * ) represents the optimized allocation score, λ1, λ2, λ3 and λ4 represent the weight coefficients of the matching function, and represent the optimization weight coefficients, which are used to balance the influence of priority and resource attributes. r (j * ) represents the jth * The computing power of resource nodes, B r (j * ) represents the jth * The network bandwidth of resource nodes, A r (j * ) represents the jth * The current availability of resource nodes; S54, if the assignment relationship of task i (i, j * ) score is low, then rematch the resource nodes and look for feasible alternative nodes that meet: O(i,j′)>O(i,j * ); Among them, O(i,j') represents the new assigned score.

7. The operation and maintenance method of a multi-cloud management platform based on cloud-edge collaboration according to claim 1 is characterized in that: The S6 specifically includes: S61. Monitor the operating status of edge nodes in real time, collect multi-dimensional monitoring data, including CPU usage, storage occupancy, network latency, and task execution status, and pre-process the multi-dimensional monitoring data; S62. Use a gated temporal convolutional network in combination with an autoencoder to construct a time series anomaly detection model. The gated temporal convolutional network includes causal convolution, dilated convolution, and a gating mechanism. The autoencoder consists of an encoder and a decoder. S63. Use the gated temporal convolutional network to extract multi-scale temporal features from the pre-processed multi-dimensional monitoring data and output temporal embedding features: H t =σ(W f *S t +b f )⊙tanh(W g *S t +b g ); Among them, H t represents the temporal embedding feature of time step t, W f and W g Represent the weights of the gate and main convolution kernel, b f and b g Represents the corresponding bias term, σ represents the Sigmoid activation function, ⊙ represents the point-by-point product, S t represents the input sequence; S64: Input the temporal embedding feature into an autoencoder, compress the high-dimensional temporal embedding feature into a low-dimensional potential representation, and decode the low-dimensional potential representation back into reconstructed data of the original input dimension through a decoder of the autoencoder; S65. Compare the input data sequence and the reconstructed data generated by the autoencoder and calculate the reconstruction error: Where E represents the reconstruction error, t represents the time step, and x i represents the input data sequence, Represents the reconstructed data generated by the autoencoder; S66, comparing the reconstruction error with a preset threshold, and determining that it is abnormal when the reconstruction error is greater than the preset threshold; S67. When an abnormal edge node is detected, an autonomous recovery mechanism is triggered according to the task queue and resource allocation status. The autonomous recovery mechanism includes task migration, resource switching and task reallocation. The task migration means migrating high-priority tasks on the abnormal node to other normal nodes. The resource switching means enabling redundant resource nodes to take over the tasks of the abnormal node. The task reallocation means recalculating the priority of unfinished tasks and reallocating them to suitable resource nodes.

8. An operation and maintenance system for a multi-cloud management platform based on cloud-edge collaboration, executing the operation and maintenance method for a multi-cloud management platform based on cloud-edge collaboration according to any one of claims 1 to 7, characterized in that: include: The cloud management module is used to manage the configuration, distributed deployment and resource association of cloud resources with edge nodes; Edge management module, used to monitor the resource status, task allocation and operation status of edge nodes; The task scheduling module is used to generate matching relationships between tasks and resources based on task characteristics and resource status, and optimize task resource allocation strategies; Edge autonomy module, which is used to implement task priority classification and dynamic policy adjustment of edge nodes by optimizing network and load prediction; Anomaly detection module, which is used to monitor the status of edge nodes in real time, detect anomalies and make relevant adjustments; Resource distribution map generation module, used to generate and synchronize cloud-edge collaborative resource distribution maps and display resource status; The task management module is used to record task lifecycle data, update task status, and support scheduling and recovery operations; The communication interface module is used to realize task data transmission and status feedback between the cloud and edge nodes.

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