AI-driven elastic synchronization method and system for overseas cloud mobile phone

Abstract

The embodiment of the invention provides an overseas cloud mobile phone AI-driven elastic synchronization method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: collecting a user operation sequence and transnational network link quality data; predicting a session interruption risk value R by using an AI model in which GNN and Transform are fused; when R exceeds a threshold value, selecting an optimal target node from the compliance node list to trigger hierarchical elastic scheduling; layering state changes according to high and low priorities through a semantic-level differentiated state synchronization engine, and performing incremental coding on high-priority operation to generate minimum instruction set synchronization; a data compliance strategy is automatically executed during cross-border transmission; and the global strong consistency of the metadata is guaranteed by adopting a Raft protocol optimized by a wide area network. Through the scheme disclosed by the invention, the problems of high delay, easy interruption and data compliance of overseas cloud mobile phones in a complex network environment are solved, and high continuity, low delay synchronization and reliable operation of sessions are realized.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to an AI-driven elastic synchronization method and system for overseas cloud phones. Background Technology

[0002] Currently, existing cloud phone synchronization solutions generally face the following technical challenges when applied across regions:

[0003] Cross-domain network latency: Traditional solutions rely on fixed central nodes or simple regional clusters, leading to significantly increased network latency during cross-border access, causing lag in cloud phone operations and impacting user experience; Inefficient synchronization and performance bottlenecks: Existing technologies mainly employ periodic full or incremental synchronization, transmitting large amounts of redundant data, which is inefficient in high-concurrency and low-bandwidth environments. Traditional consistency protocols in wide area network (WAN) environments struggle to meet real-time synchronization requirements due to their convergence speed and communication overhead; Data compliance and risks: Global data privacy regulations (such as GDPR and CCPA) are becoming increasingly stringent. Existing systems lack automated compliance review mechanisms, making it difficult to ensure legal compliance during cross-border data transmission, posing significant legal and operational risks.

[0004] It is evident that there is an urgent need for a flexible synchronization method that can provide seamless, efficient, secure, and compliant AI-driven cloud phone services for users in different regions. Summary of the Invention

[0005] In view of this, the present disclosure provides an AI-driven elastic synchronization method and system for overseas cloud phones, which at least partially solves some of the problems existing in the prior art.

[0006] In a first aspect, embodiments of this disclosure provide an AI-driven elastic synchronization method for overseas cloud phones, including:

[0007] Step 1: Collect real-time quality data of user operation sequences and cross-border network links;

[0008] Step 2: Using an AI model based on a fusion architecture of graph neural network (GNN) and Transformer, process user operation sequences and real-time quality data to predict the interruption risk value R of the user session;

[0009] Step 3: If the interruption risk value R exceeds the preset threshold, then in the candidate node list, hard constraints are applied to filter based on data sovereignty and compliance policies, and the optimal target node is selected from the compliant nodes to trigger hierarchical elastic scheduling.

[0010] Step 4: When user operations cause changes in the cloud phone's state, the cloud phone's state is semantically structured into high-priority and low-priority layers. For state changes in the high-priority layer, incremental encoding is performed based on the optimized remote desktop protocol to generate a minimal instruction set for synchronization.

[0011] Step 5: When conducting cross-border data transfer, automatically execute predefined compliance policies based on data sovereignty labels and destination regulations;

[0012] Step 6: Utilize the Raft strong consistency protocol optimized for wide area networks to achieve global strong consistency of session state metadata among all cross-border cloud nodes.

[0013] According to a specific implementation of an embodiment of this disclosure, the hierarchical elastic scheduling includes:

[0014] When the interruption risk value R reaches the first risk level, data preloading to the predicted low-risk node is triggered;

[0015] When the interruption risk value R reaches the second risk level, which is higher than the first risk level, a millisecond-level seamless hot migration to the optimal target node is triggered.

[0016] According to a specific implementation of this disclosure, the step of semantically structuring the cloud phone status according to high-priority and low-priority layers includes:

[0017] The high-priority layer is defined to include core UI components that directly impact the user interaction experience and immediate action response commands;

[0018] The low-priority layer is defined to include background application status and non-real-time synchronized data;

[0019] A semantically differentiated coding strategy is used for state synchronization of low-priority layers.

[0020] According to one specific implementation of this disclosure, the compliance strategy includes at least one of end-to-end encryption, zero-knowledge proof, and routing data to neutral country nodes for compliance relay.

[0021] According to a specific implementation of an embodiment of this disclosure, step 6 specifically includes:

[0022] By sharing the consensus pressure of the central node through edge arbitration nodes and implementing a read-write separation mechanism, consensus performance can be optimized in cross-border high-latency environments.

[0023] Secondly, embodiments of this disclosure provide an AI-driven elastic synchronization system for overseas cloud phones, comprising:

[0024] A cross-domain session interruption risk prediction and elastic scheduling system is used to predict the interruption risk value R through an AI model and trigger elastic scheduling.

[0025] A semantic-level differential state synchronization engine is used to structure the state of cloud phones and generate a minimal synchronization instruction set;

[0026] A data sovereignty and compliance automation engine is used to dynamically filter nodes based on a global regulatory database and automatically execute compliance policies.

[0027] The Raft strong consistency protocol module optimized for wide area networks is used to ensure global strong consistency of session state metadata.

[0028] According to one specific implementation of this disclosure, the AI ​​model is a fusion architecture of GNN and Transformer;

[0029] The GNN is used to model the topology and state relationships of cross-border network links;

[0030] The Transformer is used for temporal modeling and attention analysis of user operation sequences.

[0031] According to a specific implementation of an embodiment of this disclosure, the data sovereignty and compliance automation engine includes:

[0032] A global regulatory database that stores data sovereignty laws and regulations from different countries and regions;

[0033] The dynamic filtering module is used to match the geographical location of candidate migration nodes with data sovereignty tags and the global regulatory database to filter out non-compliant nodes.

[0034] The policy execution module is used to automatically invoke encryption or routing relay policies during data transmission.

[0035] According to a specific implementation of an embodiment of this disclosure, the semantic-level differentiated state synchronization engine includes:

[0036] The semantic analysis module is used to identify the semantics of user operation commands and classify them into high-priority or low-priority layers.

[0037] The incremental encoding module is used to optimize the encoding of high-priority layer operation instructions based on VNC or RDP protocols, generating incremental instruction sets.

[0038] The AI-driven elastic synchronization scheme for overseas cloud phones in this embodiment includes: Step 1, collecting user operation sequences and real-time quality data of cross-border network links; Step 2, using an AI model based on a graph neural network (GNN) and Transformer fusion architecture to process user operation sequences and real-time quality data, and predicting the interruption risk value R of the user session; Step 3, if the interruption risk value R exceeds a preset threshold, then in the candidate node list, hard constraints are applied to filter based on data sovereignty and compliance policies, and the optimal target node is selected from the compliant nodes to trigger hierarchical elastic scheduling; Step 4, when user operations cause changes in the cloud phone state, the cloud phone state is semantically structured into high-priority and low-priority layers, and for high-priority layer state changes, incremental encoding is performed based on an optimized remote desktop protocol to generate a minimized instruction set for synchronization; Step 5, during cross-border data transmission, predefined compliance policies are automatically executed based on data sovereignty labels and destination regulations; Step 6, using a Raft strong consistency protocol optimized for wide area networks, global strong consistency of session state metadata is achieved among all cross-border cloud nodes.

[0039] The beneficial effects of this disclosure are as follows: Through the solution of this disclosure, the risk of cross-border network interruption is accurately predicted by an AI model based on the fusion architecture of GNN and Transformer, and a hierarchical elastic scheduling is triggered accordingly. Combined with the semantic-level differentiated state synchronization engine, high-priority operations are incrementally encoded and transmitted, which greatly reduces synchronization latency and data volume. At the same time, the data sovereignty and compliance automation engine ensures the legality and compliance of cross-border data transmission by dynamically filtering nodes and automatically executing encryption strategies. Finally, the Raft protocol optimized for wide area networks ensures strong consistency of global data, thereby achieving high continuity, low latency synchronization and compliant and reliable operation of cloud mobile phone sessions in complex and ever-changing overseas network environments. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating an AI-driven elastic synchronization method for overseas cloud phones provided in this embodiment of the disclosure;

[0042] Figure 2 This is a schematic diagram illustrating the specific implementation process of an AI-driven elastic synchronization method for overseas cloud phones provided in this embodiment of the disclosure. Detailed Implementation

[0043] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0044] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0045] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0046] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0047] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0048] This disclosure provides an AI-driven elastic synchronization method for overseas cloud phones, which can be applied to the cloud phone synchronization process in Internet scenarios.

[0049] See Figure 1 This is a flowchart illustrating an AI-driven elastic synchronization method for overseas cloud phones provided in this embodiment of the disclosure. Figure 1 As shown, the method mainly includes the following steps:

[0050] Step 1: Collect real-time quality data of user operation sequences and cross-border network links;

[0051] Step 2: Using an AI model based on a fusion architecture of graph neural network (GNN) and Transformer, process user operation sequences and real-time quality data to predict the interruption risk value R of the user session;

[0052] Step 3: If the interruption risk value R exceeds the preset threshold, then in the candidate node list, hard constraints are applied to filter based on data sovereignty and compliance policies, and the optimal target node is selected from the compliant nodes to trigger hierarchical elastic scheduling.

[0053] Step 4: When user operations cause changes in the cloud phone's state, the cloud phone's state is semantically structured into high-priority and low-priority layers. For state changes in the high-priority layer, incremental encoding is performed based on the optimized remote desktop protocol to generate a minimal instruction set for synchronization.

[0054] Step 5: When conducting cross-border data transfer, automatically execute predefined compliance policies based on data sovereignty labels and destination regulations;

[0055] Step 6: Utilize the Raft strong consistency protocol optimized for wide area networks to achieve global strong consistency of session state metadata among all cross-border cloud nodes.

[0056] In practice, the system first continuously collects operation events (such as touch clicks and swipe trajectories) from user clients and real-time performance data (such as latency, jitter, and packet loss rate) of cross-border network links. This data is then sent to a central management node. Subsequently, an AI model based on a fusion architecture of GNN and Transformer, deployed on this node, executes subsequent operation processes. The GNN component models each node and link in the network as a graph structure, analyzing its topological relationships and state associations; the Transformer component encodes the user operation sequence, capturing its temporal characteristics and dependencies. The model outputs a quantified outage risk value R.

[0057] When the risk value R exceeds a preset threshold (e.g., 0.7), the elastic scheduler is triggered. It first invokes the compliance engine, providing a list of candidate nodes containing information such as geographic location and carrier. The compliance engine dynamically filters out non-compliant nodes (e.g., nodes that do not allow EU data to leave the country) from the list based on data sovereignty labels (e.g., "EU user data") and destination regulations. The scheduler then selects the optimal target node from the remaining compliant nodes, taking into account both network quality and computing resources.

[0058] Next, when user actions trigger changes in the cloud phone's state, the semantic synchronization engine distinguishes the type of change. For high-priority UI refreshes or immediate response operations, the engine compares them with the previous frame, performs incremental encoding using an optimized VNC protocol, and transmits only the differential instruction set of the changed portion, rather than the entire screen image. Simultaneously, any data packets requiring cross-border transmission are processed by the compliance engine, automatically applying end-to-end encryption according to policy. Finally, all overseas nodes participating in the synchronization synchronize session metadata (such as connection state and resource locks) through an optimized Raft protocol cluster. This protocol alleviates the slow consensus speed caused by high latency over wide area networks by introducing edge arbitration nodes and separating read and write operations, thereby ensuring strong global consistency of the entire system state.

[0059] The AI-driven elastic synchronization method for overseas cloud phones provided in this embodiment accurately predicts cross-border network outage risks through an AI model based on a fusion architecture of GNN and Transformer, and triggers hierarchical elastic scheduling accordingly. Combined with a semantic-level differentiated state synchronization engine, high-priority operations are incrementally encoded and transmitted, significantly reducing synchronization latency and data volume. At the same time, the data sovereignty and compliance automation engine ensures the legality and compliance of cross-border data transmission by dynamically filtering nodes and automatically executing encryption strategies. Finally, relying on the Raft protocol optimized for wide area networks, strong global data consistency is guaranteed, thereby achieving high continuity, low-latency synchronization, and compliant and reliable operation of cloud phone sessions in complex and ever-changing overseas network environments.

[0060] Based on the above embodiments, the hierarchical elastic scheduling includes:

[0061] When the interruption risk value R reaches the first risk level, data preloading to the predicted low-risk node is triggered;

[0062] When the interruption risk value R reaches the second risk level, which is higher than the first risk level, a millisecond-level seamless hot migration to the optimal target node is triggered.

[0063] For example, two risk thresholds can be set: = 0.5 (medium risk) and = 0.8 (High Risk). When the risk value R predicted by the AI ​​model first exceeds... But it did not reach the target. At this time, the system performs the first level of scheduling: data preloading. The system identifies the application the user is currently using and its related data, and asynchronously and silently pushes this data to a predicted low-risk backup node in the background for unforeseen circumstances. This does not interrupt the user's current session.

[0064] When the risk value R further climbs and exceeds Upon arrival, the system immediately triggers the second level of scheduling: seamless hot migration of the session. The system quickly selects the best target node from the list of compliant nodes, and then migrates the user's current complete session state (including memory image, CPU state, and network connection) through a high-speed network. The entire migration process is completed within milliseconds. Users may only perceive a very brief delay in operation or a slight decrease in image quality, but there will be no noticeable interruption of connection or application restart, achieving a "seamless" experience.

[0065] Furthermore, the step of semantically structuring the cloud phone status according to high-priority and low-priority layers includes:

[0066] The high-priority layer is defined to include core UI components that directly impact the user interaction experience and immediate action response commands;

[0067] The low-priority layer is defined to include background application status and non-real-time synchronized data;

[0068] A semantically differentiated coding strategy is used for state synchronization of low-priority layers.

[0069] In practice, the system can divide the state of the cloud phone into two layers. The high-priority layer directly affects the smoothness of the user experience, including: the rendering state of the UI components of the currently foreground application, user input response commands (such as clicks and typing), and audio and video streams. The low-priority layer includes: cached data of background applications, downloaded but unviewed files, system logs, etc.

[0070] For high-priority state changes, the synchronization engine employs incremental encoding, which is crucial for real-time performance. For example, if a user simply enters a single character in an input box, the engine recognizes this as a "text update" semantic operation and generates a concise command (such as `UpdateTextWidget(ID=123, Value="A")`) for synchronization, instead of transmitting the entire screenshot. For low-priority states, such as when a background application updates its internal data, the engine uses a semantically differentiated encoding strategy. This might involve batch, compressed full data synchronization during periods of network idle, or synchronizing only a change notification and fetching the latest data when the user switches to the application.

[0071] Based on the above embodiments, the compliance strategy includes at least one of end-to-end encryption, zero-knowledge proof, and routing data to neutral country nodes for compliance relay.

[0072] In practice, suppose a cloud phone node located in country A needs to transmit data containing user personal information to an analytics node located in country B. Before transmission, a compliance automation engine intervenes. First, it queries a global regulatory database to confirm whether the data transmission complies with relevant data export regulations in Singapore and the United States. Based on a predefined strategy, the engine decides to implement an "end-to-end encryption" strategy. It calls an encryption service and uses a key possessed only by the sender and receiver to encrypt the data packet, ensuring that no intermediate node during transmission can decrypt the data content. For some highly sensitive operations, the strategy may require the use of "zero-knowledge proof" technology, allowing one party to prove to another that it knows a secret value without revealing the value itself. Furthermore, if the engine determines that direct transmission poses a compliance risk, it will automatically initiate a "routing to a neutral country node" strategy. For example, the data is first encrypted and sent to a compliant relay node in country C, which then forwards it to the final destination, leveraging country C's neutral status to circumvent certain data sovereignty conflicts.

[0073] Based on the above embodiments, step 6 specifically includes:

[0074] By sharing the consensus pressure of the central node through edge arbitration nodes and implementing a read-write separation mechanism, consensus performance can be optimized in cross-border high-latency environments.

[0075] In practical implementation, in the traditional Raft protocol, all write requests must be processed by the Leader node and synchronized to the majority of nodes. In cross-border high-latency environments, this leads to extremely high write latency. The optimization strategy of this solution is as follows: First, an "edge arbitration" mechanism is introduced. Multiple lightweight arbiter nodes are deployed in multiple regions such as North America, Europe, and Asia. These arbiter nodes do not store the complete state; they only participate in voting. When a node in a certain region needs to commit logs, it can quickly obtain majority approval from geographically nearest arbiter nodes, greatly reducing the latency of cross-oceanic network voting. Second, a "read-write separation" mechanism is adopted. Follower nodes are allowed to directly handle client read requests without forwarding them to the Leader node. This reduces the load on the Leader node and allows users to read data from the nearest node, significantly reducing read operation latency and thus improving overall consensus efficiency.

[0076] Corresponding to the above method embodiments, this disclosure also provides an AI-driven elastic synchronization system for overseas cloud phones, including:

[0077] A cross-domain session interruption risk prediction and elastic scheduling system is used to predict the interruption risk value R through an AI model and trigger elastic scheduling.

[0078] A semantic-level differential state synchronization engine is used to structure the state of cloud phones and generate a minimal synchronization instruction set;

[0079] A data sovereignty and compliance automation engine is used to dynamically filter nodes based on a global regulatory database and automatically execute compliance policies.

[0080] The Raft strong consistency protocol module optimized for wide area networks is used to ensure global strong consistency of session state metadata.

[0081] Based on the above embodiments, the AI ​​model is a fusion architecture of GNN and Transformer;

[0082] The GNN is used to model the topology and state relationships of cross-border network links;

[0083] The Transformer is used for temporal modeling and attention analysis of user operation sequences.

[0084] Furthermore, the data sovereignty and compliance automation engine includes:

[0085] A global regulatory database that stores data sovereignty laws and regulations from different countries and regions;

[0086] The dynamic filtering module is used to match the geographical location of candidate migration nodes with data sovereignty tags and the global regulatory database to filter out non-compliant nodes.

[0087] The policy execution module is used to automatically invoke encryption or routing relay policies during data transmission.

[0088] Based on the above embodiments, the semantic-level differentiated state synchronization engine includes:

[0089] The semantic analysis module is used to identify the semantics of user operation commands and classify them into high-priority or low-priority layers.

[0090] The incremental encoding module is used to optimize the encoding of high-priority layer operation instructions based on VNC or RDP protocols, generating incremental instruction sets.

[0091] The AI-driven elastic synchronization system for overseas cloud phones in this embodiment can execute the content described in the above method embodiments. This system is deployed on a cluster consisting of multiple overseas data center nodes. The cross-domain session interruption risk prediction and elastic scheduling system runs as an independent microservice on the central management node, and it has a built-in AI model inference engine. The semantic-level differentiated state synchronization engine runs as a kernel module or user-space daemon on the host machine of each cloud phone instance, intercepting and processing graphical commands in real time. The data sovereignty and compliance automation engine is also deployed as a centralized service, possessing a highly available global regulatory database. The WAN-optimized Raft strong consistency protocol module is integrated as a library into the central management service and the state management services of each node, responsible for maintaining metadata consensus. These components collaborate via RPC calls and message communication through a high-speed internal network to jointly complete the elastic synchronization task.

[0092] The data processing flow of the AI ​​model in the GNN and Transformer fusion architecture during training and inference is as follows: The input to the GNN component is a dynamic network topology graph, where nodes represent cloud mobile servers or network gateways, edges represent links between them, and edge weights contain real-time network metrics. The GNN aggregates neighbor information through graph convolution operations, learning the latent representation of each node in its network context. The input to the Transformer component is a sequence of user actions, which calculates the importance of each action relative to other actions in the sequence through a self-attention mechanism, and outputs a feature vector containing the temporal context. Finally, the network state representation output by the GNN and the user behavior representation output by the Transformer are fused (e.g., through concatenation or attention weighting), input into a fully connected network for computation, and finally output the interruption risk value R.

[0093] The global regulatory database is stored using a relational database. Its table structure includes fields such as "Country / Region," "Regulatory Type," "Data Restriction Clauses," "Encryption Requirements," and "Effective Date," and is scheduled to fetch updates from official channels. The dynamic filtering module, acting as a policy decision point (PDP), works as follows: it receives scheduling requests (containing data tags and target node regions), queries the database, matches relevant regulatory clauses, and if the regulations of the target node region allow the data with that tag to enter the country, it adds the node to the compliance list; otherwise, it filters it. The policy enforcement module, acting as a policy enforcement point (PEP), is deployed at the data egress gateway. Based on the PDP's decision, it invokes the corresponding encryption algorithm library or routing rule table to enforce the policy on incoming data packets.

[0094] The semantic analysis module is integrated into the cloud phone's graphics rendering engine. It monitors the frame buffer and input events. It uses a predefined rule base (e.g., identifying continuous pixel changes accompanied by mouse movement events classifies it as "drag" semantics; identifying specific texture updates within a text input box classifies it as "text input") to classify and label operations in real time. The incremental encoding module then selects an encoding strategy based on the semantic tags. For operations classified as high-priority, it utilizes Hextile or Tight encoders in the VNC protocol to encode and transmit only the rectangular areas on the screen that have changed, thereby generating the incremental instruction set and minimizing data volume.

[0095] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0096] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A flexible synchronization method driven by AI for overseas cloud phones, characterized in that, include: Step 1: Collect real-time quality data of user operation sequences and cross-border network links; Step 2: Using an AI model based on a fusion architecture of graph neural network (GNN) and Transformer, process user operation sequences and real-time quality data to predict the interruption risk value R of the user session; Step 3: If the interruption risk value R exceeds the preset threshold, then in the candidate node list, hard constraints are applied to filter based on data sovereignty and compliance policies, and the optimal target node is selected from the compliant nodes to trigger hierarchical elastic scheduling. Step 4: When user operations cause changes in the cloud phone's state, the cloud phone's state is semantically structured into high-priority and low-priority layers. For state changes in the high-priority layer, incremental encoding is performed based on the optimized remote desktop protocol to generate a minimal instruction set for synchronization. Step 5: When conducting cross-border data transfer, automatically execute predefined compliance policies based on data sovereignty labels and destination regulations; Step 6: Utilize the Raft strong consistency protocol optimized for wide area networks to achieve global strong consistency of session state metadata among all cross-border cloud nodes.

2. The method according to claim 1, characterized in that, The hierarchical elastic scheduling includes: When the interruption risk value R reaches the first risk level, data preloading to the predicted low-risk node is triggered; When the interruption risk value R reaches the second risk level, which is higher than the first risk level, a millisecond-level seamless hot migration to the optimal target node is triggered.

3. The method according to claim 2, characterized in that, The step of semantically structuring the cloud phone status according to high-priority and low-priority layers includes: The high-priority layer is defined to include core UI components that directly impact the user interaction experience and immediate action response commands; The low-priority layer is defined to include background application status and non-real-time synchronized data; A semantically differentiated coding strategy is used for state synchronization of low-priority layers.

4. The method according to claim 3, characterized in that, The compliance strategy includes at least one of end-to-end encryption, zero-knowledge proofs, and routing data to neutral country nodes for compliance relay.

5. The method according to claim 4, characterized in that, Step 6 specifically includes: By sharing the consensus pressure of the central node through edge arbitration nodes and implementing a read-write separation mechanism, consensus performance can be optimized in cross-border high-latency environments.

6. An AI-driven elastic synchronization system for overseas cloud phones, used to execute the AI-driven elastic synchronization method for overseas cloud phones as described in any one of claims 1 to 5, characterized in that, include: A cross-domain session interruption risk prediction and elastic scheduling system is used to predict the interruption risk value R through an AI model and trigger elastic scheduling. A semantic-level differential state synchronization engine is used to structure the state of cloud phones and generate a minimal synchronization instruction set; A data sovereignty and compliance automation engine is used to dynamically filter nodes based on a global regulatory database and automatically execute compliance policies. The Raft strong consistency protocol module optimized for wide area networks is used to ensure global strong consistency of session state metadata.

7. The system according to claim 6, characterized in that, The AI ​​model is a fusion architecture of GNN and Transformer; The GNN is used to model the topology and state relationships of cross-border network links; The Transformer is used for temporal modeling and attention analysis of user operation sequences.

8. The system according to claim 6, characterized in that, The data sovereignty and compliance automation engine includes: A global regulatory database that stores data sovereignty laws and regulations from different countries and regions; The dynamic filtering module is used to match the geographical location of candidate migration nodes with data sovereignty tags and the global regulatory database to filter out non-compliant nodes. The policy execution module is used to automatically invoke encryption or routing relay policies during data transmission.

9. The system according to claim 6, characterized in that, The semantic-level differential state synchronization engine includes: The semantic analysis module is used to identify the semantics of user operation commands and classify them into high-priority or low-priority layers. The incremental encoding module is used to optimize the encoding of high-priority layer operation instructions based on VNC or RDP protocols, generating incremental instruction sets.

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