Cross-platform network training environment seamless migration and synchronization method

Abstract

The invention provides a cross-platform network practical training environment seamless migration and synchronization method, which specifically comprises the following steps of: packaging a configured practical training environment into a container mirror image at a practical training system management end of teacher equipment, generating an initial snapshot S0 according to the container mirror image, and storing the initial snapshot S0 into a distributed file system; at a practical training system client of the student equipment, after the student equipment is compatible with a practical training environment by utilizing equipment detection and a dynamic adaptation mechanism, carrying out primary loading of an initial snapshot S0 through a distributed file system, and completing practical training environment deployment so as to carry out practical training operation; in the practical training operation, the practical training system records the state change of the practical training environment in real time and generates differential data delta S, and the differential data delta S is synchronized to the teacher equipment or other student equipment through the distributed file system so as to synchronize and update the state of the practical training environment. According to the scheme, the problems of poor equipment compatibility, low synchronization efficiency, difficulty in solving conflicts and the like in cross-platform migration are effectively solved.

Description

Technical Field

[0001] The present invention relates to the fields of educational technology and network computers, and in particular to a method for seamless migration and synchronization of a cross-platform network training environment. Background Art

[0002] Traditional university training courses have complex and diverse requirements for network environment and system platform. Common training environment deployment has the following characteristics and pain points:

[0003] 1. Diversified platform requirements: Students and teachers may use non-operating system devices such as Windows, Linux, and MacOS at the same time, which poses a challenge to the unified deployment of the training environment;

[0004] 2. Dynamic environment changes: Network experiments require frequent configuration adjustments, while the update and migration of existing environments are inefficient;

[0005] 3. Limited resources: Colleges and universities have limited teaching equipment, and frequent reconfiguration of the environment consumes a lot of time and resources;

[0006] 4. High technical threshold: Complex environment deployment and maintenance require a high level of technical skills, which is difficult for ordinary teachers to handle;

[0007] At present, the more common solutions include virtualization technology, container technology and cloud computing, but they all have shortcomings, as follows:

[0008] 1. Traditional virtualization technology: Virtual machines (such as VMware and VirtualBox) provide a way to isolate environments, but the migration operation is complex and requires high resources; the migration efficiency is low, the environment synchronization is cumbersome, and it is difficult to meet real-time requirements;

[0009] 2. Containerization technology: Container technology (such as Docker and Kubernetes) achieves environment migration through lightweight packaging, but large-scale deployment still takes a lot of time; and manual configuration adjustment is required, and there is a lack of automatic adaptation mechanism for heterogeneous devices;

[0010] 3. Cloud solution: Cloud computing can achieve centralized management and unified deployment of the environment, but it relies on network bandwidth and cloud resources. When the network is disconnected or the bandwidth is insufficient, it will cause serious performance problems. It is highly dependent on the network and cannot meet the needs of offline scenarios.

[0011] In view of this, the present invention proposes a method for seamless migration and synchronization of a cross-platform network training environment. Summary of the invention

[0012] The purpose of the present invention is to propose a cross-platform network training environment seamless migration and synchronization method; to detect environmental changes in real time through differential migration technology, and only transmit changed data, thereby greatly improving migration efficiency and reducing bandwidth requirements; to automatically identify the operating system and hardware configuration of the target device through dynamic adaptation technology, and dynamically adjust the environment adaptation layer to ensure the compatibility and consistency of the environment; based on a distributed file system, support multi-user concurrent environment synchronization, and provide a breakpoint resume mechanism to improve system stability.

[0013] To achieve the above purpose, the technical solution of the present invention is: a method for seamless migration and synchronization of a cross-platform network training environment, the method specifically comprises:

[0014] On the training system management end of the teacher's device, the configured training environment is encapsulated into a container image and an initial snapshot S0 is generated based on the container image and stored in a distributed file system;

[0015] On the training system client of the student device, after making the student device compatible with the training environment by using the device detection and dynamic adaptation mechanism, the initial snapshot S0 is initially loaded through the distributed file system to complete the deployment of the training environment for training operations;

[0016] During the training operation, the training system records the changes in the training environment status in real time and generates differential data ΔS, which is synchronized to the teacher's device or other student devices through the distributed file system to synchronize and update the training environment status.

[0017] Preferably, encapsulating the configured training environment into a container image specifically comprises: utilizing container technologies including Docker or Podman to uniformly encapsulate the operating system, software packages, dependencies, and configuration files into the container image.

[0018] Preferably, the container image is built based on a layered file system, with the base image serving as a read-only layer and new configurations and modifications stored as incremental layers.

[0019] Preferably, the device detection and dynamic adaptation mechanism is used to make the student device compatible with the training environment, specifically:

[0020] Detect the operating system type, processor architecture, memory and network bandwidth performance of student devices and generate device capability reports;

[0021] The predefined adaptation strategy table is searched according to the device parameters in the device capability report, and the adaptation layer matching the student device is determined and automatically loaded according to the search result of the adaptation strategy table.

[0022] Preferably, the training system records the changes in the training environment status in real time and generates differential data ΔS, specifically: the training system records the changes in the training environment status in real time, including newly added files and modified configurations, encapsulates them into the incremental layer of the container image, and generates a snapshot S of the training environment status at the change submission time t. t , and uses the differential algorithm DSDIFF to generate a differential patch: ΔS = f(S0, S t ), where f represents the differential data generation function, which is used to generate the differential data ΔS between the initial snapshot S0 and S t .

[0023] Preferably, the differential data ΔS is synchronized to the teacher device or other student devices through a distributed file system for synchronizing and updating the training environment status, specifically: after being compressed, the differential data ΔS is synchronized to the teacher device or other student devices through a distributed file system, and the teacher device or other student devices apply the differential patch ΔS to update the training environment status after decompression: S t+1 = S0 + ΔS.

[0024] Preferably, the compression uses the Brotli algorithm.

[0025] Preferably, the distributed file system uses a Ceph or GlusterFS distributed storage system; the teacher device and student devices are respectively connected to the distributed file system through different nodes, and the initial snapshot S0 of the training environment and the differential data ΔS are cut into multiple small segments and distributed and stored on multiple nodes of the distributed file system.

[0026] Preferably, during the training operation, when multiple users modify the same file simultaneously, that is, when none of the multiple users has completed the system synchronization and update of the file modification, and multiple users modify the exactly same file, the training system adopts the following conflict detection and resolution method:

[0027] Detect modification conflicts based on timestamps: Conflict condition = (T1 < T2 ∧ H(S1) ≠ H(S2)), where T1 and T2 respectively represent the timestamps of the modification operations of different operators, and the timestamp T2 is later than the timestamp T1, and H(S1) and H(S2) respectively represent the file status hash values corresponding to the timestamps T1 and T2; when the training system detects that the modified file corresponding to the timestamp T2 is the same as the modified file corresponding to the timestamp T1, and the file status hash value corresponding to the timestamp T2 is different from the file status hash value corresponding to the timestamp T1, it is determined that a conflict has occurred;

[0028] The training system resolves conflicts according to the preset priority and automatically merges the modifications:

[0029] For unstructured data, the training system uses the diff3 merge tool to automatically merge. When the modifications of two versions occur in different parts of the file, the diff3 merge tool directly completes the automatic merge and generates a merged file containing all the modifications. If the modifications occur in the same location but there is no contradiction (such as the same modification or can be resolved according to the rules), the diff3 tool automatically completes the merge. If the conflict cannot be automatically resolved (such as conflicting modification content), the diff3 tool will mark the conflict and prompt the user to make manual adjustments.

[0030] For structured data, prompt the user to make manual adjustments.

[0031] Preferably, the preset priorities include: priority to users with higher authority levels, wherein the priority of teachers is higher than that of students; priority to operations with later timestamps at the same authority level.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. Cross-platform compatibility and seamless migration capability: The present invention realizes seamless migration across multiple operating systems and devices through dynamic adaptation strategies and containerization technology, solving the problem of poor compatibility between platforms and operating systems in the prior art.

[0034] 2. Efficient data synchronization and incremental update: The use of incremental synchronization and differential data transmission technology reduces the amount of transmitted data, improves synchronization efficiency, and avoids bandwidth waste and transmission delays caused by full synchronization in existing technologies.

[0035] 3. Containerization and tiered storage optimization: Containerization technology makes environment migration more flexible and efficient. The tiered storage strategy reduces storage space usage and optimizes resource consumption during data transmission.

[0036] 4. Intelligent conflict detection and automatic merging: The present invention ensures data consistency and accuracy of version control when synchronizing multiple users and multiple devices through intelligent conflict detection and automatic merging algorithms, avoiding manual intervention and synchronization errors in traditional technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart of a practical training business in one embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of a container packaging process in one embodiment of the present invention;

[0039] Figure 3 Schematic diagram of a distributed storage architecture in one embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following is combined with Figure 1-3, the technical solution of the present invention is specifically described.

[0041] The present invention proposes a method for seamless migration and synchronization of a cross-platform network training environment, and the method is specifically as follows:

[0042] On the training system management end of the teacher's device, the configured training environment is encapsulated into a container image and an initial snapshot S0 is generated based on the container image and stored in a distributed file system;

[0043] On the training system client of the student device, after making the student device compatible with the training environment by using the device detection and dynamic adaptation mechanism, the initial snapshot S0 is initially loaded through the distributed file system to complete the deployment of the training environment for training operations;

[0044] During the training operation, the training system records the changes in the training environment status in real time and generates differential data ΔS, which is synchronized to the teacher's device or other student devices through the distributed file system to synchronize and update the training environment status.

[0045] The overall architecture design of the present invention is composed of five parts: containerized environment encapsulation, multi-platform dynamic adaptation, differential synchronization and real-time update and distributed storage support, and conflict detection and merging, as shown below.

[0046] 1. Containerized environment packaging

[0047] The present invention completely encapsulates the training environment into a lightweight container image to ensure consistency when running on different operating systems or hardware devices:

[0048] refer to Figure 2 The present invention uses container technologies such as Docker or Podman to encapsulate the training environment, and uniformly encapsulates the operating system, software packages, dependencies and configuration files in the container image, ensuring the independence and portability of the environment.

[0049] The container image is built based on a layered file system (UnionFS) to reduce data redundancy through layered storage; the base image (such as Ubuntu, Alpine) is used as a read-only layer, and new configurations and modifications are stored as incremental layers.

[0050] The present invention uses a dynamic adaptation layer to solve multi-platform compatibility issues. For example, on Windows systems, a container operating environment is built with the help of WSL (Windows Subsystem for Linux), and on MacOS, HyperKit is directly called to provide virtualization support.

[0051] 2. Dynamic adaptation to multiple platforms

[0052] Due to the diversity of student devices (such as Windows, MacOS, Linux or mobile devices), traditional training environments often fail to deploy due to incompatibility between devices. The device detection and dynamic adaptation mechanism of the present invention enables the environment to run seamlessly on multiple platforms:

[0053] Detect the operating system type, processor architecture (x86, ARM, etc.), memory and network bandwidth performance of student devices, and generate device capability reports;

[0054] Searching a predefined adaptation strategy table according to the device parameters in the device capability report, and determining and automatically loading an adaptation layer that matches the student device according to the search result of the adaptation strategy table;

[0055] For example, on low-performance devices, a lightweight adaptation layer can be loaded, such as disabling some unnecessary services; on devices that do not support direct container operation, a virtualization engine (such as KVM or Hyper-V) is provided;

[0056] The algorithm flow of the adaptation process is as follows:

[0057] Collect device parameters D = {OS, CPU, RAM, Network}

[0058] According to parameter D, find the predefined adaptation strategy table T: Adaptation layer = T(D)

[0059] According to the result of T(D), the adaptation module is dynamically loaded.

[0060] 3. Differential synchronization and real-time update

[0061] During cross-device synchronization, it is inefficient to fully transmit the entire environment data, especially when the network bandwidth is limited. To this end, the present invention adopts a differential synchronization combined with an incremental update method to achieve efficient data synchronization:

[0062] During the initial synchronization, the system generates an initial snapshot S0 as the baseline snapshot, and S0 is downloaded when the student device is loaded for the first time;

[0063] During the training operation, the training system records the changes in the training environment status in real time, including the addition of new files and configuration changes, encapsulates them into incremental layers of the container image, and generates a snapshot S of the training environment status at the change submission time t. t , use the difference algorithm DSDIFF to generate the difference patch: ΔS = f(S0,S t ), where f represents the differential data generation function, which is used to generate the initial snapshots S0 and S t The differential data ΔS.

[0064] After the differential data ΔS is compressed (using the Brotli algorithm), it is synchronized to the teacher device or other student devices through a distributed file system. After decompression on the teacher device or other student devices, the differential patch ΔS is applied to update the training environment state: S t+1 = S0 + ΔS.

[0065] 4. Distributed storage support

[0066] To achieve cross-device and cross-network environment synchronization, referring to Figure 3 the schematic diagram of the distributed storage architecture, this application introduces a distributed file system (such as Ceph or GlusterFS) to provide consistent data sharing:

[0067] Data sharding: The initial snapshot S0 of the training environment and the differential data ΔS (base image data and incremental data) are cut into multiple small segments and distributed and stored on multiple nodes of the distributed file system; among them, the teacher device and the student devices access the distributed file system through different nodes respectively;

[0068] Redundancy mechanism: Improve data reliability through the replica strategy;

[0069] Real-time synchronization: Each time the state changes, the system ensures the atomicity of the updated data through a distributed transaction.

[0070] 5. Conflict detection and merging

[0071] When multiple users synchronously modify the same file, that is, when any user among the multiple users has not completed the system synchronization and update of the file modification, and multiple users modify the exactly same file, the training system adopts the following conflict detection and resolution methods:

[0072] Detect modification conflicts based on timestamps: Conflict condition = (T1 < T2 ∧ H(S1) ≠ H(S2)), where T1 and T2 respectively represent the timestamps of the modification operations of different operators, and the timestamp T2 is later than the timestamp T1, and H(S1) and H(S2) respectively represent the file state hash values corresponding to the timestamps T1 and T2; when the training system detects that the modified file corresponding to the timestamp T2 is the same as the modified file corresponding to the timestamp T1, and the file state hash value corresponding to the timestamp T2 is different from the file state hash value corresponding to the timestamp T1, it is determined that a conflict has occurred;

[0073] The training system resolves conflicts according to the preset priority and automatically merges the modifications:

[0074] For unstructured data, the training system uses the diff3 merge tool to automatically merge. When the modifications of two versions occur in different parts of the file, the diff3 merge tool directly completes the automatic merge and generates a merged file containing all the modifications. If the modifications occur in the same location but there is no contradiction, the diff3 tool automatically completes the merge. If the conflict cannot be resolved automatically, the diff3 tool will mark the conflict and prompt the user to make manual adjustments.

[0075] For structured data, prompt the user to make manual adjustments.

[0076] The preset priorities include: users with higher authority levels are given priority, among which teachers have a higher priority than students; operations with later timestamps are given priority at the same authority level.

[0077] refer to Figure 1 , a specific practical training business example is provided below:

[0078] (1) Teachers create and publish a training environment. Teachers complete the training environment configuration in the management interface, which mainly includes the following tasks:

[0079] Software environment configuration: Select the operating system image and related applications.

[0080] Network topology settings: define the network connections and access permissions required for the training.

[0081] Permission assignment: Assign access rights to different users, such as read-only, editable, etc.

[0082] The system generates an environment template (container image) and snapshot S0 based on the configuration and stores them in distributed storage.

[0083] (2) Students load and adapt the training environment; after the student device starts the training client, the system performs the following steps:

[0084] Device detection: Detect the operating system, hardware architecture, and network status of student devices.

[0085] Dynamic adaptation: Generate an adaptation layer based on the detection results to make the template environment compatible with the device.

[0086] Initial loading: Download the template environment S0 to the student device and complete the deployment.

[0087] (3) Status recording and synchronization during training;

[0088] During the student's operation, the system records the environment changes (such as new files, configuration modifications, etc.) in real time and generates differential data ΔS;

[0089] The differential data is synchronized to the teacher's device or other student devices through a distributed file system.

[0090] (4) Conflict detection and resolution

[0091] When multiple users modify the same resource at the same time, the system resolves conflicts using the following rules:

[0092] Operations with later timestamps take precedence;

[0093] Users with higher authority levels are given priority (teachers > students);

[0094] Unresolved conflicts are marked and the user is prompted to make manual adjustments.

[0095] (5) Clean up the environment after the training. Teachers can choose to keep or destroy the students’ training environment, and the system will automatically archive important data (such as operation logs).

[0096] In summary, the present invention has the following advantages:

[0097] Through containerization technology and dynamic adaptation strategies, we ensure that the network training environment can be seamlessly migrated and synchronized between multiple operating systems (such as Windows, Linux, macOS) and multiple devices (PC, tablets, mobile phones, etc.); the introduction of device feature parameter collection and dynamic adaptation mechanism solves the problem of inconsistent environmental configuration between multiple devices and platforms.

[0098] Based on the layered storage model (basic image layer + differential data layer), lightweight migration and storage optimization of the environment are achieved; by transmitting only differential data (differential layer), the amount of migrated data is reduced, greatly improving synchronization efficiency.

[0099] Snapshot management and differential computing techniques are used to accurately capture the state changes of the training environment; differential synchronization algorithm (ΔS = f(S0, S t ))Achieve efficient status update and data transmission, significantly reducing resource usage.

[0100] The conflict detection strategy based on the file status hash value ensures the accuracy of data synchronization; the automatic merge strategy supports efficient conflict resolution without user intervention when code files or unstructured data are updated.

[0101] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for seamless migration and synchronization of a cross-platform network training environment, characterized in that: The method is specifically as follows: On the training system management end of the teacher device, encapsulate the configured training environment into a container image and generate an initial snapshot S0 based on the container image, and store it in the distributed file system; On the training system client of the student device, after making the student device compatible with the training environment by using the device detection and dynamic adaptation mechanism, perform the initial loading of the initial snapshot S0 through the distributed file system to complete the deployment of the training environment for training operations; During the training operation, the training system records the changes in the training environment status in real time and generates differential data ΔS. The differential data ΔS is synchronized to the teacher device or other student devices through the distributed file system to synchronize and update the training environment status.

2. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 1, characterized in that: The specific process of encapsulating the configured training environment into a container image is as follows: use container technologies including Docker or Podman to encapsulate the operating system, software packages, dependencies, and configuration files into the container image uniformly.

3. A cross-platform network training environment seamless migration and synchronization method according to claim 1, characterized in that: The container image is built based on a layered file system, with the base image as the read-only layer and the newly added configurations and modifications stored as the incremental layer.

4. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 1, characterized in that: The specific process of making the student device compatible with the training environment by using the device detection and dynamic adaptation mechanism is as follows: Detect the operating system type, processor architecture, memory, and network bandwidth performance of the student device, and generate a device capability report; According to the device parameters in the device capability report, search the predefined adaptation policy table, and determine and automatically load the adaptation layer that matches the student device according to the search result of the adaptation policy table.

5. A cross-platform network training environment seamless migration and synchronization method according to claim 1, characterized in that: The training system records the training environment status changes in real time and generates differential data ΔS. Specifically, the training system records the training environment status changes in real time, including adding new files and modifying configurations, encapsulating them into incremental layers of container images and generating a training environment status snapshot S at the change submission time t. t , use the difference algorithm DSDIFF to generate the difference patch: ΔS = f(S0,S t ), where f represents the differential data generation function, which is used to generate the initial snapshots S0 and S t The differential data ΔS.

6. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 1, characterized in that: The differential data ΔS is synchronized to the teacher's device or other student devices through the distributed file system to synchronize and update the training environment status. Specifically, the differential data ΔS is compressed and synchronized to the teacher's device or other student devices through the distributed file system. The teacher's device or other student device applies the differential patch ΔS to update the training environment status after decompression: S t+1 =S0+ΔS.

7. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 6, characterized in that: The compression uses the Brotli algorithm.

8. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 1, characterized in that: The distributed file system uses a Ceph or GlusterFS distributed storage system; the teacher device and the student device are respectively connected to the distributed file system through different nodes, and the initial snapshot S0 of the training environment and the differential data ΔS are split into multiple small segments and stored on multiple nodes of the distributed file system.

9. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 1, characterized in that: During the training operation, when multiple users modify the same file simultaneously, that is, when none of the multiple users has completed the system synchronization and update of the file modification and all multiple users modify the exactly same file, the training system adopts the following conflict detection and resolution method: Detect modification conflicts based on timestamps: Conflict condition = (T1 < T2 ∧ H(S1) ≠ H(S2)), where T1 and T2 respectively represent the timestamps of the modification operations of different operators, and the timestamp T2 is later than the timestamp T1, and H(S1) and H(S2) respectively represent the file status hash values corresponding to the timestamps T1 and T2; when the training system detects that the modified file corresponding to the timestamp T2 is the same as the modified file corresponding to the timestamp T1, and the file status hash value corresponding to the timestamp T2 is different from the file status hash value corresponding to the timestamp T1, it is determined that a conflict has occurred; The training system resolves conflicts according to the preset priority and performs automatic merging of the modifications: For unstructured data, the training system uses the diff3 merge tool for automatic merging; when the modifications of two versions occur in different parts of the file, the diff3 merge tool directly completes the automatic merging and generates a merged file containing all the modifications. If the modifications occur in the same location but there is no conflict, the diff3 tool will automatically complete the merge; if the conflict cannot be resolved automatically, the diff3 tool will mark the conflict and prompt the user to make manual adjustments; For structured data, prompt the user to make manual adjustments.

10. A method for seamless migration and synchronization of a cross-platform network training environment according to claim 9, characterized in that: The preset priorities include: users with higher authority levels are given priority, among which teachers have a higher priority than students; operations with later timestamps are given priority at the same authority level.

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