Physical experiment teaching platform based on cloud desktop

Abstract

The invention relates to the technical field of physical experiment teaching, in particular to a physical experiment teaching platform based on a cloud desktop, which comprises an infrastructure layer, a platform service layer, an application function layer and a user access layer. According to the physical experiment teaching platform based on the cloud desktop, decoupling of bottom layer resources, platform services, application functions and user access is achieved through a four-layer architecture design, the resource utilization rate and experiment performance are improved through the virtualization technology, meanwhile, data access efficiency can be optimized through SSD cache acceleration and access frequency layering, and the data access efficiency is improved. A three-network separation strategy is combined with a VXLAN tunnel and SR-IOV technology, delay is reduced so as to improve experiment interaction fluency, through an AR auxiliary operation unit and a 3D experiment operation table rendering engine, superposition of equipment installation guidance and parameter setting is realized, experiment operation intuition is improved, multi-user experiment table sharing and remote labeling are supported, resource demands are predicted through AI, and experiment efficiency is improved. Idle nodes are automatically closed, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of physics experiment teaching, and in particular to a physics experiment teaching platform based on a cloud desktop. Background Art

[0002] The physics experiment teaching platform combined with cloud desktop technology is an information platform that deeply integrates cloud computing virtualization technology with physics experiment teaching. Its core is to virtualize the experimental environment through cloud servers, and students and teachers remotely access the virtual desktop through terminal devices to complete experimental operations, data analysis and collaborative learning, thereby effectively solving the problems of low resource utilization and limited experimental types in traditional experimental teaching, and can effectively improve teaching efficiency and teaching experience.

[0003] After searching, it was found that according to the invention patent with Chinese patent publication number CN112256374A, a university physics experiment teaching platform based on cloud desktop is disclosed. The university physics experiment teaching platform in this invention patent is designed to include a data storage module, an experimental teaching module, an experimental course management module, an experimental equipment module, an experimental problem solving module, an experimental report completion module and an experimental performance module, thereby diversifying the experimental content and effectively improving students' learning enthusiasm.

[0004] However, the university physics experiment teaching platform based on cloud desktop only includes three levels: experimental teaching platform, teacher side and student side. The technical architecture is relatively simple and the module functions are relatively basic. It lacks hierarchical design, and further improvement is needed in the underlying resource scheduling and network optimization interaction. Therefore, a physics experiment teaching platform based on cloud desktop is proposed. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a cloud desktop-based physics experiment teaching platform with a layered architecture design, which realizes resource scheduling optimization and focuses on the teaching interactive experience. It solves the problem in the above-mentioned background technology that the existing university physics experiment teaching platform relies on basic functional modules and cannot further realize resource scheduling and network optimization interactive operations.

[0007] (2) Technical solution

[0008] To achieve the above-mentioned layered architecture design, optimize resource scheduling, and focus on the interactive teaching experience, the present invention provides the following technical solutions: a cloud desktop-based physics experiment teaching platform, including an infrastructure layer that integrates servers, storage devices, and network devices into a unified resource pool. Through KVM virtualization technology, physical resources are converted into flexibly allocated virtual resources to achieve three-network separation, multi-copy storage, and automatic fault migration.

[0009] The platform service layer intelligently allocates virtual machine resources and dynamically selects the optimal access mode based on terminal type, implementing zero-trust authentication and fine-grained permission control, automatically scaling resources based on AI prediction, and energy-saving hibernation processing for idle nodes.

[0010] The application function layer completes course scheduling, equipment reservation, and performance evaluation operations, and provides a digital twin laboratory to implement AR operation guidance, AI Q&A, and collaborative experimental space support;

[0011] The user access layer optimizes access based on terminal type and unifies the workspace, enabling immersive interaction through AR device guidance, multi-screen collaboration, and voice control.

[0012] Preferably, the infrastructure layer includes a virtualized server resource pool, a distributed storage module, and a network switching module. The virtualized server resource pool includes a physical server cluster, a virtualization program management unit, and a resource scheduling engine. The specific steps include:

[0013] 1) Resource Receiving Phase: a. The virtualization program management unit uses KVM virtualization technology to receive a virtual machine creation request from the platform service layer;

[0014] b. The resource scheduling engine implements intelligent allocation based on OpenStack Nova. It selects physical nodes that meet the requirements based on the CPU and GPU nodes stored in the physical server cluster. The selection condition is set to memory utilization less than 70%.

[0015] c. Select the node with the least load as the target node based on the weighted score and create a virtual machine on the target node;

[0016] 2) Virtualization implementation phase: CPU virtualization: a. Divide the physical core into vCPUs using Intel VT-x / AMD-V technology to implement CPU virtualization processing;

[0017] b. Divide the physical GPU card into four vGPU instances, install the vGPU driver for the virtual machine, and establish a hardware passthrough channel to implement GPU virtualization processing;

[0018] c. Use transparent huge page (THP) technology to reduce address translation overhead for memory allocation;

[0019] 3) Real-time optimization phase: Automatically adjust resources by monitoring the experimental load, and migrate VMs to healthy nodes according to the hot migration mechanism when a physical server fails.

[0020] Preferably, the distributed storage module includes a metadata server, an object storage node, an SSD cache acceleration layer, and a storage network, and the specific steps include:

[0021] 1) Image loading: Receives the startup command from the virtual machine, locates the image replica through the metadata server, reads the image file from the three-replica storage area, selects the OSD node with the lowest network latency, and transmits the image data through the storage network;

[0022] 2) Data writing: Data is collected in real time and written to the SSD cache layer. The cached data is transferred to the EC coding area using the 8+3 erasure code strategy.

[0023] 3) Set access frequency for cache tiering and preload data based on experiment type, including:

[0024] When the access frequency is greater than 50 times / min, the data is retained in the SSD layer; when the access frequency is less than 5 times / min, the data is moved to the HDD layer;

[0025] The network switching module includes a desktop access switch, a storage dedicated switch, a management network switch and an SDN controller. The specific steps include:

[0026] 1) Three-network traffic separation strategy: a. For the desktop network, prioritize video streaming to ensure screen image and interactive command operations; b. For the storage network, maximize bandwidth to ensure virtual machine storage data operations; c. For the management network, implement high-reliability transmission to ensure health monitoring and migration command operations;

[0027] 2) Experimental Interaction: The user terminal sends mouse trajectory data to the desktop interactive machine, which forwards the trajectory data to the +VM server via a VXLAN tunnel. The physical GPU then performs rendering calculations and returns H.265 encoded video frames to the desktop switch, which decodes the experimental images and displays them on the user terminal.

[0028] 3) Use SR-IOV technology to virtualize the physical network card into multiple VFs, and mount the VFs to the virtual machine to achieve μs-level delay data collection process.

[0029] Preferably, the platform service layer includes a virtualization management module, a cloud desktop delivery module, and a security authentication module. The virtualization management module includes a main management server cluster, a Libvirt API interface layer, a resource monitoring unit, and a virtual machine lifecycle management unit. The specific steps include:

[0030] 1) Abstract physical server resources into logical resource pools through the Libvirt API, connect to KVM hosts, and detect GPU devices to build a dynamic resource view;

[0031] 2) Receive request instructions from the application function layer, analyze the request instructions and perform intelligent scheduling, including:

[0032] a. Experiments are divided into basic experiments and GPU experiments according to the experiment type. General computing nodes are used for basic experiments.

[0033] b. For the GPU experiment, select idle vGPU nodes and select the node with the highest load score. The scheduling algorithm is expressed as follows:

[0034]

[0035] c. When the CPU usage of the experimental virtual machine is continuously greater than 80%, the vCPU expansion is triggered, and a failover is performed after the physical node is detected to be down.

[0036] Preferably, the cloud desktop delivery module includes a connection agent unit, a multi-architecture engine, a protocol optimization unit and a user configuration database, and the specific steps include:

[0037] 1) Execute the connection agent and establish a session when the information is logged in, and select the delivery mode according to the terminal type;

[0038] 2) When the experimental data contains real-time video streams, H.265 hard encoding and dynamic bit rate processing are enabled. When the experimental data mainly consists of text operations, lossless compression and instruction transmission processing are enabled;

[0039] 3) Realize cross-terminal synchronization through unified workspace services;

[0040] The security authentication module includes a secure access gateway, an RBAC authority center, a behavior audit database, and a two-factor authentication unit. The specific steps include:

[0041] 1) Zero Trust Access Control: The secure access gateway submits the device fingerprint to the zero trust engine to verify the user's identity. The zero trust engine queries the access policy in the policy center, which returns a dynamic permission token to the secure access gateway and establishes an encrypted channel through the connection agent.

[0042] 2) Build a role-based permission model, refine permission management based on different roles, and record key events to implement full-link audit processing.

[0043] Preferably, the application function layer includes an experimental teaching management system, a virtual experimental environment and an interactive support system. The experimental teaching management system includes a course scheduling engine, an elastic image management subsystem, an experimental equipment virtualization unit, and a performance evaluation center. The specific steps include:

[0044] 1) Log in to the experimental teaching management system, generate new experimental courses based on the course arrangement engine, and divide the experiments into basic experiments and innovative experiments according to the experiment type;

[0045] 2) For basic experiments, call preset templates to automatically match resource requirements; for innovative experiments, customize parameters, set GPU and sensor requirements, and automatically allocate virtual machine specifications;

[0046] 3) Construct hierarchical modules at the foundation, course, and personal levels;

[0047] 4) Real-time data collection of physical experimental equipment, generating visual waveforms through digital signal processing, and realizing equipment virtualization processing;

[0048] The virtual experiment environment includes an interdisciplinary experiment suite, a real-time data acquisition engine, a collaborative experiment space, and an experiment sandbox system. The specific steps include:

[0049] 1) Set experimental parameters in a virtual environment, output data structure through the physical engine, and display data distribution curves in real time after collection;

[0050] 2) Establish a safety isolation mechanism, run dangerous experiments in dedicated isolation areas and monitor alarms, and create collaborative spaces to generate shared lab benches;

[0051] The interactive support system includes a multimodal guidance subsystem, an intelligent question-answering engine, an experimental report factory, and a learning analysis dashboard. The specific steps include:

[0052] 1) The help-seeking method is divided into AR guidance and manual assistance. For AR guidance, the user scans the QR code of the experimental equipment and overlays 3D operation instructions. For manual assistance, the user initiates a screen sharing request and remotely annotates the operation steps.

[0053] 2) Retrieve the knowledge graph through the intelligent question-answering engine, match similar experimental data, recommend historical solutions, and evaluate the generated experimental reports.

[0054] Preferably, the user access layer includes a terminal access system, a workspace service module and an experimental interface. The terminal access system includes a terminal adaptation engine, a cross-platform client, a protocol conversion gateway and a peripheral virtualization driver unit. The specific steps include:

[0055] 1) Detect device type through terminal connection request, including:

[0056] a. Enable VDI mode and PCoIP protocol for thin clients; b. Enable WebRTC and touch optimization for iPad Pro; c. Enable VOI local computing mode for reusable PCs;

[0057] 2) Build a device fingerprint, including the device model, GPU model, and OS version. After identifying the device type based on the device fingerprint, perform cloud virtualization penetration processing to enable peripheral access.

[0058] The workspace service module includes environment snapshot management, cross-terminal synchronization engine, personal experiment data lake and breakpoint resumption controller. The specific steps include:

[0059] 1) The user enters UWS to pause the experiment, requests to freeze the VM virtual environment through UWS, and returns a memory snapshot and disk increment. The snapshot is encrypted and stored in the storage system, and a device QR code containing positioning information is generated through UWS.

[0060] 2) Generate an experimental VM to capture increments, locate the personal data lake through the message queue, build a real-time synchronization path, and use the Operational Transformation algorithm to resolve conflicts and synchronize data;

[0061] The experimental interface includes a 3D experimental console rendering engine, an AR auxiliary operation unit, a multi-screen collaboration space, and a voice interaction interface. The specific steps include:

[0062] 1) Loading quantum tunneling scenes, using binocular rendering and gesture recognition to interact with the real-time physics engine, and accessing the VR laboratory;

[0063] 2) Scan the experimental equipment with your mobile phone, identify the equipment QR code and load the 3D model overlay. According to the operation mode, it is divided into installation guidance and parameter setting. For installation guidance, the laser installation path is displayed, and for parameter setting, the knob adjustment scale is superimposed;

[0064] 3) Using the main screen as the screen role, establish an optical simulation interface, and interact through precise keyboard and mouse operations; using the auxiliary screen as the screen role, build an experimental principle animation, and achieve interaction through touch and scrolling; using the mobile phone as the screen role, build a real-time data dashboard, and achieve interaction through voice control.

[0065] (3) Beneficial effects

[0066] Compared with the existing technology, the present invention provides a physics experiment teaching platform based on cloud desktop, which has the following beneficial effects:

[0067] 1. This cloud desktop-based physics experiment teaching platform achieves decoupling of underlying resources, platform services, application functions, and user access through a four-layer architecture design, and improves resource utilization and experimental performance through virtualization technology. At the same time, SSD cache acceleration and access frequency stratification can optimize data access efficiency. Combining the three-network separation strategy with VXLAN tunneling and SR-IOV technology, it reduces latency to improve the smoothness of experimental interaction.

[0068] 2. This cloud-desktop-based physics experiment teaching platform uses AR-assisted operation units and a 3D experimental console rendering engine to superimpose equipment installation guidance and parameter settings, making experimental operations more intuitive. It also supports multi-user sharing of the experimental console and remote annotation. It tracks student experimental data through a learning analysis dashboard, recommends personalized learning paths, and uses AI to predict resource requirements, automatically shutting down idle nodes and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a schematic diagram of the physical experiment teaching platform architecture of the present invention. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0071] Example

[0072] In this embodiment, the specific steps for the physics experiment teaching platform to achieve full-process collaboration of experimental teaching include:

[0073] 1) Experimental environment preparation stage

[0074] a. The teacher logs in to the platform through the teacher terminal, creates or selects an experiment template image through the image management module, and installs specific physics simulation software;

[0075] b. The administrator monitors the resource pool status through the cluster management module and dynamically allocates resources based on the course size;

[0076] c. The system automatically generates a test environment configuration report, including resource allocation, network status, and estimated concurrent capacity;

[0077] 2) Experimental preparation and resource allocation stage

[0078] a. Students log in to the experimental course management module through the student terminal and select experimental projects. The system automatically allocates personal experimental space;

[0079] b. The platform pushes pre-study material packages, including electronic teaching plans, experimental videos, and virtual equipment operation guides;

[0080] c. Students use the experimental equipment module to preview instrument operations and conduct virtual pre-experiments. The system records the preview duration and operation trajectory;

[0081] 3) Experimental implementation phase

[0082] a. Students access their own virtual desktops through the Unified Workspace (UWS) and load pre-configured lab environments.

[0083] b. For basic experiments, preset simulation models are called, and for open experiments, customized parameters and models are supported;

[0084] c. The teacher uses the screen broadcast function to demonstrate key operations, and students initiate consultations or join group discussions through the problem-solving module. The platform monitors the progress of the experiment in real time and automatically issues warnings for abnormal operations.

[0085] 4) Experimental report and evaluation stage

[0086] a. Students use the lab report to automatically import experimental process data, use data analysis tools to generate charts, and write structured reports online;

[0087] b. Teachers use the scoring unit to evaluate the operation process, data analysis quality, and innovation based on the operation logs recorded by the system;

[0088] c. The system supports peer review mechanism to promote collaborative learning;

[0089] 5) Resource recovery and optimization stage

[0090] a. After the experiment is over, the system automatically releases the temporarily allocated computing resources and persistently stores personal experimental data and environment configuration;

[0091] b. The learning analysis engine generates experimental reports, including resource utilization analysis, experimental difficulty heat map, and equipment utilization efficiency evaluation, providing optimization suggestions for the next round of experimental resource allocation;

[0092] 6) Mobile expansion and continuous learning

[0093] a. Through the desktop portable service, students can continue to access the experimental environment using devices such as tablets after class;

[0094] b. Teachers use mobile office functions to correct reports and answer questions at any time;

[0095] c. Resource space services provide extracurricular extension experimental projects and open research topics.

[0096] In summary, this cloud desktop-based physics experiment teaching platform achieves decoupling of underlying resources, platform services, application functions, and user access through a four-layer architecture design. It also improves resource utilization and experimental performance through virtualization technology. At the same time, SSD cache acceleration and access frequency stratification can optimize data access efficiency. Combining the three-network separation strategy with VXLAN tunneling and SR-IOV technology can reduce latency and improve the smoothness of experimental interaction.

[0097] Through the AR-assisted operation unit and 3D experimental console rendering engine, equipment installation guidance and parameter setting are superimposed to improve the intuitiveness of experimental operations, and support multi-user sharing of the experimental console and remote annotation. Through the learning analysis dashboard, student experimental data is tracked and personalized learning paths are recommended. Through AI, resource requirements are predicted, idle nodes are automatically shut down, and energy consumption is reduced.

[0098] The relevant modules involved in this system are all hardware system modules or functional modules that combine computer software programs or protocols with hardware in the existing technology. The computer software programs or protocols involved in the functional modules are themselves technologies that are well known to those skilled in the art and are not improvements to this system. The improvements to this system are the interaction or connection relationships between the modules, that is, improvements to the overall structure of the system to solve the corresponding technical problems to be solved by this system.

[0099] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A physics experiment teaching platform based on cloud desktop, characterized by: This includes the infrastructure layer, which integrates servers, storage devices, and network devices into a unified resource pool. Using KVM virtualization technology, physical resources are converted into flexibly allocated virtual resources, enabling three-network separation, multi-copy storage, and automatic fault migration. The platform service layer intelligently allocates virtual machine resources and dynamically selects the optimal access mode based on terminal type, implementing zero-trust authentication and fine-grained permission control, automatically scaling resources based on AI prediction, and energy-saving hibernation processing for idle nodes. The application function layer completes course scheduling, equipment reservation, and performance evaluation operations, and provides a digital twin laboratory to implement AR operation guidance, AI Q&A, and collaborative experimental space support; The user access layer optimizes access based on terminal type and unifies the workspace, enabling immersive interaction through AR device guidance, multi-screen collaboration, and voice control.

2. A physics experiment teaching platform based on cloud desktop according to claim 1, characterized in that: The infrastructure layer includes a virtualized server resource pool, a distributed storage module, and a network switching module. The virtualized server resource pool includes a physical server cluster, a virtualization program management unit, and a resource scheduling engine. The specific steps include: 1) Resource Receiving Phase: a. The virtualization program management unit uses KVM virtualization technology to receive a virtual machine creation request from the platform service layer; b. The resource scheduling engine implements intelligent allocation based on OpenStack Nova. It selects physical nodes that meet the requirements based on the CPU and GPU nodes stored in the physical server cluster. The selection condition is set to memory utilization less than 70%. c. Select the node with the least load as the target node based on the weighted score and create a virtual machine on the target node; 2) Virtualization implementation phase: CPU virtualization: a. Divide the physical core into vCPUs using Intel VT-x / AMD-V technology to implement CPU virtualization processing; b. Divide the physical GPU card into four vGPU instances, install the vGPU driver for the virtual machine, and establish a hardware passthrough channel to implement GPU virtualization processing; c. Use transparent huge page (THP) technology to reduce address translation overhead for memory allocation; 3) Real-time optimization phase: Automatically adjust resources by monitoring the experimental load, and migrate VMs to healthy nodes according to the hot migration mechanism when a physical server fails.

3. A physics experiment teaching platform based on cloud desktop according to claim 2, characterized in that: The distributed storage module includes a metadata server, an object storage node, an SSD cache acceleration layer, and a storage network. The specific steps include: 1) Image loading: Receives the startup command from the virtual machine, locates the image replica through the metadata server, reads the image file from the three-replica storage area, selects the OSD node with the lowest network latency, and transmits the image data through the storage network; 2) Data writing: Data is collected in real time and written to the SSD cache layer. The cached data is transferred to the EC coding area using the 8+3 erasure code strategy. 3) Set access frequency for cache tiering and preload data based on experiment type, including: When the access frequency is greater than 50 times / min, the data is retained in the SSD layer; when the access frequency is less than 5 times / min, the data is moved to the HDD layer; The network switching module includes a desktop access switch, a storage dedicated switch, a management network switch and an SDN controller. The specific steps include: 1) Three-network traffic separation strategy: a. For the desktop network, prioritize video streaming to ensure screen image and interactive command operations; b. For the storage network, maximize bandwidth to ensure virtual machine storage data operations; c. For the management network, implement high-reliability transmission to ensure health monitoring and migration command operations; 2) Experimental Interaction: The user terminal sends mouse trajectory data to the desktop interactive machine, which forwards the trajectory data to the +VM server via a VXLAN tunnel. The physical GPU then performs rendering calculations and returns H.265 encoded video frames to the desktop switch, which decodes the experimental images and displays them on the user terminal. 3) Use SR-IOV technology to virtualize the physical network card into multiple VFs, and mount the VFs to the virtual machine to achieve μs-level delay data collection process.

4. A physics experiment teaching platform based on cloud desktop according to claim 1, characterized in that: The platform service layer includes a virtualization management module, a cloud desktop delivery module, and a security authentication module. The virtualization management module includes a main management server cluster, a Libvirt API interface layer, a resource monitoring unit, and a virtual machine lifecycle management unit. The specific steps include: 1) Abstract physical server resources into logical resource pools through the Libvirt API, connect to KVM hosts, and detect GPU devices to build a dynamic resource view; 2) Receive request instructions from the application function layer, analyze the request instructions and perform intelligent scheduling, including: a. Experiments are divided into basic experiments and GPU experiments according to the experiment type. General computing nodes are used for basic experiments. b. For the GPU experiment, select idle vGPU nodes and select the node with the highest load score. The scheduling algorithm is expressed as follows: c. When the CPU usage of the experimental virtual machine is continuously greater than 80%, the vCPU expansion is triggered, and a failover is performed after the physical node is detected to be down.

5. A physics experiment teaching platform based on cloud desktop according to claim 4, characterized in that: The cloud desktop delivery module includes a connection agent unit, a multi-architecture engine, a protocol optimization unit, and a user configuration database. The specific steps include: 1) Execute the connection agent and establish a session when the information is logged in, and select the delivery mode according to the terminal type; 2) When the experimental data contains real-time video streams, H.265 hard encoding and dynamic bit rate processing are enabled. When the experimental data mainly consists of text operations, lossless compression and instruction transmission processing are enabled; 3) Realize cross-terminal synchronization through unified workspace services; The security authentication module includes a secure access gateway, an RBAC authority center, a behavior audit database, and a two-factor authentication unit. The specific steps include: 1) Zero Trust Access Control: The secure access gateway submits the device fingerprint to the zero trust engine to verify the user's identity. The zero trust engine queries the access policy in the policy center, which returns a dynamic permission token to the secure access gateway and establishes an encrypted channel through the connection agent. 2) Build a role-based permission model, refine permission management based on different roles, and record key events to implement full-link audit processing.

6. A physics experiment teaching platform based on cloud desktop according to claim 1, characterized in that: The application function layer includes an experimental teaching management system, a virtual experimental environment and an interactive support system. The experimental teaching management system includes a course scheduling engine, an elastic image management subsystem, an experimental equipment virtualization unit, and a performance evaluation center. The specific steps include: 1) Log in to the experimental teaching management system, generate new experimental courses based on the course arrangement engine, and divide the experiments into basic experiments and innovative experiments according to the experiment type; 2) For basic experiments, call preset templates to automatically match resource requirements; for innovative experiments, customize parameters, set GPU and sensor requirements, and automatically allocate virtual machine specifications; 3) Construct hierarchical modules at the foundation, course, and personal levels; 4) Real-time data collection of physical experimental equipment, generating visual waveforms through digital signal processing, and realizing equipment virtualization processing; The virtual experiment environment includes an interdisciplinary experiment suite, a real-time data acquisition engine, a collaborative experiment space, and an experiment sandbox system. The specific steps include: 1) Set experimental parameters in a virtual environment, output data structure through the physical engine, and display data distribution curves in real time after collection; 2) Establish a safety isolation mechanism, run dangerous experiments in dedicated isolation areas and monitor alarms, and create collaborative spaces to generate shared lab benches; The interactive support system includes a multimodal guidance subsystem, an intelligent question-answering engine, an experimental report factory, and a learning analysis dashboard. The specific steps include: 1) The help-seeking method is divided into AR guidance and manual assistance. For AR guidance, the user scans the QR code of the experimental equipment and overlays 3D operation instructions. For manual assistance, the user initiates a screen sharing request and remotely annotates the operation steps. 2) Retrieve the knowledge graph through the intelligent question-answering engine, match similar experimental data, recommend historical solutions, and evaluate the generated experimental reports.

7. A physics experiment teaching platform based on cloud desktop according to claim 1, characterized in that: The user access layer includes a terminal access system, a workspace service module and an experimental interface. The terminal access system includes a terminal adaptation engine, a cross-platform client, a protocol conversion gateway and a peripheral virtualization driver unit. The specific steps include: 1) Detect device type through terminal connection request, including: a. Enable VDI mode and PCoIP protocol for thin clients; b. Enable WebRTC and touch optimization for iPad Pro; c. Enable VOI local computing mode for reusable PCs; 2) Build a device fingerprint, including the device model, GPU model, and OS version. After identifying the device type based on the device fingerprint, perform cloud virtualization penetration processing to enable peripheral access. The workspace service module includes environment snapshot management, cross-terminal synchronization engine, personal experiment data lake and breakpoint resumption controller. The specific steps include: 1) The user enters UWS to pause the experiment, requests to freeze the VM virtual environment through UWS, and returns a memory snapshot and disk increment. The snapshot is encrypted and stored in the storage system, and a device QR code containing positioning information is generated through UWS. 2) Generate an experimental VM to capture increments, locate the personal data lake through the message queue, build a real-time synchronization path, and use the Operational Transformation algorithm to resolve conflicts and synchronize data; The experimental interface includes a 3D experimental console rendering engine, an AR auxiliary operation unit, a multi-screen collaboration space, and a voice interaction interface. The specific steps include: 1) Loading quantum tunneling scenes, using binocular rendering and gesture recognition to interact with the real-time physics engine, and accessing the VR laboratory; 2) Scan the experimental equipment with your mobile phone, identify the equipment QR code and load the 3D model overlay. According to the operation mode, it is divided into installation guidance and parameter setting. For installation guidance, the laser installation path is displayed, and for parameter setting, the knob adjustment scale is superimposed; 3) Using the main screen as the screen role, establish an optical simulation interface, and interact through precise keyboard and mouse operations; using the auxiliary screen as the screen role, build an experimental principle animation, and achieve interaction through touch and scrolling; using the mobile phone as the screen role, build a real-time data dashboard, and achieve interaction through voice control.

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