AIOT platform multi-experimental environment and multi-cloud adaptive combination method and equipment medium

Abstract

The invention discloses an AIOT platform multi-experiment environment and multi-cloud adaptive combination method and an equipment medium. The method comprises the following steps: configuring different experiment environments corresponding to different functions on an experiment service platform; the experiment service platform obtains configuration information from the configuration center, and determines a corresponding target server according to the configuration information; the user side obtains the configured experimental environment information from the experimental service platform and determines the functions according to the experimental environment information; the user side initiates a service function starting request to the experiment service platform, and the experiment service platform matches a corresponding target experiment environment according to the service function starting request and deploys the target experiment environment in the target server; the user side uses the target server to perform function access; and when the user end finishes accessing, a service function stopping request is sent to the experiment service platform, and the experiment service platform deletes the target experiment environment in the target server according to the service function stopping request. According to the invention, the experiment service adaptive to the hybrid cloud can be provided.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an AIOT platform multi-experimental environment and multi-cloud adaptation combination method and device medium. Background Art

[0002] In the teaching and training of majors such as the Internet of Things, blockchain, and artificial intelligence, it is necessary to provide one or more related professional training operating environments. If student users study related majors for industry case training, using their own local computer installation environment requires a relatively high computer configuration and the installation environment is relatively troublesome. Currently, there are relatively few platforms that provide online training environments, and even fewer online platforms that provide multi-professional training environments.

[0003] Different cloud computing vendors can provide cloud servers (terminals) to students or users, but cloud computing vendors only provide a server or terminal, and do not provide the corresponding professional industry training environment. Students or users still need to build the relevant training environment on the service terminal by themselves. The servers provided by cloud computing vendors cannot be turned on and off immediately without losing data. In order to prevent the environment from being built and the data from being lost when it is turned off and on again, the environment and data need to be stored. The storage methods and strategies of different cloud computing vendors are inconsistent.

[0004] As the environment deployed by online cloud computing vendors is inconsistent with the server environment purchased by offline customers for private deployment, the platform's online training environment needs to provide consistent operations and user experience, and the experimental services need to adapt to the online cloud computing vendors and offline physical server deployments to complete the provision of the training environment. Summary of the invention

[0005] In view of this, the purpose of the present invention is to propose a multi-experimental environment and multi-cloud adaptation combination method for an AIOT platform, to abstractly extract the similarities between the business functions implemented by the experimental service and the interfaces related to docking with multi-cloud service providers and self-built K8S, to implement the API docking interface through configuration, and to provide an experimental service adapted to hybrid clouds.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0007] The present invention provides an AIOT platform multi-experimental environment and multi-cloud adaptation combination method, which is applied to a user terminal, a configuration center, an experimental service platform, and servers corresponding to different cloud computing vendors; and comprises the following steps:

[0008] Step 1: configuring different experimental environments corresponding to different functions on the experimental service platform;

[0009] Step 2: The experimental service platform obtains configuration information from the configuration center during startup, and determines the corresponding target server according to the configuration information;

[0010] Step 3: The user terminal obtains the configured experimental environment information from the experimental service platform, and determines the functions it has according to the experimental environment information;

[0011] Step 4: The user terminal initiates a service function activation request to the experiment service platform, and the experiment service platform matches the corresponding target experiment environment according to the service function activation request and deploys the target experiment environment in the target server;

[0012] Step 5: The user terminal uses the target server to access functions;

[0013] Step 6: When the user terminal ends the access, it sends a stop service function request to the experimental service platform, and the experimental service platform deletes the target experimental environment deployed in the target server according to the stop service function request.

[0014] Furthermore, the step 1 specifically includes:

[0015] Step 11: The administrator plans different experimental environments required on the experimental service platform according to functional requirements;

[0016] Step 12, the administrator configures the planned different experimental environments on the experimental service platform; the different experimental environments include CPU / GPU type terminal environment, VSCode environment, ThingsBoard environment, Jupyter environment, 2D / 3D simulation environment, Remix environment, WeBASE environment and AIGC environment;

[0017] The CPU / GPU type terminal environment is used for transmitting, storing and processing data;

[0018] The VSCode environment is used for cross-platform source code editing and supports multiple programming languages;

[0019] The ThingsBoard environment is used to collect and visualize data from IoT devices;

[0020] The Jupyter environment is used for data analysis, computing, and machine learning;

[0021] The 2D / 3D simulation environment is used to simulate the wiring, data generation and processing, testing and debugging, and cloud platform access of real IoT devices, so as to realize the simulation of interconnection and communication collaboration between devices;

[0022] The Remix environment is used for basic compilation and deployment to local or test networks and contract execution;

[0023] The WeBASE environment is used to build between blockchain applications and FISCO-BCOS nodes;

[0024] The AIGC environment is used for intelligent teaching assistants, answering related questions and intelligent scoring of teaching tasks in majors related to the Internet of Things, artificial intelligence and blockchain.

[0025] Furthermore, the servers corresponding to the different cloud computing vendors include Huawei Cloud Server, Alibaba Cloud Server, Microsoft Cloud Server and self-built K8S server; the step 2 specifically includes:

[0026] Step 21: The experimental service platform obtains the configuration information of the cloud computing vendor from the configuration center during the startup process;

[0027] Step 22: Determine the corresponding cloud computing vendor implementation class according to the configuration information;

[0028] If the configuration information describes a Huawei cloud computing vendor, obtain the Huawei cloud computing vendor implementation class;

[0029] If the configuration information describes an Alibaba Cloud computing vendor, obtain the Alibaba Cloud computing vendor implementation class;

[0030] If the configuration information describes a Microsoft cloud computing vendor, then obtain the Microsoft cloud computing vendor implementation class;

[0031] If the configuration information describes a K8S cloud computing vendor, obtain the K8S cloud computing vendor implementation class;

[0032] Step 23: Load the corresponding cloud computing vendor implementation class object into the Spring container according to the cloud computing vendor implementation class;

[0033] If it is a Huawei cloud computing vendor implementation class, load the Huawei cloud computing vendor implementation class object into the corresponding Spring container;

[0034] If it is an Alibaba Cloud Computing vendor implementation class, load the Alibaba Cloud Computing vendor implementation class object into the corresponding Spring container;

[0035] If it is a Microsoft cloud computing vendor implementation class, load the Microsoft cloud computing vendor implementation class object into the corresponding Spring container;

[0036] If it is a K8S cloud computing vendor implementation class, load the K8S cloud computing vendor implementation class object into the corresponding Spring container;

[0037] Step 24: Use the server used to run the Spring container as the target server;

[0038] If the Spring container used to load the Huawei cloud computing vendor implementation class objects is running on the Huawei cloud server, the Huawei cloud server is determined as the target server;

[0039] If the Spring container used to load the Alibaba Cloud computing vendor implementation class object is running on the Alibaba Cloud server, the Alibaba Cloud server is determined as the target server;

[0040] If the Spring container used to load the Microsoft cloud computing vendor implementation class object is running on a Microsoft cloud server, the Microsoft cloud server is determined as the target server;

[0041] If the Spring container used to load the K8S cloud computing vendor implementation class object is running on the K8S cloud server, the K8S cloud server is determined as the target server;

[0042] Step 25: Register the experimental service platform to a registration center.

[0043] Furthermore, the step 3 specifically includes:

[0044] Step 31, the user terminal obtains the configured experimental environment information from the experimental service platform;

[0045] Step 32: Obtain scripts and image information for different experimental environment configurations according to the experimental environment information;

[0046] Step 33: Determine the functions of different experimental environments based on the script and image information.

[0047] Furthermore, the step 32 specifically includes:

[0048] Step 321: determine whether there is a CPU / GPU type terminal environment according to the experimental environment information. If so, obtain the script and image information configured for the CPU / GPU type terminal environment; otherwise, start the specified training environment;

[0049] Step 322: determine whether there is a VSCode environment according to the experimental environment information. If so, obtain the script and image information configured in the VSCode environment; otherwise, start the specified training environment;

[0050] Step 323: determine whether there is a ThingsBoard environment according to the experimental environment information. If so, obtain the script and image information configured for the ThingsBoard environment; otherwise, start the specified training environment;

[0051] Step 324: determine whether there is a Jupyter environment according to the experimental environment information. If so, obtain the script and image information configured for the Jupyter environment; otherwise, start the specified training environment;

[0052] Step 325: determine whether there is a 2D / 3D simulation environment according to the experimental environment information; if so, obtain the script and image information configured for the 2D / 3D simulation environment; otherwise, start the specified training environment;

[0053] Step 326: Determine whether there is a Remix environment according to the experimental environment information. If so, obtain the script and image information configured in the Remix environment; otherwise, start the specified training environment;

[0054] Step 327: determine whether there is a WeBASE environment according to the experimental environment information. If so, obtain the script and image information configured for the WeBASE environment; otherwise, start the specified training environment;

[0055] Step 328: Determine whether there is an AIGC environment based on the experimental environment information. If so, obtain the script and image information configured for the AIGC environment; otherwise, start the specified training environment.

[0056] Furthermore, the step 4 specifically includes:

[0057] Step 41: The user terminal initiates a service function activation request to the experiment service platform according to the functions of different experiment environments, and the service function request includes the specific experiment environment to be deployed;

[0058] Step 42: the experiment service platform matches the corresponding target experiment environment according to the service function start request;

[0059] Step 43: Obtain the script and image information of the target experimental environment, run the image information according to the script, and store the image directory data mounted by the script; and return the access information to the experimental service platform;

[0060] If the target server is a Huawei cloud server, step 43 specifically includes:

[0061] A1. Obtain the script and image information of the target experimental environment;

[0062] A2. Call the service interface of Huawei Cloud Server according to the script to create a pod container corresponding to the target experimental environment;

[0063] A3. Determine whether the user already has a disk in the Huawei Cloud server. If so, call the service interface of the Huawei Cloud server to bind the disk to the pod container. Otherwise, call the service interface of the Huawei Cloud server to create a new disk and bind the disk to the pod container.

[0064] A4. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the disk;

[0065] A5. Call the service interface of Huawei Cloud Server to obtain the access IP address and bind it to the pod container and return it to the experimental service platform.

[0066] A6. The processing status of the pod container is called back through the pod probe and returned to the experimental service platform;

[0067] If the target server is an Alibaba Cloud server, step 43 specifically includes:

[0068] B1. Obtain the script and image information of the target experimental environment;

[0069] B2. Call the service interface of the Alibaba Cloud server according to the script to create a pod container corresponding to the target experimental environment;

[0070] B3. Determine whether the user already has a disk in the Alibaba Cloud server. If so, call the service interface of the Alibaba Cloud server to bind the disk to the pod container; otherwise, call the service interface of the Alibaba Cloud server to create a new disk and bind the disk to the pod container;

[0071] B4. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the disk;

[0072] B5. Call the service interface of the Alibaba Cloud server to obtain the access IP address from the Alibaba Cloud IP pool and bind it to the pod container and return it to the experimental service platform;

[0073] B6. Call back the processing status of the pod container through the pod probe and return it to the experimental service platform;

[0074] If the target server is a Microsoft cloud server, step 43 specifically includes:

[0075] C1. Obtain the script and image information of the target experimental environment;

[0076] C2. Determine whether there is an existing virtual machine in the Microsoft cloud server. If so, call the service interface of the Microsoft cloud server to start the corresponding virtual machine; otherwise, call the service interface of the Microsoft cloud server to create a new virtual machine and start the corresponding virtual machine;

[0077] C3, call the service interface of the Microsoft cloud server to obtain the access IP address and bind it to the virtual machine and then return it to the experimental service platform;

[0078] If the target server is a K8S cloud server, step 43 specifically includes:

[0079] D1. Obtain the script and image information of the target experimental environment;

[0080] D2. Call the service interface of the K8S cloud server according to the script to create a pod container corresponding to the target experimental environment;

[0081] D3. Create an NFS storage server based on the script information and bind the NFS storage server to the pod container;

[0082] D4. Map the access IP addresses and ports of the virtual machines and pod containers in the K8S cloud server according to the script information and return them to the experimental service platform;

[0083] D5. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the NFS storage server;

[0084] D6. Call back the processing status of the pod container through the pod probe and return it to the experimental service platform.

[0085] Furthermore, the step 5 specifically includes:

[0086] Step 51: The experimental service platform receives the access information and sends it to the user terminal;

[0087] If the target server is Huawei Cloud Server, Alibaba Cloud Server or Microsoft Cloud Server, the access information is the access IP address;

[0088] If the target server is a self-built K8S server, the access information is the access IP address and port;

[0089] Step 52: The user terminal receives the access information and accesses the target experimental environment of the target server according to the access information.

[0090] Further, the step 6 comprises:

[0091] Step 61: When the user terminal ends access, the user terminal sends a stop service function request to the experimental service platform;

[0092] Step 62: The experimental service platform calls the service interface of the target server according to the stop service function request to delete the target experimental environment deployed in the target server;

[0093] If the target server is a Huawei cloud server, step 62 specifically includes:

[0094] a1. Call the service interface of Huawei Cloud Server to unbind and delete the access IP address in Huawei Cloud Server;

[0095] a2. Call the service interface of Huawei Cloud Server to delete the binding relationship between the disk and the pod container in Huawei Cloud Server;

[0096] a3. Call the service interface of Huawei Cloud Server to delete the pod container in Huawei Cloud Server;

[0097] If the target server is an Alibaba Cloud server, step 62 specifically includes:

[0098] b1. Call the service interface of the Alibaba Cloud server to unbind and delete the access IP address in the Alibaba Cloud server;

[0099] b2. Call the service interface of the Alibaba Cloud server to delete the binding relationship between the disk and the pod container in the Alibaba Cloud server;

[0100] b3. Call the service interface of Alibaba Cloud Server to delete the pod container in Alibaba Cloud Server;

[0101] If the target server is a Microsoft cloud server, step 62 specifically includes:

[0102] c1. Call the service interface of the Microsoft cloud server to unbind and delete the access IP address in the Microsoft cloud server;

[0103] c2. Call the service interface of Microsoft Cloud Server to shut down the virtual machine;

[0104] If the target server is a K8S cloud server, step 62 specifically includes:

[0105] d1. Call the service interface of the Alibaba Cloud server to unbind and delete the port in the K8S cloud server and unbind the access IP address;

[0106] d2. Call the service interface of the K8S cloud server to delete the binding relationship between the NFS storage server and the pod container in the K8S cloud server;

[0107] d3. Call the service interface of the K8S cloud server to delete the pod container in the K8S cloud server;

[0108] Step 63: After the deletion operation is completed, the target server feeds back the operation success information to the user terminal through the experimental service platform.

[0109] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the multi-experimental environment and multi-cloud adaptation combination method of the AIOT platform is implemented as described above.

[0110] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned AIOT platform multi-experimental environment and multi-cloud adaptation combination method.

[0111] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0112] 1. The experimental service platform provides unified management of experimental environment, images, and scripts, and provides training images and environment configurations for different majors and industries. It can provide unified training environment services for different majors and industries, reducing the work of student users in installing and configuring the training environment.

[0113] 2. The services deployed in the experimental environment and the related training data are persisted to the disk storage provided by the corresponding cloud computing service provider. When the student closes the experimental service, the relevant data is persisted and then the experimental environment is closed. When the experimental environment is opened again, the persistent disk of the last operation is mounted back to the opened experimental environment. The training environment can be used immediately after it is turned on. After the training environment is closed, the corresponding environment and related operation data will not be lost when it is opened next time, which can effectively solve the security of the training environment and data.

[0114] 3. The experimental environment of the experimental service platform is adapted to multiple cloud computing vendors and self-built K8S to implement a common abstract interface method, which is configured based on the configuration file. The experimental service loads the cloud computing vendor configuration during the startup process to determine which cloud computing vendor's specific implementation method provides the experimental environment. The experimental service project code can be iteratively developed based on this version branch for subsequent required functions. There is no need to pull the corresponding branch code for each specific implementation method for maintenance. The synchronization and maintenance costs of the subsequent demand development branch code are greatly reduced. For online and offline private deployment, only the relevant configuration of the specific implementation of the experimental service configuration needs to be changed, which facilitates the management of the images released by the experimental service. It can adapt to the services provided by different online cloud computing vendors and offline physical server deployments, dynamically and seamlessly switch, and achieve a consistent implementation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0116] Figure 1 It is an execution flow chart of an AIOT platform multi-experimental environment and multi-cloud adaptation combination method provided by an embodiment of the present invention.

[0117] Figure 2It is a data interaction flow chart among the user terminal, configuration center, experimental service platform and different cloud servers provided by an embodiment of the present invention.

[0118] Figure 3 It is a framework diagram of the experimental service platform and cloud computing platform provided by the embodiments of the present invention.

[0119] Figure 4 This is a flowchart of starting the experimental service platform and implementing loading by a cloud computing vendor provided by an embodiment of the present invention.

[0120] Figure 5 It is a flow chart for starting the experimental service platform provided by an embodiment of the present invention.

[0121] Figure 6 This is a flowchart of a Huawei cloud server turning on and off a CPU / GPU type terminal environment provided by an embodiment of the present invention.

[0122] Figure 7 This is a flow chart of turning on and off a CPU / GPU type terminal environment on an Alibaba Cloud server provided by an embodiment of the present invention.

[0123] Figure 8 This is a flowchart of a Microsoft cloud server turning on and off a CPU / GPU type terminal environment provided by an embodiment of the present invention.

[0124] Fig. 9 This is a flowchart of turning on and off the CPU / GPU type terminal environment of the K8S cloud server provided by an embodiment of the present invention.

[0125] Fig.10 It is a schematic diagram of an electronic device provided by an embodiment of the present invention.

[0126] Fig.11 It is a schematic diagram of a computer-readable storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0127] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0128] See also Figures 1 to 9, an AIOT platform multi-experimental environment and multi-cloud adaptation combination method of the present invention is applied to the user end, the configuration center, the experimental service platform and the servers corresponding to different cloud computing vendors; wherein the servers corresponding to different cloud computing vendors include Huawei cloud servers, Alibaba cloud servers, Microsoft cloud servers and self-built K8S servers (kubernetes, referred to as K8s, is an abbreviation formed by replacing the 8 characters "ubernete" in the middle of the name with 8. It is an open source, used to manage containerized applications on multiple hosts in a cloud platform. The goal of Kubernetes is to make the deployment of containerized applications simple and efficient (powerful). Kubernetes provides a mechanism for application deployment, planning, updating, and maintenance); comprising the following steps:

[0129] Step 1: configuring different experimental environments corresponding to different functions on the experimental service platform;

[0130] In this embodiment, the step 1 specifically includes:

[0131] Step 11: The administrator plans different experimental environments required on the experimental service platform according to functional requirements;

[0132] Step 12, the administrator configures the planned different experimental environments on the experimental service platform; the different experimental environments include CPU / GPU type terminal environment, VSCode environment, ThingsBoard environment, Jupyter environment, 2D / 3D simulation environment, Remix environment, WeBASE environment and AIGC environment;

[0133] The CPU / GPU type terminal (Docker) environment is used for data transmission, storage and processing; the AIoT online engineering training platform uses Docker container technology to provide each user with a Linux virtual machine bound to the account, so that each operating environment and resource are independent of each other and do not affect each other, and software deployment, resource occupancy and operation monitoring are performed through command input. The user end remotely logs in to the Linux virtual machine terminal through a browser, deploys the project software at the transport layer, edits the associated configuration files, realizes the southbound docking of Modbus, ZigBee, LoRaWAN, CANbus and other protocol devices, and transmits the collected data to the northbound IoT cloud platform.

[0134] The VSCode environment is used for cross-platform source code editing and supports multiple programming languages. VSCode (full name: Visual Studio Code) is a free cross-platform source code editor developed by Microsoft. It supports a large number of programming languages ​​by default, including JavaScript, TypeScript, CSS and HTML. It can also support other languages ​​including Python, C / C++, Java and Go by downloading extensions. Support functions include syntax highlighting, bracket completion, code folding and code snippets; for some languages, IntelliSense can be used. Visual Studio Code also supports debugging Node.js programs. Like GitHub's Atom, Visual Studio Code is also built on the Electron framework. Visual StudioCode supports opening multiple directories at the same time and saves information in the workspace for reuse. As a cross-platform editor, Visual Studio Code allows users to change the code page, line breaks and programming language of a file.

[0135] The ThingsBoard environment is used to collect and visualize data from IoT devices; ThingsBoard is an open source platform for collecting and visualizing data from IoT devices. Data from any number of devices can be sent to a cloud server, where it can be viewed or shared through a customizable dashboard. ThingsBoard supports standard protocols for providing device connectivity, namely MQTT, CoAP, and HTTP, and supports cloud and local deployment. It provides more than 30 customizable widgets, allowing you to build end-user custom dashboards for most IoT use cases. The main features of the thingsboard.io open source IoT platform include: real-time data visualization and remote device control, 30 customizable widgets for custom dashboards, customizable rules, plugins, widgets, and transport implementations that allow monitoring of client-side and provision of server-side device properties. Supports multi-tenant installation out of the box, supports transmission encryption for MQTT and HTTP protocols, and failed nodes can be replaced without downtime.

[0136] The Jupyter environment is used for data analysis, computing, and machine learning; Jupyter Notebook is an interactive computing environment that is widely used in fields such as data analysis, scientific computing, and machine learning. It allows users to combine code, text, formulas, and charts in one document, making the data processing process more intuitive and easy to share. Jupyter Notebook supports more than 40 programming languages, including Python, R, Julia, and Scala. Its core is IPython, an enhanced interactive shell for Python. Jupyter Notebook can be run on a local computer or used through cloud services. Jupyter Notebook is opened in the form of a web page, where you can write and run code directly on the web page, and the results of the code will be displayed directly under the code block. If you need to write an instruction document during the programming process, you can write it directly on the same page to facilitate timely instructions and explanations.

[0137] The 2D / 3D simulation environment is used to simulate the wiring, data generation and processing, testing and debugging, and cloud platform of real IoT devices, so as to realize the simulation of interconnection and communication collaboration between devices; such a simulation method and system can not only improve the accuracy and reliability of simulation testing, but also reduce R&D costs, shorten the R&D cycle, and provide strong support for the R&D and deployment of IoT devices.

[0138] The Remix environment is used for basic compilation and deployment to local or test networks and contract execution; Remix is ​​an open source Solidity smart contract development environment. Solidity is a development language officially designed and supported by Ethereum, specifically for writing smart contracts.

[0139] The WeBASE environment is used to build between blockchain applications and FISCO-BCOS nodes;

[0140] WeBASE (WeBank Blockchain Application Software Extension) is a set of common components built between blockchain applications and FISCO-BCOS nodes. Each module is designed around transactions, contracts, key management, data, and visual management. Developers can choose subsystems for deployment according to business needs. WeBASE shields the complexity of the underlying blockchain, lowers the threshold for developers, and greatly improves the development efficiency of blockchain applications. It includes subsystems such as node pre-positioning, node management, transaction links, data export, and Web management platform.

[0141] The AIGC environment is used for intelligent teaching assistants, answers to related questions and intelligent scoring of teaching tasks in majors related to the Internet of Things, artificial intelligence and blockchain; the AIGC environment is currently connected to mainstream large model environments: Microsoft aoai big model, Wenxin Yiyan, and Kimi big model, and can provide intelligent teaching assistants, answers to related questions and intelligent scoring of teaching tasks in majors related to the Internet of Things, artificial intelligence and blockchain.

[0142] Step 2: The experimental service platform obtains configuration information from the configuration center during startup, and determines the corresponding target server according to the configuration information;

[0143] In this embodiment, step 2 specifically includes:

[0144] Step 21: The experimental service platform obtains the configuration information of the cloud computing vendor from the configuration center during the startup process;

[0145] Step 22: Determine the corresponding cloud computing vendor implementation class according to the configuration information;

[0146] If the configuration information describes a Huawei cloud computing vendor, obtain the Huawei cloud computing vendor implementation class;

[0147] If the configuration information describes an Alibaba Cloud computing vendor, obtain the Alibaba Cloud computing vendor implementation class;

[0148] If the configuration information describes a Microsoft cloud computing vendor, then obtain the Microsoft cloud computing vendor implementation class;

[0149] If the configuration information describes a K8S cloud computing vendor, obtain the K8S cloud computing vendor implementation class;

[0150] Step 23: Load the corresponding cloud computing vendor implementation class object into the Spring container according to the cloud computing vendor implementation class;

[0151] If it is a Huawei cloud computing vendor implementation class, load the Huawei cloud computing vendor implementation class object into the corresponding Spring container;

[0152] If it is an Alibaba Cloud Computing vendor implementation class, load the Alibaba Cloud Computing vendor implementation class object into the corresponding Spring container;

[0153] If it is a Microsoft cloud computing vendor implementation class, load the Microsoft cloud computing vendor implementation class object into the corresponding Spring container;

[0154] If it is a K8S cloud computing vendor implementation class, load the K8S cloud computing vendor implementation class object into the corresponding Spring container;

[0155] Step 24: Use the server used to run the Spring container as the target server;

[0156] If the Spring container used to load the Huawei cloud computing vendor implementation class objects is running on the Huawei cloud server, the Huawei cloud server is determined as the target server;

[0157] If the Spring container used to load the Alibaba Cloud computing vendor implementation class object is running on the Alibaba Cloud server, the Alibaba Cloud server is determined as the target server;

[0158] If the Spring container used to load the Microsoft cloud computing vendor implementation class object is running on a Microsoft cloud server, the Microsoft cloud server is determined as the target server;

[0159] If the Spring container used to load the K8S cloud computing vendor implementation class object is running on the K8S cloud server, the K8S cloud server is determined as the target server;

[0160] Step 25: Registering the experimental service platform to the registration center not only simplifies service discovery and management, but also improves the availability and scalability of the system.

[0161] Step 3: The user terminal obtains the configured experimental environment information from the experimental service platform, and determines the functions it has according to the experimental environment information;

[0162] In this embodiment, step 3 specifically includes:

[0163] Step 31, the user terminal obtains the configured experimental environment information from the experimental service platform;

[0164] Step 32: Obtain scripts and image information for different experimental environment configurations according to the experimental environment information;

[0165] In the software development process, a playbook usually refers to a script or plan in the development process, which is used to guide developers on how to develop and test. It describes in detail the various stages and steps of software development, including requirements analysis, design, coding, testing, etc. The purpose of the playbook is to ensure the orderly development process, reduce errors and duplication of work, and improve development efficiency.

[0166] Image information refers to a duplicate version of a system, data, or file.

[0167] Step 33: Determine the functions of different experimental environments based on the script and image information.

[0168] In this embodiment, step 32 specifically includes:

[0169] Step 321: determine whether there is a CPU / GPU type terminal environment according to the experimental environment information. If so, obtain the script and image information configured for the CPU / GPU type terminal environment; otherwise, start the specified training environment (which has the same or similar functions as the CPU / GPU type terminal environment and is used to replace the CPU / GPU type terminal environment);

[0170] Step 322: determine whether there is a VSCode environment according to the experimental environment information. If so, obtain the script and image information configured in the VSCode environment; otherwise, open the specified training environment (which has the same or similar functions as the VSCode environment and is used to replace the VSCode environment);

[0171] Step 323: determine whether there is a ThingsBoard environment according to the experimental environment information. If so, obtain the script and image information configured for the ThingsBoard environment; otherwise, start a specified training environment (which has the same or similar functions as the ThingsBoard environment and is used to replace the ThingsBoard environment);

[0172] Step 324: determine whether there is a Jupyter environment according to the experimental environment information. If so, obtain the script and image information configured for the Jupyter environment; otherwise, start a specified training environment (which has the same or similar functions as the Jupyter environment and is used to replace the Jupyter environment);

[0173] Step 325: determine whether there is a 2D / 3D simulation environment according to the experimental environment information; if so, obtain the script and image information configured for the 2D / 3D simulation environment; otherwise, start a specified training environment (having the same or similar functions as the 2D / 3D simulation environment, used to replace the 2D / 3D simulation environment);

[0174] Step 326: determine whether there is a Remix environment according to the experimental environment information. If so, obtain the script and image information configured in the Remix environment; otherwise, start a specified training environment (which has the same or similar functions as the Remix environment and is used to replace the Remix environment);

[0175] Step 327: determine whether there is a WeBASE environment according to the experimental environment information; if so, obtain the script and image information configured for the WeBASE environment; otherwise, start a specified training environment (having the same or similar functions as the WeBASE environment, used to replace the WeBASE environment);

[0176] Step 328: Determine whether there is an AIGC environment based on the experimental environment information. If so, obtain the script and image information configured for the AIGC environment; otherwise, start the specified training environment (which has the same or similar functions as the AIGC environment and is used to replace the AIGC environment).

[0177] Step 4: The user terminal initiates a service function activation request to the experimental service platform. The experimental service platform matches the corresponding target experimental environment according to the service function activation request and deploys the target experimental environment in the target server. The script yaml file is run by the target server to provide different mirror environments to create different experimental environments.

[0178] In this embodiment, step 4 specifically includes:

[0179] Step 41: The user terminal initiates a service function activation request to the experiment service platform according to the functions of different experiment environments, and the service function request includes the specific experiment environment to be deployed;

[0180] Step 42: the experiment service platform matches the corresponding target experiment environment according to the service function start request;

[0181] Step 43: Obtain the script and image information of the target experimental environment, run the image information according to the script, and store the image directory data mounted by the script; and return the access information to the experimental service platform;

[0182] If the target server is a Huawei cloud server, step 43 specifically includes:

[0183] A1. Obtain the script and image information of the target experimental environment;

[0184] A2. Call the service interface of Huawei Cloud Server according to the script to create a pod container corresponding to the target experimental environment (the running status of the installed software);

[0185] A3. Determine whether the user already has a disk in the Huawei Cloud server. If so, call the Huawei Cloud server's service interface to bind the disk to the pod container. Otherwise, call the Huawei Cloud server's service interface to create a new disk and bind the disk to the pod container (PVC (Persistent Volume Claim) is the association between the disk and the pod container. Through PVC, the program (pod) started by the user can be associated with the data stored on the user's own disk).

[0186] A4. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the disk;

[0187] A5. Call the service interface of Huawei Cloud Server to obtain the access IP address and bind it to the pod container and return it to the experimental service platform.

[0188] A6. The processing status of the pod container is called back through the pod probe and returned to the experimental service platform;

[0189] If the target server is an Alibaba Cloud server, step 43 specifically includes:

[0190] B1. Obtain the script and image information of the target experimental environment;

[0191] B2. Call the service interface of the Alibaba Cloud server according to the script to create a pod container corresponding to the target experimental environment;

[0192] B3. Determine whether the user already has a disk in the Alibaba Cloud server. If so, call the service interface of the Alibaba Cloud server to bind the disk to the pod container; otherwise, call the service interface of the Alibaba Cloud server to create a new disk and bind the disk to the pod container;

[0193] B4. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the disk;

[0194] B5. Call the service interface of the Alibaba Cloud server to obtain the access IP address from the Alibaba Cloud IP pool and bind it to the pod container and return it to the experimental service platform;

[0195] B6. Call back the processing status of the pod container through the pod probe and return it to the experimental service platform;

[0196] If the target server is a Microsoft cloud server, step 43 specifically includes:

[0197] C1. Obtain the script and image information of the target experimental environment;

[0198] C2. Determine whether there is an existing virtual machine in the Microsoft cloud server. If so, call the service interface of the Microsoft cloud server to start the corresponding virtual machine; otherwise, call the service interface of the Microsoft cloud server to create a new virtual machine and start the corresponding virtual machine;

[0199] C3, call the service interface of the Microsoft cloud server to obtain the access IP address and bind it to the virtual machine and then return it to the experimental service platform;

[0200] If the target server is a K8S cloud server, step 43 specifically includes:

[0201] D1. Obtain the script and image information of the target experimental environment;

[0202] D2. Call the service interface of the K8S cloud server according to the script to create a pod container corresponding to the target experimental environment;

[0203] D3. Create an NFS storage server based on the script information and bind the NFS storage server to the pod container;

[0204] D4. Map the access IP addresses and ports of the virtual machines and pod containers in the K8S cloud server according to the script information and return them to the experimental service platform;

[0205] D5. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the NFS storage server;

[0206] D6. Call back the processing status of the pod container through the pod probe and return it to the experimental service platform.

[0207] Step 5: The user terminal uses the target server to access functions;

[0208] In this embodiment, step 5 specifically includes:

[0209] Step 51: The experimental service platform receives the access information and sends it to the user terminal;

[0210] If the target server is Huawei Cloud Server, Alibaba Cloud Server or Microsoft Cloud Server, the access information is the access IP address;

[0211] If the target server is a self-built K8S server, the access information is the access IP address and port;

[0212] Step 52: The user terminal receives the access information and accesses the target experimental environment of the target server according to the access information.

[0213] Step 6: When the user terminal ends the access, it sends a stop service function request to the experimental service platform, and the experimental service platform deletes the target experimental environment deployed in the target server according to the stop service function request.

[0214] In this embodiment, step 6 includes:

[0215] Step 61: When the user terminal ends access, the user terminal sends a stop service function request to the experimental service platform;

[0216] Step 62: The experimental service platform calls the service interface of the target server according to the stop service function request to delete the target experimental environment deployed in the target server;

[0217] If the target server is a Huawei cloud server, step 62 specifically includes:

[0218] a1. Call the service interface of Huawei Cloud Server to unbind and delete the access IP address in Huawei Cloud Server;

[0219] a2. Call the service interface of Huawei Cloud Server to delete the binding relationship between the disk and the pod container in Huawei Cloud Server (that is, delete PVC);

[0220] a3. Call the service interface of Huawei Cloud Server to delete the pod container in Huawei Cloud Server (that is, delete podDeployment, which is used to manage the resource objects of pod container);

[0221] If the target server is an Alibaba Cloud server, step 62 specifically includes:

[0222] b1. Call the service interface of the Alibaba Cloud server to unbind and delete the access IP address in the Alibaba Cloud server;

[0223] b2. Call the service interface of the Alibaba Cloud server to delete the binding relationship between the disk and the pod container in the Alibaba Cloud server;

[0224] b3. Call the service interface of Alibaba Cloud Server to delete the pod container in Alibaba Cloud Server;

[0225] If the target server is a Microsoft cloud server, step 62 specifically includes:

[0226] c1. Call the service interface of the Microsoft cloud server to unbind and delete the access IP address in the Microsoft cloud server;

[0227] c2. Call the service interface of Microsoft Cloud Server to shut down the virtual machine;

[0228] If the target server is a K8S cloud server, step 62 specifically includes:

[0229] d1. Call the service interface of the Alibaba Cloud server to unbind and delete the port in the K8S cloud server and unbind the access IP address;

[0230] d2. Call the service interface of the K8S cloud server to delete the binding relationship between the NFS storage server and the pod container in the K8S cloud server;

[0231] d3. Call the service interface of the K8S cloud server to delete the pod container in the K8S cloud server;

[0232] Step 63: After the deletion operation is completed, the target server feeds back the operation success information to the user terminal through the experimental service platform.

[0233] Operation process:

[0234] The operation and maintenance personnel log in to the configuration center and add the cloud computing vendor configuration information of the experimental service platform;

[0235] The operation and maintenance personnel start the experimental service platform. During the startup of the experimental service platform, the configuration information of the cloud computing vendor is loaded from the configuration center to decide which cloud computing server to use to implement the experimental environment service;

[0236] Administrators log in to the experimental service platform, create cases, configure training environments, and sell cases to school customers;

[0237] The school administrator logs in to the experimental service platform and creates relevant courses according to the purchased cases;

[0238] The teacher logs in to the experimental service platform, creates student tasks based on relevant courses, and distributes the tasks to students in the designated class;

[0239] Students log in to the experimental service platform, start the task to open the corresponding training environment, and the experimental service platform calls the corresponding cloud computing vendor to provide the terminal environment.

[0240] like Fig.10 As shown, an embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned AIOT platform multi-experimental environment and multi-cloud adaptation combination method is implemented.

[0241] like Fig.11 As shown, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned AIOT platform multi-experimental environment and multi-cloud adaptation combination method is implemented.

[0242] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0243] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0244] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for combining multiple experimental environments and multiple cloud adaptation on an AIOT platform, characterized in that: Applied to the user end, configuration center, experimental service platform and the corresponding servers of different cloud computing vendors; including the following steps: Step 1: configuring different experimental environments corresponding to different functions on the experimental service platform; Step 2: The experimental service platform obtains configuration information from the configuration center during startup, and determines the corresponding target server according to the configuration information; Step 3: The user terminal obtains the configured experimental environment information from the experimental service platform, and determines the functions it has according to the experimental environment information; Step 4: The user terminal initiates a service function activation request to the experiment service platform, and the experiment service platform matches the corresponding target experiment environment according to the service function activation request and deploys the target experiment environment in the target server; Step 5: The user terminal uses the target server to access functions; Step 6: When the user terminal ends the access, it sends a stop service function request to the experimental service platform, and the experimental service platform deletes the target experimental environment deployed in the target server according to the stop service function request.

2. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method according to claim 1, characterized in that: The step 1 specifically includes: Step 11: The administrator plans different experimental environments required on the experimental service platform according to functional requirements; Step 12, the administrator configures the planned different experimental environments on the experimental service platform; the different experimental environments include CPU / GPU type terminal environment, VSCode environment, ThingsBoard environment, Jupyter environment, 2D / 3D simulation environment, Remix environment, WeBASE environment and AIGC environment; The CPU / GPU type terminal environment is used for transmitting, storing and processing data; The VSCode environment is used for cross-platform source code editing and supports multiple programming languages; The ThingsBoard environment is used to collect and visualize data from IoT devices; The Jupyter environment is used for data analysis, computing, and machine learning; The 2D / 3D simulation environment is used to simulate the wiring, data generation and processing, testing and debugging, and cloud platform access of real IoT devices, so as to realize the simulation of interconnection and communication collaboration between devices; The Remix environment is used for basic compilation and deployment to local or test networks and contract execution; The WeBASE environment is used to build between blockchain applications and FISCO-BCOS nodes; The AIGC environment is used for intelligent teaching assistants, answering related questions and intelligent scoring of teaching tasks in majors related to the Internet of Things, artificial intelligence and blockchain.

3. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method according to claim 1, characterized in that: The servers corresponding to different cloud computing vendors include Huawei Cloud Server, Alibaba Cloud Server, Microsoft Cloud Server and self-built K8S Server; Step 2 specifically includes: Step 21: The experimental service platform obtains the configuration information of the cloud computing vendor from the configuration center during the startup process; Step 22: Determine the corresponding cloud computing vendor implementation class according to the configuration information; If the configuration information describes a Huawei cloud computing vendor, obtain the Huawei cloud computing vendor implementation class; If the configuration information describes an Alibaba Cloud computing vendor, obtain the Alibaba Cloud computing vendor implementation class; If the configuration information describes a Microsoft cloud computing vendor, then obtain the Microsoft cloud computing vendor implementation class; If the configuration information describes a K8S cloud computing vendor, obtain the K8S cloud computing vendor implementation class; Step 23: Load the corresponding cloud computing vendor implementation class object into the Spring container according to the cloud computing vendor implementation class; If it is a Huawei cloud computing vendor implementation class, load the Huawei cloud computing vendor implementation class object into the corresponding Spring container; If it is an Alibaba Cloud Computing vendor implementation class, load the Alibaba Cloud Computing vendor implementation class object into the corresponding Spring container; If it is a Microsoft cloud computing vendor implementation class, load the Microsoft cloud computing vendor implementation class object into the corresponding Spring container; If it is a K8S cloud computing vendor implementation class, load the K8S cloud computing vendor implementation class object into the corresponding Spring container; Step 24: Use the server used to run the Spring container as the target server; If the Spring container used to load the Huawei cloud computing vendor implementation class objects is running on the Huawei cloud server, the Huawei cloud server is determined as the target server; If the Spring container used to load the Alibaba Cloud computing vendor implementation class object is running on the Alibaba Cloud server, the Alibaba Cloud server is determined as the target server; If the Spring container used to load the Microsoft cloud computing vendor implementation class object is running on a Microsoft cloud server, the Microsoft cloud server is determined as the target server; If the Spring container used to load the K8S cloud computing vendor implementation class object is running on the K8S cloud server, the K8S cloud server is determined as the target server; Step 25: Register the experimental service platform to a registration center.

4. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method as claimed in claim 3, characterized in that: The step 3 specifically includes: Step 31, the user terminal obtains the configured experimental environment information from the experimental service platform; Step 32: Obtain scripts and image information for different experimental environment configurations according to the experimental environment information; Step 33: Determine the functions of different experimental environments based on the script and image information.

5. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method as claimed in claim 4, characterized in that: The step 32 specifically includes: Step 321: determine whether there is a CPU / GPU type terminal environment according to the experimental environment information. If so, obtain the script and image information configured for the CPU / GPU type terminal environment; otherwise, start the specified training environment; Step 322: determine whether there is a VSCode environment according to the experimental environment information. If so, obtain the script and image information configured in the VSCode environment; otherwise, start the specified training environment; Step 323: determine whether there is a ThingsBoard environment according to the experimental environment information. If so, obtain the script and image information configured for the ThingsBoard environment; otherwise, start the specified training environment; Step 324: determine whether there is a Jupyter environment according to the experimental environment information. If so, obtain the script and image information configured for the Jupyter environment; otherwise, start the specified training environment; Step 325: determine whether there is a 2D / 3D simulation environment according to the experimental environment information; if so, obtain the script and image information configured for the 2D / 3D simulation environment; otherwise, start the specified training environment; Step 326: Determine whether there is a Remix environment according to the experimental environment information. If so, obtain the script and image information configured in the Remix environment; otherwise, start the specified training environment; Step 327: determine whether there is a WeBASE environment according to the experimental environment information. If so, obtain the script and image information configured for the WeBASE environment; otherwise, start the specified training environment; Step 328: Determine whether there is an AIGC environment based on the experimental environment information. If so, obtain the script and image information configured for the AIGC environment; otherwise, start the specified training environment.

6. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method as claimed in claim 4, characterized in that: The step 4 specifically includes: Step 41: The user terminal initiates a service function activation request to the experiment service platform according to the functions of different experiment environments, and the service function request includes the specific experiment environment to be deployed; Step 42: the experiment service platform matches the corresponding target experiment environment according to the service function start request; Step 43: Obtain the script and image information of the target experimental environment, run the image information according to the script, and store the image directory data mounted by the script; and return the access information to the experimental service platform; If the target server is a Huawei cloud server, step 43 specifically includes: A1. Obtain the script and image information of the target experimental environment; A2. Call the service interface of Huawei Cloud Server according to the script to create a pod container corresponding to the target experimental environment; A3. Determine whether the user already has a disk in the Huawei Cloud server. If so, call the service interface of the Huawei Cloud server to bind the disk to the pod container. Otherwise, call the service interface of the Huawei Cloud server to create a new disk and bind the disk to the pod container. A4. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the disk; A5. Call the service interface of Huawei Cloud Server to obtain the access IP address and bind it to the pod container and return it to the experimental service platform. A6. The processing status of the pod container is called back through the pod probe and returned to the experimental service platform. If the target server is an Alibaba Cloud server, step 43 specifically includes: B1. Obtain the script and image information of the target experimental environment; B2. Call the service interface of the Alibaba Cloud server according to the script to create a pod container corresponding to the target experimental environment; B3. Determine whether the user already has a disk in the Alibaba Cloud server. If so, call the service interface of the Alibaba Cloud server to bind the disk to the pod container; otherwise, call the service interface of the Alibaba Cloud server to create a new disk and bind the disk to the pod container; B4. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the disk; B5. Call the service interface of the Alibaba Cloud server to obtain the access IP address from the Alibaba Cloud IP pool and bind it to the pod container and return it to the experimental service platform; B6. Call back the processing status of the pod container through the pod probe and return it to the experimental service platform; If the target server is a Microsoft cloud server, step 43 specifically includes: C1. Obtain the script and image information of the target experimental environment; C2. Determine whether there is an existing virtual machine in the Microsoft cloud server. If so, call the service interface of the Microsoft cloud server to start the corresponding virtual machine; otherwise, call the service interface of the Microsoft cloud server to create a new virtual machine and start the corresponding virtual machine; C3, call the service interface of the Microsoft cloud server to obtain the access IP address and bind it to the virtual machine and then return it to the experimental service platform; If the target server is a K8S cloud server, step 43 specifically includes: D1. Obtain the script and image information of the target experimental environment; D2. Call the service interface of the K8S cloud server according to the script to create a pod container corresponding to the target experimental environment; D3. Create an NFS storage server based on the script information and bind the NFS storage server to the pod container; D4. Map the access IP addresses and ports of the virtual machines and pod containers in the K8S cloud server according to the script information and return them to the experimental service platform; D5. Run the image information according to the script, start the pod container, and store the image directory data mounted by the script on the NFS storage server; D6. Call back the processing status of the pod container through the pod probe and return it to the experimental service platform.

7. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method according to claim 6, characterized in that: The step 5 specifically includes: Step 51: The experimental service platform receives the access information and sends it to the user terminal; If the target server is Huawei Cloud Server, Alibaba Cloud Server or Microsoft Cloud Server, the access information is the access IP address; If the target server is a self-built K8S server, the access information is the access IP address and port; Step 52: The user terminal receives the access information and accesses the target experimental environment of the target server according to the access information.

8. The AIOT platform multi-experimental environment and multi-cloud adaptation combination method as claimed in claim 7, characterized in that: The step 6 comprises: Step 61: When the user terminal ends access, the user terminal sends a stop service function request to the experimental service platform; Step 62: The experimental service platform calls the service interface of the target server according to the stop service function request to delete the target experimental environment deployed in the target server; If the target server is a Huawei cloud server, step 62 specifically includes: a1. Call the service interface of Huawei Cloud Server to unbind and delete the access IP address in Huawei Cloud Server; a2. Call the service interface of Huawei Cloud Server to delete the binding relationship between the disk and the pod container in Huawei Cloud Server; a3. Call the service interface of Huawei Cloud Server to delete the pod container in Huawei Cloud Server; If the target server is an Alibaba Cloud server, step 62 specifically includes: b1. Call the service interface of the Alibaba Cloud server to unbind and delete the access IP address in the Alibaba Cloud server; b2. Call the service interface of the Alibaba Cloud server to delete the binding relationship between the disk and the pod container in the Alibaba Cloud server; b3. Call the service interface of Alibaba Cloud Server to delete the pod container in Alibaba Cloud Server; If the target server is a Microsoft cloud server, step 62 specifically includes: c1. Call the service interface of the Microsoft cloud server to unbind and delete the access IP address in the Microsoft cloud server; c2. Call the service interface of Microsoft Cloud Server to shut down the virtual machine; If the target server is a K8S cloud server, step 62 specifically includes: d1. Call the service interface of the Alibaba Cloud server to unbind and delete the port in the K8S cloud server and unbind the access IP address; d2. Call the service interface of the K8S cloud server to delete the binding relationship between the NFS storage server and the pod container in the K8S cloud server; d3. Call the service interface of the K8S cloud server to delete the pod container in the K8S cloud server; Step 63: After the deletion operation is completed, the target server feeds back the operation success information to the user terminal through the experimental service platform.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the AIOT platform multi-experimental environment and multi-cloud adaptation combination method as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the AIOT platform multi-experimental environment and multi-cloud adaptation combination method as described in any one of claims 1 to 8 is implemented.

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