TWI811862B - Virtualization environment creation method and electronic device - Google Patents

Abstract

A virtualization environment creation method and an electronic device are disclosed. The method includes: training an inference model according to a training data set which includes information of first hardware equipment in at least one first virtualization environment; detecting a virtualization environment creation instruction; and executing the inference model according to the training data set to create a target virtualization environment and pre-building at least one second hardware equipment into the target virtualization environment.

Description

Virtual environment establishment method and electronic device

The present invention relates to a computer technology, and in particular to a virtual environment establishment method and electronic device.

As the data computing capabilities of electronic devices with computing functions such as notebook computers continue to improve, many types of electronic devices are shipped from the factory with the creation and execution function of a virtual environment (also known as a virtual machine (Virtualization machine, VM)). For example, Microsoft's Windows 10 operating system (Windows 10) is equipped with the Windows Subsystem for Linux (Windows Subsystem for Linux, WSL) by default. WSL can run in the windowed interface of Windows 10, allowing users to operate Linux systems in the windowed interface. However, the creation of a virtual environment requires setting a lot of information. For example, users need to manually select which types of hardware devices in the host device are to be added to the virtual environment. In addition, after the virtual environment is created, users are not allowed to add new hardware devices to the virtual environment midway. These operational limitations related to hardware devices often cause considerable trouble to users when operating virtual environments.

In view of this, the present invention provides a method and an electronic device for establishing a virtual environment, which can effectively improve the efficiency of establishing a virtual environment.

Embodiments of the present invention provide a method for establishing a virtual environment, which is used in electronic devices. The virtual environment establishment method includes: training an inference model according to a training data set, wherein the training data set includes information on at least one first hardware device in the virtual environment; detecting a virtual environment construction instruction; and according to the virtual environment The environment building instruction runs the inference model to create a target virtual environment and build at least one second hardware device in the target virtual environment.

An embodiment of the present invention further provides an electronic device, which includes a storage circuit and a processor. The storage circuit is used to store an inference model and a training data set, wherein the training data set includes information on at least one first hardware device in the virtual environment. The processor is coupled to the storage circuit. The processor is used to: train the inference model according to the training data set; detect a virtual environment establishment instruction; and run the inference model according to the virtual environment establishment instruction to establish a target virtual environment and at least one The second hardware device is built into the target virtual environment.

Based on the above, the inference model can be trained based on the training data set. In particular, the training data set may include information about the first hardware device in the virtual environment. After detecting the virtual environment establishment instruction, the inference model may be run according to the virtual environment establishment instruction to establish a target virtual environment and build the second hardware device into the target virtual environment. This can effectively improve the efficiency of creating a virtual environment.

FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention. Referring to FIG. 1 , the electronic device 10 may include various electronic devices with data processing functions such as smartphones, tablet computers, notebook computers, desktop computers, industrial computers or servers.

The electronic device 10 includes a processor 11 , a storage circuit 12 and an input/output (I/O) interface 13 . The processor 11 is responsible for all or part of the operation of the electronic device 10 . For example, the processor 11 may include a central processing unit (CPU), a graphics processing unit (GPU), or other programmable general-purpose or special-purpose microprocessors or digital signal processors. (Digital Signal Processor, DSP), programmable controller, Application Specific Integrated Circuits (ASIC), Programmable Logic Device (PLD) or other similar devices or a combination of these devices.

The storage circuit 12 is used to store data. For example, storage circuit 12 may include volatile storage circuits and non-volatile storage circuits. Volatile storage circuits are used to store data volatilely. For example, the volatile storage circuit may include random access memory (RAM) or similar volatile storage media. Non-volatile storage circuits are used to store data in a non-volatile manner. For example, the non-volatile storage circuit may include a read-only memory (ROM), a solid state disk (SSD), a traditional hard disk drive (HDD) or similar non-volatile storage media. .

The input/output interface 13 may include various signal input/output devices such as a camera lens, a communication circuit (such as a network interface card), a mouse, a keyboard, a touch pad, a screen, a speaker, and/or a microphone. The present invention does not limit the device type of the input/output interface 13.

In one embodiment, the storage circuit 12 can be used to store the training data set 101 and the inference model 102. Training data set 101 may be used to train inference model 102. The inference model 102 may include a deep learning (deep learning) model or a neural network (Neural Network) model and other various artificial intelligence models that can be trained to autonomously perform specific functions.

In one embodiment, the training data set 101 may include information on at least one hardware device (also referred to as the first hardware device) in the virtual environment. For example, the information may include the device type of the first hardware device and other information that can be used to describe the first hardware device. In addition, the first hardware device refers to a hardware device used in the at least one virtual environment. For example, the first hardware device may include any hardware device in the electronic device 10 or other electronic devices.

In one embodiment, the training data set 101 may further include at least one of device identification information and information used to run a program in the virtual environment. For example, the device identification information may include information such as a motherboard serial number or a Media Access Control address (MAC address) of a specific electronic device (such as the electronic device 10 or other electronic devices) that can be used to identify the specific electronic device. In addition, the information about the program used to run the virtual environment may include the program name of the program used to run the virtual environment and other information that may be used to describe the program used to run the virtual environment.

FIG. 2 is a schematic diagram of training an inference model based on a training data set according to an embodiment of the present invention. Referring to FIG. 2 , the training data set 101 may include training data 21 and 22 . The training data 21 includes hardware information 211, identification information 212 and program information 213 related to a certain virtual environment (also called the first virtual environment). The hardware information 211 may include description information (eg, device type) applied to the hardware device in the first virtual environment. The identification information 212 may include identification information (eg, motherboard serial number) of the electronic device running the first virtual environment. The program information 213 may include description information (eg, program name) used to run the program in the first virtual environment.

The training data 22 includes hardware information 221, identification information 222 and program information 223 related to another virtual environment (also called a second virtual environment). The hardware information 221 may include description information (eg, device type) applied to the hardware device in the second virtual environment. The identification information 222 may include identification information (eg, motherboard serial number) of the electronic device running the second virtual environment. The program information 223 may include description information (eg, program name) of the program used to run the second virtual environment. By analogy, training data related to more virtual environments may also be included in the training data set 101 .

In an embodiment, the processor 11 can train the inference model 102 according to the training data set 101 . For example, during the process of training the inference model 102, various information in the training data set 101 can be sent to the inference model 102 to adjust the decision logic of the inference model 102.

Please return to Figure 1. After training the inference model 102, the processor 11 can detect the virtual environment establishment instruction. For example, the processor 11 can detect user operations through the input/output interface 13 and obtain virtual environment construction instructions according to the user operations. This virtual environment creation command instructs the creation of a virtual environment (also called a target virtual environment). Taking Microsoft Windows 10 as an example, the target virtual environment can be established based on WSL. Or, taking the Chrome operating system as an example, the target virtual environment can be established based on Crostini.

In one embodiment, the processor 11 can run the inference model 102 according to the virtual environment establishment instruction to establish the target virtual environment and build at least one hardware device (also referred to as a second hardware device) in the target. in a virtual environment. Alternatively, in one embodiment, the processor 11 can run the inference model 102 to select hardware devices (ie, second hardware devices) suitable for being built into the target virtual environment. After the second hardware device is built into or added to the target virtual environment, the second hardware device can be used in the created target virtual environment.

In one embodiment, the processor 11 may obtain device identification information corresponding to the target electronic device according to the virtual environment construction instruction. The target electronic device refers to an electronic device used to run the target virtual environment. For example, the target electronic device may be the electronic device 10 or other electronic devices. Processor 11 may input this device identification information to inference model 102 . Then, the processor 11 may determine the second hardware device according to the output of the inference model 102 .

FIG. 3 is a schematic diagram of determining a second hardware device according to an embodiment of the present invention. Referring to FIG. 3 , the processor 11 can obtain the identification information 31 according to the virtual environment establishment instruction. The identification information 31 may include device identification information (such as a motherboard serial number) of the target electronic device. The processor 11 may input the identification information 31 to the inference model 102 . The inference model 102 can output hardware information 32 according to the identification information 31 . The hardware information 32 may reflect the hardware device predicted by the inference model 102 to be suitable for being built into the target virtual environment (ie, the second hardware device). For example, the hardware information 32 may include description information (eg, device type) of the second hardware device. The processor 11 can determine the second hardware device according to the hardware information 32 . For example, the second hardware device may include at least part of the hardware device in the target electronic device.

In one embodiment, the processor 11 can automatically add the second hardware device to the created target virtual environment. Thereby, in the process of establishing the target virtual environment, the user does not even need to manually select the hardware devices to be added to the target virtual environment.

In one embodiment, the processor 11 may provide a device list based on the second hardware device. This device list may record descriptive information (such as device type) of the second hardware device. During the process of establishing the target virtual environment, the processor 11 may decide whether to directly add the second hardware device to the established target virtual environment based on user operations, or whether to delete or supplement the second hardware device. In one embodiment, the processor 11 can add the second hardware device adjusted by the user to the target virtual environment.

In one embodiment, after establishing the target virtual environment, the processor 11 may detect at least one interrupt packet related to at least one hardware device (also referred to as a third hardware device) during running of the target virtual environment. For example, the processor 11 may detect the interrupt packet through the operating system of the target electronic device. The interrupt packet may reflect that an abnormality occurs in the current operation behavior of the third hardware device. In response to the interrupt packet, the processor 11 may determine whether the third hardware device is operating normally in the target virtual environment. In response to the third hardware device not operating normally in the target virtual environment, the processor 11 may retrain the inference model 102 according to the information of the third hardware device (eg, device type) to optimize the inference model 102 . In addition, if the third hardware device operates normally in the target virtual environment, the processor 11 may not change the inference model 102.

In one embodiment, in response to the interrupt packet, the processor 11 may determine whether the third hardware device is not configured in the target virtual environment, whether the third hardware device is abnormal in the target virtual environment, and/or whether the third hardware device is abnormal in the target virtual environment. Whether the driver of the hardware device is abnormal in the target virtual environment. In response to the third hardware device not being configured in the target virtual environment, the third hardware device being abnormal in the target virtual environment, and/or the driver of the third hardware device being abnormal in the target virtual environment, the processor 11 may It is determined that the third hardware device does not operate normally in the target virtual environment.

In one embodiment, once an interrupt packet related to the third hardware device is detected, the processor 11 may determine whether the third hardware device is operating normally in the target virtual environment. If the third hardware device does not operate normally in the target virtual environment, the processor 11 can perform subsequent optimization of the inference model 102 .

In one embodiment, after detecting an interrupt packet related to the third hardware device, the processor 11 may determine whether the occurrence frequency of the interrupt packet related to the third hardware device is higher than a preset value. In response to the occurrence frequency of interrupt packets related to the third hardware device being higher than a preset value (for example, the number of occurrences of interrupt packets related to the third hardware device being higher than a preset number within a period of time), the processor 11 may Perform the above-mentioned operation of determining whether the third hardware device is operating normally in the target virtual environment. However, if the occurrence frequency of interrupt packets related to the third hardware device is not higher than the preset value (for example, the number of occurrences of interrupt packets related to the third hardware device within a period of time is not higher than the preset number), then The processor 11 may temporarily not perform the above-mentioned operation of determining whether the third hardware device is operating normally in the target virtual environment.

In one embodiment, during the process of retraining the inference model 102 based on the information of the third hardware device (eg, device type), the processor 11 may add the information of the third hardware device to the retraining of the training data set 101 in the manifest, and retrain the inference model 102 using the data in the retraining manifest.

Taking FIG. 2 as an example, the processor 11 can add the training data 21 corresponding to the target virtual environment to the retraining list. Thus, in the training data 21 of the retraining list, the hardware information 211 may include the description information of the third hardware device (such as the device type), and the identification information 212 may include the identification information of the target electronic device (such as the motherboard serial number). , and the program information 213 may include description information (such as program name) used to run the program in the target virtual environment. The processor 11 can retrain the inference model 102 according to the training data 21 to optimize the inference model 102.

FIG. 4 is a flow chart of a method for establishing a virtual environment according to an embodiment of the present invention. Referring to FIG. 4 , in step S401 , an inference model is trained based on a training data set, where the training data set includes information on at least one first hardware device in the virtual environment. In step S402, a virtual environment establishment instruction is detected. In step S403, the inference model is run according to the virtual environment construction instruction to establish a target virtual environment and build at least one second hardware device in the target virtual environment.

FIG. 5 is a flow chart of a method for establishing a virtual environment according to an embodiment of the present invention. Referring to FIG. 5 , in step S501 , during running of the target virtual environment, at least one interrupt packet related to at least a third hardware device is detected via the operating system of the electronic device. In step S502, it is determined whether the third hardware device is operating normally in the target virtual environment. If the third hardware device does not operate normally in the target virtual environment, in step S503, the inference model is retrained based on the information of the third hardware device. In addition, if the third hardware device operates normally in the target virtual environment, step S501 can be returned without changing the inference model temporarily.

However, each step in Figure 4 and Figure 5 has been described in detail above, and will not be described again here. It is worth noting that each step in Figure 4 and Figure 5 can be implemented as multiple program codes or circuits, and the present invention is not limited thereto. In addition, the methods of FIG. 4 and FIG. 5 can be used in conjunction with the above exemplary embodiments or can be used alone, and are not limited by the present invention.

In summary, the embodiments proposed by the present invention can train an inference model based on the information related to a specific virtual environment in the training data set, so as to reflect the operating habits of one or more users into the inference model. The trained inference model can be used to predict hardware devices suitable to be built into the target virtual environment and pre-add these hardware devices to the target virtual environment while establishing the target virtual environment. Thereby, the built-in hardware equipment in the target virtual environment can be automatically set according to the operating habits of the user who sets the specific electronic device, effectively improving the efficiency of creating the virtual environment. In addition, after the target virtual environment is established, the inference model can also be dynamically optimized based on interrupt packets detected during the operation of the target virtual environment.

Although the present invention has been disclosed above through embodiments, they are not intended to limit the present invention. Anyone with ordinary knowledge in the technical field may make some modifications and modifications without departing from the spirit and scope of the present invention. Therefore, The protection scope of the present invention shall be determined by the appended patent application scope.

10: Electronic devices 11: Processor 12: Storage circuit 13: Input/output (I/O) interface 101: Training data set 102: Inference Model 21, 22: Training materials 211, 221, 32: Hardware information 212, 222, 31: Identification information 213, 223: Program information S401~S403, S501~S503: steps

FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention. FIG. 2 is a schematic diagram of training an inference model based on a training data set according to an embodiment of the present invention. FIG. 3 is a schematic diagram of determining a second hardware device according to an embodiment of the present invention. FIG. 4 is a flow chart of a method for establishing a virtual environment according to an embodiment of the present invention. FIG. 5 is a flow chart of a method for establishing a virtual environment according to an embodiment of the present invention.

S401~S403: steps

Claims (8)

A virtual environment creation method for an electronic device. The virtual environment creation method includes: training an inference model according to a training data set, wherein the training data set includes at least one information of a first hardware device in the virtual environment and a target corresponding to the first hardware device in the virtual environment. Device identification information of the electronic device, and the target electronic device is used to run the target virtual environment; detects a virtual environment construction instruction; runs the inference model according to the virtual environment construction instruction to establish the target virtual environment and at least a first Two hardware devices are built into the target virtual environment; during running of the target virtual environment, detect at least one interrupt packet related to at least a third hardware device; in response to the at least one interrupt packet, determine that the at least one Whether a third hardware device operates normally in the target virtual environment; and in response to the at least one third hardware device not operating normally in the target virtual environment, retraining based on the information of the at least one third hardware device This inference model. The virtual environment creation method of claim 1, wherein the training data set further includes information for running a program of the at least one virtual environment. The method of establishing a virtual environment as claimed in claim 1, wherein the inference model is run according to the virtual environment establishment instruction to establish the target virtual environment and build the at least one second hardware device in the target virtual environment. The steps include: obtaining the device identification information according to the virtual environment construction instruction; inputting the device identification information into the inference model; and The at least one second hardware device is determined according to the output of the inference model. The virtual environment establishment method according to claim 1, further comprising: in response to the at least one third hardware device not being configured in the target virtual environment and the at least one third hardware device experiencing an abnormality in the target virtual environment. Or the driver of the at least one third hardware device has an abnormality in the target virtual environment, and it is determined that the at least one third hardware device does not operate normally in the target virtual environment. An electronic device includes: a storage circuit for storing an inference model and a training data set, wherein the training data set includes at least one information of a first hardware device in a virtual environment and device identification information corresponding to a target electronic device, and The target electronic device is used to run the target virtual environment; and a processor is coupled to the storage circuit, wherein the processor is used to: train the inference model according to the training data set; detect virtual environment construction instructions; and according to the virtual environment The build instruction runs the inference model to create the target virtual environment and build at least one second hardware device in the target virtual environment; during running of the target virtual environment, detect and detect at least one third hardware device at least one interrupt packet related to the device; in response to the at least one interrupt packet, determining whether the at least one third hardware device is operating normally in the target virtual environment; and in response to the at least one third hardware device not operating normally in the target virtual environment. The target is virtual In the environment, the inference model is retrained based on the information of the at least one third hardware device. The electronic device of claim 5, wherein the training data set further includes information for running a program of the at least one virtual environment. The electronic device of claim 5, wherein the processor runs the inference model according to the virtual environment establishment instruction to establish the target virtual environment and build the at least one second hardware device in the target virtual environment. The operations include: obtaining the device identification information according to the virtual environment construction instruction; inputting the device identification information into the inference model; and determining the at least one second hardware device according to the output of the inference model. The electronic device of claim 5, wherein the processor is further configured to: in response to the at least one third hardware device not being configured in the target virtual environment, the at least one third hardware device being in the target virtual environment. An exception occurs in the system or the driver of the at least one third hardware device appears abnormal in the target virtual environment, and it is determined that the at least one third hardware device does not operate normally in the target virtual environment.

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