Software and hardware separation test method and device, electronic equipment and medium

Abstract

The invention discloses a software and hardware separation test method and device, electronic equipment and a medium. According to the method, to-be-tested software is installed in a container instance of hardware equipment; simulating an interface of the hardware equipment through the virtual equipment node; when it is recognized that the to-be-tested software calls the hardware device through the interface, calling of the to-be-tested software to the real hardware device is intercepted through a hijacking technology, and the calling is dynamically replaced with an AI image driven by the virtual camera; and testing the to-be-tested software based on the AI image. According to the method, the real hardware equipment is subjected to hardware simulation, and the to-be-tested software calls the simulated hardware equipment container instance through a hijacking technology and dynamic replacement, so that the real hardware does not need to be matched with a test, and test images with different characteristics and different hardware parameters can be flexibly generated through the hardware equipment container instance; therefore, the influence of the test images with different hardware parameters and different features on the AI diagnosis result can be verified, and the test accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence medical assistant diagnosis, and particularly to a software and hardware separation testing method, device, electronic device and medium. Background Art

[0002] The AI (Artificial Intelligence) face-to-face diagnosis system is an innovative solution that combines artificial intelligence technology and medical diagnosis, aiming to provide health assessment and disease diagnosis through image recognition and data analysis. Such systems usually consist of two major parts: hardware devices and a background AI analysis platform. Hardware devices: These include devices for collecting user images or videos, such as skin detectors, cameras, etc. These devices are designed to capture high-quality images or videos for subsequent analysis. Some advanced devices may also have specific functions, such as magnification functions, different light source modes (for more accurately displaying skin conditions), etc. Background AI analysis platform: Once the user's image data is uploaded to the cloud through a dedicated application, it will be received by this platform. The platform uses machine learning and deep learning algorithms to analyze this data. In the example of skin disease diagnosis, the AI model can be trained to identify various skin conditions, from relatively common ones such as acne and eczema to more serious ones such as skin cancer. In addition, with the progress of technology, the accuracy of the AI face-to-face diagnosis system is constantly improving, and the types of data that can be processed are also becoming more extensive.

[0003] It can be seen that in the existing AI face-to-face diagnosis system, the hardware device is responsible for collecting real user images and uploading them to the cloud through an APP (Application), and then processed by the AI analysis platform in the cloud to complete the entire test process. However, this testing method has the following defects: Strong hardware dependence: Real hardware is required for cooperation in testing, and it is impossible to quickly verify the impact of different hardware parameters (such as resolution, color space) on the AI diagnosis result.

[0004] Insufficient coverage of test scenarios: It is impossible to flexibly generate test images with specific features, which affects the test results. Summary of the Invention

[0005] Embodiments of the present invention provide a software and hardware separation testing method, device, electronic device and medium, so as to improve the testing accuracy and efficiency.

[0006] In a first aspect, embodiments of the present invention provide a software and hardware separation testing method, and the method includes: Install the software to be tested in the container instance of the hardware device; Simulate the interface of the hardware device through a virtual device node; When it is recognized that the software under test calls a hardware device through the interface, intercept the call of the software under test to the real hardware device through hijacking technology, and dynamically replace it with an AI image of a virtual camera driver; Test the software under test based on the AI image.

[0007] Furthermore, the construction process of the container instance of the hardware device includes: Based on the docker-Android framework, configure an Android system image consistent with the hardware device, and the Android system image includes the following parameter configuration information: interface level, screen resolution, virtual camera; Build a running instance of the Android system image to obtain the container instance of the hardware device.

[0008] Furthermore, the dynamic replacement with an AI image of a virtual camera driver includes: If the image input source is local, convert the locally selected image to an AI image according to the parameters of the hardware device, and inject the AI image into the image buffer in the container instance; If the image input source is the cloud, push the face image generated by the cloud to the image queue in the container instance through the gRPC interface, and use the face image in the image queue as the AI image.

[0009] Furthermore, before the dynamic replacement with an AI image of a virtual camera driver, it also includes: Mount a hybrid image source through the user-mode file system, and the hybrid image source includes local and cloud; According to the dynamic image source switching request, switch the image input source in real time.

[0010] Furthermore, after it is recognized that the software under test calls a hardware device through the interface, before intercepting the call of the software under test to the real hardware device through hijacking technology and dynamically replacing it with an AI image of a virtual camera driver, it also includes: Use the transparent proxy server deployed in the container instance to dynamically modify the request header and encryption key for the call request sent by the software under test according to the currently simulated network protocol and encryption scenario, and obtain the processed call request; Redirect the processed call request to the proxy service.

[0011] Furthermore, the intercepting the call of the software under test to the real hardware device through hijacking technology includes: Intercept the ImageReader call of the App to the Camera2 API through the LD_PRELOAD hijacking technology.

[0012] Further, the hardware device is an AI face diagnosis hardware device, and the AI image is an AI face diagnosis image.

[0013] In a second aspect, an embodiment of the present invention provides a software and hardware separation test device, and the device includes: An installation module, configured to install a software to be tested in a container instance of a hardware device; A simulation module, configured to simulate an interface of a hardware device through a virtual device node; A replacement module, configured to intercept a call of the software to be tested to a real hardware device through hijacking technology and dynamically replace it with an AI image driven by a virtual camera driver when it is recognized that the software to be tested calls the hardware device through the interface; A test module, configured to test the software to be tested based on the AI image.

[0014] Further, it further includes a container construction module, configured to configure an Android system image consistent with the hardware device based on the docker-Android framework. The Android system image includes the following parameter configuration information: interface level, screen resolution, virtual camera; construct a running instance of the Android system image to obtain a container instance of the hardware device.

[0015] Further, the replacement module is specifically configured to, if the image input source is local, convert the locally selected image into an AI image according to the parameters of the hardware device and inject the AI image into the image buffer in the container instance; if the image input source is the cloud, push the face image generated by the cloud to the image queue in the container instance through the gRPC interface, and use the face image in the image queue as the AI image.

[0016] Further, the replacement module is further configured to mount a mixed image source through a user-space file system, and the mixed image source includes local and cloud; and switch the image input source in real time according to a dynamic image source switching request.

[0017] Further, the replacement module is further configured to use a transparent proxy server deployed in the container instance to dynamically modify the request header and encryption key for a call request sent by the software to be tested according to the currently simulated network protocol and encryption scenario, and obtain a processed call request; redirect the processed call request to the proxy service.

[0018] Further, the replacement module is specifically configured to intercept an ImageReader call of an App to the Camera2 API through the LD_PRELOAD hijacking technology.

[0019] Further, the hardware device is an AI face diagnosis hardware device, and the AI image is an AI face diagnosis image.

[0020] In a third aspect, an embodiment of the present invention provides an electronic device, which includes a processor and a memory. The memory is used to store program instructions, and the processor is used to implement the steps of the above software and hardware separation test method when executing the computer program stored in the memory.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above software and hardware separation test method are implemented.

[0022] In the embodiment of the present invention, the software to be tested is installed in the container instance of the hardware device; the interface of the hardware device is simulated through a virtual device node; when it is recognized that the software to be tested calls the hardware device through the interface, the call of the software to be tested to the real hardware device is intercepted through hijacking technology and dynamically replaced with the AI image driven by the virtual camera; based on the AI image, the software to be tested is tested. In this invention, the real hardware device is hardware-simulated, and through hijacking technology and dynamic replacement, the software to be tested calls the container instance of the simulated hardware device, without the need for real hardware to cooperate with the test. Different characteristic test images and different hardware parameters can be flexibly generated through the container instance of the hardware device. Therefore, the influence of different hardware parameters and different characteristic test images on the AI diagnosis result can be verified, and the test accuracy and efficiency are improved. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a process schematic diagram of a software and hardware separation test method provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a software and hardware separation test device provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. Detailed Embodiments

[0025] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] To improve the test accuracy and efficiency, an embodiment of the present invention provides a software-hardware separation test method, device, electronic device, and medium.

[0027] Embodiment 1: Figure 1 The following is a schematic diagram of the process of a software-hardware separation test method provided by an embodiment of the present invention. The process includes the following steps: S101: Install the software to be tested in the container instance of the hardware device.

[0028] S102: Simulate the interface of the hardware device through a virtual device node.

[0029] S103: When it is recognized that the software to be tested calls the hardware device through the interface, intercept the call of the software to be tested to the real hardware device through hijacking technology, and dynamically replace it with the AI image of the virtual camera driver.

[0030] S104: Based on the AI image, test the software to be tested.

[0031] Since in the embodiment of the present invention, the real hardware device is hardware-simulated, and through hijacking technology and dynamic replacement, the software to be tested calls the container instance of the simulated hardware device, without the need for real hardware to cooperate in the test. Different characteristic test images and different hardware parameters can be flexibly generated through the container instance of the hardware device. Therefore, the influence of different hardware parameters and different characteristic test images on the AI diagnosis result can be verified, improving the test accuracy and efficiency.

[0032] The software-hardware separation test method provided by the embodiment of the present invention is applied to an electronic device. The electronic device simulates the real hardware device by constructing a containerized environment, and designs a dynamic image injection and background communication proxy mechanism to achieve software-hardware separation testing. For example, the hardware device is an AI face diagnosis hardware device.

[0033] In one implementation, the electronic device can construct a containerized Android environment (docker-Android) to simulate a real hardware device. For example, the construction process of the container instance of the hardware device may include the following process: Based on the docker-Android framework, configure an Android system image that is consistent with the hardware device. The Android system image includes the following parameter configuration information: interface level, screen resolution, virtual camera. Build a running instance of the Android system image to obtain a container instance of the hardware device. Different hardware parameters, such as but not limited to resolution, color space, etc., can be quickly simulated in the container instance of the hardware device, so as to quickly verify the influence of different hardware parameters on the AI diagnosis result. And when simulating the containerized hardware, through docker-Android, rapid cloning and parameterized configuration of the hardware device system environment can be achieved.

[0034] In the above S101, the APP to be tested (i.e., the software to be tested) can be installed in the container of the hardware device, and camera and storage permissions can be granted. Then in S102, the hardware camera interface is simulated through a virtual device node, and the virtual device node can be, for example, / dev / videoX.

[0035] The hijacking technology in the above S103 may include the LD_PRELOAD hijacking technology (dynamic link library hijacking). In one implementation, when intercepting the call of the software to be tested to the real hardware device through the hijacking technology, the ImageReader call of the App (i.e., the software to be tested) to the Camera2 API can be intercepted through the LD_PRELOAD hijacking technology. In this implementation, based on the system API (Application Programming Interface), hijacking and virtual device nodes are used to replace the image source without modifying the APP code, and non-intrusive image injection can be achieved.

[0036] The image source includes local images and cloud images. Therefore, when dynamically replacing the AI image with the virtual camera driver in step 103 above, different processing can be performed according to the different image input sources. For example, if the image input source is local, the locally selected image is format-converted according to the parameters of the hardware device to obtain the AI image, and the AI image is injected into the image buffer in the container instance. Specifically, the locally selected image (such as JPG / DICOM format) can be injected into the buffer after being real-time converted according to the hardware device parameters (resolution, color space YUV420→NV21). Another example is that if the image input source is the cloud, the face image generated in the cloud is pushed to the image queue in the container instance through the gRPC (Google Remote Procedure Call) interface, and the face image in the image queue is used as the AI image. Specifically, based on the existing platform, diverse face images are generated to enrich the test data, and are pushed to the image queue in the container through the gRPC interface to improve the coverage and accuracy of the test. For example, the AI image is an AI face consultation image.

[0037] In one implementation, in order to ensure real-time switching of the input source during the test, before dynamically replacing the AI image with the virtual camera driver, a hybrid image source including local and cloud can also be mounted through the user-space file system; according to the dynamic image source switching request, the image input source is real-time switched. The user-space file system includes, for example, FUSE (Filesystem in Userspace). Mounting the hybrid image source (local + cloud) through the user-space file system supports real-time switching of the input source during the test, and through the real-time push mechanism of the local image format conversion and the generative AI image included in the dynamic image injection method, the simulated injection of the image can be ensured.

[0038] After it is recognized that the software under test calls a hardware device through an interface, the communication data between the software under test and the background system can be intercepted and recorded, and different network protocols and encryption scenarios can be simulated. For example, after it is recognized that the software under test calls a hardware device through an interface, before intercepting the call of the software under test to the real hardware device through hijacking technology and dynamically replacing it with the AI image of the virtual camera driver, the transparent proxy server deployed in the container instance can be used to dynamically modify the request header and encryption key for the call request sent by the software under test according to the currently simulated network protocol and encryption scenario, and obtain the processed call request; redirect the processed call request to the proxy service. In this implementation method, a transparent proxy server is deployed inside the container instance of the hardware device, and all requests of the software under test are redirected to the proxy service, and then the protocol simulation is implemented by dynamically modifying the request header and encryption key, etc. The transparent proxy server can be MITMproxy, and the request can include HTTP (Hypertext Transfer Protocol) / HTTPS (Hypertext Transfer Protocol Secure), and the request header can include information such as User-Agent (user agent) and device serial number, and the encryption key can include information such as AES (Advanced Encryption Standard) / GCM (Galois / Counter Mode).

[0039] It can be understood that the embodiments of the present invention are mainly directed to the products of traditional Chinese medicine face diagnosis instrument devices, but in actual applications, they can also be applied to other software and hardware combination test processes. Taking traditional Chinese medicine face diagnosis as an example, the embodiments of the present invention are further described in detail. The embodiments of the present invention propose an AI face diagnosis software and hardware separation test method based on a containerized Android environment, which is used to simulate the image transmission process between a medical hardware device and a background system. Specifically, it is applied to an AI face diagnosis image transmission simulation test system based on a containerized Android environment and a virtual camera driver. The core process includes the following parts: Hardware simulation layer: Simulate the system environment of the hardware device (such as Android version, sensor configuration) through a containerized Android image.

[0040] Image transmission layer: Provide local / cloud image injection interfaces and dynamically replace the camera image input called by the App.

[0041] Communication proxy layer: Intercept and record the communication data between the App and the background system, and simulate different network protocols and encryption scenarios.

[0042] The specific implementation includes the following steps: Step 1: Construction of Containerized Hardware Environment Based on the docker-Android framework, configure an Android system image (such as API Level, screen resolution, virtual camera driver) that is consistent with the target hardware device.

[0043] Install the App under test in the container, grant camera and storage permissions, and simulate the hardware camera interface through the virtual device node ( / dev / videoX).

[0044] Step 2: Image Simulation Injection Module Local Image Injection: Create a virtual image buffer in the container, and use the LD_PRELOAD hijacking technique to intercept the ImageReader call of the App to the Camera2 API.

[0045] Inject the locally selected image (such as JPG / DICOM format) into the buffer after real-time conversion according to the hardware device parameters (resolution, color space YUV420→NV21).

[0046] Cloud Image Generation: Based on the existing platform, generate diverse facial images to enrich the test data, and push them to the image queue in the container through the gRPC interface to improve the test coverage and accuracy.

[0047] In this step, dynamic image source switching: Mount a hybrid image source (local + cloud) through FUSE (user-space file system) to support real-time switching of the input source during the test.

[0048] Step 3: Background Communication Proxy Module Deploy a transparent proxy server (MITMproxy) inside the container to redirect all HTTP / HTTPS requests of the App to the proxy service.

[0049] Implement protocol simulation: Dynamically modify the request headers (such as User-Agent, device serial number), encryption keys (AES / GCM).

[0050] Through this embodiment, the image transmission process between the medical hardware device and the background system can be simulated to achieve the separate testing of the AI face diagnosis software and hardware.

[0051] Embodiment 2: On the basis of the above embodiments, Figure 2 This is a schematic structural diagram of a software and hardware separation testing device provided by an embodiment of the present invention. The device includes: An installation module 201 for installing the software under test in the container instance of the hardware device; A simulation module 202, used to simulate the interface of the hardware device through a virtual device node; The replacement module 203 is used to intercept the call of the software under test to the real hardware device through the hijacking technology when it is recognized that the software under test calls the hardware device through the interface, and dynamically replace it with the AI ​​image driven by the virtual camera; The testing module 204 is used to test the software to be tested based on the AI ​​image.

[0052] Furthermore, it also includes a container construction module, which is used to configure an Android system image consistent with the hardware device based on the docker-Android framework. The Android system image includes the following parameter configuration information: interface level, screen resolution, virtual camera; build a running instance of the Android system image to obtain a container instance of the hardware device.

[0053] Furthermore, the replacement module 203 is specifically used to, if the image input source is local, convert the format of the locally selected image according to the parameters of the hardware device to obtain an AI image, and inject the AI ​​image into the image buffer in the container instance; if the image input source is cloud-based, push the facial image generated in the cloud-based image to the image queue in the container instance through the gRPC interface, and use the facial image in the image queue as the AI ​​image.

[0054] Furthermore, the replacement module 203 is also used to mount a hybrid image source through a user-mode file system, where the hybrid image source includes a local image source and a cloud image source; and to switch the image input source in real time according to a dynamic image source switching request.

[0055] Furthermore, the replacement module 203 is also used to utilize the transparent proxy server deployed in the container instance to dynamically modify the request header and encryption key of the call request sent by the software under test according to the currently simulated network protocol and encryption scenario, and obtain a processed call request; and redirect the processed call request to the proxy service.

[0056] Furthermore, the replacement module 203 is specifically used to intercept the App's call to the ImageReader of the Camera2API through the LD_PRELOAD hijacking technology.

[0057] Furthermore, the hardware device is AI facial diagnosis hardware device, and the AI ​​image is AI facial diagnosis image.

[0058] Embodiment 3: Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Based on the above embodiments, an electronic device is further provided in an embodiment of the present invention, including a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304; The memory 303 stores a computer program. When the program is executed by the processor 301, the processor 301 executes the steps of implementing the software and hardware separation testing method in the above embodiment.

[0059] For the specific implementation process, please refer to the above embodiment, and the similarities are not repeated here.

[0060] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0061] The communication interface 302 is used for communication between the electronic device and other devices.

[0062] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0063] The above-mentioned processor can be a general-purpose processor, including a central processing unit, a network processor (NP), etc.; it can also be a digital signal processing processor (DSP), an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0064] Embodiment 4: Based on the above embodiments, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the software and hardware separation testing method in the above embodiments.

[0065] For the specific implementation process, please refer to the above embodiment, and the similarities are not repeated here.

[0066] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0067] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0068] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0070] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A software and hardware separation testing method, characterized in that, The method includes: Install the software to be tested within the container instance of the hardware device; Simulate the interface of the hardware device through a virtual device node; When it is recognized that the software to be tested calls the hardware device through the interface, intercept the call of the software to be tested to the real hardware device through hijacking technology, and dynamically replace it with the AI image of the virtual camera driver; Based on the AI image, test the software to be tested.

2. The method according to claim 1, wherein The construction process of the container instance of the hardware device includes: Based on the docker-Android framework, configure an Android system image consistent with the hardware device, and the Android system image includes the following parameter configuration information: interface level, screen resolution, virtual camera; Build a running instance of the Android system image to obtain the container instance of the hardware device.

3. The method according to claim 1, characterized in that The dynamic replacement with the AI image of the virtual camera driver includes: If the image input source is local, convert the locally selected image into an AI image according to the parameters of the hardware device, and inject the AI image into the image buffer within the container instance; If the image input source is the cloud, push the facial image generated in the cloud to the image queue within the container instance through the gRPC interface, and use the facial image in the image queue as the AI image.

4. The method according to claim 3, wherein Before the dynamic replacement with the AI image of the virtual camera driver, it further includes: Mount a hybrid image source through the user-space file system, and the hybrid image source includes local and cloud; According to the dynamic image source switching request, switch the image input source in real time.

5. The method according to any one of claims 1 to 4, characterized in that, After it is recognized that the software to be tested calls the hardware device through the interface, before intercepting the call of the software to be tested to the real hardware device through hijacking technology and dynamically replacing it with the AI image of the virtual camera driver, it further includes: Utilize the transparent proxy server deployed in the container instance to dynamically modify the request header and encryption key of the call request sent by the software to be tested for the current simulated network protocol and encryption scenario, and obtain the processed call request; Redirect the processed call request to the proxy service.

6. The method according to claim 1, wherein The intercepting of the call of the software to be tested to the real hardware device through hijacking technology includes: Intercept the ImageReader call of the App to the Camera2 API through the LD_PRELOAD hijacking technology.

7. The method according to claim 1, characterized in that, The hardware device is an AI face diagnosis hardware device, and the AI image is an AI face diagnosis image.

8. A software and hardware separation test device, characterized in that, The device includes: An installation module for installing the software to be tested within the container instance of the hardware device; A simulation module for simulating the interface of the hardware device through a virtual device node; A replacement module for intercepting the call of the software to be tested to the real hardware device through hijacking technology and dynamically replacing it with the AI image of the virtual camera driver when it is recognized that the software to be tested calls the hardware device through the interface; A testing module for testing the software to be tested based on the AI image.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory. The memory is used to store program instructions, and the processor is used to implement the steps of the software and hardware separation test method according to any one of claims 1-7 when executing the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, It stores a computer program, and when the computer program is executed by the processor, it implements the steps of the software and hardware separation test method according to any one of claims 1-7.

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