Method for converting OCI mirror image and embedded mirror image in embedded environment

Abstract

The invention relates to the technical field of embedded mirror image conversion, and discloses a method for converting an OCI mirror image and an embedded mirror image in an embedded environment, which comprises the following steps of: generating mirror image demand information by acquiring a task execution type and a task execution time period of a running task of each embedded device; according to the task execution idle time period and the current mirror image storage information of the embedded device, considering the sequence of the operation task execution time of the embedded device and the adjustment time of the embedded mirror image, the network condition between the embedded device and the container engine, the adjustment frequency of the embedded mirror image and other factors; carrying out distribution and mirror image adjustment strategy generation on a plurality of embedded device operation tasks in a target time period, and guiding mirror image conversion and task execution between a container engine and each embedded device at different moments in the target time period. The reasonability of switching the OCI mirror image in the container engine into the embedded mirror image and storing the OCI mirror image into the embedded device is improved, and the task execution suitability of the embedded device is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of embedded image conversion, and in particular to a method for converting an OCI image and an embedded image in an embedded environment. Background Art

[0002] Currently, in the application of embedded device groups, different embedded devices perform different types of tasks at different times and require different embedded images, which makes the use flexibility of embedded devices limited when only using the embedded images stored in their own embedded image modules to perform tasks.

[0003] Although OCI images play an important role in cloud-native environments, and can help embedded devices convert OCI images in the OCI image module of the cloud container engine into embedded images for use in embedded devices by connecting to the container engine, there are still some challenges in directly applying it to embedded systems. Embedded systems usually have limited resources (embedded image storage is limited on the embedded device side), network uncertainty between the cloud container engine (the time for converting the OCI image in the container engine to the embedded system as an embedded image is limited), and high real-time requirements (the response speed of the embedded system directly pulling the OCI image from the container engine to execute tasks is poor), which are somewhat different from the original design intention of the OCI image. When considering the execution of multiple tasks at different times by an embedded device group, it is necessary to ensure that the embedded image stored in the embedded device meets the current task requirements and has sufficient flexibility to meet the execution changes of multiple tasks, which is difficult to achieve in the existing technology.

[0004] Therefore, when an embedded device group executes multiple tasks, how to improve the control rationality of switching the OCI image in the container engine to the embedded image and storing it in the embedded device, and ensure that the task execution of the embedded device is compatible with the image, is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a method for converting an OCI image and an embedded image in an embedded environment, aiming to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a method for converting an OCI image and an embedded image in an embedded environment, the method comprising the following steps: S1: Acquire a number of embedded device running tasks in a target period; wherein each of the embedded device running tasks includes a task execution type and a task execution period; S2: Based on the task execution type and task execution period, generate image requirement information for each embedded device to run the task; S3: Obtain an idle embedded device set within a target period, call a corresponding task execution and image retention list based on each embedded device identifier in the embedded device set, and extract the task execution idle period and current image storage information of each embedded device; S4: according to the image demand information of each embedded device running task and the idle period of task execution of each embedded device, considering the current image storage information of each embedded device and the network status prediction information of the container engine in the target period, several embedded device running tasks in the target period are allocated, and the image adjustment strategy and task allocation strategy are generated; S5: Utilizing the image adjustment strategy and the task allocation strategy, executing the image conversion action and the task allocation action between the container engine and the embedded device.

[0007] Optionally, step S1 specifically includes: S11: when the server receives the embedded device requirements sent by the user, it generates an embedded device requirement list according to the requirement period in each embedded device requirement and arranges them in chronological order; S12: calling the embedded device requirement list stored in the server, determining the task execution type of the embedded device requirement according to the usage scenario description in each embedded device requirement, and then selecting several embedded device running tasks in the target period based on the requirement period of the embedded device requirement.

[0008] Optionally, in step S12, the task execution type required by the embedded device is determined according to the usage scenario description in each embedded device requirement, specifically including: S121: extracting a plurality of scenario function keywords described in the usage scenario in each embedded device requirement and a plurality of task execution keywords corresponding to each task execution type of the embedded device; S122: Based on a number of scenario function keywords and a number of task execution keywords, calculate the correlation between each embedded device requirement and each task execution type, and determine the task execution type of each embedded device requirement with the highest correlation.

[0009] Optionally, step S2 specifically includes: S21: Based on the task execution type, matching the required image category of each embedded device running task in the mapping relationship between different task execution types and image categories recorded in the image database; S22: Generate image requirement information for each embedded device running task by using the required image category and the task execution period.

[0010] Optionally, step S3 specifically includes: S31: querying embedded devices that are in an idle state within a target period, and constructing the identifiers of the embedded devices in the idle state into an embedded device set; S32: Based on each embedded device identifier in the embedded device set, call the corresponding task execution and image retention list, and determine the task execution idle period and current image storage information of each embedded device according to the task execution period and current image storage information of each embedded device recorded in the task execution and image retention list.

[0011] Optionally, step S4 specifically includes: S41: Obtain network status prediction information of the container engine within the target time period; wherein the network status prediction information is configured as the network bandwidth status allocated to the container engine within the target time period; S42: Based on the image requirement information of each embedded device running tasks and the idle period of task execution of each embedded device, the current image storage information of each embedded device and the network communication bandwidth allocated to the container engine in the target period are considered, and the embedded image stored by each embedded device at each moment in the target period contains the image category required by the embedded device at that moment as the first constraint condition, and the network bandwidth status of each embedded device during two consecutive executions of image adjustment actions satisfies the network conditions required for the embedded image to be imported from the container engine to the embedded device as the second constraint condition, and the space occupied by the embedded image in the embedded image module of each embedded device at each moment in the target period is less than the storage space allocated to the embedded device as the third constraint condition, and the number of times that all embedded devices import the embedded image from the OCI image module of the container engine to the embedded image module of the embedded device within the target period is minimized as the goal, and the image storage information of the embedded image module of each embedded device at each moment in the target period is solved; S43: solving the image adjustment strategy and task allocation strategy of each embedded device according to the image storage information of the embedded image module of each embedded device at each moment in the target period.

[0012] Optionally, step S43 specifically includes: S431: generating an image adjustment strategy for each embedded device according to the image storage information of the embedded image module of each embedded device at each moment in the target period and according to the difference between the image storage information at adjacent moments; S432: According to the image storage information of the embedded image module of each embedded device at each moment in the target period, according to the image requirement information of each embedded device running task, the embedded device running task is allocated to the corresponding embedded device, and a task allocation strategy for each embedded device is generated.

[0013] Optionally, step S5 specifically includes: S51: Using the image adjustment strategy, control the OCI image module in the container engine and the embedded image module of the corresponding embedded device to perform an image conversion action at a corresponding time within a target period; S52: Utilizing the task allocation strategy, executing a task allocation action of each embedded device running the task to the corresponding embedded device.

[0014] Optionally, in step S51, the OCI image module in the container engine and the embedded image module of the corresponding embedded device perform an image conversion action, specifically including: using the OverlayFS tool to assemble the OCI image module that performs image conversion in the OCI image module of the container engine and map it to the OCI image temporary directory, and copying the OCI image temporary directory to the embedded image temporary directory and then importing it into the embedded image directory to achieve the image conversion action.

[0015] Optionally, the method further includes step S6: the user selects the corresponding embedded device to execute the current embedded device running task according to the task allocation action of the embedded device running task to the corresponding embedded device.

[0016] The beneficial effects of the present invention are as follows: a method for converting an OCI image to an embedded image in an embedded environment is proposed, image demand information is generated by obtaining the task execution type and task execution period of each embedded device running task, and then according to the task execution idle period of the embedded device and the current image storage information, the order of the embedded device running task execution time and the embedded image adjustment time, the network conditions between the embedded device and the container engine, and the number of embedded image adjustments are considered, and several embedded device running tasks within the target period are allocated and image adjustment strategies are generated, guiding the image conversion actions and task allocation actions between the container engine and the embedded device at different times within the target period, and being able to improve the control rationality of switching the OCI image in the container engine to the embedded image and storing it in the embedded device when the embedded device group executes multiple tasks, ensuring that the task execution of the embedded device is compatible with the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of an embodiment of a method for converting an OCI image to an embedded image in an embedded environment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] The embodiment of the present invention provides a method for converting an OCI image and an embedded image in an embedded environment, referring to Figure 1 , Figure 1 The figure is a flow chart of an embodiment of a method for converting an OCI image to an embedded image in an embedded environment of the present invention.

[0020] In this embodiment, a method for converting an OCI image and an embedded image in an embedded environment is provided, and the method comprises the following steps: S1: Acquire a number of embedded device running tasks in a target period; wherein each of the embedded device running tasks includes a task execution type and a task execution period; S2: Based on the task execution type and task execution period, generate image requirement information for each embedded device to run the task; S3: Obtain an idle embedded device set within a target period, call a corresponding task execution and image retention list based on each embedded device identifier in the embedded device set, and extract the task execution idle period and current image storage information of each embedded device; S4: according to the image demand information of each embedded device running task and the idle period of task execution of each embedded device, considering the current image storage information of each embedded device and the network status prediction information of the container engine in the target period, several embedded device running tasks in the target period are allocated, and the image adjustment strategy and task allocation strategy are generated; S5: Utilizing the image adjustment strategy and the task allocation strategy, executing the image conversion action and the task allocation action between the container engine and the embedded device.

[0021] It should be noted that OCI images play an important role in cloud-native environments. By connecting to the container engine, embedded devices can convert the OCI images in the OCI image module of the cloud container engine into embedded images for use in embedded devices. However, there are still some challenges in directly applying it to embedded systems. Embedded systems usually have limited resources (embedded image storage is limited on the embedded device side), network uncertainty between the cloud container engine (the time for converting the OCI image in the container engine to the embedded system as an embedded image is limited), and high real-time requirements (the response speed of the embedded system directly pulling the OCI image from the container engine to execute tasks is poor). These are somewhat different from the original design intention of the OCI image. When considering the execution of multiple tasks at different times by an embedded device group, it is necessary to ensure that the embedded image stored in the embedded device meets the current task requirements and has sufficient flexibility to meet the execution changes of multiple tasks, which is difficult to achieve in the existing technology.

[0022] In order to solve the above problems, this embodiment obtains the task execution type and task execution period of each embedded device running task, generates image demand information, and then allocates several embedded device running tasks within the target period and generates image adjustment strategies based on the embedded device's task execution idle period and current image storage information, taking into account factors such as the sequence of embedded device running task execution time and embedded image adjustment time, the network conditions between the embedded device and the container engine, and the number of embedded image adjustments. It guides the image conversion and task execution between the container engine and each embedded device at different times within the target period, improves the rationality of switching the OCI image in the container engine to the embedded image and storing it in the embedded device, and ensures the task execution adaptability of the embedded device.

[0023] In a preferred embodiment, step S1 specifically includes: S11: when the server receives the embedded device requirements sent by the user, it generates an embedded device requirement list according to the requirement period in each embedded device requirement and arranges them in chronological order; S12: calling the embedded device requirement list stored in the server, determining the task execution type of the embedded device requirement according to the usage scenario description in each embedded device requirement, and then selecting several embedded device running tasks in the target period based on the requirement period of the embedded device requirement.

[0024] Furthermore, in step S12, the task execution type required by the embedded device is determined according to the usage scenario description in each embedded device requirement, specifically including: S121: extracting a plurality of scenario function keywords described in the usage scenario in each embedded device requirement and a plurality of task execution keywords corresponding to each task execution type of the embedded device; S122: Based on a number of scenario function keywords and a number of task execution keywords, calculate the correlation between each embedded device requirement and each task execution type, and determine the task execution type of each embedded device requirement with the highest correlation.

[0025] In this embodiment, after the user terminal sends the embedded device demand, the server calculates the correlation with different task execution types through keywords, determines the task execution type corresponding to the embedded device demand, and then selects the embedded device demand in the target time period, and finally generates several embedded device running tasks including task execution type and task execution time period.

[0026] In a preferred embodiment, step S2 specifically includes: S21: Based on the task execution type, matching the required image category of each embedded device running task in the mapping relationship between different task execution types and image categories recorded in the image database; S22: Generate image requirement information for each embedded device running task by using the required image category and the task execution period.

[0027] In this embodiment, the task execution type in the embedded device running task is used to match the required image category in the scenario in the image database, so as to generate image demand information, which can provide data support for the subsequent generation of image adjustment strategy and task allocation strategy.

[0028] In a preferred embodiment, step S3 specifically includes: S31: querying embedded devices that are in an idle state within a target period, and constructing the identifiers of the embedded devices in the idle state into an embedded device set; S32: Based on each embedded device identifier in the embedded device set, call the corresponding task execution and image retention list, and determine the task execution idle period and current image storage information of each embedded device according to the task execution period and current image storage information of each embedded device recorded in the task execution and image retention list.

[0029] In this embodiment, it is necessary to obtain the embedded device identification of the embedded device in the idle state, and then use the embedded device identification in the task execution and image retention list of each embedded device to query the task execution period of the embedded device and the embedded image stored in the current embedded image module, and determine the task execution idle period of each embedded device in the target period.

[0030] In a preferred embodiment, step S4 specifically includes: S41: Obtain network status prediction information of the container engine within the target time period; wherein the network status prediction information is configured as the network bandwidth status allocated to the container engine within the target time period; S42: Based on the image requirement information of each embedded device running tasks and the idle period of task execution of each embedded device, the current image storage information of each embedded device and the network communication bandwidth allocated to the container engine in the target period are considered, and the embedded image stored by each embedded device at each moment in the target period contains the image category required by the embedded device at that moment as the first constraint condition, and the network bandwidth status of each embedded device during two consecutive executions of image adjustment actions satisfies the network conditions required for the embedded image to be imported from the container engine to the embedded device as the second constraint condition, and the space occupied by the embedded image in the embedded image module of each embedded device at each moment in the target period is less than the storage space allocated to the embedded device as the third constraint condition, and the number of times that all embedded devices import the embedded image from the OCI image module of the container engine to the embedded image module of the embedded device within the target period is minimized as the goal, and the image storage information of the embedded image module of each embedded device at each moment in the target period is solved; S43: solving the image adjustment strategy and task allocation strategy of each embedded device according to the image storage information of the embedded image module of each embedded device at each moment in the target period.

[0031] Furthermore, step S43 specifically includes: S431: generating an image adjustment strategy for each embedded device according to the image storage information of the embedded image module of each embedded device at each moment in the target period and according to the difference between the image storage information at adjacent moments; S432: According to the image storage information of the embedded image module of each embedded device at each moment in the target period, according to the image requirement information of each embedded device running task, the embedded device running task is allocated to the corresponding embedded device, and a task allocation strategy for each embedded device is generated.

[0032] In this embodiment, according to the idle period of task execution of the embedded device and the current image storage information, the order of the embedded device running task execution time and the embedded image adjustment time, the network conditions between the embedded device and the container engine, and the number of embedded image adjustments are considered, and several embedded device running tasks within the target period are allocated and the image adjustment strategy is generated.

[0033] It should be noted that the image adjustment strategy finally obtained is: the type of embedded image stored in the embedded image module of each embedded device at each moment in the target period; the task allocation strategy finally obtained is: the embedded device to which each embedded device running task is assigned. Therefore, through the generation of image adjustment strategy and task allocation strategy, it is possible to guide the image conversion action and task allocation action between the container engine and the embedded device at different moments in the target period, and when the embedded device group executes multiple tasks, improve the control rationality of switching the OCI image in the container engine to the embedded image and storing it in the embedded device, and ensure that the task execution of the embedded device is compatible with the image.

[0034] In a preferred embodiment, step S5 specifically includes: S51: using the image adjustment strategy, controlling the OCI image module in the container engine and the embedded image module of the corresponding embedded device to perform an image conversion action at a corresponding time within a target period; S52: Utilizing the task allocation strategy, executing a task allocation action of each embedded device running the task to the corresponding embedded device.

[0035] Furthermore, in step S51, the OCI image module in the container engine and the embedded image module of the corresponding embedded device perform an image conversion action, specifically including: using the OverlayFS tool to assemble the OCI image module that performs image conversion in the OCI image module of the container engine and map it to the OCI image temporary directory, and by copying the OCI image temporary directory to the embedded image temporary directory and then importing it into the embedded image directory, the image conversion action is realized.

[0036] In this embodiment, after obtaining the image adjustment strategy and the task allocation strategy, in order to achieve the optimal execution process of multiple tasks by the embedded device group within the target time period, first, at the corresponding time within the target time period, the OCI image module in the container engine and the embedded image module of the corresponding embedded device are controlled to perform the image conversion action (that is, the conversion and import of the OCI image to the embedded image are completed at the corresponding time), and then the task allocation of each embedded device running task to the corresponding embedded device is executed.

[0037] In a preferred embodiment, the method further includes step S6: the user selects the corresponding embedded device to execute the current embedded device running task according to the task allocation action of the embedded device running task to the corresponding embedded device.

[0038] In this embodiment, after executing the task allocation, each embedded device executes the allocated embedded device running task at a corresponding time, and each embedded device adjusts the embedded image stored in the embedded image module according to the image adjustment strategy.

[0039] In addition, the present invention also proposes a conversion device for an OCI image and an embedded image in an embedded environment, wherein the conversion device for an OCI image and an embedded image in an embedded environment comprises: a memory, a processor, and a conversion program for an OCI image and an embedded image in an embedded environment stored in the memory and executable on the processor, wherein the conversion program for an OCI image and an embedded image in an embedded environment implements the steps of the method for converting an OCI image and an embedded image in an embedded environment as described above when the conversion program for an OCI image and an embedded image in an embedded environment is executed by the processor.

[0040] The specific implementation of the device for converting an OCI image and an embedded image in an embedded environment of the present application is basically the same as the embodiments of the method for converting an OCI image and an embedded image in the above-mentioned embedded environment, and will not be repeated here.

[0041] It is understood that, in the description of this specification, the description with reference to the terms "one embodiment", "another embodiment", "other embodiments", or "first to Nth embodiments" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0042] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0043] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure 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 converting an OCI image and an embedded image in an embedded environment, characterized in that: The method comprises the following steps: S1: Acquire a number of embedded device running tasks in a target period; wherein each of the embedded device running tasks includes a task execution type and a task execution period; S2: Based on the task execution type and task execution period, generate image requirement information for each embedded device to run the task; S3: Obtain an idle embedded device set within a target period, call a corresponding task execution and image retention list based on each embedded device identifier in the embedded device set, and extract the task execution idle period and current image storage information of each embedded device; S4: according to the image demand information of each embedded device running task and the idle period of task execution of each embedded device, considering the current image storage information of each embedded device and the network status prediction information of the container engine in the target period, several embedded device running tasks in the target period are allocated, and the image adjustment strategy and task allocation strategy are generated; S5: Utilizing the image adjustment strategy and the task allocation strategy, executing the image conversion action and the task allocation action between the container engine and the embedded device.

2. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 1, characterized in that: Step S1 specifically includes: S11: when the server receives the embedded device requirements sent by the user, it generates an embedded device requirement list according to the requirement period in each embedded device requirement and arranges them in chronological order; S12: calling the embedded device requirement list stored in the server, determining the task execution type of the embedded device requirement according to the usage scenario description in each embedded device requirement, and then selecting several embedded device running tasks in the target period based on the requirement period of the embedded device requirement.

3. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 2, characterized in that: In step S12, the task execution type required by the embedded device is determined according to the usage scenario description in each embedded device requirement, specifically including: S121: extracting a plurality of scenario function keywords described in the usage scenario in each embedded device requirement and a plurality of task execution keywords corresponding to each task execution type of the embedded device; S122: Based on a number of scenario function keywords and a number of task execution keywords, calculate the correlation between each embedded device requirement and each task execution type, and determine the task execution type of each embedded device requirement with the highest correlation.

4. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 1, characterized in that: Step S2 specifically includes: S21: Based on the task execution type, matching the required image category of each embedded device running task in the mapping relationship between different task execution types and image categories recorded in the image database; S22: Generate image requirement information for each embedded device running task by using the required image category and the task execution period.

5. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 1, characterized in that: Step S3 specifically includes: S31: querying embedded devices that are in an idle state within a target period, and constructing the identifiers of the embedded devices in the idle state into an embedded device set; S32: Based on each embedded device identifier in the embedded device set, call the corresponding task execution and image retention list, and determine the task execution idle period and current image storage information of each embedded device according to the task execution period and current image storage information of each embedded device recorded in the task execution and image retention list.

6. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 1, characterized in that: Step S4 specifically includes: S41: Obtain network status prediction information of the container engine within the target time period; wherein the network status prediction information is configured as the network bandwidth status allocated to the container engine within the target time period; S42: Based on the image requirement information of each embedded device running tasks and the idle period of task execution of each embedded device, the current image storage information of each embedded device and the network communication bandwidth allocated to the container engine in the target period are considered, and the embedded image stored by each embedded device at each moment in the target period contains the image category required by the embedded device at that moment as the first constraint condition, and the network bandwidth status of each embedded device during two consecutive executions of image adjustment actions satisfies the network conditions required for the embedded image to be imported from the container engine to the embedded device as the second constraint condition, and the space occupied by the embedded image in the embedded image module of each embedded device at each moment in the target period is less than the storage space allocated to the embedded device as the third constraint condition, and the number of times that all embedded devices import the embedded image from the OCI image module of the container engine to the embedded image module of the embedded device within the target period is minimized as the goal, and the image storage information of the embedded image module of each embedded device at each moment in the target period is solved; S43: solving the image adjustment strategy and task allocation strategy of each embedded device according to the image storage information of the embedded image module of each embedded device at each moment in the target period.

7. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 6, characterized in that: Step S43 specifically includes: S431: generating an image adjustment strategy for each embedded device according to the image storage information of the embedded image module of each embedded device at each moment in the target period and according to the difference between the image storage information at adjacent moments; S432: According to the image storage information of the embedded image module of each embedded device at each moment in the target period, according to the image requirement information of each embedded device running task, the embedded device running task is allocated to the corresponding embedded device, and a task allocation strategy for each embedded device is generated.

8. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 1, characterized in that: Step S5 specifically includes: S51: using the image adjustment strategy, controlling the OCI image module in the container engine and the embedded image module of the corresponding embedded device to perform an image conversion action at a corresponding time within a target period; S52: Utilizing the task allocation strategy, executing a task allocation action of each embedded device running the task to the corresponding embedded device.

9. The method for converting an OCI image and an embedded image in an embedded environment as claimed in claim 8, characterized in that: In step S51, the OCI image module in the container engine and the embedded image module of the corresponding embedded device perform an image conversion action, specifically including: using the OverlayFS tool to assemble the OCI image module that performs image conversion in the OCI image module of the container engine and map it to the OCI image temporary directory, and by copying the OCI image temporary directory to the embedded image temporary directory and then importing it into the embedded image directory, the image conversion action is realized.

10. The method for converting an OCI image and an embedded image in an embedded environment according to claim 1, characterized in that: The method further comprises step S6: the user selects the corresponding embedded device to execute the current embedded device running task according to the task allocation action of the embedded device running task to the corresponding embedded device.

Images