Android container dynamic collaborative scheduling method, system and equipment based on edge cloud

Abstract

The invention belongs to the cross technical field of cloud computing and mobile equipment management, and particularly relates to an edge cloud-based Android container dynamic collaborative scheduling method, system and equipment. Transmitting the issued task and data information related to the task to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud node receives the issued task and the data information related to the task, and executes at least one preprocessing task to obtain a first processing result; utilizing a preset edge cloud to dynamically allocate the issued tasks based on a preset task allocation strategy according to the calculation requirement, the delay requirement and the current resource condition in the first processing result; and executing the issued task based on the allocated main body, and feeding back the task to the corresponding Android container. Through cooperative processing of the edge cloud and the Android container, low-delay response of tasks, efficient resource utilization and data security protection are achieved, and the method is suitable for scenes such as real-time interaction and industrial automation.

Description

Technical Field

[0001] The present invention belongs to the cross - technical field of cloud computing and mobile device management, and particularly relates to an Android container dynamic cooperative scheduling method, system and device based on an edge cloud. Background Art In the current information technology field, as an extension of cloud computing, the edge cloud pushes resources such as computing and storage to the network edge, getting closer to user terminal devices, which can effectively reduce data transmission latency and improve response speed, and is suitable for application scenarios with high real - time requirements. The Android container technology provides a lightweight and isolatable running environment for Android applications, facilitating the deployment and management of applications.

[0002] However, currently, in the aspect of combining the edge cloud and Android containers for Android application management, a complete and efficient collaborative management system and implementation method have not been formed. Most of the existing solutions are to apply the edge cloud or Android containers separately, lacking a deep collaborative cooperation mechanism between the two, resulting in the inability to fully utilize the respective advantages of the edge cloud and Android containers in actual applications and unable to effectively meet the increasingly complex Android application management requirements.

[0003] Currently, there are some similar solutions that attempt to combine the edge cloud with Android application management, but their main method is to simply migrate some functions of Android applications to the edge cloud server for processing, while the Android container is only used as a simple isolation running environment for local applications, without forming a real - sense collaborative management.

[0004] For example, some solutions only use the computing resources of the edge cloud to remotely process some data - intensive tasks of Android applications (such as large - volume image analysis, etc.), and then return the results to the applications in the local Android container. However, in this solution, there is a lack of a systematic and continuous interaction mechanism between the edge cloud and the Android container, and there is no comprehensive collaborative processing for aspects such as the overall deployment, update, running state monitoring, and security management of applications, resulting in low application management efficiency and inability to effectively ensure the safe and stable operation of applications in complex environments.

[0005] That is, traditional Android application deployment depends on local device resources, having problems such as low resource utilization rate, complex management, and poor real - time performance. Existing solutions only simply migrate some tasks to the edge cloud and lack a systematic cooperation mechanism. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an Android container dynamic cooperative scheduling method, system and device based on an edge cloud, so as to solve the problems in the prior art that traditional Android application deployment depends on local device resources, has problems such as low resource utilization rate, complex management, and poor real - time performance, and existing solutions only simply migrate some tasks to the edge cloud and lack a systematic cooperation mechanism. According to the first aspect of the embodiments of the present invention, there is provided a method for dynamic collaborative scheduling of Android containers based on an edge cloud, and the method includes: Obtain the issued task and the data information related to the task by using a preset Android container; Transmit the issued task and the data information related to the task to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud includes at least one edge cloud node; The edge cloud node receives the issued task and the data information related to the task, executes at least one preprocessing task, and obtains a first processing result; the preprocessing tasks include: data filtering, feature extraction, and preliminary analysis; the first processing result carries a computing requirement and a latency requirement; Use a preset edge cloud to dynamically allocate the issued task based on the computing requirement, latency requirement, and current resource status in the first processing result, and based on a preset task allocation strategy; Execute the issued task based on the allocated entity and feedback it to the corresponding Android container.

[0007] Further, the preset lightweight protocol is the MQTT protocol, and the MQTT protocol uses a heartbeat detection mechanism to ensure link stability.

[0008] Further, the edge cloud node executes the preprocessing tasks, including: For data filtering, screen the data information related to the task according to preset rules, and remove invalid or redundant data; For feature extraction, use a machine learning algorithm to extract key features related to the task from the filtered data; For preliminary analysis, perform simple statistical analysis or pattern recognition based on the extracted features to generate a preliminary analysis result.

[0009] Further, the preset task allocation strategy includes: If the issued task is a latency-sensitive task, it is preferentially allocated to an edge cloud node for processing; If the issued task is a resource-intensive task and the current resource status of the edge cloud node cannot meet the computing requirement, the issued task is sent to the cloud for processing.

[0010] Further, the executing the issued task based on the allocated entity and feedbacking it to the corresponding Android container includes: If the assigned entity is an edge cloud node, the edge cloud node uses local resources to execute the task, generates a second processing result, and feeds back the second processing result to the corresponding Android container through the preset lightweight protocol; If the assigned entity is the cloud, the cloud uses its large-scale computing resources to execute the task, generates a third processing result, and feeds back the third processing result to the edge cloud node through a preset network, and then the edge cloud node forwards the result to the corresponding Android container.

[0011] Further, before the edge cloud node executes the preprocessing task, it further includes: The step of performing integrity and legality verification on the issued task and related data information. If the verification fails, the task is rejected for processing, and an error message is fed back to the task issuer.

[0012] Further, the edge cloud monitors the resource status and task execution status of each edge cloud node in real time, and dynamically adjusts the preset task allocation strategy according to the monitoring results.

[0013] Further, a two-way communication mechanism is established between the edge cloud and the Android container. The Android container feeds back the satisfaction information of the task execution result to the edge cloud, and the edge cloud further optimizes the task allocation according to the satisfaction information.

[0014] According to the second aspect of the embodiments of the present invention, there is provided an Android container dynamic cooperative scheduling system based on an edge cloud, which is applied to the Android container dynamic cooperative scheduling method described in any one of the above. The system includes: An acquisition module, configured to use a preset Android container to acquire the issued task and the data information related to the task; A first processing module, configured to transmit the issued task and the data information related to the task to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud includes at least one edge cloud node; A second processing module, configured to receive the issued task and the data information related to the task by the edge cloud node, execute at least one preprocessing task, and obtain a first processing result; the preprocessing task includes: data filtering, feature extraction, and preliminary analysis; the first processing result carries computing requirements and latency requirements; A third processing module, configured to use a preset edge cloud to dynamically allocate the issued task based on a preset task allocation strategy according to the computing requirements, latency requirements, and current resource status in the first processing result; A fourth processing module, configured to execute the issued task based on the assigned entity and feed back to the corresponding Android container.

[0015] According to the third aspect of the embodiments of the present invention, there is provided a device for dynamic collaborative scheduling of Android containers based on an edge cloud, the device including: A memory on which an executable program is stored; A processor configured to execute the executable program in the memory to implement the steps of the method described in any one of the above.

[0016] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: Improve resource utilization efficiency: By reasonably deploying Android applications on the edge cloud platform and in Android containers, making full use of the powerful computing and storage resources of the edge cloud and the isolation characteristics of Android containers, resource waste is avoided and the overall resource utilization efficiency is improved.

[0017] Optimize application running performance: It can monitor the running status of applications in real time and dynamically adjust resource allocation, enabling Android applications to obtain better running conditions in the collaborative environment of the edge cloud and Android containers, thereby improving the response speed, reducing latency, and enhancing the running performance of the applications.

[0018] Enhance security protection: Under the collaborative management of the edge cloud platform and Android containers, a more complete security protection system can be established. On the one hand, the edge cloud platform can provide professional security protection measures such as data encryption and intrusion detection; on the other hand, the isolation characteristics of Android containers also help prevent interference between applications and the spread of malware, ensuring the data security and running security of Android applications.

[0019] Facilitate application management: The present invention provides a centralized management method, which uniformly manages aspects such as the deployment, running status monitoring, resource scheduling, and update of Android applications through a collaborative management module, greatly simplifying the management process of Android applications and improving the management efficiency.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0022] Figure 1 is a schematic diagram of the steps of providing a method for dynamic collaborative scheduling of Android containers based on an edge cloud shown according to an exemplary embodiment; Figure 2 is a design flow chart of providing a method for dynamic collaborative scheduling of Android containers based on an edge cloud shown according to an exemplary embodiment; Figure 3It is a system architecture flowchart showing a method for dynamically collaborative scheduling of Android containers based on edge cloud according to an exemplary embodiment; Figure 4 It is a schematic diagram showing the composition of a system for dynamically collaborative scheduling of Android containers based on edge cloud according to an exemplary embodiment; Figure 5 It is a schematic diagram showing the composition of a device for dynamically collaborative scheduling of Android containers based on edge cloud according to an exemplary embodiment. Detailed implementation manners

[0023] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0024] Embodiment 1 Please refer to Figure 1 , Figure 1 It is a step schematic diagram showing a method for dynamically collaborative scheduling of Android containers based on edge cloud according to an exemplary embodiment. The method includes: S1. Use a preset Android container to obtain the issued task and the data information related to the task; S2. Transmit the issued task and the data information related to the task to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud includes at least one edge cloud node; S3. The edge cloud node receives the issued task and the data information related to the task, and executes at least one preprocessing task to obtain a first processing result; the preprocessing tasks include: data filtering, feature extraction, and preliminary analysis; the first processing result carries a computing requirement and a latency requirement; S4. Use a preset edge cloud to dynamically allocate the issued task based on the computing requirement, latency requirement, and current resource status in the first processing result according to a preset task allocation strategy; S5. Execute the issued task based on the allocated entity and feedback it to the corresponding Android container.

[0025] In one embodiment, the Android container realizes the efficient acquisition and processing of tasks and data through technologies such as lightweight protocol communication and dynamic resource negotiation. In scenarios such as cloud gaming and industrial Internet of Things, it can ensure the low-latency execution of real-time tasks and ensure data security through encryption technology.

[0026] In one embodiment, in the edge cloud and Android container collaborative management system, these two parts play key but different roles: (I) Roles of the edge cloud: 1. Data processing and analysis: Edge cloud nodes can process a large amount of data transmitted from devices (including Android devices), reducing dependence on the cloud. This includes data filtering, preliminary analysis, and performing low-latency computing tasks.

[0027] 2. Reducing latency: By executing computing tasks at the edge nodes, the edge cloud can significantly reduce the latency of data processing and decision-making, which is crucial for applications that require real-time responses (such as autonomous driving and telemedicine).

[0028] 3. Improving privacy and security: Much data can be processed at the edge, reducing the need to transmit data to the cloud, enhancing data privacy protection, and reducing the risk of cyberattacks.

[0029] 4. Resource optimization: The edge cloud can intelligently allocate tasks according to local computing resources and network conditions, optimizing system performance, especially when the network connection is unstable or costly.

[0030] 5. Local caching and services: Edge nodes can cache content or services, improving access speed and reducing dependence on remote servers, which is particularly common in content delivery networks (CDNs).

[0031] (II) Roles of Android containers: 1. Application isolation and security: Through containerization, applications on Android devices can run in isolation, enhancing security and preventing one application from affecting other parts of the system or other applications.

[0032] 2. Unified management and deployment: Container technology makes the deployment and management of applications more standardized and controllable. Through cloud or edge cloud management platforms, application containers on Android devices can be uniformly managed to achieve automatic updates, monitoring, and resource allocation.

[0033] 3. Efficient resource utilization: The lightweight nature of containers allows multiple application instances to run on Android devices without the need to configure a complete operating system environment for each application separately, thus saving system resources.

[0034] 4. Application portability: Containerized applications have good portability and can be migrated between different Android devices or from edge cloud nodes to the cloud and vice versa, simplifying application distribution and maintenance.

[0035] 5. Quick startup and stop: Containers can be quickly started and stopped, suitable for scenarios that require dynamic resource scheduling, such as quickly switching between different applications.

[0036] (III) Synergy: 1. Collaborative computing: Edge cloud and Android containers can collaborate to achieve hierarchical task processing. The edge cloud is responsible for more complex or real-time response tasks, while Android containers handle the specific requirements of the application layer.

[0037] 2. Data flow management: Data can be transmitted from Android devices to the edge cloud for preliminary processing or analysis, and then to the cloud for further processing or storage if necessary.

[0038] 3. Application consistency: Through container technology, ensure that the application logic is consistent whether running on Android devices or edge clouds, providing a consistent user experience.

[0039] This collaborative management system provides an effective solution for various scenarios that require low latency, high security, and resource-optimized applications by integrating the real-time processing capabilities of the edge cloud and the application management advantages of Android containers.

[0040] In specific implementation, as described in steps S1 - S2, it includes: Use an Android device to obtain tasks issued by users. The Android device collects data through sensors, cameras, etc., and sends the data to the edge node through a lightweight protocol (such as MQTT).

[0041] In specific implementation, as described in step S3, after receiving the data, the edge node performs preprocessing tasks, such as data filtering, feature extraction, and preliminary analysis. If the task requires more complex processing or long-term storage, the data will be further transmitted to the cloud.

[0042] Specifically, for data filtering, screen the data information related to the task according to preset rules to remove invalid or redundant data; For feature extraction, use machine learning algorithms to extract key features related to the task from the filtered data; For preliminary analysis, perform simple statistical analysis or pattern recognition based on the extracted features to generate preliminary analysis results.

[0043] In specific implementation, as described in step S4, the preset task allocation strategy includes: If the assigned task is a latency-sensitive task, it is preferentially allocated to the edge cloud node for processing; If the assigned task is a resource-intensive task and the current resource status of the edge cloud node cannot meet the computing requirements, the assigned task is sent to the cloud for processing.

[0044] Further, executing the assigned task based on the assigned entity and feeding back to the corresponding Android container includes: If the assigned entity is an edge cloud node, the edge cloud node uses local resources to execute the task, generates a second processing result, and feeds back the second processing result to the corresponding Android container through the preset lightweight protocol; If the assigned entity is the cloud, the cloud uses its large-scale computing resources to execute the task, generates a third processing result, and feeds back the third processing result to the edge cloud node through a preset network, and then the edge cloud node forwards the result to the corresponding Android container.

[0045] In specific implementation, before the edge cloud node executes the preprocessing task, it further includes: Steps for performing integrity and legality verification on the assigned task and related data information. If the verification fails, the task is rejected for processing, and an error message is fed back to the task sender.

[0046] In specific implementation, TLS / SSL encryption is used in the transmission from the Android device to the edge node, and the transmission from the edge node to the cloud is also encrypted.

[0047] Specifically, the edge cloud monitors the resource status and task execution status of each edge cloud node in real time, and dynamically adjusts the preset task allocation strategy according to the monitoring results.

[0048] It should be noted that the edge cloud dynamically adjusts task allocation according to the current network status and device resource usage. For example, if the CPU usage rate of the Android device is high, the computing task may be transferred to the edge node; Implement load balancing among multiple edge nodes to ensure that no single node becomes a bottleneck.

[0049] More specifically, a two-way communication mechanism is established between the edge cloud and the Android container. The Android container feeds back satisfaction information on the task execution result to the edge cloud, and the edge cloud further optimizes task allocation according to the satisfaction information.

[0050] In specific implementation, a unified API interface is defined to allow communication between the Android device and the edge cloud, including task requests, status reports, and data transmission.

[0051] In specific implementation, a message queue system (such as RabbitMQ) is used to process asynchronous tasks and events to ensure the reliability and scalability of the system.

[0052] In specific implementation, for container management and coordination, it includes: Applications on Android devices run through containers (such as Docker) to isolate the application environment and ensure the independence and security of the applications.

[0053] Container orchestration tools (such as KubeEdge extended from Kubernetes, OpenYurt) are used to manage containers on edge nodes. These tools provide: Automated deployment: Deploy applications from the cloud or edge cloud to containers on Android devices.

[0054] Update and rollback: Manage application updates and perform quick rollbacks when necessary.

[0055] Monitoring and logging: Real-time monitor the running status of containers and collect logs.

[0056] Task coordination: The edge cloud coordinates container tasks on Android devices. Through APIs or message queues (such as Kafka), the edge cloud can send task instructions or data to containers on Android devices.

[0057] In a system where the edge cloud and Android containers work together, the process of data production and processing can be divided into the following detailed steps: One: Data production (Android device side) 1.1 Data collection: Sensors and devices: Android devices collect data through built-in or external sensors (such as accelerometers, light sensors, GPS, etc.).

[0058] User interaction: Users generate data through the application interface, such as clicks, input of information, taking photos, etc. After the data generated by users through the application interface is submitted to the background, various tasks are generated; Application operation: Applications running on the device generate data such as logs and status information.

[0059] 1.2 Data preprocessing: Local caching: Data may be first stored in the local memory or storage of the device to prevent data loss in case of unstable network.

[0060] Initial processing: Some data may be basically processed on the device, such as compressing pictures and filtering noisy data to reduce the amount of data transmitted.

[0061] 1.3 Data sending: Protocol Selection: Use an appropriate communication protocol (such as MQTT, CoAP, HTTP) to send data to the edge node. MQTT is often used in scenarios with high real-time requirements.

[0062] It should be noted that the data needs to be transmitted encrypted. The data is encrypted before sending to ensure security during the transmission process.

[0063] II. Data Processing (Edge and Cloud) 2.1 Data Reception: The edge node receives data from multiple Android devices. The node may use a load balancing mechanism to share the data traffic.

[0064] 2.2 Data Decoding and Analysis: Data Decryption: Decrypt the data after receiving it.

[0065] Real-time Analysis: The edge node uses its computing resources to perform a more in-depth analysis of the data, such as: Feature Extraction: Extract useful features from the sensor data.

[0066] Pattern Recognition: Identify specific patterns or anomalies.

[0067] Decision Support: Make decisions or trigger actions based on the analysis results (such as automatically adjusting smart home devices).

[0068] 2.3 Task Allocation and Processing: Task Judgment: The edge cloud node determines whether further processing is required: Local Processing: If the task requires low latency and resources permit, it is processed at the edge node.

[0069] Cloud Processing: Complex tasks or data that requires long-term storage are sent to the cloud.

[0070] Caching and Services: Content Caching: The edge node caches commonly used data or service content to improve access speed and reduce network load.

[0071] Service Response: Directly respond to certain queries or requests to reduce dependence on remote servers.

[0072] III. Further Processing of Data in the Cloud 3.1 Data Upload: If the data needs further analysis, storage, or for a global view, the edge node will upload the data to the cloud.

[0073] 3.2 Cloud Analysis: Big Data Analysis: The cloud can perform more complex analyses, including machine learning model training, long-term trend analysis, etc.

[0074] 3.3 Data storage: long-term storage and backup of data.

[0075] 3.3 Feedback and control: Feedback mechanism: The results or decisions processed by the cloud can be fed back to the edge nodes or directly to the Android device for adjustment or control.

[0076] Update strategy: According to the analysis results of the cloud, the strategies or applications of the edge nodes or Android devices may be updated.

[0077] In specific implementation, as the core connecting the edge cloud platform and the Android container cluster, the integrity and effectiveness of its functions are crucial for the coordinated operation of the entire system. It can accurately analyze the requirements of applications, monitor the running status, and dynamically adjust resource allocation, which is the key to achieving efficient coordinated management.

[0078] Furthermore, it can formulate a reasonable deployment plan in the edge cloud platform and Android containers according to the characteristics of different Android applications (such as real-time requirements, resource requirements, etc.), which is an important link to make full use of the respective advantages of the edge cloud and Android containers and improve the application running efficiency.

[0079] Furthermore, ensuring the stability and efficiency of the communication link between the edge cloud platform and the Android container cluster, and adopting a customized efficient communication protocol to reduce communication latency is one of the key factors to ensure the overall performance of the system.

[0080] Please refer to Figure 4 , Figure 4 which is a schematic diagram showing the composition of an Android container dynamic cooperative scheduling system based on the edge cloud according to an exemplary embodiment. The system includes: An acquisition module 40, configured to obtain the tasks and the data information related to the tasks issued by using a preset Android container. A first processing module 41, configured to transmit the issued tasks and the data information related to the tasks to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud includes at least one edge cloud node. A second processing module 42, configured to receive the issued tasks and the data information related to the tasks by the edge cloud node, and execute at least one preprocessing task to obtain a first processing result; the preprocessing tasks include: data filtering, feature extraction, and preliminary analysis; the first processing result carries calculation requirements and latency requirements. A third processing module 43, configured to use a preset edge cloud to dynamically allocate the issued tasks based on the calculation requirements, latency requirements, and current resource status in the first processing result, according to a preset task allocation strategy. The fourth processing module 44 is configured to execute the assigned task based on the assigned main body and feed back to the corresponding Android container.

[0081] Please refer to Figure 5 , Figure 5 which is a schematic diagram of the composition of a device for providing dynamic cooperative scheduling of Android containers based on an edge cloud according to an exemplary embodiment. The device includes: A memory 51 storing an executable program thereon; A processor 52 configured to execute the executable program in the memory 51 to implement the steps of the method described in any one of the above.

[0082] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content in other embodiments.

[0083] It should be noted that in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise stated, the meaning of "a plurality of" refers to at least two.

[0084] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a manner that is not shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0085] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following well-known technologies in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0086] Those of ordinary skill in the technical field of the present invention can understand that all or part of the steps carried by the method for implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0087] In addition, each functional unit in various embodiments of the present invention may be integrated into a processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0088] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, or the like.

[0089] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0090] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A dynamic collaborative scheduling method for Android containers based on edge cloud, characterized in that , the method comprises: Use the preset Android container to obtain the issued tasks and data information related to the tasks; The issued task and data information related to the task are transmitted to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud includes at least one edge cloud node; The edge cloud node receives the issued task and data information related to the task, performs at least one preprocessing task, and obtains a first processing result; the preprocessing task includes: data filtering, feature extraction, and preliminary analysis; the first processing result carries computing requirements and delay requirements; Using a preset edge cloud to dynamically allocate the issued tasks based on a preset task allocation strategy according to the computing requirements, delay requirements, and current resource conditions in the first processing result; The issued tasks are executed based on the assigned subjects and fed back to the corresponding Android container.

2. According to the edge cloud-based Android container dynamic collaborative scheduling method of claim 1, it is characterized in that: The preset lightweight protocol is the MQTT protocol, and the MQTT protocol uses a heartbeat detection mechanism to ensure link stability.

3. According to the edge cloud-based Android container dynamic collaborative scheduling method of claim 1, it is characterized in that: The edge cloud node performs the preprocessing task, including: For data filtering, the data information related to the task is screened according to preset rules to remove invalid or redundant data; For feature extraction, machine learning algorithms are used to extract key features related to the task from the filtered data; For preliminary analysis, simple statistical analysis or pattern recognition is performed based on the extracted features to generate preliminary analysis results.

4. According to the edge cloud-based Android container dynamic collaborative scheduling method of claim 1, it is characterized in that: The preset task allocation strategy includes: If the task is a delay-sensitive task, it will be assigned to the edge cloud node for processing first; If the task sent is a resource-intensive task and the current resource status of the edge cloud node cannot meet the computing requirements, the task sent is sent to the cloud for processing.

5. According to the edge cloud-based Android container dynamic collaborative scheduling method of claim 1, it is characterized in that: The subject based on the allocation executes the issued task and feeds back to the corresponding Android container, including: If the assigned subject is an edge cloud node, the edge cloud node uses local resources to perform the task, generates a second processing result, and feeds back the second processing result to the corresponding Android container through the preset lightweight protocol; If the assigned entity is the cloud, the cloud uses its large-scale computing resources to perform the task, generates a third processing result, and feeds the third processing result back to the edge cloud node through a preset network, and the edge cloud node then forwards the result to the corresponding Android container.

6. According to the edge cloud-based Android container dynamic collaborative scheduling method of claim 1, it is characterized in that: Before the edge cloud node performs preprocessing tasks, it also includes: The step of checking the integrity and legality of the task and related data information issued, if the check fails, the task is rejected and an error message is fed back to the task issuer.

7. The method for dynamic collaborative scheduling of Android containers based on edge cloud according to claim 1 is characterized in that: The edge cloud monitors the resource status and task execution status of each edge cloud node in real time, and dynamically adjusts the preset task allocation strategy according to the monitoring results.

8. The method for dynamic collaborative scheduling of Android containers based on edge cloud according to claim 1 is characterized in that: A two-way communication mechanism is established between the edge cloud and the Android container. The Android container feeds back satisfaction information of task execution results to the edge cloud, and the edge cloud further optimizes task allocation based on the satisfaction information.

9. The Android container dynamic collaborative scheduling system based on edge cloud is applied to the Android container dynamic collaborative scheduling method based on edge cloud as described in any one of claims 1 to 8, characterized in that , the system comprises: An acquisition module, used to acquire the issued tasks and data information related to the tasks by using a preset Android container; A first processing module, configured to transmit the issued task and data information related to the task to an edge cloud node of a preset edge cloud through a preset lightweight protocol; the edge cloud includes at least one edge cloud node; A second processing module is used for the edge cloud node to receive the issued task and the data information related to the task, perform at least one pre-processing task, and obtain a first processing result; the pre-processing task includes: data filtering, feature extraction, and preliminary analysis; the first processing result carries computing requirements and delay requirements; A third processing module is used to dynamically allocate the issued tasks based on a preset task allocation strategy using a preset edge cloud according to the computing requirements, delay requirements, and current resource conditions in the first processing result; The fourth processing module is used to execute the issued task based on the assigned subject and feed back to the corresponding Android container.

10. Android container dynamic collaborative scheduling device based on edge cloud, characterized in that: The device comprises: a memory having an executable program stored therein; A processor, configured to execute the executable program in the memory to implement the steps of the method according to any one of claims 1 to 8.

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