A body intelligent end cloud collaborative control method and system

Abstract

This invention relates to the field of embodied intelligence technology and discloses an embodied intelligence edge-cloud collaborative control method and system. The method includes: receiving environmental perception data and task instructions collected by an embodied intelligence device; determining the task category based on the task instructions; establishing an edge-side execution record queue and a cloud-side execution record queue for each task category; the execution record queue is a fixed-length circular queue structure of length N, used to record the results of the most recent N task executions; calculating the edge-side execution success rate and the cloud-side execution success rate based on the queue data; when the difference between the two is greater than a preset threshold T, selecting the side with the higher success rate as the current task execution position; and updating the corresponding circular queue after the task execution is completed. This invention achieves the updability and stability of edge-cloud execution decisions through a fixed-length statistical structure.

Description

Technical Field

[0001] This invention relates to the field of embodied intelligence technology, and in particular to an edge-cloud collaborative control method and system for embodied intelligent devices. Background Technology

[0002] In the process of performing tasks, existing embodied intelligent devices typically need to allocate execution locations between edge devices and cloud servers. Existing technologies mostly use fixed rules or preset strategies for execution selection, lacking an update mechanism based on historical execution result statistics, which may lead to fluctuations in execution performance or unstable resource utilization efficiency.

[0003] Therefore, it is necessary to provide a well-structured, statistically updatable end-to-cloud collaborative control method. Summary of the Invention

[0004] The purpose of this invention is to provide an edge-cloud collaborative control method and system based on execution result statistics, so as to improve the stability of execution location selection. Technical solution

[0005] This invention provides an embodied intelligent edge-cloud collaborative control method, comprising the following steps: Step S1: Task Reception The embodied intelligent device receives environmental perception data and task instructions. The environmental perception data includes, but is not limited to, image data, location information, or sensor data. The task instructions are used to instruct the embodied intelligent device to perform specific operations.

[0006] Step S2: Determine the task category The tasks are categorized according to the task instructions to determine the corresponding task categories. A separate execution record structure is established for each different task category.

[0007] Step S3: Establish an execution record queue Create separate instances for each task category: End-side execution record queue Cloud Execution Record Queue The execution record queue is a circular queue structure with a fixed length of N.

[0008] N is a preset positive integer, with a value range of 5–50.

[0009] Step S4: Execution Result Recording Method The execution record queue is used to store the results of the N most recent task executions.

[0010] The execution result is recorded in binary format: Successful execution is recorded as 1. Execution failure is recorded as 0. When the queue length exceeds N, delete the oldest record.

[0011] Step S5: Success Rate Calculation Calculate the execution success rate on the client side and the execution success rate on the cloud side separately: Success rate = Sum of values ​​in the queue ÷ N Step S6: Execution Location Selection The difference between the execution success rate on the computing side and the execution success rate on the cloud.

[0012] When the difference is greater than the preset threshold T, the side with the higher success rate is selected as the current task execution position.

[0013] The threshold T is a preset value, ranging from 0.05 to 0.5.

[0014] Step S7: Queue Update After the task is completed, the execution result is written to the corresponding execution record queue, and the oldest record is deleted. This completes one end-to-end cloud collaborative control process.

[0015] System Solution The present invention also provides an embodied intelligent edge-cloud collaborative control system, comprising: The sensing data acquisition module is used to acquire environmental sensing data and task instructions; The task identification module is used to determine the task category; The execution record storage module is used to establish and maintain circular queues of execution records on the client side and in the cloud. The success rate calculation module is used to calculate the execution success rate on the client side and the execution success rate on the cloud side. The execution location selection module is used to determine the execution location based on the success rate difference; The queue update module is used to update the execution record queue after a task is completed.

[0016] Storage Media Solution The present invention also includes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method steps.

[0017] Beneficial Effects: This invention records historical execution results through a fixed-length circular queue structure and selects between edge and cloud execution based on success rate statistics, giving execution decisions a statistical basis and update mechanism. The structure is clear and easy to implement in engineering. Attached Figure Description To more clearly illustrate the technical solution of the present invention, the accompanying drawings involved in the present invention are briefly described below. Obviously, the drawings described below are merely illustrative drawings of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort. Figure 1 This is a flowchart illustrating the intelligent edge-cloud collaborative control method of the present invention.

Claims

1. A method for integrated intelligent edge-cloud collaborative control, characterized in that, include: Receive environmental perception data and task instructions collected by the embodied intelligent device; The task category is determined based on the task instructions; Establish a client-side execution record queue and a cloud-based execution record queue for each of the aforementioned task categories; The execution record queue is a circular queue structure of fixed length N, used to record the results of the most recent N task executions; The task execution result is recorded in binary form, where successful execution is recorded as 1 and failed execution is recorded as 0; Calculate the execution success rate on the client side and the execution success rate on the cloud side separately. The calculation formula is: Success rate = Sum of values ​​in the queue / N; When the difference between the success rate of execution on the device side and the success rate of execution on the cloud side is greater than the preset threshold T, the side with the higher success rate is selected as the current task execution position. After the task is completed, the execution result is written to the corresponding circular queue, and the oldest record is deleted.

2. The method according to claim 1, characterized in that, The value of N ranges from 5 to 50.

3. The method according to claim 1, characterized in that, The preset threshold T has a value range of 0.05–0.

5.

4. A personalized intelligent edge-cloud collaborative control system, characterized in that, include: Sensing data acquisition module Task identification module Execution record storage module Success rate calculation module Execution location selection module Queue update module The modules work together to implement the steps of the method described in claim 1.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 3.

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