Intelligent scheduling system for humanoid robot under multitasking

Abstract

The present application relates to the technical field of scheduling system, specifically relates to humanoid robot intelligent scheduling system under multi-task processing, the system includes analysis module, control module, sequencing module and scheduling module;The analysis module is used for analyzing and obtaining task related information, and is transmitted to the control module;The control module obtains task factor according to task related information, and is transmitted to the sequencing module;The sequencing module sorts multiple different task factors according to the numerical ordering mode, and transmits the multiple task factors sorted to the scheduling module;The scheduling module schedules the task corresponding to the maximum task factor value after sorting, and transmits the scheduling information to the robot.

Description

Technical Field

[0001] This invention relates to the technical field of scheduling systems, and more specifically to an intelligent scheduling system for humanoid robots under multi-task processing. Background Technology

[0002] Multitasking refers to a robot's ability to perform multiple tasks simultaneously or within a time segment. For a robotic system, multitasking typically presents the following challenges: different tasks have varying degrees of urgency and importance, requiring scheduling based on task priority. The robot needs to determine the execution order of tasks based on their urgency, dependencies, and other factors.

[0003] Application CN109917705A discloses a multi-task scheduling method applied to a robot, which includes multiple resources. The method includes: Step S1, sequentially acquiring resource scheduling requests for several tasks; Step S2, arbitrating conflicts in the currently acquired resource scheduling requests according to a conflict arbitration strategy to obtain a scheduling result for the current resource scheduling request; Step S3, executing the scheduling result corresponding to the current resource scheduling request; Step S4, repeating steps S2 to S3 until the scheduling result corresponding to the resource scheduling request of the last task is obtained and executed; Step S5, ending the process.

[0004] Existing technologies are based on pre-set conflict arbitration strategies and lack intelligent judgment and dynamic adaptation. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings by proposing an intelligent scheduling system for humanoid robots under multi-tasking conditions.

[0006] The present invention adopts the following technical solution:

[0007] A humanoid robot intelligent scheduling system for multi-task processing includes an analysis module, a control module, a sorting module, and a scheduling module. The analysis module analyzes and derives task-related information, which is then transmitted to the control module. The control module derives task factors based on the task-related information and transmits them to the sorting module. The sorting module sorts multiple different task factors according to their numerical values ​​and transmits the sorted task factors to the scheduling module. The scheduling module schedules the task corresponding to the task factor with the highest numerical value after sorting and transmits the scheduling information to the robot.

[0008] Optionally, the analysis module includes a data setting submodule, a data storage submodule, and a detection submodule. The data setting submodule is used to set the task priority index and transmit it to the control module. The data storage submodule is used to store the task deadline, the total allowable delay time, the ideal value of the ambient temperature, the ideal value of the ambient humidity, and the ideal value of the obstacle density in the environment, and transmit them to the control module. The detection submodule is used to detect and obtain the length of the task queue, the current time, the actual value of the ambient temperature, the actual value of the ambient humidity, and the actual value of the obstacle density in the environment, and transmit them to the control module. The control module calculates the environmental adaptation index required for the task based on the actual values ​​of the ambient temperature, humidity, and obstacle density, the ideal values ​​of the ambient temperature, humidity, and obstacle density, and calculates the task urgency index based on the task deadline, the current time, and the total allowable delay time. It also calculates the task factor based on the task priority index, the task urgency index, the required environmental adaptation index, and the length of the task queue.

[0009] Optionally, the detection submodule includes a queue length detection unit, a time detection unit, a temperature detection unit, a humidity detection unit, and an obstacle density detection unit; the queue length detection unit is used to detect and determine the length of the queue in which the task is located, and transmit it to the control module; the time detection unit is used to detect and determine the current time, and transmit it to the control module; the temperature detection unit is used to detect and determine the actual value of the ambient temperature of the task, and transmit it to the control module; the humidity detection unit is used to detect and determine the actual value of the ambient humidity of the task, and transmit it to the control module; the obstacle density detection unit is used to detect and determine the actual value of the obstacle density of the environment in which the task is located, and transmit it to the control module.

[0010] Optionally, the obstacle density detection unit includes an environmental scanner, a data acquisition unit, an obstacle detector, and a data calculator; the environmental scanner is used to scan the surrounding environment and collect obstacle information in the space; the data acquisition unit removes noise interference from the collected information; the obstacle detector is used to identify the location and size of obstacles; the data calculator obtains the actual value of the obstacle density in the environment where the task is located through gridded calculation and distance distribution analysis, and transmits it to the control module.

[0011] Optionally, when the control module calculates the task factor, it satisfies the following formula:

[0012] F task =PR×2+max(10 -6 F(EX) + max(0, ENV) + ln(2 + LD). Where Ftask The task factor is PR, which is the task priority index. PR can take the following values: PR = 10, PR = 6, or PR = 3. When PR = 10, the task priority is urgent. When PR = 6, the task priority is less urgent. When PR = 3, the task priority is low. EX is the task urgency index. ENV is the environmental adaptation index required for the task. LD is the length of the queue in which the task is located.

[0013] The beneficial effects achieved by this invention are:

[0014] 1. Through the collaboration of the analysis module and the control module, this application can analyze task information in real time and adjust task priority by calculating and sorting task factors. It can dynamically respond to changes in the urgency of tasks, execution environment, etc., and ensure that tasks can be scheduled in a timely manner according to their actual needs.

[0015] 2. By using an intelligent sorting mechanism, system resources can be allocated reasonably to avoid unnecessary resource conflicts and improve resource utilization efficiency, especially in complex multi-tasking scenarios;

[0016] 3. It introduces dynamic calculation and scheduling of task factors, which can flexibly respond to changes in the environment, such as external environmental factors like temperature and humidity, obstacle density, and changes in the task itself, further improving the intelligence and adaptability of task scheduling.

[0017] 4. The system adopts a modular design (analysis, control, sorting, and scheduling modules), which allows the system to be customized and optimized under different task scheduling requirements, increasing the system's scalability and maintainability.

[0018] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0020] Figure 2 This is a schematic diagram of the analysis module in this invention;

[0021] Figure 3 This is a schematic diagram of the detection submodule in this invention;

[0022] Figure 4 This is a schematic diagram of the obstacle density detection unit in this invention;

[0023] Figure 5 This is a rendering of the invention;

[0024] Figure 6 This is a schematic diagram of the overall structure of Embodiment 2 of the present invention;

[0025] Figure 7 This is a schematic diagram of the resource monitoring module in Embodiment 2 of the present invention;

[0026] Figure 8 This is a rendering of an embodiment of the present invention. Detailed Implementation

[0027] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0028] Example 1: This example provides an intelligent scheduling system for humanoid robots under multi-task processing, combined with... Figures 1 to 5 As shown.

[0029] A humanoid robot intelligent scheduling system for multi-task processing includes an analysis module, a control module, a sorting module, and a scheduling module. The analysis module analyzes and derives task-related information, which is then transmitted to the control module. The control module derives task factors based on the task-related information and transmits them to the sorting module. The sorting module sorts multiple different task factors according to their numerical values ​​and transmits the sorted task factors to the scheduling module. The scheduling module schedules the task corresponding to the task factor with the highest numerical value after sorting and transmits the scheduling information to the robot.

[0030] Optionally, the analysis module includes a data setting submodule, a data storage submodule, and a detection submodule. The data setting submodule is used to set the task priority index and transmit it to the control module. The data storage submodule is used to store the task deadline, the total allowable delay time, the ideal value of the ambient temperature, the ideal value of the ambient humidity, and the ideal value of the obstacle density in the environment, and transmit them to the control module. The detection submodule is used to detect and obtain the length of the task queue, the current time, the actual value of the ambient temperature, the actual value of the ambient humidity, and the actual value of the obstacle density in the environment, and transmit them to the control module. The control module calculates the environmental adaptation index required for the task based on the actual values ​​of the ambient temperature, humidity, and obstacle density, the ideal values ​​of the ambient temperature, humidity, and obstacle density, and calculates the task urgency index based on the task deadline, the current time, and the total allowable delay time. It also calculates the task factor based on the task priority index, the task urgency index, the required environmental adaptation index, and the length of the task queue.

[0031] Optionally, the detection submodule includes a queue length detection unit, a time detection unit, a temperature detection unit, a humidity detection unit, and an obstacle density detection unit; the queue length detection unit is used to detect and determine the length of the queue in which the task is located, and transmit it to the control module; the time detection unit is used to detect and determine the current time, and transmit it to the control module; the temperature detection unit is used to detect and determine the actual value of the ambient temperature of the task, and transmit it to the control module; the humidity detection unit is used to detect and determine the actual value of the ambient humidity of the task, and transmit it to the control module; the obstacle density detection unit is used to detect and determine the actual value of the obstacle density of the environment in which the task is located, and transmit it to the control module.

[0032] Optionally, the obstacle density detection unit includes an environmental scanner, a data acquisition unit, an obstacle detector, and a data calculator; the environmental scanner is used to scan the surrounding environment and collect obstacle information in the space; the data acquisition unit removes noise interference from the collected information; the obstacle detector is used to identify the location and size of obstacles; the data calculator obtains the actual value of the obstacle density in the environment where the task is located through gridded calculation and distance distribution analysis, and transmits it to the control module.

[0033] Optionally, when the control module calculates the task factor, it satisfies the following formula:

[0034] F task =PR×2+max(10 -6 F(EX) + max(0, ENV) + ln(2 + LD). Where Ftask The task factor is PR, which is the task priority index. PR can take the following values: PR = 10, PR = 6, or PR = 3. When PR = 10, the task priority is urgent. When PR = 6, the task priority is less urgent. When PR = 3, the task priority is low. EX is the task urgency index. ENV is the environmental adaptation index required for the task. LD is the length of the queue in which the task is located.

[0035] Optionally, when the control module calculates the task factor, it satisfies the following formula:

[0036]

[0037] Among them, t due t is the task deadline. current t represents the current time. delate The total allowed delay time for the task;

[0038] temp c temp represents the actual ambient temperature of the mission. r The ideal temperature for the mission's environment, hum c The actual humidity value of the environment where the mission takes place. r The ideal humidity level for the mission environment, dob c dob represents the actual value of the obstacle density in the environment where the mission takes place. r This represents the ideal value for the density of obstacles in the environment where the mission takes place.

[0039] When the control module calculates the task factor, refer to the following program code:

[0040]

[0041]

[0042] Specifically, different tasks can calculate different task factors, and then all task factors are sorted from largest to smallest, with larger values ​​being processed first.

[0043] When setting task priority indices, the following points should be noted. Task priority indices are set by those skilled in the art based on actual circumstances. For example, "urgent" usually refers to tasks that, if not resolved in a timely manner, will lead to significant losses, impacts, or risks. "Secondary urgent" refers to tasks that are usually somewhat urgent but do not have immediate pressure to be completed. "Minor" tasks often have little impact on the current work progress and do not involve urgent decisions, and can be postponed without affecting other tasks.

[0044] The total allowable delay for a task is in minutes.

[0045] The purpose of calculating the environmental adaptability index required for a task is that the worse the environmental conditions, the more time the robot needs to adapt to the working environment, and therefore, the better the environment, the more priority will be given to processing.

[0046] The units for both the actual and ideal ambient temperatures of the task are degrees Celsius. The ideal ambient temperature is set by those skilled in the art and refers to the suitable temperature for the robot to work.

[0047] Both the actual and ideal humidity values ​​of the task environment are expressed as percentages. The ideal humidity value of the task environment is set by those skilled in the art and refers to the humidity suitable for the robot to work.

[0048] The units for both the actual and ideal values ​​of obstacle density in the task environment are per square meter. The ideal value of obstacle density in the task environment is set by those skilled in the art. The ideal value of obstacle density in the task environment refers to the obstacle density suitable for robot operation. Since robots walk on the ground, obstacles refer to obstacles that appear on the ground along the robot's preset walking path. Other obstacles in the air are not considered.

[0049] The purpose of calculating the length of the task queue is to understand the current workload of the robot. The length of the task queue refers to the total backlog of tasks for the robot. When the length of the task queue is long, scheduling is prioritized to reduce latency.

[0050] The above units are just examples. Those skilled in the art can set different units according to actual needs when implementing this solution.

[0051] This embodiment solves the problem of the lack of flexibility in traditional scheduling systems. Through the collaboration of the analysis module and the control module, this application can analyze task information in real time and adjust the priority of tasks by calculating and sorting task factors. It can dynamically respond to changes in the urgency of tasks, execution environment, etc., and ensure that tasks can be scheduled in a timely manner according to their actual needs.

[0052] Example 2: This example includes all the content of Example 1, providing an intelligent scheduling system for humanoid robots under multi-task processing, combined with... Figures 6 to 8 As shown.

[0053] A humanoid robot intelligent scheduling system for multi-tasking, which also includes a resource monitoring module;

[0054] The resource monitoring module is used to monitor and determine the total power required by the task, the total memory required by the task, the total time the task occupies the central processing unit, and the bandwidth required for task transmission, and then transmits this information to the control module.

[0055] The control module calculates the resource index required by the task based on the total power required by the task, the total memory required by the task, the total time the task occupies the central processing unit, and the bandwidth required for the task transmission, and then transmits the resource index required by the task to the sorting module.

[0056] The sorting module selects tasks corresponding to multiple task factors with the same value, sorts the resource indices required by the corresponding tasks according to the numerical sorting method, and transmits the resource indices required by the sorted tasks to the scheduling module.

[0057] The scheduling module schedules the task corresponding to the resource index required by the task with the largest value after sorting, and transmits the scheduling information to the robot.

[0058] Optionally, the resource monitoring module includes a power monitoring submodule, a memory monitoring submodule, a task usage duration monitoring submodule, and a bandwidth monitoring submodule;

[0059] The power monitoring submodule is used to monitor and determine the total power required for the task, and then transmit it to the control module;

[0060] The memory monitoring submodule is used to monitor and determine the total power required for the task, and then transmit this information to the control module.

[0061] The task duration monitoring submodule is used to monitor and calculate the total time that a task occupies the central processing unit, and then transmit this result to the control module.

[0062] The bandwidth monitoring submodule is used to monitor and determine the bandwidth required for task transmission and transmit it to the control module.

[0063] Optionally, when calculating the resource index required for the task, the control module satisfies the following formula:

[0064] RES=ln(2+0.2×pow+0.3×mem+0.4×cpu+0.1×nt).

[0065] Where RES is the resource index required by the task, pow is the total power required by the task, mem is the total memory required by the task, cpu is the total time the task occupies the central processing unit, and nt is the bandwidth required for task transmission.

[0066] When the control module calculates the resource index required for the task, refer to the following program code:

[0067]

[0068] Specifically, the purpose of calculating the resource index required for a task is to compare the resource indices required for different tasks when the task factor values ​​are the same. The higher the value of the resource index required for a task, the higher its priority. When the resource indices required for tasks are the same, those skilled in the art can select the task to be prioritized based on experience or actual needs.

[0069] The purpose of calculating the resource index required for a task is to assume that the more resources a task requires, the more likely it is to become a scheduling bottleneck under limited resources, preventing other tasks from executing smoothly. Therefore, it needs to be prioritized to avoid blocking other tasks. For complex and resource-intensive tasks, the system often needs to schedule these tasks in advance. The total power required by the task, the total time the task occupies the central processing unit, and the bandwidth required for task transmission can be obtained by averaging historical data. For example, for the same task (meaning that the task objective and task type are the same, such as the robot's walking path and the amount of tasks to be performed, which are the task objectives, and the task type refers to the actions performed by the robot, such as cleaning and carrying), all historical data can be summed and then averaged. Assuming that there are no identical tasks, those skilled in the art can select tasks with the same type and similar steps to estimate the corresponding values.

[0070] The unit for the total power required for the task is watts.

[0071] The total memory required for the task is measured in GB.

[0072] The total CPU time required for a task is measured in seconds. This means that when you run a program, it performs a series of calculations in the background, such as mathematical operations and data processing. These calculations all require the CPU to complete, and CPU time requirement describes the amount of CPU time occupied by these calculations.

[0073] The unit of bandwidth required for task transmission is megabits per second.

[0074] The above units are just examples. Those skilled in the art can set different units according to actual needs when implementing this solution.

[0075] This embodiment addresses the issue of poor intelligence in traditional scheduling systems by introducing a resource monitoring module. The system can acquire real-time data on task resource consumption, including power, memory, CPU usage time, and bandwidth. This allows the system to dynamically adjust task scheduling strategies based on actual resource requirements, preventing excessive resource consumption or conflicts and improving the intelligence and efficiency of task scheduling.

[0076] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A humanoid robot intelligent scheduling system for multi-task processing, characterized in that, The system includes an analysis module, a control module, a sorting module, and a scheduling module; The analysis module is used to analyze and obtain task-related information, and then transmit it to the control module; The control module derives task factors based on task-related information and transmits them to the sorting module; The sorting module sorts multiple different task factors according to their numerical values ​​and then transmits the sorted task factors to the scheduling module. The scheduling module schedules the task corresponding to the task factor with the largest value after sorting and transmits the scheduling information to the robot. The analysis module includes a data setting submodule, a data storage submodule, and a detection submodule; The data setting submodule is used to set the task priority index and transmit it to the control module; The data storage submodule is used to store the task deadline, the total allowable delay of the task, the ideal value of the ambient temperature of the task, the ideal value of the ambient humidity of the task, and the ideal value of the obstacle density of the task, and transmit them to the control module. The detection submodule is used to detect and obtain the length of the task queue, the current time, the actual value of the ambient temperature of the task, the actual value of the ambient humidity of the task, and the actual value of the density of obstacles in the environment of the task, and transmit them to the control module. The control module calculates the environmental adaptability index required for the task based on the actual values ​​of the ambient temperature, humidity, and obstacle density of the task's environment, as well as the ideal values ​​of these values. It also calculates the task urgency index based on the task deadline, current time, and the total allowable delay time. Finally, it calculates the task factor based on the task priority index, urgency index, required environmental adaptability index, and the length of the queue in which the task is located.

2. The humanoid robot intelligent scheduling system under multi-task processing as described in claim 1, characterized in that, The detection submodule includes a queue length detection unit, a time detection unit, a temperature detection unit, a humidity detection unit, and an obstacle density detection unit; The queue length detection unit is used to detect and determine the length of the queue where the task is located, and transmit it to the control module; The time detection unit is used to detect and obtain the current time, and transmit it to the control module; The temperature detection unit is used to detect and obtain the actual value of the ambient temperature where the task is located, and transmit it to the control module; The humidity detection unit is used to detect and obtain the actual humidity value of the environment where the task is located, and transmit it to the control module; The obstacle density detection unit is used to detect and obtain the actual value of the obstacle density in the environment where the task is located, and transmit it to the control module.

3. The humanoid robot intelligent scheduling system under multi-task processing as described in claim 2, characterized in that, The obstacle density detection unit includes an environmental scanner, a data acquisition unit, an obstacle detector, and a data calculator; The environmental scanner is used to scan the surrounding environment and collect information on obstacles in the space; The data acquisition device removes noise interference from the acquired information; The obstacle detector is used to identify the location and size of obstacles; The data calculator obtains the actual value of the obstacle density in the environment where the task is located through gridded calculation and distance distribution analysis, and transmits it to the control module.

4. The humanoid robot intelligent scheduling system under multi-task processing as described in claim 3, characterized in that, When the control module calculates the task factor, it satisfies the following formula: ； in, As a task factor, This is the task priority index, which has the following possible values: or or ,when When the task priority is urgent, it indicates that the task is urgent. When the time is indicated as the task priority being the second most urgent, This indicates that the task priority is low. The urgency index is the indicator of the mission. The environmental adaptability index required for the task. The length of the queue containing the task.

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