Humanoid robot intelligent scheduling system under multi-task processing

Abstract

The invention relates to the technical field of scheduling systems, in particular to a humanoid robot intelligent scheduling system under multi-task processing, which comprises an analysis module, a control module, a sorting module and a scheduling module, the analysis module is used for analyzing and obtaining task-related information and transmitting the task-related information to the control module; the control module obtains task factors according to information related to tasks and transmits the task factors to the sorting module; the sorting module sorts the plurality of different task factors according to a numerical sorting mode, and transmits the plurality of sorted task factors to the scheduling module; and the scheduling module carries out scheduling processing on the task corresponding to the task factor with the maximum numerical value after sorting, and transmits scheduling information to the robot.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling systems, and particularly to an intelligent scheduling system for humanoid robots under multi-task processing. Background Art

[0002] Multi-task processing means that a robot can execute multiple tasks at the same time or within a time segment. For a robot system, multi-task processing usually faces the following problems: different tasks have different urgencies and importance levels, and scheduling needs to be carried out according to the priorities of the tasks. The robot needs to determine the execution order of tasks based on the urgency, dependency relationships, etc. of the tasks.

[0003] The application document with the publication number CN109917705A discloses a multi-task scheduling method applied to a robot, and the robot includes multiple resources; it includes: step S1, sequentially obtaining resource scheduling requests for several tasks in order; step S2, performing conflict arbitration on the currently obtained resource scheduling request according to a conflict arbitration strategy so that the currently obtained resource scheduling request obtains a scheduling result; step S3, executing the scheduling result corresponding to the currently obtained 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.

[0004] The prior art is based on a pre-set conflict arbitration strategy and lacks intelligent judgment and dynamic adaptation. Summary of the Invention

[0005] The purpose of the present invention is to propose an intelligent scheduling system for humanoid robots under multi-task processing in view of the above-mentioned deficiencies.

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

[0007] An intelligent scheduling system for humanoid robots under multi-task processing, 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 transmit it to the control module; the control module obtains task factors according to the task-related information and transmits them to the sorting module; the sorting module sorts multiple different task factors in the way of numerical sorting and transmits the sorted multiple 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.

[0008] Optionally, the analysis module includes a data setting sub-module, a data storage sub-module, and a detection sub-module; the data setting sub-module is used to set a task priority index and transmit it to the control module; the data storage sub-module is used to store the task deadline, the total duration allowed for task delay, 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 environment, and transmit them to the control module; the detection sub-module is used to detect and obtain the length of the queue where the task is located, 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 obstacle density of the task environment, and transmit them to the control module; the control module obtains the environmental adaptation index required for the task based on the actual value of the ambient temperature of the task, the actual value of the ambient humidity of the task, the actual value of the obstacle density of the task environment, 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 environment, obtains the task urgency index based on the task deadline, the current time, and the total duration allowed for task delay, and obtains the task factor based on the task priority index, the task urgency index, the environmental adaptation index required for the task, and the length of the queue where the task is located.

[0009] Optionally, the detection sub-module 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 obtain 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 of the task and transmit it to the control module; the humidity detection unit is used to detect and obtain 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 obtain the actual value of the obstacle density of the task environment and transmit it to the control module.

[0010] Optionally, the obstacle density detection unit includes an environmental scanner, a data collector, 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 collector removes noise interference from the collected information; the obstacle detector is used to identify the position and size of the obstacle; the data calculator obtains the actual value of the obstacle density of the task environment through grid calculation and distance distribution analysis and transmits it to the control module.

[0011] Optionally, when the control module calculates the task factor, the following formula is satisfied:

[0012] F task = PR × 2 + max(10 -6 , EX) + max(0, ENV) + ln(2 + LD). Where, Ftask Let TF be the task factor, PR be the task priority index, which has the following values: PR = 10 or PR = 6 or PR = 3. When PR = 10, it means the task priority is urgent; when PR = 6, it means the task priority is less urgent; when PR = 3, it means the task priority is mild. EX is the task emergency index, ENV is the environmental adaptation index required for the task, and LD is the length of the task queue.

[0013] The beneficial effects achieved by the present invention are as follows:

[0014] 1. Through the cooperation of the analysis module and the control module, the present application can analyze the task information in real time, and adjust the task priority by calculating and sorting the task factors, so as to dynamically respond to changes such as the urgency of the task and the execution environment, and ensure that the task can be scheduled in a timely manner according to its actual needs;

[0015] 2. Through the intelligent sorting mechanism, the system resources are reasonably allocated, unnecessary resource conflicts are avoided, and the utilization efficiency of resources is improved, especially in complex multi-task scenarios;

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

[0017] 4. The modular design (analysis, control, sorting, and scheduling modules) is adopted, so that the system can be customized and optimized under different task scheduling requirements, increasing the scalability and maintainability of the system.

[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 provided drawings are only for reference and illustration, and are not used to limit the present invention. Brief Description of the Drawings

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

[0020] Figure 2 It is a schematic diagram of the structure of the analysis module in the present invention;

[0021] Figure 3 It is a schematic diagram of the structure of the detection sub-module in the present invention;

[0022] Figure 4 It is a schematic diagram of the structure of the obstacle density detection unit in the present invention;

[0023] Figure 5 It is the effect diagram of the present invention;

[0024] Figure 6 Schematic diagram of the overall structure of the second embodiment of the present invention;

[0025] Figure 7 Schematic diagram of the structure of the resource monitoring module in the second embodiment of the present invention;

[0026] Figure 8 Effect diagram of the second embodiment of the present invention. Specific implementation manners

[0027] The following are specific embodiments to illustrate the implementation manners 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. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to actual dimensions. This is stated in advance. The following implementation manners will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention.

[0028] Embodiment 1: This embodiment provides an intelligent scheduling system for humanoid robots under multi-task processing, as shown in combination with Figures 1 to 5 shown.

[0029] An intelligent scheduling system for humanoid robots under multi-task processing, 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 transmit it to the control module; the control module obtains task factors based on the task-related information and transmits them to the sorting module; the sorting module sorts multiple different task factors in the way of numerical sorting and transmits the sorted multiple 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.

[0030] Optionally, the analysis module includes a data setting sub-module, a data storage sub-module, and a detection sub-module; the data setting sub-module is used to set a task priority index and transmit it to the control module; the data storage sub-module is used to store the task deadline, the total duration allowed for task delay, 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 environment, and transmit them to the control module; the detection sub-module is used to detect and obtain the length of the queue where the task is located, 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 obstacle density of the task environment, and transmit them to the control module; the control module calculates the environmental adaptation index required for the task based on the actual value of the ambient temperature of the task, the actual value of the ambient humidity of the task, the actual value of the obstacle density of the task environment, 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 environment, calculates the task urgency index based on the task deadline, the current time, and the total duration allowed for task delay, and calculates the task factor based on the task priority index, the task urgency index, the environmental adaptation index required for the task, and the length of the queue where the task is located.

[0031] Optionally, the detection sub-module 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 obtain 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 of the task and transmit it to the control module; the humidity detection unit is used to detect and obtain 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 obtain the actual value of the obstacle density of the task environment and transmit it to the control module.

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

[0033] Optionally, when the control module calculates the task factor, the following formula is satisfied:

[0034] F task = PR × 2 + max(10 -6 , EX) + max(0, ENV) + ln(2 + LD). Where, Ftask Let TF be the task factor, PR be the task priority index, which can take the following values: PR = 10 or PR = 6 or PR = 3. When PR = 10, it indicates that the task priority is urgent; when PR = 6, it indicates that the task priority is less urgent; when PR = 3, it indicates that the task priority is mild. EX is the task emergency index, ENV is the environmental adaptation index required for the task, and LD is the length of the task queue where the task is located.

[0035] Optionally, when the control module calculates the task factor, the following formula is satisfied:

[0036]

[0037] where, t due is the task deadline, t current is the current time, t delate is the total allowable delay duration of the task;

[0038] temp c is the actual value of the temperature of the environment where the task is located, temp r is the ideal value of the temperature of the environment where the task is located, hum c is the actual value of the humidity of the environment where the task is located, hum r is the ideal value of the humidity of the environment where the task is located, dob c is the actual value of the obstacle density of the environment where the task is located, dob r is the ideal value of the obstacle density of the environment where the task is located.

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

[0040]

[0041]

[0042] Specifically, different tasks can calculate different task factors, and then all the task factors are sorted from largest to smallest, and the larger the value, the higher the priority for processing.

[0043] The following points need to be noted when setting the task priority index. The task priority index is set by those skilled in the art according to the actual situation. For example, "urgent" usually refers to those tasks that will cause significant losses, impacts or risks if not solved in time; "less urgent" usually refers to those tasks that have a certain degree of urgency but no immediate pressure to be completed; "mild" often has little impact on the current work progress and does not involve urgent decisions, and can be postponed without affecting other tasks.

[0044] The unit of the total allowable delay duration of the task is minutes.

[0045] The purpose of calculating the environmental adaptation index required for a computing task is that the worse the environmental conditions are, the more time the corresponding robot needs to adapt to the working environment. Therefore, when the environment is better, it will be processed preferentially.

[0046] The unit of both the actual value and the ideal value of the environmental temperature where the task is located is degrees Celsius. The ideal value of the environmental temperature where the task is located is set by those skilled in the art, and the ideal value of the environmental temperature where the task is located refers to the temperature suitable for the robot to work.

[0047] The actual value and the ideal value of the environmental humidity where the task is located are both expressed in the form of a percentage. The ideal value of the environmental humidity where the task is located is set by those skilled in the art, and the ideal value of the environmental humidity where the task is located refers to the humidity suitable for the robot to work.

[0048] The unit of both the actual value and the ideal value of the obstacle density in the environment where the task is located is number per square meter. The ideal value of the obstacle density in the environment where the task is located is set by those skilled in the art, and the ideal value of the obstacle density in the environment where the task is located refers to the obstacle density suitable for the robot to work. Since the robot walks on the ground, the obstacles refer to the obstacles that appear on the ground along the preset walking route of the robot, and the obstacles in the air are not considered.

[0049] The purpose of calculating the length of the task queue where the task is located is to understand the current task volume of the robot. The length of the task queue where the task is located refers to the total backlogged task volume of the robot. When the length of the task queue where the task is located is relatively long, in order to reduce latency, scheduling is preferentially performed.

[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 that the traditional scheduling system lacks flexibility. Through the cooperation of the analysis module and the control module, this application can analyze the task information in real time, and adjust the task priority through task factor calculation and sorting, and can dynamically respond to changes such as the urgency of the task and the execution environment, ensuring that the task can be scheduled in a timely manner according to its actual needs.

[0052] Embodiment 2: This embodiment includes all the content of Embodiment 1 and provides an intelligent scheduling system for a humanoid robot under multi-task processing, as shown in Figures 6 to 8 shown.

[0053] An intelligent scheduling system for a humanoid robot under multi-task processing, and this system further includes a resource monitoring module;

[0054] The resource monitoring module is used to monitor and obtain the total power required for the task, the total memory required for the task, the total duration of the task occupying the central processing unit, and the bandwidth required during the task transmission, and transmit them to the control module;

[0055] The control module obtains the resource index required for the task based on the total power required for the task, the total memory required for the task, the total duration of the task occupying the central processing unit, and the bandwidth required during the task transmission, and transmits the resource index required for the task to the sorting module;

[0056] The sorting module selects the tasks corresponding to multiple task factors with the same value, sorts the corresponding resource indexes required for the tasks in the order of numerical values, and transmits the sorted multiple resource indexes required for the tasks to the scheduling module;

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

[0058] Optionally, the resource monitoring module includes a power monitoring sub-module, a memory monitoring sub-module, a task occupation duration monitoring sub-module, and a bandwidth monitoring sub-module;

[0059] The power monitoring sub-module is used to monitor and obtain the total power required for the task, and transmit it to the control module;

[0060] The memory monitoring sub-module is used to monitor and obtain the total power required for the task, and transmit it to the control module;

[0061] The task occupation duration monitoring sub-module is used to monitor and obtain the total duration of the task occupying the central processing unit, and transmit it to the control module;

[0062] The bandwidth monitoring sub-module is used to monitor and obtain the bandwidth required during the task transmission, and transmit it to the control module.

[0063] Optionally, when the control module calculates the resource index required for the task, the following formula is satisfied:

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

[0065] Where RES is the resource index required for the task, pow is the total power required for the task, mem is the total memory required for the task, cpu is the total duration of the task occupying the central processing unit, and nt is the bandwidth required during the task transmission.

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

[0067]

[0068] Specifically, the purpose of the resource index required for a computing task is that when the values of task factors are the same for different tasks, the resource indices required for the tasks can be compared. The larger the value of the resource index required for a task, the higher the priority for processing. When the resource indices required for the tasks are also the same, those skilled in the art can select the task to be processed first according to experience or actual requirements.

[0069] The purpose of calculating the resource index required for a computing task is as follows. Assuming that the more resources a task requires, it means that under limited resources, this task may become a bottleneck in scheduling, and other tasks may not be able to execute smoothly. Therefore, it is necessary to process it with priority to avoid other tasks from being blocked. For complex and resource-intensive tasks, the system often needs to schedule these tasks in advance; the total power required for the corresponding task, the total power required for the task, the total duration of the task occupying the central processing unit, and the bandwidth required during task transmission can be obtained by averaging historical data. For example, for the same task (referring to the same task objective and task type, such as the path of a robot walking and the task volume of the executed action, etc. This is the task objective, and the task type refers to the actions performed by the robot, such as cleaning and handling), all historical data can be summed and then averaged. Assuming there are no identical tasks, those skilled in the art can select tasks with the same task type and then screen for similar steps to estimate the corresponding values.

[0070] The unit of the total power required for a task is watt.

[0071] The unit of the total memory required for a task is GB.

[0072] The unit of the total duration of a task occupying the central processing unit is second. It means that when you run a program, the program will perform a series of calculation operations in the background, such as mathematical operations and data processing. These calculation operations all need to be completed by the CPU, and the CPU time requirement describes the amount of CPU time occupied by these calculation operations.

[0073] The unit of the bandwidth required during task transmission is megabit per second.

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

[0075] This embodiment solves the problem of poor intelligence in traditional scheduling systems. By introducing a resource monitoring module, the system can obtain the resource consumption situation of tasks in real time, including power, memory, CPU occupancy time, and bandwidth. This enables the system to dynamically adjust the task scheduling strategy according to the actual resource requirements of tasks, avoid excessive resource consumption or resource conflicts, and improve the intelligence and efficiency of task scheduling.

[0076] The content disclosed above is only the preferred and feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, with the development of technology, the elements therein can be updated.

Claims

1. The intelligent scheduling system of humanoid robots under multi-tasking processing is characterized by: 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 transmit it to the control module; The control module obtains the task factor according to the task-related information and transmits it to the sorting module; The sorting module sorts the multiple different task factors in numerical order, and transmits the sorted multiple 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.

2. The multi-tasking humanoid robot intelligent scheduling system according to claim 1, characterized in that: 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 duration of the task delay allowed, the ideal value of the task environment temperature, the ideal value of the task environment humidity and the ideal value of the task environment obstacle density, 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 task environment temperature, the actual value of the task environment humidity and the actual value of the task environment obstacle density, and transmit them to the control module; The control module calculates the environmental adaptability index required for the task based on the actual value of the task's ambient temperature, the actual value of the task's ambient humidity, the actual value of the task's ambient obstacle density, the ideal value of the task's ambient temperature, the ideal value of the task's ambient humidity and the ideal value of the task's ambient obstacle density; calculates the task urgency index based on the task deadline, the current time and the total duration of the task's allowed delay; and calculates the task factor based on the task priority index, the task urgency index, the environmental adaptability index required for the task and the length of the queue where the task is located.

3. The multi-tasking humanoid robot intelligent scheduling system according to claim 2, 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 obtain 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 of the task environment, and transmit it to the control module; The humidity detection unit is used to detect and obtain the actual value of the humidity of the environment in which 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 task environment and transmit it to the control module.

4. The multi-tasking humanoid robot intelligent scheduling system according to claim 3, characterized in that: The obstacle density detection unit includes an environment scanner, a data collector, an obstacle detector and a data calculator; The environment scanner is used to scan the surrounding environment and collect obstacle information in the space; The data collector removes noise interference from the collected information; The obstacle detector is used to identify the position and size of the obstacle; The data calculator obtains the actual value of the obstacle density in the mission environment through grid calculation and distance distribution analysis, and transmits it to the control module.

5. The multi-tasking humanoid robot intelligent scheduling system according to claim 4, characterized in that: When the control module calculates the task factor, the following formula is satisfied: F task =PR×2+max(10 -6 ,EX)+max(0,ENV)+ln(2+LD); Among them, F task is the task factor, PR is the task priority index, which has the following values: PR=10 or PR=6 or PR=3. When PR=10, it means the task priority is urgent, when PR=6, it means the task priority is second urgent, when PR=3, it means the task priority is light, EX is the task urgency index, ENV is the environment adaptation index required for the task, and LD is the length of the queue where the task is located.

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