Robot cluster scheduling method, system, electronic device, medium and program product

Abstract

The present invention provides a robot cluster scheduling method, system, electronic device, medium and program product, the method comprising: dividing all robots into a current working queue and a current charging queue; based on the power information of each robot in the current time period, using a corresponding simulation method to simulate, and obtaining the simulation output results of each simulation method in a future preset time period; comparing the simulation output results corresponding to a plurality of simulation methods, and determining the robot scheduling scheme according to the comparison results. The present invention simulates charging of the robot cluster according to different simulation methods, finds the most suitable charging strategy, and better utilizes future expectations, so that the robot cluster can avoid excessive charging while covering the expected tasks, and can give full play to the role of the robot.

Description

Technical Field

[0001] The present invention relates to the field of robot scheduling, and more specifically, to a robot cluster scheduling method, system, electronic equipment, medium and program product. Background Art

[0002] During the execution of robot tasks, the robot's working status and charging status need to be coordinated so that the robot can meet work requirements and avoid waste caused by overcharging.

[0003] Currently common robot charging strategies cannot cope well with the concentrated explosive demand for robots in future time periods, but the maximum charging strategy will lead to excessive power storage, resulting in the problem of missing unexpected demand, and the extra reserve power is not well utilized.

[0004] Therefore, it is necessary to provide a charging strategy that better utilizes future expectations so that the robot cluster can avoid excessive charging while covering the expected tasks. Summary of the invention

[0005] The present invention aims at the technical problems existing in the prior art and provides a robot cluster scheduling method, system, electronic equipment, medium and program product.

[0006] According to a first aspect of the present invention, a robot cluster scheduling method based on charging simulation is provided, comprising: obtaining status information of each robot in the robot cluster in a current time period, and dividing all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue; based on the power information of each robot in the current time period, different simulation methods are used for simulation to obtain simulation output results of each simulation method in a future preset time period; comparing the simulation output results corresponding to multiple simulation methods, and determining a scheduling plan for the robot based on the comparison results, wherein the scheduling plan includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work.

[0007] Based on the above technical solution, the present invention can also make the following improvements.

[0008] Optionally, the simulation mode includes adding a working simulation mode, a current status simulation mode and adding a charging simulation mode.

[0009] Optionally, when the simulation mode is an increase-work simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including: finding the robot A with the highest power in the current charging queue, subtracting the work power consumption from the power of each robot in the current work queue, adding the charging increment to the power of each robot in the current charging queue except A, and subtracting the work power consumption from the power of A, where the work power consumption refers to the power consumed by a single robot to perform a task in a time period, and the charging increment refers to the power that can be increased by charging a single robot in a time period;

[0010] When the simulation mode is a current status simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including:

[0011] Subtract the working power consumption from the power of each robot in the current working queue, and add the charging power to the power of each robot in the current charging queue;

[0012] When the simulation mode is to increase the charging simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including:

[0013] Find the robot B with the lowest power in the current work queue, subtract the work power consumption from the power of each robot in the current work queue except B, add the charging increment to the power of B, and add the charging increment to the power of each robot in the current charging queue.

[0014] Optionally, the simulation output result is the number of working robots that can meet the expectation in the current time period, and the simulation output results corresponding to the multiple simulation methods are compared, and the robot scheduling scheme is determined according to the comparison result, including:

[0015] Let the simulation output result after the current situation simulation mode be s1, the simulation output result after the added charging simulation mode be s2, and the simulation output result after the added working simulation mode be s3;

[0016] Determine whether s1≥s2+threshold. If not, dispatch robot B to charge;

[0017] If s1≥s2+threshold, determine whether s3>s1+threshold. If so, schedule A to work. If not, maintain the status quo.

[0018] Optionally, the power information of each robot in the current time period is simulated by using different simulation methods to obtain the simulation output result of each simulation method, including:

[0019] a. For each time period in the next day, obtain the power information of each robot in the robot cluster in the current time period, and sort all robots in order from high to low power to form a robot sequence;

[0020] b. Calculate the number of working robots V1 that can meet the expectations in the current time period based on the power information of each robot in the robot cluster in the current time period, the expected number of working robots in the current time period, the total number of robots, and the number of available charging piles in the current time period;

[0021] c. Subtract the working power consumption from the power of the first V1 robots in the robot sequence, and add the charging increment to the power of the non-first V1 robots to obtain the estimated power of each robot, which will be used as the power information of each robot in the robot cluster in the next time period;

[0022] d. Repeat steps a and b to calculate the number of working robots V2 that can meet the expectation in the next time period;

[0023] e. Repeat steps a to d to calculate the number of working robots Vm that can meet the expectation in the last time period of the next day;

[0024] The simulation output result V=V1+V2+...+Vm, where m is the number of time periods in the future day.

[0025] Optionally, the step b calculates the number of working robots V1 that can meet the expectation in the current time period in the following manner:

[0026] Assume A = the number of robots in the robot cluster whose power level is greater than the working threshold in the current time period, E = the expected number of working robots in the current time period, C = the number of available charging piles in the current time period, then V1 = max(min(A, E), total number of robots - C).

[0027] According to a second aspect of the present invention, there is provided a robot cluster scheduling system based on charging simulation, comprising:

[0028] A division module is used to obtain the status information of each robot in the robot cluster in the current time period, and divide all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue;

[0029] The simulation module is used to simulate in different ways based on the power information of each robot in the current time period, and obtain the simulation output results of each simulation method;

[0030] The scheduling module is used to compare the simulation output results corresponding to multiple simulation methods, and determine the robot scheduling plan based on the comparison results. The scheduling plan includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work.

[0031] According to a third aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the processor is configured to implement the steps of a robot cluster scheduling method based on charging simulation when executing a computer program stored in the memory.

[0032] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a robot cluster scheduling method based on charging simulation are implemented.

[0033] According to a fifth aspect of the present invention, there is provided a computer program product, comprising a computer program or instructions, which, when executed by a processor, implement the steps of the method according to the first aspect of the present invention.

[0034] The present invention provides a robot cluster scheduling method, system, electronic device, medium and program product, which divides all robots into a current working queue and a current charging queue; based on the power information of each robot in the current time period, different simulation methods are used for simulation to obtain the simulation output results of each simulation method in the future preset time period; the simulation output results corresponding to multiple simulation methods are compared, and the robot scheduling plan is determined according to the comparison results. The present invention simulates the charging of the robot cluster according to different simulation methods, finds the most suitable charging strategy, and better uses future expectations, so that the robot cluster can avoid excessive charging while covering the expected tasks, and can give full play to the role of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of a robot cluster scheduling method based on charging simulation provided by the present invention;

[0036] Figure 2 It is an overall schematic diagram of the robot cluster scheduling method based on charging simulation;

[0037] Figure 3 A schematic diagram of the simulation process of charging a robot cluster;

[0038] Figure 4 A schematic diagram of the structure of a robot cluster scheduling system based on charging simulation provided by the present invention;

[0039] Figure 5 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0040] Figure 6 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0041] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0042] Before describing the robot cluster scheduling method based on charging simulation provided by the present invention, the terms and concepts involved in the present invention are introduced, wherein the basic logic and terms involved include:

[0043] Point: A physical location that a robot can perceive. The basic information of a point consists of location information and description information, where the location information is the map where it is located and its coordinates in the map.

[0044] Robot mission: used to describe the robot's work. Mission types include charging, delivering food, picking up goods, guiding, patrolling, etc. The specific description of the mission will vary depending on the type, but generally includes at least the start time of the mission and the points passed.

[0045] Robot status: including robot type, current location, angle, load, power, tasks being executed and tasks accepted, etc. Robot type is used to determine the types of tasks that the robot can accept; power information includes current power and maximum battery capacity; the same robot can accept multiple tasks at the same time and determine the execution order of the tasks.

[0046] Working power consumption: Estimate the power consumed by a single robot to perform tasks during a specified time interval. The power consumption may vary depending on the type and number of tasks, but this article will not elaborate on this in order to keep the description concise.

[0047] Charging increment: Estimate the amount of power that can be added by charging a single robot during a specified time interval. Depending on factors such as the robot's charging circuit structure, battery type, specifications, and aging, the charging benefits of different robots in the same time period are also different, but to keep the description concise, the present invention does not expand on this.

[0048] Embodiment 1

[0049] See also Figure 1, a robot cluster scheduling method based on charging simulation is provided in the present invention, including: obtaining the status information of each robot in the robot cluster in the current time period, and dividing all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue; based on the power information of each robot in the current time period, different simulation methods are used for simulation to obtain the simulation output results of each simulation method in a future preset time period; the simulation output results corresponding to multiple simulation methods are compared, and according to the comparison results, the robot scheduling plan is determined, and the scheduling plan includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work.

[0050] The present invention simulates the charging of the robot cluster according to different simulation methods, finds the most suitable charging strategy, and better utilizes future expectations, so that the robot cluster can avoid excessive charging while covering the expected tasks, and can give full play to the role of the robots.

[0051] Embodiment 2

[0052] A robot cluster scheduling method based on charging simulation is provided, which is mainly completed in the cloud, wherein the robot communicates with the cloud via a 4G data link. The scheduling method mainly includes: S1, obtaining the status information of each robot in the robot cluster in the current time period, and dividing all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue.

[0053] Specifically, the cloud obtains the status information of all robots in the robot cluster in the current time period. The status information of the robot includes whether the robot is in working state or charging state. According to the status information of each robot, the robot in working state can be placed in the current working queue, and the robot in charging state can be placed in the current charging queue, in preparation for the subsequent simulation or estimation of the power of all robots.

[0054] S2, based on the power information of each robot in the current time period, different simulation methods are used to perform simulation, and the simulation output results of each simulation method in the future preset time period are obtained.

[0055] Among them, the simulation methods include adding a working simulation method, a current status simulation method and adding a charging simulation method.

[0056] Specifically, when the simulation mode is to increase the work simulation mode, the power information of each robot in the robot cluster in each preset time period in the future is simulated accordingly, including: finding the robot A with the highest power in the current charging queue, subtracting the working power consumption from the power of each robot in the current working queue, adding the charging increment to the power of each robot in the current charging queue except robot A, and subtracting the working power consumption from the power of robot A. The working power consumption refers to the power that a single robot will consume when performing a task in a time period, and the charging increment refers to the power that can be increased by charging a single robot in a time period.

[0057] When the simulation mode is the current status simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including: subtracting the working power consumption from the power of each robot in the current working queue, and adding the charging power to the power of each robot in the current charging queue.

[0058] When the simulation mode is the charging increase simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including: finding the robot B with the lowest power in the current work queue, subtracting the working power consumption from the power of each robot except B in the current work queue, adding the charging increment to the power of B, and adding the charging increment to the power of each robot in the current charging queue.

[0059] Specifically, based on the power information of each robot in the current time period, different simulation methods are used to perform simulations, and the simulation output results of each simulation method in the future preset time period are obtained, including:

[0060] a. For each time period in the future day, obtain the power information of each robot in the robot cluster in the current time period, and sort all robots in order from high to low power to form a robot sequence. b. According to the power information of each robot in the robot cluster in the current time period, the expected number of working robots in the current time period, the total number of robots, and the number of available charging piles in the current time period, simulate and calculate the number of working robots V1 that can meet the expectations in the current time period. c. Subtract the working power consumption from the power of the first V1 robots in the robot sequence, and add the charging increment to the power of the non-first V1 robots to obtain the power of each robot after simulation, which is used as the power information of each robot in the robot cluster in the next time period; d. Repeat a and b to calculate the number of working robots V2 that can meet the expectations in the next time period; e. Repeat a to d to calculate the number of working robots Vm that can meet the expectations in the last time period in the future day; where the simulation output result V=V1+V2+...+Vm, m is the number of preset time periods in the future day.

[0061] Among them, step b calculates the expected number of working robots V1 that can be met in the current time period in the following way: let A = the number of robots in the current time period (the power level in the robot cluster > the working threshold), E = the expected number of working robots in the current time period, C = the number of available charging piles in the current time period, then V1 = max(min(A, E), total number of robots - C).

[0062] Specifically, the expected number of working robots in each time period in the next day is calculated with a certain time interval (30 minutes) as the starting point. Although the expected number of working robots is changing according to the real-time correction strategy, the expectation before correction remains unchanged before the next training (end of the day). Therefore, these original expectations are cached to avoid repeated calculations.

[0063] The number of robots expected to work in each time period is related to the needs of people in that time period, and there is a phenomenon of concentrated outbreaks (for example: the lunch rush hour). In order to take relevant factors into account and maintain consistency in decisions at any point in time, the embodiment of the present invention always simulates the expected number of working robots in the time period of the next day. Since the training is based on the timeline of a day, expectations for more than one day are made in a cyclical manner. For example, the expectation 12 hours later at 20 o'clock in the evening adopts the original expectation at 8 o'clock in the morning. For the expectation of the next day, it can be further modified according to the attributes related to the number of robots required on the next day, such as holidays, activities and even weather.

[0064] Based on the working power of each robot in the current working queue and the current charging queue and the expected number of working robots in the current time period, we simulated adding a working robot, maintaining the status quo, and adding a charging robot (skipping when all charging piles are occupied) to obtain the number of working robots that can meet expectations under the three simulation methods. The simulation here means estimating the robot power in each time period in the future day according to a certain strategy, and taking the total number of working robots that meet expectations in each time period as the output result after simulation.

[0065] When the output result of the current simulation is greater than or equal to the output result of the simulation after adding the charging robot, it means that adding the charging robot cannot bring more coverage of the expected number of robots, so the current situation is maintained; otherwise, it means that reducing the number of working robots to charge now will increase the coverage of future workloads, so the robot with the lowest power in the current work queue is scheduled to charge. To avoid frequent jitters in decision-making, a threshold is added.

[0066] Similarly, when the output result of the simulation after adding a working robot is greater than the output result of the current simulation + the threshold, the robot with the highest power in the current charging queue is dispatched to work; otherwise, the status quo is maintained.

[0067] See also Figure 2 , the process of the robot cluster scheduling method based on charging simulation of the present invention is explained. Specifically, the robot cluster in the current time period is divided into a working queue and a charging queue. Based on the power information of the robots in the current time period, different simulation methods are used to perform simulation to obtain simulation output results.

[0068] After obtaining the simulation output result, determine whether the output result of the current simulation ≥ the simulation result after charging + the threshold. If not, schedule the robot with the lowest power in the work queue to charge. If so, continue to determine whether the simulation output result after work is greater than the current simulation output result + the threshold. If so, schedule the robot with the highest power in the charging queue to work. Otherwise, maintain the status quo.

[0069] Among them, see Figure 3 , introduced the simulation process. Since each simulation needs to iteratively calculate the number of working robots that meet the expectations in each period of the next day, it is equivalent to the number of robots that need to execute the original strategy dozens of times each time. In order to reduce space occupation and improve computing efficiency, the embodiment of the present invention simplifies the single calculation process according to the following assumptions:

[0070] Assume that the robot can quickly perform the dispatch task: In real scenarios, according to the principle of "service priority", the robot may not be able to quickly perform the charging task. Assume that the dispatch task can be performed normally: In real scenarios, the robot may not be able to perform the dispatch task as scheduled due to emergencies, such as: blocked access, human intervention, etc. Assume that there is no power and time loss when the robot goes to the charging pile: For the convenience of calculation, this part of the impact will be compensated by reducing the charging increment. Assume that the power consumption and charging efficiency of all robots are not much different.

[0071] The simulation process mainly includes the following steps:

[0072] Step 1: Based on the time interval (taking 30 minutes as an example), estimate the amount of power that a single robot will consume when performing tasks in a period of time and the amount of power that can be added by charging (working power consumption and charging increment). The estimation method can be weighted average of historical data or experimental measurement. Considering the power and time consumption of the robot during the scheduling process, and the fact that redundant working robots are more likely to be deployed than a small number of robots, a certain upward adjustment is made to the working power consumption, and a certain downward adjustment is made to the charging increment.

[0073] Step 2: Simplify the robot status information into the current power of the robot and store it in an array to form a robot sequence.

[0074] Step 3: Loop through the expected number of working robots for each period of the next day and perform the following operations:

[0075] 3.1. Arrange all robots in the robot sequence in descending order of power;

[0076] 3.2. Calculate the number of working robots V1 that can meet expectations in the current period according to the following formula:

[0077] A = the number of robots with (battery level > working threshold);

[0078] E = expected number of working robots;

[0079] C = number of charging stations;

[0080] V1=max(min(A,E),number of robots-C).

[0081] 3.3. The simulation score is increased by V1. The power consumption of each robot in the first V positions in the robot sequence is reduced by the working power consumption, and the power of each remaining robot is increased by the charging increment to obtain the power of all robots in the next time period;

[0082] 3.4. Enter the simulation of the next time period until the expected number of working robots in all time periods in the next day is traversed to obtain the final simulation score V.

[0083] S3, comparing the simulation output results corresponding to the multiple simulation methods, and determining the robot scheduling plan based on the comparison results, wherein the scheduling plan includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work.

[0084] Specifically, the simulation output result is the number of working robots that can meet the expectation in the current time period, wherein the simulation output results corresponding to the various simulation methods are compared, and the robot scheduling scheme is determined based on the comparison results, including: the simulation output result after the current simulation method is simulated is s1, the simulation output result after the charging simulation method is added is s2, and the simulation output result after the working simulation method is added is s3. Determine whether s1≥s2+threshold, if not, dispatch robot B to charge; if so, determine whether s3>s1+threshold, if so, dispatch A to work, if not, maintain the current status.

[0085] Embodiment 3

[0086] Figure 4 A structural diagram of a robot cluster scheduling system based on charging simulation provided by an embodiment of the present invention, such as Figure 4 As shown, the system includes a partitioning module 401, a simulation module 402 and a scheduling module 403, wherein:

[0087] The division module 401 is used to obtain the status information of each robot in the robot cluster in the current time period, and divide all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue;

[0088] The simulation module 402 is used to simulate using different simulation methods based on the power information of each robot in the current time period, and obtain the simulation output results of each simulation method in the future preset time period;

[0089] The scheduling module 403 is used to compare the simulation output results corresponding to multiple simulation methods, and determine the robot scheduling plan based on the comparison results. The scheduling plan includes maintaining the status quo, scheduling the robot in the current working queue to charge, or scheduling the robot in the current charging queue to work.

[0090] It can be understood that the robot cluster scheduling system based on charging simulation provided by the present invention corresponds to the robot cluster scheduling method based on charging simulation provided by the aforementioned embodiments. The relevant technical features of the robot cluster scheduling system based on charging simulation can refer to the relevant technical features of the robot cluster scheduling method based on charging simulation, which will not be repeated here.

[0091] Embodiment 4

[0092] See also Figure 5 , Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520 and a computer program 511 stored in the memory 510 and executable on the processor 520, wherein the processor 520 implements the following steps when executing the computer program 511: obtaining the status information of each robot in the robot cluster in the current time period, and dividing all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue; based on the power information of each robot in the current time period, different simulation methods are used for simulation to obtain the simulation output results of each simulation method in a future preset time period; the simulation output results corresponding to multiple simulation methods are compared, and according to the comparison results, the scheduling plan of the robot is determined, wherein the scheduling plan includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work.

[0093] The electronic device 500 and the processor 520 may implement the method in the second embodiment when executing the computer program.

[0094] Embodiment 5

[0095] See also Figure 6 , Figure 6 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Figure 6 As shown, this embodiment provides a computer-readable storage medium 600, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented: obtaining the status information of each robot in the robot cluster in the current time period, and dividing all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging queue; based on the power information of each robot in the current time period, different simulation methods are used for simulation to obtain the simulation output results of each simulation method in a future preset time period; comparing the simulation output results corresponding to multiple simulation methods, and determining the robot scheduling plan based on the comparison results, wherein the scheduling plan includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work.

[0096] The computer-readable storage medium 600 may also implement the method in the second embodiment when being processed and executed.

[0097] Embodiment 6

[0098] A computer program product includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the method described in Embodiment 1 or Embodiment 2 is implemented.

[0099] The embodiments of the present invention provide a robot cluster scheduling method, system, electronic device, medium and program product, which simulate the charging of the robot cluster in different simulation methods, find the most suitable charging strategy, and better utilize future expectations, so that the robot cluster can avoid excessive charging while covering the expected tasks, and can give full play to the role of the robots.

[0100] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0105] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0106] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A robot cluster scheduling method based on charging simulation, characterized in that: include: Acquire the status information of each robot in the robot cluster in the current time period, and divide all robots into a current working queue and a current charging queue according to the status information, wherein each robot in the current working queue is in a working state, and each robot in the current charging queue is in a charging state; Based on the power information of each robot in the current time period, different simulation methods are used to simulate and obtain the simulation output results of each simulation method in the future preset time period; The simulation output results corresponding to the multiple simulation modes are compared, and the robot scheduling scheme is determined according to the comparison results. The scheduling scheme includes maintaining the status quo, scheduling the robots in the current working queue to charge, or scheduling the robots in the current charging queue to work. The simulation modes include adding a working simulation mode, a current status simulation mode, and adding a charging simulation mode. When the simulation mode is adding a working simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including: Find the robot A with the highest power in the current charging queue, subtract the working power consumption from the power of each robot in the current working queue, add the charging increment to the power of each robot in the current charging queue except A, and subtract the working power consumption from the power of A. The working power consumption refers to the power consumed by a single robot to perform a task in a time period, and the charging increment refers to the power that can be increased by charging a single robot in a time period; When the simulation mode is a current status simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including: Subtract the working power consumption from the power of each robot in the current working queue, and add the charging power to the power of each robot in the current charging queue; When the simulation mode is to increase the charging simulation mode, the power information of each robot in the robot cluster in the current time period is simulated accordingly, including: Find the robot B with the lowest power in the current work queue, subtract the work power consumption from the power of each robot in the current work queue except B, add the charging increment to the power of B, and add the charging increment to the power of each robot in the current charging queue.

2. The robot cluster scheduling method based on charging simulation according to claim 1 is characterized in that: The simulation output result is the number of working robots that can meet the expectation in the current time period. The simulation output results corresponding to the multiple simulation methods are compared, and the robot scheduling plan is determined according to the comparison results, including: Let the simulation output result after the current situation simulation mode be s1, the simulation output result after the added charging simulation mode be s2, and the simulation output result after the added working simulation mode be s3; Determine whether s1≥s2+threshold. If not, dispatch robot B with the lowest power to charge. If s1≥s2+threshold, determine whether s3>s1+threshold. If so, dispatch robot A with the highest power to work. If not, maintain the status quo.

3. The robot cluster scheduling method based on charging simulation according to claim 1 is characterized in that: The power information of each robot in the current time period is simulated by using different simulation methods to obtain the simulation output results of each simulation method in the future preset time period, including: a. For each time period in the next day, obtain the power information of each robot in the robot cluster in the current time period, and sort all robots in order from high to low power to form a robot sequence; b. Calculate the number of working robots V1 that can meet the expectations in the current time period based on the expected number of working robots in the current time period, the number of robots with power greater than the working threshold, and the number of available charging piles in the current time period; c. Subtract the working power consumption from the power of the first V1 robots in the robot sequence, and add the charging increment to the power of the non-first V1 robots to obtain the power of each robot after simulation, which will be used as the power information of each robot in the robot cluster in the next time period; d. Repeat steps a and b to calculate the number of working robots V2 that can meet the expectation in the next time period; e. Repeat steps a to d to calculate the number of working robots Vm that can meet the expectation in the last time period of the next day; The simulation output result V=V1+V2+...+Vm, where m is the number of time periods in the future day.

4. The robot cluster scheduling method based on charging simulation according to claim 3 is characterized in that: The step b calculates the number of working robots V1 that can meet the expectation in the current time period in the following way: Assume A = the number of robots in the robot cluster whose power level is greater than the working threshold in the current time period, E = the expected number of working robots in the current time period, C = the number of available charging piles in the current time period, then V1 = max(min(A, E), total number of robots - C).

5. An electronic device, characterized in that: It includes a memory and a processor, and the processor is used to implement the steps of the robot cluster scheduling method based on charging simulation as described in any one of claims 1 to 4 when executing the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the robot cluster scheduling method based on charging simulation as described in any one of claims 1 to 4 are implemented.

7. A computer program product, characterized in that The method comprises a computer program or an instruction, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 4.

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