An intelligent scheduling method for autonomous charging of mobile charging robots

Abstract

The present invention discloses an intelligent scheduling method for autonomous charging of a mobile charging robot, which belongs to the technical field of robot control systems, and includes periodically collecting power data of robots that have completed tasks to obtain a first power value, scheduling the robot to a preset location and listing it in a preset idle list according to the first power value; periodically collecting power data of robots that are charging to obtain a second power value, listing the robot in a preset execution list according to the second power value; judging whether there are robots in the idle list and the execution list; and adjusting the scheduling mode to schedule the robot to perform tasks and charge according to the judgment result. The present invention adjusts the robot position by real-time monitoring of the robot's power status, and can dynamically adjust the scheduling strategy according to changes in the task volume, thereby optimizing the process of charging and task execution, and improving the intelligence level and operation efficiency of the overall system.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control systems, and in particular to an intelligent scheduling method for autonomous charging of a mobile charging robot. Background Art

[0002] In the fields of automated warehousing and intelligent manufacturing, mobile charging robots are an important type of automated equipment and are widely used in tasks such as cargo handling and sorting. These robots usually need to work continuously for a long time, so power management becomes a key issue. Traditional robot charging scheduling methods often rely on manual intervention or simple preset rules, lack flexibility and intelligence, and are difficult to adapt to complex and changing working environments and task requirements.

[0003] Traditional scheduling methods cannot dynamically schedule robots based on their real-time power and task requirements, causing the robots to be idle when they have sufficient power or unable to charge in time when they are low on power, thus affecting work efficiency. Due to unreasonable scheduling, some robots may need to be charged frequently while others are idle for a long time, resulting in a waste of electricity and robot resources. Scheduling strategies cannot be adjusted in real time based on changes in the working environment and adjustments in task demand, and there is a lack of the ability to respond to emergencies. The scheduling of some robots requires manual monitoring and intervention in the robot's charging and scheduling process, which increases labor costs and is prone to human errors. Summary of the invention

[0004] To solve the above problems, the present invention provides an intelligent scheduling method for autonomous charging of mobile charging robots. The method adjusts the robot position by real-time monitoring of the robot's power status and can dynamically adjust the scheduling strategy according to changes in the task volume, thereby achieving efficient, flexible and autonomous management of robot resources, optimizing the charging and task execution processes, reducing operating costs, and improving the intelligence level and operating efficiency of the overall system.

[0005] The above objectives can be achieved through the following solutions:

[0006] An intelligent scheduling method for autonomous charging of a mobile charging robot comprises periodically collecting power data of a robot that has completed a task to obtain a first power value, scheduling the robot to a preset location and listing it in a preset idle list according to the first power value; periodically collecting power data of a robot that is charging to obtain a second power value, listing the robot in a preset execution list according to the second power value; judging whether there is a robot in the idle list and the execution list; and adjusting an idle scheduling mode, a charging scheduling mode and a compatible scheduling mode according to the judgment result to schedule the robot to perform tasks and charge.

[0007] Optionally, the periodic collection of power data of the robot that has completed the task to obtain a first power value, and the dispatching of the robot to a preset location and listing it in a preset idle list according to the first power value include: collecting the power data of the robot that has completed the task to obtain a first power value; determining whether the first power value is greater than a preset first threshold; if not, dispatching the robot to a charging station for charging; if so, dispatching the robot to a preset location and listing it in the idle list.

[0008] Optionally, the periodic collection of power data of the robot being charged to obtain a second power value, and listing the robot in a preset execution list according to the second power value include: collecting the power data of the robot being charged to obtain a second power value; determining whether the second power value is greater than a preset second threshold; if not, scheduling the robot to continue charging; if yes, listing the robot in the execution list.

[0009] Optionally, the method also includes: determining whether there is a robot in the idle list; if there is a robot in the idle list, selecting the idle scheduling mode to schedule the robot; if there is no robot in the idle list, determining whether there is a robot in the execution list; if there is a robot in the execution list, selecting the charging scheduling mode to schedule the robot; if there is no robot in the execution list, selecting the compatible scheduling mode to schedule the robot.

[0010] Optionally, the method further includes: establishing an evaluation function for characterizing the comprehensive state value and initializing a weight coefficient; collecting historical device data and historical power data of the robot to construct a historical data set; using the historical data set to train the evaluation function to determine the weight coefficients of each item, and obtaining a final evaluation function. ,have

[0011] ;

[0012] In the formula, is the error term coefficient, is the weight coefficient of the power data, is the input power value, The first Device parameters, For the The function of the degree of influence of the device parameters on the robot state, For the The weight coefficient corresponding to the function of the influence of each device parameter on the robot state, For the The function of the degree of influence of the interaction between the device parameters and the power value on the robot state, For the The weight coefficient corresponding to the function of the degree of influence of the interaction between the device parameters and the power value on the robot state.

[0013] Optionally, selecting the idle scheduling mode to schedule the robot includes: collecting device data of the robots in the idle list to obtain a first device parameter set; inputting the first device parameter set and the first power value into the evaluation function to calculate the comprehensive status value of each robot in the idle list; sorting the scheduling priority of the robots in the idle list according to the size of the comprehensive status value to obtain a first sorting result; scheduling the robots in the idle list to perform tasks according to the first sorting result, and scheduling the robots in the idle list that have not performed tasks to charge.

[0014] Optionally, selecting the charging scheduling mode to schedule the robot includes: collecting equipment data of the robots in the execution list to obtain a second equipment parameter set; inputting the second power data and the second equipment parameter set into the evaluation function to calculate the current comprehensive status value of each robot in the execution list; sorting the scheduling priority of the robots in the execution list according to the size of the current comprehensive status value to obtain a second sorting result; scheduling the robots in the execution list to perform tasks according to the second sorting result, and scheduling the robots in the execution list that have not performed tasks to continue charging.

[0015] Optionally, selecting the compatible scheduling mode to schedule the robot includes: determining whether there is a robot in the idle list or the execution list; if there is a robot in the idle list, scheduling the robot in the idle list to perform a task; if there is a robot in the execution list, scheduling the robot in the execution list to perform a task; if there is no robot in either the idle list or the execution list, collecting the power data of the robot currently being charged to obtain a third power value; sorting the third power values ​​corresponding to each robot from large to small; calculating the time required for the third power value corresponding to the first-ranked robot to charge to the second threshold to obtain the waiting time; setting the waiting time as the scheduling waiting time and broadcasting an early warning.

[0016] Optionally, the method further includes: collecting the number of robots in the idle list in real time to obtain a reset number value; determining whether the reset number value is greater than a preset third threshold; if not, maintaining the current mode to schedule the robot; if so, switching to the idle scheduling mode to schedule the robot.

[0017] Based on the same inventive concept, the present invention also provides an intelligent scheduling system for autonomous charging of mobile charging robots, the system comprising: a data acquisition module, used to periodically collect power data of robots that have completed tasks to obtain a first power value, and periodically collect power data of robots that are charging to obtain a second power value; a list management module, used to list robots that have completed tasks in a preset idle list according to the first power value, and to list robots that are charging in a preset execution list according to the second power value; a judgment module, used to judge the robots in the idle list and the execution list, and select a scheduling mode according to the judgment result; the judgment module is also used to collect the number of robots in the idle list in real time, obtain a reset number value, and when the reset number value is greater than a preset third threshold value, switch to the idle scheduling mode to schedule the robot; the idle scheduling module is used to, when there is a robot in the idle list, calculate the number of robots in the idle list according to the evaluation function. The comprehensive status value is used to sort the scheduling priorities of the robots in the idle list, and the robots are scheduled to perform tasks or charge according to the sorting results; the charging scheduling module is used to sort the scheduling priorities of the robots in the execution list according to the comprehensive status value calculated by the evaluation function when there are robots in the execution list, and schedule the robots to perform tasks or continue charging according to the sorting results; the compatible scheduling module is used to schedule the robots in the idle list to perform tasks if there are robots in the idle list; the compatible scheduling module is also used to schedule the robots in the execution list to perform tasks if there are robots in the execution list; the compatible scheduling module is also used to collect the third power value of the robot currently being charged if there are no robots in both the idle list and the execution list, calculate the time required to charge to the second threshold according to the sorting result of the third power value, set the scheduling waiting time and broadcast an early warning; the evaluation function module is used to establish and train an evaluation function for characterizing the comprehensive status value.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] 1. The present invention realizes refined management of robot resources by periodically collecting robot power data, adjusting the robot's position and dynamically placing the robot in an idle list or execution list according to the power status; the intelligent scheduling algorithm can adjust the scheduling strategy in real time according to the change of task volume and robot status, ensuring that the robot executes tasks in time when the power is sufficient and charges quickly when the power is insufficient, thereby significantly improving work efficiency;

[0020] 2. The present invention provides multiple scheduling modes, which can be flexibly switched according to the status of the idle list and the execution list to cope with different working environments and changes in task volume; by establishing an evaluation function and comprehensively considering the robot's power, equipment parameters and the interaction between them, the present invention can more accurately evaluate the comprehensive status of the robot, thereby making more reasonable scheduling decisions;

[0021] 3. The present invention realizes the autonomous charging and task execution of the mobile charging robot without human intervention or preset rules, which reduces labor costs and human errors; the intelligent scheduling system can make autonomous decisions based on real-time data and dynamically adjust the charging and task allocation of the robot, thus improving the autonomy and intelligence of the system;

[0022] 4. Through intelligent scheduling, the present invention can maximize the use of robot resources and avoid frequent charging or long-term idleness of robots, thereby optimizing the use of electricity and robot resources; real-time monitoring and dynamic adjustment of scheduling strategies can ensure that the robot performs tasks in the best state, extend the service life of the robot, and reduce maintenance costs.

[0023] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a flow chart of an intelligent scheduling method for autonomous charging of a mobile charging robot according to an embodiment of the present invention.

[0026] Figure 2 It is an execution flow chart of putting a robot that has completed a task into an idle list according to an embodiment of the present invention.

[0027] Figure 3 It is an execution flow chart of adding a charging robot to an execution list according to an embodiment of the present invention.

[0028] Figure 4 It is an execution flow chart of the switching scheduling mode according to an embodiment of the present invention.

[0029] Figure 5 It is a structural schematic diagram of an intelligent scheduling system for autonomous charging of a mobile charging robot according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Reference Figure 1 An embodiment of the present invention proposes an intelligent scheduling method for autonomous charging of a mobile charging robot, which adjusts the robot position by real-time monitoring of the robot's power status and can dynamically adjust the scheduling strategy according to changes in the task volume, thereby achieving efficient, flexible and autonomous management of robot resources, optimizing the charging and task execution processes, reducing operating costs, and improving the overall system's intelligence level and operating efficiency.

[0032] The method of this embodiment specifically includes:

[0033] Periodically collect power data of robots that have completed tasks to obtain a first power value, and dispatch the robot to a preset location and list it in a preset idle list according to the first power value;

[0034] Specifically, for robots that have completed their tasks and are in an idle state, their power data will be collected periodically (for example, every hour), and this data is the first power value; based on the first power value, robots with sufficient power are included in the idle list, indicating that these robots currently have no tasks and have sufficient power to accept new tasks at any time; the preset locations can be set at the center of the task-intensive areas so that they can be quickly dispatched to perform new tasks when needed.

[0035] Periodically collecting power data of the robot being charged to obtain a second power value, and listing the robot in a preset execution list according to the second power value;

[0036] Specifically, for robots that are charging, their power data will also be periodically collected, and this data is the second power value; based on the second power value, robots that are charging and whose power has reached a certain level are included in the execution list, indicating that although these robots are charging, their power is sufficient to perform certain tasks and can perform tasks while charging or immediately after charging is completed; dispatching them to charging stations facilitates faster charging of the robots.

[0037] Determining whether there are robots in the idle list and the execution list;

[0038] Specifically, the idle list and the execution list are checked periodically to determine whether there are robots therein.

[0039] According to the judgment result, the idle scheduling mode, charging scheduling mode and compatible scheduling mode are adjusted to schedule the robot to perform tasks and charge.

[0040] Specifically, if there are robots in the idle list and the execution list is empty or has insufficient power, the idle scheduling mode will be selected to select a robot from the idle list to perform the task; if there are robots in the execution list and their power is sufficient to perform the current task, the charging scheduling mode will be selected to prioritize these robots to perform the task while continuing to charge; if there are robots in both the idle list and the execution list and both meet the conditions for performing the task, the system will select the most suitable robot to perform the task and adjust the charging plan based on a comprehensive consideration of factors such as the urgency of the task, the robot's power level, and the availability of charging stations.

[0041] For example, suppose there are 10 robots in a warehouse, 5 of which have completed the task and have sufficient first power values, then they will be included in the idle list; if the warehouse receives a handling task at this time, it will first check the idle list and find that 5 robots are available, so it will switch to the idle scheduling mode, and can select the robot with the highest power and the closest location to perform the task, and the remaining robots will be called out of the idle list for charging.

[0042] For example, suppose there are 10 robots in a warehouse, 5 of which have completed the task and have sufficient first power values, and they will be included in the idle list; the other 3 robots are charging at the charging station, and their second power values ​​reach 40%, 70% and 90% respectively. If it is set that the robots with second power values ​​greater than 50% will be included in the execution list, then the robots with second power values ​​of 70% and 90% will be included in the execution list; if the warehouse receives 6 handling tasks at this time, it will first check the idle list and find that there are 5 robots, so it will switch to idle scheduling mode and select the 5 robots in the idle list to perform the tasks; at this time there are no robots in the idle list, and then start to check the execution list, and find that there are robots with second power values ​​of 90% and 70%, so it will switch to charging scheduling mode, select the robot with second power value of 90% to perform the remaining task, and the robot with 70% power will continue to charge.

[0043] For example, suppose there are 10 robots in a warehouse, 5 of which have completed the task and have sufficient first power values, and they will be included in the idle list; the other 3 robots are charging at the charging station, and the second power values ​​are 40%, 70% and 90% respectively. If it is set that the robots with a second power value greater than 50% will be included in the execution list, then the robots with a second power value of 70% and a second power value of 90% will be included in the execution list; if the warehouse receives 8 handling tasks at this time, it will first check the idle list and find that there are 5 robots, so it switches to idle scheduling mode and selects 5 robots in the idle list to perform the task. At this time, there is no robot in the idle list, so the execution list is checked and it is found that there are robots with a second power value of 90% and a second power value of 70%. So the charging scheduling mode is switched and these two robots are selected to perform 2 tasks. At this time, there is 1 task left, so it is switched to compatible mode and periodically checks whether there are robots in the idle list and the execution list. If the other 2 robots have just completed the tasks and returned, one robot has a first power value of 70% and is included in the idle list, and the other robot has a first power value of 30% and is called for charging. Then the robot with the first power value of 70% will be scheduled to perform the remaining task.

[0044] Alternatively, if Figure 2 As shown, the periodic collection of power data of the robot that completes the task to obtain a first power value, and scheduling the robot to a preset location and listing it in a preset idle list according to the first power value include:

[0045] Collecting power data of the robot that completes the task to obtain a first power value;

[0046] Specifically, after completing the task, the mobile charging robot will periodically (such as every hour or according to actual needs) collect its power data, and this data will be recorded as the first power value; this step is the basis of intelligent scheduling. By understanding the robot's power status in real time, reasonable charging and task allocation decisions can be made.

[0047] Determine whether the first power value is greater than a preset first threshold;

[0048] Specifically, after the first power value is collected, it will be compared with a preset first threshold; the first threshold is set based on factors such as the robot's work requirements, battery capacity and charging efficiency, and aims to ensure that the robot has sufficient power when performing tasks.

[0049] If not, dispatch the robot to a charging station for charging;

[0050] Specifically, if the first power value is less than or equal to the first threshold, it indicates that the robot is low on power and needs to be charged immediately; at this time, the robot will be dispatched to a charging station for charging to ensure that it can quickly recover its power and prepare to perform subsequent tasks.

[0051] If so, the robot is dispatched to a preset location and included in the idle list.

[0052] Specifically, if the first power value is greater than the first threshold, it indicates that the robot currently has sufficient power and can perform tasks or remain in standby mode; at this time, the robot will be included in a preset idle list, waiting for further scheduling based on task requirements and power status; the idle list is used to manage a queue of robots that have sufficient power and are on standby to perform tasks; when a new task request is received, a suitable robot will be selected from the idle list for task allocation; the dynamic management of the idle list ensures the effective use of robot resources and the efficient execution of tasks.

[0053] For example, assuming that 10 mobile charging robots are deployed, their power data will be collected periodically after completing the cargo handling task; after completing the task, a robot collects its first power value of 30% (assuming the first threshold is 50%); since the power is lower than the threshold, the system will immediately dispatch the robot to the charging station for charging to avoid affecting the execution of subsequent tasks due to insufficient power; after completing the task, another robot collects its power data of 70% (greater than the first threshold of 50%); at this time, the robot will be dispatched to a preset location and included in the idle list, waiting for new tasks to be assigned; if there is no new task request for the time being, the robot will remain in standby mode until the power drops below the threshold or receives a new task instruction; through the above-mentioned intelligent scheduling method, the mobile charging robot can autonomously charge and assign tasks according to its own power status and task requirements, thereby improving work efficiency and autonomy; at the same time, the method also ensures the reasonable allocation and effective utilization of robot resources through dynamic management of the idle list.

[0054] Alternatively, if Figure 3 As shown, the periodic collection of power data of the robot being charged to obtain a second power value, and the inclusion of the robot in a preset execution list according to the second power value includes:

[0055] Collecting power data of the robot being charged to obtain a second power value;

[0056] Specifically, power data of the robot being charged will be collected periodically; this power data is called the second power value; by real-time monitoring of power changes during the charging process, the charging progress and power status of the robot can be accurately grasped.

[0057] Determining whether the second power value is greater than a preset second threshold;

[0058] Specifically, after the second power value is collected, it will be compared with a preset second threshold; the second threshold is set based on factors such as the robot's task requirements, battery capacity, charging efficiency, and expected working time; it represents the minimum power level required for the robot to perform the task.

[0059] If not, the robot is dispatched to continue charging;

[0060] Specifically, if the second power value is less than or equal to the second threshold, it means that the robot's power is not enough to support it to perform the current or upcoming assigned task; at this time, the robot's charging state will be maintained and it will continue to be charged until the power reaches or exceeds the threshold.

[0061] If so, the robot is included in the execution list.

[0062] Specifically, if the second power value is greater than the second threshold, it means that the robot's power is sufficient to support its task execution; at this time, the robot will be included in the preset execution list, waiting for task assignment based on factors such as its power status, task requirements, and idle status; the execution list is used to manage a queue of robots that have sufficient power and can execute tasks; when a new task request is received, a suitable robot will be selected from the execution list for task assignment; the dynamic management of the execution list ensures the balance between charging and task execution of the robot, and improves the robot's work efficiency and the timeliness of task execution.

[0063] For example, assume that in an automated warehouse, several mobile charging robots are deployed for cargo handling and sorting; these robots will automatically go to the charging station for charging after completing a round of tasks; during the charging process, a robot periodically collects power data of 45% (assuming the second threshold is 50%); since the power is lower than the threshold, the robot's charging state will be maintained and it will continue to be charged until the power reaches or exceeds 50%; during the charging process, another robot periodically collects power data of 55% (greater than the second threshold of 50%); at this time, the robot will be included in the execution list, waiting for new tasks to be assigned; if a new cargo handling task happens to be received at this time, and the robot's position, power and load capacity meet the requirements, then the task will be assigned to the robot, and it will be instructed to end the charging state and start executing the task.

[0064] Alternatively, if Figure 4 As shown, the method also includes:

[0065] Determine whether there is a robot in the idle list;

[0066] Specifically, in the autonomous charging intelligent scheduling system of the mobile charging robot, in addition to listing the robot in the idle list or execution list according to the power data, the corresponding scheduling mode will be selected according to the list status to optimize the robot's task allocation and charging management.

[0067] If there is a robot in the idle list, select the idle scheduling mode to schedule the robot;

[0068] Specifically, when it is determined that there are robots in the idle list, the idle scheduling mode will be selected; in this mode, priority will be given to selecting robots from the idle list to perform tasks; since these robots currently have sufficient power and have no tasks on hand, they can quickly respond to new task requests, thereby improving the timeliness of task execution.

[0069] If there is no robot in the idle list, determining whether there is a robot in the execution list;

[0070] If there is a robot in the execution list, selecting the charging scheduling mode to schedule the robot;

[0071] Specifically, when it is determined that there are no robots available in the idle list, but there are robots in the execution list that are charging and whose power is close to or has reached the threshold required to perform the task, the charging scheduling mode will be selected; in this mode, priority will be given to selecting robots with sufficient power from the execution list to perform new tasks. At the same time, these robots can be allowed to continue charging while performing the task, or return to the charging station for recharging immediately after the task is completed; the advantage of this is that the resources of the robots that are charging can be fully utilized, waiting time can be reduced, and the overall task execution efficiency can be improved.

[0072] If the execution list does not contain a robot, the compatible scheduling mode is selected to schedule the robot.

[0073] Specifically, when it is determined that there are no robots in both the idle list and the execution list, the compatible scheduling mode will be selected. In this mode, the battery level, location, task progress and other factors of all robots will be comprehensively considered to try to find an optimal scheduling plan. This may include allowing a robot that is charging but whose battery level is close to a threshold to end charging early to perform a task, or allowing a robot that is performing a task to go directly to another task location to perform a new task after completing the current task.

[0074] Optionally, the method further comprises:

[0075] Establish an evaluation function for characterizing the comprehensive state value and initialize the weight coefficient;

[0076] Specifically, in the intelligent scheduling system of the mobile charging robot, in order to more accurately evaluate the comprehensive status of the robot and thus make more reasonable scheduling decisions, an evaluation function can be established to comprehensively consider the robot's power level, equipment parameters, and the interactions between them.

[0077] Collect the robot's historical equipment data and historical power data to build a historical data set;

[0078] The evaluation function is trained using the historical data set to determine the weight coefficients of each item, and the final evaluation function is obtained. ,have

[0079] ;

[0080] In the formula, is the error term coefficient, is the weight coefficient of the power data, is the input power value, The first Device parameters, For the The function of the degree of influence of the device parameters on the robot state, For the The weight coefficient corresponding to the function of the influence of each device parameter on the robot state, For the The function of the degree of influence of the interaction between the device parameters and the power value on the robot state, For the The weight coefficient corresponding to the function of the degree of influence of the interaction between the device parameters and the power value on the robot state.

[0081] Specifically, in order to determine the weight coefficient and error term coefficient in the evaluation function, it is necessary to collect the robot's historical equipment data and historical power data to build a historical data set; then, use these historical data to train the evaluation function and optimize the weight coefficient by minimizing the difference between the evaluation result and the actual state; this process usually involves the application of machine learning or optimization algorithms.

[0082] For example, suppose that in an automated factory, the comprehensive status of a mobile robot needs to be evaluated to determine whether it is suitable for a new task. The following historical data are collected: , equipment parameters running speed , Equipment parameter load capacity , through the training process, the optimized evaluation function weight coefficient is obtained; now, when a new task request is received, it will collect the current robot's power and device parameters , The value of is then substituted into the evaluation function Calculation is performed in Will reflect the current comprehensive status of the robot, and can be based on this comprehensive status value To decide whether to schedule the robot to perform the task; if the comprehensive state value output by the evaluation function If the overall status value is high, it means that the robot is in good overall condition and has sufficient power and performance to perform the task; if the overall status value is high, it means that the robot is in good overall condition and has sufficient power and performance to perform the task. If the status is low, you may need to put the robot in the charging list or idle list and wait for the status to recover before scheduling it.

[0083] Optionally, selecting the idle scheduling mode to schedule the robot includes:

[0084] Collecting device data of the robots in the idle list to obtain a first device parameter set;

[0085] Specifically, the device data of each robot in the idle list will be collected first. These data include but are not limited to the robot's running speed, load capacity, working time, etc. These data constitute the robot's first device parameter set; these parameters are important indicators for evaluating the robot's current status and are crucial for subsequent task allocation.

[0086] Inputting the first device parameter set and the first power value into the evaluation function to calculate the comprehensive state value of each robot in the idle list;

[0087] Specifically, the first device parameter set and the current power value of the robot (first power value) are input into the evaluation function established previously; the evaluation function calculates the comprehensive status value of each robot based on these input data; this comprehensive status value reflects the overall performance of the robot in the current state, including power, device status and other aspects.

[0088] According to the size of the comprehensive state value, the scheduling priorities of the robots in the idle list are sorted to obtain a first sorting result;

[0089] Specifically, the robots in the idle list are sorted according to the calculated comprehensive status values; the higher the comprehensive status value of a robot, the higher its scheduling priority, which means that it is more suitable for being assigned a new task; through sorting, a first sorting result is obtained, which determines the order in which the robots perform tasks.

[0090] The robots in the idle list are scheduled to perform tasks according to the first sorting result, and the robots in the idle list that are not performing tasks are scheduled to be charged.

[0091] Specifically, the robots in the idle list are scheduled according to the first sorting result; robots with higher sorting will be given priority in being assigned new tasks to execute; and for those robots that have not been assigned tasks, they will be scheduled to go to the charging station for charging to ensure that their power is always maintained at a sufficient level and are ready to execute new tasks at any time.

[0092] For example, suppose that in a smart warehouse, the system detects that there are three robots A, B, and C in the idle list; first, the equipment data of the three robots are collected to obtain their first equipment parameter sets; then, these equipment data and the current power values ​​of the robots are input into the evaluation function to calculate the comprehensive status value of each robot; suppose the calculation result is that the comprehensive status value of robot A is 90, the comprehensive status value of robot B is 80, and the comprehensive status value of robot C is 70; according to this result, the robots are sorted, and robot A has the highest scheduling priority, followed by robot B, and finally robot C; in the task allocation stage, a new cargo handling task is first assigned to robot A; since robots B and C have not been assigned tasks, they are subsequently scheduled to go to the charging station for charging to ensure that they have sufficient power; in this way, the resources of idle robots are effectively managed through the idle scheduling mode, which not only ensures the timely execution of new tasks, but also ensures that the robots have sufficient power.

[0093] Optionally, selecting the charging scheduling mode to schedule the robot includes:

[0094] Collecting device data of the robots in the execution list to obtain a second device parameter set;

[0095] Specifically, the equipment data of each robot in the execution list is collected, and these data constitute the second equipment parameter set of the robot; these data are crucial for evaluating the performance and reliability of the robot when performing tasks.

[0096] Inputting the second power data and the second device parameter set into the evaluation function, and calculating the current comprehensive state value of each robot in the execution list;

[0097] Specifically, the collected second power data (i.e., current power) and the second device parameter set are input into a pre-established evaluation function; the evaluation function calculates the current comprehensive state value of each robot based on these data; this value reflects the overall execution capability of the robot under the current power and device status.

[0098] Sorting the scheduling priorities of the robots in the execution list according to the current comprehensive state value to obtain a second sorting result;

[0099] Specifically, the robots in the execution list are sorted according to the calculated comprehensive status values; a robot with a higher comprehensive status value has a higher scheduling priority, which means that it is more suitable for being assigned a new task, and the sorting result is the second sorting result.

[0100] The robots in the execution list are scheduled to execute tasks according to the second sorting result, and the robots in the execution list that have not executed tasks are scheduled to continue charging.

[0101] Specifically, the robots in the execution list are scheduled according to the second sorting result; robots with higher sorting will be given priority in assigning new tasks; for those robots that have not been assigned new tasks, if they are still charging and the battery is not fully charged, they will be allowed to continue charging; if the battery is sufficient to complete the task but for the purpose of optimizing the charging cycle, it may be decided whether to let them continue charging or standby according to the overall scheduling strategy.

[0102] For example, suppose that on an automated production line, it is detected that a task needs to be assigned, but there is no robot available in the idle list, so it switches to the charging scheduling mode; at this time, it turns to the detection execution list and finds that three robots D, E, and F are charging; the device data (the second device parameter set) and current power (the second power data) of the three robots are collected; the collected data is input into the evaluation function to calculate the current comprehensive state value of each robot; it is assumed that the result is that the comprehensive state value of robot D is 85, the comprehensive state value of robot E is 78, and the comprehensive state value of robot F is 65; according to the comprehensive state value, the system evaluates the robot The second sorting result is D>E>F. A new production task is assigned to robot D with the highest comprehensive status value. Although robot E is not assigned a new task, the system decides to let it continue charging for later use because its battery is not fully charged and its comprehensive status value is high. Robot F has a low comprehensive status value and its battery may be close to meeting the needs of performing the task. Therefore, the overall strategy will decide whether to let it continue charging or remain on standby so that it can respond quickly when needed. In this way, the charging scheduling mode ensures that even when there are no idle robots available, task allocation can be optimized through intelligent scheduling to improve overall production efficiency.

[0103] Optionally, selecting the compatible scheduling mode to schedule the robot includes:

[0104] Determine whether there is a robot in the idle list or the execution list;

[0105] Specifically, check whether there are robots in the idle list and the execution list, in order to determine whether there are robots that can be directly scheduled to perform the task.

[0106] If there are robots in the idle list, scheduling the robots in the idle list to perform tasks;

[0107] Specifically, if there are robots in the idle list, these robots will be scheduled to perform tasks first; because these robots are not currently performing tasks, they can be put into new work immediately.

[0108] If there is a robot in the execution list, scheduling the robot in the execution list to execute the task;

[0109] Specifically, if there are no robots in the idle list, but there are robots in the execution list, these robots will be considered for scheduling to perform new tasks.

[0110] If there is no robot in the idle list or the execution list, collecting power data of the robot currently being charged to obtain a third power value;

[0111] Specifically, if there are no robots available in the idle list and the execution list, the power data of the robot currently being charged will be collected to obtain the third power value; these values ​​reflect the current power sufficiency of the robot.

[0112] Sorting the third power values ​​corresponding to the robots from large to small;

[0113] Calculate the time required for the third power value corresponding to the first-ranked robot to be charged to the second threshold value to obtain the waiting time;

[0114] Specifically, the collected third power values ​​are sorted from large to small to determine which robot's power is closest to being fully charged or close to the threshold for executing the task; then, the time required for the first-ranked robot (i.e., the robot with the highest power) to charge to the second threshold is calculated, and this time is marked as waiting time.

[0115] The waiting time is set as the scheduling waiting time and a warning is broadcast.

[0116] Specifically, the calculated waiting time is set as the scheduling waiting time, and a warning is broadcast; this means that it is necessary to wait for this period of time before the robot with the highest power can be dispatched to perform the task; the warning broadcast can allow relevant personnel to understand the current scheduling status and make corresponding preparations or adjustments.

[0117] For example, suppose that on an automated production line in a large manufacturing plant, robots need to be scheduled to complete a series of production tasks; the idle list and the execution list are checked and it is found that there are no robots in the idle list, but two robots A and B are being charged and their power levels are greater than a second threshold, so they are included in the execution list; these two robots will be immediately scheduled to perform the tasks; while these two robots are performing their tasks, they will continue to monitor the idle list and the execution list; if new robots complete the tasks and are added to the idle list at this time, these robots will be immediately scheduled to share the workload; if a robot completes the task and is detected to have a first power value less than the first threshold, it will be charged and its power level (second power value) will be monitored, and when it is charged to be greater than the second threshold, it will also be immediately scheduled to perform the task.

[0118] Exemplarily, if there are no robots in both the idle list and the execution list at this time, the power data of the robot being charged will be collected, and the third power values ​​will be 40% and 50% respectively; these two power values ​​will be sorted to obtain 50% greater than 40%; then the time required for the robot with the third power value of 50% to charge to the second threshold (assuming it is 80%) is calculated, assuming it is 10 minutes; 10 minutes is set as the scheduling waiting time, and an early warning is broadcast through the warehouse broadcast system to inform relevant personnel that they need to wait up to 10 minutes before scheduling the robot to perform the handling task.

[0119] Alternatively, if Figure 4 As shown, the method also includes:

[0120] Collect the number of robots in the idle list in real time to obtain a reset number value;

[0121] Specifically, the number of robots in the idle list is collected in real time to obtain a dynamic data called the reset quantity value; this data reflects the sufficiency of the current idle robots.

[0122] Determining whether the reset quantity value is greater than a preset third threshold;

[0123] Specifically, the reset quantity value is compared with a preset third threshold value; the third threshold value is a value preset according to demand and resource conditions, and is used to determine whether there are enough idle robots to support switching to the idle scheduling mode.

[0124] If not, maintain the current mode to schedule the robot;

[0125] Specifically, if the reset quantity value is not greater than the third threshold, it means that the number of idle robots is insufficient, and the current charging scheduling mode or compatible scheduling mode will continue to be maintained, and all available robots will continue to be used to perform tasks.

[0126] If so, switch to the idle scheduling mode to schedule the robot.

[0127] Specifically, if the reset quantity value is greater than the third threshold, it means that there are sufficient idle robots and the amount of tasks is small, and it will switch to the idle scheduling mode; in the idle scheduling mode, the robots in the idle list will be scheduled first to perform tasks to maximize the use of idle resources, reduce waiting time and improve task execution efficiency.

[0128] For example, assume that on an automated production line, a compatible scheduling mode is being used to schedule robots to perform production tasks; the number of robots in the idle list is collected in real time, and a reset number value of 8 is obtained; the preset third threshold is 4; the reset number value 8 is compared with the third threshold value 4, and it is found that the reset number value is greater than the third threshold value; therefore, the idle scheduling mode is switched to schedule the robot to perform the task; if the collected reset value is 1, the reset number value is less than the third threshold value, and the number of idle robots is insufficient, the current compatible scheduling mode is maintained, which means that all available resources will continue to be used to perform tasks to ensure the continuous operation of the production line.

[0129] Based on the same inventive concept, Figure 5 As shown, the present invention also provides an intelligent scheduling system for autonomous charging of a mobile charging robot, the system comprising:

[0130] A data collection module, used to periodically collect power data of robots that have completed tasks to obtain a first power value, and to periodically collect power data of robots that are being charged to obtain a second power value;

[0131] a list management module, configured to list robots that have completed tasks into a preset idle list according to the first power value, and to list robots that are being charged into a preset execution list according to the second power value;

[0132] A judgment module is used to judge the robots in the idle list and the execution list, and select a scheduling mode according to the judgment result; the judgment module is also used to collect the number of robots in the idle list in real time, obtain a reset number value, and switch to the idle scheduling mode to schedule the robot when the reset number value is greater than a preset third threshold value;

[0133] The idle scheduling module is used to sort the robots in the idle list according to the comprehensive state value calculated by the evaluation function when there are robots in the idle list, and schedule the robots to perform tasks or charge according to the sorting results;

[0134] The charging scheduling module is used to sort the scheduling priorities of the robots in the execution list according to the comprehensive state value calculated by the evaluation function when there are robots in the execution list, and schedule the robots to perform tasks or continue charging according to the sorting results;

[0135] A compatible scheduling module is used to schedule the robots in the idle list to perform tasks if there are robots in the idle list; the compatible scheduling module is also used to schedule the robots in the execution list to perform tasks if there are robots in the execution list; the compatible scheduling module is also used to collect the third power value of the robot currently being charged if there are no robots in both the idle list and the execution list, calculate the time required to charge to the second threshold value according to the sorting result of the third power value, set the scheduling waiting time and broadcast an early warning;

[0136] The evaluation function module is used to establish and train the evaluation function used to characterize the comprehensive state value.

[0137] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between the lines. The indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0138] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. An intelligent scheduling method for autonomous charging of a mobile charging robot, characterized in that: The method comprises: Periodically collecting power data of robots that have completed tasks to obtain a first power value, and dispatching the robots to a preset location and listing them in a preset idle list according to the first power value; Periodically collecting power data of the robot being charged to obtain a second power value, and listing the robot in a preset execution list according to the second power value; Determining whether there are robots in the idle list and the execution list; According to the judgment result, the idle scheduling mode, charging scheduling mode and compatible scheduling mode are adjusted to schedule the robot to perform tasks and charge; Wherein, determining whether there is a robot in the idle list; If there is a robot in the idle list, select the idle scheduling mode to schedule the robot; If there is no robot in the idle list, determining whether there is a robot in the execution list; If there is a robot in the execution list, selecting the charging scheduling mode to schedule the robot; If the execution list does not contain a robot, the compatible scheduling mode is selected to schedule the robot.

2. According to claim 1, an intelligent scheduling method for autonomous charging of a mobile charging robot is characterized in that: The periodic collection of power data of the robot that has completed the task to obtain a first power value, and dispatching the robot to a preset location and listing it in a preset idle list according to the first power value includes: Collecting power data of the robot that completes the task to obtain a first power value; Determine whether the first power value is greater than a preset first threshold; If not, dispatch the robot to a charging station for charging; If so, the robot is dispatched to a preset location and included in the idle list.

3. The intelligent scheduling method for autonomous charging of a mobile charging robot according to claim 1, characterized in that: The periodic collection of the power data of the robot being charged to obtain a second power value, and the inclusion of the robot in a preset execution list according to the second power value includes: Collecting power data of the robot being charged to obtain a second power value; Determining whether the second power value is greater than a preset second threshold; If not, the robot is dispatched to continue charging; If so, the robot is included in the execution list.

4. The intelligent scheduling method for autonomous charging of a mobile charging robot according to claim 3, characterized in that: The method further comprises: Establish an evaluation function for characterizing the comprehensive state value and initialize the weight coefficient; Collect the robot's historical equipment data and historical power data to build a historical data set; The evaluation function is trained using the historical data set to determine the weight coefficients of each item, and the final evaluation function is obtained. , have ; In the formula, is the error term coefficient, is the weight coefficient of the power data, is the input power value, The first Device parameters, For the The function of the degree of influence of the device parameters on the robot state, For the The weight coefficient corresponding to the function of the influence of each device parameter on the robot state, For the The function of the degree of influence of the interaction between the device parameters and the power value on the robot state, For the The weight coefficient corresponding to the function of the degree of influence of the interaction between the device parameters and the power value on the robot state.

5. The intelligent scheduling method for autonomous charging of a mobile charging robot according to claim 4, characterized in that: The selecting the idle scheduling mode to schedule the robot comprises: Collecting device data of the robots in the idle list to obtain a first device parameter set; Inputting the first device parameter set and the first power value into the evaluation function to calculate the comprehensive status value of each robot in the idle list; According to the size of the comprehensive state value, the scheduling priorities of the robots in the idle list are sorted to obtain a first sorting result; The robots in the idle list are scheduled to perform tasks according to the first sorting result, and the robots in the idle list that are not performing tasks are scheduled to be charged.

6. The intelligent scheduling method for autonomous charging of a mobile charging robot according to claim 4, characterized in that: The selecting the charging scheduling mode to schedule the robot includes: Collecting device data of the robots in the execution list to obtain a second device parameter set; Inputting the second power value and the second device parameter set into the evaluation function to calculate the current comprehensive state value of each robot in the execution list; Sorting the scheduling priorities of the robots in the execution list according to the current comprehensive state value to obtain a second sorting result; The robots in the execution list are scheduled to execute tasks according to the second sorting result, and the robots in the execution list that have not executed tasks are scheduled to continue charging.

7. The intelligent scheduling method for autonomous charging of a mobile charging robot according to claim 4, characterized in that: The selecting the compatible scheduling mode to schedule the robot comprises: Determine whether there is a robot in the idle list or the execution list; If there are robots in the idle list, scheduling the robots in the idle list to perform tasks; If there is a robot in the execution list, scheduling the robot in the execution list to execute the task; If there is no robot in the idle list or the execution list, collecting power data of the robot currently being charged to obtain a third power value; Sorting the third power values ​​corresponding to the robots from large to small; Calculate the time required for the third power value corresponding to the first-ranked robot to be charged to the second threshold value to obtain the waiting time; The waiting time is set as the scheduling waiting time and a warning is broadcast.

8. The intelligent scheduling method for autonomous charging of a mobile charging robot according to claim 7, characterized in that: The method further comprises: Collect the number of robots in the idle list in real time to obtain a reset number value; Determining whether the reset quantity value is greater than a preset third threshold; If not, maintain the current mode to schedule the robot; If so, switch to the idle scheduling mode to schedule the robot.

9. An intelligent scheduling system for autonomous charging of a mobile charging robot, using the method as described in any one of claims 1 to 8, characterized in that: The system comprises: A data collection module, used to periodically collect power data of robots that have completed tasks to obtain a first power value, and to periodically collect power data of robots that are being charged to obtain a second power value; a list management module, configured to list robots that have completed tasks into a preset idle list according to the first power value, and to list robots that are being charged into a preset execution list according to the second power value; A judgment module is used to judge the robots in the idle list and the execution list, and select a scheduling mode according to the judgment result; the judgment module is also used to collect the number of robots in the idle list in real time, obtain a reset number value, and switch to the idle scheduling mode to schedule the robot when the reset number value is greater than a preset third threshold value; The idle scheduling module is used to sort the robots in the idle list according to the comprehensive state value calculated by the evaluation function when there are robots in the idle list, and schedule the robots to perform tasks or charge according to the sorting results; The charging scheduling module is used to sort the scheduling priorities of the robots in the execution list according to the comprehensive state value calculated by the evaluation function when there are robots in the execution list, and schedule the robots to perform tasks or continue charging according to the sorting results; A compatible scheduling module is used to schedule the robots in the idle list to perform tasks if there are robots in the idle list; the compatible scheduling module is also used to schedule the robots in the execution list to perform tasks if there are robots in the execution list; the compatible scheduling module is also used to collect the third power value of the robot currently being charged if there are no robots in both the idle list and the execution list, calculate the time required to charge to the second threshold value according to the sorting result of the third power value, set the scheduling waiting time and broadcast an early warning; The evaluation function module is used to establish and train the evaluation function used to characterize the comprehensive state value.

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