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

Abstract

The present invention provides a robot cluster charging method, system, electronic device, medium, and program product. The method includes: obtaining the status of charging piles and the status of each robot in a designated work type robot cluster during the current time period; reading the target power of the designated work type robot cluster during the current time period; calculating the corresponding expected minimum average power based on the number of available charging piles in the current time period and the status of each robot in the designated work type robot cluster; and adjusting the working state and charging state of the robots based on the relationship between the expected minimum average power and a preset power value. The present invention uses the expected minimum average power as an adjustable parameter to construct a robot cluster charging strategy, providing a robot cluster charging strategy that has a certain degree of autonomy and supports regulation. While being able to learn from historical data, it can also accept human arrangements and adjustments, making the robot cluster charging strategy more flexible.

Description

Technical Field

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

[0002] Electricity is the primary energy source for most robots today. Until wireless charging technology matures and becomes widely adopted, robots will often have to recharge at designated locations. To maximize the value created by a robot swarm, a good charging strategy must consider the number and timing of potential tasks, utilizing off-peak periods to reserve power for potential peak periods.

[0003] Fixed charging plans not only rely heavily on the planner's knowledge but also lack the ability to adapt to changes. To reduce the manual maintenance costs associated with such plans, we use historical data to predict future mission conditions, thereby implementing a series of fully autonomous cluster charging strategies. However, while these strategies reduce manual workload, they also prevent people from participating in improvements through their own knowledge. All decisions are based on a limited data source, which in turn makes them inflexible. In practice, we have found that fully autonomous strategies are difficult for people to understand and cannot meet certain management requirements. Summary of the Invention

[0004] The present invention addresses the technical problems existing in the prior art and provides a robot cluster charging method, system, electronic equipment, medium and program product.

[0005] According to a first aspect of the present invention, a robot cluster charging method supporting average power regulation is provided, comprising:

[0006] Obtain the status of the charging pile in the current time period and the status of each robot in the robot cluster of the specified type;

[0007] Read the target power of the specified type of robot cluster in the current time period from the configuration file;

[0008] Calculate the expected minimum average power consumption based on the number of available charging stations in the current time period and the status of each robot in the robot cluster of the specified type;

[0009] When the expected minimum average power level is less than the preset power level, the robot in working state with the lowest power level is arranged to charge;

[0010] When the expected minimum average power is greater than or equal to the preset power value, the robot in the charging state with the highest power is assigned to work;

[0011] The preset power value is the difference between the target power and the threshold.

[0012] On the basis of the above technical solution, the present invention can also make the following improvements.

[0013] Optionally, the calculation of the corresponding expected minimum average power consumption based on the number of available charging piles in the current time period and the status of each robot in the robot cluster of the specified type includes:

[0014] a. Extract the remaining power of each robot from the status of each robot in the robot cluster of the specified type, and divide all robots of the specified type into a working power array and a charging power array according to their working status and charging status;

[0015] b. Set the initial expected minimum average power V to 100% and read the expected number of working robots in the current time period;

[0016] c. Calculate the average power V1 of all robots in the specified job type during the current time period;

[0017] d. If V1 is less than V, let V = V1. Then, based on the remaining power of each robot, the expected number of working robots in the current time period, and the total number of robots in the robot cluster of the specified type of work, determine the number of robots in the working state to be arranged for charging, and re-divide the working power array and the charging power array;

[0018] e. Read the data for the next time period, calculate the average power of all robots, and execute step d until the average power of all robots in the next time period is greater than or equal to the average power of all robots in the current time period. Then exit and use the average power of all robots at this time as the expected minimum average power for the current time period.

[0019] Optionally, in step d, determining the number of robots in working state to be arranged to charge includes:

[0020] Set A = the number of robots with remaining power greater than the working threshold power among all robots;

[0021] E = the expected number of working robots in the current time period;

[0022] Determine the number of robots in working state to be scheduled for charging: C = min (the total number of robots in the robot cluster of the specified type of work - min(A, E), the number of available charging piles in the current time period);

[0023] Among them, C robots that are in working condition and have the lowest power are arranged to charge.

[0024] Optionally, in step d, the step of determining the number of robots in working state to be arranged to be charged and re-dividing the working power array and the charging power array further includes:

[0025] Obtain the working power consumption and charging power increment of each robot in the specified work type in the current time period, and calculate the remaining power of each robot in the next time period based on the re-divided working power array and charging power array;

[0026] Accordingly, in step e, reading the data of the next time period and calculating the average power of all robots includes:

[0027] Based on the calculated remaining power of each robot in the next time period, the average power of all robots in the next time period is calculated.

[0028] Optionally, when the expected minimum average power level is less than a preset power level, arranging a robot in a working state with the lowest power level to charge further includes:

[0029] When all charging piles are occupied at this time, a robot of the same type of work that is in charging state, has the highest power and has a higher power than the robot scheduled to be charged is selected for exchange. The exchange refers to the exchange of the working state or charging state of the two robots.

[0030] Optionally, it also includes: for robot clusters of multiple types, charging is allocated to each type of robot cluster according to the priority order of robots of different types of work, wherein the result of each charging allocation will affect the number of available charging piles when charging is allocated to the next type of robot cluster.

[0031] According to a second aspect of the present invention, there is provided a robot cluster charging system that supports adjusting average power, comprising:

[0032] An acquisition module is used to obtain the status of the charging pile and each robot in the specified work robot cluster in the current time period; and read the target power of the specified work robot cluster in the current time period from the configuration file;

[0033] A calculation module is used to calculate the corresponding expected minimum average power based on the number of available charging piles in the current time period and the status of each robot in the robot cluster of a specified type;

[0034] The adjustment module is used to adjust and arrange the robot in the working state with the lowest power to charge when the expected minimum average power is less than the preset power value; and to adjust and arrange the robot in the charging state with the highest power to work when the expected minimum average power is greater than or equal to the preset power value; wherein the preset power value is the difference between the target power and the threshold.

[0035] According to a third aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the processor is configured to implement the steps of a robot cluster charging method supporting adjustment of average power when executing a computer program stored in the memory.

[0036] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the robot cluster charging method that supports adjusting the average power are implemented.

[0037] In a fifth aspect of the present invention, a computer program product is provided, comprising a computer program or instructions, which, when executed by a processor, implement the steps of the robot cluster charging method that supports adjusting the average power.

[0038] The present invention provides a robot cluster charging method, system, electronic device, medium and program product, which takes the expected minimum average power as an adjustable parameter to construct a robot cluster charging strategy, providing a robot cluster charging strategy that has a certain degree of autonomy and supports regulation. While being able to learn historical data, it can also accept human arrangements and adjustments, making the robot cluster charging strategy more flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a robot cluster charging method that supports adjusting average power provided by the present invention;

[0040] Figure 2 A schematic diagram of the process of calculating the expected minimum average power of a robot cluster;

[0041] Figure 3 A schematic diagram of the structure of a robot cluster charging system that supports adjusting average power provided by the present invention;

[0042] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0043] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0044] The following embodiments of the present invention are described in further detail with reference to 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.

[0045] Before introducing the robot cluster charging method supporting average power adjustment provided by the present invention, relevant terms and concepts involved in the embodiments of the present invention are explained.

[0046] Point: A physical location that a robot can perceive. The basic information of a point consists of location information and description information. The location information is the map where it is located and its coordinates within that map.

[0047] Robot Mission: This describes the robot's task. Mission types include charging, delivering food, picking up goods, guiding, patrolling, and more. The specific mission description varies depending on the type, but generally includes at least the mission's start time and the locations it will pass through.

[0048] Robot status: including the robot's type of work, current location, angle, load (maximum number of tasks that can be accepted), power, charging status (whether the robot is charging), tasks being performed and tasks already accepted, etc. In some embodiments, the robot status includes the robot's type of work, current location, load, tasks being performed and tasks already accepted, charging status, and power information. The robot's type of work corresponds to the type of tasks that can be accepted, such as: delivery robot, environmental protection robot, security robot, disinfection robot, etc.; power information includes current voltage, current, battery temperature, maximum battery capacity, current remaining battery capacity, etc. For simplicity, the power information in this application specifically refers to the remaining power in percentage, that is:

[0049]

[0050] The same robot can accept multiple tasks simultaneously and decide the order in which they will be executed. Operating power consumption refers to the amount of power a single robot consumes while executing a task within a specified time period. Charging power increase refers to the amount of power a single robot can generate by charging within a specified time period.

[0051] Due to differences in task energy consumption, circuit structure, battery type, specifications, and aging, different robots will have different working power consumption and charging benefits during the same period of time. However, to maintain simplicity, this application does not expand on this issue and assumes that robots of the same type have similar parameters in all aspects. For example, the power consumption during the same period of time is roughly the same, and the charging power gain is also similar.

[0052] It should be noted that the threshold values ​​and working threshold values ​​mentioned in the following embodiments are all pre-set values, and their reference values ​​are in the range of 0-100 in percentage.

[0053] Example 1

[0054] A robot cluster charging method that supports regulating the average power, such as Figure 1As shown, the method includes: 101. Obtaining the status of the charging piles and the status of each robot in the designated work robot cluster in the current time period; reading the target power of the designated work robot cluster in the current time period from the configuration file; 102. Calculating the corresponding expected minimum average power according to the number of available charging piles in the current time period and the status of each robot in the designated work robot cluster; 103. When the expected minimum average power is less than the preset power value, arranging the robot in the working state with the lowest power to charge; 104. When the expected minimum average power is greater than or equal to the preset power value, arranging the robot in the charging state with the highest power to work; wherein, the preset power value is the difference between the target power and the threshold.

[0055] It can be understood that based on the defects in the background technology, the embodiment of the present invention proposes a robot cluster charging method that supports the adjustment of average power, takes the expected minimum average power as an adjustable parameter, constructs a robot cluster charging strategy, and provides a robot cluster charging strategy that has a certain degree of autonomy and supports regulation. While being able to learn historical data, it can also accept human arrangements and adjustments. The charging strategy of the robot cluster is more flexible.

[0056] Example 2

[0057] A robot cluster charging method supporting average power regulation mainly includes the following steps:

[0058] 101. Obtain the status of the charging pile and the status of each robot in the designated work robot cluster in the current time period; and read the target power of the designated work robot cluster in the current time period from a configuration file.

[0059] It is understandable that for the current time period, the status of each robot in the robot cluster of a specified type of work (which can be understood as a certain type of work) is obtained, where the robot status refers to the robot's type of work, current location, angle, load, power, tasks being executed, and tasks accepted. Here, the main purpose is to obtain the power of each robot and whether it is in working state or charging state. The charging pile status of the current time period includes whether each charging pile is supplying power and the power supply object, that is, whether each charging pile is supplying power, and if so, which type of robot is being supplied power.

[0060] While obtaining the status of the charging pile in the current time period and the status of each robot in the specified work robot cluster, the target power of the specified work robot cluster in the current time period can be read from the configuration file. The format of the target power of the robot in each time period in the configuration file is as follows:

[0061] Delivery robot: {"0:00-7:00":100};

[0062] The target power of the delivery robot cluster between 0:00 and 7:00 is 100.

[0063] 102. Calculate the corresponding expected minimum average power consumption based on the number of available charging piles in the current time period and the status of each robot in the robot cluster of the specified type of work.

[0064] Step 101 obtains the status of the charging piles for the current time period. Based on this status, the number of available charging piles for the current time period can be determined. It should be noted that the number of available charging piles here refers to the number of charging piles available for the robot cluster of the specified work type. The number of available charging piles = the number of unoccupied charging piles + the number of charging piles currently occupied by robots of the specified work type. Based on the robot status and the number of available charging piles for the current time period, the expected minimum average power consumption for the work type is calculated.

[0065] It can be understood that there are many ways to obtain the number of available charging piles, for example, it can be obtained based on the status of the charging piles in the current time period, or by retrieving the available charging pile data stored in real time in the background; there are different ways to obtain the number of available charging piles, and the present invention will not give examples one by one.

[0066] The flowchart of the calculation process of the expected minimum average power can be found in Figure 2 , mainly including the following steps:

[0067] Step 1: Calculate the expected number of working robots for each period of the next day, starting at the next time point, at intervals of 30 minutes. Although the expected number of working robots changes based on the real-time correction strategy, the expected number of working robots before the correction remains unchanged until the next training session (at the end of the day). Therefore, these original expectations are cached to avoid repeated calculations.

[0068] The number of robots expected to work in each time period is related to the needs of people during that 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 next day. Since the training is based on a one-day timeline, expectations for more than one day are made in a cyclical manner. For example, the expectation 12 hours later at 8 pm is based on the original expectation at 8 am. For the next day's expectation, it can be further modified according to the attributes related to the number of robots required for the next day, such as holidays, activities, and even weather.

[0069] Based on the time interval (30 minutes), the working power consumption and charging power increment of a single robot in a period of time are estimated. The estimation method can be weighted averaging of historical data or experimental measurement.

[0070] Step 2: Extract the remaining power of each robot from the robot status, distinguish them by working and charging status, obtain the working power array and the charging power array, and set the initial expected minimum average power V = 100%.

[0071] Step 3: Loop through the expected number of working robots for each period of the next day and perform the following operations:

[0072] 3.1, combine the working power array and the charging power array, and calculate the average power V1 of all robots;

[0073] 3.2, when V>V1, let V=V1, and calculate the amount of electricity C to be allocated to the charging array according to the following formula:

[0074] Set A = the number of robots with remaining power greater than the working threshold power among all robots;

[0075] E = expected number of working robots;

[0076] C = min (total number of robots of this type of work - min (A, E), number of available charging piles).

[0077] Assign the C robots with the lowest battery levels in the working state to the charging power array, and the remaining robots to the working power array. Re-divide the working and charging power arrays. Then, for each battery level in the charging power array, increment the charging power level. If the battery level is greater than 100, set it to 100. For each battery level in the working power array, decrement the working power level. If the battery level is less than 0, set it to 0.

[0078] Step 4: Repeat step 3 until V≤V1 or all time periods are traversed.

[0079] Among them, after the working power array and charging power array of all robots are redivided, the working power consumption and charging power increment of each robot of the specified work type in the current time period are obtained, and the remaining power of each robot in the next time period is calculated based on the redivided working power array and charging power array.

[0080] Calculating the average power of all robots in the next time period includes: calculating the average power of all robots in the next time period based on the calculated remaining power of each robot in the next time period.

[0081] Based on the average power of all robots in the next time period, execute step 3 until the average power of all robots in the next time period is greater than or equal to the average power of all robots in the current time period, and exit. At this time, the average power of all robots is the expected minimum average power of the current time period.

[0082] It's important to note that each step in the loop schedules charging as much as possible after the expected number of robots working is met. When the number of robots and charging stations is sufficient, the average battery charge in the next period will increase. When the number of robots is insufficient to meet the expected number of tasks, both scheduling robots to charge in advance and waiting until the limit is reached will result in lost tasks during the charging period, and neither option is superior in terms of the desired number of tasks completed. Therefore, when V ≤ V 1, the loop is exited and the return value is returned to V. This results in a smaller probability of difference from the result after iterating through all periods and significantly reduces the computational effort.

[0083] 103. When the expected minimum average power is less than the preset power value, the robot with the lowest power in the working state is arranged to charge; when the expected minimum average power is greater than or equal to the preset power value, the robot with the highest power in the charging state is arranged to work; wherein the preset power value is the difference between the target power and the threshold.

[0084] Among them, when the expected minimum average power < target power - threshold, traverse the robots working in this type of work, add robots without immediate tasks and robots interfered by other things to the alternative queue, and arrange the robot with the lowest power in the alternative queue to charge. When all charging piles are occupied, the robot with the same type of work that is charging, has the highest power and a power higher than the power scheduled to be charged is selected for exchange. The exchange refers to the exchange of the working status or charging status of the two robots. When the expected minimum average power > target power + threshold, the robot with the highest power in charging is arranged to work. Since the allocation process is executed regularly (every minute), the overall allocation can be achieved by arranging only one robot at a time.

[0085] In addition, for robot clusters of various types, charging is allocated to each type of robot cluster according to the priority order of robots of different types. The result of each charging allocation will affect the number of available charging piles when the next type of robot cluster is charged.

[0086] Example 3

[0087] Figure 3 A structural diagram of a robot cluster charging system that supports adjusting average power provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, a robot cluster charging system that supports adjusting the average power includes an acquisition module 301, a calculation module 302 and an adjustment module 303, wherein:

[0088] The acquisition module 301 is used to obtain the status of the charging pile and each robot in the specified work robot cluster in the current time period; and read the target power of the specified work robot cluster in the current time period from the configuration file;

[0089] A calculation module 302 is configured to calculate the corresponding expected minimum average power consumption based on the number of available charging stations in the current time period and the status of each robot in the robot cluster of a specified type of work;

[0090] The adjustment module 303 is used to adjust and arrange the robot in the working state with the lowest power to charge when the expected minimum average power is less than the preset power value; and to adjust and arrange the robot in the charging state with the highest power to work when the expected minimum average power is greater than or equal to the preset power value; wherein the preset power value is the difference between the target power and the threshold.

[0091] As an embodiment, the acquisition module 301 is further configured to acquire the number of available charging piles.

[0092] As an embodiment, the calculation module 302 is further configured to calculate the robots that are included in the charging array C.

[0093] It can be understood that the robot cluster charging system that supports adjusting the average power provided by the present invention corresponds to the robot cluster charging method that supports adjusting the average power provided by the aforementioned embodiments. The relevant technical features of the robot cluster charging system that supports adjusting the average power can refer to the relevant technical features of the robot cluster charging method that supports adjusting the average power, and will not be repeated here.

[0094] Example 4

[0095] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: obtaining the status of the charging piles and the status of each robot in the specified work robot cluster in the current time period; reading the target power of the specified work robot cluster in the current time period from the configuration file; calculating the corresponding expected minimum average power according to the number of available charging piles in the current time period and the status of each robot in the specified work robot cluster; when the expected minimum average power is less than the preset power value, arranging the robot in the working state with the lowest power to charge; when the expected minimum average power is greater than or equal to the preset power value, arranging the robot in the charging state with the highest power to work; wherein, the preset power value is the difference between the target power and the threshold.

[0096] As an embodiment, the computer program 411 stored in the memory 410 of the electronic device 400 can implement the method in the second embodiment when executed.

[0097] Example 5

[0098] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: obtaining the status of the charging piles and the status of each robot in the specified work robot cluster in the current time period; reading the target power of the specified work robot cluster in the current time period from the configuration file; calculating the corresponding expected minimum average power according to the number of available charging piles in the current time period and the status of each robot in the specified work robot cluster; when the expected minimum average power is less than the preset power value, arranging the robot in the working state with the lowest power to charge; when the expected minimum average power is greater than or equal to the preset power value, arranging the robot in the charging state with the highest power to work; wherein, the preset power value is the difference between the target power and the threshold.

[0099] As an embodiment, the computer program 411 stored on the computer-readable storage medium 500 may also implement the method in the second embodiment when executed.

[0100] Example 6

[0101] A computer program product includes a computer program or instructions, characterized in that when the computer program or instructions are executed by a processor, the steps of the robot cluster charging method supporting adjustment of average power described in Example 1 or Example 2 are implemented.

[0102] The embodiments of the present invention provide a robot cluster charging method, system, electronic device, medium and program product, which use the expected minimum average power as an adjustable parameter to construct a robot cluster charging strategy, providing a robot cluster charging strategy that has a certain degree of autonomy and supports regulation. While being able to learn historical data, it can also accept human arrangements and adjustments, making the robot cluster charging strategy more flexible.

[0103] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0104] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 produce 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 flowcharts and / or block diagrams. 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.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0108] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional 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.

[0109] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A robot cluster charging method that supports adjusting average power, characterized in that: include: Obtain the status of the charging pile in the current time period and the status of each robot in the robot cluster of the specified type; Read the target power of the specified type of robot cluster in the current time period from the configuration file; Calculate the expected minimum average power consumption for the next day based on the number of available charging stations in each time period and the status of each robot in the designated robot cluster. When the expected minimum average power is less than the preset power value, the robot in the working state with the lowest power is assigned to charge; when the expected minimum average power is greater than or equal to the preset power value, the robot in the charging state with the highest power is assigned to work; The preset power value is the difference between the target power and the threshold value; The calculation of the expected minimum average power consumption for the next day based on the number of available charging piles in each time period in the next day and the status of each robot in the robot cluster of a specified type includes: a. Extract the remaining power of each robot from the status of each robot in the robot cluster of the specified type, and divide all robots of the specified type into a working power array and a charging power array according to their working status and charging status; b. Set the initial expected minimum average power V to 100% and read the expected number of working robots in each period of the next day; c. Calculate the average power consumption V1 of all robots in a specified job type in each period of the next day; d. If V1 is less than V, set V = V1. Then, based on the remaining power of each robot, the expected number of working robots in each period of the next day, and the total number of robots in the robot cluster of the specified type of work, determine the number of robots in the working state to be arranged for charging, and re-divide the working power array and the charging power array; e. Read the data for the next time period, calculate the average power of all robots, and execute step d until the average power of all robots in the next time period is greater than or equal to the average power of all robots in this time period. Then exit and use the average power of all robots at this time as the expected minimum average power for the next day.

2. The robot cluster charging method supporting average power regulation according to claim 1, characterized in that: In step d, determining the number of robots in working state to be arranged to charge includes: Set A = the number of robots with remaining power greater than the working threshold power among all robots; E = the expected number of working robots in each period of the next day; Determine the number of robots in working state to be scheduled for charging: C = min(total number of robots in the specified robot cluster - min(A, E), number of available charging piles in each time period in the next day); Among them, C robots that are in working condition and have the lowest power are arranged to charge.

3. The robot cluster charging method supporting average power regulation according to claim 1, characterized in that: In the step d, the step of determining the number of robots in working state to be arranged to be charged and re-dividing the working power array and the charging power array further includes: Obtain the working power consumption and charging power increment of each robot of the specified type in each period of the next day. Calculate the remaining power of each robot in the next time period based on the re-divided working power array and charging power array. Accordingly, in step e, reading the data of the next time period and calculating the average power of all robots includes: Based on the calculated remaining power of each robot in the next time period, the average power of all robots in the next time period is calculated.

4. The robot cluster charging method supporting average power regulation according to claim 1, characterized in that: When the expected minimum average power is less than a preset power value, arranging the robot in a working state with the lowest power to charge, further comprising: When all charging piles are occupied at this time, a robot of the same type of work that is in charging state, has the highest power and has a higher power than the robot scheduled to be charged is selected for exchange. The exchange refers to the exchange of the working state or charging state of the two robots.

5. The robot cluster charging method supporting average power adjustment according to any one of claims 1 to 4, characterized in that: Also includes: For robot clusters of various types, charging is allocated to each type of robot cluster according to the priority order of the robots of different types. The result of each charging allocation will affect the number of available charging piles when the next type of robot cluster is charged.

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 charging method supporting the adjustment of average power as described in any one of claims 1 to 5 are implemented.

7. An electronic device comprising a memory and a processor, wherein the processor is configured to implement the steps of the robot cluster charging method supporting average power adjustment as described in any one of claims 1 to 5 when executing a computer program stored in the memory.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by the processor, the steps of the robot cluster charging method supporting the adjustment of the average power as described in any one of claims 1 to 5 are implemented.

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