Multi-robot charging control method and device, and electronic device

Abstract

The present invention discloses a multi-robot charging control method and device, and electronic equipment, belonging to the field of intelligent hardware technology. The method includes: when monitoring that multiple robots are to be charged, determining the value of a target selection factor; wherein the target selection factor is the difference between the power supply capacity of the distribution transformer and the total load; determining the optimization target corresponding to the value; selecting a target optimization function set corresponding to the optimization target from pre-constructed optimization functions; solving the target optimization function set to obtain the charging strategy of each robot to be charged; and controlling each robot to be charged to be charged according to the corresponding charging strategy. The multi-robot charging control method disclosed by the present invention can effectively reduce the impact of the robot charging process on the power grid, and ensure the safety and stability of the power grid.

Description

Technical Field

[0001] The present invention relates to the field of intelligent hardware technology, and in particular to a multi-robot charging control method and device, and electronic equipment. Background Art

[0002] With the development of science and technology, robots generally have automatic charging functions. When they need to be charged, the robots can automatically charge by locating and navigating to the charging piles, so as to improve the robot's ability to work autonomously and continuously for a long time without supervision.

[0003] When multiple robots in the system need to charge at the same time, the robot charging process will cause changes in the grid load, which may even cause fires and pose safety hazards. Therefore, how to improve grid safety during the robot charging process is a technical problem that needs to be solved urgently in the field of robotics. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a multi-robot charging control method and device, and electronic equipment, which can solve the problem of poor power grid security when charging multiple robots in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An embodiment of the present invention provides a multi-robot charging control method, wherein the method includes: when monitoring that multiple robots are to be charged, determining the value of a target selection factor; wherein the target selection factor is the difference between the power supply capacity of the distribution transformer and the total load; determining the optimization target corresponding to the value; selecting a target optimization function set corresponding to the optimization target from pre-constructed optimization functions, wherein different optimization targets correspond to different target optimization function sets; solving the target optimization function set to obtain a charging strategy for each of the robots to be charged; and controlling each of the robots to be charged to charge according to the corresponding charging strategy.

[0007] Optionally, the charging strategy includes: charging power and starting charging time.

[0008] Optionally, the step of solving the target optimization function set to obtain the charging strategy of each of the robots to be charged includes:

[0009] Determine respectively the charging emergency type of each of the robots to be charged, wherein the charging emergency type includes: a first type for indicating emergency charging and a second type for suspending charging;

[0010] For the first robots to be charged whose charging urgency type is the first type, solving the target optimization function set to obtain the charging power of each of the first robots to be charged;

[0011] For the second robots to be charged whose charging urgency type is the second type, the target optimization function set is solved to obtain the starting charging time of each of the second robots to be charged and set the charging power to the rated charging power.

[0012] Optionally, the step of respectively determining the charging emergency type of each of the robots to be charged includes:

[0013] For one of the robots to be charged, determining a relationship between an actual power level of the robot to be charged and a preset power level threshold;

[0014] When the actual power level is less than the preset power level threshold, determining that the charging emergency type of the robot to be charged is the first type;

[0015] When the actual power level is greater than or equal to the preset power level threshold, it is determined that the charging emergency type of the robot to be charged is the second type.

[0016] Optionally, the step of determining the optimization target corresponding to the numerical value includes:

[0017] When the value of the target selection factor is greater than or equal to 0 and less than a first preset threshold, determining that the optimization target corresponding to the value is power grid operation safety;

[0018] When the value of the target selection factor is greater than or equal to the first preset value, determining that the optimization target corresponding to the value is cost;

[0019] When the value of the target selection factor is less than 0, the optimization target corresponding to the value is determined to be power grid security and stability.

[0020] Optionally, the pre-constructed optimization function includes: a charging load peak minimum function, a charging load peak-to-valley difference minimum function, a charging load variance minimum function, a charging pile voltage offset minimum function, an energy input minimum function, and a robot expenditure minimum function; the step of selecting a target optimization function set corresponding to the optimization target from the pre-constructed optimization function includes:

[0021] When the optimization target is power grid operation safety, all pre-built optimization functions are selected as the target optimization function set;

[0022] When the optimization target is cost, a minimum energy input function and a minimum robot expenditure function are selected from pre-built optimization functions as a target optimization function set;

[0023] When the optimization target is the safety and stability of the power grid, a charging load peak minimum function and a robot expenditure minimum function are selected from pre-built optimization functions as a target optimization function set.

[0024] Optionally, when multiple robots are detected to be charged, the step of determining the value of the target selection factor includes:

[0025] When multiple robots are detected to be charged, determining whether the number of robots to be charged reaches a second preset threshold;

[0026] When the number of robots to be charged reaches the second preset threshold, determining a value of the target selection factor;

[0027] When the number of robots to be charged does not reach the second preset threshold, each of the robots to be charged is directly charged.

[0028] An embodiment of the present invention further provides a multi-robot charging control device, wherein the device comprises:

[0029] A first determination module is used to determine the value of a target selection factor when it is detected that multiple robots are to be charged; wherein the target selection factor is the difference between the power supply capacity of the distribution transformer and the total load;

[0030] A second determination module is used to determine the optimization target corresponding to the numerical value;

[0031] A selection module, used to select a target optimization function set corresponding to the optimization target from pre-built optimization functions, wherein different optimization targets correspond to different target optimization function sets;

[0032] A solution module, used for solving the target optimization function set to obtain a charging strategy for each of the robots to be charged;

[0033] The control module is used to control each of the robots to be charged to charge according to a corresponding charging strategy.

[0034] Optionally, the charging strategy includes: charging power and starting charging time.

[0035] Optionally, the solution module includes:

[0036] A first submodule is used to respectively determine the charging emergency type of each of the robots to be charged, wherein the charging emergency type includes: a first type for indicating emergency charging and a second type for suspending charging;

[0037] A second submodule is used to solve the target optimization function set for the first robots to be charged whose charging urgency type is the first type, so as to obtain the charging power of each of the first robots to be charged;

[0038] The third submodule is used to solve the target optimization function set for the second robots to be charged whose charging urgency type is the second type, obtain the starting charging time of each of the second robots to be charged and set the charging power to the rated charging power.

[0039] Optionally, the first submodule is specifically used for:

[0040] For one of the robots to be charged, determining a relationship between an actual power level of the robot to be charged and a preset power level threshold;

[0041] When the actual power level is less than the preset power level threshold, determining that the charging emergency type of the robot to be charged is the first type;

[0042] When the actual power level is greater than or equal to the preset power level threshold, it is determined that the charging emergency type of the robot to be charged is the second type.

[0043] Optionally, the second determining module includes:

[0044] A fourth submodule is used to determine that the optimization target corresponding to the value is power grid operation safety when the value of the target selection factor is greater than or equal to 0 and less than a first preset threshold;

[0045] A fifth submodule, configured to determine, when the value of the target selection factor is greater than or equal to the first preset value, that the optimization target corresponding to the value is cost;

[0046] The sixth submodule is used to determine, when the value of the target selection factor is less than 0, that the optimization target corresponding to the value is power grid security and stability.

[0047] Optionally, the pre-built optimization function includes: a charging load peak minimum function, a charging load peak-to-valley difference minimum function, a charging load variance minimum function, a charging pile voltage offset minimum function, an energy input minimum function, and a robot expenditure minimum function; the selection module is specifically used for:

[0048] When the optimization target is power grid operation safety, all pre-built optimization functions are selected as the target optimization function set;

[0049] When the optimization target is cost, a minimum energy input function and a minimum robot expenditure function are selected from pre-built optimization functions as a target optimization function set;

[0050] When the optimization target is the safety and stability of the power grid, a charging load peak minimum function and a robot expenditure minimum function are selected from pre-built optimization functions as a target optimization function set.

[0051] Optionally, the first determining module is specifically configured to:

[0052] When multiple robots are detected to be charged, determining whether the number of robots to be charged reaches a second preset threshold;

[0053] When the number of robots to be charged reaches the second preset threshold, determining a value of the target selection factor;

[0054] When the number of robots to be charged does not reach the second preset threshold, each of the robots to be charged is directly charged.

[0055] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of any one of the above-mentioned multi-robot charging control methods.

[0056] An embodiment of the present invention provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of any one of the above-mentioned multi-robot charging control methods are implemented.

[0057] The multi-robot charging control scheme provided by the embodiment of the present invention determines the value of the target selection factor when multiple robots are monitored to be charged; determines the optimization target corresponding to the value; selects the target optimization function set corresponding to the optimization target from the pre-constructed optimization function; solves the target optimization function set to obtain the charging strategy of each robot to be charged; controls each robot to be charged to charge according to the corresponding charging strategy. The multi-robot charging control scheme sets the target selection factor as the difference between the power supply capacity of the distribution transformer and the total load. The power grid load condition can be determined by the value of the target selection factor. Different charging strategies are formulated for the robots to be charged according to different power grid load conditions, thereby effectively reducing the impact of the robot charging process on the power grid and ensuring the safety and stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flowchart showing the steps of a multi-robot charging control method according to an embodiment of the present application;

[0059] Figure 2 is a structural block diagram of a multi-robot charging control device according to an embodiment of the present application;

[0060] Figure 3 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0062] The multi-robot charging control scheme provided in the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0063] The multi-robot charging control method of the embodiment of the present application comprises the following steps:

[0064] Step 101: When multiple robots are detected to be charged, the value of the target selection factor is determined.

[0065] The target selection factor is the difference between the power supply capacity of the distribution transformer and the total load.

[0066] The specific expression of the target selection factor is as follows:

[0067]

[0068] Among them, α,S T are the efficiency and rated capacity of the transformer respectively.

[0069] The multi-robot charging control method of the embodiment of the present application is applied to a multi-robot system, which includes multiple robots to be charged and electronic devices. The electronic device can be a device with analysis function such as a server or a computer. The storage medium in the electronic device stores a multi-robot charging control program, and the processor of the electronic device runs the program in the storage medium to execute the multi-robot charging control process, generates a charging strategy for each robot to be charged in the system, and controls each robot to be charged to charge according to the corresponding charging strategy.

[0070] In an optional embodiment, when multiple robots are monitored to be charged, it is determined whether the number of robots to be charged reaches a second preset threshold; when the number of robots to be charged reaches the second preset threshold, the value of the target selection factor is determined; when the number of robots to be charged does not reach the second preset threshold, each robot to be charged is directly charged.

[0071] The second preset value can be flexibly set by those skilled in the art, for example, to 5, 8 or 10, etc., and no specific limitation is made in the embodiments of the present application.

[0072] In the actual implementation process, the power grid will be greatly affected only when the number of robots to be charged is large. In this optional embodiment, the multi-robot charging control process provided in the embodiment of the present application is executed when and only when the number of robots to be charged reaches a second preset threshold, otherwise the robots to be charged are directly charged normally.

[0073] Step 102: Determine the optimization target corresponding to the value of the target selection factor.

[0074] The value of the target selection factor can characterize the grid load. Different levels of grid load correspond to different optimization targets, which ultimately lead to different target optimization function sets. When the grid load is at a low level, the optimization target should be mainly economic benefits; when the grid load is at a high level, the optimization target should be mainly grid operation safety; in extreme cases where the grid power supply capacity is insufficient, the optimization target should be mainly grid safety and stability.

[0075] An optional way to determine the optimization target corresponding to the value of the target selection factor is:

[0076] When the value of the target selection factor is greater than or equal to 0 and less than the first preset threshold, the optimization target corresponding to the value of the target selection factor is determined to be the safe operation of the power grid; when the value of the target selection factor is greater than or equal to the first preset value, the optimization target corresponding to the value of the target selection factor is determined to be the cost; when the value of the target selection factor is less than 0, the optimization target corresponding to the value is determined to be the safety and stability of the power grid.

[0077] Step 103: Select a target optimization function set corresponding to the optimization target from the pre-built optimization functions.

[0078] Among them, different optimization objectives correspond to different sets of target optimization functions.

[0079] In an embodiment of the present application, the optimization function pre-constructed in the system by a technician in this field, when executing the multi-robot charging control process, after determining the optimization target, selects the required optimization function from the pre-constructed optimization function based on the preset target optimization function selection rule.

[0080] The optimization functions pre-built in the system may include but are not limited to: charging load peak minimum function, charging load peak-to-valley difference minimum function, charging load variance minimum function, charging pile voltage offset minimum function, energy input minimum function and robot expenditure minimum function.

[0081] In the embodiment of the present application, the optimization functions are constructed in advance from three aspects: power grid operation safety, power grid load level, and user economic benefits:

[0082] Optimization function 1: Charging load peak minimum function

[0083]

[0084] Among them, p i,t is the total charging load of node i at time t, and N is the total number of nodes.

[0085] Optimization function 2: Charging load peak-to-valley difference minimum function

[0086]

[0087] Optimization function 3: Charging load variance minimum function

[0088]

[0089] Among them, p av is the average charging load of node i during period T.

[0090] Optimization function 4: Minimum voltage offset function for each charging pile

[0091]

[0092] Among them, U i,t is the voltage of node i at time t, U i,o is the rated voltage of node i.

[0093] Optimization function 5: Energy input minimum function

[0094]

[0095] Among them, C m is the operation and maintenance cost of the power grid, p ev,t is the charging power of the robot at time t, C t is the electricity price at time t.

[0096] Optimization function 6: Minimum robot expenditure function

[0097]

[0098] Among them, C a Maintain the cost of the robot.

[0099] A feasible way to select the optimization function set corresponding to the optimization target is: based on a preset target optimization function selection rule, the target optimization function selection rule can be defined by the following target selection function:

[0100]

[0101] Among them, f op (t) is the optimization target at time t, and θ is the preset threshold. It is the weight coefficient preset according to the average charging load in the T period.

[0102] It can be seen from the above definition of the target selection function that when the value of the target selection factor is small, that is, less than the first preset threshold, it indicates that the grid load is at a high level at the current moment and the grid margin is low, then the optimization target is mainly based on the safety of grid operation. When the optimization target is the safety of grid operation, all pre-constructed optimization functions are selected as the target optimization function set, so the optimization functions f1, f2, f3, f4, and f6 are selected; when the value of the target selection factor is less than 0, it indicates that the grid operation is overloaded at the current moment, the grid power supply capacity is insufficient, and there are certain operating risks. At this time, the optimization target should be mainly based on grid safety and stability. When the optimization target is the safety and stability of the grid, the charging load peak minimum function and the robot expenditure minimum function are selected from the pre-constructed optimization functions as the target optimization function set, so the optimization functions f1 and f6 are selected.

[0103] When the value of the target selection factor is large, that is, greater than or equal to the first preset threshold, it means that the grid load is at a low level at this moment and the grid margin is large. The optimization goal is mainly economic benefit. Therefore, the optimization goal is determined to be cost. When the optimization goal is cost, the minimum energy input function and the minimum robot expenditure function are selected from the pre-constructed optimization functions as the target optimization function set, so the target optimization functions f5 and f6 are selected.

[0104] In addition, in the embodiments of the present application, from the perspective of power grid safety and stable operation, a target optimization function set is constructed to ensure that the power grid load peak is as low as possible and the peak-to-valley difference and load fluctuation are as small as possible. By solving the target optimization function set, the robot's charging strategy is generated, which effectively reduces the impact of the robot's charging process on the power grid, thereby ensuring the safety and stability of the power grid.

[0105] Step 104: Solve the target optimization function set to obtain the charging strategy of each robot to be charged.

[0106] The charging strategy may specifically be the charging power of the robot, preferably the charging power of the robot at each moment, or may be the charging period of the robot, such as the starting charging time.

[0107] An optional way to determine the charging strategy of each robot to be charged may be as follows:

[0108] First, the charging emergency type of each robot to be charged is determined respectively, wherein the charging emergency type includes: a first type for indicating emergency charging and a second type for suspended charging.

[0109] A feasible way to determine the charging emergency type of each robot to be charged is:

[0110] For a robot to be charged, the relationship between the actual power level of the robot to be charged and a preset power threshold is determined; when the actual power level is less than the preset power threshold, the charging emergency type of the robot to be charged is determined to be the first type; when the actual power level is greater than or equal to the preset power threshold, the charging emergency type of the robot to be charged is determined to be the second type.

[0111] The specific value of the preset power threshold can be flexibly set by those skilled in the art, and there is no specific limitation on this in the embodiments of the present application. In the present application, the actual power of the robot to be charged is compared with the preset power threshold to determine the urgency of charging the robot to be charged, and the robot to be charged is divided into a suspended charging robot and an emergency charging robot according to the urgency. The charging strategy in this optional embodiment includes: charging power and starting charging time.

[0112] Secondly, for the first robots to be charged whose charging urgency type is the first type, the target optimization function set is solved to obtain the charging power of each first robot to be charged.

[0113] Again, for the second robots to be charged whose charging urgency type is the second type, the target optimization function set is solved to obtain the starting charging time of each second robot to be charged and set the charging power to the rated charging power.

[0114] This method of dividing the robots to be charged into two types and distinguishing them to determine the charging strategy can ensure that resources are focused on emergency charging robots and that the robots that most urgently need to be charged can be charged stably.

[0115] Step 105: Control each robot to be charged to charge according to the corresponding charging strategy.

[0116] After determining the charging strategy of each robot to be charged through steps 101 to 104, the system can control the robot to charge according to the charging strategy corresponding to each robot to be charged.

[0117] The multi-robot charging control method provided in the embodiment of the present application sets the target selection factor as the difference between the power supply capacity of the distribution transformer and the total load. The grid load condition can be determined by the value of the target selection factor. Different charging strategies are formulated for the robots to be charged according to different grid load conditions, thereby effectively reducing the impact of the robot charging process on the grid and ensuring the safety and stability of the grid.

[0118] Figure 2 A structural block diagram of a multi-robot charging control device for implementing an embodiment of the present application.

[0119] The multi-robot charging control device provided in the embodiment of the present application includes the following functional modules:

[0120] The first determination module 201 is used to determine the value of the target selection factor when it is detected that multiple robots are to be charged; wherein the target selection factor is the difference between the power supply capacity of the distribution transformer and the total load;

[0121] A second determination module 202 is used to determine the optimization target corresponding to the value;

[0122] A selection module 203 is used to select a target optimization function set corresponding to the optimization target from pre-built optimization functions, wherein different optimization targets correspond to different target optimization function sets;

[0123] A solution module 204 is used to solve the target optimization function set to obtain a charging strategy for each of the robots to be charged;

[0124] The control module 205 is used to control each of the robots to be charged to charge according to a corresponding charging strategy.

[0125] Optionally, the charging strategy includes: charging power and starting charging time.

[0126] Optionally, the solution module includes:

[0127] A first submodule is used to respectively determine the charging emergency type of each of the robots to be charged, wherein the charging emergency type includes: a first type for indicating emergency charging and a second type for suspending charging;

[0128] A second submodule is used to solve the target optimization function set for the first robots to be charged whose charging urgency type is the first type, so as to obtain the charging power of each of the first robots to be charged;

[0129] The third submodule is used to solve the target optimization function set for the second robots to be charged whose charging urgency type is the second type, obtain the starting charging time of each of the second robots to be charged and set the charging power to the rated charging power.

[0130] Optionally, the first submodule is specifically used for:

[0131] For one of the robots to be charged, determining a relationship between an actual power level of the robot to be charged and a preset power level threshold;

[0132] When the actual power level is less than the preset power level threshold, determining that the charging emergency type of the robot to be charged is the first type;

[0133] When the actual power level is greater than or equal to the preset power level threshold, it is determined that the charging emergency type of the robot to be charged is the second type.

[0134] Optionally, the second determining module includes:

[0135] A fourth submodule is used to determine that the optimization target corresponding to the value is power grid operation safety when the value of the target selection factor is greater than or equal to 0 and less than a first preset threshold;

[0136] A fifth submodule, configured to determine, when the value of the target selection factor is greater than or equal to the first preset value, that the optimization target corresponding to the value is cost;

[0137] The sixth submodule is used to determine, when the value of the target selection factor is less than 0, that the optimization target corresponding to the value is power grid security and stability.

[0138] Optionally, the pre-built optimization function includes: a charging load peak minimum function, a charging load peak-to-valley difference minimum function, a charging load variance minimum function, a charging pile voltage offset minimum function, an energy input minimum function, and a robot expenditure minimum function; the selection module is specifically used for:

[0139] When the optimization target is power grid operation safety, all pre-built optimization functions are selected as the target optimization function set;

[0140] When the optimization target is cost, a minimum energy input function and a minimum robot expenditure function are selected from pre-built optimization functions as a target optimization function set;

[0141] When the optimization target is the safety and stability of the power grid, a charging load peak minimum function and a robot expenditure minimum function are selected from pre-built optimization functions as a target optimization function set.

[0142] Optionally, the first determining module is specifically configured to:

[0143] When multiple robots are detected to be charged, determining whether the number of robots to be charged reaches a second preset threshold;

[0144] When the number of robots to be charged reaches the second preset threshold, determining a value of the target selection factor;

[0145] When the number of robots to be charged does not reach the second preset threshold, each of the robots to be charged is directly charged.

[0146] The multi-robot charging control device provided in the embodiment of the present application sets the target selection factor as the difference between the power supply capacity of the distribution transformer and the total load. The grid load condition can be determined by the value of the target selection factor, and different charging strategies are formulated for the robots to be charged according to different grid load conditions, thereby effectively reducing the impact of the robot charging process on the grid and ensuring the safety and stability of the grid.

[0147] In the present application embodiment Figure 2 The multi-robot charging control device shown can be a device, or a component, integrated circuit, or chip in a server. Figure 2 The multi-robot charging control device shown may be a device having an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0148] The embodiments of the present application provide Figure 3 The multi-robot charging control device shown can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0149] Alternatively, if Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a processor 301, a memory 302, and a program or instruction stored in the memory 302 and executable on the processor 301. When the program or instruction is executed by the processor 301, each process of the above-mentioned multi-robot charging control method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0150] It should be noted that the electronic device in the embodiment of the present application includes the server described above.

[0151] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned multi-robot charging control method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0152] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0153] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above multi-robot charging control method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0154] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0155] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0156] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A multi-robot charging control method, characterized in that: The method comprises: When multiple robots are detected to be charged, a value of a target selection factor is determined; wherein the target selection factor is the difference between the power supply capacity of the distribution transformer and the total load; Determine an optimization target corresponding to the numerical value; Selecting a target optimization function set corresponding to the optimization target from pre-built optimization functions, wherein different optimization targets correspond to different target optimization function sets; Solving the target optimization function set to obtain a charging strategy for each robot to be charged; Controlling each of the robots to be charged to charge according to a corresponding charging strategy; Wherein, the step of determining the optimization target corresponding to the numerical value includes: When the value of the target selection factor is greater than or equal to 0 and less than a first preset threshold, determining that the optimization target corresponding to the value is power grid operation safety; When the value of the target selection factor is greater than or equal to the first preset threshold, determining that the optimization target corresponding to the value is cost; When the value of the target selection factor is less than 0, determining that the optimization target corresponding to the value is power grid security and stability; The pre-built optimization functions include: a charging load peak minimum function, a charging load peak-to-valley difference minimum function, a charging load variance minimum function, a charging pile voltage offset minimum function, an energy input minimum function, and a robot expenditure minimum function; the step of selecting a target optimization function set corresponding to the optimization target from the pre-built optimization functions includes: When the optimization target is power grid operation safety, all pre-built optimization functions are selected as the target optimization function set; When the optimization target is cost, a minimum energy input function and a minimum robot expenditure function are selected from pre-built optimization functions as a target optimization function set; When the optimization goal is power grid safety and stability, a charging load peak minimum function and a robot expenditure minimum function are selected from pre-built optimization functions as a target optimization function set; The step of determining the value of the target selection factor when multiple robots are detected to be charged includes: When multiple robots are detected to be charged, determining whether the number of robots to be charged reaches a second preset threshold; When the number of robots to be charged reaches the second preset threshold, determining a value of the target selection factor; When the number of robots to be charged does not reach the second preset threshold, directly charging each of the robots to be charged; Among them, the constructed optimization function is as follows: Optimization function 1: Charging load peak minimum function ; in, is the total charging load of node i at time t, and N is the total number of nodes; Optimization function 2: Charging load peak-to-valley difference minimum function ; Optimization function 3: Charging load variance minimum function ; in, is the average charging load of node i during period T; Optimization function 4: Minimum voltage offset function for each charging pile ; Among them, U i,t is the voltage of node i at time t, U i,o is the rated voltage of node i; Optimization function 5: Energy input minimum function ; Among them, C m is the operation and maintenance cost of the power grid, The charging power of the robot at time t, C t is the electricity price at time t; Optimization function 6: Minimum robot expenditure function ; Among them, C a Maintaining costs for robots; The optimization function set corresponding to the optimization target is selected based on the preset target optimization function selection rule: the target optimization function selection rule is defined by the following target selection function: ； Among them, f op (t) is the optimization target at time t, and θ is the preset threshold; is the weight coefficient preset according to the average charging load in the T period, Select the factor for the target.

2. The method according to claim 1, characterized in that The charging strategy includes: charging power and starting charging time.

3. The method according to claim 2, characterized in that The step of solving the target optimization function set to obtain the charging strategy of each of the robots to be charged includes: Determine respectively the charging emergency type of each of the robots to be charged, wherein the charging emergency type includes: a first type for indicating emergency charging and a second type for suspending charging; For the first robots to be charged whose charging urgency type is the first type, solving the target optimization function set to obtain the charging power of each of the first robots to be charged; For the second robots to be charged whose charging urgency type is the second type, the target optimization function set is solved to obtain the starting charging time of each of the second robots to be charged and set the charging power to the rated charging power.

4. The method according to claim 3, characterized in that The step of respectively determining the charging emergency type of each of the robots to be charged comprises: For one of the robots to be charged, determining a relationship between an actual power level of the robot to be charged and a preset power level threshold; When the actual power level is less than the preset power level threshold, determining that the charging emergency type of the robot to be charged is the first type; When the actual power level is greater than or equal to the preset power level threshold, it is determined that the charging emergency type of the robot to be charged is the second type.

5. A multi-robot charging control device, characterized in that: The device comprises: A first determination module is used to determine the value of a target selection factor when it is detected that multiple robots are to be charged; wherein the target selection factor is the difference between the power supply capacity of the distribution transformer and the total load; A second determination module is used to determine the optimization target corresponding to the numerical value; A selection module, used to select a target optimization function set corresponding to the optimization target from pre-built optimization functions, wherein different optimization targets correspond to different target optimization function sets; A solution module, used for solving the target optimization function set to obtain a charging strategy for each robot to be charged; A control module, used to control each of the robots to be charged to charge according to a corresponding charging strategy; Wherein, the second determining module includes: A fourth submodule is used to determine that the optimization target corresponding to the value is power grid operation safety when the value of the target selection factor is greater than or equal to 0 and less than a first preset threshold; A fifth submodule, configured to determine, when the value of the target selection factor is greater than or equal to the first preset threshold, that the optimization target corresponding to the value is cost; A sixth submodule is used to determine, when the value of the target selection factor is less than 0, that the optimization target corresponding to the value is power grid security and stability; The pre-built optimization functions include: charging load peak minimum function, charging load peak-to-valley difference minimum function, charging load variance minimum function, each charging pile voltage offset minimum function, energy input minimum function and robot expenditure minimum function; the selection module is specifically used for: When the optimization target is power grid operation safety, all pre-built optimization functions are selected as the target optimization function set; When the optimization target is cost, a minimum energy input function and a minimum robot expenditure function are selected from pre-built optimization functions as a target optimization function set; When the optimization goal is power grid safety and stability, a charging load peak minimum function and a robot expenditure minimum function are selected from pre-built optimization functions as a target optimization function set; The first determination module is specifically used to determine whether the number of robots to be charged reaches a second preset threshold when multiple robots are monitored to be charged; if the number of robots to be charged reaches the second preset threshold, determine the value of the target selection factor; if the number of robots to be charged does not reach the second preset threshold, directly charge each of the robots to be charged; Among them, the optimization function is constructed as follows: Optimization function 1: Charging load peak minimum function ; in, is the total charging load of node i at time t, and N is the total number of nodes; Optimization function 2: Charging load peak-to-valley difference minimum function ; Optimization function 3: Charging load variance minimum function ; in, is the average charging load of node i during period T; Optimization function 4: Minimum voltage offset function for each charging pile ; Among them, U i,t is the voltage of node i at time t, U i,o is the rated voltage of node i; Optimization function 5: Energy input minimum function ; Among them, C m is the operation and maintenance cost of the power grid, is the charging power of the robot at time t, C t is the electricity price at time t; Optimization function 6: Minimum robot expenditure function ; Among them, C a Maintaining costs for robots; The optimization function set corresponding to the optimization target is selected based on the preset target optimization function selection rule; the target optimization function selection rule is defined by the following target selection function: ； Among them, f op (t) is the optimization target at time t, and θ is the preset threshold; is the weight coefficient preset according to the average charging load in the T period, Select the factor for the target.

6. The device according to claim 5, characterized in that The charging strategy includes: charging power and starting charging time.

7. The device according to claim 6, characterized in that The solution module includes: A first submodule is used to respectively determine the charging emergency type of each of the robots to be charged, wherein the charging emergency type includes: a first type for indicating emergency charging and a second type for suspending charging; A second submodule is used to solve the target optimization function set for the first robots to be charged whose charging urgency type is the first type, so as to obtain the charging power of each of the first robots to be charged; The third submodule is used to solve the target optimization function set for the second robots to be charged whose charging urgency type is the second type, obtain the starting charging time of each of the second robots to be charged and set the charging power to the rated charging power.

8. The device according to claim 7, characterized in that The first submodule is specifically used for: For one of the robots to be charged, determining a relationship between an actual power level of the robot to be charged and a preset power level threshold; When the actual power level is less than the preset power level threshold, determining that the charging emergency type of the robot to be charged is the first type; When the actual power level is greater than or equal to the preset power level threshold, it is determined that the charging emergency type of the robot to be charged is the second type.

9. The device according to claim 5, characterized in that The first determining module is specifically used for: When multiple robots are detected to be charged, determining whether the number of robots to be charged reaches a second preset threshold; When the number of robots to be charged reaches the second preset threshold, determining a value of the target selection factor; When the number of robots to be charged does not reach the second preset threshold, each of the robots to be charged is directly charged.

10. An electronic device, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the multi-robot charging control method according to any one of claims 1 to 4.

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