Three-phase balance control method, device and equipment for mobile shared charging robot

Abstract

The present application relates to a three-phase balance control method, device and equipment for a mobile shared charging robot. The method includes: when receiving a charging demand initiated by a vehicle to be charged and there are mobile shared charging robots in an idle state in the charging station, constructing a plurality of phase allocation probability matrices based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state, calculating the imbalance degrees of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power and the output power; determining the probability update speed and the probability update direction based on a target phase allocation probability matrix whose imbalance degree meets a preset condition, updating the plurality of phase allocation probability matrices, iteratively executing until the imbalance degree meets a preset stop condition, and performing phase adjustment based on the phase allocation strategy corresponding to the imbalance degree. Using this method can improve the three-phase imbalance problem in the transformer substation area of the charging station.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle charging, and particularly to a three-phase balance control method, device, and equipment for a mobile shared charging robot. Background Art

[0002] With the continuous increase in the ownership of new energy vehicles, the construction demand for supporting charging facilities has been further stimulated. Currently, traditional charging piles have problems of space binding of "vehicles looking for piles" and waste of charging pile resources of "fuel vehicles occupying positions". Therefore, mobile shared charging robots have become an effective solution to solve the above problems.

[0003] Most low-voltage distribution networks adopt a three-phase four-wire distribution method, while electric vehicle charging usually uses single-phase AC charging. A large number of single-phase loads will cause a three-phase imbalance problem. For distribution transformers, the three-phase imbalance problem will lead to an increase in the copper loss and iron loss of the transformer, reduce the operating efficiency of the transformer, and affect its safe operation. Therefore, there is an urgent need for a three-phase balance control method for mobile shared charging robots to improve the three-phase imbalance problem in the transformer area of the charging station. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a three-phase balance control method, device, and equipment for a mobile shared charging robot that can improve the three-phase imbalance problem in the transformer area of the charging station.

[0005] In a first aspect, the present application provides a three-phase balance control method for a mobile shared charging robot, including:

[0006] When receiving a charging demand initiated by at least one vehicle to be charged and there is an idle mobile shared charging robot in the charging station, determine candidate mobile shared charging robots that match the number of at least one vehicle to be charged among the idle mobile shared charging robots, and obtain the remaining charging power of the vehicles connected to the mobile shared charging robots in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robots in the charging state;

[0007] Based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, construct multiple phase allocation probability matrices, and calculate the imbalance degrees under the phase allocation strategies corresponding to the multiple phase allocation probability matrices based on the remaining charging power, required charging power, and output power;

[0008] Based on the target phase allocation probability matrix among the multiple phase allocation probability matrices whose imbalance degrees meet the preset conditions, determine the probability update speed and probability update direction;

[0009] Based on the probability update speed and the probability update direction, update the multiple phase allocation probability matrices, return the steps of calculating the imbalance degree under the phase allocation strategies corresponding to the multiple phase allocation probability matrices based on the remaining charge, the required charge, and the output power, and continue to execute until the imbalance degree meets the preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance degree.

[0010] In one embodiment, calculating the imbalance degree under the phase allocation strategies corresponding to the multiple phase allocation probability matrices based on the remaining charge, the required charge, and the output power includes:

[0011] For each phase allocation probability matrix, determine the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the output power; determine the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the remaining charge and the required charge; and weight the power imbalance degree and the energy imbalance degree to obtain the imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0012] In one embodiment, determining the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the output power includes:

[0013] For the phase allocation strategy corresponding to the current phase allocation probability matrix, sum the output power by phase to obtain the total output power under each phase;

[0014] Obtain the power difference between the maximum power value and the minimum power value among the total output powers under each phase, and the power average value among the total output powers under each phase;

[0015] Take the quotient between the power difference and the power average value as the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0016] In one embodiment, determining the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the remaining charge and the required charge includes:

[0017] For the phase allocation strategy corresponding to the current phase allocation probability matrix, sum the remaining charge and the required charge by phase to obtain the total charge under each phase;

[0018] Obtain the charge difference between the maximum charge value and the minimum charge value among the total charges under each phase, and the charge average value among the total charges under each phase;

[0019] Take the quotient between the power difference and the average power as the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0020] In one embodiment, before determining the probability update speed and the probability update direction based on the target phase allocation probability matrix whose imbalance degree in multiple phase allocation probability matrices meets a preset condition, it further includes:

[0021] For each phase allocation probability matrix, determine the difference between the phase allocation strategy corresponding to the phase allocation probability matrix and the real-time phase allocation strategy;

[0022] Update the imbalance degree based on the difference to obtain the updated imbalance degree corresponding to each phase allocation probability matrix.

[0023] In one embodiment, based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, construct multiple phase allocation probability matrices, including:

[0024] Take the sum between the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state as the number of rows of each phase allocation probability matrix, and take the number of phases of the mobile shared charging robots as the number of columns of each phase allocation probability matrix;

[0025] According to the number of rows and columns, determine multiple groups of random probability values, and use the multiple groups of random probability values as the elements of the multiple phase allocation probability matrices, where the sum of the elements in each row of each phase allocation probability matrix is a preset value.

[0026] In one embodiment, each column of the phase allocation probability matrix corresponds to each phase of the mobile shared charging robot one by one; each row of the phase allocation probability matrix corresponds to the candidate mobile shared charging robots and the mobile shared charging robots in the charging state one by one; the determination steps of the phase allocation strategy include:

[0027] For each row in the phase allocation probability matrix, determine the target mobile shared charging robot corresponding to the current row, determine the target column where the element with the largest value in the current row elements is located, and take the phase corresponding to the target column as the allocated phase of the target mobile shared charging robot.

[0028] In a second aspect, the present application also provides a three-phase balance control device for a mobile shared charging robot, including:

[0029] An acquisition module, configured to, when receiving a charging demand initiated by at least one vehicle to be charged and there is a mobile shared charging robot in an idle state in a charging station, determine a candidate mobile shared charging robot that matches the number of at least one vehicle to be charged from the mobile shared charging robots in the idle state, and acquire the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state;

[0030] A first determination module, configured to construct multiple phase allocation probability matrices based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, and calculate the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power;

[0031] A second determination module, configured to determine the probability update speed and the probability update direction based on the target phase allocation probability matrix among the multiple phase allocation probability matrices whose imbalance degree meets a preset condition;

[0032] An adjustment module, configured to update the multiple phase allocation probability matrices based on the probability update speed and the probability update direction, return to the step of calculating the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power and continue to execute, until the imbalance degree meets a preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance degree.

[0033] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0034] When receiving a charging demand initiated by at least one vehicle to be charged and there is a mobile shared charging robot in an idle state in a charging station, determine a candidate mobile shared charging robot that matches the number of at least one vehicle to be charged from the mobile shared charging robots in the idle state, and acquire the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state;

[0035] Based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, construct multiple phase allocation probability matrices, and calculate the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power;

[0036] Based on the target phase allocation probability matrix among the multiple phase allocation probability matrices whose imbalance degree meets the preset conditions, determine the probability update speed and the probability update direction;

[0037] Based on the probability update speed and the probability update direction, update the multiple phase allocation probability matrices, return to the step of calculating the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power and continue to execute until the imbalance degree meets the preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state according to the phase allocation strategy corresponding to the imbalance degree.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0039] When receiving the charging requirements initiated by at least one vehicle to be charged and there are mobile shared charging robots in the idle state in the charging station, determine candidate mobile shared charging robots that match the number of at least one vehicle to be charged among the mobile shared charging robots in the idle state, and obtain the remaining charging power of the vehicles connected to the mobile shared charging robots in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robots in the charging state;

[0040] Based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, construct multiple phase allocation probability matrices, and calculate the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power;

[0041] Based on the target phase allocation probability matrix among the multiple phase allocation probability matrices whose imbalance degree meets the preset conditions, determine the probability update speed and the probability update direction;

[0042] Update the probability matrices for multiple phases based on the probability update speed and probability update direction, return the steps of calculating the imbalance degree under the phase allocation strategy corresponding to each of the probability matrices for multiple phases based on the remaining charge, the required charge, and the output power, and continue to execute until the imbalance degree meets the preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance degree.

[0043] The above three-phase balance control method, device, computer device, and computer-readable storage medium for mobile shared charging robots, when receiving the charging demands initiated by at least one vehicle to be charged and there are mobile shared charging robots in the idle state in the charging station, allocate candidate mobile shared charging robots for at least one mobile shared charging robot among the mobile shared charging robots in the idle state. The candidate mobile shared charging robots and the mobile shared charging robots in the charging state are used as the phase control objects. Therefore, multiple phase allocation probability matrices are constructed based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state. Each phase allocation probability matrix contains the probability values for performing phase control on each phase control object. For each phase allocation probability matrix, based on the remaining charge, the required charge, and the output power, the imbalance degree under the phase allocation strategy corresponding to this phase allocation probability matrix can be calculated. The imbalance degree is used to characterize the balance situation of each phase in the charging station. Based on the target phase allocation probability matrix whose imbalance degree in the multiple phase allocation probability matrices meets the preset conditions, determine the probability update speed and probability update direction, so as to update the multiple phase allocation probability matrices. Through multiple rounds of iteration until the imbalance degree meets the preset stop condition, perform phase adjustment on each mobile shared charging robot with the phase allocation strategy corresponding to this imbalance degree, which is beneficial to improving the three-phase imbalance problem in the transformer substation area of the charging station. Description of the Drawings

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0045] Figure 1 It is an application environment diagram of the three-phase balance control method for mobile shared charging robots in an embodiment;

[0046] Figure 2 It is a flowchart of the three-phase balance control method for mobile shared charging robots in an embodiment;

[0047] Figure 3 It is a schematic diagram of the composition of a three-phase balance control system for a mobile shared charging robot in an embodiment;

[0048] Figure 4 It is a structural block diagram of a three-phase balance control device for a mobile shared charging robot in an embodiment;

[0049] Figure 5 It is an internal structure diagram of a computer device in an embodiment. Specific implementation manners

[0050] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] The three-phase balance control method for the mobile shared charging robot provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed on the cloud or other network servers. In this embodiment, an example is given where the method is applied to the terminal. It can be understood that the method can also be applied to the server, and can also be applied to a system including the terminal and the server, and is implemented through the interaction between the terminal and the server. When the terminal 102 receives the charging requirements initiated by at least one vehicle to be charged and there is a mobile shared charging robot in the charging station in an idle state, a candidate mobile shared charging robot that matches the number of at least one vehicle to be charged is determined among the mobile shared charging robots in the idle state, and the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state are obtained; based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, a plurality of phase allocation probability matrices are constructed, and based on the remaining charging power, the required charging power, and the output power, the unbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices is calculated; based on the target phase allocation probability matrix among the plurality of phase allocation probability matrices whose unbalance degree meets the preset conditions, the probability update speed and the probability update direction are determined; based on the probability update speed and the probability update direction, the plurality of phase allocation probability matrices are updated, and the step of calculating the unbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power is returned and continued to be executed until the unbalance degree meets the preset stop condition, and the phase of the candidate mobile shared charging robot and the mobile shared charging robot in the charging state is adjusted based on the phase allocation strategy corresponding to the unbalance degree. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0052] In an exemplary embodiment, as Figure 2 shown, a three-phase balance control method for a mobile shared charging robot is provided, with the method applied to Figure 1Taking the terminal 102 in it as an example for illustration, it includes the following steps 202 to step 208. Among them:

[0053] Step 202, when receiving a charging demand initiated by at least one vehicle to be charged and there is a mobile shared charging robot in the charging station in an idle state, determine candidate mobile shared charging robots that match the number of at least one vehicle to be charged from the mobile shared charging robots in the idle state, and obtain the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state.

[0054] Among them, the mobile shared charging robot refers to a charging device that combines robot technology and charging technology and can provide charging services for new energy vehicles and other devices. The charging station includes multiple mobile shared charging robots. The mobile shared charging robot in the idle state refers to the mobile shared charging robot in the charging station that charges the vehicle, and the mobile shared charging robot in the charging state refers to the mobile shared charging robot in the charging station that is currently charging the vehicle.

[0055] The vehicle to be charged refers to a new energy vehicle that sends a charging demand to the terminal. This solution can respond to at least one vehicle to be charged that initiates a charging demand at the same moment or within a period of time. The charging demand carries information such as the required charging power of the vehicle to be charged.

[0056] The candidate mobile shared charging robot refers to the mobile shared charging robot determined from the mobile shared charging robots in the idle state and is used to charge the vehicle to be charged. In some embodiments, each mobile shared charging robot can charge a single vehicle, and the data of the candidate mobile shared charging robot is consistent with the number of vehicles to be charged. For example, there are 11 mobile shared charging robots in the charging station, among which 8 are in the charging state and 3 are in the idle state. The number of vehicles to be charged does not exceed 3, for example, there are 2, then the candidate mobile shared charging robots are 2 mobile shared charging robots selected from the 3 mobile shared charging robots in the idle state. If the number of vehicles to be charged is more than 3, for example, there are 4, then the 3 mobile shared charging robots in the idle state are used as candidate mobile shared charging robots, and when there is a mobile shared charging robot in the idle state in the charging station for the remaining 1 vehicle to be charged, a candidate mobile shared charging robot is allocated for this vehicle to be charged.

[0057] In some embodiments, the candidate mobile shared charging robots can be selected based on at least one of the remaining battery levels of the mobile shared charging robots in the idle state and the required charging amounts of the vehicles to be charged, so as to ensure that a suitable candidate mobile shared charging robot is allocated to each vehicle to be charged.

[0058] The remaining charging amount refers to the remaining amount of electricity that the vehicle connected to the mobile shared charging robot in the charging state needs to be charged. The required charging amount refers to the amount of electricity that the vehicle to be charged needs to be charged, which can be obtained from the charging demand.

[0059] Mobile shared charging robots usually use three-phase electricity, including phase A, phase B, and phase C respectively. When a mobile shared charging robot connects to a vehicle, usually one of the phases is connected to the vehicle. The output power of each phase refers to the power output by the phase connected to the vehicle in each mobile shared charging robot.

[0060] Step 204: Based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, construct multiple phase allocation probability matrices, and calculate the imbalance degrees under the phase allocation strategies corresponding to the multiple phase allocation probability matrices based on the remaining charging amount, the required charging amount, and the output power.

[0061] Among them, the candidate mobile shared charging robots and the mobile shared charging robots in the charging state are connected to vehicles, which will affect the three-phase imbalance of the charging station. Therefore, the candidate mobile shared charging robots and the mobile shared charging robots in the charging state are the objects that need to be phase-controlled.

[0062] The number of phase allocation probability matrices can be set according to requirements. The phase allocation probability matrix refers to a matrix composed of multiple probability values. Each probability value in the phase allocation probability matrix indicates the possibility of using each phase of the mobile shared charging robot to connect to the vehicle. For example, (0.7, 0.2, 0.1) indicates that the possibility of using phase A to connect to the vehicle is 0.7, the possibility of using phase B to connect to the vehicle is 0.2, and the possibility of using phase C to connect to the vehicle is 0.1.

[0063] The phase allocation strategy indicates the strategy of using each phase of the mobile shared charging robot to connect to the vehicle. The phase allocation probability matrix and the phase allocation strategy are in one-to-one correspondence. For example, the phase allocation strategy corresponding to (0.7, 0.2, 0.1) indicates using phase A to connect to the vehicle.

[0064] The unbalance degree is used to characterize the balance situation of each phase in the charging station. The smaller the unbalance degree, the more balanced each phase in the charging station is. Calculating the unbalance degree corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power is beneficial to determining a phase allocation strategy that can improve the three-phase unbalance problem in the transformer substation area of the charging station.

[0065] Step 206: Based on the target phase allocation probability matrix among the multiple phase allocation probability matrices whose unbalance degree meets the preset condition, determine the probability update speed and the probability update direction.

[0066] Among them, the target phase allocation probability matrix refers to the phase allocation probability matrix among the multiple phase allocation probability matrices whose unbalance degree meets the preset condition. For example, the preset condition can be the phase allocation probability matrix with the smallest unbalance degree, or the phase allocation probability matrix whose unbalance degree is less than the preset balance degree, etc.

[0067] The probability update speed is the probability value for updating each element in the phase allocation probability matrix each time. The probability update direction indicates whether to increase or decrease each element in the phase allocation probability matrix.

[0068] Step 208: Based on the probability update speed and the probability update direction, update the multiple phase allocation probability matrices, return to the step of calculating the unbalance degree corresponding to each phase allocation strategy of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power and continue to execute until the unbalance degree meets the preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the unbalance degree.

[0069] Among them, update the elements in the multiple phase allocation probability matrices according to the probability update speed and the probability update direction to obtain the updated phase allocation probability matrix. For example, if the phase allocation probability matrix is (0.7, 0.2, 0.1), the probability update speed is 0.01, and the probability update direction is the increasing direction, then the updated phase allocation probability matrix is (0.71, 0.21, 0.11).

[0070] After the phase allocation probability matrix is updated, continue to calculate the unbalance degree, and after multiple rounds of iteration until the unbalance degree meets the preset stop condition. For example, the preset stop condition is that the unbalance degree is stable at a fixed value, or the unbalance degree is less than the preset value, etc. After multiple rounds of iteration, using the phase allocation strategy corresponding to the finally determined unbalance degree to perform phase adjustment on the corresponding mobile shared charging robots can improve the three-phase phase balance problem of the charging station.

[0071] In the above three-phase balance control method of the mobile shared charging robot, when at least one charging demand initiated by a vehicle to be charged is received and there is an idle mobile shared charging robot in the charging station, candidate mobile shared charging robots are assigned to at least one mobile shared charging robot among the idle mobile shared charging robots. The candidate mobile shared charging robots and the mobile shared charging robots in the charging state are used as phase control objects. Therefore, multiple phase allocation probability matrices are constructed based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state. Each phase allocation probability matrix contains probability values for performing phase control on each phase control object. For each phase allocation probability matrix, based on the remaining charging power, the required charging power, and the output power, the unbalance degree under the phase allocation strategy corresponding to the phase allocation probability matrix can be calculated. The unbalance degree is used to characterize the balance situation of each phase in the charging station. Based on the target phase allocation probability matrix whose unbalance degree in multiple phase allocation probability matrices meets the preset conditions, the probability update speed and the probability update direction are determined, so as to update multiple phase allocation probability matrices. Through multiple rounds of iteration until the unbalance degree meets the preset stop condition, the phase of each mobile shared charging robot is adjusted according to the phase allocation strategy corresponding to the unbalance degree, which is beneficial to improving the three-phase unbalance problem of the transformer substation area in the charging station.

[0072] In an exemplary embodiment, calculating the unbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power includes: for each phase allocation probability matrix, determining the power unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the output power; determining the energy unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the remaining charging power and the required charging power; and weighting the power unbalance degree and the energy unbalance degree to obtain the unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0073] Among them, for the calculation of the unbalance degree, the embodiment of the present application proposes a two-dimensional unbalance evaluation scheme, that is, calculating the power unbalance degree and the energy unbalance degree, and weighting the two to obtain the unbalance degree. The power unbalance degree is used to indicate the unbalance situation of each phase in the charging station in terms of output power, and the energy unbalance degree is used to indicate the unbalance situation of each phase in the charging station in terms of charging power.

[0074] For each phase allocation probability matrix, calculating the power unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the output power; calculating the energy unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the remaining charging power and the required charging power.

[0075] In this embodiment, based on the output power, the power imbalance degree corresponding to each phase allocation probability matrix is calculated, and based on the remaining charge and the required charge, the energy imbalance degree corresponding to each phase allocation probability matrix is calculated, so as to evaluate the phase imbalance situation of the charging station from two dimensions of output power and charging energy, which is beneficial to improving the accuracy of the imbalance degree.

[0076] In an exemplary embodiment, based on the output power, determining the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix includes: for the phase allocation strategy corresponding to the current phase allocation probability matrix, summing the output power by phase respectively to obtain the total output power under each phase; obtaining the power difference between the maximum power value and the minimum power value among the total output powers under each phase, and the power average value among the total output powers under each phase; and taking the quotient between the power difference and the power average value as the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0077] Among them, for each phase allocation strategy, the phases of the output powers of each mobile shared charging robot may be different. Therefore, by summing the output power by phase respectively, the total output power under each phase can be obtained. For example, mobile shared charging robots 1-5 respectively use the output power 1 of phase A, the output power 2 of phase B, the output power 3 of phase C, the output power 4 of phase A, and the output power 5 of phase B. Therefore, after summing by phase, the total output power under phase A is the sum of output power 1 and output power 4, the total output power under phase B is the sum of output power 2 and output power 5, and the total output power under phase C is output power 3.

[0078] The power difference is the difference between the maximum power value and the minimum power value among the total output powers under each phase, and the power average value is the average value of the total output powers under each phase. The quotient between the power difference and the power average value is used as the power imbalance degree under the phase allocation strategy.

[0079] In this embodiment, using the quotient between the power difference between the maximum power value and the minimum power value among the total output powers under each phase and the power average value among the total output powers under each phase as the power imbalance degree, the obtained power imbalance degree can reflect the imbalance situation of each phase of the charging station in terms of output power.

[0080] In an exemplary embodiment, based on the remaining charge and the required charge, determining the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix includes: for the phase allocation strategy corresponding to the current phase allocation probability matrix, summing the remaining charge and the required charge by phase respectively to obtain the total charge under each phase; obtaining the charge difference between the maximum charge value and the minimum charge value among the total charges under each phase, and the average charge among the total charges under each phase; taking the quotient between the charge difference and the average charge as the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0081] Among them, for each phase allocation strategy, the phases of the vehicles connected by each mobile shared charging robot may be different, and the remaining charge and the required charge are the charging charges of the vehicles connected by each mobile shared charging robot when phase control is performed according to the corresponding phase allocation strategy. Since the phases of the vehicles connected by each mobile shared charging robot may be different, the remaining charge and the required charge are summed by phase respectively to obtain the total charge under each phase. For example, mobile shared charging robots 1-5 connect to vehicles using phases A, B, C, A, and B respectively, and the corresponding remaining charges of each vehicle are remaining charge 1, remaining charge 2, remaining charge 3, required charge 1, and required charge 2 respectively. Therefore, after summing by phase, the total charge under phase A is the sum of remaining charge 1 and required charge 1, the total charge under phase B is the sum of remaining charge 2 and required charge 2, and the total charge under phase C is remaining charge 3.

[0082] The charge difference is the difference between the maximum charge value and the minimum charge value among the total charges under each phase, and the average charge is the average of the total charges under each phase. The quotient between the charge difference and the average charge is used as the energy imbalance degree under the phase allocation strategy.

[0083] In some embodiments, when the current moment is in a three-phase balanced state, if a new vehicle has a charging demand, connecting the vehicle to any phase will cause a power imbalance phenomenon. The embodiments of the present application take energy imbalance into consideration. For example, at the current moment, the remaining charge of the vehicle connected to phase A is the least, which means that a vehicle connected to phase A will soon complete charging. To minimize the energy imbalance, the new vehicle will be connected to phase A at this time to achieve energy balance among the three phases. When the previous vehicle connected to phase A completes charging and leaves, there is no need to adjust the phase, thus reducing the switching operation.

[0084] In this embodiment, the quotient of the power difference between the maximum power value and the minimum power value in the total power of each phase and the average power between the total powers of each phase is used as the energy imbalance degree. The obtained energy imbalance degree can reflect the imbalance of the charging power of each phase in the charging station. The method for determining the energy imbalance degree proposed in the embodiment of the present application takes into account the current charging power imbalance and the situation of future vehicle charging termination, and uses the energy imbalance degree as a constraint for the commutation switch, which is beneficial to minimizing the operation of the switch as much as possible.

[0085] In an exemplary embodiment, before determining the probability update speed and the probability update direction based on the target phase allocation probability matrix whose imbalance degree meets the preset condition among multiple phase allocation probability matrices, it further includes: for each phase allocation probability matrix, determining the difference between the phase allocation strategy corresponding to the phase allocation probability matrix and the real-time phase allocation strategy; updating the imbalance degree based on the difference to obtain the updated imbalance degree corresponding to each phase allocation probability matrix.

[0086] Among them, in order to suppress frequent phase switching during three-phase phase balance control, the embodiment of the present application proposes to introduce a penalty term in the calculation process of the imbalance degree.

[0087] The real-time phase allocation strategy refers to the connection situation of each phase of each mobile shared charging robot at the current moment. The terminal determines the difference between the phase allocation strategy corresponding to each phase allocation probability matrix and the real-time phase allocation strategy, and updates the imbalance degree based on the difference. For example, if the phase allocation strategy corresponding to the current phase allocation probability matrix indicates that the mobile shared charging robot 1 is connected to phase A, and the real-time phase allocation strategy indicates that the mobile shared charging robot 1 is connected to phase B, there is a difference between the two, and a phase switch is required when using the current phase allocation probability matrix; if the real-time phase allocation strategy indicates that the mobile shared charging robot 1 is connected to phase A, there is no difference between the two, and no phase switch is required when using the current phase allocation probability matrix.

[0088] For each phase allocation probability matrix, for the mobile shared charging robots with differences, a penalty coefficient λ is introduced, and the product of the number of mobile shared charging robots with differences and the penalty coefficient λ is used as the penalty term. The imbalance degree corresponding to the current phase allocation probability matrix is added with the penalty term to obtain the updated imbalance degree.

[0089] In this embodiment, by introducing a penalty term in the calculation process of the balance degree, the penalty term can represent the number of mobile shared charging robots that need to switch phases. By introducing the penalty term to increase the imbalance degree corresponding to the phase allocation probability matrix that needs to switch phases, it is beneficial to suppressing frequent phase switching.

[0090] In an exemplary embodiment, based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state, multiple phase allocation probability matrices are constructed, including: taking the sum of the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state as the number of rows of each phase allocation probability matrix, and taking the number of phases of the mobile shared charging robots as the number of columns of each phase allocation probability matrix; determining multiple groups of random probability values according to the number of rows and the number of columns, and taking the multiple groups of random probability values as elements of multiple phase allocation probability matrices, wherein the sum of the elements in each row of each phase allocation probability matrix is a preset value.

[0091] Among them, the random probability value is a randomly generated probability value. Multiple phase allocation probability matrices are composed of multiple groups of randomly generated probability values. The number of rows of each phase allocation probability matrix is equal to the sum of the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state, that is, each row of the phase allocation probability matrix corresponds to a mobile shared charging robot. The number of columns of each phase allocation probability matrix is the number of phases of the mobile shared charging robot, for example, three phases. For example, the sum of the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state is 8, and the number of phases of the mobile shared charging robot is three phases, then the phase allocation probability matrix is a matrix of 8 rows and 3 columns.

[0092] The number of elements of each phase allocation probability matrix can be determined by the number of rows and columns. The number of each group of random probability values is the same as the number of elements of each phase allocation probability matrix. Each group of random probability values is assigned to each phase allocation probability matrix. At the same time, it should be ensured that the sum of each row element in each phase allocation probability matrix is a preset value. In some embodiments, when generating random probability values, multiple random probability values matching the number of columns are generated each time, and the sum of the multiple random probability values is a preset value, for example, the preset value can be 1. For example, the random probability value generated at a certain time can be 0.7, 0.2, 0.1, or 0.8, 0.1, 0.1, etc.

[0093] In this embodiment, random probability values are used as elements of multiple phase allocation probability matrices, the number of rows of the phase allocation probability matrix is the sum of the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state, and the number of columns is the number of phases of the mobile shared charging robots, so that multiple phase allocation probability matrices can be determined. The multiple phase allocation probability matrices are used to determine a phase allocation strategy in which the imbalance degree meets preset conditions, which can improve the three-phase imbalance problem in the transformer area of the charging station.

[0094] In an exemplary embodiment, each column of the phase allocation probability matrix corresponds to each phase of the mobile shared charging robot one by one; each row of the phase allocation probability matrix corresponds to the candidate mobile shared charging robots and the mobile shared charging robots in the charging state one by one; the determining step of the phase allocation strategy includes: for each row in the phase allocation probability matrix, determining the target mobile shared charging robot corresponding to the current row, determining the target column where the element with the largest value in the elements of the current row is located, and taking the phase corresponding to the target column as the allocated phase of the target mobile shared charging robot.

[0095] Among them, since each row of the phase allocation probability matrix corresponds to the candidate mobile shared charging robots and the mobile shared charging robots in the charging state one by one, therefore, each row in the phase allocation probability matrix indicates the corresponding target mobile shared charging robot.

[0096] The terminal takes the target column where the element with the largest value in the elements of each row as the allocated phase of the corresponding target mobile shared charging robot, so that the phase allocation strategy corresponding to each phase allocation probability matrix can be obtained. For example, a certain row of the phase allocation probability matrix is (0.7, 0.2, 0.1), this row corresponds to the target mobile shared charging robot, 0.7 is the largest element of this row, the target column where 0.7 is located corresponds to phase A, then the allocated phase of the target mobile shared charging robot is phase A.

[0097] In this embodiment, the target column where the element with the largest value in the elements of each row of the phase allocation probability matrix is determined, and the phase corresponding to the target column can be used as the allocated phase of the target mobile shared charging robot. The probability value can determine the corresponding phase adjustment strategy. By iteratively adjusting the probability value to calculate the imbalance degree, the adjustment amplitude is more refined, which is beneficial to determining the accurate imbalance degree.

[0098] To illustrate the effect of the three-phase balance control method of the mobile shared charging robot in this solution in detail, the following is described with a most detailed embodiment:

[0099] For the scenario where there are multiple mobile shared charging robots in the charging station, the three-phase balance control method of the mobile shared charging robot is applied to the three-phase balance control system of the mobile shared charging robot. As Figure 3The figure shows a schematic diagram of the composition of a three-phase balance control system for a mobile shared charging robot in some embodiments. The system includes a data acquisition module 310, a data processing and calculation module 320, an optimization decision module 330, a control execution module 340, and a storage and log module 350. Among them, the data acquisition module 310 is used to determine candidate mobile shared charging robots that match the number of at least one vehicle to be charged among the mobile shared charging robots in the idle state when receiving a charging demand initiated by at least one vehicle to be charged and there are mobile shared charging robots in the idle state in the charging station, and obtain the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state. The data processing and calculation module 320 is used to construct multiple phase allocation probability matrices based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, and calculate the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, required charging power, and output power. The optimization decision module 330 is used to determine the probability update speed and probability update direction based on the target phase allocation probability matrix in the multiple phase allocation probability matrices whose imbalance degree meets the preset conditions; update the multiple phase allocation probability matrices based on the probability update speed and probability update direction, return to the step of calculating the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, required charging power, and output power, and continue to execute until the imbalance degree meets the preset stop condition. The control execution module 340 is used to adjust the phases of the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance degree. The storage and log module 350 is used to store historical data, optimization results, and operation logs for subsequent analysis and troubleshooting, record data such as the connection phases, output powers of the mobile shared charging robots, and the remaining charging powers of the vehicles, generate operation logs, and record system status and abnormal events, etc.

[0100] In some embodiments, the determination process of the imbalance degree is as follows:

[0101] represents the phase to which the kth mobile shared charging robot is connected, represented by a binary number. When = 1, it means that there is a vehicle connected to this phase of the mobile shared charging robot. When = 0, it means that there is no vehicle connected to this phase of the mobile shared charging robot.

[0102]

[0103] In the formula, Indicates three phases A, B, and C. When the k-th mobile shared charging robot is connected to the phase, the corresponding status is 1.

[0104] Indicates the output power of the mobile shared charging robot, indicates the remaining charging power of the vehicle connected to the mobile shared charging robot, then the remaining charging power at the next moment can be expressed as follows:

[0105]

[0106] where is the current remaining charging power, is the remaining charging power at the next moment, and the remaining charging power at the next moment is equal to the current charging power minus the charged power within the time step.

[0107] According to the connection phases of each mobile shared charging robot charging in the charging station, calculate the total output power of each of the three phases A, B, and C. The total output power under each phase is equal to the sum of the output powers of the mobile shared charging robots connected to that phase:

[0108]

[0109] In the formula, is the output power of the mobile shared charging robot, is the connection phase of the mobile shared charging robot, is the total output power under each phase.

[0110] Determine the maximum power value in the total output power under each phase:

[0111]

[0112] Determine the minimum power value in the total output power under each phase:

[0113]

[0114] Subtract the minimum power value from the maximum power value in each phase to obtain the power difference under each phase:

[0115]

[0116] Average the total output powers under each phase to obtain the average power :

[0117]

[0118] Divide the power difference by the average power to obtain the power imbalance :

[0119]

[0120] Calculate the total electricity of each of the three phases A, B, and C according to the connection phases of each mobile shared charging robot in the charging station. The total electricity of each phase is equal to the sum of the remaining charging electricity and the required charging electricity of the vehicles connected to that phase:

[0121]

[0122] In the formula, represents the connection phase of the k-th mobile shared charging robot, represents the remaining charging electricity or the required charging electricity of the vehicle connected by the k-th mobile shared charging robot.

[0123] Determine the maximum electricity value among the total electricity of each phase:

[0124]

[0125] Determine the minimum electricity value among the total electricity of each phase:

[0126]

[0127] Subtract the minimum electricity value from the maximum electricity value of each phase to obtain the electricity difference of each phase:

[0128]

[0129] Average the total electricity of each phase to obtain the average electricity value of each phase :

[0130]

[0131] Divide the electricity difference by the average electricity value to obtain the energy imbalance:

[0132]

[0133] Based on the calculated power imbalance and energy imbalance, establish a multi-objective optimization model. Taking minimizing the power imbalance and energy imbalance as the objective function and the connection phases of the mobile shared charging robots as the decision variables. First, construct a single-objective model with the goal of minimizing the power imbalance to obtain the model objective function:

[0134]

[0135] The constraint conditions include the connection phase of the mobile shared charging robot and the update of the remaining charging power, as shown in the above formulas. Considering that the mobile shared charging robot uses a commutation switch for phase switching, frequent commutation actions will cause damage to the commutation switch. Therefore, energy imbalance is introduced to minimize the number of commutation switch actions as much as possible. Thus, the single-objective function becomes a multi-objective function:

[0136]

[0137] To achieve better dynamic regulation and meet the different emphasis requirements of different charging stations for the above two objectives, the entropy weight method is used to transform the multi-objective function into a single-objective function. The entropy weight method is to assign different weights to each objective and ensure that the sum of all weights is 1. First, standardize each index, as shown in the following formula:

[0138]

[0139] According to the standardized data, the proportion of samples under the index is calculated as shown in the following formula:

[0140]

[0141] According to the proportion value, calculate the information entropy of each index, as shown in the following formula:

[0142]

[0143] Based on the information entropy, calculate the deviation coefficient of the importance of each index, as shown in the following formula:

[0144]

[0145] Finally, calculate the weight coefficients of each objective, as shown in the following formula:

[0146]

[0147] Through the above entropy weight method, determine the weights of the power imbalance degree and the energy imbalance degree, and finally transform the multi-objective function into a single-objective function. The objective function is as shown in the following formula:

[0148]

[0149] At the same time, it should be ensured that the sum of the objective weights is 1. Therefore, the constraint condition adds the formula:

[0150]

[0151] Thus, a multi-objective optimization model is established, with the goal of minimizing the weighted sum of the power imbalance degree and the energy imbalance degree. The connection phase of the mobile shared charging robot is used as the decision variable. By dynamically adjusting the connection phase of the mobile shared charging robot, the minimization of the three-phase imbalance is achieved, while ensuring a reduction in the number of switching operations of the commutator switch.

[0152] Solve the optimization model, dynamically adjust the connection phase of the mobile shared charging robot, and construct a three-phase dynamic adjustment method for the mobile shared charging robot.

[0153] First, construct multiple phase allocation probability matrices. For the k-th mobile shared charging robot, use a three-dimensional vector to represent its phase selection (A / B / C), where one element is 1 (connected to this phase) and the rest are 0. The dimension of the multiple phase allocation probability matrices is the sum between the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state.

[0154] According to the phase allocation strategies corresponding to each of the multiple phase allocation probability matrices, calculate the imbalance degree under each phase allocation strategy corresponding to each of the multiple phase allocation probability matrices. The probability update speed is initialized to a random value, usually in the range of . According to the objective function, obtain the calculation formula for the imbalance degree:

[0155]

[0156] where, is the power imbalance degree, is the energy imbalance degree, and are the weights determined by the entropy weight method. The smaller the imbalance degree, the better the improvement of the three-phase balance.

[0157] After completing the initialization, update the probability update speed and the probability update direction, as shown in the following formula:

[0158]

[0159] where, is the inertia weight, , are the learning factors, , are random numbers within [0, 1].

[0160] Based on the probability update speed and the probability update direction, update the multiple phase allocation probability matrices, as shown in the following formula:

[0161]

[0162] Convert the continuous position value into a binary connection state. Select the phase position 1 corresponding to the maximum value from the three-dimensional vectors corresponding to each mobile shared charging robot, and set the rest to 0, as shown in the following formula:

[0163]

[0164] Pbest represents the optimal value of multiple phase allocation probability matrices, that is, the target phase allocation probability matrix. If the current imbalance is less than the imbalance corresponding to the historical optimum, update Pbest to the target phase allocation probability matrix; Gbest represents the optimal value of the phase allocation probability matrix in the current iteration process. In each iteration, update Gbest to the phase allocation probability matrix with the smallest imbalance.

[0165] According to the charging state change period (for example, set to every 5 minutes), iteratively execute the above steps of calculating the imbalance to update the phase allocation. At the same time, introduce a penalty term into the imbalance calculation formula to suppress frequent phase switching, as shown in the following formula:

[0166]

[0167] Where λ is the penalty coefficient, which balances the optimization objective and the switching cost. Exemplarily, set the fixed number of iterations to 100 times, and terminate the iteration when the change of the imbalance Gbest is less than the threshold (for example, 0.001) for several consecutive iterations, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance.

[0168] The above three-phase balance control method for mobile shared charging robots aims at the three-phase imbalance problem caused by the random access of single-phase charging loads in charging stations. It proposes a two-dimensional imbalance evaluation system. By calculating the power imbalance degree and energy imbalance degree in real time, a multi-objective optimization model is constructed with the goal of minimizing both, and the connection phase of the mobile shared charging robot is dynamically adjusted. This method represents the phase state through three-dimensional vector coding, adaptively assigns target weights in combination with the entropy weight method, introduces a penalty term for the number of commutation switch toggles, and periodically re-optimizes the phase allocation scheme. While reducing the losses of distribution network transformers and improving power quality, it effectively reduces the mechanical losses of equipment. The dual-dimensional quantization evaluation of power and energy, the dynamic optimization algorithm, and the hardware control are deeply integrated, solving the problems of low efficiency of "vehicle finding charger" in traditional charging piles, transformer efficiency degradation and safety hazards caused by three-phase imbalance, realizing the improvement of the resource utilization rate of the mobile shared charging robot system and the intelligent regulation of three-phase balance, and providing technical support for the efficient and safe operation of new energy charging infrastructure. At the same time, through the collaborative work of modules such as data collection, processing, optimization decision-making, and control execution, the intelligent regulation of the three-phase dynamic balance of the mobile shared charging robot is realized, effectively reducing the three-phase imbalance degree, improving the operation efficiency and power quality of the charging station, and prolonging the service life of the equipment, providing reliable technical support for the intelligent management of charging infrastructure.

[0169] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0170] Based on the same inventive concept, the embodiments of the present application also provide a three-phase balance control device for a mobile shared charging robot for implementing the above-mentioned three-phase balance control method for a mobile shared charging robot. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the three-phase balance control device for a mobile shared charging robot provided below can refer to the limitations on the three-phase balance control method for a mobile shared charging robot in the above text, and will not be repeated here.

[0171] In an exemplary embodiment, as Figure 4As shown, a three-phase balance control device 100 for a mobile shared charging robot is provided, including: an acquisition module 120, a first determination module 140, a second determination module 160, and an adjustment module 180, where:

[0172] The acquisition module 120 is configured to, when receiving a charging demand initiated by at least one vehicle to be charged and there is an idle mobile shared charging robot in the charging station, determine a candidate mobile shared charging robot that matches the number of at least one vehicle to be charged among the idle mobile shared charging robots, and acquire the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state;

[0173] The first determination module 140 is configured to construct multiple phase allocation probability matrices based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, and calculate the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power;

[0174] The second determination module 160 is configured to determine the probability update speed and the probability update direction based on a target phase allocation probability matrix among the multiple phase allocation probability matrices whose imbalance degree meets a preset condition;

[0175] The adjustment module 180 is configured to update the multiple phase allocation probability matrices based on the probability update speed and the probability update direction, return to the step of calculating the imbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power and continue to execute until the imbalance degree meets a preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance degree.

[0176] The three-phase balance control device of the above-mentioned mobile shared charging robot, when receiving the charging demands initiated by at least one vehicle to be charged and there are mobile shared charging robots in the charging station in an idle state, assigns candidate mobile shared charging robots to at least one mobile shared charging robot among the mobile shared charging robots in the idle state. The candidate mobile shared charging robots and the mobile shared charging robots in the charging state are used as phase control objects. Therefore, multiple phase allocation probability matrices are constructed based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state. Each phase allocation probability matrix contains the probability values for phase control of each phase control object; for each phase allocation probability matrix, based on the remaining charging power, the required charging power, and the output power, the unbalance degree under the phase allocation strategy corresponding to this phase allocation probability matrix can be calculated. The unbalance degree is used to characterize the balance situation of each phase in the charging station; based on the target phase allocation probability matrix among the multiple phase allocation probability matrices whose unbalance degree meets the preset conditions, the probability update speed and the probability update direction are determined, thereby updating the multiple phase allocation probability matrices. Through multiple rounds of iteration until the unbalance degree meets the preset stop condition, the phase of each mobile shared charging robot is adjusted according to the phase allocation strategy corresponding to this unbalance degree, which is beneficial to improving the three-phase unbalance problem of the transformer substation area in the charging station.

[0177] In one embodiment, based on the remaining charging power, the required charging power, and the output power, calculating the unbalance degree under the phase allocation strategy corresponding to each of the multiple phase allocation probability matrices, the first determination module 140 is further configured to: for each phase allocation probability matrix, based on the output power, determine the power unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix; based on the remaining charging power and the required charging power, determine the energy unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix; weight the power unbalance degree and the energy unbalance degree to obtain the unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0178] In one embodiment, based on the output power, determining the power unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix, the first determination module 140 is further configured to: for the phase allocation strategy corresponding to the current phase allocation probability matrix, sum the output power according to the phases respectively to obtain the total output power of each phase; obtain the power difference between the maximum power value and the minimum power value among the total output powers of each phase, and the power average value among the total output powers of each phase; use the quotient between the power difference and the power average value as the power unbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0179] In one embodiment, based on the remaining charging power and the required charging power, to determine the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix, the first determination module 140 is further configured to: for the phase allocation strategy corresponding to the current phase allocation probability matrix, sum the remaining charging power and the required charging power according to phases respectively to obtain the total power under each phase; obtain the power difference between the maximum power value and the minimum power value among the total powers under each phase, and the average power among the total powers under each phase; and use the quotient between the power difference and the average power as the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

[0180] In one embodiment, before determining the probability update speed and the probability update direction based on the target phase allocation probability matrix whose imbalance degree among multiple phase allocation probability matrices meets the preset condition, the second determination module 160 is further configured to: for each phase allocation probability matrix, determine the difference between the phase allocation strategy corresponding to the phase allocation probability matrix and the real-time phase allocation strategy; and update the imbalance degree based on the difference to obtain the updated imbalance degree corresponding to each phase allocation probability matrix.

[0181] In one embodiment, based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state, to construct multiple phase allocation probability matrices, the first determination module 140 is further configured to: use the sum of the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state as the number of rows of each phase allocation probability matrix, and use the number of phases of the mobile shared charging robots as the number of columns of each phase allocation probability matrix; determine multiple groups of random probability values according to the number of rows and the number of columns, and use the multiple groups of random probability values as the elements of the multiple phase allocation probability matrices, wherein the sum of the elements in each row of each phase allocation probability matrix is a preset value.

[0182] In one embodiment, each column of the phase allocation probability matrix corresponds to each phase of the mobile shared charging robot one by one; each row of the 0-phase allocation probability matrix corresponds to the candidate mobile shared charging robots and the mobile shared charging robots in the charging state one by one; in terms of the determination of the phase allocation strategy, the first determination module 140 is further configured to: for each row in the phase allocation probability matrix, determine the target mobile shared charging robot corresponding to the current row, determine the target column where the element with the largest value in the current row is located, and use the phase corresponding to the target column as the allocated phase of the target mobile shared charging robot.

[0183] Each module in the three-phase balance control device of the above-mentioned mobile shared charging robot can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0184] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structural diagram can be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it realizes a three-phase balance control method for a mobile shared charging robot. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0185] Those skilled in the art can understand that Figure 5 the structure shown in

[0186] is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0187] In one embodiment, 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 in the foregoing method embodiments are implemented.

[0188] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0190] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logics, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0191] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0192] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for three-phase balance control of a mobile shared charging robot, characterized in that, The method includes: When receiving a charging demand initiated by at least one vehicle to be charged and there is a mobile shared charging robot in an idle state in the charging station, determining candidate mobile shared charging robots that match the number of at least one vehicle to be charged from the mobile shared charging robots in the idle state, and obtaining the remaining charging power of the vehicle connected to the mobile shared charging robot in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robot in the charging state; Based on the number of the candidate mobile shared charging robots and the number of the mobile shared charging robots in the charging state, constructing a plurality of phase allocation probability matrices, and calculating the imbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power; Based on the target phase allocation probability matrix whose imbalance degree in the plurality of phase allocation probability matrices meets the preset condition, determining the probability update speed and the probability update direction; Based on the probability update speed and the probability update direction, updating the plurality of phase allocation probability matrices, returning to the step of calculating the imbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power and continuing to execute until the imbalance degree meets the preset stop condition, and performing phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in the charging state based on the phase allocation strategy corresponding to the imbalance degree.

2. The method according to claim 1, wherein The calculating the imbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power includes: For each phase allocation probability matrix, determining the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the output power; determining the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the remaining charging power and the required charging power; and weighting the power imbalance degree and the energy imbalance degree to obtain the imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

3. The method according to claim 2, wherein The determining the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the output power includes: For the phase allocation strategy corresponding to the current phase allocation probability matrix, summing the output power according to phases respectively to obtain the total output power of each phase; Obtaining the power difference between the maximum power value and the minimum power value in the total output power of each phase, and the power average value between the total output powers of each phase; Taking the quotient between the power difference and the power average value as the power imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

4. The method according to claim 2, wherein The determining the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix based on the remaining charging power and the required charging power includes: For the phase allocation strategy corresponding to the current phase allocation probability matrix, sum the remaining charging power and the required charging power according to phases respectively to obtain the total power under each phase; Obtain the power difference between the maximum power value and the minimum power value among the total powers under each phase, and the average power among the total powers under each phase; Take the quotient between the power difference and the average power as the energy imbalance degree under the phase allocation strategy corresponding to the current phase allocation probability matrix.

5. The method according to claim 1, characterized in that, Before determining the probability update speed and the probability update direction based on the target phase allocation probability matrix whose imbalance degree among multiple phase allocation probability matrices meets the preset conditions, it further includes: For each phase allocation probability matrix, determine the difference between the phase allocation strategy corresponding to the phase allocation probability matrix and the real-time phase allocation strategy; Update the imbalance degree based on the difference to obtain the updated imbalance degree corresponding to each phase allocation probability matrix.

6. The method according to claim 1, wherein The constructing of multiple phase allocation probability matrices based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state includes: Take the sum of the number of candidate mobile shared charging robots and the number of mobile shared charging robots in the charging state as the number of rows of each phase allocation probability matrix, and take the number of phases of the mobile shared charging robots as the number of columns of each phase allocation probability matrix; According to the number of rows and columns, determine multiple groups of random probability values, and take the multiple groups of random probability values as the elements of the multiple phase allocation probability matrices, where the sum of the elements in each row of each phase allocation probability matrix is a preset value.

7. The method according to claim 1, wherein Each column of the phase allocation probability matrix corresponds to each phase of the mobile shared charging robots one by one; each row of the phase allocation probability matrix corresponds to the candidate mobile shared charging robots and the mobile shared charging robots in the charging state one by one; the determining step of the phase allocation strategy includes: For each row in the phase allocation probability matrix, determine the target mobile shared charging robot corresponding to the current row, determine the target column where the element with the largest value in the current row elements is located, and take the phase corresponding to the target column as the allocated phase of the target mobile shared charging robot.

8. A three-phase balance control device for a mobile shared charging robot, characterized in that, The device includes: An obtaining module, configured to, when receiving a charging demand initiated by at least one vehicle to be charged and there are mobile shared charging robots in the charging station in the idle state, determine candidate mobile shared charging robots that match the number of at least one vehicle to be charged among the mobile shared charging robots in the idle state, and obtain the remaining charging power of the vehicles connected to the mobile shared charging robots in the charging state, the required charging power of at least one vehicle to be charged, and the output power of each phase of the mobile shared charging robots in the charging state; The first determination module is configured to construct a plurality of phase allocation probability matrices based on the number of candidate mobile shared charging robots and the number of mobile shared charging robots in a charging state, and calculate the imbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power; The second determination module is configured to determine the probability update speed and the probability update direction based on a target phase allocation probability matrix among the plurality of phase allocation probability matrices whose imbalance degree meets a preset condition; The adjustment module is configured to update the plurality of phase allocation probability matrices based on the probability update speed and the probability update direction, return to the step of calculating the imbalance degree under the phase allocation strategy corresponding to each of the plurality of phase allocation probability matrices based on the remaining charging power, the required charging power, and the output power, and continue to execute until the imbalance degree meets a preset stop condition, and perform phase adjustment on the candidate mobile shared charging robots and the mobile shared charging robots in a charging state based on the phase allocation strategy corresponding to the imbalance degree.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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