Power allocation path determination method, apparatus, and computer readable storage medium

Abstract

The application provides a power distribution path determination method, device and computer readable storage medium, and relates to the technical field of power systems, wherein the method comprises: calculating an upper limit value of the number of times of running a greedy algorithm according to the maximum acceptance delay of a charging system, the number of nodes expected to be traversed by the charging system and the time required to traverse a single node, the node being a power module; calculating a plurality of candidate path groups through the greedy algorithm and an adjacency list of the charging system, the plurality of candidate path groups each comprising at least one power distribution path of a charging gun; wherein the at least one charging gun is a charging gun that needs to be subjected to power distribution, and the number of times of running the greedy algorithm is less than or equal to the upper limit value of the number of times of running the greedy algorithm; calculating the path quality of the plurality of candidate path groups, and taking a candidate path in a candidate path group with the highest path quality as the power distribution path of the at least one charging gun, so that the method can take into account the calculation efficiency and path quality of power distribution and optimize the power distribution path.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a power distribution path determination method and device and a computer readable storage medium. BACKGROUND

[0002] With the rapid development of the new energy automobile industry, the new energy automobile high-voltage platform and high-current ratio technology are rapidly popularized, the charging power is greatly improved, and accordingly, the charging pile industry also successively puts forward high-power main machines and charging terminals, and the power topology design of the charging system is more and more complex, which puts forward higher requirements and challenges to the power distribution method of the charging system.

[0003] The traditional power distribution method is based on the design idea of maximum utilization rate and equal distribution of system power modules, and under the background of the great improvement of the charging power of new energy automobiles, the problems of reducing the operation efficiency of the station and affecting the experience of the charging vehicle owner may be caused, which need to be optimized and solved. SUMMARY

[0004] The application provides a power distribution path determination method, device and computer readable storage medium, which can consider the calculation efficiency and path quality of power distribution, improve the timeliness of power distribution, optimize the power distribution path and improve the user experience.

[0005] To achieve the above-mentioned purpose, the application adopts the following technical scheme:

[0006] In a first aspect, the application provides a power distribution path determination method, comprising:

[0007] According to the maximum acceptance delay of the charging system, the number of nodes expected to be traversed by the charging system and the time required to traverse a single node, the upper limit value of the running number of the greedy algorithm is calculated, and the node is a power module;

[0008] A plurality of candidate path groups are calculated by the greedy algorithm and the charging system adjacency list, and each of the plurality of candidate path groups comprises a power distribution path of at least one charging gun; wherein the at least one charging gun is a charging gun that needs to be distributed power, and the running number of the greedy algorithm is less than or equal to the upper limit value of the running number of the greedy algorithm;

[0009] The path quality of the plurality of candidate path groups is calculated, and the candidate path in the candidate path group with the highest path quality is taken as the power distribution path of the at least one charging gun.

[0010] This method controls the number of times the greedy algorithm runs by considering factors such as the maximum accepting delay of the charging system and the expected number of nodes to be traversed. This avoids excessive algorithm time that slows down efficiency, ensures rapid path determination, and generates the optimal candidate path group through multiple rounds of the greedy algorithm. It avoids blindly distributing power evenly, taking into account both the computational efficiency and path quality of power allocation, thereby improving the timeliness of power allocation, optimizing power allocation paths, and enhancing the user experience.

[0011] In one possible implementation, the dequeue order of the at least one charging gun is randomized each time the greedy algorithm runs, and the candidate optimal node of each node reached by the greedy algorithm is determined based on the potential and selection probability of the branch nodes of the reached node; the dequeue order is the calculation order of the power allocation path of the at least one charging gun. This method, by randomly dequeueing the charging guns each time the greedy algorithm runs, increases the diversity of the candidate path group, facilitating the subsequent selection of a better path.

[0012] In one possible implementation, the selection probability of the first node among the branch nodes of the node reached by the greedy algorithm each time is calculated by the potential of the first node, the maximum potential of the branch of the node reached, the initial iteration parameter, and the number of iterations; the first node can be any node among the branch nodes of the node reached, and the value of the initial iteration parameter is between 0 and 1. This method avoids the algorithm from getting trapped in local optima too early by making the selection probability of nodes with lower potential increasingly smaller, thereby ensuring or achieving overall path optimization.

[0013] In one possible implementation, the charging system adjacency list includes an initial adjacency list or a real-time adjacency list. The initial adjacency list is constructed based on the power topology of the charging system and whether there are parallel contactors directly connecting the nodes. The real-time adjacency list is updated in real-time based on the fault status of the charging system and / or changes in the charging gun's demand. This method ensures that the real-time adjacency list updates with changes in the system state, thereby guaranteeing that the calculated path adapts to the current system state and preventing path failure or inconsistencies due to system changes.

[0014] In one possible implementation, the number of nodes the charging system is expected to traverse is the sum of the number of nodes required by the at least one charging gun. This optional method explicitly determines the number of nodes the charging system needs to traverse, ensuring the efficiency and accuracy of the algorithm.

[0015] In one possible implementation, the upper limit of the number of times the greedy algorithm can run is the floor value of the ratio of the maximum receiving delay of the charging system to a first value, where the first value is the product of the expected number of nodes to be traversed by the charging system and the time required to traverse a single node. This method, by floor value, avoids the total time exceeding the maximum receiving delay of the charging system due to an excessively large floor value, thus ensuring the effectiveness of the time constraint on the algorithm's execution.

[0016] In one possible implementation, the greedy algorithm is a greedy algorithm that embeds a local node breadth-first search algorithm. Each time the greedy algorithm calculates the power allocation path for the first charging gun, if the power allocation requirement of the first charging gun is not met and there are still idle nodes in the charging system, then the local node breadth-first search algorithm determines a power allocation path that satisfies the power allocation requirement of the first charging gun. Here, the first charging gun is any one of the at least one charging guns. This method embeds the local node breadth-first search algorithm into the greedy algorithm, which can improve the power requirement of the charging gun even when its power requirement is not met and there are still idle nodes in the charging system. It also makes full use of the system's idle resources, avoids resource waste, and improves the power allocation capability of the charging system.

[0017] In one possible implementation, the calculation of multiple candidate path groups using a greedy algorithm and the adjacency list of the charging system includes:

[0018] Step 1: Calculate candidate path group i using the i-th greedy algorithm and the adjacency list of the charging system, where i=1;

[0019] Step 2: Add the candidate path group i to the candidate path table;

[0020] Step 3: Let i = i + 1;

[0021] Step 4: Determine whether i is greater than the upper limit of the number of times the greedy algorithm can be run. If yes, determine the candidate path group in the candidate path table as the multiple candidate path groups. If no, proceed to step 5.

[0022] Step 5: Calculate candidate path group j using the i-th greedy algorithm and the adjacency list of the charging system;

[0023] Step 6: Compare the candidate path group j with the last candidate path group in the candidate path table. If the number of nodes in the candidate path group j is greater than the number of nodes in the last candidate path group, clear the candidate path table and add the candidate path group j to the candidate path table. If the number of nodes in the candidate path group j is less than the number of nodes in the last candidate path group, discard the candidate path group j. If the number of nodes in the candidate path group j is equal to the number of nodes in the last candidate path group, add the candidate path group j to the candidate path table.

[0024] Step 7: Determine whether the number of candidate path groups in the candidate path table is equal to the first threshold. If yes, determine that the candidate path group in the candidate path table is the multiple candidate path groups. If no, return to step 3. The first threshold is a preset value.

[0025] This method can make multiple candidate path groups into a determined optimal multiple candidate path group, thereby optimizing the power allocation path.

[0026] Secondly, this application provides a power distribution path determination device, including a processing module, the processing module being used for:

[0027] Based on the maximum accepting delay of the charging system, the expected number of nodes to be traversed by the charging system, and the time required to traverse a single node, the upper limit of the number of times the greedy algorithm can be run is calculated, where the node is a power module.

[0028] Multiple candidate path groups are calculated using a greedy algorithm and the adjacency list of the charging system. Each candidate path group includes a power allocation path for at least one charging gun. The at least one charging gun is a charging gun that requires power allocation. The number of times the greedy algorithm is run is less than or equal to the upper limit of the number of times the greedy algorithm is run.

[0029] Calculate the path quality of the multiple candidate path groups, and select the candidate path in the candidate path group with the highest path quality as the power distribution path for the at least one charging gun.

[0030] In one possible implementation, the dequeue order of the at least one charging gun is random each time the greedy algorithm runs, and the candidate optimal node of the node reached by the greedy algorithm each time is determined based on the potential and selection probability of the branch nodes of the node reached; the dequeue order is the calculation order of the power allocation path of the at least one charging gun.

[0031] In one possible implementation, the probability of selecting the first node among the branch nodes of the node reached by the greedy algorithm each time is calculated by the potential of the first node, the maximum potential of the branch of the node reached, the initial iteration parameter, and the iteration round; the first node is any node among the branch nodes of the node reached, and the value of the initial iteration parameter is between 0 and 1.

[0032] In one possible implementation, the charging system adjacency list includes an initial adjacency list or a real-time adjacency list. The initial adjacency list is constructed based on the power topology of the charging system and whether there are parallel contactors directly connected between the nodes of the charging system. The real-time adjacency list is updated in real time based on the fault status of the charging system and / or changes in the demand of the charging guns.

[0033] In one possible implementation, the number of nodes that the charging system is expected to traverse is the sum of the number of nodes required by the at least one charging gun.

[0034] In one possible implementation, the upper limit of the number of times the greedy algorithm can be run is the floor value of the ratio of the maximum acceptance delay of the charging system to a first value, where the first value is the product of the number of nodes that the charging system is expected to traverse and the time required to traverse a single node.

[0035] In one possible implementation, the greedy algorithm is a greedy algorithm that embeds a local node breadth-first search algorithm. After each calculation of the power allocation path for the first charging gun using the greedy algorithm, if the power allocation requirement of the first charging gun is not met and there are still idle nodes in the charging system, then the local node breadth-first search algorithm is used to determine the power allocation path that meets the power allocation requirement of the first charging gun; wherein, the first charging gun is any one of the at least one charging guns.

[0036] In one possible implementation, the processing module is specifically used for:

[0037] Step 1: Calculate candidate path group i using the i-th greedy algorithm and the adjacency list of the charging system, where i=1;

[0038] Step 2: Add the candidate path group i to the candidate path table;

[0039] Step 3: Let i = i + 1;

[0040] Step 4: Determine whether i is greater than the upper limit of the number of times the greedy algorithm can be run. If yes, determine the candidate path group in the candidate path table as the multiple candidate path groups. If no, proceed to step 5.

[0041] Step 5: Calculate candidate path group j using the i-th greedy algorithm and the adjacency list of the charging system;

[0042] Step 6: Compare the candidate path group j with the last candidate path group in the candidate path table. If the number of nodes in the candidate path group j is greater than the number of nodes in the last candidate path group, clear the candidate path table and add the candidate path group j to the candidate path table. If the number of nodes in the candidate path group j is less than the number of nodes in the last candidate path group, discard the candidate path group j. If the number of nodes in the candidate path group j is equal to the number of nodes in the last candidate path group, add the candidate path group j to the candidate path table.

[0043] Step 7: Determine whether the number of candidate path groups in the candidate path table is equal to the first threshold. If yes, determine that the candidate path group in the candidate path table is the multiple candidate path groups. If no, return to step 3. The first threshold is a preset value.

[0044] Thirdly, this application provides a power allocation path determination apparatus, including a memory and a processor:

[0045] The memory is used to store computer programs;

[0046] The processor is configured to read a computer program from the memory and execute any of the methods described in the first aspect.

[0047] Fourthly, this application provides a computer-readable storage medium storing a computer program for causing a computer to perform any of the methods described in the first aspect.

[0048] Fifthly, a computer program product comprising computer execution instructions is provided, which, when executed on a computer, cause the computer to perform any of the methods described in the first aspect.

[0049] Compared with the prior art, the beneficial effects of this application embodiment are as follows: by controlling the maximum accepting delay of the charging system and the expected number of nodes traversed, the number of times the greedy algorithm runs is controlled, avoiding excessive algorithm time that slows down efficiency, ensuring rapid path determination, generating the optimal candidate path group through multiple rounds of the greedy algorithm, avoiding blindly distributing power equally, taking into account the computational efficiency and path quality of power allocation, improving the timeliness of power allocation, optimizing the power allocation path, improving user experience, and preventing the algorithm from getting trapped in local optima too early by making the selection probability of nodes with lower potential smaller and smaller, thereby ensuring or achieving overall path optimization.

[0050] Furthermore, the randomization of the charging gun dequeue order during each greedy algorithm run increases the diversity of candidate path groups, facilitating the selection of better paths later. The system's real-time adjacency list updates with system state changes, ensuring that the calculated paths adapt to the current system state and preventing path failures or inconsistencies due to system changes. The clearly defined number of nodes to be traversed in the charging system guarantees the algorithm's efficiency and accuracy. Rounding down prevents the total time from exceeding the charging system's maximum accepting delay due to excessive rounding during algorithm runs, ensuring the effectiveness of the algorithm's time constraints. Embedding the local node breadth-first search algorithm into the greedy algorithm allows for the refinement of charging gun power requirements even when the charging system still has idle nodes, fully utilizing idle system resources, avoiding resource waste, and improving the charging system's power allocation capability. It also ensures that multiple candidate path groups are identified as optimal, thereby optimizing the power allocation path. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of a charging system architecture proposed in an embodiment of this application;

[0052] Figure 2 This is a schematic diagram of a ring topology for a charging system proposed in an embodiment of this application;

[0053] Figure 3 This is a flowchart illustrating the implementation of a power allocation path determination method proposed in an embodiment of this application.

[0054] Figure 4 A schematic diagram of node relationships provided in an embodiment of this application;

[0055] Figure 5 A flowchart illustrating the implementation of another power allocation path determination method provided in this application embodiment;

[0056] Figure 6 This is a schematic diagram of the composition of a power distribution path determination device provided in an embodiment of this application;

[0057] Figure 7 This is a schematic diagram of the hardware structure of a power distribution path determination device provided in an embodiment of this application. Detailed Implementation

[0058] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In the description of this application, unless otherwise stated, "at least one" means one or more, and "more than" means two or more.

[0059] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0060] In this application's embodiments, "equal to" can be used with either "greater than" or "less than," and this application does not impose any restrictions. When "equal to" and "greater than" are used together, the scheme corresponding to "greater than" is adopted; when "equal to" and "less than" are used together, the scheme corresponding to "less than" is adopted.

[0061] This application provides a charging system, such as... Figure 1 As shown, the charging system (also known as a charging pile system or charging gun system) may include a host, one or more terminals, one or more vehicles, and may also include a ring topology of the charging system. Figure 1 Not shown, an example of a ring topology for a charging system can be found in [reference needed]. Figure 2 The ring topology of the charging system can exist independently or be embedded in the host as part of the host; this application makes no restriction. The charging system adopts a centralized hierarchical architecture, with the host acting as the central node of the entire architecture. It establishes topological connections with multiple terminals through bus-type communication links. All terminals are directly connected to the host, forming a master-slave communication network with the host at its core. Terminals can establish a one-to-one connection with the vehicle.

[0062] The host can be a charging operation management server or a cloud energy management platform, etc. Its hardware can integrate high-performance multi-core processors, redundant storage modules and multi-protocol communication gateways, etc. The host can manage and distribute power in the ring topology of the charging system.

[0063] The terminal can be specifically a charging gun, which may or may not integrate a local controller or intelligent control module. It can receive charging strategies, power adjustment commands from the host computer and perform localized parsing and power output control. The terminal can be connected to the ring topology of the charging system to output the corresponding power.

[0064] The vehicle can be a specific electric passenger car, electric bus or heavy electric truck, etc., equipped with AC and / or DC fast charging interfaces. As the service object of the charging system, its battery management system can communicate with the terminal one-way or two-way, and send data such as charging demand and battery status to the terminal.

[0065] This application also provides a ring topology diagram of a charging system, such as... Figure 2 As shown, K1-K24 represent 24 contactors connected in parallel, 1#-12# represent 12 charging guns, ①- This represents 12 power modules. It's understandable that the charging gun here can be the terminal mentioned above; for ease of description and understanding, it will be referred to as the charging gun in the following text.

[0066] Figure 2 The charging system's ring topology diagram shown employs a layout combining ring topology and cross-connection. Twelve power modules serve as the core power nodes of the charging system, all connected to the ring power bus. Twenty-four parallel contactors act as dynamic switching units, establishing configurable electrical paths between each power module and multiple charging guns in a many-to-many connection manner. This allows each charging gun to flexibly selectively connect with one or more power modules through the on / off combinations of corresponding parallel contactors, enabling the charging gun to output different power levels according to varying needs. The output power of different power modules can be the same or different; this application does not impose any restrictions.

[0067] It is understood that the ring topology diagram of the charging system above is only an example. In actual implementation, the number of power modules, charging guns, etc. in the ring topology of the charging system can be more or less, and this application does not impose any restrictions.

[0068] Based on the problems raised in the background art, this application provides a power allocation path determination method, such as... Figure 3 As shown, the method includes:

[0069] 301. Based on the maximum receiving delay of the charging system, the expected number of nodes to be traversed by the charging system, and the time required to traverse a single node, calculate the upper limit of the number of times the greedy algorithm can be run, where the node is a power module.

[0070] The execution entity in this application embodiment can be the aforementioned host.

[0071] Among them, the maximum acceptance delay of the charging system refers to the longest allowable time threshold from the start of the scheduling command to the completion of power module selection, link establishment and meeting of charging execution conditions when the charging system performs core operations such as power module scheduling and charging strategy configuration. This threshold is jointly determined by the charging response performance requirements of the charging system, the real-time requirements of electric vehicle charging demand and the transmission characteristics of the system control link.

[0072] The expected number of nodes to be traversed in the charging system refers to the total number of power modules that the charging system plans to traverse in a single greedy algorithm scheduling process in order to select power modules that meet the requirements of charging power, voltage, and current adaptation. Its value is determined by the actual deployment scale of power modules in the charging system, the node requirements of charging guns, etc.

[0073] The time required to traverse a single node refers to the average time taken by the greedy algorithm to complete the node's working status detection, electrical parameter acquisition, compatibility judgment with the charging gun, and data interaction feedback in a single traversal operation. It is determined by the system's hardware computing performance, communication link transmission rate, and power module status response characteristics.

[0074] The maximum number of times a greedy algorithm can run refers to the maximum number of iterations or scheduling runs allowed for the algorithm to be executed so that its overall execution time does not exceed the maximum accepting delay of the charging system during node scheduling.

[0075] Optionally, step 301 can be implemented in at least one of the following methods:

[0076] Method 1: The upper limit of the number of times the greedy algorithm can run (denoted as times) is the floor value of the ratio of the maximum acceptance delay of the charging system to the first value, where the first value is the product of the number of nodes that the charging system is expected to traverse and the time required to traverse a single node.

[0077] In other words, times= In this application, " "Represents rounding down," "" represents multiplication. Rounding down can prevent the total time from exceeding the maximum receiving delay of the charging system due to an excessively large number of algorithm runs, thus ensuring the effectiveness of the algorithm's time constraints.

[0078] Method 2: The upper limit of the number of times the greedy algorithm can run (denoted as times) is the floor value of the ratio of "the difference between the maximum acceptance delay of the charging system and the delay margin" to "the first value".

[0079] In other words, times= The time delay margin is a non-deterministic time consumption factor that takes into account communication jitter in the charging system control link and the small delay of local algorithm computation. It is the time margin deducted from the maximum receiving delay of the charging system. It can be determined based on the historical operating data of the charging system, the jitter coefficient of the communication link, and the fluctuation characteristics of hardware computation. The time delay margin is less than the maximum receiving delay of the charging system, thereby further avoiding the total consumption exceeding the maximum receiving delay of the charging system due to the rounding of the number of algorithm runs being too large, and ensuring the effectiveness of the time constraint of the algorithm operation.

[0080] Optionally, the number of nodes the charging system is expected to traverse is the sum of the number of nodes required for at least one charging gun. Here, at least one charging gun is a charging gun that requires power distribution.

[0081] At least one charging gun can be some or all of the total number of charging guns. For accuracy, the at least one charging gun is described below as M charging guns, where M is greater than 0 and less than or equal to the total number of charging guns. For example, the M charging guns can be #9, #7, #4, and #8.

[0082] In other words, the expected number of nodes traversed by the charging system is the sum of the number of nodes required for the M charging guns. For example, assuming the M charging guns are #9, #7, #4, and #8, and the number of nodes required for #9, #7, #4, and #8 are 3, 1, 3, and 1 respectively, then the sum of the number of nodes required for the M charging guns is 8, and the expected number of nodes traversed by the charging system is 8. The number of nodes required for a single charging gun can be determined based on the charging demand of that single charging gun. For example, if a single charging gun requires 60 kilowatts (kW) or 50 kW to charge the corresponding vehicle, and a single node can provide 20 kW, then the number of nodes required for that single charging gun is 3.

[0083] It is understandable that if the sum of the number of nodes required for M charging guns is greater than the total number of nodes in the charging system, then the expected number of nodes traversed by the charging system can be equal to the total number of nodes in the charging system.

[0084] This optional method clarifies the number of nodes that the charging system needs to traverse, ensuring the efficiency and accuracy of the algorithm.

[0085] 302. Multiple candidate path groups are obtained by using a greedy algorithm and the adjacency list of the charging system. Each candidate path group includes the power allocation path of M charging guns. The number of times the greedy algorithm is run is less than or equal to the upper limit of the number of times the greedy algorithm is run.

[0086] Among them, the charging system adjacency list is a structured data format specifically used to record the connection relationship between various charging guns in the charging system. It takes all charging guns as basic units and the charging lines connecting these guns as connection relationships. By showing which adjacent guns each charging gun corresponds to, the physical connection and electrical relationship of the entire charging system are recorded clearly and orderly.

[0087] The adjacency list of the charging system can be generated based on the connection relationship of each power module. For example, each charging gun is defined as a vertex of the graph, and two charging guns are considered to be adjacent if there is a parallel contactor directly connected between them.

[0088] For example, based on Figure 2 The charging system has a ring topology, and the adjacency list of the charging system can be shown in Table 1.

[0089] Table 1

[0090]

[0091] Optionally, the adjacency list of the charging system includes an initial adjacency list or a real-time adjacency list. The initial adjacency list is constructed based on the power topology of the charging system and whether there are parallel contactors directly connected between the nodes of the charging system. The real-time adjacency list is updated in real time based on the fault status of the charging system and / or the changes in the demand of the charging guns.

[0092] The system's real-time adjacency list can be generated based on the system's initial adjacency list or based on the previous system's real-time adjacency list.

[0093] For example, the adjacency list of the charging system shown in Table 1 above can be the initial adjacency list of the system. Assuming that the charging system is fault-free and the charging guns with charging needs (i.e., the above M charging guns) are 9#, 7#, 4#, and 8#, the real-time adjacency list of the system can be found in Table 2.

[0094] Table 2

[0095]

[0096] In other words, in the specific implementation of step 302 above, multiple candidate path groups can be calculated using the system's initial adjacency list or the system's real-time adjacency list.

[0097] In this application, the system's real-time adjacency list can be updated as the system state changes, thereby ensuring that the calculated path can adapt to the current system state and avoiding path failure or unreasonableness due to system changes.

[0098] Optionally, the dequeue order of the M charging guns is random each time the greedy algorithm runs, and the candidate optimal node of the node reached by the greedy algorithm each time is determined based on the potential and selection probability of the branch nodes of the node reached; the dequeue order of the M charging guns is the calculation order of the power allocation path of the M charging guns.

[0099] The dequeue order of the M charging guns is random. For example, in the first run of the greedy algorithm, the dequeue order of the M charging guns could be 9#, 7#, 4#, 8#; in the second run of the greedy algorithm, the dequeue order of the M charging guns could be 7#, 9#, 8#, 4#; and in the third run of the greedy algorithm, the dequeue order of the M charging guns could be 7#, 4#, 9#, 8#.

[0100] The method provided in this application randomly dequeues the charging guns each time the greedy algorithm is run, which can increase the diversity of candidate path groups and facilitate the subsequent selection of better paths.

[0101] Among them, the branch nodes of a node refer to the nodes that the node can be adjacent to. For example, as shown in Table 1, the branch nodes of 4# can be 3#, 5#, 1#, and 7#.

[0102] The potential of a node's branch node refers to the potential probability / value weight that the node and its branch node can exert when combined. For example, if the combined power meets the requirements, the potential is high; otherwise, the potential is low. Furthermore, the more stable the combined operation, the higher the potential. The potential of a node's branch nodes can be preset based on historical operating data.

[0103] The selection probability of a branch node of a given node refers to the probability of selecting that branch node under that given node. In the embodiments of this application, the selection probability of a branch node with low potential is not 0, that is, in this application, there is a certain probability of accepting a branch node with low potential as a candidate optimal node.

[0104] In practical implementation, the probability of extracting a branch node can be determined based on the potential and selection probability of the branch node. For example, a weighted random selection formula can be used to combine the potential and selection probability of the branch node into a comprehensive weight, and the extraction probability can be determined based on this comprehensive weight.

[0105] Specifically, the potential of the x-th branch node of a node (denoted as P) can be considered. 潜力,x ) and the selection probability of the x-th branch node (denoted as P) 选择,x Multiply by , and obtain the comprehensive weight of the x-th branch node (denoted as W). x ), that is, W x =P 潜力,x P 选择,xThe probability of extracting the x-th branch node can be denoted as P. 抽取,x ) is P 抽取,x = Where n is the total number of branch nodes of this node, k is an integer greater than 1 and less than or equal to n+1, and x is an integer greater than 0 and less than n+1.

[0106] For example, see Figure 4 For node ④ (i.e., power module ④), its branch nodes include nodes ③, ⑤, ①, and ⑦, with potentials of 50%, 25%, 15%, and 10%, respectively, and selection probabilities of 80%, 40%, 24%, and 16%, respectively. The probability of selecting node ③ is... ≈72.46%; the probability of extracting node ⑤ = ≈18.12%; the probability of drawing node ① = ≈6.52%; the probability of drawing node ⑦ = ≈2.90%.

[0107] The candidate optimal nodes for each node encountered during the process can be selected based on the extraction probability. The number of candidate optimal nodes can be preset. Different nodes can have the same or different number of candidate optimal nodes. For example, see [link to example]. Figure 4 The number of candidate optimal nodes is 2. Assuming the node reached is node ④, the selected candidate optimal nodes can include nodes ③ and ⑦. In subsequent processes, a node can be randomly selected from the candidate optimal nodes as the next node, and the selection of subsequent nodes can be carried out in a similar way to node ④, until the number of nodes required for 4# is met or there are no branches to choose from, at which point the greedy algorithm calculation for 4# ends.

[0108] Optionally, the probability of selecting the first node among the branch nodes of the node reached by the greedy algorithm each time is calculated from the potential of the first node, the maximum potential of the branch of the node reached, the initial iteration parameters, and the iteration round; the first node can be any node among the branch nodes of the node reached, and the value of the initial iteration parameters is between 0 and 1.

[0109] Specifically, the probability of selecting the first node = ,in, These are the initial iteration parameters. For iteration rounds, It is an exponential function. express . The value of is 1. The initial iteration parameter can be between 0 and 1.

[0110] The iteration rounds for different charging guns among the M charging guns can be calculated separately.

[0111] For example, see Figure 4 For charging gun #4 out of M, assuming the node reached is node ④ (i.e., power module ④), and node ④ has branches ③, ⑤, ①, and ⑦ with potentials of 50%, 25%, 15%, and 10% respectively, and assuming the first node is node ⑤, with an initial iteration parameter of 0.8 and an iteration round of 1, then the probability of selecting the first node is = =40%. The selection probabilities of nodes ③, ①, and ⑦ can be calculated similarly.

[0112] After running node ④, the node reached (i.e., the node selected from the candidate optimal node of node ④) is assumed to be node ③. The branch nodes of node ③ include node ②, node ④, and node ③. Nodes 6 and 7 have potentials of 40%, 25%, 20%, and 15% respectively. Assuming the first node is node 6, the initial iteration parameter is 0.8, and the iteration round is 2, then the probability of selecting the first node is... =24%. Node ②, Node ④, Node The selection probability can be calculated similarly.

[0113] This alternative approach avoids the algorithm from getting trapped in local optima too early by making the selection probability of nodes with lower potential smaller and smaller, thereby ensuring or achieving overall path optimization.

[0114] Optionally, the greedy algorithm is a greedy algorithm that embeds a local node breadth-first search (BFS) algorithm. After each calculation of the power allocation path for the first charging gun using the greedy algorithm, if the power allocation requirement of the first charging gun is not met (i.e., the number of nodes required by the first charging gun is not met) and there are still idle nodes in the charging system, then the power allocation path that meets the power allocation requirement of the first charging gun is determined by BFS; where the first charging gun is any one of the M charging guns.

[0115] For example, if we consider charging gun #4 out of M charging guns, and assume that #4 requires 3 nodes, and a greedy algorithm calculates the power allocation path for the first charging gun as: node ④ — node ③, then the power allocation requirement for the first charging gun is not met and there are still idle nodes in the charging system. In this case, we randomize the allocated nodes of #4 (i.e., nodes ④ and ③) and sequentially calculate the BFS result of each node until the power allocation requirement of #4 is met or all allocated nodes have been traversed.

[0116] By embedding BFS into the greedy algorithm, the power demand of the charging gun can be improved by BFS when the power demand of the charging gun is not yet met and there are still idle nodes in the charging system. This fully utilizes the idle resources of the system, avoids resource waste, and improves the power allocation capability of the charging system.

[0117] Optionally, step 302 may include the following in its implementation:

[0118] Step 1: Calculate candidate path group i using the i-th greedy algorithm and the adjacency list of the charging system, where i=1;

[0119] Step 2: Add candidate path group i to the candidate path table;

[0120] Step 3: Let i = i + 1;

[0121] Step 4: Determine if i is greater than the upper limit of the number of times the greedy algorithm can be run. If yes, determine that there are multiple candidate path groups in the candidate path table. If not, proceed to step 5.

[0122] Step 5: Calculate candidate path group j using the i-th greedy algorithm and the adjacency list of the charging system;

[0123] Step 6: Compare candidate path group j with the last candidate path group in the candidate path table. If the number of nodes in candidate path group j is greater than the number of nodes in the last candidate path group, clear the candidate path table and add candidate path group j to the candidate path table. If the number of nodes in candidate path group j is less than the number of nodes in the last candidate path group, discard candidate path group j. If the number of nodes in candidate path group j is equal to the number of nodes in the last candidate path group, add candidate path group j to the candidate path table.

[0124] Step 7: Determine whether the number of candidate path groups in the candidate path table is equal to the first threshold. If yes, determine that there are multiple candidate path groups in the candidate path table. If no, return to step 3. The first threshold is a preset value.

[0125] Therefore, it can be understood that the multiple candidate path groups are the optimal multiple candidate path groups determined after step 302, thereby optimizing the power allocation path.

[0126] For example, assuming the M charging guns are #9, #7, #4, and #8, and the first threshold is 10, the 10 optimal candidate path groups can be found in Table 3. In Table 3 and Table 4 below, the numbers in the specific candidate paths represent power modules. For example, the candidate path "9-12-1-2-5" for #9 in candidate path group 1 represents "⑨- -①-②-⑤”, the rest are similar and will not be repeated.

[0127] Table 3

[0128]

[0129] 303. Calculate the path quality of multiple candidate path groups, and use the candidate path in the candidate path group with the highest path quality as the power allocation path for M charging guns.

[0130] In specific implementation, step 303 can determine the path quality of multiple candidate path groups based on the system path quality evaluation criteria. For example, path quality can be calculated as: Determine the path quality of multiple candidate path groups, where q is an integer greater than or equal to 1 and less than or equal to M. This represents the priority coefficient of the q-th charging gun. This represents the number of nodes in the candidate path for the q-th charging gun. The priority coefficient for each charging gun can be preset.

[0131] For example, assuming that the priority coefficient of each charging gun decreases by 0.05 in sequence according to the order of each charging gun, where the priority coefficient of the first charging gun is 1, then the priority coefficient of 9# is 1, the priority coefficient of 7# is 0.95, the priority coefficient of 4# is 0.90, and the priority coefficient of 8# is 0.85, then the path quality calculation results of the candidate path group shown in Table 3 above can be found in Table 4.

[0132] Table 4

[0133]

[0134] In step 303, if there are multiple candidate path groups with the highest path quality, one of them can be selected as the power distribution path for the M charging guns.

[0135] It should be noted that after determining the candidate path group with the highest path quality, its validity can be verified. If valid, the candidate paths in the candidate path group with the highest path quality are used as the power allocation paths for the M charging guns. Specifically, if there are no duplicate nodes in different candidate paths in the candidate path group with the highest path quality, and any adjacent nodes in the same candidate path are connected, then the combination of candidate paths with the highest path quality is valid; otherwise, it is invalid.

[0136] For example, if the candidate paths in candidate path group 2 of Table 4 are used as the power allocation paths for the M charging guns, then the power allocation path for #9 is 9-6-5-2-11, the power allocation path for #7 is 7, the power allocation path for #4 is 4-1-10-3-12, and the power allocation path for #8 is 8. Taking #9 as an example, this means that the power output of #9 can be ⑨, ⑥, ⑤, ②, The sum of the output power.

[0137] The method provided in the above embodiments of this application controls the number of times the greedy algorithm runs by taking into account factors such as the maximum acceptable delay of the charging system and the expected number of nodes to be traversed. This avoids excessive algorithm time that slows down efficiency, ensures rapid path determination, generates the optimal candidate path group through multiple rounds of the greedy algorithm, avoids blindly distributing power equally, takes into account both the computational efficiency and path quality of power allocation, improves the timeliness of power allocation, optimizes the power allocation path, and enhances the user experience.

[0138] The embodiments described above utilize the characteristic of the greedy algorithm to quickly obtain local optimal solutions. By introducing a system perturbation part (i.e., the selection probability of the aforementioned branch nodes), the defects of the greedy algorithm locking the local optimal solution too early and missing the global better solution are avoided, thereby ensuring or achieving the overall path optimization. At the same time, BFS is used as a supplement, which can quickly obtain a solution close to the current system optimal solution while achieving better space complexity.

[0139] This application can be applied to scenarios with limited embedded hardware resources. Since the embedded hardware resource requirements are low, this application can make the computational complexity controllable.

[0140] The method provided in this application has strong scalability and is applicable to the power topology of common charging systems. It can adaptively adjust the number of algorithm runs according to the topology complexity and real-time charging demand. It reserves a system path quality evaluation interface and can load different system path evaluation strategies according to different needs to meet various charging modes.

[0141] To make the embodiments of this application clearer, the execution process of the embodiments of this application is described below as an example. See also Figure 5 The process includes:

[0142] 500. Generate the initial adjacency table of the system based on the connection relationship of each power module in the charging system.

[0143] For example, the initial adjacency list of the system can be found in Table 1 above.

[0144] 501. Monitor the fault status of the charging system and / or whether the demand for the charging gun changes.

[0145] If yes, proceed to step 502. If no, proceed to step 503.

[0146] 502. Generate the system's real-time adjacency list based on the system's initial adjacency list.

[0147] For example, assuming the charging system is fault-free and the charging guns that need charging (i.e., the M charging guns mentioned above) are 9#, 7#, 4#, and 8#, the real-time adjacency table of the system can be found in Table 2 above.

[0148] 503. Determine the expected number of nodes to be traversed in the charging system.

[0149] The expected number of nodes traversed by the charging system is the sum of the number of nodes required for the M charging guns. For example, assuming the M charging guns are #9, #7, #4, and #8, and the number of nodes required for #9, #7, #4, and #8 are 5, 1, 5, and 3 respectively, then the sum of the number of nodes required for the M charging guns is 18. Figure 2 The charging system shown has a ring topology with a maximum of 12 nodes. Therefore, the expected number of nodes to be traversed in the charging system is 12.

[0150] 504. Based on the maximum accepting delay of the charging system, the expected number of nodes to be traversed by the charging system, and the time required to traverse a single node, calculate the upper limit of the number of times the greedy algorithm can be run.

[0151] For example, assuming the maximum acceptance delay of the charging system is 100 milliseconds (ms) and the time required to traverse a single node is 0.1ms, then the maximum number of times the greedy algorithm can run is 83.

[0152] 505. The candidate path group i is obtained by calculating the i-th greedy algorithm and the adjacency list of the charging system, where i=1.

[0153] 506. Add candidate path group i to the candidate path table.

[0154] 507. Let i=i+1.

[0155] 508. Determine if i is greater than the upper limit of the number of times the greedy algorithm can be run. If yes, proceed to step 512; otherwise, proceed to step 509.

[0156] 509. The candidate path group j is obtained by calculating the i-th greedy algorithm and the adjacency list of the charging system.

[0157] 510. Compare candidate path group j with the last candidate path group in the candidate path table. If the number of nodes in candidate path group j is greater than the number of nodes in the last candidate path group, clear the candidate path table and add candidate path group j to the candidate path table. If the number of nodes in candidate path group j is less than the number of nodes in the last candidate path group, discard candidate path group j. If the number of nodes in candidate path group j is equal to the number of nodes in the last candidate path group, add candidate path group j to the candidate path table.

[0158] Step 510 can be implemented in the following ways: compare candidate path group j with the last candidate path group in the candidate path table, and determine whether the number of nodes in candidate path group j is greater than the number of nodes in the last candidate path group. If yes, clear the candidate path table and add candidate path group j to the candidate path table. If no, determine whether the number of nodes in candidate path group j is less than the number of nodes in the last candidate path group. If yes, discard candidate path group j; if no, add candidate path group j to the candidate path table. Of course, the implementation process can also be different and is not limited.

[0159] 511. Determine whether the number of candidate path groups in the candidate path table is equal to the first threshold. If yes, proceed to step 512; otherwise, return to step 507.

[0160] For example, the first threshold can be 10.

[0161] 512. Determine that the candidate path group in the candidate path table is a multiple candidate path group.

[0162] For example, the 10 optimal candidate path groups determined after steps 505-512 can be found in Table 3.

[0163] 513. Calculate the path quality of multiple candidate path groups.

[0164] The specific implementation of step 513 can be found above and will not be repeated here. For example, based on the 10 optimal candidate path groups shown in Table 3, assuming that the priority coefficient of each charging gun decreases by 0.05 in sequence according to the order of each charging gun, where the priority coefficient of the first charging gun is 1, then the priority coefficient of 9# is 1, the priority coefficient of 7# is 0.95, the priority coefficient of 4# is 0.90, and the priority coefficient of 8# is 0.85. The path quality calculation results of the candidate path groups shown in Table 3 can be found in Table 4.

[0165] 514. Identify the candidate path group with the highest path quality among multiple candidate path groups.

[0166] 515. Determine whether the candidate path group with the highest path quality is valid.

[0167] If valid, proceed to step 516; if invalid, proceed to step 517.

[0168] Among them, if the nodes in different candidate paths in the candidate path group with the highest path quality are not duplicated, and any adjacent nodes in the same candidate path can be connected, then the candidate path combination method with the highest path quality is valid; otherwise, it is invalid.

[0169] 516. Select the candidate path from the candidate path group with the highest path quality as the power allocation path for the M charging guns.

[0170] 517. Allocation error confirmed.

[0171] After step 517, you can continue to select a suitable candidate path group from other candidate path groups besides the candidate path group with the highest path quality determined in step 514, or you can redetermine the candidate path group with the highest path quality starting from step 500 above. This application does not impose any restrictions.

[0172] The foregoing primarily describes the solutions of the embodiments of this application from a methodological perspective. It is understood that, in order to achieve the aforementioned functions, the host includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0173] For example, Figure 6 A possible structural schematic diagram of the power allocation path determination device (denoted as power allocation path determination device 60) involved in the above embodiments is shown. The power allocation path determination device 60 includes a processing module 601 for performing the actions in the above method. For specific implementation details, please refer to the above description, which will not be repeated here. For example, the power allocation path determination device 60 can be a device, a chip, or a chip system.

[0174] This application also provides a hardware structure diagram of a power allocation path determination device. See [link to diagram]. Figure 7 The power distribution path determination device includes a processor 701 and a memory 702. The processor 701 and the memory 702 can be connected via a communication bus.

[0175] Processor 701 can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program according to the present application. Processor 701 may also include multiple CPUs, and processor 701 can be a single-core processor or a multi-core processor. Here, processor can refer to one or more devices, circuits, or processing cores used to process data (e.g., computer program instructions).

[0176] The memory 702 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or it may be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer. This application embodiment does not impose any limitations on this. The memory 702 may exist independently (in this case, the memory 702 may be located outside or within the power allocation path determination device), or it may be integrated with the processor 701. The memory 702 may contain computer program code. The processor 701 is used to execute computer program code stored in the memory 702, thereby implementing the method provided in the embodiments of this application.

[0177] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0178] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, implements the methods described in the above-described method embodiments.

[0179] This application also provides a computer program product that, when run on a computer, enables the computer to implement the methods described in the above-described method embodiments.

[0180] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying the computer program code to a photographic device / terminal device, a recording medium, computer memory, ROM, RAM, electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0181] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0182] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0183] In the embodiments provided in this application, it should be understood that the disclosed apparatus / devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0184] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0185] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0186] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0187] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for determining a power allocation path, characterized in that, include: Based on the maximum receiving delay of the charging system, the expected number of nodes to be traversed by the charging system, and the time required to traverse a single node, the upper limit of the number of times the greedy algorithm can be run is calculated. The node is a power module. The upper limit of the number of times the greedy algorithm can be run is the floor value of the ratio of the maximum receiving delay of the charging system to a first value. The first value is the product of the expected number of nodes to be traversed by the charging system and the time required to traverse a single node. Multiple candidate path groups are calculated using a greedy algorithm and the adjacency list of the charging system. Each candidate path group includes a power allocation path for at least one charging gun. The at least one charging gun is a charging gun that requires power allocation. The number of times the greedy algorithm is run is less than or equal to the upper limit of the number of times the greedy algorithm is run. Calculate the path quality of the multiple candidate path groups, and use the candidate path in the candidate path group with the highest path quality as the power distribution path of the at least one charging gun. The probability of selecting the first node among the branch nodes of the node reached by the greedy algorithm each time is calculated from the potential of the first node, the maximum potential of the branch of the node reached, the initial iteration parameter and the iteration round; the first node is any node among the branch nodes of the node reached, and the value of the initial iteration parameter is between 0 and 1.

2. The power distribution path determination method according to claim 1, characterized in that, Each time the greedy algorithm runs, the dequeue order of the at least one charging gun is random, and the candidate optimal node of the node reached by the greedy algorithm each time is determined according to the potential and selection probability of the branch node of the node reached; the dequeue order is the calculation order of the power allocation path of the at least one charging gun.

3. The power distribution path determination method according to claim 1 or 2, characterized in that, The charging system adjacency list includes an initial adjacency list or a real-time adjacency list. The initial adjacency list is constructed based on the power topology of the charging system and whether there are parallel contactors directly connected between the nodes of the charging system. The real-time adjacency list is updated in real time based on the fault status of the charging system and / or changes in the demand of the charging guns.

4. The power distribution path determination method according to claim 1 or 2, characterized in that, The number of nodes that the charging system is expected to traverse is the sum of the number of nodes required for the at least one charging gun.

5. The power distribution path determination method according to claim 1 or 2, characterized in that, The greedy algorithm is a greedy algorithm that embeds a local node breadth-first search algorithm. After each calculation of the power allocation path for the first charging gun using the greedy algorithm, if the power allocation requirement of the first charging gun is not met and there are still idle nodes in the charging system, the local node breadth-first search algorithm is used to determine the power allocation path that meets the power allocation requirement of the first charging gun. Here, the first charging gun is any one of the at least one charging guns.

6. The power distribution path determination method according to claim 1 or 2, characterized in that, The process of calculating multiple candidate path groups using a greedy algorithm and the adjacency list of the charging system includes: Step 1: Calculate candidate path group i using the i-th greedy algorithm and the adjacency list of the charging system, where i=1; Step 2: Add the candidate path group i to the candidate path table; Step 3: Let i = i + 1; Step 4: Determine whether i is greater than the upper limit of the number of times the greedy algorithm can be run. If yes, determine the candidate path group in the candidate path table as the multiple candidate path groups. If no, proceed to step 5. Step 5: Calculate candidate path group j using the i-th greedy algorithm and the adjacency list of the charging system; Step 6: Compare the candidate path group j with the last candidate path group in the candidate path table. If the number of nodes in the candidate path group j is greater than the number of nodes in the last candidate path group, clear the candidate path table and add the candidate path group j to the candidate path table. If the number of nodes in the candidate path group j is less than the number of nodes in the last candidate path group, discard the candidate path group j. If the number of nodes in the candidate path group j is equal to the number of nodes in the last candidate path group, add the candidate path group j to the candidate path table. Step 7: Determine whether the number of candidate path groups in the candidate path table is equal to the first threshold. If yes, determine that the candidate path group in the candidate path table is the multiple candidate path groups. If no, return to step 3. The first threshold is a preset value.

7. A power distribution path determination device, characterized in that, Including memory and processor: The memory is used to store computer programs; The processor is configured to read the computer program in the memory and execute the method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that causes a computer to perform the method of any one of claims 1-6.

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