An intelligent scheduling method and system based on multi-pile charging modules

Abstract

The application discloses a kind of intelligent scheduling method and system based on multiple pile charging module, it is related to charging module intelligent scheduling technical field, the charging data of user is collected to generate charging application, corresponding charging demand bubble is generated according to charging request;According to the physical connection relationship and capacity limit of charging facility, a virtual pipe network model is constructed;In the virtual pipe network model, the transmission path from the entrance to the target charging pile is matched for the charging demand bubble, and a path matching result is generated;Based on the size of charging demand bubble and the real-time available width of virtual pipe on transmission path, size verification is carried out, and a passability verification result is generated;According to the passability verification result, corresponding resource scheduling operation is executed, and a scheduling scheme is generated;The application converts charging scheduling into the dynamic adaptation process of bubble in pipe, realizes the flexible matching and global optimization of charging resources under strict power grid constraints.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology for charging modules, and specifically to an intelligent scheduling method and system based on multiple charging pile modules. Background Technology

[0002] Existing multi-pile charging scheduling technologies can be mainly divided into two typical methods. One is the static rule-based method, which directly assigns charging piles and power to vehicles according to the principles of first-come-first-served, fixed priority, or rotational allocation. The other is the centralized optimization model-based method, which usually establishes a mathematical model that includes constraints such as grid capacity and user demand, and uses linear programming or heuristic algorithms to solve for a theoretically optimal global scheduling scheme. However, existing technologies have certain limitations. Rule-based methods are too rigid and cannot perceive the real-time changes in capacity status and line coupling relationships in the power grid topology, which can easily lead to local overload and uneven resource utilization. Although centralized optimization methods consider global constraints, they are computationally complex and slow to respond, making it difficult to cope with continuously arriving dynamic requests and second-level fluctuations in power grid status. At the same time, both types of methods generally treat user charging demand as an unadjustable fixed parameter, lacking quantitative modeling and proactive utilization of the inherent elasticity of demand, such as the acceptable range of time and power. This results in a lack of flexibility in scheduling strategies when the system faces capacity bottlenecks, often resorting to inefficient methods such as denial of service or long queuing, and failing to achieve efficient and smooth utilization of overall resources through multi-pile coordination and minor adjustments on the demand side while ensuring the core needs of users. Therefore, developing an intelligent scheduling method based on multi-pile charging modules is of great significance. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent scheduling method and system based on multi-pile charging modules to solve the problems in the background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent scheduling method based on a multi-pile charging module, comprising: Collect user charging data to generate charging requests, and generate corresponding charging demand bubbles based on the charging requests; A virtual network model is constructed based on the physical connections and capacity limitations of the charging facilities; In the virtual pipeline model, a transmission path from the inlet to the target charging pile is matched for the charging demand bubble, and a path matching result is generated; The size of the charging demand bubble is verified based on the real-time available width of the virtual pipe on the transmission path, and a passability verification result is generated. Based on the pass / fail verification result, perform the corresponding resource scheduling operation and generate a scheduling scheme.

[0005] In a preferred embodiment, the step of collecting user charging data to generate a charging request, and generating a corresponding charging demand bubble based on the charging request, is as follows: The system collects user charging data to generate a charging request. The charging request includes the vehicle identifier, the target charging station identifier, the vehicle's current battery level, the target battery level, the maximum allowable charging power of the vehicle's battery, and the charging mode selected by the user through the interactive interface. Based on the charging application, determine the core demand parameters and elastic demand parameters of the charging demand bubble; The core requirements parameters include the minimum required charging amount calculated based on the vehicle's current battery level and the target battery level, as well as the latest charging completion deadline determined based on the charging mode. The flexible demand parameters include the allowable charging completion time fluctuation range determined based on charging mode and historical behavior data, and the acceptable charging power adjustment range determined based on the vehicle battery's maximum allowable charging power and charging mode. Based on the determined core demand parameters and flexible demand parameters, a charging demand bubble is generated. The core demand parameters are abstracted as the inner diameter of the bubble, and the flexible demand parameters are abstracted as the outer diameter of the bubble.

[0006] In a preferred embodiment, the step of constructing a virtual network model based on the physical connection relationship and capacity limitations of the charging facilities is as follows: Obtain electrical topology data and power distribution capacity configuration data of charging facilities; Electrical topology data includes the physical connection hierarchy and pathway relationships from the upstream power access point to each charging pile at the end. The power distribution capacity configuration data includes the rated safe current carrying capacity and power limits of each level of power distribution lines; Based on the electrical topology data, each physical electrical path with an independent power distribution capacity configuration is abstracted and created as a virtual pipe object; Associate each virtual pipe object with the rated safe current carrying capacity and power limit from the power distribution capacity configuration data, and abstract the rated safe current carrying capacity and power limit into the pipe's equivalent width; Abstract and create each charging pile and each level of power distribution node that provides power to multiple charging piles as a network node object; Based on the physical connection hierarchy and path relationships in the electrical topology data, all network node objects are connected using virtual pipe objects to generate a virtual pipe network model.

[0007] In a preferred embodiment, the step of matching a transmission path from the inlet to the target charging pile for a charging demand bubble in the virtual pipeline model and generating a path matching result is as follows: Based on the target charging pile identifier in the charging demand bubble, locate the corresponding target network node in the virtual network model; Determine the entry network node of the charging demand bubble. The entry network node is the logical node in the virtual pipeline model that corresponds to the upstream power supply that supplies power to the line where the target charging pile is located. In the virtual pipeline model, starting from the ingress network node and ending at the target network node, a topology search is performed based on the connection relationship established by the virtual pipeline object to retrieve all candidate transmission paths that are physically feasible, which are then used as path matching results.

[0008] In a preferred embodiment, the step of performing size verification based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path to generate a passability verification result is as follows: The inner diameter of the charging demand bubble is used as the power value requested at the current scheduling moment. For each candidate transmission path in the path matching results, perform size verification; Calculate the real-time available pipe width for each virtual pipe object in the candidate transmission path. The real-time available pipe width is calculated based on the equivalent pipe width of the virtual pipe object and the currently occupied width. The system sequentially determines whether the bubble size of the current request is less than or equal to the real-time available pipe width of each virtual pipe object in the candidate transmission path, and generates a judgment result. Based on the judgment results, a passability verification result is generated for each candidate transmission path; The passability verification results include passable, requires compression, and failable.

[0009] In a preferred embodiment, the step of generating the corresponding resource scheduling operation based on the pass / fail verification result is as follows: If the passability verification result shows that there is a candidate transmission path that can be directly accessed, then select a candidate transmission path that can be directly accessed and generate a scheduling instruction for the target charging pile to charge at the power corresponding to the outer diameter of the bubble. If the passability verification result indicates that compression is required and there is no impassable candidate transmission path, then bubble compression scheduling is executed. For the candidate transmission path, a scheduling instruction is generated to charge the target charging pile with the power corresponding to the inner diameter of the bubble. If the passability check results indicate that all candidate transmission paths are impassable, then cooperative scheduling is performed. Scheduling instructions and coordinated scheduling are used as scheduling schemes.

[0010] In a preferred embodiment, if the passability verification result indicates that all candidate transmission paths are impassable, the step of performing cooperative scheduling is as follows: For charging bubble requirements that cannot be passed through, the charging bubble requirement is divided into multiple sub-bubbles that are consecutive in time. Multiple sub-bubbles are re-input into the virtual pipeline model, and the dimensions are re-verified on all candidate transmission paths to generate updated passability verification results; Based on the update pass verification results, a corresponding scheduling scheme is generated.

[0011] This invention also provides an intelligent scheduling system based on a multi-pile charging module, comprising: The bubble generation module collects user charging data to generate charging requests and generates corresponding charging demand bubbles based on the charging requests. The pipeline construction module, connected to the bubble generation module, constructs a virtual pipeline model based on the physical connection relationship and capacity limitations of the charging facilities. The path planning module, connected to the pipeline construction module, matches the transmission path from the entrance to the target charging pile for the charging demand bubble in the virtual pipeline model and generates path matching results. The size verification module, connected to the path planning module, performs size verification based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path, and generates a passability verification result. The scheduling decision module is connected to the size verification module. Based on the pass / fail verification results, it performs the corresponding resource scheduling operation and generates a scheduling scheme.

[0012] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention abstracts charging demand into bubble objects with inner and outer diameters and dynamically maps the electrical topology of charging facilities into a virtual pipeline model, transforming the charging scheduling problem into the movement problem of bubbles in the pipeline network. Traditional scheduling methods are often based on simple first-come, first-served or fixed priority rules, which cannot perceive the micro-capacity status of the power grid in real time, resulting in the coexistence of local overload and resource idleness. This solution calculates the available width of each virtual pipeline in real time and performs precise matching and verification with the size of the bubble, realizing refined control of charging power. The system can actively guide the charging load to the path and time period with sufficient power grid capacity, just like guiding water flow, thereby significantly improving the average utilization rate of the charging pile cluster as a whole, effectively smoothing the peak and valley difference of the power grid load, and realizing refined collaborative scheduling of multi-pile charging load and active guidance of capacity bottlenecks. 2. This invention constructs a scheduling logic with flexible adaptability by designing a decision-making process that includes multi-state verification results and corresponding hierarchical response strategies. When a charging request cannot be directly satisfied due to immediate capacity limitations, the system does not simply accept or reject it, but can activate multiple flexible collaborative mechanisms, including bubble compression and time slicing. This layered and progressive response strategy enables the system to exhibit stronger adaptability and robustness when facing common constraints such as peak loads and local equipment limitations. It can accommodate more demands by flexibly adjusting non-critical parameters while prioritizing core charging needs. This helps to balance efficiency and fairness under complex operating conditions, improve service experience, and achieve a synergistic improvement in the reliability, fairness, and overall operational efficiency of charging services under strict physical constraints. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a flowchart of the method of the present invention.

[0015] Figure 2 This is a system block diagram of the present invention.

[0016] Figure 3 This is a logic block diagram of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 and Figure 3 As shown in this embodiment, an intelligent scheduling method based on a multi-pile charging module includes: S1. Collect user charging data to generate a charging request, and generate a corresponding charging demand bubble based on the charging request. S2. Construct a virtual pipeline model based on the physical connection relationship and capacity limitations of the charging facilities; S3. In the virtual pipeline model, match the transmission path from the entrance to the target charging pile for the charging demand bubble, and generate the path matching result; S4. Based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path, perform size verification and generate passability verification results. S5. Execute the corresponding resource scheduling operation based on the pass / fail verification result to generate a scheduling scheme; As described in steps S1-S5 above, existing multi-pile charging scheduling technologies can be mainly divided into two typical methods. One is a static rule-based method, such as directly assigning charging piles and power to vehicles according to the principles of first-come-first-served, fixed priority, or rotational allocation. The other is a centralized optimization model-based method, which usually establishes a mathematical model that includes constraints such as grid capacity and user demand, and uses linear programming or heuristic algorithms to solve for a theoretically optimal global scheduling scheme. However, existing technologies have certain limitations. Rule-based methods are too rigid and cannot perceive the real-time changes in capacity status and line coupling relationships in the power grid topology, which can easily lead to local overload and uneven resource utilization. Although centralized optimization methods consider global constraints, they are computationally complex and slow to respond, making it difficult to cope with continuously arriving dynamic requests and second-level fluctuations in power grid status. At the same time, both types of methods generally treat user charging demand as an unadjustable fixed parameter, lacking quantitative modeling and proactive utilization of the inherent elasticity of demand, such as the acceptable range of time and power. This results in a lack of flexibility in scheduling strategies when the system faces capacity bottlenecks, often resorting to inefficient methods such as denial of service or long queuing, and failing to achieve efficient and smooth utilization of overall resources through multi-pile coordination and minor adjustments on the demand side while ensuring the core needs of users. This invention abstracts charging demand into bubble objects with inner and outer diameters and dynamically maps the electrical topology of charging facilities into a virtual pipeline model, transforming the charging scheduling problem into the movement problem of bubbles in the pipeline network. Traditional scheduling methods are often based on simple first-come, first-served or fixed priority rules, which cannot perceive the micro-capacity status of the power grid in real time, resulting in the coexistence of local overload and resource idleness. This solution calculates the available width of each virtual pipeline segment in real time and performs precise matching and verification with the size of the bubble, realizing fine-grained control of charging power. The system can actively guide the charging load to the path and time period with sufficient power grid capacity, just like guiding water flow, thereby significantly improving the average utilization rate of the charging pile cluster as a whole, effectively smoothing the peak-valley difference of the power grid load, and realizing fine-grained collaborative scheduling of multi-pile charging load and active guidance of capacity bottlenecks. By designing a decision-making process that includes multi-state verification results and corresponding hierarchical response strategies, a scheduling logic with elastic adaptability is constructed. When a charging request cannot be directly satisfied due to immediate capacity limitations, the system does not simply accept or reject it, but can activate multiple flexible collaborative mechanisms, including bubble compression and time slicing. This layered and progressive response strategy enables the system to exhibit stronger adaptability and robustness when facing common constraints such as peak loads and local device limitations. Under the premise of prioritizing core charging needs, it can accommodate more demands by flexibly adjusting non-critical parameters, which helps to balance efficiency and fairness under complex operating conditions, improve service experience, and achieve a synergistic improvement in charging service reliability, fairness, and overall operational efficiency under strict physical constraints.

[0019] In one embodiment, step S1, which involves collecting user charging data to generate a charging request and generating a corresponding charging demand bubble based on the charging request, includes: S11. Collect user's charging data to generate a charging request. The charging request includes vehicle identification, target charging pile identification, current vehicle battery level, target battery level, maximum allowable charging power of the vehicle battery, and the charging mode selected by the user through the interactive interface. S12. Based on the charging application, determine the core demand parameters and elastic demand parameters of the charging demand bubble; S13. Core requirement parameters include the minimum required charging amount calculated based on the vehicle's current battery level and target battery level, and the latest charging completion deadline determined based on the charging mode. S14. The flexible demand parameters include the allowable charging completion time fluctuation range determined based on the charging mode and historical behavior data, and the acceptable charging power adjustment range determined based on the maximum allowable charging power of the vehicle battery and the charging mode. S15. Based on the determined core demand parameters and flexible demand parameters, generate charging demand bubbles, and abstract the core demand parameters as the inner diameter of the bubble, and the flexible demand parameters as the outer diameter of the bubble. As described in steps S11-S15 above, after generating a structured charging request based on the vehicle identifier, target charging pile identifier, current vehicle battery level, target battery level, maximum allowable charging power of the vehicle battery, and the charging mode selected by the user through the interactive interface, the system first calculates the minimum required charging amount based on the difference between the current vehicle battery level and the target battery level. Simultaneously, it maps the corresponding latest charging completion deadline based on the user-selected charging mode. These two parameters together constitute the core demand parameters of the charging demand bubble. Next, the system analyzes the user-selected charging mode and historical behavior data to calculate a reasonable range for allowable charging completion time fluctuations. Based on the maximum allowable charging power of the vehicle battery and the current charging mode, it determines an acceptable range for charging power adjustment. For example, in economy mode, the charging power can be continuously adjusted between 50% and 100% of the rated power, thus forming the elastic demand parameters of the charging demand bubble. Finally, the system abstracts the core demand parameters as the inner diameter of the charging demand bubble to represent the minimum power that must be guaranteed or the most urgent time constraint, while abstracting the elastic demand parameters as the outer diameter of the bubble to represent the maximum adjustable value. The system generates a charging demand bubble object with a clear elastic boundary by determining the power or time buffer range. The steps are as follows: First, establish a linear mapping relationship between power values ​​and geometric dimensions. Set a conversion factor for the unit radius corresponding to the base power. Convert the calculated minimum required charging power into the inner diameter value of the bubble. Simultaneously, convert the battery's maximum allowable charging power and the power adjustment upper limit determined by the charging mode into the outer diameter value of the bubble, forming a geometric model that characterizes the power demand range with radius. For time constraints, the system calculates the required average power by the difference between the charging cutoff time and the current time. This power value is also converted into the equivalent inner diameter in the time dimension according to the above mapping relationship. The allowable charging time fluctuation range determined based on historical behavior data corresponds to the equivalent outer diameter. Finally, the parameters of the power and time dimensions are merged into a unified spherical model. In terms of data structure implementation, the system creates a bubble object containing inner diameter attributes, outer diameter attributes, and current requested size attributes. The inner diameter attribute stores the fixed radius value corresponding to the core demand, the outer diameter attribute stores the maximum radius value corresponding to the elastic demand, and the current requested size attribute is initialized to the outer diameter value and can be dynamically adjusted during scheduling.

[0020] In one embodiment, step S2, which involves constructing a virtual network model based on the physical connectivity and capacity limitations of the charging facilities, includes: S21. Obtain electrical topology data and power distribution capacity configuration data of the charging facilities; S22. Electrical topology data includes the physical connection hierarchy and path relationships from the upstream power access point to each charging pile at the end. S23. Distribution capacity configuration data includes the rated safe current carrying capacity and power limit of each level of distribution line; S24. Based on the electrical topology data, abstract each physical electrical path with independent power distribution capacity configuration and create a virtual pipe object. S25. Associate the rated safe current carrying capacity and power limit from the power distribution capacity configuration data for each virtual pipe object, and abstract the rated safe current carrying capacity and power limit as the pipe's equivalent width. S26. Abstract and create each charging pile and each level of power distribution node that provides power to multiple charging piles as a network node object; S27. Based on the physical connection hierarchy and path relationships in the electrical topology data, connect all network node objects based on virtual pipe objects to generate a virtual pipe network model. As described in steps S21-S27 above, the system obtains complete electrical wiring diagrams and equipment parameter tables as electrical topology data and power distribution capacity configuration data by accessing the charging station's monitoring and data acquisition system or reading a pre-configured database. The electrical topology data clarifies all series and parallel connections from the upstream substation or grid access point, through various levels of distribution cabinets and cable branches, down to each charging pile. The power distribution capacity configuration data lists the rated safe current carrying capacity and corresponding maximum allowable power value for each cable segment, each switch, and each transformer. Based on the electrical topology data, the system analyzes each electrically independent physical path that carries power transmission functions, such as a complete line from a specific transformer output to a distribution cabinet and then connected to a group of charging piles, creating a uniquely identified virtual conduit object for it. The system matches and associates the corresponding rated safe current carrying capacity and power limit with the power distribution capacity configuration data for the virtual pipeline object. Using a preset conversion factor, these electrical capacity parameters are transformed into the equivalent width of the pipeline measured in abstract units, thus representing the upper limit of power that the virtual pipeline can stably transmit. Simultaneously, the system abstracts each physical entity in the topology, including each charging pile and each distribution box or transformer that distributes power to downstream equipment, into a network node object with a unique identifier and type, such as a charging pile node or a distribution node. Based on this, according to the connection relationships described by the electrical topology data, the system inserts the corresponding virtual pipeline object among all created network node objects in the actual physical connection order, thereby constructing a complete virtual pipeline network model that maps the real electrical connections of the charging facilities and includes the topological relationships between nodes and pipelines.

[0021] In one embodiment, step S3, which matches a transmission path from the inlet to the target charging pile for a charging demand bubble in the virtual pipeline model and generates a path matching result, includes: S31. Based on the target charging pile identifier in the charging demand bubble, locate the corresponding target network node in the virtual network model; S32. Determine the entry network node of the charging demand bubble. The entry network node is the logical node in the virtual pipeline model that corresponds to the upstream power supply that supplies power to the line where the target charging pile is located. S33. In the virtual pipeline model, with the ingress network node as the starting point and the target network node as the ending point, a topology search is performed based on the connection relationship established by the virtual pipeline object to retrieve all candidate transmission paths that are physically feasible, as the path matching result. As described in steps S31-S33 above, the system queries the unique network node identifier bound to the target charging pile carried by the charging demand bubble in the constructed virtual pipeline model, thereby accurately locating the target network node. Based on the predefined power supply relationship backtracking logic in the virtual pipeline model, the system traces the power source of the virtual pipeline object backward from the target network node until it finds a logical node directly connected to the upstream power grid or serving as the system's preset power supply starting point. This node is then identified as the entry network node of the current charging demand bubble. On this basis, the system uses the entry network node as the initial point and the target network node as the endpoint. Based on the bidirectional or unidirectional connection relationships between nodes established by the virtual pipeline object, it uses depth-first search or breadth-first search algorithms in graph theory to traverse all possible node-pipe connection sequences, eliminating invalid paths that form loops. Finally, it retrieves all complete ordered sequences of virtual pipeline objects that are physically connected from the starting point to the endpoint. These sequences are then used as all feasible candidate transmission paths for the charging demand bubble in the current virtual pipeline model, i.e., the path matching results.

[0022] In one embodiment, step S4, which performs size verification based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path to generate a passability verification result, includes: S41. The inner diameter value of the charging demand bubble is used as the power value requested at the current scheduling moment. S42. Perform size verification for each candidate transmission path in the path matching results; S43. Calculate the real-time available pipe width of each virtual pipe object in the candidate transmission path. The real-time available pipe width is calculated based on the equivalent pipe width of the virtual pipe object and the width that is currently occupied. S44. Sequentially determine whether the bubble size of the current request is less than or equal to the real-time available pipe width of each virtual pipe object in the candidate transmission path, and generate the determination result. S45. Based on the judgment results, generate a passability verification result for each candidate transmission path; S46, where the passability verification results include passable, require compression, and failable; As described in steps S41-S46 above, the system first converts the inner diameter of the charging demand bubble into the specific power value requested at the current scheduling moment using a predefined power-size mapping coefficient, and uses this value as the current requested size of the bubble. For each candidate transmission path retrieved in the path matching results, the system executes a size verification process one by one. This process first traverses each virtual pipe object contained in the candidate transmission path, queries the equivalent width attribute of the virtual pipe object, and obtains the total width currently occupied by other charging demand bubbles in real time. The equivalent width of the pipe is subtracted from the currently occupied width to accurately calculate the real-time available pipe width of the virtual pipe object. Subsequently, the system sets the current size of the bubble... The requested size is compared sequentially with the real-time available pipe width of each virtual pipe object in the current candidate transmission path. If the current requested size is less than or equal to the real-time available pipe width of all virtual pipe objects on the path, the path is recorded as passable. If the current requested size is greater than the real-time available pipe width of at least one virtual pipe object on the path, but the outer diameter of the charging demand bubble is greater than its inner diameter, the path is recorded as needing compression. If the current requested size is greater than the real-time available pipe width of at least one virtual pipe object on the path, and the outer diameter of the bubble is equal to its inner diameter, the path is recorded as impassable. Based on the above judgment results for each candidate transmission path, the system generates a passability verification result set containing three explicit states: passable, needing compression, and impassable.

[0023] In one embodiment, step S5, which generates the corresponding resource scheduling operation based on the pass / fail verification result, includes: S51. If the passability verification result shows that there is a candidate transmission path that can be directly accessed, then select a candidate transmission path that can be directly accessed and generate a scheduling instruction for the target charging pile to charge with the power corresponding to the outer diameter of the bubble. S52. If the passability verification result indicates that there is a conclusion that compression is required, and the conclusion is that there is no unpassable candidate transmission path, then bubble compression scheduling is executed. For the candidate transmission path, a scheduling instruction is generated to charge the target charging pile with the power corresponding to the inner diameter of the bubble. S53. If the passability check result indicates that all candidate transmission paths are impassable, then cooperative scheduling is performed. S54. Use scheduling instructions and coordinated scheduling as scheduling schemes; As described in steps S51-S54 above, the system first parses the passability verification result set. If at least one candidate transmission path is found to be passable, then according to a preset path optimization strategy, such as the shortest pipe hop count or the maximum average real-time available width, one of the passable candidate transmission paths is selected as the final transmission path. Then, the outer diameter value of the charging demand bubble is converted into a specific charging power value using predefined size and power inverse calculation coefficients, and a scheduling instruction containing the target charging pile identifier, the power value, and an immediate start charging command is generated. If the verification result shows that there are no passable paths, but at least one path needs to be compressed, the system performs bubble compression scheduling. Specifically, it selects one path from all paths that need compression according to a strategy, such as selecting the path with the shortest real-time available pipe width. The larger path ensures maximum operational space. Then, the compression upper limit is set based on the critical width value, and the lower limit is set based on the bubble inner diameter value. Within this range, a specific compressed power value is determined according to the preset optimization goal, such as maximizing charging efficiency, and a corresponding compression scheduling instruction is generated. If the verification result shows that all candidate transmission paths are unusable, the system initiates a collaborative scheduling process. This process may involve dividing the current charging demand bubble into multiple smaller power sub-bubbles according to the time dimension and resubmitting the scheduling, or actively coordinating other bubbles that are occupying critical pipeline resources to temporarily reduce their power occupation to free up capacity. Finally, a set of corresponding collaborative scheduling instructions is generated. The system integrates and formats any of the above-generated scheduling instructions and outputs a complete specific scheduling scheme that can be sent to the charging pile controller for execution.

[0024] In one embodiment, step S53, which involves performing cooperative scheduling if the passability verification result indicates that all candidate transmission paths are impassable, includes: S531. For a charging bubble demand that cannot be passed through, the charging bubble demand is divided into multiple sub-bubbles that are continuous in time. S532. Re-enter the multiple sub-bubbles into the virtual pipeline model, and re-verify the size of all candidate transmission paths to generate updated passability verification results. S533. Based on the update passability verification result, generate the corresponding scheduling scheme; As described in steps S531-S533 above, when all candidate transmission paths are determined to be impassable, the system initiates a collaborative scheduling process. First, based on the core demand parameters of the current charging demand bubble, especially the minimum required charging amount and the latest charging completion deadline, an intelligent segmentation algorithm based on historical load patterns is used to divide the total charging demand of the original bubble into several sub-bubbles arranged consecutively on the time axis with smaller power requirements. Each sub-bubble inherits the target charging pile identifier of the original bubble and is assigned an independent start and end time window. The steps involve first calling the historical database to analyze the power supply of the target charging pile. By analyzing the average load curve of the line on similar dates and during the same time periods in the past, the algorithm identifies continuously available low-load time windows. Based on the minimum required total charging energy determined by the original bubble inner diameter and the power elasticity range defined by the difference between the outer and inner diameters, the algorithm combines the duration of the identified low-load windows with the expected available capacity. The optimization objective is to maximize the utilization of each window's capacity while minimizing the total completion time. In practice, the algorithm prioritizes scheduling charging tasks within upcoming low-load windows. It determines the power value of each sub-bubble based on the expected available pipe width of that window, ensuring that this power value is not lower than the bubble's inner diameter. To ensure core requirements are met while maintaining feasibility, the power value is set no higher than the smaller of the expected available window width and the bubble's outer diameter. The duration of the sub-bubble is then calculated based on this power value and the required allocated electricity, thus determining its start and end times. If a low-load window cannot accommodate all the required electricity, the algorithm continues searching for the next available low-load window, repeating the process to create a second sub-bubble until all necessary charging is allocated. Finally, the algorithm generates a sequence of sub-bubble objects with determined power values ​​and time windows. Each sub-bubble inherits the target charging station identifier and core requirement parameters from the original bubble. The time windows are arranged continuously or with slight intervals on the timeline to ensure the original latest charging completion deadline. Subsequently, the system resubmits these newly generated sub-bubbles as entirely new charging requests to the virtual network model. This involves re-executing the complete process for each sub-bubble, starting from locating the target and ingress network nodes. This includes performing a topology search based on virtual pipeline connections to retrieve feasible candidate transmission paths, and calculating the real-time available pipeline width for each virtual pipeline object on these paths. This width is derived by subtracting the width currently occupied by other bubbles and previously arranged sub-bubbles from the equivalent pipeline width. The system then compares and verifies the power dimension of each sub-bubble, represented by its inner diameter, with the real-time available pipeline width of its respective candidate paths, thereby generating an updated passability verification result for each sub-bubble.Finally, based on the updated verification results of all sub-bubbles, the system generates corresponding scheduling instructions for sub-bubbles whose status is either "straight-through" or "requires compression." These instructions are then arranged and combined according to the time window sequence of the sub-bubbles to form a time-continuous collaborative scheduling scheme that includes multiple stages of charging tasks and fully covers the original core requirements. Furthermore, for the power supply in different time zones, the system executes a parallel process of power supply status monitoring and virtual pipeline dynamic update. This process detects increases in total power supply by collecting data such as real-time upper-level grid dispatch instructions, distributed generation output within the station, or discharge power of energy storage systems. Once the power supply increase is confirmed, the system immediately calculates the additional capacity that each virtual pipe object directly affected by the increase can carry based on the preset electrical topology and capacity allocation model, and dynamically increases the pipe equivalent width attribute value of these virtual pipe objects proportionally accordingly. The system triggers a targeted re-verification, not only for newly generated sub-bubbles, but also for the original bubbles currently waiting in the queue with the status of "unpassable". The system recalculates the real-time available pipe width of the relevant path using the updated larger pipe equivalent width, and compares the size of the charging demand bubble (the size corresponding to its allocated power for sub-charging demand bubbles, and its inner diameter value for original charging demand bubbles) with the new real-time available pipe width. For bubbles that change from "unpassable" to "requires compression" or "can be straight through" due to this update, the system will interrupt the original segmentation scheduling thread and instead regenerate better scheduling instructions for these bubbles, such as directly allocating power or performing a smaller degree of compression, thereby prioritizing the use of the newly released pipe capacity.

[0025] Example 2, please refer to Figure 2 As shown in this embodiment, an intelligent scheduling system based on a multi-pile charging module includes: The bubble generation module collects user charging data to generate charging requests and generates corresponding charging demand bubbles based on the charging requests. The pipeline construction module, connected to the bubble generation module, constructs a virtual pipeline model based on the physical connection relationship and capacity limitations of the charging facilities. The path planning module, connected to the pipeline construction module, matches the transmission path from the entrance to the target charging pile for the charging demand bubble in the virtual pipeline model and generates path matching results. The size verification module, connected to the path planning module, performs size verification based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path, and generates a passability verification result. The scheduling decision module is connected to the size verification module. Based on the pass / fail verification results, it performs the corresponding resource scheduling operation and generates a scheduling scheme.

[0026] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart scheduling method based on multi-pile charging modules, characterized in that, Collect user charging data to generate charging requests, and generate corresponding charging demand bubbles based on the charging requests; A virtual network model is constructed based on the physical connections and capacity limitations of the charging facilities; In the virtual pipeline model, a transmission path from the inlet to the target charging pile is matched for the charging demand bubble, and a path matching result is generated; The size of the charging demand bubble is verified based on the real-time available width of the virtual pipe on the transmission path, and a passability verification result is generated. Based on the pass / fail verification result, perform the corresponding resource scheduling operation and generate a scheduling scheme.

2. The intelligent scheduling method based on a multi-charging pile module according to claim 1, characterized in that, The steps for collecting user charging data to generate charging requests and generating corresponding charging demand bubbles based on charging requests are as follows: The system collects user charging data to generate a charging request. The charging request includes the vehicle identifier, the target charging station identifier, the vehicle's current battery level, the target battery level, the maximum allowable charging power of the vehicle's battery, and the charging mode selected by the user through the interactive interface. Based on the charging application, determine the core demand parameters and elastic demand parameters of the charging demand bubble; The core requirements parameters include the minimum required charging amount calculated based on the vehicle's current battery level and the target battery level, and the latest charging completion deadline determined based on the charging mode. The flexible demand parameters include the allowable charging completion time fluctuation range determined based on charging mode and historical behavior data, and the acceptable charging power adjustment range determined based on the vehicle battery's maximum allowable charging power and charging mode. Based on the determined core demand parameters and flexible demand parameters, a charging demand bubble is generated. The core demand parameters are abstracted as the inner diameter of the bubble, and the flexible demand parameters are abstracted as the outer diameter of the bubble.

3. The intelligent scheduling method based on a multi-charging pile module according to claim 1, characterized in that, The steps for constructing a virtual network model based on the physical connection relationships and capacity limitations of charging facilities are as follows: Obtain electrical topology data and power distribution capacity configuration data of charging facilities; Electrical topology data includes the physical connection hierarchy and path relationships from the upstream power access point to each charging pile at the end; The power distribution capacity configuration data includes the rated safe current carrying capacity and power limits of each level of power distribution lines; Based on the electrical topology data, each physical electrical path with an independent power distribution capacity configuration is abstracted and created as a virtual pipe object; Associate each virtual pipe object with the rated safe current carrying capacity and power limit from the power distribution capacity configuration data, and abstract the rated safe current carrying capacity and power limit into the pipe's equivalent width; Abstract and create each charging pile and each level of power distribution node that provides power to multiple charging piles as a network node object; Based on the physical connection hierarchy and path relationships in the electrical topology data, all network node objects are connected using virtual pipe objects to generate a virtual pipe network model.

4. The intelligent scheduling method based on a multi-charging pile module according to claim 1, characterized in that, In the virtual pipeline model, the steps for matching the transmission path from the inlet to the target charging pile for the charging demand bubble and generating the path matching result are as follows: Based on the target charging pile identifier in the charging demand bubble, locate the corresponding target network node in the virtual network model; Determine the entry network node of the charging demand bubble. The entry network node is the logical node in the virtual pipeline model that corresponds to the upstream power supply that supplies power to the line where the target charging pile is located. In the virtual pipeline model, starting from the ingress network node and ending at the target network node, a topology search is performed based on the connection relationship established by the virtual pipeline object to retrieve all candidate transmission paths that are physically feasible, which are then used as path matching results.

5. The intelligent scheduling method based on a multi-pile charging module according to claim 1, characterized in that, The step of performing size verification based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path to generate a passability verification result is as follows: The inner diameter of the charging demand bubble is used as the power value requested at the current scheduling moment. For each candidate transmission path in the path matching results, perform size verification; Calculate the real-time available pipe width for each virtual pipe object in the candidate transmission path. The real-time available pipe width is calculated based on the equivalent pipe width of the virtual pipe object and the currently occupied width. The system sequentially determines whether the bubble size of the current request is less than or equal to the real-time available pipe width of each virtual pipe object in the candidate transmission path, and generates a judgment result. Based on the judgment results, a passability verification result is generated for each candidate transmission path; The passability verification results include passable, requires compression, and failable.

6. The intelligent scheduling method based on a multi-charging pile module according to claim 1, characterized in that, The steps for generating the corresponding resource scheduling operation based on the pass / fail verification result are as follows: If the passability verification result shows that there is a candidate transmission path that can be directly accessed, then select a candidate transmission path that can be directly accessed and generate a scheduling instruction for the target charging pile to charge at the power corresponding to the outer diameter of the bubble. If the passability verification result indicates that compression is required and there is no impassable candidate transmission path, then bubble compression scheduling is executed. For the candidate transmission path, a scheduling instruction is generated to charge the target charging pile with the power corresponding to the inner diameter of the bubble. If the passability check results indicate that all candidate transmission paths are impassable, then cooperative scheduling is performed. Scheduling instructions and coordinated scheduling are used as scheduling schemes.

7. The intelligent scheduling method based on a multi-charging pile module according to claim 6, characterized in that, If the passability verification result indicates that all candidate transmission paths are impassable, then the steps for performing cooperative scheduling are as follows: For charging bubble requirements that cannot be passed through, the charging bubble requirement is divided into multiple sub-bubbles that are consecutive in time. Multiple sub-bubbles are re-input into the virtual pipeline model, and the dimensions are re-verified on all candidate transmission paths to generate updated passability verification results; Based on the update pass verification results, a corresponding scheduling scheme is generated.

8. An intelligent scheduling system based on a multi-charging pile module, used to implement the intelligent scheduling method based on a multi-charging pile module as described in any one of claims 1-7, characterized in that, The bubble generation module collects user charging data to generate charging requests and generates corresponding charging demand bubbles based on the charging requests. The pipeline construction module, connected to the bubble generation module, constructs a virtual pipeline model based on the physical connection relationship and capacity limitations of the charging facilities. The path planning module, connected to the pipeline construction module, matches the transmission path from the entrance to the target charging pile for the charging demand bubble in the virtual pipeline model and generates path matching results. The size verification module, connected to the path planning module, performs size verification based on the size of the charging demand bubble and the real-time available width of the virtual pipe on the transmission path, and generates a passability verification result. The scheduling decision module is connected to the size verification module. Based on the pass / fail verification results, it performs the corresponding resource scheduling operation and generates a scheduling scheme.

Images