Charging pile distribution system based on dynamic load balancing

Abstract

The invention discloses a charging pile distribution system based on dynamic load balancing, and relates to the technical field of intelligent scheduling of new energy automobile charging facilities. Updating the dynamic load characterization information to a central database of the charging pile distribution system; generating a charging pile distribution scheme; and updating the charging service scheduling state. According to the method, the problem that pile position idling and local overload coexist due to traditional static distribution is effectively avoided, and the utilization rate of charging facilities and the operation stability of a power grid are improved; the robustness and service continuity of the system in a sudden high-concurrency scene are enhanced, so that more efficient, reliable and safe charging experience is provided for users, and high-quality development of related infrastructures of new energy automobiles is powerfully supported.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology for new energy vehicle charging facilities, and in particular to a charging pile allocation system based on dynamic load balancing. Background Technology

[0002] With the rapid development of the new energy vehicle industry and the continuous increase in the number of new energy vehicles, the scale and service capacity of charging infrastructure, as a key link supporting the development of the industry, have increasingly become the focus of social attention. The demand for efficient, intelligent and reliable charging services in scenarios such as urban public charging stations, residential areas and commercial complexes is constantly growing. Users also have higher and higher requirements for charging convenience, waiting time and grid load stability. Against this background, how to scientifically allocate limited charging pile resources and improve the overall operating efficiency of the charging network has become a core issue in promoting the high-quality development of new energy vehicle-related facilities.

[0003] However, existing charging pile allocation systems generally suffer from static allocation and a lack of real-time response capabilities. Most systems simply schedule charging piles based on their idle status, failing to comprehensively consider dynamic factors such as grid load, user queuing, charging power demand, and regional peak electricity consumption. This results in some charging piles operating under overload while others remain idle, not only wasting resources but also potentially triggering localized grid overload risks. Furthermore, traditional systems struggle to cope with sudden surges in charging demand during holidays or extreme weather, lacking effective load balancing mechanisms, which severely impacts user experience and the stability of the charging network. Summary of the Invention

[0004] In view of the problems existing in the current charging pile allocation system based on dynamic load balancing, this invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is that existing charging pile allocation systems lack comprehensive perception and real-time response to dynamic factors such as grid load, user queuing, and vehicle demand, resulting in uneven resource allocation, local overload, poor user experience, and grid safety risks.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a charging pile allocation system based on dynamic load balancing, which includes a data acquisition module for acquiring multi-dimensional dynamic load sensing data of the charging pile. The load status assessment module is used to quantitatively assess the comprehensive load status of the charging pile and the power supply area to which the charging pile belongs based on the multi-dimensional dynamic load perception data obtained by the data acquisition module, generate dynamic load characterization information, and update the dynamic load characterization information to the central database of the charging pile distribution system. The dynamic scheduling decision module is used to generate a charging pile allocation scheme based on the dynamic load characterization information output by the load status assessment module and combined with a multi-objective optimization strategy. The charging pile control execution module is used to receive the charging pile allocation scheme issued by the dynamic scheduling decision module, send the charging pile access control command to the target charging pile, update the charging service scheduling status, introduce an abnormal response mechanism, and detect charging service faults in the charging service scheduling status.

[0007] As a preferred embodiment of the charging pile allocation system based on dynamic load balancing described in this invention, the data acquisition module includes a charging pile operation status submodule, a vehicle charging demand submodule, a user queuing information submodule, and a regional power grid load submodule. The charging pile operation status submodule is used to monitor and collect the operating characteristic parameters of each charging pile body, and obtain the operating load characteristics of the charging pile body. The vehicle charging demand submodule is used to obtain vehicle charging task characteristic parameters when a vehicle is connected or scheduled for charging. The user queuing information submodule is used to record and update the waiting queue information corresponding to each charging pile; The regional power grid load submodule is used to obtain the regional power grid carrying capacity characteristic parameters of the power supply area where the charging pile is located, and to assess the power grid operation safety margin of the external power grid for charging behavior.

[0008] As a preferred embodiment of the charging pile allocation system based on dynamic load balancing described in this invention, the load status assessment module includes a charging pile individual load assessment submodule, a regional aggregated load assessment submodule, a multi-dimensional fusion index calculation submodule, and a dynamic characterization information update submodule. The charging pile individual load assessment submodule is used to normalize and model the current single-pile comprehensive service load factor of each charging pile based on the charging pile body operation characteristic parameters and vehicle charging task characteristic parameters output by the data acquisition module, and generate a single-pile real-time load score. The regional aggregated load assessment submodule is used to combine the waiting queue information provided by the user queuing information submodule with the regional power grid carrying capacity characteristic parameters obtained by the regional power grid load submodule to perform coupled analysis on the overall service pressure and power grid operation safety margin of the power supply area to which the charging pile belongs, and calculate the regional comprehensive load index. The multidimensional fusion index calculation submodule is used to weight and fuse the real-time load score of a single charging pile with the regional comprehensive load index, introduce dynamic scheduling and adjustment parameters, and construct unified dynamic load characterization information. This unified dynamic load characterization information refers to the comprehensive load status of each charging pile and power supply area at the current moment in the form of structured data. The dynamic characterization information update submodule is used to write the unified dynamic load characterization information generated by the multi-dimensional fusion index calculation submodule into the central database of the charging pile allocation system, and synchronously maintain the load status data index information of the unified dynamic load characterization information.

[0009] As a preferred embodiment of the charging pile allocation system based on dynamic load balancing described in this invention, the dynamic scheduling decision module includes a load state analysis submodule, a multi-objective optimization modeling submodule, a feasible solution generation submodule, and an allocation scheme output submodule. The load status analysis submodule is used to read the unified dynamic load characterization information generated by the load status assessment module from the central database, extract the schedulability characteristics of all current charging piles and power supply areas, and form a scheduling input dataset. The multi-objective optimization modeling submodule is used to construct a multi-objective optimization decision rule set based on the scheduling input dataset, and to embed the dynamic scheduling adjustment parameters as constraint weight factors into the multi-objective optimization decision rule set. The feasible solution generation submodule is used to solve the multi-objective optimization decision rule set and generate feasible charging pile allocation candidate solutions; The allocation scheme output submodule is used to decapsulate feasible charging pile allocation candidates into a charging pile allocation scheme, push it to the charging pile control execution module, and record it.

[0010] As a preferred embodiment of the charging pile allocation system based on dynamic load balancing described in this invention, the charging pile control execution module includes an allocation scheme parsing submodule, an access control command issuance submodule, a scheduling status synchronization submodule, and an anomaly monitoring and response submodule. The allocation scheme parsing submodule is used to receive the charging pile allocation scheme pushed by the dynamic scheduling decision module, parse the charging pile allocation scheme, verify the consistency between the charging pile allocation scheme and the running context of the charging pile allocation system based on dynamic load balancing, and generate a local control instruction set. The access control command sending submodule is used to send a charging pile access control command to the target charging pile according to the local control command set, and to start or prepare the charging service process. The scheduling status synchronization submodule is used to update the charging service scheduling status in the central database after the charging pile access control command is issued. The anomaly monitoring and response submodule is used to continuously monitor the running data flow under the charging service scheduling status. When a charging service failure event is detected, the anomaly response mechanism is triggered to generate a fault alarm log and notify the dynamic scheduling decision module to start the rescheduling process.

[0011] As a preferred embodiment of the charging pile allocation system based on dynamic load balancing described in this invention, the calculation of the regional comprehensive load index includes the following formula:

[0012] in, This represents the regional comprehensive load index. This indicates the current total number of users in the queue. This indicates the maximum tolerable number of people in the queue. This indicates the current total charging power. Indicates the remaining power supply capacity. This represents the measured value of the voltage stability index. This represents the standard voltage stability reference value. , , This represents the normalized weighting coefficient.

[0013] As a preferred embodiment of the charging pile allocation system based on dynamic load balancing described in this invention, the step of solving the multi-objective optimization decision rule set includes calculating the adaptation score of each allocatable charging pile using a weighted comprehensive scoring formula, and selecting several solutions with high scores as feasible candidate solutions for charging pile allocation. The weighted comprehensive scoring formula is as follows:

[0014] in, This represents the overall compatibility score of charging pile j. , , Indicates the weighting coefficient. This represents the real-time load score of charging pile j. This represents the regional comprehensive load index of the power supply area r to which charging pile j belongs. This represents the current number of users queuing for charging station j.

[0015] Secondly, embodiments of the present invention provide a charging pile allocation method based on dynamic load balancing, which includes: collecting multi-dimensional dynamic load sensing data of charging piles; Based on the multi-dimensional dynamic load perception data obtained by the data acquisition module, the comprehensive load status of the charging pile and the power supply area to which the charging pile belongs is quantitatively evaluated, and dynamic load characterization information is generated and updated to the central database of the charging pile distribution system. Based on the dynamic load characterization information output by the load status assessment module, and combined with a multi-objective optimization strategy, a charging pile allocation scheme is generated. It receives the charging pile allocation plan issued by the dynamic scheduling decision module, sends the charging pile access control command to the target charging pile, updates the charging service scheduling status, introduces an abnormal response mechanism, and detects charging service faults in the charging service scheduling status.

[0016] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the steps of the above-described charging pile allocation system based on dynamic load balancing.

[0017] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any of the steps of the above-described charging pile allocation system based on dynamic load balancing.

[0018] The beneficial effects of this invention are as follows: By constructing a charging pile allocation system based on dynamic load balancing, this invention achieves refined, real-time, and intelligent scheduling of charging pile resources. The system integrates multi-dimensional dynamic data such as equipment operating status, vehicle charging demand, user queuing status, and regional power grid load to quantitatively evaluate the comprehensive load status of individual charging piles and the region. Based on a multi-objective optimization strategy, it generates an allocation scheme that takes into account user waiting time, load balance, and power grid safety margin. This technology effectively avoids the problems of idle charging piles and local overload caused by traditional static allocation, significantly improving the utilization rate of charging facilities and the stability of power grid operation. At the same time, through an anomaly response mechanism and dynamic rescheduling capability, it enhances the robustness and service continuity of the system under sudden high-concurrency scenarios, thereby providing users with a more efficient, reliable, and safe charging experience and strongly supporting the high-quality development of new energy vehicle-related infrastructure. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of a charging pile allocation system based on dynamic load balancing, provided as an embodiment of the present invention.

[0020] Figure 2 This is a flowchart of a charging pile allocation system based on dynamic load balancing, provided as an embodiment of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0025] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0026] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0027] Example

[0028] Reference Figure 1 and Figure 2This is the first embodiment of the present invention, which provides a charging pile allocation system based on dynamic load balancing, including: S1: Data acquisition module, used to collect multi-dimensional dynamic load sensing data of charging piles.

[0029] The data acquisition module includes a charging pile operation status submodule, a vehicle charging demand submodule, a user queuing information submodule, and a regional power grid load submodule. The charging pile operation status submodule is used to monitor and collect the operating characteristic parameters of each charging pile body, and obtain the operating load characteristics of the charging pile body. The vehicle charging demand submodule is used to obtain the characteristic parameters of the vehicle charging task when the vehicle is connected or scheduled for charging. The user queue information submodule is used to record and update the waiting queue information corresponding to each charging pile. The regional power grid load submodule is used to obtain the regional power grid carrying capacity characteristic parameters of the power supply area where the charging pile is located, and to assess the power grid operation safety margin of the external power grid for charging behavior.

[0030] Furthermore, the data acquisition module consists of four collaborative sub-modules, comprehensively sensing the real-time operating status of the charging system from four dimensions: equipment, vehicles, users, and the power grid. Specifically, the charging pile operation status sub-module continuously monitors the operational status of each charging pile, including whether it is idle, charging, or faulty, its current output power, connection status, and historical usage frequency, forming operational characteristic parameters reflecting its own load capacity and health status; the vehicle charging demand sub-module instantly acquires information such as the vehicle's remaining battery power, target charging amount, maximum allowable charging power, and estimated charging time when a vehicle physically connects to a charging pile or reserves a charging service through the platform, constructing complete single charging task characteristic parameters; and the user queuing information sub-module dynamically maintains the waiting queue corresponding to each charging pile, recording the waiting status of each charging pile. The number of users queuing, arrival order, reservation priority, and estimated waiting time are used to quantify the spatiotemporal distribution pressure of service requests. The regional power grid load submodule connects to the distribution network monitoring system or local smart meters to collect data in real time on the total power load, peak and valley time period identifiers, voltage stability level, and remaining available power capacity in the power supply area where the charging pile is located. This data forms regional power grid carrying capacity characteristic parameters, which are then used to assess the safe acceptance capacity of the external power grid for current and expected charging behavior. The above four submodules collect data synchronously and aggregate them in a unified manner to form the multi-dimensional dynamic load perception data foundation required to support subsequent dynamic load assessment and intelligent scheduling decisions.

[0031] Furthermore, the data acquisition module, through four highly collaborative sub-modules, constructs a real-time perception system covering all elements of equipment, vehicles, users, and the power grid, ensuring that the system has comprehensive and accurate insights into the dynamic changes of the charging scenario. Specifically, the charging pile operation status sub-module not only identifies whether the charging pile is currently idle, charging, or out of service, but also continuously tracks details such as its output power fluctuations, the stability of the charging gun connection, and historical usage frequency, thereby accurately characterizing the current availability and load capacity of the equipment; the vehicle charging demand sub-module, upon receiving a charging request from a user, whether through plug-and-charge or online reservation, captures the vehicle's core power needs, including the current remaining battery power, the desired charging level, the vehicle's maximum allowable charging power, and the calculated service duration, forming a quantitative description of the resource consumption intensity of a single charging behavior. The user queuing information submodule synchronously maintains the virtual or physical waiting queue in front of each charging pile, accurately recording the number of queuing users, the order in which they entered the queue, and whether they enjoy priority service privileges, such as VIP or emergency vehicles. Based on this, it estimates the expected waiting time for each user to reflect the cumulative degree of local service pressure. Meanwhile, the regional power grid load submodule obtains key operating indicators from the power system side, including the current overall electricity load level of the area, whether it is during peak or off-peak hours, whether the voltage is stable within a safe range, and how much additional charging power the power grid can safely support, thereby determining the external power supply environment's capacity limit for new charging tasks. These four submodules sample synchronously in time, are precisely aligned spatially by charging pile location and region, and complement each other logically, ultimately fusing to generate a unified and semantically complete set of multi-dimensional dynamic load perception data, providing a solid and reliable data foundation for subsequent load assessment, optimized scheduling, and fault response.

[0032] S2: Load status assessment module, which is used to quantitatively assess the comprehensive load status of charging piles and the power supply area to which the charging piles belong based on the multi-dimensional dynamic load perception data obtained by the data acquisition module, generate dynamic load characterization information, and update the dynamic load characterization information to the central database of the charging pile distribution system.

[0033] The load status assessment module includes a charging pile individual load assessment submodule, a regional aggregated load assessment submodule, a multi-dimensional fusion index calculation submodule, and a dynamic characterization information update submodule. The charging pile individual load assessment submodule is used to normalize and model the current single-pile comprehensive service load factor of each charging pile based on the charging pile's own operating characteristic parameters and vehicle charging task characteristic parameters output by the data acquisition module, and generate a single-pile real-time load score. The regional aggregated load assessment submodule combines the waiting queue information provided by the user queuing information submodule with the regional power grid carrying capacity characteristic parameters obtained by the regional power grid load submodule to perform coupled analysis on the overall service pressure and power grid operation safety margin of the power supply area to which the charging pile belongs, and calculates the regional comprehensive load index. The multidimensional fusion index calculation submodule is used to weight and fuse the real-time load score of a single charging pile with the regional comprehensive load index, introduce dynamic scheduling and adjustment parameters, and construct unified dynamic load characterization information. This unified dynamic load characterization information refers to the comprehensive load status of each charging pile and power supply area at the current moment in the form of structured data. The dynamic characterization information update submodule is used to write the unified dynamic load characterization information generated by the multi-dimensional fusion index calculation submodule into the central database of the charging pile allocation system, and synchronously maintain the load status data index information of the unified dynamic load characterization information.

[0034] Furthermore, the load status assessment module, through four closely interconnected sub-modules, constructs a multi-level, dynamic load assessment mechanism, ranging from individual charging piles to the power supply area, and from equipment status to grid security. First, the individual charging pile load assessment sub-module, based on the charging pile's operational characteristics (such as working status, output power, and fault information) and vehicle charging task characteristics (such as required power and charging duration) provided by the data acquisition module, comprehensively quantifies the service pressure currently borne by each charging pile. Through normalization, a standardized real-time load score for each charging pile is generated, which directly reflects the resource occupancy intensity and service burden of the pile at the current moment. Second, the regional aggregated load assessment sub-module, from a higher dimension, integrates waiting queue data from each charging pile provided by the user queuing information sub-module (such as the number of people queuing and priority distribution) and grid carrying capacity characteristics collected by the regional grid load sub-module (such as total load, remaining capacity, and voltage stability). It jointly analyzes the overall service congestion status and grid security margin of the power supply area to which the charging pile belongs, deriving a regional-level comprehensive load assessment that characterizes the overall operational tension of the region. The system calculates a combined load index. Based on this, the multi-dimensional fusion index calculation submodule organically integrates the aforementioned single-pile load score with the regional load index, and introduces dynamic scheduling adjustment parameters, such as time decay factors and user priority weights, to adaptively weight scheduling objectives under different scenarios. This ultimately constructs unified dynamic load characterization information. This information, in structured data form, accurately describes the comprehensive load status of each charging pile and its corresponding power supply area at the current moment, encompassing both micro-level individual details and macro-level regional constraints. Finally, the dynamic characterization information update submodule is responsible for writing this characterization information into the system's central database in real time and simultaneously maintaining its corresponding load status data index information, including timestamps, version numbers, and charging pile identifiers. This ensures that all modules in the system always make subsequent decisions and controls based on the latest, consistent, and traceable load status data, thus laying a solid foundation for achieving efficient, safe, and balanced dynamic scheduling.

[0035] Furthermore, the load status assessment module constructs a dynamic load assessment system that evolves in real time, from point to surface, from micro to macro, through four logically progressive and data-connected sub-modules. Specifically, the individual charging pile load assessment submodule first focuses on each charging pile itself, comprehensively considering its operational characteristics such as whether it is currently charging, its output power, connection stability, and the presence of fault alarms, as well as task characteristics such as the battery demand of the vehicles being served, the maximum allowable charging power, and the expected occupancy time. This allows for a refined modeling of the comprehensive service pressure borne by a single charging pile at the current moment, and transforms it into a standardized real-time load score for each pile using a unified normalization method. This score not only reflects whether the equipment is idle but also the depth and persistence of its resource occupancy. Building on this, the regional aggregated load assessment submodule moves beyond the single-pile perspective, expanding its view to the entire power supply area. On one hand, it integrates the waiting queue details for each pile provided by the user queuing information submodule, including the actual number of people in the queue, the order of user arrival, and whether there are high-priority reservations. On the other hand, it combines key grid-side indicators obtained by the regional power grid load submodule, such as the current total regional power load, remaining dispatchable power capacity, voltage fluctuations, and whether it is during peak electricity consumption periods. This allows for a coupled analysis of the overall service supply and demand imbalance and grid carrying capacity within the region, generating an accurate assessment. The system accurately characterizes the regional comprehensive load index to determine whether the area is overheated or underloaded. Subsequently, the multi-dimensional fusion index calculation submodule, as a crucial link, weights and fuses the aforementioned single-pile scores with the regional index, introducing dynamic scheduling adjustment parameters. These include time decay factors that automatically adjust weights based on time periods, or priority correction coefficients set according to user type. This allows the evaluation results to adapt to different operating scenarios such as holidays, extreme weather, or peak hours. Ultimately, it outputs a unified dynamic load characterization information with a clear structure and complete semantics. This information assigns a comprehensive, real-time, and comparable load status label to each charging pile and its associated region. Finally, the dynamic characterization information update submodule ensures that this critical status data is written to the system's central database in a timely and reliable manner, and simultaneously maintains its corresponding index information, including timestamps to ensure timeliness, version numbers to support status backtracking, and unique identifiers precisely linked to specific charging piles. This ensures that subsequent scheduling decisions, control execution, and anomaly responses are always based on a consistent, accurate, up-to-date, and traceable load understanding, truly achieving a closed-loop logic from perception to understanding to decision support.

[0036] Preferably, the dynamic scheduling adjustment parameters are a set of configurable coefficients used to dynamically adjust the weights of load assessment and scheduling strategies. Their values ​​are typically in the range of [0,1]. The specific values ​​are automatically set by the system rule engine or adaptive algorithm based on the real-time operating scenario, such as peak and off-peak electricity consumption periods, holidays, extreme weather, or user priority levels. The adjustment rules follow the principle of enhancing grid security and queuing fairness in high-pressure scenarios and focusing on improving service efficiency and resource utilization in low-pressure scenarios, ensuring that the scheduling strategy maintains optimal balance under different operating conditions.

[0037] S3: Dynamic scheduling decision module, used to generate charging pile allocation scheme based on the dynamic load characterization information output by the load status assessment module and combined with multi-objective optimization strategy.

[0038] The dynamic scheduling decision module includes a load status analysis submodule, a multi-objective optimization modeling submodule, a feasible solution generation submodule, and an allocation scheme output submodule. The load status analysis submodule is used to read the unified dynamic load characterization information generated by the load status assessment module from the central database, extract the schedulability characteristics of all current charging piles and power supply areas, and form a scheduling input dataset. The multi-objective optimization modeling submodule is used to construct a set of multi-objective optimization decision rules based on the scheduling input dataset, and to embed dynamic scheduling adjustment parameters as constraint weight factors into the set of multi-objective optimization decision rules. The feasible solution generation submodule is used to solve the multi-objective optimization decision rule set and generate feasible candidate solutions for charging pile allocation; The allocation scheme output submodule is used to decapsulate feasible charging pile allocation candidates into charging pile allocation schemes, push them to the charging pile control execution module, and record them.

[0039] Furthermore, the dynamic scheduling decision-making module constructs a complete scheduling logic chain from state understanding to intelligent decision-making and solution output through four orderly and collaborative sub-modules. First, the load state analysis sub-module, as the starting point of the scheduling process, actively reads the unified dynamic load characterization information generated by the load state assessment module from the central database. It accurately extracts the current availability status of each charging pile, such as whether it is serviceable, its load intensity, and the overall operational constraints of its power supply area, such as grid margin and queuing pressure. This multi-dimensional heterogeneous data is then structured and organized into a standardized scheduling input dataset, providing a clear and consistent decision-making basis for subsequent optimization. Next, the multi-objective optimization modeling sub-module, based on this dataset, constructs a multi-objective optimization decision rule set that integrates multiple business objectives. This rule set explicitly incorporates core objectives such as reducing average user waiting time, balancing the load intensity of each charging pile, and ensuring grid safety margin into a unified framework. Dynamic scheduling adjustment parameters, such as time period weights, user priorities, and regional risk coefficients, are embedded as configurable constraint weight factors, enabling the scheduling strategy to adjust according to actual conditions. The system flexibly adjusts the target focus based on the operational scenario. Building upon this, the feasible solution generation submodule efficiently solves the aforementioned decision rule set, employing highly adaptable heuristic or intelligent search algorithms. Under the premise of meeting grid capacity limitations, equipment physical constraints, and user reservation conditions, it quickly generates a set of technically feasible and goal-coordinated charging pile allocation candidate solutions, ensuring that the solution neither overloads nor wastes resources. Finally, the allocation scheme output submodule selects the optimal solution from the candidate solutions based on preset optimization criteria, such as the highest comprehensive score or lowest risk. This optimal solution is then encapsulated into a structured charging pile allocation scheme, containing key information such as target pile locations, user mapping relationships, and execution instructions. This scheme is pushed to the charging pile control execution module in real time to trigger subsequent operations. Simultaneously, the system records the scheme's generation time, associated requests, and decision context, providing complete log support for scheduling traceability, performance analysis, and anomaly rollback. This achieves efficient and reliable transformation from perceived data to executable scheduling instructions.

[0040] S4: Charging pile control execution module, used to receive the charging pile allocation scheme issued by the dynamic scheduling decision module, send the charging pile access control command to the target charging pile, update the charging service scheduling status, introduce an abnormal response mechanism, and detect charging service faults in the charging service scheduling status.

[0041] The charging pile control execution module includes a distribution scheme parsing submodule, an access control command issuance submodule, a scheduling status synchronization submodule, and an anomaly monitoring and response submodule. The allocation scheme parsing submodule is used to receive the charging pile allocation scheme pushed by the dynamic scheduling decision module, parse the charging pile allocation scheme, verify the consistency between the charging pile allocation scheme and the running context of the charging pile allocation system based on dynamic load balancing, and generate a local control instruction set. The access control command issuing submodule is used to send charging pile access control commands to the target charging pile according to the local control command set, and to start or prepare the charging service process. The scheduling status synchronization submodule is used to update the charging service scheduling status in the central database after the charging pile access control command is issued. The anomaly monitoring and response submodule is used to continuously monitor the running data flow under the charging service scheduling status. When a charging service failure event is detected, the anomaly response mechanism is triggered to generate a fault alarm log and notify the dynamic scheduling decision module to start the rescheduling process.

[0042] Furthermore, the charging pile control execution module consists of four closely linked sub-modules, forming a complete control execution chain from instruction reception, execution issuance, status synchronization to anomaly closure. First, the allocation scheme parsing sub-module, as the execution entry point, receives the charging pile allocation scheme pushed by the dynamic scheduling decision module in real time. It performs structured parsing of the scheme, extracting key fields such as the target charging pile identifier, user identity information, and service priority. It then immediately performs consistency verification with the current system operating context, such as whether the charging pile is still online, whether it has been occupied by other users, and whether there have been sudden changes in the regional power grid status, ensuring that the allocation instructions are executable at both the physical and logical levels. Subsequently, it generates a set of local control instructions for specific device operations. Next, the access control instruction issuance sub-module, based on this instruction set, accurately sends standardized charging pile access control instructions to the designated target charging pile, including prompts guiding users to the corresponding charging pile location, authorization signals to unlock the charging interface, and pre-configured power limit parameters, thereby formally initiating or preparing the charging service process. Simultaneously, the scheduling status synchronization sub-module, after successfully issuing the instructions,... The charging service scheduling status in the central database is immediately updated, the target charging pile is marked as occupied, the assigned user is removed from the corresponding queue, the service start time is recorded, and the occupancy status of related resources is updated synchronously to ensure that all modules in the system have a consistent understanding of the current resource distribution. Finally, the anomaly monitoring and response submodule continuously monitors the operation data stream from the charging pile, user terminal, and power grid during service execution. Once a charging service failure event such as charging pile communication interruption, unexpected charging termination, abnormal power fluctuation, or user-initiated cancellation is detected, the anomaly response mechanism is immediately triggered. On the one hand, a fault alarm log containing the fault type, occurrence time, and associated resources is generated for operation and maintenance tracing. On the other hand, the dynamic scheduling decision module is proactively notified to start the rescheduling process, freeze the abnormal resources, and quickly allocate alternative charging piles to the affected users, thereby ensuring service continuity and system robustness and truly realizing closed-loop control of execution, feedback, and self-healing.

[0043] Further refinement of step S2: The calculation of the regional comprehensive load index includes the following formula:

[0044] in, This represents the regional comprehensive load index. This indicates the current total number of users in the queue. This indicates the maximum tolerable number of people in the queue. This indicates the current total charging power. Indicates the remaining power supply capacity. This represents the measured value of the voltage stability index. This represents the standard voltage stability reference value. , , This represents the normalized weighting coefficient.

[0045] Preferably, the formula comprehensively characterizes the regional load status through three indicators with clear physical meaning: the proportion of people queuing reflects service demand pressure, the ratio of charging power to remaining capacity reflects the tightness of grid power supply, and voltage stability deviation measures power quality risk. These three correspond to key constraints on the user side, power side, and safety side, respectively. The weighted summation method has good interpretability and real-time performance in engineering practice. Although it is a linear model, it can effectively capture the dominant influence of key operating characteristics. The weighting coefficients... , , Based on power grid operation specifications and historical data settings, such as increasing power consumption during peak hours... Prioritizing power grid safety and increasing [efforts] during holidays. To alleviate queuing congestion and ensure that the strategy adapts to different scenarios, the system supports online learning or manual calibration mechanisms to dynamically optimize weights, balancing accuracy and robustness, avoiding excessive complexity caused by nonlinear coupling, and meeting the practical needs of actual scheduling systems.

[0046] Further refinement of step S3: Solving the multi-objective optimization decision rule set includes calculating the adaptation score of each allocable charging pile using a weighted comprehensive scoring formula, and selecting several solutions with high scores as feasible candidate solutions for charging pile allocation. The weighted comprehensive scoring formula is as follows:

[0047] in, This represents the overall compatibility score of charging pile j. , , Indicates the weighting coefficient. This represents the real-time load score of charging pile j. This represents the regional comprehensive load index of the power supply area r to which charging pile j belongs. This represents the current number of users queuing for charging station j.

[0048] Preferably, the weighting coefficients are not fixed and preset, but dynamically adjusted according to the real-time operating scenario. Based on time periods, such as peak and off-peak hours, regional power grid load status, weather warnings, and user demand distribution, the system automatically adjusts the weights through a rule engine or lightweight adaptive algorithm. For example, it increases the weights during peak electricity consumption periods. Prioritizing power grid safety and enhancing [safety measures] during holidays or extreme weather. To accelerate service response and ensure that scheduling strategies always match the current environment, dynamic load balancing is achieved.

[0049] Furthermore, the calculation of the regional comprehensive load index is not a simple superposition of a single indicator, but rather a systematic quantification of the overall operational pressure of the power supply area to which the charging piles belong by integrating three key dimensions: user side, grid side, and power quality. Specifically, the index first considers the ratio of the total number of users queuing in front of all charging piles in the current area to the system's preset maximum tolerance threshold to reflect the congestion level of user service demand; secondly, it introduces the ratio of the current total charging power in the area to the remaining power supply capacity of the grid to measure the tension of power resources; at the same time, it also combines the deviation of the measured voltage stability value from the standard reference value to assess the safety margin of the grid under the current load. These three indicators respectively characterize the load status at the human, power, and grid levels, and are weighted and integrated through normalized weight coefficients to ultimately form a unified, dimensionless regional comprehensive load index, comprehensively depicting whether the area is in a high-risk, critical, or relaxed operating state, providing a reliable regional constraint basis for upper-level scheduling.

[0050] The solution process for the multi-objective optimization decision rule set employs an efficient and interpretable scoring mechanism, rather than relying on complex global optimization algorithms. This mechanism dynamically calculates a comprehensive suitability score for each allocable charging pile, considering its individual load status, the overall load level of its region, and the current number of people in the queue. Specifically, a lower real-time load score for a single charging pile (i.e., more idle pile) results in a higher score; a lower overall load index for its region (i.e., a more relaxed region) also results in a higher score; and fewer queued users indicate a faster expected service response time, leading to a higher score. Through weighted fusion of these three factors, the system can quickly rank all candidate charging piles and select several allocation schemes with the best overall performance as feasible candidate solutions. This scoring-based solution method is not only computationally efficient and fast, but its weighting coefficients can also be flexibly adjusted according to different time periods, scenarios, or strategies, thereby achieving lightweight, real-time intelligent scheduling decisions while ensuring multi-objective collaboration.

[0051] In a preferred embodiment, a charging pile allocation method based on dynamic load balancing includes: collecting multi-dimensional dynamic load perception data of charging piles; quantitatively evaluating the comprehensive load status of charging piles and their power supply areas based on the multi-dimensional dynamic load perception data acquired by the data acquisition module, generating dynamic load characterization information, and updating the dynamic load characterization information to the central database of the charging pile allocation system; generating a charging pile allocation scheme based on the dynamic load characterization information output by the load status evaluation module and combined with a multi-objective optimization strategy; receiving the charging pile allocation scheme issued by the dynamic scheduling decision module, sending charging pile access control commands to target charging piles, updating the charging service scheduling status, and introducing an anomaly response mechanism to detect charging service faults in the charging service scheduling status.

[0052] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0053] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0054] In summary, this invention constructs a charging pile allocation system based on dynamic load balancing, achieving refined, real-time, and intelligent scheduling of charging pile resources. The system integrates multi-dimensional dynamic data such as equipment operating status, vehicle charging demand, user queuing status, and regional power grid load to quantitatively assess the comprehensive load status of individual charging piles and the region. Based on a multi-objective optimization strategy, it generates allocation schemes that balance user waiting time, load balance, and power grid safety margins. This technology effectively avoids the coexistence of idle charging piles and localized overloads caused by traditional static allocation, significantly improving the utilization rate of charging facilities and the stability of power grid operation. Simultaneously, through anomaly response mechanisms and dynamic rescheduling capabilities, it enhances the system's robustness and service continuity under sudden high-concurrency scenarios, thereby providing users with a more efficient, reliable, and safe charging experience and strongly supporting the high-quality development of new energy vehicle-related infrastructure.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A charging pile allocation system based on dynamic load balancing, characterized in that: include, The data acquisition module is used to collect multi-dimensional dynamic load sensing data of the charging pile; The load status assessment module is used to quantitatively assess the comprehensive load status of the charging pile and the power supply area to which the charging pile belongs based on the multi-dimensional dynamic load perception data obtained by the data acquisition module, generate dynamic load characterization information, and update the dynamic load characterization information to the central database of the charging pile distribution system. The dynamic scheduling decision module is used to generate a charging pile allocation scheme based on the dynamic load characterization information output by the load status assessment module and combined with a multi-objective optimization strategy. The charging pile control execution module is used to receive the charging pile allocation scheme issued by the dynamic scheduling decision module, send the charging pile access control command to the target charging pile, update the charging service scheduling status, introduce an abnormal response mechanism, and detect charging service faults in the charging service scheduling status.

2. The charging pile allocation system based on dynamic load balancing as described in claim 1, characterized in that: The data acquisition module includes a charging pile operation status submodule, a vehicle charging demand submodule, a user queuing information submodule, and a regional power grid load submodule. The charging pile operation status submodule is used to monitor and collect the operating characteristic parameters of each charging pile body, and obtain the operating load characteristics of the charging pile body. The vehicle charging demand submodule is used to obtain vehicle charging task characteristic parameters when a vehicle is connected or scheduled for charging. The user queuing information submodule is used to record and update the waiting queue information corresponding to each charging pile; The regional power grid load submodule is used to obtain the regional power grid carrying capacity characteristic parameters of the power supply area where the charging pile is located, and to assess the power grid operation safety margin of the external power grid for charging behavior.

3. The charging pile allocation system based on dynamic load balancing as described in claim 2, characterized in that: The load status assessment module includes a charging pile individual load assessment submodule, a regional aggregated load assessment submodule, a multi-dimensional fusion index calculation submodule, and a dynamic characterization information update submodule. The charging pile individual load assessment submodule is used to normalize and model the current single-pile comprehensive service load factor of each charging pile based on the charging pile body operation characteristic parameters and vehicle charging task characteristic parameters output by the data acquisition module, and generate a single-pile real-time load score. The regional aggregated load assessment submodule is used to combine the waiting queue information provided by the user queuing information submodule with the regional power grid carrying capacity characteristic parameters obtained by the regional power grid load submodule to perform coupled analysis on the overall service pressure and power grid operation safety margin of the power supply area to which the charging pile belongs, and calculate the regional comprehensive load index. The multidimensional fusion index calculation submodule is used to weight and fuse the real-time load score of a single charging pile with the regional comprehensive load index, introduce dynamic scheduling and adjustment parameters, and construct unified dynamic load characterization information. This unified dynamic load characterization information refers to the comprehensive load status of each charging pile and power supply area at the current moment in the form of structured data. The dynamic characterization information update submodule is used to write the unified dynamic load characterization information generated by the multi-dimensional fusion index calculation submodule into the central database of the charging pile allocation system, and synchronously maintain the load status data index information of the unified dynamic load characterization information.

4. The charging pile allocation system based on dynamic load balancing as described in claim 3, characterized in that: The dynamic scheduling decision module includes a load status analysis submodule, a multi-objective optimization modeling submodule, a feasible solution generation submodule, and an allocation scheme output submodule. The load status analysis submodule is used to read the unified dynamic load characterization information generated by the load status assessment module from the central database, extract the schedulability characteristics of all current charging piles and power supply areas, and form a scheduling input dataset. The multi-objective optimization modeling submodule is used to construct a multi-objective optimization decision rule set based on the scheduling input dataset, and to embed the dynamic scheduling adjustment parameters as constraint weight factors into the multi-objective optimization decision rule set. The feasible solution generation submodule is used to solve the multi-objective optimization decision rule set and generate feasible charging pile allocation candidate solutions; The allocation scheme output submodule is used to decapsulate feasible charging pile allocation candidates into a charging pile allocation scheme, push it to the charging pile control execution module, and record it.

5. The charging pile allocation system based on dynamic load balancing as described in claim 4, characterized in that: The charging pile control execution module includes an allocation scheme parsing submodule, an access control command issuance submodule, a scheduling status synchronization submodule, and an anomaly monitoring and response submodule. The allocation scheme parsing submodule is used to receive the charging pile allocation scheme pushed by the dynamic scheduling decision module, parse the charging pile allocation scheme, verify the consistency between the charging pile allocation scheme and the running context of the charging pile allocation system based on dynamic load balancing, and generate a local control instruction set. The access control command sending submodule is used to send a charging pile access control command to the target charging pile according to the local control command set, and to start or prepare the charging service process. The scheduling status synchronization submodule is used to update the charging service scheduling status in the central database after the charging pile access control command is issued. The anomaly monitoring and response submodule is used to continuously monitor the running data stream under the charging service scheduling status. When a charging service failure event is detected, the anomaly response mechanism is triggered to generate a fault alarm log and notify the dynamic scheduling decision module to start the rescheduling process.

6. The charging pile allocation system based on dynamic load balancing as described in claim 3, characterized in that: The calculation of the regional comprehensive load index includes the following formula: in, This represents the regional comprehensive load index. This indicates the current total number of users in the queue. This indicates the maximum tolerable number of people in the queue. This indicates the current total charging power. Indicates the remaining power supply capacity. This represents the measured value of the voltage stability index. This represents the standard voltage stability reference value. , , This represents the normalized weighting coefficient.

7. The charging pile allocation system based on dynamic load balancing as described in claim 4, characterized in that: The process of solving the multi-objective optimization decision rule set includes calculating the adaptation score of each allocable charging pile using a weighted comprehensive scoring formula, and selecting several solutions with high scores as feasible candidate solutions for charging pile allocation. The weighted comprehensive scoring formula is as follows: in, This represents the overall compatibility score of charging pile j. , , Indicates the weighting coefficient. This represents the real-time load score of charging pile j. This represents the regional comprehensive load index of the power supply area r to which charging pile j belongs. This represents the current number of users queuing for charging station j.

8. A charging pile allocation method based on dynamic load balancing, based on the charging pile allocation system based on dynamic load balancing as described in any one of claims 1 to 7, characterized in that: include, Collect multi-dimensional dynamic load sensing data of charging piles; Based on the multi-dimensional dynamic load perception data obtained by the data acquisition module, the comprehensive load status of the charging pile and the power supply area to which the charging pile belongs is quantitatively evaluated, and dynamic load characterization information is generated and updated to the central database of the charging pile distribution system. Based on the dynamic load characterization information output by the load status assessment module, and combined with a multi-objective optimization strategy, a charging pile allocation scheme is generated. It receives the charging pile allocation plan issued by the dynamic scheduling decision module, sends the charging pile access control command to the target charging pile, updates the charging service scheduling status, introduces an abnormal response mechanism, and detects charging service faults in the charging service scheduling status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the charging pile allocation system based on dynamic load balancing as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the charging pile allocation system based on dynamic load balancing as described in any one of claims 1 to 7.

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