Fixed-point charging pile management method for intelligent electric bicycle, medium and equipment

Abstract

A fixed-point charging pile management method for an intelligent electric bicycle is suitable for the intelligent electric bicycle connected with a fixed-point charging pile, and the fixed-point charging pile management method comprises the following steps: S10, the fixed-point charging pile obtains the battery power of the intelligent electric bicycle, and obtains a charging mode selected by an application program through a cloud; s20, the fixed-point charging pile determines the time period electricity price, and a dynamic charging curve is formed based on an optimization algorithm in combination with the charging mode and the battery electric quantity; s30, according to the dynamic charging curve, the fixed-point charging pile controls output power to charge the intelligent electric bicycle; and S40, continuously detecting the battery electric quantity and the charging mode by the fixed-point charging pile during charging, and updating the dynamic charging curve in real time according to the time period electricity price so as to dynamically adjust the output power of the fixed-point charging pile to charge the intelligent electric bicycle.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation vehicles, and more particularly to a method, medium, and equipment for managing fixed-point charging stations for intelligent electric bicycles. Background Technology

[0002] With the promotion of green urban travel concepts, smart electric bicycles and their associated fixed-point charging station networks have been widely adopted. Currently, the mainstream charging station management method typically involves charging the battery at a fixed, pre-set power after the user connects the vehicle, until it reaches full charge or the user manually stops the charging. However, this traditional charging management model reveals significant technical limitations in complex real-world application scenarios.

[0003] First, existing charging strategies are rigid and singular, failing to adapt to diverse and immediate user needs, resulting in poor user experience and low charging efficiency. In practice, user charging needs are dynamic. For example, during peak commuting hours, users may only need to replenish a small amount of power for urgent matters, such as charging from 30% to 50% to reach their next destination. In this case, attempting to fully charge is both time-consuming and wastes scarce charging resources. Other users may be price-sensitive, hoping to charge during the lowest electricity price periods to save costs. However, existing technology lacks a mechanism to identify and respond to these differentiated charging intentions, consistently implementing a single strategy of charging to the end. This results in the inability to provide rapid service to users with emergency charging needs, and also fails to offer economical solutions to cost-sensitive users. Personalized user needs are not met, thus reducing the overall utilization efficiency and social benefits of charging infrastructure. Second, existing technology fails to effectively link with the grid's time-of-use pricing policy, leading to high user charging costs and exacerbating the peak-valley load difference in the power grid. To balance supply and demand, the power system generally implements a time-of-use pricing mechanism, resulting in significant price differences at different times. Most existing charging stations ignore this price signal or lack intelligent guidance when users make their choices. As a result, many users may unintentionally concentrate their charging during peak electricity consumption periods when electricity prices are high, not only paying unnecessary extra electricity fees, but also further exacerbating the peak load on the power grid and putting pressure on its stable operation. Conversely, during off-peak electricity consumption periods when electricity prices are low, charging facilities may be idle, resulting in a waste of resources.

[0004] Therefore, it is necessary to provide a fixed-point charging pile management method for smart electric bicycles that can avoid being unable to adapt to the diverse on-demand charging needs of smart electric bicycles and can reduce users' charging costs by linking with the grid's time-of-use electricity pricing. Summary of the Invention

[0005] The purpose of this invention is to provide a fixed-point charging pile management method for smart electric bicycles that can avoid the inability to adapt to the diverse and immediate charging needs of smart electric bicycles and can reduce the user's charging costs by linking with the grid's time-of-use electricity pricing.

[0006] According to one aspect of this application, a method for managing fixed-point charging piles for smart electric bicycles is provided, applicable to smart electric bicycles electrically connected to fixed-point charging piles, the method comprising: S10 The fixed-point charging pile obtains the battery power of the smart electric bicycle and obtains the charging mode selected by the application through the cloud. The fixed-point charging pile described in S20 determines the electricity price for a given time period and forms a dynamic charging curve based on an optimization algorithm combined with the charging mode and battery capacity. S30, based on the dynamic charging curve, controls the output power of the fixed-point charging pile to charge the smart electric bicycle; When the S40 is charging, the fixed-point charging pile continuously detects the battery level and charging mode, and updates the dynamic charging curve in real time according to the electricity price during the time period to dynamically adjust the output power of the fixed-point charging pile to charge the smart electric bicycle.

[0007] More preferably, the charging modes include an emergency charging mode, a cost-optimized mode, and a standard full charge mode, and the dynamic charging curves include a first charging curve, a second charging curve, and a third charging curve.

[0008] More preferably, in step S20, If the charging mode is the emergency power replenishment mode, then the first charging curve is formed; If the charging mode is the cost-optimal mode, then the second charging curve is formed; If the charging mode is the standard full charge mode, then the third charging curve is formed.

[0009] More preferably, the output power includes a first power P1, a second power P2 and a third power P3, and satisfies the relationship: P1 > P3 > P2.

[0010] More preferably, in step S30, If the dynamic charging curve is the first charging curve, then control the fixed-point charging pile to charge the smart electric bicycle at the first power. If the dynamic charging curve is the second charging curve, then the fixed-point charging pile is controlled to charge the smart electric bicycle at the second power or the third power. If the dynamic charging curve is the third charging curve, then the fixed-point charging pile is controlled to charge the smart electric bicycle at the third power.

[0011] More preferably, the partial charge threshold and full charge threshold of the smart electric bicycle battery are determined. In step S30, when the fixed-point charging pile charges the smart electric bicycle at the first power, if the fixed-point charging pile detects that the battery charge has reached the partial charge threshold, the fixed-point charging pile stops charging the smart electric bicycle.

[0012] More preferably, when the fixed-point charging pile charges the smart electric bicycle at the third power, if the fixed-point charging pile detects that the battery power has reached the full power threshold, the fixed-point charging pile stops charging the smart electric bicycle.

[0013] More preferably, the time-based electricity price includes the off-peak electricity price within the daily time period T1 and the peak electricity price within the daily time period T2, in step S30, If the fixed-point charging pile determines that it is within time period T1, then the fixed-point charging pile charges the smart electric bicycle with the third power. If the fixed-point charging pile determines that it is within time period T2, then the fixed-point charging pile charges the smart electric bicycle at the second power. When the designated charging station detects that the battery level has reached the full charge threshold, the designated charging station stops charging the smart electric bicycle.

[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method.

[0015] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method.

[0016] The present invention has the following beneficial effects: By acquiring the charging mode selected by the application through the cloud and generating a dynamic charging curve based on that mode, designated charging stations can charge smart e-bikes according to the user's selected charging mode and the dynamic charging curve. This avoids the inability of designated charging stations to adapt to the diverse and immediate charging needs of smart e-bikes. Furthermore, by updating the dynamic charging curve in real time based on the electricity price during the specified time period, the output power of the designated charging stations is dynamically adjusted to charge the smart e-bikes. This allows the designated charging stations to use different output power during the charging process based on the electricity price at different times, effectively reducing the user's charging costs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the steps of a fixed-point charging pile management method according to an embodiment of this application. Figure 2 This is a block diagram showing the connection between the fixed-point charging pile described in one embodiment of this application and the cloud, the smart electric bicycle, and the application. Figure 3 This is a structural block diagram of the computer device described in one embodiment of this application; Explanation of icon numbers: 100. Fixed-point charging piles; 200. Smart electric bicycles; 300. Cloud computing; 400. Applications; 500. Computer equipment. Detailed Implementation

[0019] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0020] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Please refer to Figure 1 - Figure 3 This application provides a method for managing a fixed-point charging pile 100 for a smart electric bicycle 200, applicable to a smart electric bicycle 200 electrically connected to a fixed-point charging pile 100. The method for managing the fixed-point charging pile 100 includes: S10 The fixed-point charging pile 100 obtains the battery power of the smart electric bicycle 200 and obtains the charging mode selected by the application 400 through the cloud 300.

[0022] The fixed-point charging pile 100 in S20 determines the electricity price for a given period and forms a dynamic charging curve based on an optimization algorithm that combines the charging mode and battery capacity.

[0023] S30 According to the dynamic charging curve, the fixed-point charging pile 100 controls the output power to charge the smart electric bicycle 200.

[0024] During S40 charging, the fixed-point charging pile 100 continuously detects the battery level and charging mode, and updates the dynamic charging curve in real time according to the electricity price of the time period to dynamically adjust the output power of the fixed-point charging pile 100 to charge the smart electric bicycle 200.

[0025] In step S10, during the data perception phase, the system simultaneously acquires two key pieces of information from the vehicle and the user. First, it reads the current battery level in real time through the communication interface between the charging pile and the vehicle's battery management system to accurately grasp the physical starting point and basic state of the charging process. Second, it receives the charging mode command actively selected by the user from the mobile application 400 via a cloud-based 300 service relay. This step lays the data foundation for the entire system's intelligence, transforming the user's abstract intentions, such as seeking speed or cost savings, into explicit signals that the machine can recognize. In step S20, based on the aforementioned perceived data, the system enters the intelligent decision-making and planning phase. The core of step S20 is an embedded optimization algorithm. It does not perform a single, fixed calculation but rather integrates and analyzes three variables: charging mode, battery level, and real-time electricity price, generating a dynamic charging curve that changes over time. Its decision-making logic is multi-objective. After the decision is made, the system proceeds to step S30. The charging pile's control system receives the dynamic charging curve command and drives its internal power conversion module to precisely adjust the output voltage and current, safely and stably delivering electrical energy to the smart electric bicycle 200 at the specific power specified by the algorithm. Finally, the system does not stop at a one-time execution; step S40 is a stage of continuous monitoring and dynamic optimization. In this stage, the system forms a complete closed-loop feedback. The fixed-point charging pile 100 continuously monitors the battery level, whether the user changes the charging mode, and whether the electricity price changes during the charging period. Once any change in input conditions is detected, the system immediately feeds back the latest data to step S20, triggering the optimization algorithm to recalculate and then generating an updated charging curve for execution. This makes the entire charging process no longer static, but a dynamic system capable of responding to changes in real time and continuously tracking the optimal solution, ensuring that the final result best meets the user's immediate wishes and economic interests under any circumstances, thus achieving intelligent charging management.

[0026] More preferably, the charging modes include an emergency charging mode, a cost-optimized mode, and a standard full charge mode, and the dynamic charging curves include a first charging curve, a second charging curve, and a third charging curve.

[0027] Firstly, the emergency charging mode and its corresponding first charging curve are solutions for users with urgent time needs. The sole core optimization goal of this mode is speed. When users face the awkward situation of urgently needing to use their vehicle but having insufficient battery power, their biggest pain point is to minimize waiting time. Therefore, the first charging curve is designed as a high-power, non-linear fast charging curve. It utilizes the maximum power that the charging station can safely provide, and within the limits allowed by the battery chemistry, efficiently and quickly increases the battery power to the user's desired target threshold, such as 50%. Secondly, the cost-optimal mode and its corresponding second charging curve are solutions for users' economic needs. This mode elevates the external economic signal of time-of-use electricity prices from a background parameter to the primary basis for decision-making. The second charging curve is a highly dynamic and intelligent scheduling curve. It has two power outputs, which are not fixed but closely tied to the real-time electricity price. During peak hours when electricity prices are high, the algorithm instructs the charging station to operate at a very low maintenance power. Once the system detects the arrival of a low-price valley, it immediately switches to a higher power to fully charge the battery. Finally, the standard full-charge mode and the third charging curve represent conventional charging modes that cater to the charging needs of most users and meet their stable expectations in typical scenarios. This three-mode, three-curve architecture essentially transforms a complex multi-objective optimization problem into clear, user-selectable options. By pre-setting distinct optimization weights for each mode, the system generates a unique charging strategy, enabling the algorithm to accurately serve the user's real-world scenarios and intentions. Ultimately, this improves the user experience while also achieving efficient utilization of charging station and grid resources.

[0028] More preferably, in step S20, If the charging mode is the emergency power replenishment mode, then the first charging curve is formed.

[0029] If the charging mode is the cost-optimal mode, then the second charging curve is formed.

[0030] If the charging mode is the standard full charge mode, then the third charging curve is formed.

[0031] The system ensures efficient decision-making and predictable results by pre-setting a highly specialized response strategy for each specific user intent. When a user selects the emergency charging mode, their core requirement is minimizing time. The system directly invokes the first charging curve tailored to this goal, responding quickly with maximum power output, avoiding any delays or uncertainties that might arise from weighing speed against economy. Similarly, selecting the cost-optimal mode means the user authorizes the system to prioritize electricity cost as the overriding optimization objective. In this case, the second charging curve is activated, designed to fully utilize time-of-use pricing signals, intelligently scheduling charging periods to achieve the lowest total cost, no longer constrained by standard charging schedules. As for the standard full-charge mode, it corresponds to a universal solution that achieves the best balance between charging efficiency, battery life maintenance, and ease of operation. This one-to-one correspondence logic essentially presents the complex internal trade-offs of the algorithm to the front-end user in a transparent manner. By locking in the optimization direction in advance through the user's choice, it ensures that the system response can always accurately match the user's initial expectations. This simplifies the complexity of real-time decision-making and fundamentally eliminates the risk of target deviation that may occur due to the system's own judgment.

[0032] More preferably, the output power includes a first power P1, a second power P2 and a third power P3, and satisfies the relationship: P1 > P3 > P2.

[0033] The charging mode is divided into three power levels. The first power level, P1, corresponds to the emergency charging mode. Its primary goal is charging speed, so it's typically set to the maximum safe fast charging power allowed by the charging station and the vehicle's battery management system, such as 1.5 kW. This achieves rapid charging through high voltage and high current, but it sacrifices some battery life and may result in higher charging costs. The third power level, P3, corresponds to the standard full-charge mode. Balancing efficiency and battery health, it's typically set to a standard medium-speed power of 1.0 kW, which can complete charging within a reasonable time. The second power level, P2, corresponds to the cost-optimal mode. Its core objective is to avoid peak electricity prices during time-of-use pricing. Therefore, it can be set to a maintenance trickle charge of around 0.3 kW. This power is only used to maintain vehicle connectivity and supply power to the battery management system during peak electricity price periods, generating almost no effective charging to avoid high electricity bills. Charging is only switched to P3 once the system detects off-peak electricity prices. This tiered design ensures the precise achievement of each mode's objectives, strictly adheres to battery safety charging standards, and enhances the user's charging experience by differentiating the power levels based on the actual effects of user choices.

[0034] More preferably, in step S30, If the dynamic charging curve is the first charging curve, then the fixed-point charging pile 100 is controlled to charge the smart electric bicycle 200 at the first power.

[0035] If the dynamic charging curve is the second charging curve, then the fixed-point charging pile 100 is controlled to charge the smart electric bicycle 200 at the second power or the third power.

[0036] If the dynamic charging curve is the third charging curve, then the fixed-point charging pile 100 is controlled to charge the smart electric bicycle 200 with the third power.

[0037] When the system determines to execute the first charging curve, it means it has confirmed that the user's primary demand is extreme speed. At this point, controlling the designated charging pile 100 to output a constant first power P1, such as 1.5kW, is the most direct strategy. This maximum power output ensures that the battery level is increased to the target value within the shortest time window, perfectly achieving emergency charging. For the second charging curve, the corresponding cost-optimal mode objective is more complex, and its execution logic is therefore more dynamic. Controlling the charging pile to switch between the second power P2, such as 0.3kW, and the third power P3, such as 1.0kW, is a manifestation of the intelligence of this mode. The system does not simply select a power level, but uses power selection as an execution command to respond to changes in the external electricity price environment. During peak periods with high electricity prices, the system instructs the charging pile to operate at a very low second power, not for charging, but to maintain connection, monitor signals, and minimize costs. Once it detects the arrival of a low-price off-peak period, the system immediately issues a command to switch the power to the effective third power and begin actual charging. The third charging curve controls the charging station to charge at a constant third power P3, such as 1.0kW, which is the common standard charging mode for smart e-bikes. This power value achieves the optimal balance between charging efficiency and long-term battery health. It avoids the stress of maximum power fast charging and is much faster than maintenance trickle charging. Users do not need to worry about fluctuations in grid electricity prices; the system will reliably and steadily charge the battery, achieving universal charging compatibility.

[0038] More preferably, the partial charge threshold and full charge threshold of the battery of the smart electric bicycle 200 are determined. In step S30, when the fixed charging pile 100 charges the smart electric bicycle 200 at the first power, if the fixed charging pile 100 detects that the battery charge has reached the partial charge threshold, the fixed charging pile 100 stops charging the smart electric bicycle 200.

[0039] When users choose emergency charging mode, their underlying need is usually not to fully charge the battery, but to alleviate battery anxiety as quickly as possible. However, the chemical characteristics of lithium-ion batteries dictate that their charging process is non-linear. Once the battery reaches a relatively high level, typically above 80%, the battery management system actively reduces the charging current to protect battery safety. This means that the charging time for the final 20% may be almost equivalent to that for the first 80%. Therefore, by pre-setting a partial charge threshold in the algorithm as the charging termination condition, the system essentially makes a rational trade-off: it decisively abandons the trickle charging phase, which offers negligible improvement to the user experience but has a very high time cost, thereby maximizing the efficiency of rapid charging and enabling users to obtain sufficient effective range for most travel scenarios in the shortest possible time. This not only perfectly matches the real-world emergency scenarios of users and avoids the ineffective use of charging resources, but also reduces deep lithium plating damage to the battery, achieving the best balance between meeting immediate needs and protecting long-term assets.

[0040] More preferably, when the fixed-point charging pile 100 charges the smart electric bicycle 200 at the third power, if the fixed-point charging pile 100 detects that the battery power has reached the full power threshold, the fixed-point charging pile 100 stops charging the smart electric bicycle 200.

[0041] When users select the standard charging mode, their explicit demand is to obtain maximum driving range and complete peace of mind regarding battery safety. However, the physical characteristics of lithium-ion batteries require that the charging process must be precisely terminated when the full charge voltage is reached. Any overcharging can lead to serious safety risks such as electrolyte decomposition, gas generation, and even thermal runaway. Therefore, the system monitors in real time and compares it with the full charge threshold to achieve millisecond-level power cut-off at the critical point of battery saturation, providing users with worry-free, complete driving range.

[0042] More preferably, the time-based electricity price includes the off-peak electricity price within the daily time period T1 and the peak electricity price within the daily time period T2. In step S30, if the fixed-point charging pile 100 determines that it is within time period T1, then the fixed-point charging pile 100 charges the smart electric bicycle 200 at the third power. If the fixed-point charging pile 100 determines that it is within time period T2, then the fixed-point charging pile 100 charges the smart electric bicycle 200 at the second power. When the fixed-point charging pile 100 detects that the battery power has reached the full power threshold, then the fixed-point charging pile 100 stops charging the smart electric bicycle 200.

[0043] The system operates on two time periods: T1 typically runs from 10:00 PM to 8:00 AM the following day, while T2 typically runs from 8:00 AM to 10:00 PM. The system determines in real-time whether the current time period falls within the low-electricity-price T1 or the high-electricity-price T2 period and adjusts its output power accordingly. Specifically, it uses effective P3 power for charging during T1 and switches to P2 power to maintain connection during T2, thus controlling charging costs. During the high-electricity-price T2 period, the system operates at extremely low P2 power not for charging but to minimize current electricity costs, preventing any valuable electricity from being used for charging during this period. Once the system enters the low-electricity-price T1 period, it immediately charges at full effective P3 power, fully utilizing the low electricity price for energy replenishment. The final boundary of the entire process is set by a full-capacity threshold, ensuring immediate termination upon reaching the target capacity and avoiding any unnecessary energy waste.

[0044] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described herein.

[0045] The present invention also provides a computer device 500, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method.

[0046] Figure 3 An internal structural diagram of a computer device 500 in one embodiment is shown. This computer device 500 can specifically be a terminal or a server. Figure 3 As shown, the computer device 500 includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium of the computer device 500 stores an operating system and may also store a computer program. When the computer program is executed by the processor, it causes the processor to implement the methods described in this embodiment. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the methods described in this embodiment. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0047] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0048] This allows the charging mode selected by the application 400 to be obtained via the cloud 300, and a dynamic charging curve to be generated based on the charging mode. This enables the fixed-point charging station 100 to charge the smart electric bicycle 200 according to the user's selected charging mode and the dynamic charging curve, avoiding the inability of the fixed-point charging station 100 to adapt to the diverse and immediate charging needs of the smart electric bicycle 200. Furthermore, by updating the dynamic charging curve in real time according to the electricity price during the specified time period, the output power of the fixed-point charging station 100 to charge the smart electric bicycle 200 is dynamically adjusted. This allows the fixed-point charging station 100 to use different output power during the charging process based on the electricity price at different times, effectively reducing the user's charging cost.

[0049] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for managing fixed-point charging stations for smart electric bicycles, characterized in that, The method for managing smart electric bicycles that are electrically connected to designated charging stations includes: S10 The fixed-point charging pile obtains the battery power of the smart electric bicycle and obtains the charging mode selected by the application through the cloud. The fixed-point charging pile described in S20 determines the electricity price for a given time period and forms a dynamic charging curve based on an optimization algorithm combined with the charging mode and battery capacity. S30, based on the dynamic charging curve, controls the output power of the fixed-point charging pile to charge the smart electric bicycle; When the S40 is charging, the fixed-point charging pile continuously detects the battery level and charging mode, and updates the dynamic charging curve in real time according to the electricity price during the time period to dynamically adjust the output power of the fixed-point charging pile to charge the smart electric bicycle.

2. The method for managing fixed-point charging piles according to claim 1, characterized in that, The charging modes include emergency charging mode, cost-optimized mode, and standard full charge mode, and the dynamic charging curves include a first charging curve, a second charging curve, and a third charging curve.

3. The fixed-point charging pile management method according to claim 2, characterized in that, In step S20 If the charging mode is the emergency power replenishment mode, then the first charging curve is formed; If the charging mode is the cost-optimal mode, then the second charging curve is formed; If the charging mode is the standard full charge mode, then the third charging curve is formed.

4. The fixed-point charging pile management method according to claim 3, characterized in that, The output power includes a first power P1, a second power P2, and a third power P3, and satisfies the relationship: P1 > P3 > P2.

5. The method for managing fixed-point charging piles according to claim 4, characterized in that, In step S30 If the dynamic charging curve is the first charging curve, then control the fixed-point charging pile to charge the smart electric bicycle at the first power. If the dynamic charging curve is the second charging curve, then the fixed-point charging pile is controlled to charge the smart electric bicycle at the second power or the third power. If the dynamic charging curve is the third charging curve, then the fixed-point charging pile is controlled to charge the smart electric bicycle at the third power.

6. The method for managing fixed-point charging piles according to claim 5, characterized in that, In step S30, when the designated charging pile charges the smart electric bicycle at the first power, if the designated charging pile detects that the battery power has reached the partial power threshold, the designated charging pile stops charging the smart electric bicycle.

7. The method for managing fixed-point charging piles according to claim 6, characterized in that, In step S30, when the fixed-point charging pile charges the smart electric bicycle at the third power, if the fixed-point charging pile detects that the battery power has reached the full power threshold, the fixed-point charging pile stops charging the smart electric bicycle.

8. The method for managing fixed-point charging piles according to claim 7, characterized in that, The time-based electricity price includes the off-peak electricity price within the daily time period T1 and the peak electricity price within the daily time period T2. In step S30... If the fixed-point charging pile determines that it is within time period T1, then the fixed-point charging pile charges the smart electric bicycle with the third power. If the fixed-point charging pile determines that it is within time period T2, then the fixed-point charging pile charges the smart electric bicycle at the second power. When the designated charging station detects that the battery level has reached the full charge threshold, the designated charging station stops charging the smart electric bicycle.

9. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, causes the processor to perform the steps of the fixed-point charging pile management method as described in any one of claims 1 to 8.

10. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the fixed-point charging pile management method as described in any one of claims 1 to 8.

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