Intelligent charging management system and method based on Internet of Things data synchronization

Abstract

The invention discloses an intelligent charging management system and method based on Internet of Things data synchronization, and belongs to the technical field of intelligent charging management, the intelligent charging management system based on Internet of Things data synchronization comprises a data acquisition module, a cloud platform, a threshold management module, a charging terminal control module and a charging management module; the data acquisition module is used for monitoring power consumption conditions and user charging preferences, the cloud platform is used for receiving power consumption load data published by a provincial platform, and the threshold value management module is used for grading charging demand quantity based on historical load data and performing classification management on charging equipment according to demand load. The charging terminal control module is responsible for comparing expected and actual electric energy requirements and adjusting power output of the charging piles, the charging management module is used for formulating a charging strategy according to load requirements, and the output power of the orderly charging piles is dynamically adjusted through charging requirements and power grid curve changes.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent charging management, and in particular to an intelligent charging management system and method based on Internet of Things data synchronization. Background Art

[0002] With the popularization of electric vehicles, the demand for charging infrastructure has grown rapidly. At the same time, in order to respond to the electricity load curve released by the provincial grid platform and accept real-time supervision of electricity consumption by the provincial grid platform.

[0003] However, the existing charging management system has the following challenges: first, the traditional charging mode cannot respond to the load changes and demands of the power grid in real time, resulting in the concentrated charging demand of charging piles exceeding the power supply capacity of the power grid during the peak hours of the power grid, thus affecting the stability of the power grid; second, the lack of effective load management strategies, charging stations will cause overloads on the power grid during peak hours, resulting in unstable power supply and even tripping problems; then, under the existing charging pile management mode, there is an imbalance in the utilization of charging piles, some charging piles may be idle for a long time, while others may be overloaded; then, the existing charging management system often lacks intelligent control means, and cannot dynamically adjust the charging strategy according to real-time data and prediction models to adapt to changes in the power grid load; finally, the lack of personalized charging strategies and optimizations may prevent users from charging at the best time, resulting in low charging efficiency and poor charging experience. Summary of the invention

[0004] The object of the present invention is to provide an intelligent charging management system and method based on Internet of Things data synchronization to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent charging management system based on Internet of Things data synchronization, the system comprising: a data acquisition module, a cloud platform, a threshold management module, a charging terminal control module and a charging management module; the data acquisition module is used to monitor the user's charging preferences and the electricity consumption data of the substation, the cloud platform comprises a power supply company cloud platform and an orderly charging cloud platform, which is used to receive the electricity load data released by the provincial platform, the threshold management module classifies the charging demand based on the historical load data, and classifies the charging equipment according to the demand load, the charging terminal control module is responsible for receiving the electricity consumption data of the orderly charging piles, comparing the expected total power demand with the actual power demand, and adjusting the output power of the charging piles, and the charging management module is used to formulate corresponding charging strategies according to the load demand.

[0006] Furthermore, the data acquisition module includes a power data acquisition unit and a charging preference recording unit. The power data acquisition unit is used to monitor the power consumption data of the substation master meter and the regional master meter, including the charging power and output voltage of the ordered charging piles. The charging preference recording unit is used to record the charging mode selected by the user, wherein the charging mode includes automatic full charge, fixed power, fixed amount and fixed time, and all collected data are transmitted to the charging terminal control module via HPLC+RF dual mode.

[0007] Furthermore, the threshold management module includes a charging demand grading module and a charging equipment classification unit. The charging demand grading unit is used to grade the charging demand according to the historical load data of the charging station and the power demand equipment, collect the real-time charging power and grid load changes flowing through each charging device, and obtain the power output difference of each charging device in each level of charging demand and the normal threshold value of the grid load fluctuation range. The charging equipment classification unit is used to be divided into high-load charging equipment, standard-power charging equipment and low-load charging equipment according to historical load records.

[0008] Furthermore, the charging terminal control module includes an electric energy demand comparison unit and a power grid load prediction unit. The electric energy demand comparison unit is used to manage area 1 and area 2 through the orderly charging management terminal, and is connected to the regional master table of area 1 and area 2 using HPLC+RF dual mode, to receive the power consumption data of the orderly charging piles, and to compare the expected total electric energy demand with the actual electric energy demand. The power grid load prediction unit is used to establish a power grid load prediction model, predict the power grid load curve, and transmit the prediction result to the cloud platform, and the cloud platform calls the orderly charging terminal to adjust the power of the charging pile.

[0009] Furthermore, the charging management module includes a charging strategy optimization unit and a feedback unit. The charging strategy optimization unit is used to formulate a charging strategy based on real-time load demand, the charging mode selected by the user and the grid load status: when the grid load is low, the power is increased to shorten the charging time; when the load is peak, the power is reduced to reduce the grid load, and personalized charging strategies are provided according to user preferences. The feedback unit monitors the load demand and grid load status of each charging pile in real time during the charging process, and feeds back the data to the charging terminal control module. At the same time, the charging strategy is automatically adjusted based on the feedback information, and the power output is optimized to adapt to changes in real-time load demand and grid conditions.

[0010] An intelligent charging management method based on Internet of Things data synchronization includes the following steps:

[0011] S1: Obtain historical load data of charging stations and charging equipment, real-time charging power of orderly charging piles and grid load curve;

[0012] S2: Compare the expected total power demand with the actual power demand. If the expected total power demand is greater than the actual power demand, update the output power of the low-load charging pile. If the expected total power demand is less than the actual power demand, update the output power of the high-load charging pile.

[0013] S3: Adjust the charging power of the orderly charging pile according to the updated low-load charging power and high-load charging power.

[0014] In step S1, the demand level classification and orderly charging pile classification are performed based on the historical load data of charging stations and charging equipment, the real-time charging power of orderly charging piles, and the grid load curve. The steps are as follows:

[0015] S1-1: Based on the historical load data of charging stations and charging equipment, including the charging power, charging time and power demand of historical orderly charging piles in each period, the charging demand is collected and classified into different levels, which is helpful to plan the power demand of charging stations and optimize the allocation of power resources;

[0016] S1-2: Obtain the charging power of orderly charging piles during peak, normal and off-peak time periods, divide the orderly charging piles into high-load charging piles, standard-power charging piles and low-load charging piles according to historical data, classify the orderly charging piles according to their power usage in different load time periods, and perform differentiated management according to the load characteristics of the charging piles. This classification helps to reasonably allocate the power of the charging piles during peak grid load periods and reduce the pressure on the grid.

[0017] In step S2, the expected total power demand is compared with the actual power demand, and the output power of the orderly charging pile is updated. The calculation process includes the following steps:

[0018] S2-1: Obtain the expected total power demand. The calculation formula is as follows:

[0019]

[0020] Among them, D actual Indicates the actual power demand, P rated Indicates the rated output power of the orderly charging pile, N total represents the total number of orderly charging piles HC, standard power charging piles SC and low load charging piles LC in the area, and η represents the charging efficiency of the orderly charging piles;

[0021] S2-2: The expected total power demand is compared with the actual power demand. When the expected total power demand is greater than the actual power demand, the output power of the low-load charging pile is updated. The calculation formula for updating the LC output of the low-load charging pile is as follows:

[0022]

[0023] Among them, P LC_new represents the updated low-load charging pile power, D expected represents the expected total power demand, NLC represents the number of low-load orderly charging piles, Δ Padjusted Indicates the power difference adjusted in real time according to the grid load;

[0024] When the expected total power demand is less than the actual power demand, the output power of the high-load charging pile is updated, and the calculation formula for the updated high-load charging pile LC output is as follows:

[0025]

[0026] Among them, P HC_new Represents the updated high-load orderly charging pile power, N HC Indicates the number of high-load orderly charging piles, ΔP balance Indicates the power adjustment item that is dynamically balanced according to the actual load difference;

[0027] S2-3: Introduce prediction models and feedback mechanisms combined with machine learning to predict the grid load curve and adjust the power output of the charging pile in advance based on the prediction results. The power adjustment term in the formula can be added with a historical data feedback mechanism and optimized based on the previous load adjustment effect. The power calculation formula adjusted in real time according to the grid load is as follows:

[0028] P adjust =α*P previous +(1-α)*P predicated ;

[0029] Among them, α represents the adjustment coefficient, P previous Indicates the power adjusted last time, P predicated It means that according to the predicted power of load forecast, by comparing the expected power demand with the actual power demand, the output power of the charging pile is adjusted in real time to ensure that its operation is consistent with the grid load. At the same time, the prediction model and machine learning algorithm are introduced to predict the grid load curve in advance and optimize the power output, which enhances the flexibility and adaptability of the charging process. The historical data feedback mechanism is used to continuously learn and optimize the charging strategy, improve the charging efficiency and reduce energy waste.

[0030] In step S3, the charging power of the orderly charging pile is adjusted according to the updated low-load charging power and high-load charging power, and the power is optimized according to the remaining power of the charging vehicle connected to the current charging pile in combination with the real-time detection data. Specifically, for vehicles with high remaining power, the output power of the low-load charging pile is selected for charging; for vehicles with low power, the output power of the high-load charging pile is selected for charging according to the load of the power grid. In addition, the charging strategy is further optimized according to the charging mode selected by the user, including automatic full charge, fixed power, fixed amount and fixed time. Specifically, for users who choose automatic full charge, fixed power and fixed amount, the output power of the high-load charging pile is selected for charging during the low load period of the power grid, and during the low load period of the power grid. During peak load periods, low-load charging piles are selected for charging. For users who choose fixed time, when the selected time is at peak load, the charging rate is maintained within the safety range of the power grid, and during low load periods, high-load charging piles are selected for charging. The charging power is adjusted according to the remaining power of the vehicle to provide the most suitable charging solution for each vehicle, thereby improving charging efficiency. During the low load period of the power grid, the system gives priority to high-load charging piles, and during peak periods, low-load charging piles are used to balance the overall load of the power grid and reduce the impact on the stability of the power grid. At the same time, the charging mode optimization strategy selected by the user is used to meet personalized needs. By intelligently adjusting the charging power and optimization strategy, a more flexible and convenient charging experience is provided to improve user satisfaction.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0032] 1. The present invention ensures the safe and compliant operation of charging piles by responding to the power load and demand of the substations released by the power grid and combining it with real-time monitoring of the operating status of charging facilities, thus avoiding the potential safety hazards caused by disorderly construction. At the same time, the system supports peak load shaving and valley filling of electricity, optimizes the power grid load, improves the stability of the power grid, and ensures that the charging needs of new energy vehicles can be met in a timely manner.

[0033] 2. Through orderly charging, the ratio of the station's box transformer capacity to the pile capacity has reached 1:1.6, which has significantly improved the utilization rate of the charging piles and increased the number of charging equipment configured at the station. Compared with traditional charging stations, orderly charging stations have higher construction subsidies and significantly reduced operating costs. At the same time, after being regulated, the station's certification process and electricity approval have become simpler and faster, further improving the station's economic benefits and operational efficiency.

[0034] 3. The present invention ensures that the station will not be closed due to power restriction during the peak period of social electricity consumption, ensuring the normal charging needs of the vehicle. By reasonably regulating the charging power, users can enjoy stable and reliable charging services under load fluctuations without affecting the normal use of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a structural diagram of an intelligent charging management system based on Internet of Things data synchronization of the present invention;

[0036] Figure 2 It is a flow chart of an intelligent charging management method based on Internet of Things data synchronization of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1-Figure 2 As shown, the present invention provides a technical solution: an intelligent charging management system based on Internet of Things data synchronization, the system comprising: a data acquisition module, a cloud platform, a threshold management module, a charging terminal control module and a charging management module; the data acquisition module is used to monitor the user's charging preferences and the electricity consumption data of the substation, the cloud platform comprises a power supply company cloud platform and an orderly charging cloud platform, which is used to receive the electricity load data released by the provincial platform, the threshold management module classifies the charging demand based on the historical load data, and classifies the charging equipment according to the demand load, the charging terminal control module is responsible for receiving the electricity consumption data of the orderly charging pile, comparing the expected total power demand with the actual power demand, and adjusting the output power of the charging pile, and the charging management module is used to formulate corresponding charging strategies according to the load demand.

[0039] The data acquisition module includes a power data acquisition unit and a charging preference recording unit. The power data acquisition unit is used to monitor the power consumption data of the substation master meter and the regional master meter, including the charging power and output voltage of the ordered charging piles. The charging preference recording unit is used to record the charging mode selected by the user, wherein the charging mode includes automatic full charge, fixed power, fixed amount and fixed time, and all collected data are transmitted to the charging terminal control module via HPLC+RF dual mode.

[0040] The threshold management module includes a charging demand grading module and a charging equipment classification unit. The charging demand grading unit is used to grade the charging demand according to the historical load data of the charging station and the power demand equipment, collect the real-time charging power and grid load changes flowing through each charging device, and obtain the power output difference of each charging device in each level of charging demand and the normal threshold value of the grid load fluctuation range. The charging equipment classification unit is used to be divided into high-load charging equipment, standard-power charging equipment and low-load charging equipment according to historical load records.

[0041] The charging terminal control module includes an electric energy demand comparison unit and a power grid load prediction unit. The electric energy demand comparison unit is used to manage area 1 and area 2 through the orderly charging management terminal, and is connected to the regional master table of area 1 and area 2 using HPLC+RF dual mode, receiving the power consumption data of the orderly charging piles, and comparing the expected total electric energy demand with the actual electric energy demand. The power grid load prediction unit is used to establish a power grid load prediction model, predict the power grid load curve, and transmit the prediction results to the cloud platform, and the cloud platform calls the orderly charging terminal to adjust the power of the charging pile.

[0042] The charging management module includes a charging strategy optimization unit and a feedback unit. The charging strategy optimization unit is used to formulate a charging strategy based on real-time load demand, the charging mode selected by the user and the grid load status: when the grid load is low, the power is increased to shorten the charging time; when the load is peak, the power is reduced to reduce the grid load, and personalized charging strategies are provided according to user preferences. The feedback unit monitors the load demand and grid load status of each charging pile in real time during the charging process, and feeds back the data to the charging terminal control module. At the same time, the charging strategy is automatically adjusted based on the feedback information, and the power output is optimized to adapt to changes in real-time load demand and grid conditions.

[0043] An intelligent charging management method based on Internet of Things data synchronization includes the following steps:

[0044] S1: Obtain historical load data of charging stations and charging equipment, real-time charging power of orderly charging piles and grid load curve;

[0045] S2: Compare the expected total power demand with the actual power demand. If the expected total power demand is greater than the actual power demand, update the output power of the low-load charging pile. If the expected total power demand is less than the actual power demand, update the output power of the high-load charging pile.

[0046] S3: Adjust the charging power of the orderly charging pile according to the updated low-load charging power and high-load charging power.

[0047] In step S1, the demand level classification and orderly charging pile classification are performed based on the historical load data of charging stations and charging equipment, the real-time charging power of orderly charging piles, and the grid load curve. The steps are as follows:

[0048] S1-1: Based on the historical load data of charging stations and charging equipment, including the charging power, charging time and power demand of historical orderly charging piles in each period, the charging demand is collected and classified into different levels, which is helpful to plan the power demand of charging stations and optimize the allocation of power resources;

[0049] S1-2: Obtain the charging power of orderly charging piles during peak, normal and off-peak time periods, divide the orderly charging piles into high-load charging piles, standard-power charging piles and low-load charging piles according to historical data, classify the orderly charging piles according to their power usage in different load time periods, and perform differentiated management according to the load characteristics of the charging piles. This classification helps to reasonably allocate the power of the charging piles during peak grid load periods and reduce the pressure on the grid.

[0050] In step S2, the expected total power demand is compared with the actual power demand, and the output power of the orderly charging pile is updated. The calculation process includes the following steps:

[0051] S2-1: Obtain the expected total power demand. The calculation formula is as follows:

[0052]

[0053] Among them, D actual Indicates the actual power demand, P rated Indicates the rated output power of the orderly charging pile, N total represents the total number of orderly charging piles HC, standard power charging piles SC and low load charging piles LC in the area, and η represents the charging efficiency of the orderly charging piles;

[0054] S2-2: The expected total power demand is compared with the actual power demand. When the expected total power demand is greater than the actual power demand, the output power of the low-load charging pile is updated. The calculation formula for updating the LC output of the low-load charging pile is as follows:

[0055]

[0056] Among them, P LC_new represents the updated low-load charging pile power, D expected represents the expected total power demand, NLC represents the number of low-load orderly charging piles, Δ Padjusted Indicates the power difference adjusted in real time according to the grid load;

[0057] When the expected total power demand is less than the actual power demand, the output power of the high-load charging pile is updated, and the calculation formula for the LC output of the high-load charging pile is updated as follows:

[0058]

[0059] Among them, P HC_new Represents the updated high-load orderly charging pile power, N HC Indicates the number of high-load orderly charging piles, ΔP balance Indicates the power adjustment item that is dynamically balanced according to the actual load difference;

[0060] S2-3: Introduce prediction models and feedback mechanisms combined with machine learning to predict the grid load curve and adjust the power output of the charging pile in advance based on the prediction results. The power adjustment term in the formula can be added with a historical data feedback mechanism and optimized based on the previous load adjustment effect. The power calculation formula adjusted in real time according to the grid load is as follows:

[0061] P adjust =α*P previous +(1-α)*P predicated ;

[0062] Among them, α represents the adjustment coefficient, P previous Indicates the power of the last adjustment, P predicated It means that according to the predicted power of load forecast, by comparing the expected power demand with the actual power demand, the output power of the charging pile is adjusted in real time to ensure that its operation is consistent with the grid load. At the same time, the prediction model and machine learning algorithm are introduced to predict the grid load curve in advance and optimize the power output, which enhances the flexibility and adaptability of the charging process. The historical data feedback mechanism is used to continuously learn and optimize the charging strategy, improve the charging efficiency and reduce energy waste.

[0063] In step S3, the charging power of the orderly charging pile is adjusted according to the updated low-load charging power and high-load charging power, and the power is optimized according to the remaining power of the charging vehicle connected to the current charging pile in combination with the real-time detection data. Specifically, for vehicles with high remaining power, the output power of the low-load charging pile is selected for charging; for vehicles with low power, the output power of the high-load charging pile is selected for charging according to the load of the power grid. In addition, the charging strategy is further optimized according to the charging mode selected by the user, including automatic full charge, fixed power, fixed amount and fixed time. Specifically, for users who choose automatic full charge, fixed power and fixed amount, the output power of the high-load charging pile is selected for charging during the low load period of the power grid, and during the low load period of the power grid. During peak load periods, low-load charging piles are selected for charging. For users who choose fixed time, when the selected time is at peak load, the charging rate is maintained within the safety range of the power grid, and during low load periods, high-load charging piles are selected for charging. The charging power is adjusted according to the remaining power of the vehicle to provide the most suitable charging solution for each vehicle, thereby improving charging efficiency. During the low load period of the power grid, the system gives priority to high-load charging piles, and during peak periods, low-load charging piles are used to balance the overall load of the power grid and reduce the impact on the stability of the power grid. At the same time, the charging mode optimization strategy selected by the user is used to meet personalized needs. By intelligently adjusting the charging power and optimization strategy, a more flexible and convenient charging experience is provided to improve user satisfaction.

[0064] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent charging management system based on Internet of Things data synchronization, characterized in that: The system includes: a data acquisition module, a cloud platform, a threshold management module, a charging terminal control module and a charging management module; the data acquisition module is used to monitor the user's charging preferences and the electricity consumption data of the substation; the cloud platform includes a power supply company cloud platform and an orderly charging cloud platform, which is used to receive the electricity load data released by the provincial platform; the threshold management module classifies the charging demand based on the historical load data and classifies the charging equipment according to the demand load; the charging terminal control module is responsible for receiving the electricity consumption data of the orderly charging piles, comparing the expected total power demand with the actual power demand, and adjusting the output power of the charging piles; the charging management module is used to formulate corresponding charging strategies according to the load demand.

2. According to claim 1, an intelligent charging management system based on Internet of Things data synchronization is characterized in that: The data acquisition module includes a power data acquisition unit and a charging preference recording unit. The power data acquisition unit is used to monitor the power consumption data of the substation master meter and the regional master meter, including the charging power and output voltage of the ordered charging piles. The charging preference recording unit is used to record the charging mode selected by the user, wherein the charging mode includes automatic full charge, fixed power, fixed amount and fixed time, and all collected data are transmitted to the charging terminal control module via HPLC+RF dual mode.

3. According to claim 1, the intelligent charging management system based on Internet of Things data synchronization is characterized in that: The threshold management module includes a charging demand grading module and a charging device classification unit. The charging demand grading unit is used to grade the charging demand according to the historical load data of the charging station and the power demand equipment, collect the real-time charging power and grid load changes flowing through each charging device, and obtain the power output difference of each charging device in each level of charging demand and the normal threshold value of the grid load fluctuation range. The charging device classification unit is used to be divided into high-load charging equipment, standard-power charging equipment and low-load charging equipment according to historical load records.

4. According to claim 1, the intelligent charging management system based on Internet of Things data synchronization is characterized in that: The charging terminal control module includes an electric energy demand comparison unit and a power grid load prediction unit. The electric energy demand comparison unit is used to manage area 1 and area 2 through the orderly charging management terminal, and is connected to the regional master table of area 1 and area 2 using HPLC+RF dual mode, to receive the power consumption data of the orderly charging piles, and to compare the expected total electric energy demand with the actual electric energy demand. The power grid load prediction unit is used to establish a power grid load prediction model, predict the power grid load curve, and transmit the prediction results to the cloud platform, and the cloud platform calls the orderly charging terminal to adjust the power of the charging pile.

5. According to claim 1, the intelligent charging management system based on Internet of Things data synchronization is characterized in that: The charging management module includes a charging strategy optimization unit and a feedback unit. The charging strategy optimization unit is used to formulate a charging strategy according to the real-time load demand, the charging mode selected by the user and the load status of the power grid: when the power grid load is low, the power is increased to shorten the charging time; The power is reduced during peak load periods to reduce the grid load, and personalized charging strategies are provided based on user preferences. The feedback unit monitors the load demand of each charging pile and the grid load status during the charging process in real time, and feeds the data back to the charging terminal control module. At the same time, the charging strategy is automatically adjusted based on the feedback information, and the power output is optimized to adapt to changes in real-time load demand and grid conditions.

6. An intelligent charging management method based on Internet of Things data synchronization, comprising the following steps: S1: Obtain historical load data of charging stations and charging equipment, real-time charging power of orderly charging piles and grid load curve; S2: Compare the expected total power demand with the actual power demand. If the expected total power demand is greater than the actual power demand, update the output power of the low-load charging pile. If the expected total power demand is less than the actual power demand, update the output power of the high-load charging pile. S3: Adjust the charging power of the orderly charging pile according to the updated low-load charging power and high-load charging power.

7. The intelligent charging management method based on Internet of Things data synchronization according to claim 6 is characterized in that: In step S1, the demand level classification and orderly charging pile classification are performed based on the historical load data of charging stations and charging equipment, the real-time charging power of orderly charging piles, and the grid load curve. The steps are as follows: S1-1: Based on the historical load data of charging stations and charging equipment, including the charging power, charging time and power demand of historical orderly charging piles in each period, the charging demand is collected and classified into different levels; S1-2: Obtain the charging power of the orderly charging piles during peak, normal and valley time periods, and divide the orderly charging piles into high-load charging piles, standard-power charging piles and low-load charging piles according to historical data.

8. The intelligent charging management method based on Internet of Things data synchronization according to claim 6, characterized in that: In step S2, the expected total power demand is compared with the actual power demand, and the output power of the orderly charging pile is updated. The calculation process includes the following steps: S2-1: Obtain the expected total power demand. The calculation formula is as follows: Among them, D actual Indicates the actual power demand, P rated Indicates the rated output power of the orderly charging pile, N total represents the total number of orderly charging piles HC, standard power charging piles SC and low load charging piles LC in the area, and η represents the charging efficiency of the orderly charging piles; S2-2: The expected total power demand is compared with the actual power demand. When the expected total power demand is greater than the actual power demand, the output power of the low-load charging pile is updated. The calculation formula for updating the LC output of the low-load charging pile is as follows: Among them, P LC_new represents the updated low-load charging pile power, D expected represents the expected total power demand, NLC represents the number of low-load orderly charging piles, Δ Padjusted Indicates the power difference adjusted in real time according to the grid load; When the expected total power demand is less than the actual power demand, the output power of the high-load charging pile is updated, and the calculation formula for the LC output of the high-load charging pile is updated as follows: Among them, P HC_new Represents the updated high-load orderly charging pile power, N HC Indicates the number of high-load orderly charging piles, ΔP balance Indicates the power adjustment item that is dynamically balanced according to the actual load difference; S2-3: Introduce prediction models and feedback mechanisms combined with machine learning to predict the grid load curve and adjust the power output of the charging pile in advance based on the prediction results. The power adjustment term in the formula can be added with a historical data feedback mechanism and optimized based on the previous load adjustment effect. The power calculation formula adjusted in real time according to the grid load is as follows: P adjust =α*P previous +(1-α)*P predicated ; Among them, α represents the adjustment coefficient, P previous Indicates the power adjusted last time, P predicated Indicates the predicted power based on load forecast.

9. The intelligent charging management method based on Internet of Things data synchronization according to claim 6 is characterized in that: In step S3, the charging power of the orderly charging pile is adjusted according to the updated low-load charging power and high-load charging power. At the same time, combined with the real-time detection data, the power is optimized according to the remaining power of the charging vehicle connected to the current charging pile. Specifically, for vehicles with higher remaining power, the output power of the low-load charging pile is selected for charging; for vehicles with lower power, the output power of the high-load charging pile is selected for charging according to the load of the power grid. In addition, the charging strategy is further optimized according to the charging mode selected by the user, including automatic full charge, fixed power, fixed amount and fixed time. Specifically, for users who choose automatic full charge, fixed power and fixed amount, the output power of the high-load charging pile is selected for charging during the low load period of the power grid, and the output power of the low-load charging pile is selected for charging during the peak load period; for users who choose the fixed time, when the selected time is at the peak load period, the charging rate is maintained within the safety range of the power grid, and the output power of the high-load charging pile is selected for charging during the low load period.

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