An intelligent management method and system for alternating current charging piles based on big data

Abstract

This application relates to a smart management method and system for AC charging piles based on big data, belonging to the field of smart charging pile technology. The method includes: acquiring the required output power; analyzing the required output power to define multiple-visit customers; analyzing historical charging data among multiple-visit customers to determine the charging urgency, and defining loose charging piles and urgent charging piles based on the charging urgency; determining the urgent demand power based on the urgent charging piles; determining the available loose power based on the upper limit output power and the urgent demand power, and determining the individual loose power based on the available loose power and the required output power of the loose charging piles; controlling the urgent charging piles to operate at the corresponding required output power, and controlling the loose charging piles to operate at the corresponding individual loose power. This application improves the overall charging experience for users utilizing charging piles.

Description

Technical Field

[0001] This application relates to the field of smart charging pile technology, and in particular to a smart management method and system for AC charging piles based on big data. Background Technology

[0002] Currently, a large number of AC charging piles are deployed in public parking lots, commercial centers, residential areas and other areas, forming charging stations or charging clusters. In actual operation, many stations are limited by early power planning or expansion costs, and their total access power has a clear upper limit constraint.

[0003] When the number of vehicles charging at the station increases and the total charging power demand approaches or exceeds the total power threshold that the station can provide, in order to ensure grid safety and avoid overload tripping, the operation and management system usually adopts a simple protective strategy: that is, to indiscriminately reduce the power of all charging piles that are working in the station, so as to force the total load to be controlled within the safe limit.

[0004] However, in real-world charging scenarios, users' urgency of needs is heterogeneous: some users may be in situations such as long-distance charging or urgent matters, and are highly sensitive to charging completion time; while other users, such as those parking overnight or charging during office hours, have a higher tolerance for charging completion time and are more concerned about charging cost or convenience. The current method of indiscriminately reducing the output power of all charging stations, while ensuring system safety, leads to a mismatch between demand and resources: vehicles in a hurry are forced to slow down their charging speed, severely impacting the user experience; while vehicles that are not in a hurry fail to convert their available "time flexibility" into a buffer resource for system adjustment, resulting in a decline in the overall charging experience, and there is still room for improvement. Summary of the Invention

[0005] To improve the overall charging experience for users using charging stations, this application provides a smart management method and system for AC charging stations based on big data.

[0006] Firstly, this application provides an intelligent management method for AC charging piles based on big data, employing the following technical solution:

[0007] A smart management method for AC charging piles based on big data includes:

[0008] Obtain the required output power of each charging station;

[0009] The total required power is determined by summing the output power of each demand, and charging user data is obtained from each charging pile when the total required power exceeds the preset upper limit output power.

[0010] On a preset timeline, a historical interval with a preset historical duration is constructed with the current time point as the endpoint, and historical charging data is determined within the historical interval based on charging user data.

[0011] Under a single charging user's data, the historical charging count is determined based on historical charging data, and users whose historical charging count is greater than the preset benchmark charging count are defined as multiple-visit customers;

[0012] Based on historical charging data from multiple customer visits, the charging urgency is determined. Charging piles corresponding to users whose charging urgency is less than the preset demand urgency are defined as loose charging piles, and the remaining charging piles are defined as urgent charging piles.

[0013] The urgent demand power is determined by calculating the output power required by the urgent charging pile, and the loose available power is determined by calculating the upper limit output power and the urgent demand power. The loose power of a single unit is determined by calculating the loose available power and the demand output power of the loose charging pile.

[0014] Control the urgent charging piles to operate at the corresponding required output power, and control the loose charging piles to operate at the corresponding individual loose power.

[0015] Optionally, after determining the historical number of charging sessions, the intelligent management method for AC charging piles based on big data also includes:

[0016] Obtain vehicle connection data for charging stations;

[0017] The stationary charging data corresponding to the vehicle connection data is determined based on the preset vehicle matching relationship.

[0018] Vehicle charging data is determined from vehicle connectivity data, and the charging data deviation value is calculated based on static charging data and vehicle charging data.

[0019] Determine whether the charging data deviation value is greater than the preset charging allowable deviation value;

[0020] If the charging data deviation is not greater than the charging permission deviation, then multiple customer visits are determined based on the historical number of charging visits.

[0021] If the charging data deviation value is greater than the charging permission deviation value, the current charging pile will be defined as an urgent charging pile.

[0022] Optionally, the step of analyzing historical charging data across multiple customer visits to determine charging urgency includes:

[0023] Obtain the current access point based on the current number of customer visits;

[0024] Historical access points are obtained from historical charging data, and the access interval is determined based on the historical access points and the current access point.

[0025] Historical access points with an access interval shorter than the preset similar interval are defined as similar access points, and the charging completion point and device disconnection point are determined based on the historical charging data of similar access points.

[0026] The time interval between completing the unplugging process is calculated based on the charging completion point and the device disconnection point.

[0027] The representative disconnection interval is determined by calculating the interval between all disconnections, and the charging urgency corresponding to the representative disconnection interval is determined according to the preset urgency matching relationship.

[0028] Optionally, the step of calculating and determining the representative removal interval based on all completed removal intervals includes:

[0029] Randomly select one completion time interval as the primary time interval, and define the remaining completion time intervals as secondary time intervals;

[0030] The main similar intervals are constructed by calculating based on the main interval duration and the preset similar interval duration, and the secondary intervals within the main similar intervals are counted to determine the internal secondary quantity.

[0031] The internal minor quantity with the largest value is determined according to the preset sorting rules, and the main similar interval corresponding to the internal minor quantity is defined as the behavior representative interval.

[0032] Within the representative interval of the behavior, the representative removal interval is determined by calculation based on the time interval between each removal completion.

[0033] Optionally, the step of calculating and determining the representative removal interval based on the interval between removal completions within the representative behavior interval includes:

[0034] A value is randomly selected within the behavior representative interval and defined as the simulated interval duration. The interval duration for completion and removal within the behavior representative interval is defined as the internal interval duration, and the interval duration for completion and removal outside the behavior representative interval is defined as the external interval duration.

[0035] The simulated internal distance is determined by calculation based on the simulated interval duration and the internal interval duration, and the simulated external distance is determined by calculation based on the simulated interval duration and the external interval duration.

[0036] The simulated representative coefficient is determined by calculating the simulated internal distance, the preset internal weight coefficient, the simulated external distance, and the preset external weight coefficient, where the internal weight coefficient is greater than the external weight coefficient.

[0037] The simulation representative coefficient with the largest value is determined according to the sorting rules, and the simulation interval corresponding to the simulation representative coefficient is defined as the representative removal interval.

[0038] Optionally, the step of calculating the individual loose power based on the loosely available power and the required output power of the loosely available charging pile includes:

[0039] Based on the vehicle connection data of loose charging piles and the current access point, a demand charging range is constructed, and data analysis is performed within the demand charging range based on the demand output power to determine the demand charging cost.

[0040] The proportion of loosely located charging piles is determined by calculating the required output power, and the available power of each loosely located charging pile is determined by calculating the available power of each loosely located charging pile.

[0041] A simulated available power is randomly generated under each loosely located charging pile, and the simulated available power is combined to construct a simulated power scheme;

[0042] Based on data analysis of the simulated available power within the demand charging range, the simulated charging cost is determined, and the simulated power scheme in which each simulated charging cost is not greater than the corresponding demand charging cost is defined as the effective power scheme.

[0043] Under the effective power scheme, the available power of each unit and the corresponding simulated available power are used to calculate and determine the available deviation value of each unit;

[0044] The power availability suitability is determined by calculating all available deviations for each individual unit, and the simulated available power of each effective power scheme corresponding to the maximum power availability suitability is determined as the loose power of each individual unit.

[0045] Secondly, this application provides an intelligent management system for AC charging piles based on big data, which adopts the following technical solution:

[0046] A smart management system for AC charging piles based on big data includes:

[0047] The acquisition module is used to obtain the required output power of each charging pile;

[0048] The processing module, connected to the acquisition and judgment modules, is used for information storage and processing;

[0049] The judgment module, connected to the acquisition and processing modules, is used for judging information.

[0050] The processing module calculates the total required power by summing the output power of each demand, and when the judgment module determines that the total required power is greater than the preset upper limit output power, the processing module obtains charging user data based on each charging pile.

[0051] The processing module constructs a historical interval with a preset historical duration on a preset time axis, with the current time point as the endpoint, and determines historical charging data based on charging user data within the historical interval.

[0052] The processing module counts based on historical charging data under a single charging user data to determine the number of historical charging times, and defines users whose historical charging times are greater than the preset benchmark charging times as multiple-access customers.

[0053] The processing module analyzes historical charging data from multiple customer visits to determine the charging urgency. It defines the charging piles corresponding to users whose charging urgency is less than the preset demand urgency as loose charging piles and the rest as urgent charging piles.

[0054] The processing module calculates the urgent demand power based on the output power required by the urgent charging pile, and calculates the loose available power based on the upper limit output power and the urgent demand power, and calculates the loose power of a single unit based on the loose available power and the output power required by the loose charging pile.

[0055] The processing module controls the urgent charging piles to operate at the corresponding required output power, and controls the loose charging piles to operate at the corresponding individual loose power.

[0056] In summary, this application includes at least one of the following beneficial technical effects:

[0057] During the use of AC charging stations, when the power exceeds the site's upper limit, the charging needs of each vehicle can be analyzed to reasonably schedule the charging power of each vehicle, thereby improving the overall vehicle charging experience.

[0058] Based on whether there are people in the vehicle and the user's historical charging habits, the user's charging needs can be determined more accurately, thus improving the accuracy of data analysis.

[0059] When adjusting the charging power of a vehicle, it is possible to make a better adjustment based on the cost situation, so as to avoid the occurrence of additional costs due to power adjustment. Attached Figure Description

[0060] Figure 1 This is a flowchart of an intelligent management method for AC charging piles based on big data.

[0061] Figure 2This is a flowchart of the module for a smart management method for AC charging piles based on big data. Detailed Implementation

[0062] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0063] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0064] This application discloses an intelligent management method for AC charging piles based on big data, referring to... Figure 1 The methodology for intelligent management of AC charging piles based on big data includes the following steps:

[0065] Step S100: Obtain the required output power of each charging pile.

[0066] The required output power is the power output value required by each charging station to effectively charge the vehicle after it is connected to the charging station.

[0067] Step S101: Calculate the total required power by summing the output power of each demand, and obtain charging user data based on each charging pile when the total required power is greater than the preset upper limit output power.

[0068] The total power demand is the sum of all demand output power, and the upper limit output power is the maximum output power that the current site can provide. When the total power demand is greater than the upper limit output power, it means that it is currently impossible for each charging pile to operate at the demand output power. Therefore, further analysis is needed to control the power allocation of the charging piles. The charging user data is the data collected after the user scans the code to log in to the backend system, which includes user account information.

[0069] Step S102: Construct a historical interval with a preset historical duration on the preset time axis, with the current time point as the endpoint, and determine the historical charging data in the historical interval based on the charging user data.

[0070] The timeline is a coordinate axis formed by combining various time points. This timeline points from the time points that have already passed to the time points that have not yet been reached, with the time points that have already passed being on the left, i.e. the beginning. The historical duration is the total duration of the current charging station's operation as set by the staff. Historical intervals are constructed to facilitate the acquisition and analysis of data within the historical duration. Historical charging data is the charging data of the current charging users within the historical interval.

[0071] Step S103: Under a single charging user data, count based on historical charging data to determine the number of historical charging times, and define users whose historical charging times are greater than a preset baseline number of charging times as multiple-visit customers.

[0072] Historical charging counts are the total number of times a single user charges within a historical period, which can be determined by counting the historical charging data obtained within the historical period. Baseline charging counts are the minimum number of historical charging counts required to identify and explore the charging habits of a user by recognizing multiple charging sessions. By defining multiple-visit customers, different users can be identified and distinguished, which facilitates subsequent analysis.

[0073] Step S104: Analyze historical charging data from multiple customer visits to determine the charging urgency, and define the charging piles corresponding to users whose charging urgency is less than the preset demand urgency as loose charging piles, and define the remaining charging piles as urgent charging piles.

[0074] Charging urgency is a numerical value reflecting the degree of urgency of the user's charging needs. The higher the value, the more urgent the user needs to charge. The specific determination method can be found in steps S300-S304. Demand urgency is the minimum charging urgency set by the staff when they determine that the user urgently needs to charge. By defining loose charging piles and urgent charging piles, different charging piles are identified and distinguished to facilitate subsequent analysis.

[0075] Step S105: Calculate the urgent demand power based on the demand output power of the urgent charging pile, calculate the loose available power based on the upper limit output power and the urgent demand power, and calculate the loose power of a single unit based on the loose available power and the demand output power of the loose charging pile.

[0076] The urgent demand power is the total power required by all urgent charging piles. The available loose power is the value obtained by subtracting the urgent demand power from the upper limit output power. In other words, it reflects the power value that each loose charging pile can still obtain when each urgent charging pile is operating at the required output power. When this value is negative, an alarm signal is output to allow staff to intervene. The individual loose power is the power value that can be allocated to a single loose charging pile. The actual demand of each loose charging pile can be determined by the proportion of the required output power of each loose charging pile. Then, the available loose power can be allocated according to the demand proportion. For details, please refer to steps S600-S605.

[0077] Step S106: Control the urgent charging pile to operate with the corresponding required output power, and control the loose charging pile to operate with the corresponding individual loose power.

[0078] By controlling each charging station to operate at the corresponding power, users who are in a hurry can charge according to their plan, while the charging plan can be adjusted for users who are not in a hurry, thereby improving the overall charging experience for users using charging stations.

[0079] Once the historical number of charging sessions is determined, the intelligent management method for AC charging stations based on big data also includes:

[0080] Step S200: Obtain vehicle connection data of the charging pile.

[0081] The vehicle connectivity data is real-time feedback data from the vehicles connected to the charging station.

[0082] Step S201: Determine the stationary charging data corresponding to the vehicle connection data based on the preset vehicle matching relationship.

[0083] The stationary charging data is the charging data that the current model of vehicle needs to display when charging without starting the vehicle. The vehicle connection data can be used to determine the vehicle model and other information. The vehicle matching relationship between the two is determined by the staff in advance through multiple tests.

[0084] Step S202: Determine the vehicle charging data from the vehicle connection data, and calculate the charging data deviation value based on the stationary charging data and the vehicle charging data.

[0085] Vehicle charging data is the data reported by the vehicle currently charging. The charging data deviation value is the difference between the stationary charging data and the vehicle charging data. The larger the value, the more serious the deviation between the two data.

[0086] Step S203: Determine whether the charging data deviation value is greater than the preset charging allowable deviation value.

[0087] The charging allowable deviation value is the maximum allowable deviation value of charging data when the difference between the stationary charging data and the vehicle charging data is not significant, as set by the staff. The purpose of the judgment is to determine whether the vehicle is currently running based on the analysis of the charging data, so as to objectively reflect whether the user is sitting in the car, that is, to indirectly reflect whether the customer is in a hurry to charge.

[0088] Step S2031: If the charging data deviation value is not greater than the charging permission deviation value, then determine the number of times to visit the customer based on the historical charging count.

[0089] When the charging data deviation value is not greater than the charging allowable deviation value, it means that the user is not sitting in the car. In this case, it is impossible to determine whether the user is in a hurry to charge using this method. Therefore, it is sufficient to perform multiple customer visits for analysis as usual.

[0090] Step S2032: If the charging data deviation value is greater than the charging permission deviation value, then define the current charging pile as an urgent charging pile.

[0091] When the charging data deviation value is greater than the charging permission deviation value, it indicates that the user is currently sitting in the car. At this time, it can be determined that the user is in a hurry to charge, so it is defined as an urgent charging station for subsequent analysis.

[0092] The steps for determining charging urgency by analyzing historical charging data from multiple customer visits include:

[0093] Step S300: Obtain the current access point based on the current multi-access customer.

[0094] The current access point refers to the time when the user's vehicle connects to the charging pile system, which is only relative to a 24-hour period.

[0095] Step S301: Obtain historical access points from historical charging data, and determine the access interval based on historical access points and the current access point.

[0096] Historical access points are the times when a user accesses the charging pile system during their historical charging process. The access interval is the time interval between the historical access point and the current access point within 24 hours of a day, without considering the time interval between dates.

[0097] Step S302: Define historical access points with an access interval shorter than the preset similar interval as similar access points, and determine the charging completion point and the device disconnection point based on the historical charging data of similar access points.

[0098] The similarity interval is the maximum allowed access interval set by the staff when two time points are considered to be close. By defining similar access points, historical access points with similar charging situations can be identified and distinguished to facilitate subsequent analysis. The charging completion point is the time when the vehicle finishes charging and stops the function. The device disconnection point is the time when the charging gun is unplugged from the vehicle.

[0099] Step S303: Calculate the time interval between the completion of charging and the disconnection of the device.

[0100] The time interval between disconnection and charging completion is the time interval between the charging completion point and the device disconnection point.

[0101] Step S304: Calculate the representative disconnection interval based on all the complete disconnection intervals, and determine the charging urgency corresponding to the representative disconnection interval based on the preset urgency matching relationship.

[0102] The interval between disconnection and departure represents the time between when the vehicle finishes charging and when the charging gun is disconnected and the user leaves the site under normal charging conditions. It can be determined by calculating the average of all disconnection intervals, or by using the method in steps S400-S403. The larger the interval between disconnection and departure, the less urgent the user is in charging, and therefore the lower the urgency of charging. The urgency matching relationship between the two is determined by the staff in advance through multiple tests, which will not be elaborated here.

[0103] The steps for determining the representative removal interval based on all completed removal intervals include:

[0104] Step S400: Randomly select one completion removal interval as the primary interval, and define the remaining completion removal intervals as secondary intervals.

[0105] By defining primary and secondary intervals, different completion and removal intervals can be identified and distinguished, facilitating subsequent analysis.

[0106] Step S401: Calculate based on the main interval duration and the preset similar duration to construct the main similar interval, and count based on the secondary interval duration within the main similar interval to determine the internal secondary quantity.

[0107] The "similar duration" is the maximum allowable difference between two intervals defined by staff. The upper and lower endpoints of the interval can be determined by adding and subtracting the similar duration from the primary interval. The primary similar interval is constructed based on these upper and lower endpoints. The numbers within the primary similar interval are those that are closer to the primary interval. The secondary quantities are the number of secondary intervals that are closer to the primary interval, which can be determined by counting the secondary intervals within the primary similar interval.

[0108] Step S402: Determine the largest internal minor quantity according to the preset sorting rules, and define the main similar interval corresponding to the internal minor quantity as the behavior representative interval.

[0109] The sorting rules are methods set by staff to sort numerical values, such as the bubble sort algorithm. By sorting rules, the internal secondary quantity with the largest value can be determined, which means that the current main similar interval has the longest interval between unplugging and charging. In other words, the value in this interval best reflects the user's charging habits. Therefore, a behavior representative interval is defined to distinguish different main similar intervals, which is convenient for subsequent analysis.

[0110] Step S403: Calculate the interval between removals within the representative interval based on the interval between each removal completion to determine the representative removal interval.

[0111] At this point, the representative disconnection interval can be determined by averaging the disconnection intervals within all representative behavior intervals, or by using the method in steps S500-S503, in order to improve the accuracy of determining the representative disconnection interval and thus improve the accuracy of analyzing user charging habits.

[0112] The steps for determining the representative removal interval within the representative interval based on the time interval between removal completions include:

[0113] Step S500: Randomly select a value within the behavior representative interval and define it as the simulated interval duration. Define the completion removal interval within the behavior representative interval as the internal interval duration, and define the completion removal interval outside the behavior representative interval as the external interval duration.

[0114] By defining the simulation interval duration, we can simulate and analyze the values ​​within the behavioral representative interval; by defining the internal interval duration and the external interval duration, we can identify and distinguish different completion and removal interval durations, which will facilitate subsequent analysis.

[0115] Step S501: Calculate the simulated internal distance based on the simulated interval duration and the internal interval duration, and calculate the simulated external distance based on the simulated interval duration and the external interval duration.

[0116] The simulated internal distance is the difference between the simulated time interval and the internal time interval, and the simulated external distance is the difference between the simulated time interval and the external time interval. Both of these differences are absolute values.

[0117] Step S502: Calculate and determine the simulated representative coefficient based on the simulated internal distance, the preset internal weight coefficient, the simulated external distance, and the preset external weight coefficient, wherein the internal weight coefficient is greater than the external weight coefficient.

[0118] The simulation representative coefficient is a parameter reflecting the feasibility of using the current simulation interval as a representative of the removal interval. The calculation formula is as follows: ,in To simulate representative coefficients, For the first A simulated internal distance, For the first A simulated external distance, This represents the number of internal time intervals. This represents the number of external time intervals. These are internal weighting coefficients. Here are the external weighting coefficients, where Should be 5-10 times.

[0119] Step S503: Determine the largest simulated representative coefficient according to the sorting rules, and define the simulated interval corresponding to the simulated representative coefficient as the representative removal interval.

[0120] The largest simulated representative coefficient can be determined by sorting rules, which means that the current simulated interval best represents the user's charging habits. Therefore, it can be defined as the representative disconnection interval.

[0121] The steps for determining the loose power of a single unit based on the available loose power and the required output power of the loose charging pile include:

[0122] Step S600: Construct a demand charging zone based on the vehicle connection data of the loose charging piles and the current access point, and perform data analysis based on the demand output power within the demand charging zone to determine the demand charging cost.

[0123] The demand charging interval is the theoretical time interval during which a vehicle needs to charge when it starts charging at the current access point and charges according to the required output power. The demand charging fee is the theoretical cost incurred when charging is performed according to the power at each time point within the demand charging interval.

[0124] Step S601: Calculate the required output power of loose charging piles to determine the proportion of loose individual units, and calculate the available power of individual units based on the proportion of loose individual units and the available power of loose charging piles.

[0125] The loose unit ratio is the proportion of the required output power of a single loose charging pile to the sum of the required output power of all loose charging piles. The available power of a single unit is the power value that each loose charging pile can receive after the loose available power is allocated according to the proportion. It is determined by multiplying the loose unit ratio by the loose available power.

[0126] Step S602: Randomly generate a simulated available power for each loosely located charging pile, and combine the simulated available power to construct a simulated power scheme.

[0127] The simulated available power is the power supplied by each loosely connected charging pile, and the sum of the simulated available power should be the loosely connected available power. By constructing a simulated power scheme, the allocation scheme of each power is identified and distinguished, which facilitates subsequent analysis.

[0128] Step S603: Based on the simulated available power within the demand charging range, perform data analysis to determine the simulated charging cost, and define the simulated power schemes in which each simulated charging cost is no greater than the corresponding demand charging cost as effective power schemes.

[0129] The simulated charging cost is the cost of charging a vehicle, obtained from the simulated charging analysis of the available power at the loose charging pile. When each simulated charging cost is not greater than the corresponding required charging cost, it means that there is no additional cost to the user due to the power adjustment. Therefore, it meets the most basic requirement of power adjustment and is defined as an effective power scheme to distinguish different simulated power schemes for subsequent analysis.

[0130] Step S604: Under the effective power scheme, calculate the available power of each cell and the corresponding simulated available power to determine the available deviation value of each cell.

[0131] The available deviation value for a single unit is the difference between the available power of the single unit and the corresponding simulated available power, and this difference is an absolute value.

[0132] Step S605: Calculate the power availability suitability based on all available deviation values ​​of each individual unit, and determine the simulated available power of each effective power scheme corresponding to the maximum power availability suitability as the loose power of each individual unit.

[0133] Power availability suitability is a parameter value that reflects the suitability of the output power of each charging pile under the effective power scheme. The larger the value, the more suitable the corresponding effective power scheme is. It is determined by the reciprocal of the average value of the available deviation of all individual units. At this time, the effective power scheme corresponding to the largest power availability suitability is the most suitable power adjustment scheme. Therefore, the corresponding simulated available power can be determined as the loose power of the individual unit, which is convenient for subsequent control of each loose charging pile.

[0134] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide an intelligent management system for AC charging piles based on big data, comprising:

[0135] The acquisition module is used to obtain the required output power of each charging pile;

[0136] The processing module, connected to the acquisition and judgment modules, is used for information storage and processing;

[0137] The judgment module, connected to the acquisition and processing modules, is used for judging information.

[0138] The processing module calculates the total required power by summing the output power of each demand, and when the judgment module determines that the total required power is greater than the preset upper limit output power, the processing module obtains charging user data based on each charging pile.

[0139] The processing module constructs a historical interval with a preset historical duration on a preset time axis, with the current time point as the endpoint, and determines historical charging data based on charging user data within the historical interval.

[0140] The processing module counts based on historical charging data under a single charging user data to determine the number of historical charging times, and defines users whose historical charging times are greater than the preset benchmark charging times as multiple-access customers.

[0141] The processing module analyzes historical charging data from multiple customer visits to determine the charging urgency. It defines the charging piles corresponding to users whose charging urgency is less than the preset demand urgency as loose charging piles and the rest as urgent charging piles.

[0142] The processing module calculates the urgent demand power based on the output power required by the urgent charging pile, and calculates the loose available power based on the upper limit output power and the urgent demand power, and calculates the loose power of a single unit based on the loose available power and the output power required by the loose charging pile.

[0143] The processing module controls the urgent charging piles to operate at the corresponding required output power, and controls the loose charging piles to operate at the corresponding individual loose power.

[0144] The vehicle stationary status analysis module is used to analyze the status of vehicles that are stationary and charging to determine the user's urgency for charging.

[0145] The charging urgency calculation module is used to calculate and analyze the charging urgency of vehicles at each charging station.

[0146] The module for determining the interval between representative removals is used to determine the interval between representative removals.

[0147] The module for accurately determining the interval between representative removals is used to precisely measure the interval between representative removals.

[0148] The individual loose power determination module is used to determine the loose power of each individual unit.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A smart management method for AC charging piles based on big data, characterized in that, include: Obtain the required output power of each charging station; The total required power is determined by summing the output power of each demand, and charging user data is obtained from each charging pile when the total required power exceeds the preset upper limit output power. On a preset timeline, a historical interval with a preset historical duration is constructed with the current time point as the endpoint, and historical charging data is determined within the historical interval based on charging user data. Under a single charging user data, the historical charging data is used to count to determine the number of historical charging times, and users whose historical charging times are greater than the preset benchmark charging times are defined as multiple-visit customers; Based on historical charging data from multiple customer visits, the charging urgency is determined. Charging piles corresponding to users whose charging urgency is less than the preset demand urgency are defined as loose charging piles, and the remaining charging piles are defined as urgent charging piles. The urgent demand power is determined by calculating the output power required by the urgent charging pile, and the loose available power is determined by calculating the upper limit output power and the urgent demand power. The loose power of a single unit is determined by calculating the loose available power and the demand output power of the loose charging pile. Control the urgent charging piles to operate at the corresponding required output power, and control the loose charging piles to operate at the corresponding individual loose power; Once the historical number of charging sessions is determined, the intelligent management method for AC charging stations based on big data also includes: Obtain vehicle connection data for charging stations; The stationary charging data corresponding to the vehicle connection data is determined based on the preset vehicle matching relationship. Vehicle charging data is determined from vehicle connectivity data, and the charging data deviation value is calculated based on static charging data and vehicle charging data. Determine whether the charging data deviation value is greater than the preset charging allowable deviation value; If the charging data deviation is not greater than the charging permission deviation, then multiple customer visits are determined based on the historical number of charging visits. If the charging data deviation value is greater than the charging permission deviation value, the current charging pile will be defined as an urgent charging pile. The steps for determining charging urgency by analyzing historical charging data from multiple customer visits include: Obtain the current access point based on the current number of customer visits; Historical access points are obtained from historical charging data, and the access interval is determined based on the historical access points and the current access point. Historical access points with an access interval shorter than the preset similar interval are defined as similar access points, and the charging completion point and device disconnection point are determined based on the historical charging data of similar access points. The time interval between completing the unplugging process is calculated based on the charging completion point and the device disconnection point. The representative disconnection interval is determined by calculating based on all the complete disconnection intervals, and the charging urgency corresponding to the representative disconnection interval is determined based on the preset urgency matching relationship. The steps for determining the loose power of a single unit based on the available loose power and the required output power of the loose charging pile include: Based on the vehicle connection data of loose charging piles and the current access point, a demand charging range is constructed, and data analysis is performed within the demand charging range based on the demand output power to determine the demand charging cost. The proportion of loosely located charging piles is determined by calculating the required output power, and the available power of each loosely located charging pile is determined by calculating the available power of each loosely located charging pile. A simulated available power is randomly generated under each loosely located charging pile, and the simulated available power is combined to construct a simulated power scheme; Based on data analysis of the simulated available power within the demand charging range, the simulated charging cost is determined, and the simulated power scheme in which each simulated charging cost is not greater than the corresponding demand charging cost is defined as the effective power scheme. Under the effective power scheme, the available power of each unit and the corresponding simulated available power are used to calculate and determine the available deviation value of each unit; The power availability suitability is determined by calculating all available deviations for each individual unit, and the simulated available power of each effective power scheme corresponding to the maximum power availability suitability is determined as the loose power of each individual unit.

2. The intelligent management method for AC charging piles based on big data according to claim 1, characterized in that, The steps for determining the representative removal interval based on all completed removal intervals include: Randomly select one completion time interval as the primary time interval, and define the remaining completion time intervals as secondary time intervals; The main similar intervals are constructed by calculating based on the main interval duration and the preset similar interval duration, and the secondary intervals within the main similar intervals are counted to determine the internal secondary quantity. The internal minor quantity with the largest value is determined according to the preset sorting rules, and the main similar interval corresponding to the internal minor quantity is defined as the behavior representative interval. Within the representative interval of the behavior, the representative removal interval is determined by calculation based on the time interval between each removal completion.

3. The intelligent management method for AC charging piles based on big data according to claim 2, characterized in that, The steps for determining the representative removal interval within the representative interval based on the time interval between removal completions include: A value is randomly selected within the behavior representative interval and defined as the simulated interval duration. The interval duration for completion and removal within the behavior representative interval is defined as the internal interval duration, and the interval duration for completion and removal outside the behavior representative interval is defined as the external interval duration. The simulated internal distance is determined by calculation based on the simulated interval duration and the internal interval duration, and the simulated external distance is determined by calculation based on the simulated interval duration and the external interval duration. The simulated representative coefficient is determined by calculating the simulated internal distance, the preset internal weight coefficient, the simulated external distance, and the preset external weight coefficient, where the internal weight coefficient is greater than the external weight coefficient. The simulation representative coefficient with the largest value is determined according to the sorting rules, and the simulation interval corresponding to the simulation representative coefficient is defined as the representative removal interval.

4. A big data-based intelligent management system for AC charging piles, used to implement the big data-based intelligent management method for AC charging piles as described in any one of claims 1-3, characterized in that, include: The acquisition module is used to obtain the required output power of each charging pile; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The judgment module, connected to the acquisition and processing modules, is used for judging information. The processing module calculates the total required power by summing the output power of each demand, and when the judgment module determines that the total required power is greater than the preset upper limit output power, the processing module obtains charging user data based on each charging pile. The processing module constructs a historical interval with a preset historical duration on a preset time axis, with the current time point as the endpoint, and determines historical charging data based on charging user data within the historical interval. The processing module counts based on historical charging data under a single charging user data to determine the number of historical charging times, and defines users whose historical charging times are greater than the preset benchmark charging times as multiple-access customers. The processing module analyzes historical charging data from multiple customer visits to determine the charging urgency. It defines the charging piles corresponding to users whose charging urgency is less than the preset demand urgency as loose charging piles and the rest as urgent charging piles. The processing module calculates the urgent demand power based on the output power required by the urgent charging pile, and calculates the loose available power based on the upper limit output power and the urgent demand power, and calculates the loose power of a single unit based on the loose available power and the output power required by the loose charging pile. The processing module controls the urgent charging piles to operate at the corresponding required output power, and controls the loose charging piles to operate at the corresponding individual loose power.

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