Charging method and device of charging station, electronic equipment and storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN SAN FRAN ELECTRONICS CO LTD
- Filing Date
- 2023-11-20
- Publication Date
- 2026-06-26
Smart Images
Figure CN117584794B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power supply technology, and more specifically, to a charging method, apparatus, electronic device, and storage medium for a charging station. Background Technology
[0002] As global attention to renewable energy continues to grow, electricity usage is increasing.
[0003] Currently, most charging stations utilize pre-set charging power to charge objects such as electric vehicles. However, different users often have different charging needs due to individual circumstances.
[0004] Therefore, the charging methods currently commonly used in charging stations cannot meet the diverse charging needs of different users. Consequently, the coordination among users at charging stations is not scientific enough, ultimately leading to high operation and maintenance costs for charging stations. Summary of the Invention
[0005] The purpose of this application is to provide a charging method, device, electronic equipment, and storage medium for a charging station, which can meet the different charging needs of different users by adopting different charging power, thereby reducing the operation and maintenance cost of the charging station.
[0006] In a first aspect, this application provides a charging method for a charging station, including: determining whether there is a charging power corresponding to a user based on the charging demand; if it is determined that the user's charging power exists, then charging the user's charging object according to the charging power; if it is determined that the user's charging power does not exist, then charging the user by combining the default charging power and the user's custom parameters; and recording the user's station stay data during the process of staying at the charging station and generating the charging power.
[0007] The charging method described above at charging stations determines whether a corresponding charging power exists locally when a user needs charging. If it does, the user is charged at that power; otherwise, the system combines the default charging power with the user's custom parameters, recording relevant station data to generate the appropriate charging power for the user. This allows for targeted charging based on individual user needs, improving the scientific coordination among users at the charging station. Furthermore, it reduces the time spent on maintenance during idle periods, enabling the charging station to serve more users. Ultimately, this lowers maintenance costs and improves charging efficiency.
[0008] In conjunction with the first aspect, optionally, the station stay data includes the user's entry time data and exit time data; the step of recording the user's station stay data during their stay at the charging station and generating the charging power includes: processing the entry time data and exit time data using a Gaussian distribution to obtain the user's entry time prediction model and exit time prediction model respectively; predicting the user's first predicted entry time and first predicted exit time using the entry time prediction model and exit time prediction model; calculating the user's station stay duration based on the first predicted entry time and first predicted exit time; and calculating the charging power based on the station stay duration and the charging demand.
[0009] The charging method described above uses Gaussian distribution processing on the entry and exit times in the station's data to establish a predictive model for predicting user dwell time. This improves the accuracy of predicting user dwell time. Furthermore, the charging power is calculated based on the predicted dwell time and used to charge the user, better meeting user expectations. Ultimately, this further enhances the scientific coordination among users at the charging station, reduces operating costs, and improves charging efficiency.
[0010] In conjunction with the first aspect, optionally, the step of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time includes: adjusting the first predicted entry time and the first predicted exit time according to the daylight saving time system to obtain a second predicted entry time and a second predicted exit time; and calculating the stay duration based on the second predicted entry time and the second predicted exit time.
[0011] The charging method described above, based on the prediction of the first predicted entry and exit times using entry and exit time prediction models respectively, further improves the accuracy of predicting user dwell time by adjusting the first predicted entry and exit times according to the daylight saving time system. Ultimately, this further enhances the scientific coordination among users at the charging station, reduces the operation and maintenance costs of the charging station, and improves charging efficiency.
[0012] In conjunction with the first aspect, optionally, the station stay data includes the weather data upon the user's entry into the charging station and the weather data upon the user's exit from the charging station; the step of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time includes: constructing an entry time weather correction function to correct the first predicted entry time based on the entry weather data and the corresponding entry time data; constructing an exit time weather correction function to correct the first predicted exit time based on the exit weather data and the corresponding exit time data; correcting the first predicted entry time and the first predicted exit time using the entry time weather correction function and the exit time weather correction function to obtain a third predicted entry time and a third predicted exit time; and calculating the stay duration based on the third predicted entry time and the third predicted exit time.
[0013] The charging method described above for charging stations constructs weather correction functions for arrival and departure times based on arrival and departure weather data, respectively. These functions are then used to correct the first predicted arrival and departure times, resulting in third predicted arrival and departure times. This further improves the accuracy of predicting user dwell time. Ultimately, it enhances the scientific coordination among users at the charging station, reduces operating costs, and improves charging efficiency.
[0014] In conjunction with the first aspect, optionally, the arrival weather data includes arrival rainfall, and the departure weather data includes departure rainfall; the arrival time weather correction function includes an arrival time rainfall correction function, and the departure time weather correction function includes a departure time rainfall correction function; the arrival time rainfall correction function and the departure time rainfall correction function are respectively:
[0015]
[0016]
[0017] In the formula, μ' start For the third predicted arrival time, μ start Let μ' be the first predicted arrival time. end For the third predicted departure time, μ end Let k be the first predicted departure time. start k is the in-station correction parameter of the in-station time rainfall correction function. end x is the exit correction parameter of the exit time rainfall correction function. rain Let G(x) be the rainfall amount. rainThe correction value is determined based on the rainfall amount; the station-retention data includes the duration of inbound rainfall corresponding to the inbound rainfall amount and the duration of outbound rainfall corresponding to the outbound rainfall amount; the calculation of the station-retention duration based on the third predicted inbound time and the third predicted outbound time includes: calculating the k based on the duration of inbound rainfall and the corresponding third predicted inbound time. start The k is calculated based on the duration of the rainfall at the station and the corresponding third predicted departure time. end ; using the k start and k end The third predicted arrival time and the third predicted departure time are corrected to obtain the fourth predicted arrival time and the fourth predicted departure time; and the stay duration is calculated based on the fourth predicted arrival time and the fourth predicted departure time.
[0018] The charging method at the aforementioned charging station calculates k in the rainfall correction function for the entry time and the rainfall correction function for the exit time based on the duration of rainfall. start and k end This further improves the accuracy of predicting user dwell time at charging stations. Ultimately, it enhances the scientific nature of coordination among users at charging stations, reduces operating costs, and improves charging efficiency.
[0019] In conjunction with the first aspect, optionally, the station stay data includes the entry temperature data when the user enters the charging station and the exit temperature data when the user leaves the charging station; the step of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time includes: constructing an entry time temperature correction function to correct the first predicted entry time based on the entry temperature data and the corresponding entry time data; constructing an exit time temperature correction function to correct the first predicted exit time based on the exit temperature data and the corresponding exit time data; correcting the first predicted entry time and the first predicted exit time using the entry time temperature correction function and the exit time temperature correction function to obtain a fifth predicted entry time and a fifth predicted exit time; and calculating the stay duration based on the fifth predicted entry time and the fifth predicted exit time.
[0020] The charging method described above at the charging station improves the accuracy of predicting user dwell time by constructing temperature correction functions for both entry and exit times based on temperature data. Ultimately, this enhances the scientific coordination among users at the charging station, reduces operating costs, and increases charging efficiency.
[0021] In conjunction with the first aspect, optionally, wherein the charging station has a plurality of charging piles, and the method further includes: predicting the number of users and user types entering the charging station at a target time based on the first predicted entry time; wherein the user types are classified according to the user charging priority; and reserving a corresponding number of charging piles based on the number of users and user types.
[0022] The charging method of the above-mentioned charging station predicts the number and type of users at the target time by predicting the first entry time. Based on their priority and the number of users, charging piles are reserved for the target time, which further improves the scientific nature of the charging station's overall coordination among users.
[0023] Secondly, this application also provides a charging device for a charging station, including a judgment module, a charging module, and a recording module; the judgment module is used to determine whether there is a charging power corresponding to the user based on the charging demand; the charging module is used to charge the user's charging object according to the charging power if it is determined that the user's charging power exists; the charging module is also used to charge the user by combining the default charging power and the user's custom parameters if it is determined that the user's charging power does not exist; the recording module is used to record the user's station stay data during the process of staying in the charging station and generate the charging power.
[0024] The charging device of the above-mentioned charging station has the same beneficial effects as the charging method of a charging station provided by the first aspect or any optional embodiment of the first aspect, which will not be elaborated here.
[0025] Thirdly, this application also provides an electronic device, including: a processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when executed by the processor, perform the methods described above.
[0026] The aforementioned electronic device has the same beneficial effects as the charging method of a charging station provided by the first aspect or any optional embodiment of the first aspect, which will not be elaborated here.
[0027] Fourthly, this application also provides a storage medium, the storage medium including a computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to perform the methods described above.
[0028] The aforementioned storage medium has the same beneficial effects as the charging method of a charging station provided by the first aspect or any alternative embodiment of the first aspect, which will not be elaborated here.
[0029] In summary, the charging method, device, electronic equipment, and storage medium provided in this application determine whether a charging power corresponding to the user exists locally. If it does, the user is charged according to that charging power; otherwise, the user is charged using a combination of default charging power and user-defined parameters, and the application records the station-staying data related to this charging session. This generates the charging power for the user, reducing the time spent on maintenance during the charging station's idle state and enabling the charging station to serve more users. Ultimately, this reduces the charging station's maintenance costs and improves charging efficiency. By applying Gaussian distribution processing to the entry and exit time data in the station-staying data, a predictive model for predicting user station-staying duration is established, improving the accuracy of user station-staying duration prediction. This, in turn, enhances the scientific nature of the charging station's coordination among users, further reducing maintenance costs and improving charging efficiency. Furthermore, by using daylight saving time and weather data to refine the predictive model, the maintenance costs of the charging station are further reduced, and charging efficiency is improved. Attached Figure Description
[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0031] Figure 1 A flowchart illustrating the charging method for a charging station provided in an embodiment of this application;
[0032] Figure 2 A detailed flowchart of step S40 in the charging method of the charging station provided in the embodiments of this application;
[0033] Figure 3 This is a first detailed flowchart of step S43 in the charging method of the charging station provided in the embodiments of this application;
[0034] Figure 4 A second detailed flowchart of step S43 in the charging method of the charging station provided in the embodiments of this application;
[0035] Figure 5 This is a second detailed flowchart of step S4306 in the charging method of the charging station provided in the embodiments of this application;
[0036] Figure 6 A third detailed flowchart of step S43 in the charging method of the charging station provided in the embodiments of this application;
[0037] Figure 7A functional block diagram of the charging device for a charging station provided in an embodiment of this application;
[0038] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0039] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.
[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application.
[0041] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0042] Current charging stations typically use a pre-set charging power to charge all users uniformly. Because they don't tailor the charging power to the specific needs of different users, they fail to meet diverse charging requirements, ultimately leading to high operating and maintenance costs for charging stations.
[0043] In view of this, this application provides a charging method, apparatus, electronic device, and storage medium for a charging station to solve the above-mentioned technical problems. Specifically, please refer to the embodiments and accompanying drawings provided in this application.
[0044] Please refer to Figure 1 , Figure 1 This is a flowchart of a charging method for a charging station provided in an embodiment of this application. The charging method for a charging station provided in an embodiment of this application may include:
[0045] Step S10: Determine whether there is a charging power corresponding to the user based on the charging demand.
[0046] If it is determined that there is a user's charging power, then step S20 is executed: charge the user's charging object according to the charging power.
[0047] If it is determined that there is no user charging power, then step S30 is executed: charge the user by combining the default charging power and the user's custom parameters.
[0048] Step S40: Record the user's station stay data during the charging station stay and generate charging power.
[0049] The above steps can be executed by the charging control unit. The charging station can be an electric vehicle charging station, a bicycle charging station, an electric motorcycle charging station, a battery charging station, a portable electronic device charging station, a power tool charging station, or an industrial equipment charging station, etc.
[0050] In step S10 above, taking an electric vehicle charging station as an example, when a user connects to the charging gun, the unique identifier of the electric vehicle, such as the vehicle identification number (VIN) or battery serial number (SN), can be obtained and identified to determine the user's identity. Based on the identified information, the system queries whether a charging strategy corresponding to that user has been established and stored locally. This charging strategy can be the charging power for the electric vehicle. For example, for users with ample charging time, the charging power can be lower; for users with limited charging time, the charging power can be higher. During the charging process, the charging power can be constant or it can vary over time; for the same user, there can be only one charging power or more than one.
[0051] In step S20 above, if it is determined that the charging control unit has a charging power corresponding to the user, the user can be charged according to the charging power.
[0052] In steps S30 and S40 above, the charging control unit can locally preset a default charging strategy, i.e., a default charging power. This default charging power can be set according to charging methods known to those skilled in the art. Furthermore, the charging control unit can accept user-defined parameters and adjust the default charging power in response to these parameters, charging the user with the adjusted power.
[0053] If the user's custom parameters are not obtained, charging can be performed directly based on the default charging power.
[0054] During or after charging, the system can record user data at the charging station, such as entry and exit times, weather conditions, and charging power. Based on this recorded data, the system can then generate the appropriate charging power for each user.
[0055] In the above implementation process, when a user needs charging, it checks if a corresponding charging power exists locally. If it does, the user is charged at that power; otherwise, it combines the default charging power with the user's custom parameters, and records the relevant station data to generate the appropriate charging power for the user. This allows for targeted charging based on the different charging needs of various users, improving the scientific coordination among users at the charging station. Furthermore, it reduces the time spent on maintenance during idle periods, enabling the charging station to serve more users. Ultimately, this lowers the operating costs of the charging station and improves charging efficiency.
[0056] Please refer to Figure 2 , Figure 2 This is a detailed flowchart of step S40 in the charging method for a charging station provided in this application embodiment. In some optional implementations, the station stay data may include the user's entry time data and exit time data.
[0057] Accordingly, step S40 may include:
[0058] Step S41: Use Gaussian distribution to process the arrival time data and departure time data respectively to obtain the user's arrival time prediction model and departure time prediction model respectively.
[0059] Step S42: Use the arrival time prediction model and the departure time prediction model to predict the user's first predicted arrival time and first predicted departure time.
[0060] Step S43: Calculate the duration of the user's stay at the charging station based on the first predicted entry time and the first predicted exit time.
[0061] Step S44: Calculate the charging power based on the station stay time and charging demand.
[0062] The entry time data can be the time when a user starts charging or connects to the charging device, while the exit time data can be the time when a user finishes charging or disconnects from the charging device. Entry time data can also be the time when a user enters the charging station, and exit time data can be the time when a user leaves the charging station. Taking an electric vehicle charging station as an example, the entry and exit time data of the electric vehicle can be obtained from the barrier gate.
[0063] In step S41, after obtaining the user's entry and exit time data for 21 days or more, this data can be processed using a Gaussian distribution. Taking an electric vehicle charging station as an example, by processing the user's entry time data using a Gaussian distribution, the probability distribution of a user driving into the charging station at time t within a day (t∈[0,24]) can be expressed as:
[0064]
[0065] In the formula, f start (t) represents the probability that a user drives into the charging station at time t, σstart represents the discrete coefficients of the probability function for a user driving into the charging station, and μ start The time parameter represents the probability function of a user driving into a charging station.
[0066] Similarly, the probability distribution of a user driving away from the charging station at time t can be expressed as:
[0067]
[0068] In the formula, f end (t) represents the probability that the user drives away from the charging station at time t, σend represents the discrete coefficients of the probability function of the user driving away from the charging station, and μ end The time parameter of the probability function of a user driving away from a charging station.
[0069] In steps S42 to S44, the user's first predicted entry time and first predicted exit time can be predicted using the aforementioned entry and exit probability distributions. The user's expected stay time is then calculated based on these first predicted entry and exit times. Continuing with the example of an electric vehicle charging station, after the charging gun is connected to the user's electric vehicle, the remaining battery power of the electric vehicle is obtained to determine its current charging level. Combining the predicted stay time and charging level, the charging power for the user can be calculated using the formula: "Charging Power = Charging Level ÷ Stay Time".
[0070] In the above implementation process, Gaussian distribution processing is applied to the entry and exit time data in the charging station data to establish a predictive model for predicting user dwell time. This improves the accuracy of predicting user dwell time. Furthermore, the charging power for charging users is calculated based on the predicted dwell time, and charging is performed using this power, which better aligns with user expectations. Ultimately, this further enhances the scientific nature of the charging station's overall coordination among users, reduces charging station operation and maintenance costs, and improves charging efficiency.
[0071] Please refer to Figure 3 , Figure 3 This is a first detailed flowchart of step S43 in the charging method for a charging station provided in this application embodiment. In some optional embodiments, step S43 may include:
[0072] Step S4301: Adjust the first predicted arrival time and the first predicted departure time according to the daylight saving time system to obtain the second predicted arrival time and the second predicted departure time.
[0073] Step S4302: Calculate the dwell time based on the second predicted entry time and the second predicted exit time.
[0074] The aforementioned daylight saving time system may include daylight saving time and / or winter time. Based on the first predicted arrival time and the first predicted departure time predicted using the aforementioned arrival time prediction model and departure time prediction model, respectively, the first predicted arrival time and the first predicted departure time can be adjusted based on the daylight saving time system to obtain a second predicted arrival time and a second predicted departure time. For example, the daylight saving time system may be as shown in Table 1.
[0075] Table 1
[0076]
[0077] In the above implementation process, based on the prediction of the first predicted arrival time and the first predicted departure time using the arrival time prediction model and the departure time prediction model respectively, the first predicted arrival time and the first predicted departure time are adjusted according to the daylight saving time system, further improving the accuracy of predicting the user's stay time at the station. Ultimately, this further improves the scientific nature of the charging station's overall coordination among users, reduces the charging station's operation and maintenance costs, and improves charging efficiency.
[0078] Please refer to Figure 4 , Figure 4 This is a second detailed flowchart of step S43 in the charging method for a charging station provided in this application embodiment. In some optional embodiments, the station stay data may include weather data when the user enters the charging station and weather data when the user leaves the charging station.
[0079] Accordingly, step S43 may include:
[0080] Step S4303: Construct an arrival time weather correction function to correct the first predicted arrival time based on the arrival weather data and the corresponding arrival time data.
[0081] Step S4304: Construct an exit time weather correction function to correct the first predicted exit time based on the exit weather data and the corresponding exit time data.
[0082] Step S4305: Correct the first predicted arrival time and the first predicted departure time using the weather correction function for arrival time and departure time to obtain the third predicted arrival time and the third predicted departure time.
[0083] Step S4306: Calculate the dwell time based on the third predicted entry time and the third predicted exit time.
[0084] Understandably, weather usually affects users' travel plans. In slightly worse weather, users will typically postpone or advance their trips; in extremely bad weather, users will usually not travel at all. The aforementioned weather data for entering the station can include the weather type at the time of entry, such as: rainy, sunny, cloudy, windy, snowy, etc. Rainy days can include rainfall amount, windy days can include wind speed, and snowy days can include snow depth.
[0085] For example, based on the impact of rainfall on user travel shown in Table 2, the above-mentioned weather correction functions for arrival time and departure time are constructed.
[0086] Table 2
[0087]
[0088] Based on Table 2 and practical experience, rainfall inevitably impacts people's travel. Therefore, within the range of rainfall from 0 to a specific value, the impact typically follows an approximately exponential function. However, from this specific value onwards, the impact of increasing rainfall within a certain range slows down, but after reaching a certain order of magnitude, the impact increases again. This leads to the conclusion that both the rainfall correction functions for arrival and departure times are cubic functions (greater than or equal to cubic). To reduce the computational burden on management equipment, cubic function regression fitting can be performed using surveyed user data. With a goodness of fit greater than 0.9 after correction, the obtained functions can be used as the rainfall correction functions for arrival and departure times, respectively.
[0089]
[0090]
[0091] In the formula, μ' start For the third predicted arrival time, μ start For the first predicted arrival time, μ' end For the third predicted departure time, μ end For the first predicted departure time, k start k is the in-station correction parameter for the in-station time rainfall correction function. end x is the exit correction parameter for the rainfall correction function at exit time. rain For rainfall, G(x) rain () is a correction value determined based on rainfall.
[0092] It is worth mentioning that those skilled in the art can quantify other data in the weather data based on the conceptual ideas of the embodiments of this application, and construct a prediction model for the arrival and departure times of the station based on the quantified data. Therefore, the embodiments of this application do not list them one by one.
[0093] In the above implementation process, weather correction functions for arrival and departure times are constructed based on arrival and departure weather data, respectively. These functions are then used to correct the first predicted arrival and departure times, resulting in the third predicted arrival and departure times. This further improves the accuracy of predicting user dwell time. Ultimately, it enhances the scientific coordination among users at the charging station, reduces operating costs, and improves charging efficiency.
[0094] Please refer to Figure 5 , Figure 5 This is a second detailed flowchart of step S4306 in the charging method for a charging station provided in this application embodiment. In some optional embodiments, the weather correction function for arrival time may include a rainfall correction function for arrival time, and the weather correction function for departure time may include a rainfall correction function for departure time.
[0095] The rainfall correction functions for arrival time and departure time can be as follows:
[0096]
[0097]
[0098] The data retained at the station can include the duration of rainfall at the station corresponding to the rainfall amount entering the station and the duration of rainfall at the station corresponding to the rainfall leaving the station.
[0099] Accordingly, step S4306 may include:
[0100] Step S43061: Calculate k based on the duration of rainfall entering the station and the corresponding third predicted entry time. start .
[0101] Step S43062: Calculate k based on the duration of rainfall at the station and the corresponding third predicted departure time. end .
[0102] Step S43063: Using k start and k end The third predicted arrival time and the third predicted departure time are corrected to obtain the fourth predicted arrival time and the fourth predicted departure time.
[0103] Step S43064: Calculate the dwell time based on the fourth predicted entry time and the fourth predicted exit time.
[0104] Understandably, the duration of rainfall usually affects users' travel. The charging control unit can obtain weather data for entering and leaving the station, as well as the corresponding actual entry and exit times of users, and obtain the aforementioned k through a fitting method. start and k end The value of .
[0105] For example, please refer to Table 3 for the relationship between rainfall duration and k. start and k end The value of .
[0106] Table 3
[0107]
[0108] In the above implementation process, k is calculated based on the rainfall duration in the rainfall correction function for arrival time and the rainfall correction function for departure time. start and k end This further improves the accuracy of predicting user dwell time at charging stations. Ultimately, it enhances the scientific nature of coordination among users at charging stations, reduces operating costs, and improves charging efficiency.
[0109] Please refer to Figure 6 , Figure 6 This is a third detailed flowchart of step S43 in the charging method for a charging station provided in this application embodiment. In some optional embodiments, the station data may include the entry temperature data when the user enters the charging station and the exit temperature data when the user leaves the charging station.
[0110] Accordingly, step S43 may include:
[0111] Step S4307: Construct an entry time and temperature correction function to correct the first predicted entry time based on the entry temperature data and the corresponding entry time data.
[0112] Step S4308: Construct an exit time and temperature correction function to correct the first predicted exit time based on the exit temperature data and the corresponding exit time data.
[0113] Step S4309: Correct the first predicted arrival time and the first predicted departure time using the arrival time temperature correction function and the departure time temperature correction function to obtain the fifth predicted arrival time and the fifth predicted departure time.
[0114] Step S4310: Calculate the dwell time based on the fifth predicted entry time and the fifth predicted exit time.
[0115] Based on the rainfall correction functions for arrival and departure times constructed in the aforementioned embodiments, the influence of temperature data on user arrival and departure times can be introduced. The expressions for the temperature correction functions for arrival and departure times can be:
[0116] μ′ start =μ start -G(x rain )+G(x Temp )
[0117] μ′ end =μ end +G(x rain )+G(x Temp )
[0118] In the formula, x Temp This represents the temperature, and the unit can be ℃.
[0119] In the probability function P of temperature influence Temp In (n), the influence of temperature is calculated after rounding the temperature, i.e., n = round(x Temp Therefore, we can conclude that:
[0120] G(x Temp ) = P Temp (n)·k Temp
[0121] Since temperature measurements are typically not integers, the model used here to calculate the influence of temperature is based on a Poisson distribution. Because the independent variable (the value substituted) in a Poisson distribution must be an integer, a rounding function is needed to adjust the measured value before inputting it into the model for calculation. That is, n is the rounded temperature measurement value in the formula.
[0122] In the formula, k Temp These are the correction parameters for the temperature at the arrival time.
[0123]
[0124] Similarly, the charging control unit can acquire the inbound and outbound temperature data, as well as the corresponding actual inbound and outbound times of the user, and obtain the aforementioned k through a fitting method. Temp The value of .
[0125] For example, please refer to Table 4 for the relationship between temperature and k in conjunction with rainfall. Temp The value of .
[0126] Table 4
[0127]
[0128] In the above implementation process, temperature correction functions for arrival and departure times are constructed based on temperature data. This further improves the accuracy of predicting the duration of user stays at the charging station. Ultimately, it further enhances the scientific nature of the overall coordination among users at the charging station, reduces the operation and maintenance costs of the charging station, and improves charging efficiency.
[0129] In some optional implementations, the charging station has a number of charging piles, and the charging method of the charging station provided in this application embodiment may further include:
[0130] Step S50: Predict the number of users and user types entering the charging station at the target time based on the first predicted entry time; wherein, the user type is divided according to the user's charging priority.
[0131] Step S60: Reserve the corresponding number of charging piles according to the number of users and user types.
[0132] For example, the priority of electric vehicles, from highest to lowest, could be: fire trucks, ambulances, buses, taxis, and private cars. The charging station has 10 charging stations. By predicting the arrival time of each user through the charging control unit, it can be known that more than 10 electric vehicles will enter the station for charging at 14:00 on a certain day. These vehicles only include taxis and private cars. Among the vehicles entering the station for charging at 14:30 on the same day, there are 2 fire trucks. In this case, since fire trucks have a higher priority, two charging stations can be reserved for them to avoid waiting for the fire trucks entering the station for charging at 14:30.
[0133] In the above implementation process, the number of users and user types at the target time are predicted by the first predicted entry time. Based on their priority and the number of users, charging piles are reserved for the target time, which further improves the scientific nature of the charging station's overall coordination among users.
[0134] Please refer to Figure 7 , Figure 7This is a functional block diagram of the charging device 700 for a charging station provided in this application embodiment. Based on the same concept, the charging device 700 for a charging station provided in this application embodiment may include a judgment module 710, a charging module 720, and a recording module 730. The judgment module 710 is used to determine whether there is a charging power corresponding to the user's charging needs based on the charging demand. The charging module 720 is used to charge the user's charging device according to the charging power if it is determined that the user's charging power exists. The charging module 720 is also used to charge the user if it is determined that there is no charging power for the user, combining the default charging power and the user's custom parameters. The recording module 730 is used to record the user's station stay data during the process of staying in the charging station and generate the charging power.
[0135] Please continue to refer to Figure 7 In some optional implementations, the station stay data includes the user's entry time data when entering the charging station and the exit time data when leaving the charging station.
[0136] Accordingly, in the process of recording the user's stay data during their stay at the charging station and generating charging power, the recording module 730 is specifically used to: process the entry time data and exit time data using a Gaussian distribution to obtain the user's entry time prediction model and exit time prediction model respectively; predict the user's first predicted entry time and first predicted exit time using the entry time prediction model and exit time prediction model; calculate the user's stay duration at the charging station based on the first predicted entry time and first predicted exit time; and calculate the charging power based on the stay duration and charging demand.
[0137] Please continue to refer to Figure 7 In some optional implementations, during the process of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time, the recording module 730 is more specifically used to: adjust the first predicted entry time and the first predicted exit time according to the daylight saving time system to obtain the second predicted entry time and the second predicted exit time; and calculate the stay duration based on the second predicted entry time and the second predicted exit time.
[0138] Please continue to refer to Figure 7 In some optional implementations, the station stay data includes weather data when a user enters the charging station and weather data when they leave the charging station.
[0139] Accordingly, in the process of calculating the user's stay duration at the charging station based on the first predicted arrival time and the first predicted departure time, the recording module 730 is more specifically used for: constructing an arrival time weather correction function to correct the first predicted arrival time based on arrival weather data and corresponding arrival time data; constructing an departure time weather correction function to correct the first predicted departure time based on departure weather data and corresponding departure time data; correcting the first predicted arrival time and the first predicted departure time using the arrival time weather correction function and the departure time weather correction function to obtain the third predicted arrival time and the third predicted departure time; and calculating the stay duration based on the third predicted arrival time and the third predicted departure time.
[0140] Please continue to refer to Figure 7 In some optional implementations, the weather correction function for arrival time includes a rainfall correction function for arrival time, and the weather correction function for departure time includes a rainfall correction function for departure time.
[0141] The rainfall correction functions for arrival time and departure time are as follows:
[0142]
[0143]
[0144] In the formula, μ' start For the third predicted arrival time, μ start For the first predicted arrival time, μ' end For the third predicted departure time, μ end For the first predicted departure time, k start k is the in-station correction parameter for the in-station time rainfall correction function. end x is the exit correction parameter for the rainfall correction function at exit time. rain For rainfall, G(x) rain () is a correction value determined based on rainfall.
[0145] The data retained at the station includes the duration of rainfall at the station corresponding to the rainfall amount entering the station and the duration of rainfall at the station corresponding to the rainfall leaving the station.
[0146] Accordingly, in the process of calculating the dwell time based on the third predicted arrival time and the third predicted departure time, the recording module 730 is more specifically used to: calculate k based on the duration of rainfall upon arrival and the corresponding third predicted arrival time. start k is calculated based on the duration of rainfall at the station and the corresponding third predicted departure time. end ; using kstart and k end The third predicted arrival time and the third predicted departure time are corrected to obtain the fourth predicted arrival time and the fourth predicted departure time; and the stay time is calculated based on the fourth predicted arrival time and the fourth predicted departure time.
[0147] Please continue to refer to Figure 7 In some optional implementations, the station data includes the temperature data when the user enters the charging station and the temperature data when the user leaves the charging station.
[0148] Accordingly, in the process of calculating the user's stay duration at the charging station based on the first predicted arrival time and the first predicted departure time, the recording module 730 is more specifically used for: constructing an arrival time temperature correction function to correct the first predicted arrival time based on the arrival temperature data and the corresponding arrival time data; constructing an departure time temperature correction function to correct the first predicted departure time based on the departure temperature data and the corresponding departure time data; correcting the first predicted arrival time and the first predicted departure time using the arrival time temperature correction function and the departure time temperature correction function to obtain the fifth predicted arrival time and the fifth predicted departure time; and calculating the stay duration based on the fifth predicted arrival time and the fifth predicted departure time.
[0149] In some optional implementations, the charging station has a number of charging piles. The charging device 700 for the charging station provided in this application embodiment further includes a prediction module and a reservation module. The prediction module is used to predict the number of users and user types entering the charging station at a target time based on a first predicted entry time; wherein the user types are classified according to the user charging priority. The reservation module is used to reserve a corresponding number of charging piles based on the number of users and user types.
[0150] It should be understood that this device corresponds to the charging method embodiment of the charging station described above, and is capable of performing the various steps involved in the above method embodiment. The specific functions of this device can be found in the description above, and detailed descriptions are omitted here to avoid repetition. The device includes at least one software function module that can be stored in memory or embedded in the device's operating system (OS) in the form of software or firmware.
[0151] Based on the same inventive concept, please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device 800 provided in an embodiment of this application. The electronic device 800 may include a memory 811, a memory controller 812, a processor 813, a peripheral interface 814, an input / output unit 815, and a display unit 816. Those skilled in the art will understand that... Figure 8The structure shown is for illustrative purposes only and does not limit the structure of the electronic device 800. For example, the electronic device 800 may also include components that are more... Figure 8 The more or fewer components shown, or having the same Figure 8 The different configurations shown.
[0152] The aforementioned memory 811, memory controller 812, processor 813, peripheral interface 814, input / output unit 815, and display unit 816 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The aforementioned processor 813 is used to execute executable modules stored in the memory.
[0153] The memory 811 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 811 stores programs, and the processor 813 executes these programs upon receiving execution instructions. The methods executed by the electronic device 800, as defined in any embodiment of this application, can be applied to or implemented by the processor 813.
[0154] The aforementioned processor 813 may be an integrated circuit chip with signal processing capabilities. The processor 813 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.
[0155] The peripheral interface 814 described above couples various input / output devices to the processor 813 and the memory 811. In some embodiments, the peripheral interface 814, the processor 813, and the memory controller 812 can be implemented in a single chip. In other instances, they can be implemented by separate chips.
[0156] The input / output unit 815 described above is used to provide user input data. The input / output unit 815 may be, but is not limited to, a mouse and a keyboard.
[0157] The aforementioned display unit 816 provides an interactive interface (e.g., a user interface) between the electronic device 800 and the user, or displays image data for the user's reference. In this embodiment, the display unit can be a liquid crystal display (LCD) or a touch display. If it is a touch display, it can be a capacitive touchscreen or a resistive touchscreen that supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch display can sense touch operations generated simultaneously from one or more locations on the touch display and pass the sensed touch operations to the processor for calculation and processing.
[0158] The electronic device 800 in this embodiment can be used to perform the various steps in the various methods provided in the embodiments of this application.
[0159] This application also provides a storage medium, which includes a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the methods described above.
[0160] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0161] It should be understood that the disclosed apparatus and methods can also be implemented in other ways, given the several embodiments provided in this application. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0162] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.
[0163] In summary, the charging methods, devices, electronic devices, and storage media provided in the various embodiments of this application determine whether a charging power corresponding to the user exists locally. If it does, the user is charged according to that charging power; otherwise, the user is charged using a combination of default charging power and user-defined parameters, and the station-staying data related to this charging is recorded. This generates the charging power for the user, reducing the time spent on maintenance during the charging station's idle state and enabling the charging station to serve more users. Ultimately, this reduces the maintenance cost of the charging station and improves charging efficiency. By applying Gaussian distribution processing to the entry and exit time data in the station-staying data, a prediction model for predicting user station-staying time is established, improving the accuracy of predicting user station-staying time. This, in turn, enhances the scientific nature of the charging station's coordination among users, reduces maintenance costs, and improves charging efficiency. Furthermore, by using daylight saving time and weather data to correct the prediction model, the maintenance cost of the charging station is further reduced, and charging efficiency is improved.
[0164] The above description is only an optional implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application.
Claims
1. A charging method for a charging station, characterized in that, include: Determine whether the corresponding charging power exists for the user based on the charging demand; If it is determined that the user has the charging power, then the user's charging object is charged according to the charging power; If it is determined that the user's charging power does not exist, then the user is charged by combining the default charging power and the user's custom parameters; as well as Record the user's station stay data during the charging station stay and generate the charging power; The station stay data includes the user's entry time data when entering the charging station and exit time data when leaving the charging station; The process of recording the user's station stay data during their stay at the charging station and generating the charging power includes: The entry time data and exit time data are processed using Gaussian distribution to obtain the user's entry time prediction model and exit time prediction model, respectively. The first predicted entry time and the first predicted exit time of the user are predicted using the entry time prediction model and the exit time prediction model. The duration of the user's stay at the charging station is calculated based on the first predicted entry time and the first predicted exit time; and The charging power is calculated based on the station dwell time and the charging demand. The station stay data includes the weather data when the user enters the charging station and the weather data when the user leaves the charging station. The step of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time includes: Based on the arrival weather data and the corresponding arrival time data, an arrival time weather correction function is constructed to correct the first predicted arrival time. Based on the exit weather data and the corresponding exit time data, an exit time weather correction function is constructed to correct the first predicted exit time. The first predicted arrival time and the first predicted departure time are corrected using the arrival time weather correction function and the departure time weather correction function to obtain the third predicted arrival time and the third predicted departure time; and The duration of stay is calculated based on the third predicted entry time and the third predicted exit time. The weather data upon arrival at the station includes rainfall upon arrival, and the weather data upon departure from the station includes rainfall upon departure; the weather correction function for arrival time includes a rainfall correction function for arrival time, and the weather correction function for departure time includes a rainfall correction function for departure time. The rainfall correction functions for arrival time and departure time are respectively: In the formula, μ' start The third predicted arrival time, μ start This is the first predicted arrival time. μ' end The third predicted departure time, μ end This is the first predicted departure time. k start The entry correction parameter is the entry time rainfall correction function. k end The outbound correction parameter is the outbound correction parameter of the rainfall correction function for the outbound time. x rain The rainfall amount, G （x rain ） This is a correction value determined based on the rainfall amount; The data retained at the station includes the duration of rainfall at the station corresponding to the rainfall amount entering the station and the duration of rainfall at the station corresponding to the rainfall amount leaving the station; The calculation of the dwell time based on the third predicted entry time and the third predicted exit time includes: The calculation is based on the duration of the rainfall upon arrival at the station and the corresponding third predicted arrival time. k start ; The calculation is based on the duration of the rainfall at the station and the corresponding third predicted departure time. k end ; Using the k start and k end The third predicted arrival time and the third predicted departure time are corrected to obtain the fourth predicted arrival time and the fourth predicted departure time; and The dwell time is calculated based on the fourth predicted entry time and the fourth predicted exit time.
2. The method according to claim 1, characterized in that, The step of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time includes: The first predicted arrival time and the first predicted departure time are adjusted according to the daylight saving time system to obtain the second predicted arrival time and the second predicted departure time; and The dwell time is calculated based on the second predicted entry time and the second predicted exit time.
3. The method according to claim 1, characterized in that, in, The station stay data includes the temperature data when the user enters the charging station and the temperature data when the user leaves the charging station; The step of calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time includes: Based on the arrival temperature data and the corresponding arrival time data, an arrival time temperature correction function is constructed to correct the first predicted arrival time. Based on the exit temperature data and the corresponding exit time data, an exit time and temperature correction function is constructed to correct the first predicted exit time. The first predicted arrival time and the first predicted departure time are corrected using the arrival time-temperature correction function and the departure time-temperature correction function to obtain the fifth predicted arrival time and the fifth predicted departure time; and The dwell time is calculated based on the fifth predicted entry time and the fifth predicted exit time.
4. The method according to any one of claims 1 to 3, characterized in that, in, The charging station has a number of charging piles, and the method further includes: The number and type of users entering the charging station at the target time are predicted based on the first predicted entry time; wherein, the user type is classified according to the user's charging priority; and The corresponding number of charging piles will be reserved based on the number of users and user types.
5. A charging device for a charging station, characterized in that, It includes a judgment module, a charging module, and a recording module; The judgment module is used to determine whether there is a charging power corresponding to the user based on the charging demand; The charging module is used to charge the user's charging object according to the charging power if it is determined that the user has a charging power. The charging module is also used to charge the user if it is determined that there is no charging power for the user, by combining the default charging power and the user's custom parameters; The recording module is used to record the user's station stay data during the period of the user's stay at the charging station and generate the charging power; The station stay data includes the user's entry time data when entering the charging station and exit time data when leaving the charging station; In the process of recording the user's stay data at the charging station and generating the charging power, the recording module is specifically used to: process the entry time data and exit time data using a Gaussian distribution to obtain the user's entry time prediction model and exit time prediction model respectively; predict the user's first predicted entry time and first predicted exit time using the entry time prediction model and exit time prediction model; calculate the user's stay duration at the charging station based on the first predicted entry time and first predicted exit time; and calculate the charging power based on the stay duration and the charging demand. The station stay data includes the weather data upon the user's entry into the charging station and the weather data upon the user's exit from the charging station. In calculating the user's stay duration at the charging station based on the first predicted entry time and the first predicted exit time, the recording module is specifically used for: constructing an entry time weather correction function based on the entry weather data and the corresponding entry time data to correct the first predicted entry time; constructing an exit time weather correction function based on the exit weather data and the corresponding exit time data to correct the first predicted exit time; and utilizing the entry time weather data... The weather correction function and the departure time weather correction function correct the first predicted arrival time and the first predicted departure time to obtain the third predicted arrival time and the third predicted departure time; and calculate the stay duration based on the third predicted arrival time and the third predicted departure time; wherein, the arrival weather data includes arrival rainfall, and the departure weather data includes departure rainfall; the arrival time weather correction function includes an arrival time rainfall correction function, and the departure time weather correction function includes a departure time rainfall correction function; the arrival time rainfall correction function and the departure time rainfall correction function are respectively: In the formula, μ' start The third predicted arrival time, μ start This is the first predicted arrival time. μ' end The third predicted departure time, μ end This is the first predicted departure time. k start The entry correction parameter is the entry time rainfall correction function. k end The outbound correction parameter is the outbound correction parameter of the rainfall correction function for the outbound time. x rain The rainfall amount, G （x rain ） This is a correction value determined based on the rainfall amount; The station-retention data includes the duration of inbound rainfall corresponding to the inbound rainfall amount and the duration of outbound rainfall corresponding to the outbound rainfall amount; in the process of calculating the station-retention duration based on the third predicted inbound time and the third predicted outbound time, the recording module is specifically used to: calculate the station-retention duration based on the duration of inbound rainfall and the corresponding third predicted inbound time. k start ; Calculate the following based on the duration of the rainfall at the station and the corresponding third predicted departure time. k end ; using the above k start and k end The third predicted arrival time and the third predicted departure time are corrected to obtain the fourth predicted arrival time and the fourth predicted departure time; and the stay duration is calculated based on the fourth predicted arrival time and the fourth predicted departure time.
6. An electronic device, characterized in that, include: A processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when executed by the processor, perform the method as described in any one of claims 1 to 4.
7. A storage medium, characterized in that, The storage medium includes a computer-readable storage medium; the computer-readable storage medium stores a computer program that, when executed by a processor, performs the method as described in any one of claims 1 to 4.