CN112115350A - Smart city charging intelligent recommendation method and system - Google Patents
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Abstract
The invention relates to an intelligent recommendation method and system for charging of a smart city. The intelligent charging recommendation method for the smart city comprises the following steps: predicting the busy and idle probability of each charging station in a period of time in the future based on the big data of each charging station; scoring each charging station according to the busy and idle probability of each charging station in a future period of time obtained through comprehensive prediction and historical data of each charging station; the grading of each charging station, the vehicle information and the user information of the electric automobile are integrated to obtain available charging station information which can be recommended to an electric automobile user and sent to the electric automobile user; and carrying out self-adaptive adjustment on available charging station information which can be recommended to the electric vehicle user according to the selected charging station fed back by the electric vehicle user. The system for realizing the intelligent charging recommendation method for the smart city comprises a probability prediction module, a grading module, a recommendation module and an adjustment module. The method and the device can recommend a more appropriate charging station for the electric vehicle user.
Description
Technical Field
The invention belongs to the technical field of electric vehicle service, and particularly relates to an intelligent recommendation method and system for charging in a smart city, wherein the intelligent recommendation method and system are used for recommending charging stations to electric vehicle users.
Background
At present new energy automobile production scale constantly enlarges, but the speed increase that does not catch up with electric automobile far away in laying of public charging stake, under this kind of condition, it is more difficult to find suitable own electric pile that fills, still can meet the electric pile inefficacy, and the condition that the electric pile is occupied has become a big pain point of present electric automobile user at present.
Some applications for recommending charging sites/charging piles for electric vehicle users have emerged. The existing recommendation methods generally calculate the possible queuing time based on calculating the number of charging vehicles expected to arrive at the charging station (the charging station expected to arrive depending on the battery status, the remaining mileage, and the like of the electric vehicle). But except for the national platform, other platforms have difficulty accessing all electric vehicles. Therefore, in the prior art, the recommendation of the charging pile may be disconnected from actual data of vehicles and customers, or only calculation is performed without predicting the future situation, or some important influence factors are not taken into consideration, so that the recommendation quality is not high, and the charging pile with the true high quality is not recommended to users with different requirements.
Disclosure of Invention
The invention aims to provide an intelligent charging recommendation method for a smart city, which is based on the self condition of a charging station and can recommend a more appropriate charging station for an electric vehicle user.
In order to achieve the purpose, the invention adopts the technical scheme that:
a smart city charging intelligent recommendation method is used for recommending available charging stations to electric vehicle users and comprises the following steps:
step 1: predicting the busy and idle probability of each charging station in a period of time in the future based on the big data of each charging station;
step 2: scoring each charging station according to the busy and idle probability of each charging station in a future period of time obtained through comprehensive prediction and historical data of each charging station;
and step 3: and integrating the scores of all charging stations, the vehicle information and the user information of the electric automobile to obtain available charging station information which can be recommended to the electric automobile user and sending the available charging station information to the electric automobile user.
The intelligent charging recommendation method for the smart city further comprises the following steps:
and 4, step 4: and carrying out self-adaptive adjustment on available charging station information which can be recommended to the electric vehicle user according to the selected charging station fed back by the electric vehicle user.
The step 1 comprises the following substeps:
substep 1-1: collecting attribute information of each charging station and use history data of each charging station in a past period;
substeps 1-2: dividing one day into n time periods, and marking the busy and idle states of each charging pile in each charging station in each time period in the past time period according to the use historical data of each charging station in the past time period;
substeps 1-3: the use historical data of each charging station in a past period corresponds to supplementary data characteristics;
substeps 1-4: establishing a deep learning model, taking the attribute information of each charging station and the data characteristics corresponding to the use historical data of each charging station in a past period of time as the input of the deep learning model, taking the busy-idle state of each charging station in each period of time in the past period of time as the output of the deep learning model, and training the deep learning model;
substeps 1-5: and predicting the busy and idle probability of each charging station in a future period of time by using the trained deep learning model.
In the substep 1-1, the attribute information of the charging station itself includes the number of interfaces and rated power of the charging pile in the charging station, and the geographical position of the charging station.
In the substep 1-2, the time of day is divided into 48 time segments.
In the sub-steps 1 to 3, the data characteristics include time characteristics, weather characteristics, environment characteristics and price characteristics.
In the step 3, the vehicle information of the electric vehicle includes a vehicle type, mileage, a current SOC, and remaining mileage.
In the step 3, the user information of the electric vehicle includes a location of the electric vehicle user and a common charging location.
The invention also provides a system for realizing the intelligent recommendation method for charging in the smart city, so that a more appropriate charging station can be recommended to an electric vehicle user based on the self condition of the charging station, and the scheme is as follows:
a smart city charging recommendation system for recommending available charging sites to electric vehicle users, comprising:
the probability prediction module is used for predicting the busy and idle probability of each charging station in a future period of time based on the big data of each charging station;
the scoring module is used for comprehensively predicting the busy and idle probabilities of the charging stations in a future period of time and historical data of the charging stations to score the charging stations;
and the recommending module is used for integrating the scores of all charging stations, the vehicle information and the user information of the electric automobile to obtain the available charging station information which can be recommended to the electric automobile user and sending the available charging station information to the electric automobile user.
Intelligent recommendation system charges in wisdom city still includes:
and the modulation module is used for adaptively adjusting the available charging station information which can be recommended to the electric vehicle user according to the selected charging station fed back by the electric vehicle user.
Due to the application of the technical scheme, compared with the prior art, the invention has the following advantages: according to the method and the device, the idle condition of the charging pile in the charging station is predicted based on the self condition of the charging station, and a more appropriate charging station can be recommended for an electric vehicle user by combining other information.
Drawings
Fig. 1 is a flowchart of step 1 in the intelligent charging recommendation method for smart cities according to the present invention.
Fig. 2 is a flowchart of the intelligent charging recommendation method for smart cities.
Detailed Description
The invention will be further described with reference to examples of embodiments shown in the drawings to which the invention is attached.
The first embodiment is as follows: as shown in fig. 1, a smart city charging intelligent recommendation method for recommending available charging sites to electric vehicle users includes the following steps:
step 1: and predicting the busy and idle probability of each charging station in a future period of time based on the big data of each charging station.
As shown in fig. 1, the step 1 includes the following sub-steps:
substep 1-1: attribute information of each charging station itself and usage history data thereof over a past period of time are collected. The attribute information of each charging station comprises the number and rated power of interfaces of charging piles in the charging station, the geographic position of the charging station, and the attribute information of each charging station can also comprise the brand of the charging pile, the brand of an operator and the like. The use history data of the charging station in the past period mainly comprises relevant data in the process of charging the electric automobile, including the starting and ending time of charging, the charging time and the like.
Substeps 1-2: dividing the time of a day into n time periods, and marking the busy-idle state of each charging pile in each charging station in each time period in the past time period according to the use historical data of each charging station in the past time period. A day time can be divided into 48 time segments, i.e. one time segment every half hour. And if a certain charging pile is charging the electric automobile in a time period, marking the time period of the charging pile as busy.
Substeps 1-3: the usage history data for each charging station over a period of time in the past corresponds to the supplemental data characteristics. The data characteristics include time characteristics (for example, whether the charging date is a weekday, a weekend, or a holiday), weather characteristics (season of charging, whether it is a sunny day, etc.), environmental characteristics (temperature at the time of charging, etc.), price characteristics (whether there is a preferential activity, a gear position of a peak-valley-average electricity price to which the time belongs, etc.).
Substeps 1-4: and establishing a deep learning model, taking the attribute information of each charging station and the data characteristics corresponding to the use historical data of each charging station in a past period of time as the input of the deep learning model, and taking the busy and idle states of each charging station in each period of time in the past period of time as the output of the deep learning model, so that the deep learning model is trained by utilizing the mass data to obtain the trained deep learning model.
Substeps 1-5: and predicting the idle probability of each charging station in a future period of time (for example, 24 hours in the future) by using the trained deep learning model. The busy-idle probability of the charging site can be obtained by using the ratio of the number of charging piles in a charging state to the total number of charging piles.
Step 2: and scoring the charging sites by comprehensively predicting the busy and idle probabilities of the charging sites in a future period of time and historical data (such as historical scores of the charging sites, comments of the charging sites, failure rates of charging piles, charging data of the charging piles and the like) of the charging sites.
And step 3: and (3) screening based on set recommendation standards (such as highest score in the remaining mileage, maximum idle probability and the like) by integrating the scores of all charging stations, the vehicle information (including vehicle type, mileage, current SOC, remaining mileage and the like) and the user information (including the position of the electric vehicle user, the common charging position and the like) of the electric vehicle, and obtaining the available charging station information which can be recommended to the electric vehicle user and sending the available charging station information to the electric vehicle user. Wherein, the speed of traveling can be decided to the motorcycle type, and whether electric pile's position can be confirmed to reach in the current position of user and the position of electric pile in the surplus mileage combination.
And 4, step 4: and if the electric vehicle user selects the required charging station and the charging pile based on the recommended available charging station information, and after the selection information is fed back, the available charging station information which can be recommended to the electric vehicle user is adaptively adjusted according to the selected charging station fed back by the electric vehicle user. And the recommendation model of the charging pile also tends to be accurate after long-time data training.
The intelligent recommendation system for charging the smart city, which is used for realizing the intelligent recommendation method for charging the smart city, comprises a probability prediction module, a grading module, a recommendation module and an adjustment module. The probability prediction module is used for predicting the busy and idle probability of each charging station in a future period of time based on the big data of each charging station. The scoring module is used for comprehensively predicting the busy and idle probabilities of the charging stations in a future period of time and the historical data of the charging stations to score the charging stations. The recommendation module is used for integrating the scores of all charging stations, the vehicle information and the user information of the electric automobile to obtain the available charging station information which can be recommended to the electric automobile user and sending the available charging station information to the electric automobile user. The modulation module is used for adaptively adjusting the available charging station information which can be recommended to the electric vehicle user according to the selected charging station fed back by the electric vehicle user.
In the scheme, more conditions are added, a machine learning method is used, the idle probability of the charging pile is predicted, and the situation that the charging pile is high in quality but always occupied and cannot be used is reduced roughly. Most of recommendations are calculated whether the user is idle or not, but the time of the general user arriving at the charging pile is not the calculated time at that time, so machine learning is used for probability prediction, and the probability prediction is finally calculated into a recommendation index of the user. More external factors such as weather, holidays, and peak-to-valley gear shift are added to the prediction, which have a great influence on the idle and busy charging.
According to the scheme, high-quality and good-use charging piles are recommended to users based on an artificial intelligence algorithm according to the demands of the users, the charging piles are characterized according to busy and idle charging states from the charging piles, idle probabilities of the charging piles are represented and predicted through a large amount of charging data, and different weighted influences are recommended to the charging piles by combining predicted arrival time. In the aspect of predicting the idleness of the charging pile, more influence factors are added into the scheme, wherein the influence factors comprise the portrait of a client, the frequent charging address of the client, the time for initiating the charging demand at that time, the weather condition, whether the charging demand is on a weekday or on a weekend, whether the charging demand is on a foreign place or a frequently active place and the like. And finally, vehicle information and customer information are merged into the recommendation of the charging pile, and the deviation of the automatic adjustment model is fed back by the user finally. And adding vehicle and customer information in the recommendation, considering whether the vehicle can arrive and whether the vehicle is suitable for quick charging, and recording the final customer selection into the system for secondary analysis, thereby enhancing the individuation, accuracy and self-adjustment of subsequent recommendation.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (10)
1. A smart city charging intelligent recommendation method is used for recommending available charging stations to electric vehicle users, and is characterized by comprising the following steps: the intelligent charging recommendation method for the smart city comprises the following steps:
step 1: predicting the busy and idle probability of each charging station in a period of time in the future based on the big data of each charging station;
step 2: scoring each charging station according to the busy and idle probability of each charging station in a future period of time obtained through comprehensive prediction and historical data of each charging station;
and step 3: and integrating the scores of all charging stations, the vehicle information and the user information of the electric automobile to obtain available charging station information which can be recommended to the electric automobile user and sending the available charging station information to the electric automobile user.
2. The intelligent recommendation method for charging in smart city according to claim 1, wherein: the intelligent charging recommendation method for the smart city further comprises the following steps:
and 4, step 4: and carrying out self-adaptive adjustment on available charging station information which can be recommended to the electric vehicle user according to the selected charging station fed back by the electric vehicle user.
3. The intelligent recommendation method for charging in smart city according to claim 1 or 2, wherein: the step 1 comprises the following substeps:
substep 1-1: collecting attribute information of each charging station and use history data of each charging station in a past period;
substeps 1-2: dividing one day into n time periods, and marking the busy and idle states of each charging pile in each charging station in each time period in the past time period according to the use historical data of each charging station in the past time period;
substeps 1-3: the use historical data of each charging station in a past period corresponds to supplementary data characteristics;
substeps 1-4: establishing a deep learning model, taking the attribute information of each charging station and the data characteristics corresponding to the use historical data of each charging station in a past period of time as the input of the deep learning model, taking the busy-idle state of each charging station in each period of time in the past period of time as the output of the deep learning model, and training the deep learning model;
substeps 1-5: and predicting the busy and idle probability of each charging station in a future period of time by using the trained deep learning model.
4. The intelligent recommendation method for charging in smart city according to claim 3, wherein: in the substep 1-1, the attribute information of the charging station itself includes the number of interfaces and rated power of the charging pile in the charging station, and the geographical position of the charging station.
5. The intelligent recommendation method for charging in smart city according to claim 3, wherein: in the substep 1-2, the time of day is divided into 48 time segments.
6. The intelligent recommendation method for charging in smart city according to claim 3, wherein: in the sub-steps 1 to 3, the data characteristics include time characteristics, weather characteristics, environment characteristics and price characteristics.
7. The intelligent recommendation method for charging in smart city according to claim 1 or 2, wherein: in the step 3, the vehicle information of the electric vehicle includes a vehicle type, mileage, a current SOC, and remaining mileage.
8. The intelligent recommendation method for charging in smart city according to claim 1 or 2, wherein: in the step 3, the user information of the electric vehicle includes a location of the electric vehicle user and a common charging location.
9. The utility model provides a smart city intelligent recommendation system that charges for recommend available charging website to electric automobile user, its characterized in that: wisdom city intelligent recommendation system that charges includes:
the probability prediction module is used for predicting the busy and idle probability of each charging station in a future period of time based on the big data of each charging station;
the scoring module is used for comprehensively predicting the busy and idle probabilities of the charging stations in a future period of time and historical data of the charging stations to score the charging stations;
and the recommending module is used for integrating the scores of all charging stations, the vehicle information and the user information of the electric automobile to obtain the available charging station information which can be recommended to the electric automobile user and sending the available charging station information to the electric automobile user.
10. The intelligent recommendation system for charging in smart city according to claim 9, wherein: intelligent recommendation system charges in wisdom city still includes:
and the modulation module is used for adaptively adjusting the available charging station information which can be recommended to the electric vehicle user according to the selected charging station fed back by the electric vehicle user.
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| CN115330281A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Smart city new energy automobile charging service method, system, device and medium |
| CN115578855A (en) * | 2022-09-09 | 2023-01-06 | 内蒙古尚禾能源科技有限公司 | Smart city sharing management system and method based on big data encryption |
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