CN114298770A

CN114298770A - Charging station recommendation method, device, storage medium, and program product

Info

Publication number: CN114298770A
Application number: CN202111678564.7A
Authority: CN
Inventors: 陈潇; 王凯翔; 陈仝; 王屯
Original assignee: Navinfo Co Ltd
Current assignee: Navinfo Co Ltd
Priority date: 2021-12-31
Filing date: 2021-12-31
Publication date: 2022-04-08

Abstract

The embodiment of the application provides a charging station recommendation method, a device, a storage medium and a program product, the method comprises the steps of obtaining a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle, determining a plurality of reachable charging stations from the plurality of target charging stations according to the user portrait and the vehicle real-time data, obtaining a plurality of charging station portraits of the reachable charging stations, calculating a comprehensive score of the reachable charging stations according to the charging station portraits of the reachable charging stations and the user portrait for each reachable charging station, determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to a user. According to the embodiment of the application, each recommended charging station is guaranteed to be smoothly reached by the target vehicle, so that the recommendation reasonability is improved, and the user experience is improved.

Description

Charging station recommendation method, device, storage medium, and program product

Technical Field

The embodiment of the application relates to the technical field of electric vehicles, in particular to a charging station recommendation method, charging station recommendation equipment, a storage medium and a program product.

Background

Along with the enhancement of the environmental protection consciousness of people, electric automobiles are more and more popular. The electric vehicle charging station is a station for charging electric vehicles. With the popularization of electric vehicles, electric vehicle charging stations are becoming the key point for the development of the automobile industry and the energy industry. For promoting the use experience of electric automobile users and ensuring the timeliness of electric automobile charging, it is an important factor to recommend a proper charging station to the electric automobile users in time.

In the prior art, charging station recommendation is generally performed according to data of charging piles in a charging station, such as the number of free charging piles, the rating of charging piles, and the like.

However, in the process of implementing the present application, the inventors found that at least the following problems exist in the prior art: in the recommendation process, the consideration is less, the recommendation rationality is lower, and the situation that the charging cannot be smoothly completed when the electric vehicle user selects the charging station according to the recommendation often occurs.

Disclosure of Invention

The embodiment of the application provides a charging station recommendation method, charging station recommendation equipment, a storage medium and a program product, so that the charging station recommendation rationality is improved, and the user experience is improved.

In a first aspect, an embodiment of the present application provides a charging station recommendation method, including:

acquiring a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle;

determining a plurality of reachable charging stations from a plurality of target charging stations based on the user representation and the real-time data of the vehicle;

acquiring charging station images of a plurality of reachable charging stations;

calculating a comprehensive score of each reachable charging station according to the charging station portrait of the reachable charging station and the user portrait;

and determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to the user.

In one possible design, the vehicle real-time data includes weather information and road congestion levels, and the determining a plurality of reachable charging stations from a plurality of target charging stations based on the user representation and the real-time data of the vehicle includes:

determining a travelable distance of the target vehicle according to the user portrait;

for each target charging station, acquiring the position information of the target charging station, and determining an reachable distance threshold corresponding to the target charging station according to the position information of the target charging station, the weather information and the road congestion level; and if the driving distance of the target vehicle is greater than or equal to the reachable distance threshold value of the target charging station, determining the target charging station as a reachable charging station.

In one possible design, the determining, according to the location of the target charging station, the weather information, and the road congestion level, an reachable distance threshold corresponding to the target charging station includes:

determining a linear distance and a navigation distance between the target charging station and the target vehicle according to the position of the target charging station, and determining a navigation error factor of the target charging station according to a ratio of the linear distance to the navigation distance;

determining a weather error factor of the target charging station according to the weather information and the weather-cruising loss curve;

determining a road condition error factor of the target charging station according to the road congestion level;

and determining an reachable distance threshold corresponding to the target charging station according to the linear distance, the navigation error factor, the weather error factor and the road condition error factor.

In one possible design, the calculating a composite score of the reachable charging station according to the charging station representation of the reachable charging station and the user representation includes:

determining a chargeable score for the reachable charging station based on the charging station representation of the reachable charging station;

determining a distance score of the reachable charging station according to the navigation distance between the reachable charging station and the target vehicle;

determining a similarity score of the reachable charging station according to the user representation and the charging station representation;

and carrying out weighted summation on the fullness score, the distance score and the similarity score to obtain the comprehensive score.

In one possible design, the charging station representation includes a plurality of chargeable evaluation factors; said determining a chargeable score for said reachable charging station from said charging station representation of said reachable charging station, comprising:

and carrying out weighted summation on scores corresponding to the chargeable evaluation factors to obtain the chargeable score.

In one possible design, the determining the distance score for the reachable charging station based on the navigation distance between the reachable charging station and the target vehicle includes:

determining a ratio between a preset threshold and the navigation distance as the distance score.

In one possible design, the user representation includes a plurality of user preference factors, the charging station representation includes characteristic factors corresponding to the plurality of user preference factors, and the determining the similarity score of the reachable charging station according to the user representation and the charging station representation includes:

determining a product of a plurality of the user preference factors and respective corresponding weights as a plurality of components of a first vector corresponding to the target vehicle;

determining characteristic factors corresponding to the user preference factors as a plurality of components of a second vector corresponding to the reachable charging station;

and calculating cosine similarity between the first vector and the second vector, and determining the cosine similarity as a similarity score of the reachable charging stations.

In one possible design, after the pushing the charging station recommendation list to the user, the method further includes:

determining a recommendation effect according to the exposure times, the click times and the charging times of each charging station in the recommendation list;

and adjusting the weight corresponding to the fullness score, the weight corresponding to the distance score and the weight corresponding to the similarity score according to the recommendation effect.

In one possible design, the user representation further includes a plurality of first charging station candidate lists determined according to user historical behaviors, and before pushing the charging station recommendation list to the user, further includes:

for each first charging station candidate list, if the reachable charging stations are included in the first charging station candidate list, selecting at least one reachable charging station from the first charging station candidate list to join the charging station recommendation list;

determining a plurality of second charging station candidate lists corresponding to the real-time positions according to the real-time positions of the target vehicles;

for each second charging station candidate list, if the second charging station candidate list includes the reachable charging stations, selecting at least one reachable charging station from the second charging station candidate list to join the charging station recommendation list.

In a second aspect, an embodiment of the present application provides a charging station recommendation device, including:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle;

a determination module to determine a plurality of reachable charging stations from a plurality of target charging stations based on the user representation and real-time data of the vehicle;

the second acquisition module is used for acquiring charging station portraits of a plurality of reachable charging stations;

the calculation module is used for calculating to obtain the comprehensive score of the reachable charging stations according to the charging station portrait of the reachable charging stations and the user portrait for each reachable charging station;

and the pushing module is used for determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations and pushing the best reachable charging station to the user.

In a third aspect, an embodiment of the present application provides a charging station recommendation system, including: a cloud end, a vehicle end and a plurality of charging stations,

the vehicle end is used for uploading real-time data of a vehicle to the cloud end;

the charging station is used for uploading real-time data of the charging station to the cloud;

the cloud end is used for storing user figures corresponding to the vehicle end and charging station figures corresponding to a plurality of charging stations; and the system is also used for determining a plurality of reachable charging stations from a plurality of target charging stations according to the user portrait and the real-time data of the vehicle, calculating a comprehensive score of the reachable charging stations according to the charging station portrait of the reachable charging stations and the user portrait for each reachable charging station, determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to the vehicle end.

In a fourth aspect, an embodiment of the present application provides a charging station recommendation system, including: cloud and vehicle terminal

The cloud end is used for storing a user portrait corresponding to the vehicle end, real-time data of a vehicle corresponding to the vehicle end and charging station portraits of a plurality of reachable charging stations corresponding to the vehicle end; the system is also used for receiving request information sent by the vehicle end, and sending a user portrait corresponding to the vehicle end, real-time data of a vehicle corresponding to the vehicle end and charging station portraits of a plurality of accessible charging stations to the vehicle end according to the request information;

the vehicle end is used for receiving the user portrait and the real-time data of the vehicle, determining a plurality of reachable charging stations from a plurality of target charging stations according to the user portrait and the real-time data of the vehicle, receiving charging station portraits of the reachable charging stations, calculating a comprehensive score of the reachable charging stations according to the charging station portraits of the reachable charging stations and the user portrait for each reachable charging station, determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to the vehicle end.

The charging station recommendation method, the device, the storage medium and the program product provided by the embodiment comprise the steps of obtaining a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle, determining a plurality of reachable charging stations from the plurality of target charging stations according to the user portrait and the vehicle real-time data, obtaining charging station portraits of the plurality of reachable charging stations, calculating a comprehensive score of the reachable charging stations according to the charging station portraits of the reachable charging stations and the user portrait for each reachable charging station, determining at least one best reachable charging station according to the comprehensive scores of the plurality of reachable charging stations, and pushing the best reachable charging station to a user. According to the charging station recommendation method, the plurality of reachable charging stations are obtained through the reachability analysis of the plurality of target charging stations around the target vehicle, and then recommendation is performed according to the comprehensive scoring condition from the plurality of reachable charging stations, each recommended charging station is guaranteed to be successfully reachable by the target vehicle, so that the recommendation reasonableness is improved, and the user experience is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

Fig. 1 is an application scenario diagram of a charging station recommendation method according to an embodiment of the present application;

fig. 2 is a first schematic flowchart of a charging station recommendation method according to an embodiment of the present disclosure;

fig. 3 is a second flowchart illustrating a charging station recommendation method according to an embodiment of the present application;

fig. 4 is a third schematic flowchart of a charging station recommendation method according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a charging station recommendation device according to an embodiment of the present application;

fig. 6 is a first schematic structural diagram of a charging station recommendation system according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a charging station recommendation system according to an embodiment of the present application;

fig. 8 is a block diagram of a charging station recommendation device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Along with the enhancement of the environmental protection consciousness of people, electric automobiles are more and more popular. The electric vehicle charging station is a station for charging electric vehicles. With the popularization of electric vehicles, electric vehicle charging stations are becoming the key point for the development of the automobile industry and the energy industry. Can set up a plurality of electric pile that fill in the electric automobile charging station, the electric pile that fills of different voltage classes can be for the electric automobile of different models charges, fills electric pile and can divide into direct current according to the difference of the mode of charging and fill electric pile, alternating current charging pile and alternating current-direct current an organic whole. For promoting the use experience of electric automobile users, the timeliness of electric automobile charging is guaranteed, and it is an important influence factor to recommend a proper charging station to the electric automobile users in time.

In the prior art, charging stations with more idle charging piles in a target user are generally recommended to the target user according to data of charging piles in the charging stations, such as the idle number and the good rating of the charging piles, or the target user is recommended to the user with charging stations with higher user rating. However, in the above recommendation method, there are few considerations, and there are often cases where the user of the electric vehicle cannot smoothly complete charging by selecting a charging station according to the recommendation. The recommendation is less reasonable.

In order to solve the technical problems, the inventor researches and discovers that the recommended charging stations have better conditions, for example, the number of the idle charging piles is large, the user score is high, and the like. If the electric vehicles of the target users can not arrive smoothly, charging can not be finished. Based on this, the embodiment of the application provides a charging station recommendation method, and it is guaranteed that each recommended charging station can be reached by a target vehicle smoothly, so that the recommendation rationality is improved, and the user experience is improved.

Fig. 1 is an application scenario diagram of a charging station recommendation method according to an embodiment of the present application. As shown in fig. 1, a plurality of charging stations (charging stations 1 to 6) are provided around the location of the target vehicle, and when the target vehicle satisfies the recommended condition, for example, when the electric quantity of the target vehicle is less than 30%, reachability analysis is performed on each target charging station (charging station 1 to 4) in the target area, and a plurality of charging stations that can be reached by the target vehicle are selected from each target charging station in the target area as reachable charging stations. And then, the multiple reachable charging stations can be comprehensively scored, and the target vehicle can be recommended according to the comprehensive scoring condition of each reachable charging station. According to the charging station recommendation method, the plurality of reachable charging stations are obtained through the reachability analysis of the plurality of target charging stations around the target vehicle, and then recommendation is performed according to the comprehensive scoring condition from the plurality of reachable charging stations, each recommended charging station is guaranteed to be successfully reachable by the target vehicle, so that the recommendation reasonableness is improved, and the user experience is improved.

The technical solution of the present application will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.

Fig. 2 is a first flowchart of a charging station recommendation method according to an embodiment of the present application. As shown in fig. 2, the method includes:

201. and acquiring a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle.

The execution main body of the embodiment can be a computer, a tablet, a mobile phone, a car machine and other devices with data processing capability.

In this embodiment, the user representation may include a range level of the target vehicle; the vehicle real-time data may include a vehicle real-time location of the target vehicle.

Specifically, the user profile corresponding to the target vehicle includes various factors, such as a user ID, a vehicle type, a vehicle capacity, a cruising level, a user type (e.g., private, commercial, rental, rescue, and logistics), whether or not there is a private peg, a home location, a company location, a remaining battery (SOC) preference, an ac/dc preference, a charging price preference, a parking fee price preference, a dining preference, a user tag, a travel destination preference, a favorable site, an unfavorable site, a high failure rate site, a frequent site, and the like. The factors of the user portrait can be offline data, and are stored in the vehicle-mounted equipment of the target vehicle or the handheld terminal of the user, and can also be stored in the cloud server. The offline data does not need to be updated in real time, and is updated only when the home location changes, for example, if the home location changes, the offline data can be automatically updated after being verified for many times, or the user actively inputs a new home location.

In this embodiment, the real-time data of the vehicle may include at least one of a real-time location, an SOC, a road congestion level, weather information, temperature information, and the like.

202. Determining a plurality of reachable charging stations from a plurality of target charging stations based on the user representation and the real-time data of the vehicle.

Specifically, a plurality of reachable charging stations may be determined from a plurality of target charging stations whose straight-line distances from the vehicle real-time location are smaller than a preset distance, according to the vehicle real-time data and the cruising level.

In one implementation manner, the determining a plurality of reachable charging stations according to the vehicle real-time data and the cruising level may further include: determining an acceptable minimum limit value of the residual capacity of the target vehicle according to the vehicle type of the target vehicle; determining a distance that the target vehicle can travel according to the product of the difference between the real-time remaining capacity and the acceptable minimum limit value and the cruising level; for each target charging station, acquiring the position information of the target charging station, and determining an reachable distance threshold corresponding to the target charging station according to the position information of the target charging station, the weather information and the road congestion level; and if the driving distance of the target vehicle is greater than or equal to the reachable distance threshold value of the target charging station, determining the target charging station as a reachable charging station.

Specifically, the determining the reachable distance threshold corresponding to the target charging station according to the position of the target charging station, the weather information, and the road congestion level may include: determining a linear distance and a navigation distance between the target charging station and the target vehicle according to the position information of the target charging station, determining a navigation error factor of the target charging station according to a ratio of the linear distance to the navigation distance, determining a weather error factor of the target charging station according to the weather information and a weather-cruising loss curve, determining a road condition error factor of the target charging station according to the road congestion level, and determining an reachable distance threshold value corresponding to the target charging station according to the linear distance, the navigation error factor, the weather error factor and the road condition error factor.

Illustratively, taking the preset distance as 20km as an example, a circle is drawn by taking the real-time position coordinates of the vehicle as a circle center and taking 20km as a radius, the circle is a target area, and each charging station in the target area is a target charging station. For each target charging station, calculation is performed according to the following expression (1), and if the inequality is established, the target charging station is determined as a reachable charging station.

(SOC-SOC_min)×K×a≥D×αβγ (1)

Wherein the SOC is the residual capacity of the target vehicle; SOC_minAn acceptable minimum limit value of the remaining capacity of the target vehicle, the minimum limit value being related to the vehicle type of the target vehicle, for example, if the vehicle type is an operating vehicle, the minimum limit value may be 5%, if the vehicle type is a rental vehicle, the minimum limit value may be 20%, and a default value of the minimum limit value may be set to 10%; k is the cruising level of the target vehicle, and the cruising level is related to the maximum driving mileage of the vehicle under the condition of full battery; d is the linear distance between the target vehicle and the target charging station; alpha is a navigation error factor, namely the ratio of the navigation distance to the linear distance; beta is a weather error factor which can be obtained according to a weather-cruising loss curve, and exemplarily, cruising at minus 6 ℃ is reduced by 41%, and cruising at 35 ℃ is reduced by 17%; and gamma is a road condition error factor and is related to the road congestion level.

203. And acquiring a plurality of charging station portrayals of the accessible charging stations.

In this embodiment, the charging station representation of the accessible charging station includes various factors, such as: the method comprises the steps of charging station position, charging pile total amount, direct current charging pile number, predicted time-interval vacancy, predicted time-interval fault grid-leaving rate, business time, parking fee, charging fee, whether a high-speed station is located, the ground and the underground are located, a peripheral Interest Point (POI), a vehicle type which is charged, the latest charging time, whether an area popular station is located, whether an area good-rated station is located, whether an area bad-rated station is located, the occupying situation of a fuel tank truck, the charging queuing situation and the like. Each factor of the charging station portrait can be offline data and can be stored in a cloud server. The offline data does not need to be updated in real time, and is updated only when the offline data changes, for example, if the total amount of the charging piles changes, the offline data can be updated.

204. And aiming at each reachable charging station, calculating to obtain the comprehensive score of the reachable charging station according to the charging station portrait of the reachable charging station and the user portrait.

In an implementation manner, the calculating a composite score of the reachable charging station according to the charging station representation of the reachable charging station and the user representation may include: determining a chargeable score for the reachable charging station based on the charging station representation of the reachable charging station; determining a distance score of the reachable charging station according to the navigation distance between the reachable charging station and the target vehicle; determining a similarity score of the reachable charging station according to the user representation and the charging station representation; and carrying out weighted summation on the fullness score, the distance score and the similarity score to obtain the comprehensive score. In another implementation, the composite score may be determined according to actual needs, and according to only one or two of the fullness score, the distance score, and the similarity score.

In some embodiments, a more optimal weight combination may be selected using an AB-Test experiment.

In this embodiment, the weight corresponding to the distance score may be related to the SOC of the target vehicle, and the lower the SOC, the more the user desires to have a charging station with a short distance. For example, if the SOC is 5% or less, the weight of the distance score is set to 2, if the SOC is less than 20% and greater than 5%, the weight of the distance score is set to a ratio of 10 to the SOC, and if the SOC is 20 or greater, the weight of the distance score is set to 0.5.

In some embodiments, the charging station representation includes a plurality of chargeable evaluation factors; the determining a chargeable score of the reachable charging station from the charging station representation of the reachable charging station may include: and carrying out weighted summation on scores corresponding to the chargeable evaluation factors to obtain the chargeable score.

In some embodiments, the plurality of chargeable evaluation factors includes at least one of: business hours, charging success rate, station vacancy degree, fault rate, recent charging time, vehicle type white list and vehicle type black list.

Illustratively, the chargeable score is calculated by taking chargeable evaluation factors including business hours, charging success rate, station vacancy, failure rate, recent charging time, vehicle type white list and vehicle type black list as examples.

In one implementation, the chargeable score may be divided into a base score determined by business hours, charge success rate, site idleness, failure rate, most recent charge time, and an additional score by a vehicle type white list, a vehicle type black list. If the base score of the reachable charging station is greater than the predetermined base score, e.g., 0.4, and the additional score is greater than or equal to the predetermined additional score, e.g., 0, it indicates that the reachable charging station satisfies the chargeable condition.

The business hour score is determined in the following mode: estimating the time range of the target vehicle reaching the accessible charging station, judging whether the time range is in the business time of the accessible charging station, and determining a value which is greater than or equal to 0 and less than or equal to 1 as a business time score; the determination mode of the station vacancy degree score is as follows: according to the vacancy degree of the accessible charging stations and the scale of the accessible charging stations within the predicted arrival time range of the target vehicle, converting into a vacancy degree score between 0 and 1 according to a 0-1 score model; the determination method of the charging success rate score is as follows: the charging success rate of the station within a preset time period, for example, within the past 7 days, is calculated, and a charging success rate score between 0 and 1 is calculated. And (3) fault rate scoring: according to the failure rate and the off-grid rate within the predicted user arrival time range and the scale of the accessible charging stations, converting into an idleness score between 0 and 1 according to a 0-1 score model; the latest charging time: calculating a latest charging time score between 0 and 1 according to the latest charging time; the determination mode of the vehicle type white list score is as follows: counting the charged vehicle types according to the historical charging condition of the accessible charging station, and if the vehicle type of the target vehicle exists, obtaining 1 point, otherwise, obtaining 0 point; the determination mode of the vehicle type blacklist score is as follows: and according to the user feedback and the matching condition of the target vehicle and each charging pile model of the accessible charging station, if the accessible charging station does not support the model of the target vehicle, obtaining-1 point.

The base score is a weighted sum of the business hours score, the charging success rate score, the failure and off-grid rate score, the most recent charging time and the site idleness score. The additional score is the sum of the vehicle type white list score and the vehicle type black list score.

In some embodiments, the determining the distance score of the reachable charging station from the navigation distance between the reachable charging station and the target vehicle may include: determining a ratio between a preset threshold and the navigation distance as the distance score.

For example, the preset threshold may be set empirically, and may be 500m, for example.

In some embodiments, the user representation includes a plurality of user preference factors, the charging station representation includes characteristic factors corresponding to the plurality of user preference factors, and the determining the similarity score of the reachable charging station according to the user representation and the charging station representation may include: determining a product of a plurality of the user preference factors and respective corresponding weights as a plurality of components of a first vector corresponding to the target vehicle; determining characteristic factors corresponding to the user preference factors as a plurality of components of a second vector corresponding to the reachable charging station; and calculating cosine similarity between the first vector and the second vector, and determining the cosine similarity as a similarity score of the reachable charging stations.

Illustratively, the cosine similarity between the first vector and the second vector may be calculated according to the following expression (2).

Wherein, similarity is cosine similarity, A is a first vector, Ai is the ith component of the first vector, B is a second vector, and Bi is the ith component of the second vector.

For example, the user preference factors may be a charging fee preference, a parking fee preference, a direct current preference, a dining preference, whether to be on the highway, a distance preference. Correspondingly, the characteristic factors corresponding to the user preference factors can be charging fee, parking fee, direct current charging pile ratio, catering, high speed and distance score.

The results in table 6 are calculated by combining the weight corresponding to the characteristic factor condition of the reachable charging station 1 in table 1, the characteristic factor condition of the reachable charging station 2 in table 2, the user preference factor condition of the target vehicle 1 in table 3, the user preference factor condition of the target vehicle 2 in table 4, and the user preference factor in table 5. It is apparent that the calculation results of the similarity in table 6 are in accordance with the user preference.

Table 1 reachable charging station 1

Charging fee	Parking fee	Ratio of direct current	Food and beverage	Whether or not to run at high speed	Distance score
						0.8	0	0.8	1	1	0.9

Table 2 reachable charging stations 2

Charging fee	Parking fee	Ratio of direct current	Food and beverage	Whether or not to run at high speed	Distance score
						0.4	1	0.2	0	0	0.9

TABLE 3 target vehicle 1

TABLE 4 target vehicle 2

TABLE 5 user preference factor weight assignment

TABLE 6 calculation results of similarity

	Target vehicle 1	Target vehicle 2
			Charging station 1	0.074	0.125
Charging station 2	0.108	0.107

205. And determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to the user.

For example, a charging station recommendation list may be determined based on the composite scores of the plurality of reachable charging stations, and the charging station recommendation list may be pushed to the user.

In practical applications, the top N names of the composite score, for example, the top three names, may be included in the charging station recommendation list. The recommendation method of the charging station recommendation list may adopt a top page recommendation method, a list recommendation method, and a bottom page recommendation method, which is not limited in this embodiment.

According to the charging station recommendation method provided by the embodiment, the reachability of the target charging stations around the target vehicle is analyzed, the multiple reachable charging stations are obtained through screening, and then recommendation is performed according to the comprehensive scoring condition in the multiple reachable charging stations, so that each recommended charging station can be successfully reached by the target vehicle, the recommendation rationality is improved, and the user experience is improved.

Fig. 3 is a schematic flowchart of a charging station recommendation method according to an embodiment of the present application. As shown in fig. 3, on the basis of the above embodiment, in this embodiment, a determination method of a recommendation result and a step of feedback-adjusting the recommendation result to weights of items that affect the composite score are added, and the method includes:

301. acquiring a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle; the user representation includes a range level of the target vehicle; the vehicle real-time data includes a vehicle real-time location of a target vehicle.

302. And determining a plurality of reachable charging stations from a plurality of target charging stations with the linear distance between the target charging stations and the real-time position of the vehicle being smaller than the preset distance according to the real-time data of the vehicle and the cruising level.

303. And acquiring a plurality of charging station portrayals of the accessible charging stations.

304. For each of the reachable charging stations, determining a chargeable score for the reachable charging station from the charging station representation for the reachable charging station; determining a distance score of the reachable charging station according to the navigation distance between the reachable charging station and the target vehicle; determining a similarity score of the reachable charging station according to the user representation and the charging station representation; and carrying out weighted summation on the fullness score, the distance score and the similarity score to obtain the comprehensive score.

305. And determining a charging station recommendation list according to the comprehensive scores of the plurality of reachable charging stations, and pushing the charging station recommendation list to a user.

In this embodiment, steps 301 to 305 are similar to steps 201 to 205 in the above embodiment, and are not described again here.

306. Determining a recommendation effect according to the exposure times, the click times and the charging times of each charging station in the recommendation list; and adjusting the weight corresponding to the chargeable score, the weight corresponding to the distance score and the weight corresponding to the similarity score according to the recommendation effect.

In this embodiment, for each charging station in the recommendation list, the click rate of the charging station is determined according to the exposure times and the click times of the charging station, the charging rate of the charging station is determined according to the charging times and the exposure times of the charging station, and then the recommendation effect is calculated according to the click rate and the charging rate. Illustratively, the click rate is the ratio of the number of clicks to the number of exposures; the charging rate is the ratio between the number of charges and the number of exposures.

For example, if the charging station is pushed to the user 10 times, i.e., the number of exposures is 10, and clicked 5 times by the user, i.e., the number of clicks is 5, and the user actually charges the charging station 2 times, i.e., the number of charges is 2, the click rate is 50% and the charging rate is 20%. Threshold values can be set for the charging rate and the click rate, when the actual charging rate and the actual click rate are lower than the corresponding threshold values, result feedback can be performed, the generation mode of the recommendation list can be adjusted, for example, the calculation mode of the comprehensive score can be adjusted, specifically, the weight corresponding to the chargeable score, the weight corresponding to the distance score and the weight corresponding to the similarity score can be adjusted, and the weight distribution of user preference factors in the similarity score calculation process can be adjusted.

According to the charging station recommendation method provided by the embodiment, the recommendation effect of the recommendation list is calculated, and the weight of each factor influencing the comprehensive score is adjusted according to the recommendation effect, so that the recommendation list can better meet the requirements of the user under the feedback mechanism, and the user experience is improved.

Fig. 4 is a third schematic flowchart of a charging station recommendation method according to an embodiment of the present application. As shown in fig. 4, in addition to the above-mentioned embodiment, for example, in addition to the embodiment shown in fig. 2, in this embodiment, a candidate list according to other charging stations is added, for example, a list maintained by daily evaluation of a user (user favorable site, etc.) or a list maintained in an area (area favorable site, etc.), and the method includes:

401. acquiring a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle; the user representation includes a range level of the target vehicle; the vehicle real-time data includes a vehicle real-time location of a target vehicle.

402. And determining a plurality of reachable charging stations from a plurality of target charging stations with the linear distance between the target charging stations and the real-time position of the vehicle being smaller than the preset distance according to the real-time data of the vehicle and the cruising level.

403. And acquiring a plurality of charging station portrayals of the accessible charging stations.

404. And aiming at each reachable charging station, calculating to obtain the comprehensive score of the reachable charging station according to the charging station portrait of the reachable charging station and the user portrait.

405. And determining a charging station recommendation list according to the comprehensive scores of the plurality of reachable charging stations.

Steps 401 to 405 in this embodiment are similar to steps 201 to 205 in the above embodiment, and are not described again here.

406. The user representation further comprises a plurality of first charging station candidate lists determined according to user historical behaviors, and for each first charging station candidate list, if the reachable charging stations are included in the first charging station candidate list, at least one reachable charging station is selected from the first charging station candidate list to join the charging station recommendation list.

In this embodiment, the first charging station candidate list may include: the system comprises a user favorable website, a user collection website and a user frequent charging website.

For example, the sites that are good for the user may include sites with a score result greater than 3 points according to previous review behaviors of the user. The user favorite sites may include all of the user's favorite sites based on the user's previous review behavior.

407. Determining a plurality of second charging station candidate lists corresponding to the real-time positions according to the real-time positions of the target vehicles; for each second charging station candidate list, if the second charging station candidate list includes the reachable charging stations, selecting at least one reachable charging station from the second charging station candidate list to join the charging station recommendation list.

In this embodiment, the second charging station candidate list may include: regional good-scoring sites, regional popular sites and regional low-price sites.

Exemplary, regional wellness sites include: and calculating 50 sites with the highest rating of the charging station in each regional range, such as a district-county regional range. The regional hot site includes: and calculating 50 sites with the highest effective charging times in each regional range, such as a district-county regional range. Regional low price sites include: and calculating 50 sites with the lowest comprehensive price in each regional scope, such as a regional and county regional scope.

408. And filtering the charging station recommendation list according to the user poor evaluation charging station list and the high failure rate charging station list.

In this embodiment, the charging station recommendation list may be filtered in various manners, for example, each charging station in the charging station recommendation list may be compared with each charging station in the user bad evaluation charging station list, if the user bad evaluation charging station list exists in the charging station, the charging station may be removed from the charging station list, similarly, each charging station in the charging station recommendation list may be compared with each charging station in the failure rate high charging station list, and if the failure rate high charging station list exists in the charging station, the charging station may be removed from the charging station list; in order to improve the calculation efficiency, only the charging stations with poor evaluation by the user and the charging stations with high failure rate which are ranked at the top can be selected for comparison with the charging station recommendation list. The specific manner is to be taken, which is subject to actual needs, and this embodiment does not limit this.

409. And pushing the filtered charging station recommendation list to a user.

For example, the pushing the charging station recommendation list to the user may include: and adjusting the charging station recommendation list according to a recommendation scene, and pushing the adjusted charging station recommendation list to a user.

Illustratively, the recommendation scene is home page recommendation, list recommendation, page-bottom recommendation, directional recommendation or along-the-way recommendation.

For example, for a scene recommended by a home page, a charging station with the highest comprehensive score in the charging station recommendation list may be recommended; for the list recommendation scenario, one station with the highest composite score in the charging station recommendation list and 2 stations selected from the first charging station candidate list and the second charging station candidate list may be recommended. For the scene of page bottom recommendation, a station with the highest comprehensive score and a regional low-price station in the charging station recommendation list can be recommended. For the directional recommendation scene, high-quality sites around the family can be recommended to a certain group of fixed family positions, such as sites with the highest comprehensive score and low-price sites; for the recommended scenes along the way, peripheral sites can be recommended according to the condition that the electric quantity in the driving way of the user is lower than a certain value, such as sites with the highest comprehensive score.

According to the charging station recommendation method provided by the embodiment of the application, the charging stations in the first charging station candidate list and the second charging station candidate list are added into the charging station recommendation list, the charging station recommendation list can be richer, is not limited to recommendation based on comprehensive scores, is more reasonable, and the charging station recommendation list is filtered, so that the recommendation quality can be further guaranteed, and the user experience is improved.

Fig. 5 is a schematic structural diagram of a charging station recommendation device according to an embodiment of the present application. As shown in fig. 5, the charging station recommendation apparatus 50 includes: a first obtaining module 501, a determining module 502, a second obtaining module 503, a calculating module 504 and a pushing module 505.

A first obtaining module 501, configured to obtain a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle; the user representation includes a range level of the target vehicle; the vehicle real-time data comprises a vehicle real-time location of a target vehicle;

a determining module 502, configured to determine, from a plurality of target charging stations whose linear distance from the real-time location of the vehicle is smaller than a preset distance, a plurality of reachable charging stations according to the real-time vehicle data and the cruising level;

a second obtaining module 503, configured to obtain charging station representations of a plurality of reachable charging stations;

a calculating module 504, configured to calculate, for each reachable charging station, a composite score of the reachable charging station according to the charging station representation of the reachable charging station and the user representation;

and a pushing module 505, configured to determine a charging station recommendation list according to the composite scores of the plurality of reachable charging stations, and push the charging station recommendation list to a user.

According to the charging station recommendation device provided by the embodiment of the application, a user portrait corresponding to a target vehicle and vehicle real-time data of the target vehicle are acquired through a first acquisition module 501; the user representation includes a range level of the target vehicle; the vehicle real-time data comprises a vehicle real-time location of a target vehicle; the determining module 502 determines a plurality of reachable charging stations from a plurality of target charging stations whose straight-line distances from the real-time position of the vehicle are smaller than a preset distance according to the real-time data of the vehicle and the cruising level; the second acquiring module 503 acquires charging station representations of a plurality of the reachable charging stations; the calculation module 504 calculates, for each of the reachable charging stations, a composite score of the reachable charging stations according to the user profile and the charging station profile of the reachable charging station; the pushing module 505 determines a charging station recommendation list according to the composite scores of the plurality of reachable charging stations, and pushes the charging station recommendation list to the user. Through carrying out reachability analysis to a plurality of target charging stations around the target vehicle, a plurality of reachable charging stations are obtained through screening, and then recommend according to the situation of comprehensive score from a plurality of reachable charging stations, and each charging station that has guaranteed to recommend is that the target vehicle can arrive smoothly to the rationality of recommending has been improved, user experience has been promoted.

The charging station recommendation device provided by the embodiment of the application can be used for executing the method embodiment, the implementation principle and the technical effect are similar, and the embodiment is not repeated herein.

Fig. 6 is a first schematic structural diagram of a charging station recommendation system according to an embodiment of the present application. As shown in fig. 6, the charging station recommendation system 60 includes a cloud 601 and a vehicle end 602.

The vehicle end 602 is configured to upload real-time data of a vehicle to the cloud 601.

The cloud 601 is used for storing a user portrait corresponding to the vehicle end 602 and charging station portraits corresponding to a plurality of charging stations; the method further comprises the steps of determining a plurality of reachable charging stations from a plurality of target charging stations according to the user representation and the real-time data of the vehicle, calculating a comprehensive score of the reachable charging stations according to the charging station representation of the reachable charging stations and the user representation for each reachable charging station, determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to the vehicle end 602.

The charging station recommendation system 60 provided by the embodiment of the application obtains a plurality of reachable charging stations by screening and performing reachability analysis on a plurality of target charging stations around the vehicle end 602, and then recommends according to the comprehensive scoring condition from the plurality of reachable charging stations, thereby ensuring that each recommended charging station can be reached by the vehicle end 602 smoothly, improving the recommendation rationality and improving the user experience.

The charging station recommendation system provided by the embodiment of the application can be used for executing the method embodiment, the implementation principle and the technical effect are similar, and the embodiment is not repeated herein.

Fig. 7 is a schematic structural diagram of a charging station recommendation system according to an embodiment of the present application. As shown in fig. 7, the charging station recommendation system 70 includes a cloud 701 and a vehicle end 702.

The cloud 701 is configured to store a user image corresponding to the vehicle end 702, real-time data of a vehicle corresponding to the vehicle end 702, and charging station images of a plurality of reachable charging stations corresponding to the vehicle end 702; the system is further configured to receive request information sent by the vehicle end 702, and send a user image corresponding to the vehicle end 702, real-time data of a vehicle corresponding to the vehicle end 702, and charging station images of a plurality of reachable charging stations to the vehicle end 702 according to the request information;

the vehicle end 702 is configured to obtain real-time data of a vehicle and receive the user representation sent by the cloud, determine a plurality of reachable charging stations from a plurality of target charging stations according to the user representation and the real-time data of the vehicle, receive the plurality of charging station representations of the reachable charging stations, calculate, for each reachable charging station, a composite score of the reachable charging stations according to the charging station representation of the reachable charging stations and the user representation, determine at least one best reachable charging station according to the composite scores of the reachable charging stations, and push the best reachable charging station to the user.

The charging station recommendation system 70 provided by the embodiment of the application obtains a plurality of reachable charging stations by screening through performing reachability analysis on a plurality of target charging stations around the vehicle end 702, and then recommends according to the comprehensive scoring condition from the plurality of reachable charging stations, and ensures that each recommended charging station can be reached by the vehicle end 702 smoothly, thereby improving the recommendation rationality and improving the user experience.

Fig. 8 is a block diagram of a charging station recommendation device according to an embodiment of the present application, which may be a computer, a messaging device, a tablet device, a medical device, or the like, and which may be disposed on an unmanned vehicle.

The apparatus 80 may include one or more of the following components: a processing component 801, a memory 802, a power component 803, a multimedia component 804, an audio component 805, an input/output (I/O) interface 806, a sensor component 807, and a communication component 808.

The processing component 801 generally controls overall operation of the device 80, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 801 may include one or more processors 809 for executing instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 801 may include one or more modules that facilitate interaction between the processing component 801 and other components. For example, the processing component 801 may include a multimedia module to facilitate interaction between the multimedia component 804 and the processing component 801.

The memory 802 is configured to store various types of data to support operations at the apparatus 80. Examples of such data include instructions for any application or method operating on the device 80, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 802 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 803 provides power to the various components of the device 80. The power components 803 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 80.

The multimedia component 804 includes a screen that provides an output interface between the device 80 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 804 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 80 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 805 is configured to output and/or input audio signals. For example, the audio component 805 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 80 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 802 or transmitted via the communication component 808. In some embodiments, the audio component 805 also includes a speaker for outputting audio signals.

The I/O interface 806 provides an interface between the processing component 801 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

Sensor assembly 807 includes one or more sensors for providing various aspects of condition assessment for apparatus 80. For example, sensor assembly 807 may detect the open/closed state of apparatus 80, the relative positioning of components, such as a display and keypad of apparatus 80, the change in position of apparatus 80 or a component of apparatus 80, the presence or absence of user contact with apparatus 80, the orientation or acceleration/deceleration of apparatus 80, and the change in temperature of apparatus 80. Sensor assembly 807 may comprise a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 807 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 807 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 808 is configured to facilitate wired or wireless communication between the apparatus 80 and other devices. The device 80 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 808 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 808 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 80 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 802 comprising instructions, executable by the processor 809 of the apparatus 80 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

The computer-readable storage medium may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also reside as discrete components in the apparatus.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Embodiments of the present application further provide a computer program product, which includes a computer program, and when the computer program is executed by a processor, the charging station recommendation method performed by the above charging station recommendation apparatus is implemented.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A charging station recommendation method, comprising:

2. The method of claim 1, wherein the vehicle real-time data includes weather information and road congestion levels, and wherein determining a plurality of reachable charging stations from a plurality of target charging stations based on the user representation and the real-time data of the vehicle comprises:

3. The method of claim 2, wherein determining the reachable distance threshold for the target charging station based on the location of the target charging station, the weather information, and the road congestion level comprises:

4. The method of claim 1, wherein calculating a composite score for the reachable charging station based on the charging station representation of the reachable charging station and the user representation comprises:

5. The method of claim 4, wherein the charging station representation includes a plurality of chargeable evaluation factors; said determining a chargeable score for said reachable charging station from said charging station representation of said reachable charging station, comprising:

6. The method of claim 4, wherein determining the distance score for the reachable charging station based on the navigational distance between the reachable charging station and the target vehicle comprises:

7. The method of claim 4, wherein the user representation includes a plurality of user preference factors, the charging station representation includes a plurality of characteristic factors corresponding to the plurality of user preference factors, and the determining the similarity score for the reachable charging station based on the user representation and the charging station representation comprises:

8. The method of claim 4, wherein after pushing the charging station recommendation list to a user, further comprising:

9. The method of any of claims 1-8, wherein the user representation further comprises a plurality of first charging station candidate lists determined based on historical user behavior, and wherein before pushing the charging station recommendation list to the user, further comprising:

10. A charging station recommendation device, comprising:

11. A charging station recommendation system is characterized by comprising a cloud end and a vehicle end;

12. A charging station recommendation system is characterized by comprising a cloud end and a vehicle end;

the cloud end is used for storing user images corresponding to the vehicle end and charging station images of a plurality of reachable charging stations corresponding to the vehicle end; the system is also used for receiving request information sent by the vehicle end and sending a user portrait corresponding to the vehicle end and a plurality of charging station portraits of accessible charging stations to the vehicle end according to the request information;

the vehicle end is used for acquiring real-time data of a vehicle, receiving the user portrait sent by the cloud end, determining a plurality of reachable charging stations from a plurality of target charging stations according to the user portrait and the real-time data of the vehicle, receiving charging station portraits of the reachable charging stations, calculating a comprehensive score of the reachable charging stations according to the charging station portraits of the reachable charging stations and the user portrait for each reachable charging station, determining at least one best reachable charging station according to the comprehensive scores of the reachable charging stations, and pushing the best reachable charging station to the user.