A method and device for intelligently recommending a gas station, a terminal device, and a storage medium

Abstract

The application discloses a kind of intelligent recommendation method, device terminal equipment and storage medium of gas station, method includes: generating the planning path corresponding to each gas station;Planning path includes: the first planning sub-path of vehicle to gas station and the second planning sub-path between gas station and destination;Determine dynamic influence coefficient and static influence coefficient;According to dynamic influence coefficient, static influence coefficient, calculate the predicted fuel consumption of planning sub-path, the first planning sub-path with predicted fuel consumption lower than the remaining fuel quantity of vehicle is selected as the first planning sub-path, and the planning path of the first planning sub-path is selected as the selected planning path, and the total predicted fuel consumption of the selected planning path is calculated;According to total predicted fuel consumption and vehicle remaining fuel quantity, calculate minimum refueling amount;According to minimum refueling amount, calculate refueling total price, recommend the selected planning path and corresponding gas station with lowest refueling total price, and the rationality of gas station recommendation can be improved by implementing the application.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle intelligence, and particularly relates to an intelligent recommendation method and device for a gas station, a terminal device and a storage medium. BACKGROUND

[0002] A gas station refers to a station for serving automobiles and other motor vehicles and retailing gasoline and oil supplements, and is generally used for supplementing fuel oil, lubricating oil and the like during driving. With the improvement of people's living standards, the number of motor vehicles is also growing exponentially, and choosing a gas station for refueling is a necessary procedure for people to travel.

[0003] In the prior art, in the process of recommending a gas station, a driver usually initiates a gas station search request on a navigation software, and the navigation software recommends a nearby gas station to the driver based on the location of the user terminal. In order to further improve the economy of refueling, some schemes further select a gas station with the lowest oil price from the nearby gas stations to recommend to the driver based on the above scheme, so as to reduce the refueling price of the driver. However, by simply selecting a gas station with the lowest oil price, the fuel consumption of the vehicle from the current location to the gas station is not considered, nor is the fuel consumption between the gas station and the destination, so that although the oil price of the selected gas station is the lowest, the total fuel cost for reaching the destination is not the lowest. For example: the oil price of A gas station is lower than that of B gas station, but the overall path length and the complexity of the path for the vehicle to continue to the destination after refueling at A gas station from the current location are much greater than those for the vehicle to continue to the destination after refueling at B gas station from the current location. If A gas station is selected as the refueling location at this time, although the oil price is low, the total fuel consumption may be much higher, thereby making the total fuel cost for the vehicle to reach the destination higher. Therefore, the existing gas station recommendation method is not reasonable. SUMMARY

[0004] The embodiments of the present application provide a kind of intelligent recommendation method, device, terminal equipment and computer readable storage medium for gas station, which can further reduce the refueling cost of user, improve the rationality of gas station recommendation.

[0005] An embodiment of the present application provides an intelligent recommendation method for a gas station, comprising: obtaining a destination location, a current location of a vehicle and a plurality of gas station locations within a preset range of the current location of the vehicle;

[0006] For each gas station, a planned path of the vehicle from the current location to the destination via the gas station is determined according to the gas station location, the destination location and the current location of the vehicle. Each planned path includes a first planned sub-path between the current location of the vehicle and the gas station, and a second planned sub-path between the gas station and the destination.

[0007] For each planning sub-path, a dynamic influence coefficient for representing the influence of dynamic factors on fuel consumption is calculated according to the weather forecast data and congestion level of the planning sub-path, a static influence coefficient for representing the influence of static factors on fuel consumption is calculated according to the road surface state of the planning sub-path, and a predicted fuel consumption of the planning sub-path is calculated according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle and the path length of the planning sub-path;

[0008] The first planning sub-path with a predicted fuel consumption lower than the current remaining fuel amount of the vehicle is selected as a first planning sub-path to be selected, and a planning path in which the first planning sub-path to be selected is located is selected as a planning path to be selected, and then a total predicted fuel consumption of the planning path to be selected is calculated according to the predicted fuel consumptions of the first planning sub-path and the second planning sub-path in the planning path to be selected;

[0009] According to the total predicted fuel consumption and the current remaining fuel amount of the vehicle, a minimum refueling amount required for the vehicle to travel to the destination after passing through the corresponding refueling station along each planning path to be selected is calculated;

[0010] According to the oil prices of each refueling station and the corresponding minimum refueling amount, a total refueling price of each planning path to be selected is calculated, and then the planning path to be selected with the lowest total refueling price and the corresponding refueling station are recommended.

[0011] Further, the dynamic influence coefficient for representing the influence of dynamic factors on fuel consumption is calculated according to the weather forecast data and the congestion level of the planning sub-path, including:

[0012] For each sub-region of the planning sub-path, a weather influence sub-coefficient corresponding to the sub-region is calculated according to the temperature, humidity, wind power and precipitation of the sub-region;

[0013] A congestion influence sub-coefficient corresponding to the sub-region is determined according to the congestion level of the sub-region;

[0014] A dynamic influence sub-coefficient of the sub-region is calculated according to the product of the weather influence sub-coefficient and the congestion influence sub-coefficient of the sub-region;

[0015] The dynamic influence sub-coefficients of each sub-region are weighted and averaged according to the proportion of the path length of the sub-region to the path length of the planning sub-path, to obtain the dynamic influence coefficient of the planning sub-path.

[0016] Further, the weather influence sub-coefficient corresponding to the sub-region is calculated according to the temperature, humidity, wind power and precipitation of the sub-region, including:

[0017] A temperature influence coefficient is calculated according to the temperature of the sub-region and the optimal working temperature of the engine by the following formula:

[0018]

[0019] According to the humidity of the sub-area and the preset standard humidity, the humidity influence coefficient is calculated by the following formula:

[0020] k h = 1 + γ × (H - H0)

[0021] According to the wind force of the sub-area and the angle between the vehicle driving direction and the wind direction, the wind force influence coefficient is calculated by the following formula:

[0022]

[0023] According to the precipitation of the sub-area, the precipitation influence coefficient is calculated by the following formula:

[0024]

[0025] According to the temperature influence coefficient, the humidity influence coefficient, the wind force influence coefficient and the precipitation influence coefficient, the weather influence sub-coefficient corresponding to the sub-area is calculated by the following formula:

[0026] k w = a × k t + b × k h + c × k v + d × k r ;

[0027] wherein, k t is the temperature influence coefficient; T is the temperature of the sub-area; T0 is the optimal working temperature of the engine; α and β are preset adjustment parameters of the temperature influence coefficient; k h is the humidity influence coefficient; H is the humidity of the sub-area; H0 is the standard humidity; γ is the preset adjustment parameter of the humidity influence coefficient; k v is the wind force influence coefficient; θ is the angle between the vehicle driving direction and the wind direction; V is the wind force of the sub-area; δ and ∈ are preset adjustment parameters of the wind force influence coefficient; k r is the precipitation influence coefficient; R is the precipitation of the area; ζ is the preset adjustment parameter of the precipitation influence coefficient; a is the weight of the temperature influence coefficient; b is the weight of the humidity influence coefficient; c is the weight of the wind force influence coefficient; d is the weight of the precipitation influence coefficient; a + b + c + d = 1.

[0028] Further, according to the road surface state of the planned sub-path, a static influence coefficient for representing the influence of static factors on fuel consumption is calculated, including:

[0029] For each sub-area of the planned sub-path, the road surface type contained in the sub-area is determined; wherein, the road surface type includes: flat road surface, bumpy road surface, uphill road surface and downhill road surface;

[0030] Calculate the pavement condition influence coefficient corresponding to each pavement type respectively;

[0031] Calculate the static influence coefficient of the sub-area based on the ratio of the path length of different road surface types to the path length of the sub-area and the corresponding road surface condition influence coefficient;

[0032] According to the ratio of the path length of the sub-region to the path length of the planned sub-path, the static influence sub-coefficient of each sub-region is weighted averaged to obtain the static influence coefficient of the planned sub-path.

[0033] Furthermore, when the road surface type is flat, the road surface condition influence coefficient of the bumpy road surface is 1;

[0034] When the road surface type is bumpy, the road surface condition influence coefficient of bumpy road is calculated using the following formula:

[0035] k s =1+ε(S-S0);

[0036] When the road surface type is an uphill road surface, the road surface condition influence coefficient of the uphill road surface is calculated by the following formula: k s =1+ε*tanθ';

[0037] When the road surface type is a downhill road, the road surface condition influence coefficient of the downhill road is calculated using the following formula:

[0038] k s =1-ε*|tanθ'|;

[0039] Among them, k s is the road surface condition influence coefficient; ε is the preset adjustment parameter of the road surface condition influence coefficient; S is the actual bumpiness value of the road surface; S0 is the bumpiness reference value of a flat road surface; θ' is the slope of the road surface.

[0040] Furthermore, the predicted fuel consumption of the planned sub-route is calculated based on the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle, and the path length, including:

[0041] The predicted fuel consumption of the planned sub-path is calculated using the following formula:

[0042]

[0043] Among them, C pred is the predicted fuel consumption of the planned sub-path; C base is the basic fuel consumption of the vehicle; L is the path length of the planned sub-path; K dynamic is the dynamic influence coefficient; K static is the static influence coefficient.

[0044] Further, after calculating the predicted fuel consumption of the planning sub-path according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle and the path length, the method further comprises:

[0045] obtaining historical driving records of the user;

[0046] calculating an average acceleration change rate, a brake frequency and a speed standard deviation of the user when driving the vehicle according to the historical driving records;

[0047] determining a driving style of the user according to the average acceleration change rate, the brake frequency and the speed standard deviation;

[0048] determining a fuel consumption correction coefficient corresponding to the driving style of the user;

[0049] correcting the predicted fuel consumption according to the fuel consumption correction coefficient.

[0050] On the basis of the above-mentioned embodiments of the application, device embodiments are correspondingly provided.

[0051] An embodiment of the application provides an intelligent recommendation device of a gas station, comprising: a data acquisition module, a path planning module, a sub-path fuel consumption prediction module, a total path fuel consumption prediction module, a minimum refueling amount calculation module and a path recommendation module.

[0052] The data acquisition module is configured to acquire a destination position, a current position of a vehicle and positions of a plurality of gas stations within a preset range of the current position of the vehicle.

[0053] The path planning module is configured to determine, for each gas station, a planning path of the vehicle from the current position to the destination via the gas station according to the position of the gas station, the destination position and the current position of the vehicle, wherein each planning path comprises a first planning sub-path between the current position of the vehicle and the gas station and a second planning sub-path between the gas station and the destination.

[0054] The sub-path fuel consumption prediction module is configured to calculate, for each planning sub-path, a dynamic influence coefficient for representing an influence of dynamic factors on fuel consumption according to meteorological forecast data and a congestion level of the planning sub-path, calculate a static influence coefficient for representing an influence of static factors on fuel consumption according to a road surface state of the planning sub-path, and calculate a predicted fuel consumption of the planning sub-path according to the dynamic influence coefficient, the static influence coefficient, a basic fuel consumption of the vehicle and a path length.

[0055] The path total fuel consumption prediction module is configured to take a first planning sub-path with predicted fuel consumption lower than the current vehicle remaining fuel amount as a to-be-selected first planning sub-path, take a planning path where the to-be-selected first planning sub-path is located as a to-be-selected planning path, and then calculate total predicted fuel consumption of the to-be-selected planning path according to predicted fuel consumption of the first planning sub-path and a second planning sub-path in the to-be-selected planning path;

[0056] The minimum refueling amount calculation module is configured to calculate minimum refueling amounts required for the vehicle to travel to the destination after passing through corresponding gas stations along the to-be-selected planning paths according to the total predicted fuel consumption and the current vehicle remaining fuel amount;

[0057] The path recommendation module is configured to calculate total refueling prices of the to-be-selected planning paths according to the oil prices of the gas stations and the corresponding minimum refueling amounts, and then take a to-be-selected planning path with the lowest total refueling price as a target planning path for recommendation.

[0058] On the basis of the above-mentioned method embodiment, a terminal device is correspondingly provided, and the terminal device comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the intelligent recommendation method of the gas station according to any one of the embodiments of the present application when executing the computer program.

[0059] On the basis of the above-mentioned method embodiment, the present application correspondingly provides a storage medium embodiment, and another embodiment of the present application provides a storage medium comprising a stored computer program, wherein the intelligent recommendation method of the gas station according to any one of the method embodiments of the present application is controlled when the computer program is running to control the device where the storage medium is located to execute the intelligent recommendation method of the gas station.

[0060] By implementing the embodiments of the present application, the following beneficial effects are achieved:

[0061] An embodiment of the present invention provides a method, apparatus, terminal device and storage medium for intelligent recommendation of gas stations, wherein the method first determines a number of gas stations within a preset range according to the current location of a vehicle; generates various planned paths according to the location of each gas station, the current location of the vehicle and the location of the destination; each planned path includes: a first planned sub-path between the vehicle's current location and the gas station, and a second planned sub-path between the vehicle's gas station and the destination; for each planned sub-path, a dynamic influence coefficient is determined according to weather forecast data and congestion level, and a static influence coefficient is determined according to road conditions; then, the predicted fuel consumption of the planned sub-path is calculated according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle and the path length; the accuracy of the fuel consumption prediction can be improved by creatively combining the dynamic influence coefficient and the static influence coefficient to predict the fuel consumption of each planned sub-path; further, the first planned sub-path whose predicted fuel consumption is lower than the current remaining fuel of the vehicle is used as the first planned sub-path to be selected, the planned path where the first planned sub-path to be selected is located is used as the planned path to be selected, and then Based on the predicted fuel consumption of the first and second planned sub-paths in the planned path to be selected, the total predicted fuel consumption of the planned path to be selected is calculated; the gas stations are preliminarily screened based on the predicted fuel consumption of the first planned sub-path, and gas stations that cannot be reached with the current remaining fuel of the vehicle are eliminated, thereby improving the rationality of the gas station recommendation, and the total predicted fuel consumption is calculated; then, based on the total predicted fuel consumption and the current remaining fuel of the vehicle, the minimum amount of fuel required for the vehicle to reach the destination after passing through the corresponding gas stations along each planned path to be selected is calculated; finally, based on the fuel price of each gas station and the corresponding minimum amount of fuel, the total fuel price of each planned path to be selected is calculated. This total fuel price represents the minimum cost for the user to refuel at each gas station while ensuring that the user can reach the destination. Finally, the planned path to be selected and the corresponding gas station with the lowest total fuel price are recommended, so that under the premise of ensuring that the user can reach the destination, the cost for the user to refuel at the corresponding gas station according to the corresponding planned path is the lowest, thereby further reducing the user's refueling cost and improving the rationality of the gas station recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The figure is a flow chart of an intelligent recommendation method for gas stations provided by one embodiment of the present invention.

[0063] Figure 2 The figure is a schematic structural diagram of an intelligent recommendation device for a gas station provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0064] With reference to the drawings and embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0065] As shown in the figure, an embodiment of the present application provides an intelligent recommendation method for gas stations, which comprises: Figure 1

[0066] S1: obtaining a destination position, a current position of a vehicle, and positions of a plurality of gas stations within a preset range of the current position of the vehicle.

[0067] Illustratively, the preset range can be a preset distance value, and a circular region can be generated with the current position of the vehicle as the center and the preset distance value as the radius. The positions of all the gas stations within the circular region are taken as the positions of the plurality of gas stations within the preset range. It should be noted that the preset distance value can be adjusted according to actual conditions, for example, it can be 5 km, 10 km, or 15 km, which is not limited herein.

[0068] S2: for each gas station, determining a planning path of the vehicle from the current position to the destination via the gas station according to the position of the gas station, the destination position, and the current position of the vehicle; wherein each planning path comprises a first planning sub-path between the current position of the vehicle and the gas station, and a second planning sub-path between the gas station and the destination.

[0069] Specifically, for each gas station, the current position of the vehicle is taken as the starting point, the position of the gas station is taken as the passing point, and the destination position is taken as the end point, and path planning is performed again to generate each planning path. In actual scenarios, each gas station corresponds to one or more planning paths. Each planning path comprises two sub-paths, a first planning sub-path between the current position of the vehicle and the gas station, and a second planning sub-path between the gas station and the destination.

[0070] S3: for each planning sub-path, calculating a dynamic influence coefficient for representing an influence of dynamic factors on fuel consumption according to meteorological forecast data and congestion levels of the planning sub-path, calculating a static influence coefficient for representing an influence of static factors on fuel consumption according to a road surface state of the planning sub-path, and calculating a predicted fuel consumption of the planning sub-path according to the dynamic influence coefficient, the static influence coefficient, a basic fuel consumption of the vehicle, and a path length.

[0071] ​Specifically, for each planning sub-path (the first planning sub-path or the second planning sub-path), a dynamic influence coefficient is determined according to the weather forecast data and the congestion level of the planning sub-path, a static influence coefficient is determined according to the road surface state of the planning sub-path, and finally the predicted fuel consumption of the planning sub-path is calculated according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle and the path length. In the actual vehicle driving process, the fuel consumption of the vehicle will be affected by dynamic factors such as weather and congestion, and will also be affected by static factors such as path state such as uphill, downhill and ground undulation, and the present application combines dynamic factors and static factors to accurately predict the fuel consumption of the vehicle when driving on each planning sub-path.

[0072] In a preferred embodiment, the dynamic influence coefficient for representing the influence of dynamic factors on fuel consumption is calculated according to the weather forecast data and the congestion level of the planning sub-path, including:

[0073] For each sub-region of the planning sub-path, a weather influence sub-coefficient corresponding to the sub-region is calculated according to the temperature, humidity, wind and precipitation of the sub-region;

[0074] According to the congestion level of the sub-region, a congestion influence sub-coefficient corresponding to the sub-region is determined;

[0075] The dynamic influence sub-coefficient of the sub-region is calculated according to the product of the weather influence sub-coefficient and the congestion influence sub-coefficient of the sub-region;

[0076] The dynamic influence sub-coefficients of the sub-regions are weighted and averaged according to the proportion of the path length of the sub-region to the path length of the planning sub-path, to obtain the dynamic influence coefficient of the planning sub-path.

[0077] Specifically, the above-mentioned weather forecast data includes temperature, humidity, wind and precipitation; in this embodiment, the planning sub-path can be divided into sub-regions according to key road segment nodes on the planning sub-path as the division basis: for example, intersections, highway exits, tunnel entrances, etc. These nodes often change the traffic conditions and the surrounding environment, and dividing the planning sub-path into sub-regions for calculating the dynamic influence sub-coefficient of each sub-region and finally obtaining the dynamic influence coefficient of the planning sub-path according to the dynamic influence sub-coefficients of all sub-regions can make the calculation of the dynamic influence coefficient more refined and improve the accuracy of the dynamic influence coefficient.

[0078] In a preferred embodiment, the weather influence sub-coefficient corresponding to the sub-region is calculated according to the temperature, humidity, wind and precipitation of the sub-region, including:

[0079] According to the temperature of the sub-region and the optimal working temperature of the engine, the temperature influence coefficient is calculated by the following formula:

[0080]

[0081] According to the humidity of the sub-region and the preset standard humidity, the humidity influence coefficient is calculated by the following formula:

[0082] k h = 1 + γ × (H - H0)

[0083] According to the wind force of the sub-region and the angle between the vehicle driving direction and the wind direction, the wind force influence coefficient is calculated by the following formula:

[0084]

[0085] According to the precipitation of the sub-region, the precipitation influence coefficient is calculated by the following formula:

[0086]

[0087] According to the temperature influence coefficient, the humidity influence coefficient, the wind force influence coefficient and the precipitation influence coefficient, the weather influence sub-coefficient corresponding to the sub-region is calculated by the following formula:

[0088] k w = a × k t + b × k h + c × k v + d × k r ;

[0089] Wherein, k t is the temperature influence coefficient; T is the temperature of the sub-region; T0 is the optimal working temperature of the engine; α and β are preset adjustment parameters of the temperature influence coefficient; k h is the humidity influence coefficient; H is the humidity of the sub-region; H0 is the standard humidity; γ is the preset adjustment parameter of the humidity influence coefficient; k v is the wind force influence coefficient; θ is the angle between the vehicle driving direction and the wind direction; V is the wind force of the sub-region; δ and ∈ are preset adjustment parameters of the wind force influence coefficient; k r is the precipitation influence coefficient; R is the precipitation of the region; ζ is the preset adjustment parameter of the precipitation influence coefficient; a is the weight of the temperature influence coefficient; b is the weight of the humidity influence coefficient; c is the weight of the wind force influence coefficient; d is the weight of the precipitation influence coefficient; a + b + c + d = 1.

[0090] It should be noted that the preset adjustment parameter of the temperature influence coefficient, the preset adjustment parameter of the humidity influence coefficient, the preset adjustment parameter of the wind force influence coefficient, the preset adjustment parameter of the precipitation influence coefficient, the weight of the temperature influence coefficient, the weight of the humidity influence coefficient, the weight of the wind force influence coefficient, the weight of the precipitation influence coefficient, the optimal working temperature of the engine and the standard humidity are all known values preset in advance, each preset adjustment parameter can be a value set by the technician according to experience, or the relationship between the fuel consumption and the corresponding influence factor can be obtained by data fitting through the existing technology according to the fuel consumption test data of the vehicle under the corresponding influence factor, and then the corresponding preset adjustment parameter is determined.

[0091] In a preferred embodiment, the congestion influence sub-coefficients corresponding to no congestion levels are preset in the application, specifically: the congestion levels include: smooth, slow, congestion and severe congestion; when smooth, the fuel consumption increase ratio is small, and the corresponding congestion influence sub-coefficient can be set to 1; when slow, the fuel consumption increases to a certain extent, and the congestion influence sub-coefficient is set to 1.3. When congested, the fuel consumption increases significantly, and the congestion influence sub-coefficient is set to 1.7. When severely congested, the fuel consumption increases greatly, and the congestion influence sub-coefficient is set to 2.0; of course, the specific values can be adjusted according to actual data and experience. The determination of the congestion levels of each sub-region can be determined by calling the real-time traffic data of the existing navigation software, and the determination of the congestion levels is not described here again. According to the congestion levels of the sub-regions, the congestion influence sub-coefficients corresponding to the sub-regions can be determined by looking up the table. c ;

[0092] After obtaining the congestion influence sub-coefficients corresponding to the sub-regions, the weather influence sub-coefficients corresponding to the sub-regions are multiplied, and the dynamic influence sub-coefficients of the sub-regions are obtained. c w

[0093] is the dynamic influence sub-coefficient of the i-th sub-region, is the congestion influence sub-coefficient of the i-th sub-region, is the weather influence sub-coefficient of the i-th sub-region;

[0094] In a preferred embodiment, the dynamic influence coefficients of the planning sub-path are obtained by weighting and averaging the dynamic influence sub-coefficients of each sub-region according to the proportion of the path length of the sub-region to the path length of the planning sub-path, including:

[0095] The dynamic influence coefficients of the planning sub-path are calculated by the following formula:

[0096] ​​​

[0097] K dynamic is a dynamic influence coefficient of the planning sub-path; n is the number of sub-regions in the planning sub-path, L is the path length of the planning sub-path, L i is the path length of the i-th sub-region.

[0098] In a preferred embodiment, according to the road surface state of the planning sub-path, a static influence coefficient for representing the influence of static factors on fuel consumption is calculated, including:

[0099] For each sub-region of the planning sub-path, a road surface type contained in the sub-region is determined; wherein the road surface type includes: flat road surface, bumpy road surface, uphill road surface and downhill road surface;

[0100] The road surface state influence coefficient corresponding to each road surface type is calculated respectively;

[0101] According to the proportion of the path length of different road surface types and the path length of the sub-region, and the corresponding road surface state influence coefficient, a static influence sub-coefficient of the sub-region is calculated;

[0102] According to the proportion of the path length of the sub-region and the path length of the planning sub-path, the static influence sub-coefficients of the sub-regions are weighted and averaged to obtain the static influence coefficient of the planning sub-path.

[0103] Specifically, in this embodiment of the present application, the road surface types contained in the sub-region are divided into: flat road surface, bumpy road surface, uphill road surface and downhill road surface; one sub-region contains one or more types of road surface; the specific identification of the road surface type can be determined according to the fluctuation degree of the road surface, which is a prior art and will not be described here;

[0104] In a preferred embodiment, when the road surface type is flat road surface, the road surface state influence coefficient of the bumpy road surface is 1;

[0105] When the road surface type is bumpy road surface, the road surface state influence coefficient of the bumpy road surface is calculated by the following formula:

[0106] k s = 1 + ε (S - S0);

[0107] When the road surface type is uphill road surface, the road surface state influence coefficient of the uphill road surface is calculated by the following formula: s = 1 + ε * tanθ';

[0108] When the road surface type is downhill road surface, the road surface state influence coefficient of the downhill road surface is calculated by the following formula:

[0109] k s = 1 - ε * |tanθ'|;

[0110] wherein, k s is a road surface state influence coefficient; ε is a preset adjustment parameter of the road surface state influence coefficient; S is an actual bump degree value of the road surface; S0 is a bump degree reference value of a flat road surface; θ' is a slope of the road surface.

[0111] It should be noted that the preset adjustment parameter of the road surface state influence coefficient and the bump degree reference value of the flat road surface are known values preset in advance, the bump degree reference value of the flat road surface can be set to 0, and the preset adjustment parameter of the road surface state influence coefficient can be a value set by a technician according to experience, or the relationship between fuel consumption and various road surface types can be obtained by data fitting through existing technology according to the fuel consumption test data of the vehicle on various road surface types, and then the preset adjustment parameter of the corresponding road surface state influence coefficient is determined. θ' is positive when the road surface is uphill, and θ' is negative when the road surface is downhill. The actual bump degree value of the road surface can be determined according to the fluctuation degree of the road surface relative to the flat road surface.

[0112] In a preferred embodiment, the static influence sub-coefficient of the sub-region is calculated according to the proportion of the path length of the different road surface types to the path length of the sub-region, and the corresponding road surface state influence coefficient, including:

[0113] The static influence sub-coefficient of the sub-region is calculated by the following formula:

[0114]

[0115] The static influence sub-coefficient of the i-th sub-region in the planning sub-path is calculated, L i is the path length of the i-th sub-region, m is the number of road surface types contained in the i-th sub-region, is the path length of the j-th road surface type in the i-th sub-region, is the road surface state influence coefficient of the j-th road surface type in the i-th sub-region.

[0116] In a preferred embodiment, the static influence sub-coefficients of the sub-regions are weighted and averaged according to the proportion of the path length of the sub-region to the path length of the planning sub-path, to obtain the static influence coefficient of the planning sub-path, including:

[0117] The static influence coefficient of the planning sub-path is calculated by the following formula:

[0118]

[0119] K static is the static influence coefficient of the planning sub-path.

[0120] In a preferred embodiment, the calculating the predicted fuel consumption of the planning sub-path according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle and the path length comprises:

[0121] The predicted fuel consumption of the planning sub-path is calculated by the following formula:

[0122]

[0123] Wherein, C pred is the predicted fuel consumption of the planning sub-path (unit: liter); C base is the basic fuel consumption of the vehicle (unit: liter / 100km); L is the path length of the planning sub-path (unit: km); K dynamic is the dynamic influence coefficient; K static is the static influence coefficient.

[0124] In a preferred embodiment, after the calculating the predicted fuel consumption of the planning sub-path according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle and the path length, the method further comprises:

[0125] Obtaining the historical driving record of the user;

[0126] According to the historical driving record, calculating the average acceleration change rate, the brake frequency and the speed standard deviation of the user when driving the vehicle:

[0127] According to the average acceleration change rate, the brake frequency and the speed standard deviation, determining the driving style of the user;

[0128] According to the driving style of the user, determining the corresponding fuel consumption correction coefficient;

[0129] According to the fuel consumption correction coefficient, correcting the predicted fuel consumption.

[0130] In this embodiment, the driving style includes aggressive type, moderate type and ordinary type. The aggressive type driving habit is characterized by rapid acceleration, frequent braking and large speed fluctuation. The moderate type is characterized by stable acceleration, stable braking and small speed change. The ordinary type is between the two, and the driving behavior has no obvious extreme characteristics. Different driving styles also affect fuel consumption. By combining the historical driving record of the user, the driving style of the user is determined, and then the corresponding fuel consumption correction coefficient is determined according to the driving style. The predicted fuel consumption of the planning sub-path is corrected according to the fuel consumption coefficient, which can further improve the accuracy of the calculation of the predicted fuel consumption of the planning sub-path.

[0131] Specifically, the historical driving record is obtained by collecting the driving record of the previous driving, the acceleration change rate of the user in each time period in the historical driving record is calculated, and then the average acceleration change rate in each preset time period is calculated to obtain the average acceleration change rate; the total brake times of the user in the historical driving record are calculated, and the brake frequency is obtained by calculating the ratio of the total brake times to the path length of the historical driving record; the speed standard deviation is calculated according to the speed of each time in the historical driving record:

[0132] If the average acceleration change rate is greater than the first acceleration change rate threshold, the brake frequency is greater than the first brake frequency threshold, and the speed standard deviation is greater than the first speed threshold, it is determined that the driving style of the user is aggressive; illustratively, the first acceleration change rate threshold is 0.8 m / s 3 ; the first brake frequency threshold is 1 time per kilometer; and the first speed threshold is 15 km / h; it should be noted that the above values can be set according to actual conditions. If the average acceleration change rate is less than the second acceleration change rate threshold, the brake frequency is less than the second brake frequency threshold, and the speed standard deviation is less than the second speed threshold, it is determined that the driving style of the user is moderate; illustratively, the second acceleration change rate threshold is 0.3 m / s3; the second brake frequency threshold is 0.5 times per kilometer; and the second speed threshold is 5 km / h; it should be noted that the above values can be set according to actual conditions. Otherwise, it is determined that the driving style of the user is ordinary.

[0133] If the driving style of the user is aggressive, the fuel consumption correction coefficient of the user is determined to be 1.2; if the driving style of the user is moderate, the fuel consumption correction coefficient of the user is determined to be 0.8; and if the driving style of the user is ordinary, the corresponding fuel consumption correction coefficient of the user is determined to be 1.

[0134] After the fuel consumption correction coefficient is determined, the predicted fuel consumption of the planned sub-path is multiplied by the fuel consumption correction coefficient to obtain the final corrected predicted fuel consumption.

[0135] S4: The first planned sub-path with a predicted fuel consumption lower than the remaining fuel amount of the current vehicle is selected as the first planned sub-path to be selected, the planned path in which the first planned sub-path to be selected is located is selected as the planned path to be selected, and then the total predicted fuel consumption of the planned path to be selected is calculated according to the predicted fuel consumptions of the first planned sub-path and the second planned sub-path in the planned path to be selected;

[0136] In this embodiment, the first planned sub-path whose predicted fuel consumption is lower than the current remaining fuel amount of the vehicle is used as the first planned sub-path to be selected. The gas stations are initially screened based on the predicted fuel consumption of the first planned sub-path, and gas stations that cannot be reached with the current remaining fuel amount of the vehicle are eliminated, thereby improving the rationality of the gas station recommendation. Subsequently, the planned path containing the first planned sub-path to be selected is used as the planned path to be selected, and the total predicted fuel consumption of the planned path to be selected is calculated based on the predicted fuel consumption of the first planned sub-path and the second planned sub-path in the planned path to be selected.

[0137] S5: Based on the total predicted fuel consumption and the current remaining fuel of the vehicle, calculate the minimum amount of fuel required for the vehicle to travel to the destination after passing the corresponding gas station along each planned route to be selected.

[0138] Specifically, by calculating the difference between the total predicted fuel consumption and the current remaining fuel amount of the vehicle, the minimum amount of fuel required for the vehicle to travel to the destination after passing the corresponding gas station along each planned route to be selected can be obtained.

[0139] S6: Based on the gas prices of each gas station and the corresponding minimum refueling amount, the total refueling price of each planned route to be selected is calculated, and then the planned route to be selected and the corresponding gas station with the lowest total refueling price are recommended.

[0140] For each planned route to be selected, based on the corresponding type of fuel required by the vehicle, the unit price of the gas station in the planned route to be selected and the minimum amount of fuel, the total price of fuel required for the vehicle to refuel at the corresponding gas station along the planned route to be selected, while ensuring that the user can reach the destination, can be obtained. Finally, the planned route to be selected with the lowest total price of fuel is used as the target planned route, and the gas station corresponding to the target planned route is used as the target gas station. The target planned route, target gas station and corresponding total price of fuel are recommended to the terminal (such as the car computer or mobile phone).

[0141] Based on the above method embodiment, a corresponding device embodiment is provided;

[0142] like Figure 2 An embodiment of the present invention provides an intelligent recommendation device for gas stations, comprising: a data acquisition module, a route planning module, a sub-route fuel consumption prediction module, a route total fuel consumption prediction module, a minimum fuel quantity calculation module, and a route recommendation module;

[0143] The data acquisition module is used to acquire the destination location, the current location of the vehicle, and the locations of several gas stations within a preset range of the current location of the vehicle;

[0144] The path planning module is configured to determine, for each gas station, a planned path of the vehicle from a current location of the vehicle to a destination via the gas station according to a location of the gas station, a location of the destination and the current location of the vehicle, wherein each planned path comprises a first planned sub-path between the current location of the vehicle and the gas station, and a second planned sub-path between the gas station and the destination;

[0145] The sub-path fuel consumption prediction module is configured to, for each planned sub-path, calculate a dynamic influence coefficient for representing an influence of dynamic factors on fuel consumption according to weather forecast data and congestion levels of the planned sub-path, calculate a static influence coefficient for representing an influence of static factors on fuel consumption according to a road surface state of the planned sub-path, and calculate a predicted fuel consumption of the planned sub-path according to the dynamic influence coefficient, the static influence coefficient, a basic fuel consumption of the vehicle and a path length of the planned sub-path;

[0146] The total fuel consumption prediction module is configured to select, as a first planned sub-path to be selected, a first planned sub-path whose predicted fuel consumption is lower than a current remaining fuel amount of the vehicle, select, as a planned path to be selected, a planned path in which the first planned sub-path to be selected is located, and then calculate a total predicted fuel consumption of the planned path to be selected according to the predicted fuel consumptions of the first planned sub-path and the second planned sub-path in the planned path to be selected.

[0147] The minimum refueling amount calculation module is configured to calculate a minimum refueling amount required for the vehicle to travel to the destination after passing through a corresponding gas station along each planned path to be selected according to the total predicted fuel consumption and the current remaining fuel amount of the vehicle.

[0148] The path recommendation module is configured to calculate a total refueling price of each planned path to be selected according to oil prices of each gas station and the corresponding minimum refueling amount, and then recommend a planned path to be selected with the lowest total refueling price as a target planned path.

[0149] It should be noted that the above device embodiment is corresponding to the method embodiment of the present application, and can realize the intelligent recommendation method of the gas station described in any one of the method embodiments of the present application. The device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. they can be located in one place, or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to realize the purpose of the embodiment. In addition, in the device embodiment provided by the present application, the connection relationship between the modules indicates that they have a communication connection therebetween, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0150] On the basis of the above-mentioned method embodiment, a terminal device embodiment is correspondingly provided.

[0151] An embodiment of the present application provides a terminal device, comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the intelligent recommendation method of the gas station in any one of the embodiments of the present application when executing the computer program.

[0152] It should be noted that the terminal device mentioned here can be a desktop computer, a notebook computer, a palm computer and a cloud server and the like computing device. The terminal device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that, for example, it can also include an input / output device, a network access device, a bus and the like.

[0153] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like, and the processor is a control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0154] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function (such as a sound playing function, an image playing function, etc.) and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.) and the like. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0155] Correspondingly, the storage medium item embodiments are provided based on the method item embodiments.

[0156] An embodiment of the present application provides a storage medium, the storage medium comprising a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the intelligent recommendation method of the gas station according to any one of the method item embodiments of the present application when the computer program is running.

[0157] The storage medium is a computer readable storage medium, and all or part of the processes in the above-mentioned embodiments are implemented by the present application, and the computer program can also instruct the related hardware to complete, the computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when being executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0158] The above-mentioned is the preferred embodiment of the present application, and it should be pointed out that, for the ordinary skilled in the technical field, some improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements are also regarded as the protection scope of the present application.

Claims

1. An intelligent recommendation method for gas stations, characterized in that: include: Obtaining a destination location, a current location of the vehicle, and locations of several gas stations within a preset range of the current location of the vehicle; For each gas station, a planned path for the vehicle is determined from the current location to the destination via the gas station based on the location of the gas station, the location of the destination, and the current location of the vehicle; wherein each planned path includes a first planned sub-path from the current location to the gas station and a second planned sub-path from the gas station to the destination; For each planned subpath and each subregion of the planned subpath, the temperature influence coefficient is calculated using the following formula based on the temperature of the subregion and the optimal operating temperature of the engine: ； According to the humidity of the sub-area and the preset standard humidity, the humidity influence coefficient is calculated using the following formula: ； Based on the wind force in the sub-area and the angle between the vehicle's travel direction and the wind direction, the wind force influence coefficient is calculated using the following formula: ； According to the precipitation in the sub-region, the precipitation impact coefficient is calculated using the following formula: ； According to the temperature influence coefficient, humidity influence coefficient, wind influence coefficient and precipitation influence coefficient, the weather influence sub-coefficient corresponding to the sub-region is calculated by the following formula: ； in, is the temperature influence coefficient; T is the temperature of the sub-region; is the optimal operating temperature of the engine; and It is the preset adjustment parameter of the temperature influence coefficient; is the humidity influence coefficient; H is the humidity of the sub-area; is the standard humidity; It is the preset adjustment parameter of humidity influence coefficient; is the wind influence coefficient; is the angle between the vehicle's driving direction and the wind direction; V is the wind force in the sub-area; and It is the preset adjustment parameter of wind influence coefficient; is the precipitation influence coefficient; R is the regional precipitation; is the preset adjustment parameter of the precipitation influence coefficient; a is the weight of the temperature influence coefficient; b is the weight of the humidity influence coefficient; c is the weight of the wind influence coefficient; d is the weight of the precipitation influence coefficient; a+b+c+d=1; Determine the congestion impact sub-coefficient corresponding to the sub-region based on its congestion level; calculate the sub-region's dynamic impact sub-coefficient based on the product of its weather impact sub-coefficient and its congestion impact sub-coefficient; and calculate the dynamic impact coefficient of the planned sub-path by taking a weighted average of the dynamic impact sub-coefficients of each sub-region based on the ratio of the sub-region's path length to the path length of the planned sub-path. For each sub-region of the planned sub-path, determining a road type contained in the sub-region; wherein the road type includes: flat road, bumpy road, uphill road, and downhill road; Calculate the pavement condition influence coefficient for each pavement type. Calculate the sub-area's static influence sub-coefficient based on the ratio of the path length of each pavement type to the path length of the sub-area, as well as the corresponding pavement condition influence coefficient. Take a weighted average of the static influence sub-coefficients of each sub-area based on the ratio of the sub-area's path length to the path length of the planned sub-path to obtain the static influence coefficient of the planned sub-path. Calculating predicted fuel consumption of the planned sub-path based on the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle, and the path length; The first planned sub-path whose predicted fuel consumption is lower than the current remaining fuel level of the vehicle is selected as the first planned sub-path to be selected, and the planned path where the first planned sub-path to be selected is located is selected as the planned path to be selected. Then, based on the predicted fuel consumption of the first planned sub-path and the second planned sub-path in the planned path to be selected, the total predicted fuel consumption of the planned path to be selected is calculated; Based on the total predicted fuel consumption and the current remaining fuel level of the vehicle, calculate the minimum amount of fuel required for the vehicle to reach the destination after passing the corresponding gas station along each planned route to be selected; Based on the gas prices at each gas station and the corresponding minimum refueling amount, the total refueling price of each planned route to be selected is calculated, and then the planned route to be selected with the lowest total refueling price and the corresponding gas station are recommended.

2. The intelligent recommendation method for gas stations according to claim 1, characterized in that: When the road surface type is flat, the road surface condition influence coefficient of bumpy road is 1; When the road surface type is bumpy, the road surface condition influence coefficient of bumpy road is calculated using the following formula: ； When the road surface type is an uphill road surface, the road surface condition influence coefficient of the uphill road surface is calculated using the following formula: ; When the road surface type is a downhill road, the road surface condition influence coefficient of the downhill road is calculated using the following formula: ； in, is the road surface condition influence coefficient; The preset adjustment parameter of the road surface condition influence coefficient; S is the actual bumpiness value of the road surface; It is the reference value of the bumpiness on a flat road; is the slope of the road.

3. The intelligent recommendation method for gas stations according to claim 2, characterized in that: The step of calculating the predicted fuel consumption of the planned sub-path according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle, and the path length includes: The predicted fuel consumption of the planned sub-path is calculated using the following formula: ； in, To plan the predicted fuel consumption of the sub-path; is the basic fuel consumption of the vehicle; is the path length of the planned subpath; is the dynamic influence coefficient; is the static influence coefficient.

4. The intelligent recommendation method for gas stations according to claim 3, characterized in that: After calculating the predicted fuel consumption of the planned sub-route according to the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle, and the path length, the method further includes: Get the user's historical driving records; Based on historical driving records, calculate the average acceleration change rate, braking frequency, and speed standard deviation when the user is driving the vehicle: determining the user's driving style based on the average acceleration change rate, braking frequency, and speed standard deviation; Determine the corresponding fuel consumption correction factor based on the user's driving style; The predicted fuel consumption is corrected according to the fuel consumption correction coefficient.

5. An intelligent recommendation device for a gas station, characterized in that: include: Data acquisition module, route planning module, sub-route fuel consumption prediction module, route total fuel consumption prediction module, minimum refueling amount calculation module and route recommendation module; The data acquisition module is used to acquire the destination location, the current location of the vehicle, and the locations of several gas stations within a preset range of the current location of the vehicle; The path planning module is configured to determine, for each gas station, a planned path for the vehicle from the current location to the destination via the gas station based on the location of the gas station, the location of the destination, and the current location of the vehicle; wherein each planned path includes a first planned sub-path from the current location to the gas station and a second planned sub-path from the gas station to the destination; The sub-path fuel consumption prediction module is used to calculate the temperature influence coefficient for each planned sub-path and each sub-region of the planned sub-path according to the temperature of the sub-region and the optimal operating temperature of the engine using the following formula: ； According to the humidity of the sub-area and the preset standard humidity, the humidity influence coefficient is calculated using the following formula: ； Based on the wind force in the sub-area and the angle between the vehicle's travel direction and the wind direction, the wind force influence coefficient is calculated using the following formula: ； According to the precipitation in the sub-region, the precipitation impact coefficient is calculated using the following formula: ； According to the temperature influence coefficient, humidity influence coefficient, wind influence coefficient and precipitation influence coefficient, the weather influence sub-coefficient corresponding to the sub-region is calculated by the following formula: ； in, is the temperature influence coefficient; T is the temperature of the sub-region; is the optimal operating temperature of the engine; and It is the preset adjustment parameter of the temperature influence coefficient; is the humidity influence coefficient; H is the humidity of the sub-area; is the standard humidity; It is the preset adjustment parameter of humidity influence coefficient; is the wind influence coefficient; is the angle between the vehicle's driving direction and the wind direction; V is the wind force in the sub-area; and It is the preset adjustment parameter of wind influence coefficient; is the precipitation influence coefficient; R is the regional precipitation; is the preset adjustment parameter of the precipitation influence coefficient; a is the weight of the temperature influence coefficient; b is the weight of the humidity influence coefficient; c is the weight of the wind influence coefficient; d is the weight of the precipitation influence coefficient; a+b+c+d=1; Determine the congestion impact sub-coefficient corresponding to the sub-region based on its congestion level; calculate the sub-region's dynamic impact sub-coefficient based on the product of its weather impact sub-coefficient and its congestion impact sub-coefficient; and calculate the dynamic impact coefficient of the planned sub-path by taking a weighted average of the dynamic impact sub-coefficients of each sub-region based on the ratio of the sub-region's path length to the path length of the planned sub-path. For each sub-region of the planned sub-path, determining a road type contained in the sub-region; wherein the road type includes: flat road, bumpy road, uphill road, and downhill road; Calculate the pavement condition influence coefficient for each pavement type. Calculate the sub-area's static influence sub-coefficient based on the ratio of the path length of each pavement type to the path length of the sub-area, as well as the corresponding pavement condition influence coefficient. Take a weighted average of the static influence sub-coefficients of each sub-area based on the ratio of the sub-area's path length to the path length of the planned sub-path to obtain the static influence coefficient of the planned sub-path. Calculating predicted fuel consumption of the planned sub-path based on the dynamic influence coefficient, the static influence coefficient, the basic fuel consumption of the vehicle, and the path length; The total fuel consumption prediction module for a path is configured to select a first planned sub-path whose predicted fuel consumption is lower than the current remaining fuel level of the vehicle as the first planned sub-path to be selected, select the planned path where the first planned sub-path to be selected is located as the planned path to be selected, and then calculate the total predicted fuel consumption of the planned path to be selected based on the predicted fuel consumption of the first planned sub-path and the second planned sub-path in the planned path to be selected; The minimum refueling amount calculation module is used to calculate the minimum refueling amount required for the vehicle to travel to the destination after passing the corresponding refueling station along each planned route to be selected based on the total predicted fuel consumption and the current remaining fuel amount of the vehicle; The route recommendation module is used to calculate the total refueling price of each planned route to be selected based on the oil price of each gas station and the corresponding minimum refueling amount, and then recommend the planned route to be selected with the lowest total refueling price as the target planned route.

6. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent recommendation method for gas stations according to any one of claims 1 to 4 is implemented.

7. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the intelligent recommendation method for gas stations according to any one of claims 1 to 4.

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