Vehicle type recommendation method and device, medium and electronic equipment

Abstract

The invention relates to a vehicle model recommendation method and device, a medium and electronic equipment, and the method comprises the steps: obtaining vehicle selection demand information inputted by a user, the vehicle selection demand information comprising a standby driving scene parameter set; according to the to-be-used driving scene parameter set and pre-configured vehicle model recommendation data, a target recommendation vehicle model corresponding to the user is determined, and the vehicle model recommendation data comprises energy consumption information of different vehicle models corresponding to each driving scene parameter set in multiple driving scene parameter sets. Therefore, the target recommended vehicle model corresponding to the vehicle selection demand information of the user is determined by obtaining the standby driving scene parameter set of the user and the pre-configured vehicle model recommendation data, and the target recommended vehicle model can be determined according to different vehicle selection demand information of the user, so that the personalized demand of the user can be met, and the user experience is improved. And thus, the experience and satisfaction of the user can be effectively improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically, to a method, apparatus, medium, and electronic device for recommending vehicle models. Background Technology

[0002] Currently, most car model recommendations are based on rather subjective owner reviews or brand advertising, which can only provide a certain degree of "yes / no recommendation" information and cannot recommend models specifically based on user needs.

[0003] Alternatively, existing technologies can only provide users with relevant vehicle information by collecting user query information (such as price, model, etc.). However, due to the large number of car brands on the market, their different uses, and the performance parameters and functions of the cars themselves, users cannot have a comprehensive understanding of the market and cannot choose a cost-effective model that suits their needs based on the query information. Summary of the Invention

[0004] The purpose of this disclosure is to provide a vehicle model recommendation method, apparatus, medium, and electronic device.

[0005] According to a first aspect of the present disclosure, a vehicle model recommendation method is provided, the method comprising:

[0006] Obtain user-inputted vehicle selection requirements information, which includes a set of driving scenario parameters to be used. Different sets of driving scenario parameters are used to characterize driving habits under different driving environments, or different driving habits under the same driving environment.

[0007] Based on the set of driving scenario parameters to be used and the pre-configured vehicle model recommendation data, the target recommended vehicle model corresponding to the user is determined. The vehicle model recommendation data includes energy consumption information of different vehicle models corresponding to multiple driving scenario parameter sets.

[0008] Optionally, the vehicle model recommendation data is obtained in advance in the following manner:

[0009] Through the vehicle networking platform, operational data of multiple operating vehicles of various models is obtained, including vehicle operation data and driving environment data;

[0010] Based on the vehicle operation data and the driving environment data, multiple driving scenario parameter sets are determined;

[0011] The energy consumption information of different vehicle models under each driving scenario parameter set is obtained through the vehicle network platform, so as to obtain the energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets.

[0012] Optionally, the step of obtaining energy consumption information of different vehicle models under each driving scenario parameter set through the vehicle network platform to obtain energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets includes:

[0013] For each vehicle model, obtain energy consumption information of multiple operating vehicles of that model under each driving scenario parameter set;

[0014] Based on the energy consumption information of the multiple operating vehicles under the driving scenario parameter set, the energy consumption information of multiple vehicle models corresponding to each driving scenario parameter set is generated.

[0015] Optionally, the vehicle operation data includes multiple operation parameters, the driving environment data includes multiple environmental parameters, and the step of determining multiple driving scenario parameter sets based on the vehicle operation data and the driving environment data includes:

[0016] For each vehicle type, each operating parameter in the multiple vehicle operating data corresponding to multiple operating vehicles is divided into multiple first parameter intervals according to the first interval division strategy;

[0017] Each environmental parameter in the multiple driving environment data corresponding to multiple operating vehicles is divided into multiple second parameter intervals according to the second interval division strategy;

[0018] Based on the multiple first parameter intervals and / or the multiple second parameter intervals corresponding to each vehicle model, a set of multiple driving scenario parameters corresponding to the vehicle model is determined to obtain a set of multiple driving scenario parameters corresponding to the multiple vehicle models.

[0019] Optionally, the multiple driving scenario parameter sets include a first driving scenario dataset and a second driving scenario dataset. The step of determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models includes:

[0020] Each of the first parameter intervals is combined with different second parameter intervals to obtain multiple combinations of the first driving scenario dataset;

[0021] For each operating parameter, each first parameter interval is used as a second driving scenario dataset to obtain multiple second driving scenario datasets corresponding to multiple first parameter intervals of the operating parameter.

[0022] Optionally, determining the target recommended vehicle model for the user based on the driving scenario parameter set and pre-configured vehicle model recommendation data includes:

[0023] From the vehicle model recommendation data, determine the target driving scenario parameter set that matches the driving scenario parameter set to be used, and the energy consumption information of various vehicle models corresponding to the target driving scenario parameter set;

[0024] Based on the energy consumption information of the various vehicle models, determine one or more models with the lowest energy consumption;

[0025] The one or more models with the lowest energy consumption are selected as the target recommended models.

[0026] Optionally, the vehicle operation data includes at least one of driving speed, driving time, driving route, and load.

[0027] The driving environment data includes at least one of the following: temperature, road conditions, altitude and slope, wind speed and direction, precipitation information, air quality, light intensity, air pressure, and humidity.

[0028] According to a second aspect of the present disclosure, a vehicle model recommendation device is provided, the device comprising:

[0029] The acquisition module is configured to acquire vehicle selection requirement information input by the user. This vehicle selection requirement information includes a set of driving scenario parameters to be used. Different sets of driving scenario parameters are used to characterize driving habits under different driving environments, or different driving habits under the same driving environment.

[0030] The determination module is configured to determine the target recommended vehicle model for the user based on the set of driving scenario parameters to be used and pre-configured vehicle model recommendation data. The vehicle model recommendation data includes energy consumption information of different vehicle models corresponding to multiple driving scenario parameter sets.

[0031] Optionally, the vehicle model recommendation data is obtained in advance in the following manner:

[0032] Through the vehicle networking platform, operational data of multiple operating vehicles of various models is obtained, including vehicle operation data and driving environment data;

[0033] Based on the vehicle operation data and the driving environment data, multiple driving scenario parameter sets are determined;

[0034] The energy consumption information of different vehicle models under each driving scenario parameter set is obtained through the vehicle network platform, so as to obtain the energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets.

[0035] Optionally, the step of obtaining energy consumption information of different vehicle models under each driving scenario parameter set through the vehicle network platform to obtain energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets includes:

[0036] For each vehicle model, obtain energy consumption information of multiple operating vehicles of that model under each driving scenario parameter set;

[0037] Based on the energy consumption information of the multiple operating vehicles under the driving scenario parameter set, the energy consumption information of multiple vehicle models corresponding to each driving scenario parameter set is generated.

[0038] Optionally, the vehicle operation data includes multiple operation parameters, the driving environment data includes multiple environmental parameters, and the step of determining multiple driving scenario parameter sets based on the vehicle operation data and the driving environment data includes:

[0039] For each vehicle type, each operating parameter in the multiple vehicle operating data corresponding to multiple operating vehicles is divided into multiple first parameter intervals according to the first interval division strategy;

[0040] Each environmental parameter in the multiple driving environment data corresponding to multiple operating vehicles is divided into multiple second parameter intervals according to the second interval division strategy;

[0041] Based on the multiple first parameter intervals and / or the multiple second parameter intervals corresponding to each vehicle model, a set of multiple driving scenario parameters corresponding to the vehicle model is determined to obtain a set of multiple driving scenario parameters corresponding to the multiple vehicle models.

[0042] Optionally, the multiple driving scenario parameter sets include a first driving scenario dataset and a second driving scenario dataset. The step of determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models includes:

[0043] Each of the first parameter intervals is combined with different second parameter intervals to obtain multiple combinations of the first driving scenario dataset;

[0044] For each operating parameter, each first parameter interval is used as a second driving scenario dataset to obtain multiple second driving scenario datasets corresponding to multiple first parameter intervals of the operating parameter.

[0045] Optionally, the determining module is configured to:

[0046] From the vehicle model recommendation data, determine the target driving scenario parameter set that matches the driving scenario parameter set to be used, and the energy consumption information of various vehicle models corresponding to the target driving scenario parameter set;

[0047] Based on the energy consumption information of the various vehicle models, determine one or more models with the lowest energy consumption;

[0048] The one or more models with the lowest energy consumption are selected as the target recommended models.

[0049] Optionally, the vehicle operation data includes at least one of driving speed, driving time, driving route, and load.

[0050] The driving environment data includes at least one of the following: temperature, road conditions, altitude and slope, wind speed and direction, precipitation information, air quality, light intensity, air pressure, and humidity.

[0051] According to a third aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

[0052] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising:

[0053] A memory on which computer programs are stored;

[0054] A processor is configured to execute the computer program in the memory to implement the steps of the method described in the first aspect of the present disclosure.

[0055] The above technical solution acquires user-inputted vehicle selection requirements, including a set of driving scenario parameters. Different sets of these parameters represent driving habits under different driving environments, or different driving habits under the same environment. Based on these parameters and pre-configured vehicle recommendation data, a target recommended vehicle for the user is determined. This vehicle recommendation data includes energy consumption information for different vehicle models corresponding to each driving scenario parameter set. By acquiring the user's set of driving scenario parameters and pre-configured vehicle recommendation data, a target recommended vehicle corresponding to the user's vehicle selection requirements can be determined. This allows for personalized vehicle recommendations based on different user needs, thereby meeting users' individual requirements and effectively improving user experience and satisfaction.

[0056] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0057] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0058] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings:

[0059] Figure 1 This is a flowchart illustrating a vehicle model recommendation method according to an exemplary embodiment;

[0060] Figure 2 This is a flowchart illustrating a method for configuring vehicle model recommendation data according to an exemplary embodiment;

[0061] Figure 3 It is based on Figure 2 The illustrated embodiment presents a flowchart of a method for configuring vehicle model recommendation data;

[0062] Figure 4 It is based on Figure 2 The illustrated embodiment shows a flowchart of another method for configuring vehicle model recommendation data;

[0063] Figure 5 It is based on Figure 3 The illustrated embodiment presents a flowchart of a method for configuring vehicle model recommendation data;

[0064] Figure 6 It is based on Figure 1 The illustrated embodiment presents a flowchart of a vehicle model recommendation method;

[0065] Figure 7 This is a block diagram illustrating a vehicle model recommendation device according to an exemplary embodiment;

[0066] Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0067] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0068] Before detailing the specific implementation methods of this disclosure, the application scenarios of this disclosure are explained below. This disclosure can be applied to scenarios where vehicle models are recommended to users. Currently, most vehicle model recommendations are based on subjective owner reviews or brand advertising, which can only provide a certain degree of "yes / no recommendation" information reference and cannot recommend models specifically according to user needs. Alternatively, existing technologies can only provide relevant vehicle model information to users by collecting user query information (such as price, model, etc.), but due to the large number of car brands on the market, their different uses, and the performance parameters and functions of the cars themselves, users cannot have a comprehensive understanding and cannot choose a cost-effective model that suits their needs based on the query information.

[0069] To address the aforementioned technical issues, this disclosure provides a vehicle model recommendation method, apparatus, medium, and electronic device. By acquiring user-inputted vehicle selection requirements, including a set of pending driving scenario parameters (different sets characterize driving habits under different driving environments or different driving habits under the same environment), the method determines the target recommended vehicle model for the user based on the pending driving scenario parameter set and pre-configured vehicle model recommendation data. The vehicle model recommendation data includes energy consumption information for different vehicle models corresponding to multiple driving scenario parameter sets. Thus, by acquiring the user's pending driving scenario parameter set and pre-configured vehicle model recommendation data, the target recommended vehicle model corresponding to the user's vehicle selection requirements can be determined. This allows for the identification of target recommended vehicles based on different user vehicle selection requirements, thereby meeting users' personalized needs and effectively improving user experience and satisfaction.

[0070] The specific embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0071] Figure 1 This is a flowchart illustrating a vehicle model recommendation method according to an exemplary embodiment, such as... Figure 1 As shown, the vehicle recommendation method includes the following steps:

[0072] Step 101: Obtain the vehicle selection requirement information input by the user, which includes a set of driving scenario parameters to be used.

[0073] The different driving scenario parameter sets are used to characterize driving habits under different driving environments, or different driving habits under the same driving environment. The driving scenario parameter set to be used is used to characterize the user's driving habits under different driving environments, or different driving habits under the same driving environment.

[0074] For example, driving habits under different driving environments can be as follows: In clear weather, driving habits might involve moderate speed, smooth braking, small steering angle, and using only conventional lighting. In rainy or snowy weather, driving habits might involve reduced speed, increased braking force to increase braking distance, larger steering angle to cope with slippery roads, and turning on fog lights or hazard lights to improve visibility. Different driving habits under the same driving environment can also be observed on the same city roads. For a conservative driver, this might manifest as lower speed, less acceleration and braking, and fewer lane changes; while for an aggressive driver, it might manifest as higher speed, greater acceleration and braking, and frequent lane changes in pursuit of higher driving efficiency.

[0075] Step 102: Determine the target recommended vehicle model for the user based on the set of driving scenario parameters to be used and the pre-configured vehicle model recommendation data.

[0076] The vehicle recommendation data includes multiple driving scenario parameter sets, and the energy consumption information of different vehicle models corresponding to each driving scenario parameter set.

[0077] For example, the vehicle model recommendation data includes a first driving scenario parameter set: a driving scenario where the vehicle is driving on a flat surface at a medium-low speed; a second driving scenario parameter set: a driving scenario where the vehicle is driving on a flat surface at a medium-high speed; and a third driving scenario parameter set: a driving scenario where the vehicle is driving on a flat surface at a high speed. In the first driving scenario, the energy consumption information for vehicle model A is 8.5L / 100km, the energy consumption information for vehicle model B is 7.6L / 100km, and the energy consumption information for vehicle model C is... In the first driving scenario, the fuel consumption is 9.6L / 100km. In the second driving scenario, the fuel consumption for model A is 10.5L / 100km, for model B it is 9.6L / 100km, and for model C it is 11.6L / 100km. In the third driving scenario, the fuel consumption for model A is 11.5L / 100km, for model B it is 12.6L / 100km, and for model C it is 13.6L / 100km.

[0078] In this step, a target driving scenario parameter set that matches the driving scenario parameter set to be used and the energy consumption information of multiple vehicle models corresponding to the target driving scenario parameter set are determined from the vehicle model recommendation data; one or more vehicle models with the lowest energy consumption are determined based on the energy consumption information of the multiple vehicle models; and the one or more vehicle models with the lowest energy consumption are selected as the target recommended vehicle models.

[0079] For example, in the first driving scenario, the energy consumption information for model A is 8.5L per 100km, the energy consumption information for model B is 7.6L per 100km, and the energy consumption information for model C is 9.6L per 100km. By comparing the energy consumption information of models A, B, and C, model B is selected as the target recommended model.

[0080] The above technical solution, by acquiring the user's set of driving scenario parameters and pre-configured vehicle recommendation data, determines the target recommended vehicle corresponding to the user's vehicle selection needs. It can determine the target recommended vehicle based on the user's different vehicle selection needs, thereby meeting the user's personalized needs and effectively improving the user's experience and satisfaction.

[0081] Figure 2 This is a flowchart illustrating a method for configuring vehicle model recommendation data according to an exemplary embodiment, such as... Figure 2 As shown, the recommended vehicle model data can be obtained in advance in the following ways:

[0082] Step 201: Obtain operational data of multiple operating vehicles of various models through the vehicle networking platform.

[0083] The operational data includes vehicle operation data and driving environment data. Vehicle operation data may include at least one of driving speed, driving time, driving route, and load. Driving environment data includes at least one of temperature, road conditions, altitude gradient, wind speed and direction, precipitation information, air quality, light intensity, air pressure, and humidity. The operating vehicles include those with a daily mileage greater than or equal to a preset mileage and a monthly operating number greater than or equal to a preset operating number of days.

[0084] For example, the operating vehicles are those that travel ≥10km per day and operate for ≥15 days per month.

[0085] Step 202: Determine multiple driving scenario parameter sets based on the vehicle operation data and the driving environment data.

[0086] The vehicle operation data includes multiple operating parameters, and the driving environment data includes multiple environmental parameters.

[0087] In this step, for each vehicle model, each operating parameter in the vehicle operation data corresponding to multiple operating vehicles is divided into multiple first parameter intervals according to a first interval division strategy; each environmental parameter in the driving environment data corresponding to multiple operating vehicles is divided into multiple second parameter intervals according to a second interval division strategy; and multiple driving scenario parameter sets corresponding to each vehicle model are determined based on the multiple first parameter intervals and / or the multiple second parameter intervals, so as to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models.

[0088] Step 203: Obtain energy consumption information of different vehicle models under each driving scenario parameter set through the vehicle network platform, so as to obtain energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets.

[0089] In this step, the vehicle-to-everything (V2X) platform collects real-time vehicle operating data through onboard communication modules (such as T-BOX, OBD devices, etc.) installed in the vehicles. It identifies the vehicle model information using vehicle ID and other identification information, and determines the energy consumption information for different vehicle models based on the operating data and model information. For each vehicle model, it acquires the energy consumption information of multiple operating vehicles of that model under each driving scenario parameter set. Based on the energy consumption information of these multiple operating vehicles under the driving scenario parameter sets, it generates energy consumption information for multiple vehicle models corresponding to each driving scenario parameter set.

[0090] The above technical solutions, by collecting operational data of vehicles on the vehicle network platform and energy consumption information of different models under each driving scenario parameter set, can provide data support for recommending vehicle models to users. They can also improve the accuracy of recommending vehicle models to users by collecting a large amount of operational data of vehicles as samples, thereby effectively improving the user experience and satisfaction.

[0091] Figure 3 It is based on Figure 2 The illustrated embodiment presents a flowchart of a method for configuring vehicle model recommendation data, as shown in the figure. Figure 3 As shown, when the vehicle operation data includes multiple operating parameters and the driving environment data includes multiple environmental parameters, Figure 2 Step 202, which involves determining multiple driving scenario parameter sets based on the vehicle operation data and the driving environment data, may include:

[0092] Step 2021: For each vehicle type, divide each operating parameter in the multiple vehicle operating data corresponding to multiple operating vehicles into multiple first parameter intervals according to the first interval division strategy.

[0093] The vehicle operation data may include at least one of the following: driving speed, driving time, driving route, and load.

[0094] In this step, for each vehicle type, the driving speed, driving time, driving route and load of the multiple vehicle operation data corresponding to multiple operating vehicles can be divided into multiple first parameter intervals according to the first interval division strategy.

[0095] For example, for each vehicle type, the driving speed in the multiple vehicle operation data corresponding to multiple operating vehicles is divided into multiple first parameter intervals, such as a medium-low speed interval, a medium-high speed interval, a high speed interval, and an extremely high speed interval. The first interval division strategy is as follows: 65km / h ≤ driving speed < 75km / h is assigned to the medium-low speed interval, 75km / h ≤ driving speed < 85km / h is assigned to the medium-high speed interval, 85km / h ≤ driving speed < 95km / h is assigned to the high speed interval, and 95km / h ≤ driving speed < 105km / h is assigned to the extremely high speed interval. For each vehicle type, the load in the multiple vehicle operation data corresponding to multiple operating vehicles is divided into multiple first parameter intervals, such as a light cargo transport interval, a medium-heavy cargo transport interval, and a heavy cargo transport interval. The first interval division strategy is as follows: load < 42t is assigned to the light cargo transport interval, 42t ≤ load < 46t is assigned to the medium-heavy cargo transport interval, and load ≥ 46t is assigned to the heavy cargo transport interval.

[0096] Step 2022: Divide each environmental parameter in the multiple driving environment data corresponding to multiple operating vehicles into multiple second parameter intervals according to the second interval division strategy.

[0097] The driving environment data includes at least one of the following: temperature, road conditions, altitude and slope, wind speed and direction, precipitation information, air quality, light intensity, air pressure, and humidity.

[0098] In this step, for each vehicle type, the temperature, road conditions, altitude and slope, wind speed and direction, precipitation information, air quality, light intensity, air pressure and humidity in the multiple driving environment data corresponding to multiple operating vehicles can be divided into multiple second parameter intervals according to the second interval division strategy.

[0099] For example, for each vehicle type, the altitude gradient in the multiple driving environment data corresponding to multiple operating vehicles is divided into multiple second parameter intervals, such as plains, hills and plateaus. The second interval division strategy is as follows: 0m < altitude gradient ≤ 200m is divided into the plain interval, 200m < altitude gradient ≤ 500m is divided into the hill interval, and altitude gradient > 500m is divided into the plain interval.

[0100] Step 2023: Determine multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals corresponding to each vehicle model, so as to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models.

[0101] In one implementation, determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models may include: combining each first parameter interval with different second parameter intervals to obtain multiple combined first driving scenario datasets.

[0102] In another implementation, determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals for each vehicle model to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models may include: for each operating parameter, taking each first parameter interval as a second driving scenario dataset to obtain multiple second driving scenario datasets corresponding to the multiple first parameter intervals of the operating parameter.

[0103] The above technical solution divides the operating parameters in the vehicle operation data and the environmental parameters in the driving environment data into intervals to obtain multiple driving scenario parameter sets, providing a basis for obtaining vehicle model recommendation data.

[0104] Figure 4 It is based on Figure 2 The illustrated embodiment shows a flowchart of another method for configuring vehicle model recommendation data, as shown in the flowchart. Figure 4 As shown, Figure 2 Step 203, which involves obtaining energy consumption information of different vehicle models under each driving scenario parameter set through a vehicle networking platform, to obtain energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets, may include:

[0105] Step 2031: For each vehicle model, obtain the energy consumption information of multiple operating vehicles of the vehicle model under each driving scenario parameter set.

[0106] The operating vehicles include those with a daily mileage greater than or equal to a preset mileage and a monthly operating number greater than or equal to a preset operating number of days.

[0107] Step 2032: Generate energy consumption information of multiple vehicle models corresponding to each driving scenario parameter set based on the energy consumption information of the multiple operating vehicles under the driving scenario parameter set.

[0108] In this step, the energy consumption data of multiple operating vehicles of the same model under the same driving scenario parameter set are converted into a unified unit of measurement (such as fuel consumption per 100 kilometers or electricity consumption per 100 kilometers). The converted energy consumption data of the multiple operating vehicles of the same model under the same driving scenario parameter set is then averaged to obtain the energy consumption information of the same model under the same driving scenario parameter set. Based on the energy consumption data of multiple operating vehicles of various models under the same driving scenario parameter set, energy consumption information of multiple models under the same driving scenario parameter set can be obtained. Based on the energy consumption data of multiple operating vehicles of the same model under multiple driving scenario parameter sets, energy consumption information of the same model under multiple driving scenario parameter sets can be obtained. Based on the energy consumption information of the same model under the same driving scenario parameter set, the energy consumption information of multiple models under the same driving scenario parameter set, and the energy consumption information of the same model under multiple driving scenario parameter sets, recommended data for the vehicle model can be obtained.

[0109] The above technical solution obtains energy consumption information of multiple operating vehicles of each model under each driving scenario parameter set through the vehicle networking platform, providing a basis for recommending models to users in the future.

[0110] Figure 5 It is based on Figure 3 The illustrated embodiment presents a flowchart of a method for configuring vehicle model recommendation data, as shown in the figure. Figure 5 As shown, when the multiple driving scenario parameter sets include a first driving scenario dataset and a second driving scenario dataset, Figure 3 Step 2023, which involves determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals, to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models, may include:

[0111] S11, combine each of the first parameter intervals with different second parameter intervals to obtain multiple combinations of the first driving scenario dataset.

[0112] In one implementation, each first parameter interval is combined with different second parameter intervals to obtain a first driving scenario dataset formed by multiple combinations. This may include: for each vehicle model, combining multiple first parameter intervals corresponding to each operating parameter in the vehicle operating data with multiple second parameter intervals corresponding to at least one environmental parameter in the driving environment data to obtain a first driving scenario dataset formed by multiple combinations.

[0113] For example, for the same vehicle model, the vehicle operation data includes driving speed, load, and driving route. Driving speed is divided into medium-low speed range, medium-high speed range, high speed range, and very high speed range; load is divided into light cargo transport range, medium-heavy cargo transport range, and heavy cargo transport range; and driving route is divided into highway, national highway, and rural road. The driving environment data includes altitude, slope, and precipitation information. Altitude slope is divided into plains, hills, and plateaus; and precipitation information is divided into light rain, moderate rain, heavy rain, and torrential rain. The medium-low speed range, light cargo transport range, and highway corresponding to driving speed, load, and driving route in the vehicle operation data can be combined with the plains corresponding to altitude slope in the driving environment data to obtain the first driving scenario dataset formed by multiple combinations of medium-low speed range, light cargo transport range, highway, and plain.

[0114] In another implementation, each of the first parameter intervals is combined with different second parameter intervals to obtain multiple combinations of the first driving scenario dataset. This may include: for each vehicle model, combining one or more first parameter intervals corresponding to at least one operating parameter in the vehicle operation data with multiple second parameter intervals corresponding to at least one environmental parameter in the driving environment data to obtain multiple combinations of the first driving scenario dataset.

[0115] For example, the light cargo transport sections and highways corresponding to the load and driving segments in the vehicle operation data can be combined with the plains corresponding to the elevation and slope in the driving environment data to obtain the first driving scenario dataset formed by multiple combinations of light cargo transport sections, highways, and plains.

[0116] S12, for each operating parameter, each first parameter interval is used as a second driving scenario dataset to obtain multiple second driving scenario datasets corresponding to multiple first parameter intervals of the operating parameters.

[0117] In this step, for each vehicle model, a first parameter interval corresponding to one operating parameter in the vehicle operating data is taken as a second driving scenario dataset, so as to obtain multiple second driving scenario datasets corresponding to multiple first parameter intervals of the operating parameters.

[0118] For example, for the same vehicle model, the vehicle operation data includes driving speed, load, and driving route. Driving speed is divided into medium-low speed range, medium-high speed range, high speed range, and very high speed range; load is divided into light cargo transport range, medium-heavy cargo transport range, and heavy cargo transport range; and driving route is divided into highway, national highway, and rural road. The medium-low speed range from the driving speed included in the vehicle operation data can be used as a second driving scenario dataset, or the medium-heavy cargo transport range from the load included in the vehicle operation data can be used as a second driving scenario dataset.

[0119] The above technical solution obtains multiple driving scenario parameter sets based on multiple first parameter intervals corresponding to vehicle operation data and multiple second parameter intervals corresponding to driving environment data, which can provide data support for obtaining vehicle model recommendation data in the future.

[0120] Figure 6 It is based on Figure 1 The illustrated embodiment presents a flowchart of a vehicle model recommendation method, as shown below. Figure 6 As shown, Figure 1 Step 102, which involves determining the target recommended vehicle model for the user based on the driving scenario parameter set and pre-configured vehicle model recommendation data, may include:

[0121] Step 1021: Determine from the vehicle model recommendation data a target driving scenario parameter set that matches the driving scenario parameter set to be used, and the energy consumption information of various vehicle models corresponding to the target driving scenario parameter set.

[0122] The vehicle recommendation data includes multiple driving scenario parameter sets, and the energy consumption information of different vehicle models corresponding to each driving scenario parameter set.

[0123] In this step, if the target driving scenario parameter set in the multiple driving scenario parameter sets included in the vehicle model recommendation data is consistent with the driving scenario parameter set to be used, it can be determined that the target driving scenario parameter set matches the driving scenario parameter set to be used.

[0124] Step 1022: Determine one or more models with the lowest energy consumption based on the energy consumption information of the various models.

[0125] In this step, by comparing the energy consumption information of various vehicle models corresponding to the target driving scenario parameter set, one or more vehicle models with the lowest energy consumption among the various vehicle models are determined.

[0126] For example, under the target driving scenario parameter set, the fuel consumption per 100 kilometers for model A is 20.5L / km, the fuel consumption per 100 kilometers for model B is 20.5L / km, the fuel consumption per 100 kilometers for model C is 23.5L / km, and the fuel consumption per 100 kilometers for model D is 22.5L / km. By comparing the energy consumption information of the four models A, B, C, and D, it can be determined that the models with the lowest energy consumption among the various models are model A and model B.

[0127] Step 1023: Select one or more models with the lowest energy consumption as the target recommended models.

[0128] For example, by comparing the energy consumption information of four types of vehicles, A, B, C, and D, the vehicles with the lowest energy consumption among the various types of vehicles are determined to be vehicle A and vehicle B, and vehicle A and vehicle B are provided to users as target recommended vehicles.

[0129] The above technical solution compares the energy consumption information of various car models that meet user needs, identifies one or more models with the lowest energy consumption, and uses these models as target recommended models. This allows users to select the most cost-effective target recommended models, thereby effectively improving user experience and satisfaction.

[0130] Figure 7 This is a block diagram illustrating a vehicle model recommendation device 700 according to an exemplary embodiment, the vehicle model recommendation device 700 comprising:

[0131] The acquisition module 701 is configured to acquire vehicle selection requirement information input by the user, the vehicle selection requirement information including a set of driving scenario parameters to be used.

[0132] The different driving scenario parameter sets are used to characterize driving habits under different driving environments, or different driving habits under the same driving environment.

[0133] The determination module 702 is configured to determine the target recommended vehicle model corresponding to the user based on the set of driving scenario parameters to be used and the pre-configured vehicle model recommendation data.

[0134] The vehicle recommendation data includes multiple driving scenario parameter sets, and the energy consumption information of different vehicle models corresponding to each driving scenario parameter set.

[0135] The above technical solution acquires user-inputted vehicle selection requirements, including a set of driving scenario parameters. Different sets of driving scenario parameters represent driving habits under different driving environments, or different driving habits under the same driving environment. Based on the set of driving scenario parameters and pre-configured vehicle recommendation data, a target recommended vehicle for the user is determined. The vehicle recommendation data includes energy consumption information for different vehicle models corresponding to each of multiple driving scenario parameter sets. Thus, by acquiring the user's set of driving scenario parameters and pre-configured vehicle recommendation data, a target recommended vehicle corresponding to the user's vehicle selection requirements can be determined. This allows for the identification of target recommended vehicles based on different user vehicle selection requirements, thereby meeting users' personalized needs and effectively improving user experience and satisfaction.

[0136] Optionally, the vehicle model recommendation data is obtained in advance in the following manner:

[0137] Through the vehicle networking platform, operational data of multiple operating vehicles of various models is obtained, including vehicle operation data and driving environment data;

[0138] Based on the vehicle operation data and the driving environment data, multiple driving scenario parameter sets are determined;

[0139] The energy consumption information of different vehicle models under each driving scenario parameter set is obtained through the vehicle network platform, so as to obtain the energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets.

[0140] Optionally, the step of obtaining energy consumption information of different vehicle models under each driving scenario parameter set through the vehicle network platform to obtain energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets includes:

[0141] For each vehicle model, obtain energy consumption information of multiple operating vehicles of that model under each driving scenario parameter set;

[0142] Based on the energy consumption information of the multiple operating vehicles under the driving scenario parameter set, the energy consumption information of multiple vehicle models corresponding to each driving scenario parameter set is generated.

[0143] Optionally, the vehicle operation data includes multiple operation parameters, the driving environment data includes multiple environmental parameters, and the step of determining multiple driving scenario parameter sets based on the vehicle operation data and the driving environment data includes:

[0144] For each vehicle type, each operating parameter in the multiple vehicle operating data corresponding to multiple operating vehicles is divided into multiple first parameter intervals according to the first interval division strategy;

[0145] Each environmental parameter in the multiple driving environment data corresponding to multiple operating vehicles is divided into multiple second parameter intervals according to the second interval division strategy;

[0146] Based on the multiple first parameter intervals and / or the multiple second parameter intervals corresponding to each vehicle model, a set of multiple driving scenario parameters corresponding to the vehicle model is determined to obtain a set of multiple driving scenario parameters corresponding to the multiple vehicle models.

[0147] Optionally, the multiple driving scenario parameter sets include a first driving scenario dataset and a second driving scenario dataset. The step of determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models includes:

[0148] Each of the first parameter intervals is combined with different second parameter intervals to obtain multiple combinations of the first driving scenario dataset;

[0149] For each operating parameter, each first parameter interval is used as a second driving scenario dataset to obtain multiple second driving scenario datasets corresponding to multiple first parameter intervals of the operating parameter.

[0150] Optionally, the determining module 702 is configured to:

[0151] From the vehicle model recommendation data, determine the target driving scenario parameter set that matches the driving scenario parameter set to be used, and the energy consumption information of various vehicle models corresponding to the target driving scenario parameter set;

[0152] Based on the energy consumption information of the various vehicle models, determine one or more models with the lowest energy consumption;

[0153] The one or more models with the lowest energy consumption are selected as the target recommended models.

[0154] Optionally, the vehicle operation data includes at least one of driving speed, driving time, driving route, and load.

[0155] The driving environment data includes at least one of the following: temperature, road conditions, altitude and slope, wind speed and direction, precipitation information, air quality, light intensity, air pressure, and humidity.

[0156] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0157] Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Figure 8 As shown, the electronic device 800 may include a processor 801 and a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805.

[0158] The processor 801 controls the overall operation of the electronic device 800 to complete all or part of the steps in the aforementioned vehicle model recommendation method. The memory 802 stores various types of data to support the operation of the electronic device 800. This data may include, for example, instructions for any application or method operating on the electronic device 800, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0159] In an exemplary embodiment, the electronic device 800 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, microcontrollers, microprocessors, or other electronic components to perform the vehicle model recommendation method described above.

[0160] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the vehicle model recommendation method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the electronic device 800 to complete the vehicle model recommendation method described above.

[0161] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described vehicle model recommendation method when executed by the programmable device.

[0162] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0163] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0164] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A method for recommending car models, characterized in that, The method includes: Obtain user-inputted vehicle selection requirements information, which includes a set of driving scenario parameters to be used. Different sets of driving scenario parameters are used to characterize driving habits under different driving environments, or different driving habits under the same driving environment. Based on the set of driving scenario parameters to be used and the pre-configured vehicle model recommendation data, the target recommended vehicle model corresponding to the user is determined. The vehicle model recommendation data includes energy consumption information of different vehicle models corresponding to multiple driving scenario parameter sets.

2. The vehicle model recommendation method according to claim 1, characterized in that, The recommended vehicle model data is obtained in advance using the following methods: Through the vehicle networking platform, operational data of multiple operating vehicles of various models is obtained, including vehicle operation data and driving environment data; Based on the vehicle operation data and the driving environment data, multiple driving scenario parameter sets are determined; The energy consumption information of different vehicle models under each driving scenario parameter set is obtained through the vehicle network platform, so as to obtain the energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets.

3. The vehicle model recommendation method according to claim 2, characterized in that, The step of obtaining energy consumption information of different vehicle models under each driving scenario parameter set through the vehicle network platform to obtain energy consumption information of multiple vehicle models under each driving scenario parameter set in the multiple driving scenario parameter sets includes: For each vehicle model, obtain energy consumption information of multiple operating vehicles of that model under each driving scenario parameter set; Based on the energy consumption information of the multiple operating vehicles under the driving scenario parameter set, the energy consumption information of multiple vehicle models corresponding to each driving scenario parameter set is generated.

4. The vehicle model recommendation method according to claim 2, characterized in that, The vehicle operation data includes multiple operation parameters, and the driving environment data includes multiple environmental parameters. The step of determining multiple driving scenario parameter sets based on the vehicle operation data and the driving environment data includes: For each vehicle type, each operating parameter in the multiple vehicle operating data corresponding to multiple operating vehicles is divided into multiple first parameter intervals according to the first interval division strategy; Each environmental parameter in the multiple driving environment data corresponding to multiple operating vehicles is divided into multiple second parameter intervals according to the second interval division strategy; Based on the multiple first parameter intervals and / or the multiple second parameter intervals corresponding to each vehicle model, a set of multiple driving scenario parameters corresponding to the vehicle model is determined to obtain a set of multiple driving scenario parameters corresponding to the multiple vehicle models.

5. The vehicle model recommendation method according to claim 4, characterized in that, The multiple driving scenario parameter sets include a first driving scenario dataset and a second driving scenario dataset. The step of determining multiple driving scenario parameter sets corresponding to each vehicle model based on the multiple first parameter intervals and / or the multiple second parameter intervals to obtain multiple driving scenario parameter sets corresponding to the multiple vehicle models includes: Each of the first parameter intervals is combined with different second parameter intervals to obtain multiple combinations of the first driving scenario dataset; For each operating parameter, each first parameter interval is used as a second driving scenario dataset to obtain multiple second driving scenario datasets corresponding to multiple first parameter intervals of the operating parameter.

6. The vehicle model recommendation method according to claim 1, characterized in that, The step of determining the target recommended vehicle model for the user based on the driving scenario parameter set and pre-configured vehicle model recommendation data includes: From the vehicle model recommendation data, determine the target driving scenario parameter set that matches the driving scenario parameter set to be used, and the energy consumption information of various vehicle models corresponding to the target driving scenario parameter set; Based on the energy consumption information of the various vehicle models, determine one or more models with the lowest energy consumption; The one or more models with the lowest energy consumption are selected as the target recommended models.

7. The vehicle model recommendation method according to any one of claims 1-6, characterized in that, Vehicle operation data includes at least one of the following: driving speed, driving time, driving route, and load. The driving environment data includes at least one of the following: temperature, road conditions, altitude and slope, wind speed and direction, precipitation information, air quality, light intensity, air pressure, and humidity.

8. A vehicle model recommendation device, characterized in that, The device includes: The acquisition module is configured to acquire vehicle selection requirement information input by the user. This vehicle selection requirement information includes a set of driving scenario parameters to be used. Different sets of driving scenario parameters are used to characterize driving habits under different driving environments, or different driving habits under the same driving environment. The determination module is configured to determine the target recommended vehicle model for the user based on the set of driving scenario parameters to be used and pre-configured vehicle model recommendation data. The vehicle model recommendation data includes energy consumption information of different vehicle models corresponding to multiple driving scenario parameter sets.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-7.

Images