Driving information recommendation method and device, server and storage medium

Abstract

The embodiment of the invention provides a driving information recommendation method, which comprises the following steps: acquiring driving information of a vehicle and environment information of the vehicle on a target driving road section; based on the driving information and the environment information, space-time congestion data of the vehicle in the target driving road section is constructed, and the space-time congestion data comprises congestion information of the vehicle in time and space of the target driving road section; based on the space-time congestion data, when it is determined that a target sub-road section in the target driving road section is congested, driving recommendation information of the target sub-road section is generated; and before the vehicle enters the target sub-road section, sending the driving recommendation information to vehicle-mounted equipment of the vehicle. The embodiment of the invention further provides a driving information recommendation device, electronic equipment and a storage medium.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a driving information recommendation method, device, server, and storage medium. Background Art

[0002] With the development of vehicle intelligence, the driving mode of vehicles has changed significantly. Currently, vehicles can drive with the help of an Adaptive Cruise Control (ACC) system. In the case of traffic congestion, due to the complex road conditions and vehicle conditions, the congestion situations of different congested sections vary. How to recommend personalized and energy-efficient driving information for users is a technical problem that needs to be solved. Summary of the Invention

[0003] This application provides a driving information recommendation method, device, server, and storage medium.

[0004] The technical solution of this application is implemented as follows:

[0005] In a first aspect, this application provides a driving information recommendation method, which is applied to a server. The method includes:

[0006] Obtain the driving information of the vehicle and the environmental information of the vehicle on the target driving section;

[0007] Based on the driving information and the environmental information, construct the spatio-temporal congestion data of the vehicle on the target driving section. The spatio-temporal congestion data includes the congestion information of the vehicle in the time and space of the target driving section;

[0008] Based on the spatio-temporal congestion data, when it is determined that a target sub-section in the target driving section is congested, generate driving recommendation information for the target sub-section;

[0009] Before the vehicle enters the target sub-section, send the driving recommendation information to the in-vehicle device of the vehicle.

[0010] In a second aspect, this application provides a driving information recommendation device, which is characterized by including:

[0011] An acquisition unit configured to acquire the driving information of the vehicle and the environmental information of the vehicle on the target driving section;

[0012] A data construction unit configured to construct the spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information. The spatio-temporal congestion data includes the congestion information of the vehicle in the time and space of the target driving section;

[0013] A processing unit, configured to generate driving recommendation information for the target sub-section when determining that the target sub-section in the target driving section is congested based on the spatio-temporal congestion data;

[0014] A communication unit, configured to send the driving recommendation information to an in-vehicle device of the vehicle before the vehicle enters the target sub-section.

[0015] In a third aspect, the present application provides a server, including a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium for storing a computer program, and the computer program enables a computer to execute the method described in the first aspect.

[0017] An embodiment of the present application provides a driving information recommendation method. The server can obtain the driving information of a vehicle and the environmental information of the vehicle on a target driving section. Further, the server can construct spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information. The spatio-temporal congestion data includes congestion information of the vehicle in time and space on the target driving section. In this way, when the server determines that the target sub-section in the target driving section is congested based on the spatio-temporal congestion data, it can generate driving recommendation information for the target sub-section and send the driving recommendation information to the in-vehicle device of the vehicle before the vehicle enters the target sub-section. It can be seen that the server can recommend personalized driving recommendation information for the vehicle according to the driving information of the vehicle and the environmental information along the driving section, so that the user can use the driving recommendation information to drive on congested sections, reduce the energy consumption of the vehicle, and improve the driving experience of the user. Description of the Drawings

[0018] Figure 1 Schematic diagram of a driving information recommendation method provided by an embodiment of the present application Figure One ;

[0019] Figure 2 Schematic diagram of a driving information recommendation method provided by an embodiment of the present application Figure Two ;

[0020] Figure 3 Schematic diagram of a driving information recommendation method provided by an embodiment of the present application Figure Three ;

[0021] Figure 4 Schematic diagram of a driving information recommendation method provided by an embodiment of the present application Figure Four ;

[0022] Figure 5 It is a schematic hardware structure diagram of a driving information recommendation device 500 provided by an embodiment of the present application;

[0023] Figure 6 It is a schematic structural diagram of a server provided by an embodiment of the present application. Specific embodiments

[0024] Next, the technical solutions in the embodiments of the present application will be described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0025] In practical applications, the solutions for traffic congestion include the following aspects:

[0026] 1. Through big data analysis such as navigation paths and communication information, predict the duration required for congestion and recommend the optimal road.

[0027] 2. Switch to the economic driving mode to keep the engine at a low speed and high gear matching, reducing fuel consumption. It should be noted that the economic driving mode refers to controlling the fuel injection volume through the vehicle controller, reducing unnecessary fuel injection of the vehicle, making the engine speed smoother, achieving the best fuel effect, and making the vehicle travel in the most economical mode.

[0028] 3. Set the ACC adaptive cruise control mode and manually select a fixed following distance through the lever.

[0029] Currently, in the case of traffic congestion, due to the complex road conditions and vehicle conditions, the general ACC following settings and economic modes cannot improve the user's congestion driving experience and are not applicable to all road congestion situations. At the same time, the cost of calling the third-party navigation API is also relatively high and is not suitable for the usage scenarios of a large number of users. Therefore, in the case of traffic congestion, it is particularly important to recommend personalized and energy-efficient driving information for users.

[0030] Based on this, an embodiment of the present application provides a driving information recommendation method. Among them, the server can obtain the driving information of the vehicle and the environmental information of the vehicle on the target driving section. Furthermore, the server can construct spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information. The spatio-temporal congestion data includes congestion information of the vehicle in the time and space of the target driving section. In this way, when the server determines that a target sub-section in the target driving section is congested based on the spatio-temporal congestion data, it can generate driving recommendation information for the target sub-section and send the driving recommendation information to the in-vehicle device of the vehicle before the vehicle enters the target sub-section. It can be seen that the server can recommend personalized driving recommendation information for the vehicle according to the driving information of the vehicle and the environmental information along the driving section, so that the user can use the driving recommendation information to drive on the congested section, reduce the energy consumption of the vehicle, and improve the user's driving experience.

[0031] To facilitate the understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The above related technologies can be arbitrarily combined with the technical solutions of the embodiments of the present application as optional solutions, and they all fall within the protection scope of the embodiments of the present application. The embodiments of the present application include at least some of the following contents.

[0032] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the driving information recommendation method provided by the embodiment of the present application. As Figure 1 shown, the driving information recommendation method includes the following steps:

[0033] S110. The server obtains the driving information of the vehicle and the environmental information of the vehicle on the target driving section;

[0034] S120. The server constructs spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information. The spatio-temporal congestion data includes congestion information of the vehicle in the time and space of the target driving section;

[0035] S130. When the server determines that a target sub-section in the target driving section is congested based on the spatio-temporal congestion data, it generates driving recommendation information for the target sub-section;

[0036] S140. The server sends the driving recommendation information to the in-vehicle device of the vehicle before the vehicle enters the target sub-section.

[0037] It should be noted that the server can serve multiple vehicles simultaneously, that is to say, the server can generate driving recommendation information for multiple vehicles. Exemplarily, the server can be a cloud server, an edge server or other devices capable of performing a large amount of data operations.

[0038] It can be understood that during the driving process of the vehicle, the vehicle-related driving information can be reported to the server through a wireless network.

[0039] In some embodiments, the driving information may include, but is not limited to, one or more of the following:

[0040] The target road section where the vehicle is driving, the speed information of the vehicle, the acceleration information, the gear information of the vehicle, the stepping information of the brake pedal, the distance information between the vehicle and the vehicle in front, etc., and the embodiments of the present application do not limit this.

[0041] It should be noted that the vehicle can obtain the driving information during the driving process of the vehicle through the Electronic Control Unit (ECU) in the vehicle, and report the obtained driving information to the server through the communication unit. Exemplarily, the ECU can read the data of various sensors in the vehicle to obtain speed information, acceleration information, gear information, stepping information of the brake pedal, etc. For example, the ECU can read the data in the speed sensor to obtain the speed information of the vehicle, read the data in the acceleration sensor to obtain the acceleration information of the vehicle, etc. In addition, the ECU can also read the data in the navigation application to obtain the target road section where the vehicle is driving.

[0042] In addition, in the embodiments of the present application, the server can also obtain the environmental information of the vehicle on the target driving road section

[0043] In some embodiments, the environmental information of the target driving road section includes, but is not limited to, one or more of the following:

[0044] Weather information;

[0045] The location information of the target places along the target driving road section;

[0046] The time information of the crowd gathering at the target place;

[0047] The road control information of the target driving road section;

[0048] The construction information of the target driving road section.

[0049] Among them, the weather information may include precipitation information, visibility information, temperature and other information, which is not limited in the embodiments of the present application. The target places may include schools, shopping malls, office buildings, etc., which is not limited in the embodiments of the present application. The time information of the crowd gathering in the target place may be the school going-to-school time, school leaving time, shopping mall opening time, closing time, office building working time and getting-off work time, etc.

[0050] It can be understood that the environmental information of the target road section can reflect the congestion situation of the target road section to a certain extent. Exemplarily, on a sunny day with high visibility, the possibility of congestion on the target road section is relatively low; during the school going-to-school and leaving time, the possibility of congestion within a range of 1 to 2 kilometers around the school is relatively high.

[0051] It should be noted that the above environmental information of the target driving road section can be obtained and reported to the server by the vehicle. The above environmental information of the target driving road section can also be obtained by the server from a third-party data center. It can be understood that the server can obtain the environmental information of the target driving road section from the relevant third-party data center according to the target driving road section reported by the vehicle.

[0052] Exemplarily, the server can obtain the weather information of the target driving road section from a weather service website, can obtain the location information of the target places along the road in the target driving road section from various map navigation websites, obtain the time information of the crowd gathering from the relevant websites of each target place, and the server can also obtain road control information and construction information from a government service website.

[0053] In the embodiments of the present application, after obtaining the driving information and environmental information, the server can construct the spatio-temporal congestion data of the vehicle on the target driving road section based on the driving information and environmental information of the vehicle. The spatio-temporal congestion data may include congestion information of the vehicle in the time and space of the target driving road section. Among them, the congestion information may be whether congestion occurs, the probability of congestion occurring, the degree of congestion and other information. Exemplarily, the degree of congestion can be represented by a congestion level. Congestion level 1 can indicate smooth driving and no congestion. Congestion level 2 can indicate relatively congested, and the vehicle can slowly drive at a speed greater than 10 kilometers per hour and less than 20 kilometers per hour. Congestion level 3 can indicate severe congestion, and the vehicle drives at a speed less than 10 kilometers per hour.

[0054] It can be understood that the spatio-temporal congestion data may include information such as whether congestion occurs, the probability of congestion occurring, and the degree of congestion at each moment and each position on the target driving road section.

[0055] In some embodiments, the position of a vehicle on a target driving section can be identified using sub-sections. Among them, the server can divide the target driving section into multiple sub-sections. The spatio-temporal congestion data can describe whether congestion will occur when the current vehicle is driving on each sub-section, the probability of congestion occurring when driving on each sub-section, and the degree of congestion on each sub-section.

[0056] Specifically, the server can first determine, based on the driving information of the vehicle, that the vehicle is located on a certain sub-section of the target driving section at a specific time. Exemplarily, the server can determine, based on the speed information and acceleration information of the vehicle, that the vehicle is located on a certain sub-section of the target driving section at a certain time.

[0057] Furthermore, the server can predict, based on the environmental information of the target section, information such as whether congestion will occur on the sub-sections passed by the vehicle during each time period, the probability of congestion occurring, and the degree of congestion when the vehicle is driving on the target driving section. Exemplarily, if the server determines that the vehicle passes by the school at the time of school dismissal, it can determine that the vehicle will encounter congestion when passing through the sub-section of the school, or that the probability of congestion is greater than 90%. If the server determines that the vehicle passes through the controlled section during the road control time and the weather information is snowy, the server can determine that the vehicle will be congested on the controlled road, or that the probability of congestion is greater than 80%.

[0058] That is to say, the server can establish an association relationship between the driving information of the vehicle and the environmental information of the target driving section, and determine whether congestion will occur, the probability of congestion occurring, and the degree of congestion when congestion occurs in terms of specific time and space when the vehicle is driving on the target driving section.

[0059] In the embodiments of the present application, after constructing the spatio-temporal congestion data of the vehicle on the target driving section, when the server determines that congestion actually occurs on a certain sub-section (denoted as the target sub-section in the embodiments of the present application), it can generate driving recommendation information for the target sub-section where congestion currently occurs according to the spatio-temporal congestion data. It should be noted that the target sub-section can be any one of the multiple sub-sections included in the target driving section.

[0060] In some embodiments, the driving recommendation information can include one or more of the following:

[0061] The average following vehicle speed of the target sub-section;

[0062] Engine speed;

[0063] The gear of the vehicle;

[0064] The matching rule of engine speed and gear;

[0065] A voice interaction data packet for alleviating the emotions of users in congested sections.

[0066] It can be understood that the server can recommend the average following vehicle speed, engine speed, vehicle gear, and the matching rule between engine speed and gear for the user to drive on the target sub-section according to the spatio-temporal congestion data. In addition, the server can also generate voice interaction data to alleviate the anxiety of the user in the congested target sub-section.

[0067] Exemplarily, when the server determines that congestion will occur when the vehicle is driving on the target sub-section according to the spatio-temporal congestion data, the server can recommend appropriate average following vehicle speed, engine speed, vehicle gear, and the matching rule between engine speed and gear for the user according to the congestion degree of the target sub-section. For example, if the target sub-section is a school section, the speed recommended by the server is 5 kilometers per hour, and the engine speed is the speed at idle (for example, 1000 revolutions). If the target sub-section is a regulated section and the weather information is snowy, the server can recommend an average speed of 5 kilometers per hour and an engine speed of 1500 revolutions.

[0068] Furthermore, before the vehicle enters the target sub-section, the server can send driving recommendation information to the in-vehicle device of the vehicle. It can be understood that the server can send it to the in-vehicle device of the vehicle through a wireless network. In this way, the in-vehicle device of the vehicle can display the driving recommendation information. For example, the in-vehicle device can broadcast the above driving recommendation information by voice to prompt the user to use the recommended following vehicle speed, engine speed, vehicle gear, and the matching rule between engine speed and gear when entering the target sub-section. At the same time, the in-vehicle device can also play the voice interaction data packet to alleviate the anxiety of the user in the congested section.

[0069] In summary, in the driving information recommendation method provided by the embodiments of the present application, the server can obtain the driving information of the vehicle and the environmental information of the vehicle on the target driving section; furthermore, the server can construct spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and environmental information; in this way, when the server determines that the target sub-section in the target driving section is congested based on the spatio-temporal congestion data, it generates driving recommendation information for the target sub-section; finally, before the vehicle enters the target sub-section, the server sends the driving recommendation information to the in-vehicle device of the vehicle. It can be seen that the server can recommend personalized driving recommendation information for the vehicle according to the driving information of the vehicle and the environmental information along the driving section, so that the user can use the driving recommendation information to drive in the congested section, reduce the energy consumption of the vehicle, and improve the driving experience of the user.

[0070] In an embodiment of the present application, when the server determines that a target sub-section in the target driving section is congested based on spatio-temporal congestion data in S130, the driving recommendation information for the target sub-section can be generated in the following manner:

[0071] The server predicts the predicted congestion information of each sub-section in the target driving section based on the spatio-temporal congestion data;

[0072] The server obtains the real-time congestion information of the vehicle in each sub-section;

[0073] When both the real-time congestion information and the predicted congestion information of the target sub-section indicate congestion, the server determines that the target sub-section is congested and generates the driving recommendation information for the target sub-section; the target sub-section is any one of the multiple sub-sections included in the target driving section.

[0074] It can be understood that the server can predict whether each sub-section in the target implementation section will be congested, the probability of congestion of each sub-section, and the degree of congestion of each sub-section based on the spatio-temporal congestion data constructed in step S120. At the same time, the server can also obtain the real-time congestion information of the vehicle.

[0075] It should be noted that the vehicle can report the real-time congestion information to the server during driving. Among them, the vehicle can determine the real-time congestion information according to information such as the vehicle speed, the distance from the vehicle in front, and the stepping frequency of the brake pedal. The real-time congestion information can reflect whether congestion has occurred currently and information such as the degree of congestion.

[0076] In some embodiments, the vehicle can report the current real-time congestion information to the server at a preset time period. Exemplarily, the vehicle can report the real-time congestion information to the server every few minutes / seconds.

[0077] In some embodiments, the vehicle can report the real-time congestion information of the current sub-section to the server when passing through each sub-section according to the division rules of each sub-section.

[0078] In this way, the server can compare the predicted congestion information with the real-time congestion information reported by the vehicle. When both the real-time congestion information reported by the vehicle and the predicted congestion information indicate that the current road is congested, the server can determine that the target sub-section is congested. At this time, the server can generate the driving recommendation information for the target sub-section.

[0079] Exemplarily, when predicting the probability of congestion information being congested, if the probability of congestion is greater than 60%, it can be considered that the predicted congestion information indicates that the current road is congested. When the predicted congestion information / real-time congestion information includes the degree of congestion, if the degree of congestion is level 2 or level 3, it can be considered that the predicted congestion information / real-time congestion information indicates that the current road is congested.

[0080] It can be understood that the predicted congestion information is predicted by the server based on the driving information of the vehicle and the environmental information of the vehicle on the target driving section, and cannot accurately represent the real-time congestion situation of the current road. Therefore, the server can obtain the real-time congestion information reported by the vehicle. Only when both the real-time congestion information and the predicted congestion information indicate that the road is congested, generate a recommendation information for the vehicle to improve the accuracy of the recommendation.

[0081] In an embodiment of the present application, in S130, when the server generates the driving recommendation information for the target sub-section, it can be generated in combination with the driving habit information of the user, so that the generated driving recommendation information can serve the driving habit of the user. Refer to Figure 2 As shown, in S130, the server can also implement generating the driving recommendation information for the target sub-section in the following manner:

[0082] S1301. Obtain the driving habit data of the vehicle on the congested section;

[0083] S1302. Generate the driving recommendation information for the target sub-section based on the driving habit data and the spatio-temporal congestion data.

[0084] In some embodiments, the driving habit data may include one or more of the following:

[0085] Average vehicle speed;

[0086] Maximum driving speed of the vehicle;

[0087] Gear information;

[0088] Tread frequency of the brake pedal;

[0089] Distance information from the vehicle in front.

[0090] It should be noted that different users have different driving habits on the congested section. For example, some users are used to following the vehicle in front at idle speed, and some users are used to changing lanes and accelerating. In order to provide more personalized driving recommendation information, when generating the driving recommendation information, the server can also generate the driving recommendation information for the target sub-section by combining the driving habit data of the vehicle on the congested section and the spatio-temporal congestion data.

[0091] In some embodiments, the driving habit data on congested sections may include driving habit data corresponding to different congestion levels. That is to say, under different congestion levels, the driving habit data of users may be different. For example, when the congestion level is severe, such as congestion level 3 described above, where the vehicle travels at a speed less than 10 kilometers per hour. At this time, the driving habit data corresponding to the vehicle may be that the average speed of the vehicle is 5 kilometers per hour, the maximum driving speed is 8 kilometers per hour, and the distance from the vehicle in front is less than 1 meter. When the congestion level is moderately congested, such as congestion level 2 described above, where the vehicle can slowly travel at a speed greater than 10 kilometers per hour and less than 20 kilometers per hour. At this time, the driving habit data corresponding to the vehicle may be that the average speed of the vehicle is 15 kilometers per hour, the maximum driving speed is 20 kilometers per hour, and the distance from the vehicle in front is greater than 2 meters.

[0092] In the embodiments of the present application, the server may recommend appropriate driving recommendation information for the user according to the spatio-temporal congestion data and the user's driving habit data. Exemplarily, when the server determines that congestion will occur when the vehicle travels on the target sub-section, the server may recommend appropriate average following vehicle speed, engine speed, the gear of the vehicle, and the matching rule of engine speed and gear for the user according to the congestion level of the target sub-section and the driving habit data corresponding to the congestion level. For example, when the target sub-section is a school section, the speed recommended by the server is 5 kilometers per hour, and the engine speed is the speed at idle (for example, 1000 revolutions). When the target sub-section is a controlled section and the weather information is snowy, the server may recommend an average vehicle speed of 5 kilometers per hour and an engine speed of 1500 revolutions.

[0093] It can be seen that in the driving information recommendation method provided by the embodiments of the present application, the server can recommend personalized driving recommendation information for the vehicle according to the driving information of the vehicle, the environmental information along the driving section, and the driving habit information of the user in the congested section, so that the user can use the driving recommendation information to drive in the congested section, reduce the energy consumption of the vehicle, and improve the user's driving experience.

[0094] In an embodiment of the present application, with reference to Figure 3 as shown, in S120, the server constructs the spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information, and it can also be implemented through the following steps:

[0095] S1201. Delete the invalid data and / or noise data in the driving information and the environmental information, and unify the data units to obtain the preprocessed driving information and environmental information;

[0096] S1202. Construct the spatio-temporal congestion data of the vehicle on the target driving section based on the preprocessed driving information and environmental information.

[0097] Understandably, after the server obtains the driving information and environmental information, it can clean the obtained driving information and environmental information to remove dirty data, that is, remove invalid data and / or noise data. It should be noted that the invalid data can be incomplete or duplicate data, and the noise data can be data that deviates from the expected value.

[0098] Furthermore, before the server establishes an association relationship between the driving information of the vehicle and the environmental information of the target driving section and constructs spatio-temporal congestion data, it can also unify the data units of the driving information and the environmental information. For example, the data unit of the vehicle speed information in the driving information is kilometers per hour, and the unit of the target location information in the environmental information is meters. Therefore, the data of the two can be unified to avoid errors when constructing spatio-temporal congestion data and improve the accuracy of driving information recommendation.

[0099] In summary, the embodiment of the present application provides a method for recommending driving information. Among them, the server can obtain the driving information of the vehicle and the environmental information of the vehicle on the target driving section; furthermore, the server can construct the spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information, and the spatio-temporal congestion data includes the congestion information of the vehicle in the time and space of the target driving section; in this way, when the server determines that a target sub-section in the target driving section is congested based on the spatio-temporal congestion data, it can generate driving recommendation information for the target sub-section; and before the vehicle enters the target sub-section, send the driving recommendation information to the in-vehicle device of the vehicle. It can be seen that the server can recommend personalized driving recommendation information for the vehicle according to the driving information of the vehicle and the environmental information along the driving section, so that the user can use the driving recommendation information to drive on the congested section, reduce the energy consumption of the vehicle, and improve the user's driving experience.

[0100] The method provided by the embodiment of the present application will be elaborated in detail below in combination with specific application scenarios.

[0101] In the related art, for scenarios of road congestion (such as elevated roads during morning and evening rush hours in first-tier cities), the general ACC following settings and economic models cannot improve the user's congestion driving experience and are not applicable to all road congestion situations; at the same time, the cost of calling the third-party navigation API is also relatively high and is not applicable to scenarios where a large number of users use it.

[0102] In the embodiments of the present application, it is proposed to statistically analyze the congestion data of the main driving roads of the vehicle and the user's driving habit information, and combine real-time information such as the weather of the day, the school arrival and departure times around the road, road control, and road maintenance and construction to predict vehicle congestion. Furthermore, a personalized congestion mode is pushed to the user, including the optimal average following vehicle speed of a fixed route, the matching rules of engine speed and gear, voice interaction to relieve anxiety, etc., so as to improve the user's personalized driving experience applicable to specific congestion scenarios and reduce energy consumption.

[0103] Referring to Figure 4 the flowchart shown, the method provided by the embodiments of the present application includes the following steps:

[0104] S1. On the vehicle side, after the vehicle enters a congested section (corresponding to the target driving section in the above embodiments), it reports the vehicle's driving information and driving habit information under the congested section to the server.

[0105] S2. The server obtains the environmental information of the congested section from a third-party data center.

[0106] Exemplarily, the environmental information of the congested section may include weather information, school arrival and departure time information around the road, road control, road maintenance and construction, etc.

[0107] S3. The server preprocesses the driving information and environmental information, and constructs a spatio-temporal congestion data pool based on the driving information and environmental information.

[0108] It can be understood that the server can perform data cleaning, establish an association between vehicle data and third-party data based on time information and space information, unify the data units, and eliminate dirty data to form a personalized spatio-temporal congestion data pool for the vehicle.

[0109] S4. The server performs congestion prediction to determine the predicted congestion information.

[0110] It can be understood that the server predicts the congestion information of each sub-section based on the spatio-temporal congestion data pool in S3 to obtain the predicted congestion information.

[0111] S5. The server obtains the real-time congestion information reported by the vehicle.

[0112] It should be noted that the vehicle can report real-time congestion data to the server according to a preset time period.

[0113] S6. The server determines whether the real-time congestion information matches the predicted congestion information.

[0114] Among them, whether the real-time congestion information matches the predicted congestion information may refer to whether both the real-time congestion information and the predicted congestion information indicate congestion.

[0115] Specifically, when the real-time congestion information matches the predicted congestion information, S7 is executed. When the real-time congestion information does not match the predicted congestion information, it returns to S5.

[0116] S7. The server sends driving recommendation information to the vehicle.

[0117] S8. The vehicle receives the driving recommendation information and prompts the driving recommendation information on the in-vehicle device.

[0118] Exemplarily, the in-vehicle device of the vehicle can push the driving recommendation information to the user by means of voice push.

[0119] S9. The vehicle determines whether to switch to the driving mode corresponding to the driving recommendation information.

[0120] Wherein, the vehicle receives the confirmation information of the user through the in-vehicle device.

[0121] S10. If the user confirms to switch to the mode corresponding to the driving recommendation information, the vehicle adjusts the settings of the vehicle based on the driving recommendation information.

[0122] Specifically, adjusting the settings of the vehicle mainly includes adjusting the following contents: following vehicle speed, engine speed and gear matching rules, playing voice interaction to relieve anxiety, etc., so as to reduce the use times of the vehicle's acceleration and braking pedals and reduce the vehicle's energy consumption.

[0123] S11. The vehicle automatically exits the driving mode corresponding to the driving recommendation information after leaving the fixed route or accelerating multiple times.

[0124] In summary, the method provided by the embodiment of the present application can build a spatio-temporal congestion data pool, push personalized congestion mode content to the vehicle end, reduce the use times of the user's acceleration and braking pedals, optimize the driving experience, and reduce fuel consumption.

[0125] Figure 5 Schematic structural diagram of a driving information recommendation device 500 provided by an embodiment of the present application. Refer to Figure 5 As shown, the driving information recommendation device 500 includes:

[0126] An acquisition unit 501, configured to acquire the driving information of the vehicle and the environmental information of the vehicle on the target driving section;

[0127] A data construction unit 502, configured to construct spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information, where the spatio-temporal congestion data includes congestion information of the vehicle in time and space on the target driving section;

[0128] The processing unit 503 is configured to generate driving recommendation information for the target sub-section when it is determined that the target sub-section in the target driving section is congested based on the spatio-temporal congestion data;

[0129] The communication unit 504 is configured to send the driving recommendation information to the in-vehicle device of the vehicle before the vehicle enters the target sub-section.

[0130] In some embodiments, the processing unit 503 is further configured to predict the predicted congestion information of each sub-section in the target driving section based on the spatio-temporal congestion data; obtain the real-time congestion information of the vehicle in each sub-section; determine that the target sub-section is congested and generate the driving recommendation information for the target sub-section when both the real-time congestion information and the predicted congestion information of the target sub-section indicate congestion; the target sub-section is any one of the multiple sub-sections included in the target driving section.

[0131] In some embodiments, the processing unit 503 is further configured to obtain the driving habit data of the vehicle on the congested section; generate the driving recommendation information for the target sub-section based on the driving habit data and the spatio-temporal congestion data.

[0132] In some embodiments, the driving habit data includes one or more of the following:

[0133] Average vehicle speed;

[0134] Maximum vehicle driving speed;

[0135] Gear information;

[0136] Pedal frequency of the brake pedal;

[0137] Distance information from the vehicle in front.

[0138] In some embodiments, the data construction unit 502 is further configured to delete invalid data and / or noise data in the driving information and the environment information, and unify the data units to obtain preprocessed driving information and environment information; construct the spatio-temporal congestion data of the vehicle in the target driving section based on the preprocessed driving information and environment information.

[0139] In some embodiments, the driving recommendation information includes one or more of the following:

[0140] Average following vehicle speed of the target sub-section;

[0141] Engine speed;

[0142] Gear of the vehicle;

[0143] Engine speed and gear matching rules;

[0144] A voice interaction data packet, which is used to relieve the emotions of users in congested sections.

[0145] In some embodiments, the environmental information includes one or more of the following:

[0146] Weather information;

[0147] The location information of target places along the target driving section;

[0148] The time information of the crowd gathering at the target place;

[0149] The road control information of the target driving section;

[0150] The construction information of the target driving section.

[0151] Those skilled in the art should understand that the relevant descriptions of the above-mentioned driving information recommendation device in the embodiments of the present application can be understood with reference to the relevant descriptions of the driving information recommendation method in the embodiments of the present application.

[0152] Figure 6 It is a schematic structural diagram of a server 600 provided by an embodiment of the present application. Figure 6 The server 600 shown includes a processor 610, and the processor 610 can call and run a computer program from the memory to implement the driving information recommendation method in the embodiments of the present application.

[0153] Optionally, as Figure 6 shown, the server 1100 may further include a memory 620. Among them, the processor 610 can call and run a computer program from the memory 620 to implement the driving information recommendation method in the embodiments of the present application.

[0154] Among them, the memory 620 can be a separate device independent of the processor 610, or can be integrated in the processor 610.

[0155] Optionally, the server 600 can implement the corresponding processes implemented by the server in each method of the embodiments of the present application. For the sake of brevity, it will not be elaborated here.

[0156] Optionally, the server 600 can specifically be the server in the embodiments of the present application, and the server 600 can implement the corresponding processes implemented by the server in each method of the embodiments of the present application. For the sake of brevity, it will not be elaborated here.

[0157] The embodiments of the present application also provide a computer storage medium, specifically a computer-readable storage medium. Computer instructions are stored thereon. When the computer storage medium is located in a server, any steps of the above-mentioned driving information recommendation method in the embodiments of the present application are implemented when the computer instructions are executed by a processor.

[0158] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0159] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, in each embodiment of the present application, the various functional units can all be integrated in a processing unit, or each unit can be separately used as a unit, or at least two units can be integrated in one unit; the above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0161] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: various media such as mobile storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0162] Alternatively, if the above integrated units of the present application are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0163] It should be noted that: among the technical solutions described in the embodiments of the present application, they can be arbitrarily combined without conflict.

[0164] As described above, the above are only the specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A driving information recommendation method, characterized in that, Applied to a server, the method includes: Obtain the driving information of the vehicle and the environmental information of the vehicle on the target driving section; Based on the driving information and the environmental information, construct the spatio-temporal congestion data of the vehicle on the target driving section, where the spatio-temporal congestion data includes congestion information of the vehicle in time and space on the target driving section; Based on the spatio-temporal congestion data, when it is determined that a target sub-section in the target driving section is congested, generate driving recommendation information for the target sub-section; Before the vehicle enters the target sub-section, send the driving recommendation information to the in-vehicle device of the vehicle.

2. The method according to claim 1, characterized in that, The step of, based on the spatio-temporal congestion data, when it is determined that a target sub-section in the target driving section is congested, generate driving recommendation information for the target sub-section includes: Based on the spatio-temporal congestion data, predict the predicted congestion information of each sub-section in the target driving section; Obtain the real-time congestion information of the vehicle in each sub-section; When the real-time congestion information and the predicted congestion information of the target sub-section both indicate congestion, determine that the target sub-section is congested and generate driving recommendation information for the target sub-section; the target sub-section is any one of the multiple sub-sections included in the target driving section.

3. The method according to claim 1, wherein The step of generating the driving recommendation information for the target sub-section includes: Obtain the driving habit data of the vehicle on the congested section; Based on the driving habit data and the spatio-temporal congestion data, generate the driving recommendation information for the target sub-section.

4. The method according to claim 3, characterized in that The driving habit data includes one or more of the following: Average vehicle speed; Maximum vehicle driving speed; Gear information; Pedal frequency of the brake pedal; Distance information from the vehicle in front.

5. The method according to any one of claims 1-4, characterized in that, The step of, based on the driving information and the environmental information, construct the spatio-temporal congestion data of the vehicle on the target driving section includes: Delete the invalid data and / or noise data in the driving information and the environmental information, and unify the data units to obtain the preprocessed driving information and environmental information; Based on the preprocessed driving information and environmental information, construct the spatio-temporal congestion data of the vehicle on the target driving section.

6. The method according to any one of claims 1-4, characterized in that, The driving recommendation information includes one or more of the following: Average following vehicle speed of the target sub-section; Engine speed; Gear of the vehicle; Matching rule of engine speed and gear; Voice interaction data packet, which is used to relieve the user's emotions in the congested section.

7. The method according to any one of claims 1-4, wherein The environmental information includes one or more of the following: Weather information; Location information of the target places along the target driving section; Time information of the crowd gathering at the target places; Road control information of the target driving section; Construction information of the target driving section.

8. A driving information recommendation device, characterized in that, Includes: An acquisition unit, configured to obtain the driving information of the vehicle and the environmental information of the vehicle on the target driving section; A data construction unit, configured to construct the spatio-temporal congestion data of the vehicle on the target driving section based on the driving information and the environmental information, where the spatio-temporal congestion data includes congestion information of the vehicle in time and space on the target driving section; A processing unit, configured to generate driving recommendation information for the target sub-section when it is determined that the target sub-section in the target driving section is congested based on the spatio-temporal congestion data; A communication unit, configured to send the driving recommendation information to an in-vehicle device of the vehicle before the vehicle enters the target sub-section.

9. A server, characterized in that, It includes a central processing unit and a memory, the memory is used to store a computer program, and the central processing unit is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, For storing a computer program, the computer program causes a computer to execute the method according to any one of claims 1 to 7.

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