Driving mode pushing method and system based on big data analysis

Abstract

The invention relates to the technical field of vehicles, in particular to a driving mode pushing method and system based on big data analysis, and the method comprises the steps: a data end collects vehicle feature data when a vehicle runs, and transmits the vehicle feature data to a cloud end; the cloud determines a current scene category based on the vehicle feature data, identifies a driving mode corresponding to the user portrait under the scene category, and pushes the driving mode to the user side; the user side receives the driving mode pushed by the cloud side and gives a prompt, and the driving mode of the vehicle is determined based on the interactive behavior of the user on the driving mode; the driving mode is ensured to meet the actual demand of the driver, and the driving benefit and safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a driving mode push method and system based on big data analysis. Background Art

[0002] Intelligent driving, tightly integrated with big data, marks a profound shift in the automotive industry's approach to future mobility. This adaptive driving model not only significantly improves driving safety and efficiency, but also provides users with an unprecedented personalized and convenient experience. As the cornerstone of intelligent driving, big data collects and analyzes massive amounts of driving data, road conditions, driver behavior, and even weather information, providing the basis for precise decision-making in intelligent driving systems. This big data-driven intelligent driving model not only redefines the driving experience but also demonstrates tremendous potential in safety, efficiency, environmental friendliness, and personalization. It is a key force driving the transformation and upgrading of the automotive industry and realizing the vision of smart mobility.

[0003] Most related technologies use a fixed mileage or timed interval as a threshold, and recommend a driving mode once the threshold is exceeded. This results in a lack of real-time and accuracy in driving mode push, and an inability to make personalized adjustments based on dynamic road conditions and driving habits, resulting in a poor user experience. Summary of the Invention

[0004] In view of this, the purpose of the embodiments of the present invention is to provide a driving mode push method and system based on big data analysis, aiming to ensure that the driving mode fits the actual needs of the driver and improve driving efficiency and safety.

[0005] In one aspect, an embodiment of the present invention provides a driving mode push method based on big data analysis, the method comprising the following steps: The data terminal collects vehicle feature data while the vehicle is driving and sends it to the cloud; The cloud determines the current scenario category based on vehicle feature data, identifies the driving mode corresponding to the user profile in that scenario category, and pushes it to the user end; The user end receives the driving mode pushed by the cloud and provides prompts, and determines the vehicle's driving mode based on the user's interactive behavior on the driving mode.

[0006] Optionally, the method further includes: The fixed mode selection module on the user side provides basic mode and working mode for users to choose from.

[0007] Optionally, the method further includes: The user-side custom mode selection module provides multiple driving modes for users to combine and set the driving parameters of each driving mode.

[0008] Optionally, determining the current scene category based on vehicle feature data includes: The cloud determines the current driving condition through a scenario classification module and evaluates the scenario category based on the driving condition. The scenario category includes a first scenario representing entering a completely new driving condition, a second scenario representing that the user forgets to adjust the driving mode, and a third scenario representing that a more optimal driving mode combination exists for the driving condition.

[0009] Optionally, identifying the driving mode corresponding to the user profile in the scenario category includes: The cloud uses the mode push module to identify the scene category. When the matching scene categories are identified as the first scene and the second scene, the cloud prompts the user whether to use the pushed driving mode. If no user interaction behavior is detected after the first period of time, the cloud switches to the pushed driving mode. When the matching scene category is identified as the third scene, the user is prompted whether to use the pushed driving mode. If no user interaction operation behavior is detected after the second time, the current driving mode remains unchanged.

[0010] On the other hand, an embodiment of the present invention provides a driving mode push system based on big data analysis, including: The data terminal is used to collect vehicle feature data while the vehicle is driving and send it to the cloud; The cloud is used to determine the current scene category based on vehicle feature data, identify the driving mode corresponding to the user profile under the scene category, and push it to the user end; The user side is used to receive driving mode push from the cloud and provide prompts, and determine the vehicle's driving mode based on the user's interactive behavior on the driving mode.

[0011] Optionally, the user terminal includes: The fixed mode selection module is used to provide basic mode and working mode for users to choose.

[0012] Optionally, the user terminal includes: The custom mode selection module is used to provide multiple driving modes for users to combine and set the driving parameters of each driving mode.

[0013] Optionally, the cloud includes: A scenario classification module is used to determine the current driving condition and evaluate the scenario category based on the driving condition. The scenario category includes a first scenario representing entering a completely new driving condition, a second scenario representing the user forgetting to adjust the driving mode, and a third scenario representing the driving condition where a more optimal driving mode combination exists.

[0014] Optionally, the data terminal includes: A mode push module, configured to prompt the user whether to use the pushed driving mode when the matching scene categories are identified as the first scene and the second scene, and switch to the pushed driving mode if no user interactive operation behavior is detected after a first period of time; When the matching scene category is identified as the third scene, the user is prompted whether to use the pushed driving mode. If no user interaction operation behavior is detected after the second time, the current driving mode remains unchanged.

[0015] The embodiments of the present invention include the following beneficial effects: The present invention proposes a driving mode push method and system based on big data analysis. By real-time collection of vehicle feature data and matching it with preset scene categories, the appropriate driving mode can be accurately pushed. The push strategy is dynamically adjusted according to the user's historical driving habits and preferences to ensure personalized adaptation of the driving mode. It can ensure the autonomous learning of driving modes and accurately meet the actual needs of drivers, thereby bringing them more significant driving benefits and safety improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic flow chart of the steps of a driving mode push method based on big data analysis provided by an embodiment of the present invention; Figure 2 This is a structural block diagram of a driving mode push system based on big data analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0018] It should be noted that although the device schematics illustrate a modular division and the flowcharts illustrate a logical sequence, in some cases, the steps shown or described may be performed in a different order than the modular division in the device or the sequence in the flowcharts. The terms "first," "second," and so on in the specification, claims, and drawings are used to distinguish similar items and are not necessarily used to describe a specific order or precedence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0020] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0021] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0022] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0023] like Figure 1 As shown, Figure 1 An embodiment of the present invention provides a driving mode push method based on big data analysis, the method comprising the following steps: S100, the data terminal collects vehicle feature data while the vehicle is driving and sends it to the cloud; Specifically, the data terminal is responsible for data collection. When a driver drives a vehicle equipped with the above function on the road, the vehicle's data terminal will send some characteristic data of the driver to the cloud for processing by the cloud.

[0024] S200: The cloud determines the current scenario category based on the vehicle feature data, identifies the driving mode corresponding to the user profile in the scenario category, and pushes it to the user end; Specifically, the cloud clusters the vehicle feature data collected under different scene categories. For each scene category, multiple typical driving modes under the scene category are divided, and each driving mode corresponds to a cluster of vehicle feature data.

[0025] Based on the vehicle feature data collected from the user during past driving, the typical vehicle feature data of the user in each scenario category is determined, and the corresponding driving mode of the user in each scenario category is obtained, which corresponds to the user's driving style in each scenario category; the user's driving style in each scenario category is used as the basis for matching the driving mode with the user profile.

[0026] After obtaining the vehicle feature data collected while the vehicle is driving, the current scene category is determined based on the vehicle feature data, and the driving mode corresponding to the user profile under the scene category is identified as the driving mode pushed to the user end.

[0027] In this embodiment, the cloud is responsible for data processing. Based on various vehicle characteristic data such as acceleration, the cloud analyzes the driver's current driving scenario category. This is combined with the driver's profile and grid divisions to match the corresponding driving pattern. If optimized push notifications are determined to be necessary, the driving pattern corresponding to the scenario category is pushed. Various parameters such as vehicle acceleration are also recorded to support subsequent analysis of the driver profile and grid divisions. After the vehicle characteristic data is updated, the cloud regenerates multiple typical driving patterns for each scenario category based on the updated vehicle characteristic data.

[0028] S300: The user terminal receives the driving mode pushed by the cloud and provides a prompt, and determines the vehicle's driving mode based on the user's interactive behavior on the driving mode.

[0029] Specifically, after receiving a driving mode push notification from the cloud, the system automatically compares the current driving mode with the push notification. If the push notification mode is superior, the user is prompted to confirm the switch, ensuring an optimal driving experience. Simultaneously, user feedback is recorded to optimize subsequent push notification strategies and enhance the accuracy of personalized services.

[0030] It's important to note that big data-driven autonomous driving mode optimization can intelligently identify and learn users' personalized driving preferences by continuously collecting and analyzing their driving behavior data, including speed control, route selection, and braking habits. This deep learning not only enables the vehicle to provide driving assistance more tailored to user needs, such as intelligently adjusting acceleration and braking sensitivity and optimizing navigation routes to avoid congestion, but also gradually adjusts driving strategies to reduce energy consumption and improve fuel efficiency while ensuring safety. More importantly, it helps users identify and improve potentially risky driving behaviors, such as sudden acceleration or frequent lane changes, by providing gentle reminders and suggestions, gradually guiding users to develop safer and more environmentally friendly driving habits. In short, big data-driven driving mode optimization not only makes driving smarter and more convenient, but also ensures safe and comfortable driving, representing a crucial step towards future intelligent transportation and the balance between personalized and safe travel.

[0031] Based on this, steps S100 to S300 shown in the embodiment of the present application collect various signals such as acceleration, vision, and positioning, upload them to the cloud in real time, and process the data to achieve scene analysis and driving mode push within a short period of time. It is committed to making detailed divisions of scenes that require driving mode optimization and push, and formulating differentiated push logic based on different scene types. The present invention has designed a comprehensive, big data-based driving mode optimization and intelligent push system framework. It can ensure the autonomous learning of driving modes and can accurately meet the actual needs of drivers, thereby bringing them more significant driving benefits and safety improvements.

[0032] In some embodiments, the method further comprises: The fixed mode selection module on the user side provides basic mode and working mode for users to choose from.

[0033] Specifically, the fixed mode selection module is used to provide basic mode and working mode for users to choose from, among which the basic module includes comfort mode, sports mode, and energy-saving mode; the working mode module includes highway mode, low-speed escape mode, wading mode and low-adhesion snow mode.

[0034] In some embodiments, the method further comprises: The user-side custom mode selection module provides multiple driving modes for users to combine and set the driving parameters of each driving mode.

[0035] Specifically, the custom mode selection module is used to provide a driving mode combination and set driving parameters, which include power output, suspension height, suspension software, and steering force.

[0036] In some embodiments, determining the current scene category based on vehicle feature data includes: The cloud determines the current driving condition through a scenario classification module and evaluates the scenario category based on the driving condition. The scenario category includes a first scenario representing entering a completely new driving condition, a second scenario representing that the user forgets to adjust the driving mode, and a third scenario representing that a more optimal driving mode combination exists for the driving condition.

[0037] Specifically, the scenario classification module identifies three specific scenarios where users may need to receive mode push notifications. By combining positioning signals with big data analysis, it accurately determines the user's current driving condition and comprehensively assesses whether corresponding mode push notifications are necessary.

[0038] Based on user usage scenarios, scenarios where users may need to optimize driving mode recommendations are divided into three categories, mainly including: Scenario 1: Entering a new working condition; The second scenario: The user forgets to adjust the driving mode; for example, the target driver has a preferred driving mode for a certain working condition, but the driver did not use this driving mode when entering the working condition this time.

[0039] The third scenario: There is a more optimal mode combination for a certain working condition, which is divided into grids for the target driver.

[0040] In some embodiments, identifying the driving mode corresponding to the user profile in the scenario category includes: The cloud uses the mode push module to identify the scene category. When the matching scene categories are identified as the first scene and the second scene, the cloud prompts the user whether to use the pushed driving mode. If no user interaction behavior is detected after the first period of time, the cloud switches to the pushed driving mode. When the matching scene category is identified as the third scene, the user is prompted whether to use the pushed driving mode. If no user interaction operation behavior is detected after the second time, the current driving mode remains unchanged.

[0041] Specifically, the cloud is equipped with a mode push module: the push driving mode is determined according to the push scenario, and the push logic is classified according to the scenario: Logic 1: If it is recognized that the driving mode needs to be pushed due to the first and second scenarios, the user is prompted whether to use the pushed driving mode. If the user still does not select the default user for trial after 3 minutes.

[0042] Logic 2: If it is recognized that the driving mode needs to be pushed due to the third scenario, the user is prompted whether to use the pushed driving mode. If the user still does not choose after 3 minutes, the user will not be allowed to try it by default.

[0043] refer to Figure 2 As shown, an embodiment of the present invention further provides a driving mode push system based on big data analysis, including: The data terminal is used to collect vehicle feature data while the vehicle is driving and send it to the cloud; Specifically, the data terminal is responsible for data collection. When a driver drives a vehicle equipped with the above function on the road, the vehicle's data terminal will send some characteristic data of the driver to the cloud for processing by the cloud.

[0044] The cloud is used to determine the current scene category based on vehicle feature data, identify the driving mode corresponding to the user profile under the scene category, and push it to the user end; Specifically, the cloud clusters the vehicle feature data collected under different scene categories. For each scene category, multiple typical driving modes under the scene category are divided, and each driving mode corresponds to a cluster of vehicle feature data.

[0045] Based on the vehicle feature data collected from the user during past driving, the typical vehicle feature data of the user in each scenario category is determined, and the corresponding driving mode of the user in each scenario category is obtained as the user profile of the user's driving style.

[0046] After obtaining the vehicle feature data collected while the vehicle is driving, the current scene category is determined based on the vehicle feature data, and the driving mode corresponding to the user profile under the scene category is identified as the driving mode pushed to the user end.

[0047] In this embodiment, the cloud is responsible for data processing. Based on various vehicle characteristic data such as acceleration, the cloud analyzes the driver's current driving scenario category. This is combined with the driver's profile and grid divisions to match the corresponding driving pattern. If optimized push notifications are determined to be necessary, the driving pattern corresponding to the scenario category is pushed. Various parameters such as vehicle acceleration are also recorded to support subsequent analysis of the driver profile and grid divisions. After the vehicle characteristic data is updated, the cloud regenerates multiple typical driving patterns for each scenario category based on the updated vehicle characteristic data.

[0048] The user side is used to receive driving mode push from the cloud and provide prompts, and determine the vehicle's driving mode based on the user's interactive behavior on the driving mode.

[0049] Specifically, after receiving a driving mode push notification from the cloud, the system automatically compares the current driving mode with the push notification. If the push notification mode is superior, the user is prompted to confirm the switch, ensuring an optimal driving experience. Simultaneously, user feedback is recorded to optimize subsequent push notification strategies and enhance the accuracy of personalized services.

[0050] In some embodiments, the user terminal includes: The fixed mode selection module is used to provide basic mode and working mode for users to choose.

[0051] Specifically, the fixed mode selection module is used to provide basic mode and working mode for users to choose from, among which the basic module includes comfort mode, sports mode, and energy-saving mode; the working mode module includes highway mode, low-speed escape mode, wading mode and low-adhesion snow mode.

[0052] In some embodiments, the user terminal includes: The custom mode selection module is used to provide multiple driving modes for users to combine and set the driving parameters of each driving mode.

[0053] Specifically, the custom mode selection module is used to provide a driving mode combination and set driving parameters, which include power output, suspension height, suspension software, and steering force.

[0054] In some embodiments, the cloud comprises: A scenario classification module is used to determine the current driving condition and evaluate the scenario category based on the driving condition. The scenario category includes a first scenario representing entering a completely new driving condition, a second scenario representing the user forgetting to adjust the driving mode, and a third scenario representing the driving condition where a more optimal driving mode combination exists.

[0055] Specifically, the scenario classification module identifies three specific scenarios where users may need to receive mode push notifications. By combining positioning signals with big data analysis, it accurately determines the user's current driving condition and comprehensively assesses whether corresponding mode push notifications are necessary.

[0056] Based on user usage scenarios, scenarios where users may need to optimize driving mode recommendations are divided into three categories, mainly including: Scenario 1: Entering a new working condition; The second scenario: The user forgets to adjust the driving mode; for example, the target driver has a preferred driving mode for a certain working condition, but the driver did not use this driving mode when entering the working condition this time.

[0057] The third scenario: There is a more optimal mode combination for a certain working condition, which is divided into grids for the target driver.

[0058] In some embodiments, the cloud comprises: A mode push module, configured to prompt the user whether to use the pushed driving mode when the matching scene categories are identified as the first scene and the second scene, and switch to the pushed driving mode if no user interactive operation behavior is detected after a first period of time; When the matching scene category is identified as the third scene, the user is prompted whether to use the pushed driving mode. If no user interaction operation behavior is detected after the second time, the current driving mode remains unchanged.

[0059] Specifically, the data end is equipped with a mode push module: the push driving mode is determined according to the push scenario, and the push logic is differentiated according to the scenario classification: Logic 1: If it is recognized that a driving mode needs to be pushed due to the first and second scenarios, the user is prompted whether to use the pushed driving mode. If the user still does not select the default user for trial after 3 minutes.

[0060] Logic 2: If it is recognized that the driving mode needs to be pushed due to the third scenario, the user is prompted whether to use the pushed driving mode. If the user still does not choose after 3 minutes, the user will not be allowed to try it by default.

[0061] The basic framework of the system is described below: The system is divided into three main parts: the user side (including fixed mode and custom mode modules), the cloud side, and the data side. The user side stores all driving modes and is responsible for the interaction between the vehicle and the driver. The cloud side stores optimization algorithms and data analysis methods. The data side mainly stores all vehicle driving parameters.

[0062] Driving mode optimization and intelligent push system framework based on big data and autonomous adjustment system: User end: The user end consists of two parts: fixed mode selection module and custom mode selection module.

[0063] Fixed mode selection module: Divided into basic mode and operating mode. The basic module includes the common comfort, sport, and energy-saving modes. The operating mode module includes driving modes designed for specific operating conditions, such as highway mode, low-speed escape mode, wading mode, and low-adhesion snow mode.

[0064] Custom Mode Selection: Users can customize and name driving modes for various vehicle systems. For example, they can choose power output, suspension height, suspension software, steering force, and more, and freely assign names to these modes.

[0065] Data terminal: This collects data on users' driving patterns, driving-related signals, and individual driving habits, and sends it to the cloud. A cycle is established to classify drivers and optimize driving patterns.

[0066] Specifically, the cloud collects mode combinations of users in various driving modes and groups them by user grids, forming a library of mode combination solutions for different scenarios under different driving styles. The most frequently used mode combinations are determined through driver style and keywords, and the mode combinations corresponding to different driver styles are obtained.

[0067] Identify user profiles based on previously collected vehicle feature data, and generate corresponding driving modes based on user profiles and matching scene categories.

[0068] The driving mode optimization logic is as follows: The cloud collects system mode combination solutions for each driver's driving mode and groups them by driver, forming a library of frequently used combination solutions for different scenarios under different driving styles. During optimization, the most frequently used solutions are selected based on driver style and keywords. By analyzing previously collected vehicle feature data, user profiles are identified, and corresponding driving modes are generated for drivers and usage scenarios, and driving mode push is performed. It should be noted that OEM designers can provide preliminary recommended combination solutions for some typical scenarios and add them to the library. If a scenario occurs in which the recommended solution differs from the user's frequently used combination, a prompt signal can be sent to the OEM.

[0069] Scenario Classification Module: In three specific scenarios, users may need to receive mode push notifications. By combining positioning signals with big data analysis, it accurately determines the user's current driving condition and comprehensively evaluates whether corresponding mode push notifications are necessary.

[0070] Based on user usage scenarios, scenarios where users may need to optimize driving mode recommendations are divided into three categories, mainly including: Scenario 1: Entering a new working condition; The second scenario: The user forgets to adjust the driving mode; for example, the target driver has a preferred driving mode for a certain working condition, but the driver did not use this driving mode when entering the working condition this time.

[0071] The third scenario: There is a more optimal mode combination for a certain working condition, which is divided into grids for the target driver.

[0072] Interaction Logic: This invention classifies and pushes the logic based on the above scenarios to ensure that the human-vehicle interaction better meets the driver's expectations and improves the driver's driving experience.

[0073] Mode push module: Based on the scenario, customized differentiated push interaction logic is provided in different scenarios; Logic 1: If it is recognized that the driving mode needs to be pushed due to the first and second scenarios, the user is prompted whether to use the pushed driving mode. If the user still does not select the default user for trial after 3 minutes.

[0074] Logic 2: If it is recognized that the driving mode needs to be pushed due to the third scenario, the user is prompted whether to use the pushed driving mode. If the user still does not choose after 3 minutes, the user will not be allowed to try it by default.

[0075] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program implements the method of the above embodiment when executed by the processor.

[0076] For example, the processor and memory in a vehicle controller can be connected via a bus. Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. Furthermore, the memory can include high-speed random access memory and non-transitory memory, such as at least one disk drive, flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the control device via a network.

[0077] The non-transitory software programs and instructions required to implement the methods of the above embodiments are stored in the memory, and when executed by the processor, the methods of the above embodiments are performed.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0079] An embodiment of the present invention further provides a vehicle, comprising the vehicle control device of the above embodiment.

[0080] The vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. It can also be a commercial vehicle, such as a van, bus, small truck, or large trailer. The vehicle must have an electric motor that can output power or store mechanical energy as a generator. If the vehicle is a new energy vehicle, it can be a hybrid or a pure electric vehicle.

[0081] Since the vehicle applies all the technical solutions of the above-mentioned control device or vehicle controller, it has at least all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, which will not be repeated here.

[0082] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are used to execute the above method.

[0083] It is worth noting that since the computer-readable storage medium of an embodiment of the present invention can execute the method of any of the above embodiments, the specific implementation methods and technical effects of the computer-readable storage medium of an embodiment of the present invention can refer to the specific implementation methods and technical effects of the method of any of the above embodiments.

[0084] In addition, an embodiment of the present invention also provides a computer program product, including a computer program or computer instructions, the computer program or computer instructions are stored in a computer-readable storage medium, the processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device performs the above method.

[0085] It is worth noting that since the computer program product of the embodiment of the present invention can execute the method of any of the above embodiments, the specific implementation methods and technical effects of the computer program product of the embodiment of the present invention can refer to the specific implementation methods and technical effects of the method of any of the above embodiments.

[0086] Those skilled in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0087] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

Claims

1. A driving mode push method based on big data analysis, characterized in that: The method comprises: The data terminal collects vehicle feature data while the vehicle is driving and sends it to the cloud; The cloud determines the current scenario category based on vehicle feature data, identifies the driving mode corresponding to the user profile in that scenario category, and pushes it to the user end; The user end receives the driving mode pushed by the cloud and provides prompts, and determines the vehicle's driving mode based on the user's interactive behavior on the driving mode.

2. The method according to claim 1, characterized in that The method further comprises: The fixed mode selection module on the user side provides basic mode and working mode for users to choose from.

3. The method according to claim 2, characterized in that The method further comprises: The user-side custom mode selection module provides multiple driving modes for users to combine and set the driving parameters of each driving mode.

4. The method according to claim 1, wherein Determining the current scene category based on the vehicle feature data includes: The cloud determines the current driving condition through a scenario classification module and evaluates the scenario category based on the driving condition. The scenario category includes a first scenario representing entering a completely new driving condition, a second scenario representing that the user forgets to adjust the driving mode, and a third scenario representing that a more optimal driving mode combination exists for the driving condition.

5. The method according to claim 1, wherein The identification of the driving mode corresponding to the user profile in the scenario category includes: The cloud uses the mode push module to identify the scene category. When the matching scene categories are identified as the first scene and the second scene, the cloud prompts the user whether to use the pushed driving mode. If no user interaction behavior is detected after the first period of time, the cloud switches to the pushed driving mode. When the matching scene category is identified as the third scene, the user is prompted whether to use the pushed driving mode. If no user interaction operation behavior is detected after the second time, the current driving mode remains unchanged.

6. A driving mode push system based on big data analysis, characterized in that: include: The data terminal is used to collect vehicle feature data while the vehicle is driving and send it to the cloud; The cloud is used to determine the current scene category based on vehicle feature data, identify the driving mode corresponding to the user profile under the scene category, and push it to the user end; The user side is used to receive driving mode push from the cloud and provide prompts, and determine the vehicle's driving mode based on the user's interactive behavior on the driving mode.

7. The system according to claim 6, characterized in that The user terminal includes: The fixed mode selection module is used to provide basic mode and working mode for users to choose.

8. The system according to claim 6, wherein: The user terminal includes: The custom mode selection module is used to provide multiple driving modes for users to combine and set the driving parameters of each driving mode.

9. The system according to claim 6, wherein: The cloud includes: A scenario classification module is used to determine the current driving condition and evaluate the scenario category based on the driving condition. The scenario category includes a first scenario representing entering a completely new driving condition, a second scenario representing the user forgetting to adjust the driving mode, and a third scenario representing the driving condition where a more optimal driving mode combination exists.

10. The system according to claim 6, wherein: The data terminal includes: A mode push module, configured to prompt the user whether to use the pushed driving mode when the matching scene categories are identified as the first scene and the second scene, and switch to the pushed driving mode if no user interactive operation behavior is detected after a first period of time; When the matching scene category is identified as the third scene, the user is prompted whether to use the pushed driving mode. If no user interaction operation behavior is detected after the second time, the current driving mode remains unchanged.

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