Visual field display adjusting method and system based on big data

Abstract

The invention discloses a visual field display adjusting method and system based on big data, and the method comprises the steps: collecting the driving big data of a vehicle, and switching the visual field type of the rear side according to the driving big data; detecting a blind area range on the rear side and an object in the blind area range, and calculating the nearest distance between the object and the vehicle; according to the visual field type and the nearest distance, determining a blind area early warning level, and calculating a visual field safety score; and adjusting and displaying a visual field corresponding to the visual field type, and performing corresponding early warning on a driver according to the blind area early warning level. According to the embodiment of the invention, dynamic and personalized data analysis and adjustment can be realized, the driving safety and the user experience can be remarkably improved, and various limitations of a traditional view adjustment system are overcome.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle technology, and in particular to a field of view display adjustment method and system based on big data. Background Art

[0002] With the continuous development of the automobile industry, automobile safety and driving experience have received great attention. Modern cars are already equipped with a variety of advanced driver assistance systems (ADAS), such as blind spot monitoring system (BSM), lane keeping assist system (LKA), adaptive cruise control system (ACC), etc. These systems have greatly improved driving safety. However, the driver's field of view display adjustment is still a problem that needs to be solved. Summary of the invention

[0003] The purpose of the present invention is to provide a field of view display adjustment method and system based on big data to address the deficiencies in the prior art, enable dynamic and personalized data analysis and adjustment, significantly improve driving safety and user experience, and overcome the various limitations of traditional field of view adjustment systems.

[0004] An embodiment of the present application provides a method for adjusting visual field display based on big data, the method comprising:

[0005] Collecting driving big data of the vehicle, and switching the side and rear view type according to the driving big data;

[0006] Detecting the blind spot range and objects within the blind spot range at the side and rear, and calculating the shortest distance between the objects and the vehicle;

[0007] Determine a blind spot warning level according to the field of view type and the closest distance, and calculate a field of view safety score;

[0008] The field of view corresponding to the field of view type is adjusted and displayed, and the driver is given a corresponding warning according to the blind spot warning level.

[0009] Optionally, the switching of the side and rear field of view type according to the driving big data includes:

[0010] When the gear data in the driving big data is the reverse gear, the current field of view is switched to the preset reverse field of view;

[0011] When the vehicle speed data in the driving big data is greater than or equal to the first preset speed, the current field of view is switched to the preset high-speed field of view;

[0012] When the vehicle speed data in the driving big data is lower than the first preset speed, the current field of view is switched to the preset standard field of view;

[0013] When the turning data in the driving big data is a left turn or a right turn, and the vehicle speed is lower than a second preset speed, the current field of view is switched to a preset turning field of view.

[0014] Optionally, the visual field safety score is calculated as follows:

[0015] S=(W_D·P_L·F_D+W_R·P_L·F_R+W_C·P_L·F_C)·100

[0016] Among them, S is the field of vision safety score, P_L is the warning level score, F_D is the distance attenuation factor, W_D is the distance weight, F_R is the reaction time correction factor, W_R is the reaction time weight, F_C is the road condition influencing factor, and W_C is the road condition weight.

[0017] Optionally, the distance attenuation factor is calculated as follows:

[0018] F_D=e alpha·D

[0019] Among them, alpha is the distance attenuation coefficient, and D is the distance between the object and the vehicle.

[0020] Optionally, the calculation formula of the reaction time correction factor is:

[0021]

[0022] Wherein, beta is the reaction time coefficient, T_R is the driver's reaction time, and the reaction time is determined by the driver's historical driving big data.

[0023] Optionally, the calculation formula of the road condition influencing factor is:

[0024]

[0025] Among them, gamma is the road condition coefficient and C is the road condition level.

[0026] Another embodiment of the present application provides a visual field display adjustment system based on big data, the system comprising:

[0027] A switching module, used to collect driving big data of the vehicle and switch the side and rear field of view type according to the driving big data;

[0028] A detection module is used to detect the blind spot range and objects within the blind spot range at the side and rear, and calculate the shortest distance between the object and the vehicle;

[0029] A determination module, used to determine a blind spot warning level according to the field of view type and the closest distance, and calculate a field of view safety score;

[0030] The adjustment module is used to adjust and display the field of view corresponding to the field of view type, and to give corresponding warnings to the driver according to the blind spot warning level.

[0031] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0032] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0033] Compared with the prior art, the present invention provides a field of view display adjustment method based on big data, which collects vehicle driving big data, switches the field of view type of the side rear according to the driving big data; detects the blind spot range of the side rear and the objects within the blind spot range, and calculates the closest distance between the object and the vehicle; determines the blind spot warning level according to the field of view type and the closest distance, and calculates the field of view safety score; adjusts and displays the field of view corresponding to the field of view type, and issues corresponding warnings to the driver according to the blind spot warning level, thereby enabling dynamic and personalized data analysis and adjustment, which can significantly improve driving safety and user experience, and overcome the various limitations of traditional field of view adjustment systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A hardware structure block diagram of a computer terminal for a method for adjusting visual field display based on big data provided by an embodiment of the present invention;

[0035] Figure 2 A schematic flow chart of a method for adjusting visual field display based on big data provided by an embodiment of the present invention;

[0036] Figure 3 A structural schematic diagram of a visual field display adjustment system based on big data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0038] The embodiment of the present invention first provides a method for adjusting visual field display based on big data, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0039] The following describes it in detail by taking running on a computer terminal as an example. Figure 1The hardware structure block diagram of a computer terminal for a method for adjusting the visual field display based on big data provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0040] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the field of view display adjustment method based on big data in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0041] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] See also Figure 2 , an embodiment of the present invention provides a method for adjusting visual field display based on big data, which may include the following steps:

[0043] S201, collecting driving big data of the vehicle, and switching the side and rear view type according to the driving big data;

[0044] Vehicle driving big data collection: Through the vehicle's own sensors and control system, various data during the vehicle's driving process are continuously collected and updated. These data include but are not limited to gear data, vehicle speed data, steering data, etc. The data collection frequency can be adjusted according to actual needs to ensure that the system can obtain the latest driving data in a timely manner under different driving conditions.

[0045] Data processing and analysis: Preprocess the collected raw data to filter out noise and irrelevant data to ensure data accuracy and consistency. Use data analysis algorithms to classify and analyze the processed data to extract valid information. For example, extract the current gear from the gear data, extract the current vehicle speed from the vehicle speed data, and extract the current steering state from the steering data.

[0046] Specifically, when the gear data in the driving big data is a reverse gear, the current field of view is switched to a preset reverse field of view;

[0047] Reversing vision refers to the field of view specifically for reversing conditions displayed on the rearview mirror or display screen when the vehicle is reversing. Reversing vision usually provides a wide-angle field of view, covering a large area behind the vehicle to help the driver avoid obstacles and pedestrians when reversing. The purpose of designing reversing vision is to:

[0048] -Safety: When reversing, the vision is poor and the driver cannot directly see the situation behind the vehicle. The reversing vision can provide a clear rear image to help the driver accurately judge the situation behind and avoid collisions and accidents.

[0049] - Convenience: The reversing view can display distance signs and warning information to help the driver more easily judge the distance to the rear obstacles and improve the accuracy and efficiency of reversing.

[0050] -Prevent blind spot accidents: The rear blind spots when reversing are high-risk areas for accidents. The reversing field of view can cover these blind spots and reduce the risk of collision when reversing.

[0051] When the vehicle speed data in the driving big data is greater than or equal to the first preset speed, the current field of view is switched to the preset high-speed field of view;

[0052] High-speed vision refers to the field of view specifically for high-speed driving conditions displayed on the rearview mirror or display screen when the vehicle is driving at high speed. High-speed vision usually has a longer viewing distance and a narrower viewing angle, so that the driver can see the distant situation more clearly when driving at high speed. The significance of designing high-speed vision is:

[0053] -Safety: When driving at high speed, the vehicle is traveling at a faster speed, and the driver needs to detect the situation in front and behind earlier so as to respond in time. High-speed vision provides a longer visual distance, which helps the driver to detect potential dangers early.

[0054] - Reduce fatigue: When driving at high speed, the driver needs to concentrate, and staring at the distance for a long time can easily cause eye fatigue. High-speed vision can provide a clearer distant view, reduce eye fatigue, and improve driving comfort.

[0055] - Improve reaction speed: When driving at high speeds, the driver's reaction time is critical. High-speed vision can provide clearer long-distance information, helping the driver make decisions and react faster.

[0056] When the vehicle speed data in the driving big data is lower than the first preset speed, the current field of view is switched to the preset standard field of view;

[0057] The standard field of view refers to the normal field of view displayed on the rearview mirror or display screen when the vehicle is driving normally. The standard field of view usually covers the medium range in front and behind the vehicle and is suitable for most ordinary driving situations. The purpose of designing the standard field of view is to:

[0058] -Versatile: The standard field of view is suitable for most common driving situations, such as urban roads, suburban roads, etc. It provides a balanced viewing angle and viewing distance, which can meet most driving needs.

[0059] - Driving habits: The standard field of view conforms to the driver's daily driving habits, allowing the driver to adapt easily without having to adjust the field of view frequently.

[0060] - Comprehensive safety: Standard vision provides sufficient field of view to help the driver observe the surrounding environment, including the road ahead, vehicles behind and side blind spots, thereby improving overall driving safety.

[0061] When the turning data in the driving big data is a left turn or a right turn, and the vehicle speed is lower than a second preset speed, the current field of view is switched to a preset turning field of view.

[0062] The turning field of view refers to the field of view specifically for the turning situation displayed on the rearview mirror or display screen when the vehicle is turning. The turning field of view usually expands the field of view of the vehicle's turning direction so that the driver can see the situation behind the side more clearly when turning. The purpose of designing the turning field of view is to:

[0063] - Blind spot coverage: When the vehicle is turning, the blind spots on the side and rear are easily overlooked. The turning field of view can expand the field of view of these blind spots and help the driver better judge the safety when turning.

[0064] - Prevent accidents: Collisions and scrapes are more likely to occur when turning, especially at low speeds and large steering angles. The turning field of view provides more detailed information about the side and rear, reducing the risk of accidents when turning.

[0065] - Enhanced confidence: Steering vision can help drivers feel more confident when steering, especially in complex road environments such as lane changes and turns on urban roads and ramp turns on highways.

[0066] Different types of vision play a vital role in different driving situations, helping to improve driving safety and convenience.

[0067] S202, detecting the blind spot range and the object within the blind spot range at the side and rear, and calculating the shortest distance between the object and the vehicle;

[0068] Specifically, one implementation may include:

[0069] Step 1: Install and calibrate the sensor

[0070] 1.1 Multiple sensors, including ultrasonic sensors, radars and cameras, are installed on the rear side of the vehicle to monitor objects within the blind spot in real time.

[0071] 1.2 Perform initial calibration of the sensor to ensure that the sensor can accurately capture the distance and position of surrounding objects.

[0072] Step 2: Data Collection

[0073] 2.1 When the vehicle is started, the sensor begins to continuously collect environmental data from the side and rear, generating a raw data stream.

[0074] 2.2 The data includes information such as distance, angle, speed, etc., and may also include image data depending on the type of sensor.

[0075] Step 3: Data Preprocessing

[0076] 3.1 Preprocess the collected raw data, including denoising, filtering and data fusion.

[0077] 3.2 Use data fusion technology to combine data from different sensors to improve the accuracy and reliability of object detection.

[0078] -For example: combining the distance data from the radar with the image data from the camera to confirm the specific location and type of the object through image recognition algorithms.

[0079] Step 4: Determine the blind area

[0080] 4.1 Determine the side and rear blind spot detection area. The blind spot range can be adjusted according to the model and specifications of the vehicle.

[0081] 4.2 Use the multi-sensor data fusion results to determine the specific location and attributes of all objects within the blind spot.

[0082] Step 5: Object Detection and Classification

[0083] 5.1 Perform object detection and classification on the preprocessed data through machine learning and computer vision algorithms.

[0084] -For example: Use deep learning algorithms such as YOLO (You Only Look Once) to process camera image data and identify objects such as vehicles, pedestrians, and bicycles within the blind spot.

[0085] 5.2 Match the location information of the identified object with the radar data to further confirm the exact location and distance of the object.

[0086] Step 6: Distance Calculation

[0087] 6.1 Calculate the distance between each object and the vehicle within the blind spot. Distance calculation is performed based on the data from different sensors and combined with geometric algorithms.

[0088] 6.2 Sort the distance information of each object and find the object closest to the vehicle.

[0089] Step 7: Determination and update of the closest distance

[0090] 7.1 Compare the calculated distances of all objects and determine the distance of the object closest to the vehicle.

[0091] 7.2 Update the closest distance data in real time to continuously monitor the dynamic changes of objects within the blind spot during driving.

[0092] Through the above steps, the detection of the opposite rear blind spot can be realized, and the closest distance between the object in the blind spot and the vehicle can be accurately calculated, providing basic data support for the next step of blind spot warning.

[0093] S203, determining a blind spot warning level according to the field of view type and the minimum distance, and calculating a field of view safety score;

[0094] Specifically, you can set distance thresholds for different field of view types:

[0095] - Reversing Vision: Set a shorter distance threshold because the visibility is smaller and the risk is higher when reversing.

[0096] -High-speed vision: Set a longer distance threshold because the vehicle needs to be warned of potential dangers earlier when traveling at high speeds.

[0097] - Standard Field of View: Sets a medium distance threshold suitable for most driving situations.

[0098] - Turning Vision: Set a shorter distance threshold because you need to pay special attention to the side and rear blind spots when turning.

[0099] The distance threshold of each field of view type can be set to a corresponding distance sub-threshold according to the required warning level. For example, if there are three warning levels, each distance threshold is set as: the first distance sub-threshold (such as 3m), the second distance sub-threshold (such as 2m), and the third distance sub-threshold (such as 1m).

[0100] If the closest distance between the object in the blind spot and the vehicle is less than the first distance sub-threshold and greater than the second distance sub-threshold, the blind spot warning level is determined to be the first level (for example, a low warning level);

[0101] If the closest distance is less than the second distance sub-threshold and greater than the third distance sub-threshold, the blind spot warning level is determined to be the second level (for example, a medium warning level);

[0102] If the closest distance is less than the third distance sub-threshold, the blind spot warning level is determined to be the third level (eg, a high warning level).

[0103] Specifically, a method for calculating the visual field safety score may be:

[0104] S=(W_D·P_L·F_D+W_R·P_L·F_R+W_C·P_L·F_C)·100

[0105] Among them, S is the field of vision safety score, P_L is the warning level score, F_D is the distance attenuation factor, W_D is the distance weight, F_R is the reaction time correction factor, W_R is the reaction time weight, F_C is the road condition impact factor, and W_C is the road condition weight. For example, W_D = 0.5, W_R = 0.3, and W_C = 0.2.

[0106] The blind spot warning level can be converted into a warning level score value, for example: low warning level: P_L = 1, medium warning level: P_L = 0.5, high warning level: P_L = 0.1. That is, the lower the blind spot warning level, the higher the P_L, the higher the corresponding field of vision safety score, indicating that the field of vision is safer.

[0107] Specifically, a calculation formula for the distance attenuation factor may be:

[0108] F_D=e alpha·D

[0109] Among them, alpha is the distance attenuation coefficient (for example, 0.1), and D is the distance between the object and the vehicle (which can be the closest distance). Considering the impact of distance on the safety score, the longer the distance, the greater the attenuation factor and the higher the safety score.

[0110] Specifically, a calculation formula for the reaction time correction factor may be:

[0111]

[0112] Where beta is the reaction time coefficient (e.g. 0.5), T_R is the driver's reaction time, and the reaction time is determined by the driver's historical driving big data. Considering the impact of the driver's reaction time on the safety score, the shorter the reaction time, the larger the correction factor, and the higher the safety score.

[0113] Specifically, a calculation formula for a road condition influencing factor may be:

[0114]

[0115] Among them, gamma is the road condition coefficient (for example, 0.2), and C is the road condition level (for example, level 1 means good, level 2 means fair, and level 3 means bad). Considering the impact of road conditions on the safety score, the lower the road condition level, the greater the impact factor and the higher the safety score.

[0116] Through the above steps, the function of determining the blind spot warning level based on the field of view type and the closest distance can be realized, and the field of view safety can be quantified to provide the driver with timely and effective warning information to improve driving safety.

[0117] S204, adjusting and displaying the field of view corresponding to the field of view type, and giving a corresponding warning to the driver according to the blind spot warning level.

[0118] Specifically, the type of field of view currently required to be displayed can be switched to the central control display screen or HUD (head-up display) through the display control system of the vehicle. For example, a switching instruction of the field of view type is sent to the display control module using the CAN bus communication protocol.

[0119] The corresponding warning signal can be generated according to the blind spot warning level:

[0120] - Warning level 1 (low risk): Objects in the blind spot are only shown in green on the display, with no other warning signals.

[0121] - Warning level 2 (medium risk): A slight sound prompt is triggered and objects in the blind spot are displayed in yellow on the display.

[0122] - Warning Level 3 (High Risk): Triggers an emergency sound warning and vibration reminder, while dangerous objects in the blind spot are highlighted in red on the display.

[0123] Use the multimedia interface to send warning signals to the vehicle multimedia system, and use speakers and vibrators to provide physical reminders. Display blind spot warning information on the central control display or HUD:

[0124] -Using a graphical interface, the position, distance and warning level of objects in the blind spot are intuitively displayed through icons and colors.

[0125] - In high warning level situations, use flashing icons and text prompts (such as "Danger! Please slow down").

[0126] The display information can be updated in real time, and the display content can be dynamically adjusted according to changes in sensor data and changes in warning levels. For example, through a cyclic refresh mechanism, the display data is updated at regular intervals (e.g., 500 milliseconds) to ensure that the driver obtains the latest blind spot information.

[0127] The field of view display can also be adjusted according to the blind spot warning level. For example, at a high warning level, the width and height of the field of view are increased to cover a larger area. Using image processing algorithms, the camera data is stitched and corrected to generate a wide field of view image. Through image enhancement algorithms, the clarity and contrast of the field of view image are improved so that the driver can more easily identify potential hazards.

[0128] Through the above steps, dynamic adjustment of the field of view type and display of blind spot warning information can be achieved, effectively improving the driver's perception of the surrounding environment and driving safety.

[0129] It can be seen that the vehicle's driving big data is collected, and the side and rear field of view type is switched according to the driving big data; the side and rear blind spot range and objects within the blind spot range are detected, and the closest distance between the object and the vehicle is calculated; according to the field of view type and the closest distance, the blind spot warning level is determined, and the field of view safety score is calculated; the field of view corresponding to the field of view type is adjusted and displayed, and the driver is given corresponding warning according to the blind spot warning level, thereby enabling dynamic and personalized data analysis and adjustment, which can significantly improve driving safety and user experience, and overcome the various limitations of traditional field of view adjustment systems.

[0130] Another embodiment of the present invention provides a visual field display adjustment system based on big data, see Figure 3 , the system may include:

[0131] A switching module 301 is used to collect driving big data of the vehicle and switch the side and rear view type according to the driving big data;

[0132] A detection module 302 is used to detect the blind spot range and objects within the blind spot range at the side and rear, and calculate the shortest distance between the object and the vehicle;

[0133] A determination module 303 is used to determine a blind spot warning level according to the field of view type and the closest distance, and calculate a field of view safety score;

[0134] The adjustment module 304 is used to adjust and display the field of view corresponding to the field of view type, and give corresponding warnings to the driver according to the blind spot warning level.

[0135] It can be seen that the vehicle's driving big data is collected, and the side and rear field of view type is switched according to the driving big data; the side and rear blind spot range and objects within the blind spot range are detected, and the closest distance between the object and the vehicle is calculated; according to the field of view type and the closest distance, the blind spot warning level is determined, and the field of view safety score is calculated; the field of view corresponding to the field of view type is adjusted and displayed, and the driver is given corresponding warning according to the blind spot warning level, thereby enabling dynamic and personalized data analysis and adjustment, which can significantly improve driving safety and user experience, and overcome the various limitations of traditional field of view adjustment systems.

[0136] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0137] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0138] S201, collecting driving big data of the vehicle, and switching the side and rear view type according to the driving big data;

[0139] S202, detecting the blind spot range and the object within the blind spot range at the side and rear, and calculating the shortest distance between the object and the vehicle;

[0140] S203, determining a blind spot warning level according to the field of view type and the minimum distance, and calculating a field of view safety score;

[0141] S204, adjusting and displaying the field of view corresponding to the field of view type, and giving a corresponding warning to the driver according to the blind spot warning level.

[0142] It can be seen that the vehicle's driving big data is collected, and the side and rear field of view type is switched according to the driving big data; the side and rear blind spot range and objects within the blind spot range are detected, and the closest distance between the object and the vehicle is calculated; according to the field of view type and the closest distance, the blind spot warning level is determined, and the field of view safety score is calculated; the field of view corresponding to the field of view type is adjusted and displayed, and the driver is given corresponding warning according to the blind spot warning level, thereby enabling dynamic and personalized data analysis and adjustment, which can significantly improve driving safety and user experience, and overcome the various limitations of traditional field of view adjustment systems.

[0143] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0144] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0145] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0146] S201, collecting driving big data of the vehicle, and switching the side and rear view type according to the driving big data;

[0147] S202, detecting the blind spot range and the object within the blind spot range at the side and rear, and calculating the shortest distance between the object and the vehicle;

[0148] S203, determining a blind spot warning level according to the field of view type and the minimum distance, and calculating a field of view safety score;

[0149] S204, adjusting and displaying the field of view corresponding to the field of view type, and giving a corresponding warning to the driver according to the blind spot warning level.

[0150] It can be seen that the vehicle's driving big data is collected, and the side and rear field of view type is switched according to the driving big data; the side and rear blind spot range and objects within the blind spot range are detected, and the closest distance between the object and the vehicle is calculated; according to the field of view type and the closest distance, the blind spot warning level is determined, and the field of view safety score is calculated; the field of view corresponding to the field of view type is adjusted and displayed, and the driver is given corresponding warning according to the blind spot warning level, thereby enabling dynamic and personalized data analysis and adjustment, which can significantly improve driving safety and user experience, and overcome the various limitations of traditional field of view adjustment systems.

[0151] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for adjusting visual field display based on big data, characterized in that: The method comprises: Collecting driving big data of the vehicle, and switching the side and rear view type according to the driving big data; Detecting the blind spot range and objects within the blind spot range at the side and rear, and calculating the shortest distance between the objects and the vehicle; Determine a blind spot warning level according to the field of view type and the closest distance, and calculate a field of view safety score; The field of view corresponding to the field of view type is adjusted and displayed, and the driver is given a corresponding warning according to the blind spot warning level.

2. The method according to claim 1, characterized in that The switching of the side and rear view type according to the driving big data includes: When the gear data in the driving big data is the reverse gear, the current field of view is switched to the preset reverse field of view; When the vehicle speed data in the driving big data is greater than or equal to the first preset speed, the current field of view is switched to the preset high-speed field of view; When the vehicle speed data in the driving big data is lower than the first preset speed, the current field of view is switched to the preset standard field of view; When the turning data in the driving big data is a left turn or a right turn, and the vehicle speed is lower than a second preset speed, the current field of view is switched to a preset turning field of view.

3. The method according to claim 2, characterized in that The visual field safety score is calculated as follows: S=(W_D·P_L·F_D+W_R·P_L·F_R+W_C·P_L·F_C)·100 Among them, S is the field of vision safety score, P_L is the warning level score, F_D is the distance attenuation factor, W_D is the distance weight, F_R is the reaction time correction factor, W_R is the reaction time weight, F_C is the road condition influencing factor, and W_C is the road condition weight.

4. The method according to claim 3, characterized in that The calculation formula of the distance attenuation factor is: F_D=e alpha·D Among them, alpha is the distance attenuation coefficient, and D is the distance between the object and the vehicle.

5. The method according to claim 4, characterized in that The calculation formula of the reaction time correction factor is: Wherein, beta is the reaction time coefficient, T_R is the driver's reaction time, and the reaction time is determined by the driver's historical driving big data.

6. The method according to claim 5, characterized in that The calculation formula of the road condition influencing factor is: Among them, gamma is the road condition coefficient and C is the road condition level.

7. A visual field display adjustment system based on big data, characterized in that: The system comprises: A switching module, used to collect driving big data of the vehicle and switch the side and rear field of view type according to the driving big data; A detection module is used to detect the blind spot range and objects within the blind spot range at the side and rear, and calculate the shortest distance between the object and the vehicle; A determination module, used to determine a blind spot warning level according to the field of view type and the closest distance, and calculate a field of view safety score; The adjustment module is used to adjust and display the field of view corresponding to the field of view type, and to give corresponding warnings to the driver according to the blind spot warning level.

8. The system according to claim 7, characterized in that The switching module is specifically used for: When the gear data in the driving big data is the reverse gear, the current field of view is switched to the preset reverse field of view; When the vehicle speed data in the driving big data is greater than or equal to the first preset speed, the current field of view is switched to the preset high-speed field of view; When the vehicle speed data in the driving big data is lower than the first preset speed, the current field of view is switched to the preset standard field of view; When the turning data in the driving big data is a left turn or a right turn, and the vehicle speed is lower than a second preset speed, the current field of view is switched to a preset turning field of view.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 6.

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