Vehicle intelligent cabin adjusting method and system

Abstract

The invention discloses an intelligent vehicle cockpit adjusting method and system, and relates to the technical field of intelligent vehicle cockpit adjustment.The method comprises the steps of driver basic information input, basic information adjustment, preliminary cockpit adjustment, abnormal state analysis and exception handling, and on the basis of basic adjustment of a vehicle cockpit, real-time data collection and multi-data analysis are carried out; preliminary adjustment and data updating of the cockpit are completed under specific conditions, the limitation problem existing in the current vehicle intelligent cockpit adjustment development feasibility analysis process is solved, when the vehicle is abnormal, behavior data and real-time characterization data of a driver are collected, abnormal reasons are analyzed, and the vehicle intelligent cockpit adjustment development feasibility analysis efficiency is improved. According to the method, the first abnormal state and the second abnormal state are analyzed, abnormity processing is carried out according to the analysis results of the first abnormal state and the second abnormal state, data feedback is carried out, comprehensive and objective analysis of vehicle intelligent cabin adjustment is achieved, and reliability and authenticity of the vehicle intelligent cabin adjustment analysis results are guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of vehicle intelligent cockpit adjustment, and specifically relates to a vehicle intelligent cockpit adjustment method and system. Background Art

[0002] With the development of technology, the cockpit adjustment of vehicles has gradually become more intelligent. Ensuring the comfort and safety of drivers during the cockpit adjustment process of vehicles is of utmost importance.

[0003] The prior art, such as an invention application patent with the publication number CN113353011A, discloses a vehicle comfort adjustment method and device. The method includes: obtaining the body data of the current user; matching the optimal cockpit parameters of the vehicle cockpit according to the body data; when the optimal cockpit parameters are inconsistent with the current cockpit parameters, while controlling the vehicle cockpit to perform an adjustment action according to the optimal cockpit parameters, controlling the selection of each rearview mirror to achieve the optimal rearview angle corresponding to the optimal cockpit parameters. Thus, it solves the problems in the related art that users need to manually adjust the cockpit parameters and the angles of the rearview mirrors, resulting in the inability to achieve the best match between the cockpit parameters and the angles of the rearview mirrors and the user's body data, with a low driving experience and low intelligence.

[0004] In view of the above solution, the inventors of this application found that the above technology has at least the following technical problems: 1. Currently, when the vehicle enters the driving state, it lacks the monitoring of the driver's real-time basic data and the vehicle's driving data, and at the same time, it does not conduct multi-situation analysis based on the driver's real-time state and the vehicle's driving state. It cannot ensure vehicle safety while improving the comfort of the driver's cockpit, and cannot update the driving characteristic database in real time. Moreover, the lack of setting hobby optimization reduces the cockpit adjustment experience to a certain extent, making driving less in line with personal habits and reducing the intelligence and humanization level of the cockpit.

[0005] 2. Currently, it lacks multi-angle and multi-data analysis of the vehicle's first abnormal state and second abnormal state respectively, and does not use different data for analysis according to the differences in abnormal states, which reduces the accuracy of abnormal state recognition and the comprehensiveness of judgment to a certain extent, and cannot effectively reduce the false alarm and missed alarm rates. At the same time, it lacks a triggered hierarchical intelligent abnormal handling mechanism: for vehicle body abnormalities, it cannot take measures such as early warning prompts, performance limitations, function degradation, or suggestions for emergency docking and repair; for driver state abnormalities, it may not be able to start multi-level early warnings or active comfort adjustments, etc. Without this closed-loop processing process based on precise analysis, it is impossible to achieve efficient and intelligent response to abnormal states on the premise of ensuring driving safety. Summary of the Invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of this application is to provide a vehicle intelligent cockpit adjustment method and system.

[0007] To solve the above technical problems, this application adopts the following technical solutions: In the first aspect, this application provides a vehicle intelligent cockpit adjustment method, which includes the following steps: Step 1. Driver basic information entry: The driver enters basic information, and then completes the construction of the driving characteristic database.

[0008] Step 2. Basic information adjustment: Identify the driver's identity according to face recognition technology, extract the corresponding basic information of the driver, and then complete the basic adjustment of the vehicle cockpit.

[0009] Step 3. Preliminary cockpit adjustment: When the vehicle starts to drive, obtain the driver's various real-time basic data and the vehicle's various driving data, analyze and obtain the driver's various real-time state evaluation coefficients, then analyze the driver's real-time state, thereby analyze the vehicle's driving state, and complete the preliminary adjustment of the cockpit according to the driver's selection.

[0010] Step 4. Abnormal state analysis: Extract the vehicle's various driving state evaluation coefficients and the driver's various real-time state evaluation coefficients, and obtain the driver's various behavior data and various real-time characterization data, and then analyze the first abnormal state and the second abnormal state.

[0011] Step 5. Abnormal handling: Perform abnormal handling according to the analysis results of the first abnormal state and the second abnormal state.

[0012] Preferably, the basic information includes face data, basic biometric data, basic driving data, daily data, and entertainment data.

[0013] Preferably, the analysis of the driver's real-time state includes: Transmit the driver's various real-time state evaluation coefficients to the middle cockpit adjustment model. When the driver's real-time state is abnormal, it is judged that the cockpit needs to be adjusted, output the cockpit adjustment instructions corresponding to the driver's various real-time basic data, and analyze the vehicle's driving state; when the driver's real-time state is not abnormal, it is judged that the cockpit does not need to be adjusted, do not output the cockpit adjustment instructions, and directly analyze the vehicle's driving state.

[0014] Preferably, the completion of the preliminary adjustment of the cockpit according to the driver's selection includes: C1. When the driver's real-time state is not abnormal and the vehicle's driving state is not abnormal, no preliminary adjustment of the cockpit is performed.

[0015] C2. When the driver's real-time state is not abnormal and the vehicle's driving state is abnormal, record the current state as the first abnormal state and perform Step 4.

[0016] C3. When the real-time state of the driver is abnormal and the driving state of the vehicle is abnormal, record the current state as the second abnormal state and proceed to Step 4.

[0017] C4. When the real-time state of the driver is abnormal and the driving state of the vehicle is normal, send a corresponding preliminary cockpit adjustment request to the driver. When the driver agrees to perform the preliminary cockpit adjustment, the cockpit is adjusted according to the cockpit adjustment instructions corresponding to the driver's real-time basic data, thereby completing the preliminary cockpit adjustment. At the same time, update the adjusted data to the driving characteristic database; when the driver does not agree to perform the preliminary cockpit adjustment, end the preliminary cockpit adjustment; when the driver does not agree to perform the preliminary cockpit adjustment but makes a manual adjustment, update the adjusted data to the driving characteristic database.

[0018] Preferably, the analysis of the first abnormal state includes: recording the behavior data evaluation coefficient and the real-time representation data evaluation coefficient as and , respectively, and obtaining the analysis result of the first abnormal state according to the calculation formula , where represents the weight factor corresponding to the behavior data evaluation coefficient, and represents the weight factor corresponding to the real-time representation data evaluation coefficient.

[0019] Preferably, the analysis of the second abnormal state includes: obtaining the analysis result of the second abnormal state according to the calculation formula , where represents the weight factor corresponding to the real-time state evaluation coefficient, represents the weight factor corresponding to the behavior data evaluation coefficient, and represents the weight factor corresponding to the real-time representation data evaluation coefficient.

[0020] Preferably, the abnormal handling according to the analysis results of the first abnormal state and the second abnormal state includes: D1. When the vehicle is abnormal, transmit to the abnormal handling model, output and complete the corresponding vehicle abnormal handling plan.

[0021] D2. When the driver is abnormal, transmit , and to the abnormal handling model, output and complete the corresponding driver abnormal handling plan.

[0022] D3. After the abnormal handling is completed, perform Step 3 again.

[0023] In a second aspect, the present application provides a vehicle intelligent cockpit adjustment system, including: a driver basic information entry module for the driver to enter basic information, thereby completing the construction of a driving characteristic database; A basic information adjustment module for identifying the driver's identity based on face recognition technology, extracting the corresponding basic information of the driver, and thereby completing the basic adjustment of the vehicle cockpit.

[0024] A cockpit preliminary adjustment module for, when the vehicle starts to drive, obtaining the driver's various real-time basic data and the vehicle's various driving data, analyzing to obtain the driver's various real-time state evaluation coefficients, thereby analyzing the driver's real-time state, thus analyzing the vehicle's driving state, and completing the preliminary adjustment of the cockpit according to the driver's selection.

[0025] An abnormal state analysis module for extracting the vehicle's various driving state evaluation coefficients and the driver's various real-time state evaluation coefficients, and obtaining the driver's various behavior data and various real-time characterization data, and thereby analyzing the first abnormal state and the second abnormal state.

[0026] An abnormal handling terminal for performing abnormal handling according to the analysis results of the first abnormal state and the second abnormal state.

[0027] The beneficial effects of the present application are as follows: 1. A vehicle intelligent cockpit adjustment method and system provided by the present application perform real-time data collection in the basic adjustment of the vehicle cockpit, perform multi-data analysis, complete the preliminary adjustment and data update of the cockpit under specific conditions, solve the limitation problems existing in the current feasibility analysis process of vehicle intelligent cockpit adjustment, collect the driver's various behavior data and various real-time characterization data when the vehicle is abnormal, analyze the abnormal reasons, perform abnormal handling according to the analysis results of the first abnormal state and the second abnormal state, and perform data feedback, realizing comprehensive and objective analysis of vehicle intelligent cockpit adjustment, and ensuring the reliability and authenticity of the analysis results of vehicle intelligent cockpit adjustment.

[0028] 2. The present application constructs a driving characteristic database, matches the driver's face recognition with the data in the driving characteristic database, and performs basic adjustment of the vehicle cockpit before the vehicle enters the driving state. When the vehicle enters the driving state, it monitors the driver's real-time basic data and the vehicle's driving data, and at the same time performs multi-case analysis according to the driver's real-time state and the vehicle's driving state, improves the comfort of the driver's cockpit while ensuring vehicle safety, updates the driving characteristic database in real time, and sets hobby optimization, thereby realizing a significant leap in the cockpit adjustment experience, making driving more in line with personal habits, and significantly improving the intelligent and humanized level of the cockpit.

[0029] 3. This application obtains various behavioral data and real-time characterization data of the driver to build a comprehensive driving state perception ability; it conducts multi-angle and multi-data analysis on the first abnormal state and the second abnormal state of the vehicle respectively, and uses different data for analysis according to the different abnormal states. This data analysis strategy of "tailoring to the state" significantly improves the accuracy of abnormal state recognition and the comprehensiveness of judgment, and effectively reduces the false alarm and missed alarm rates; it triggers a hierarchical intelligent abnormal handling mechanism: for vehicle body abnormalities, measures such as warning prompts, performance limitations, function degradation, or suggestions for emergency docking and repair may be taken; for driver state abnormalities, multi-level warnings or active comfort adjustments may be initiated; through this closed-loop processing flow based on precise analysis, on the premise of ensuring driving safety, it realizes an efficient and intelligent response to abnormal states. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a schematic flowchart of the method implementation steps of this application.

[0032] Figure 2 It is a schematic connection diagram of the system structure of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0034] Please refer to Figure 1 As shown, this application provides a vehicle intelligent cockpit adjustment method in the first aspect, including: Step 1: Driver basic information entry: The driver enters basic information, and then completes the construction of the driving feature database.

[0035] In a specific example, the basic information includes face data, basic biometric data, basic driving data, daily data, and entertainment data.

[0036] It should be noted that the face data includes pictures and videos of various aspects of the user's face recognition; the basic biometric data includes heart rate, blood pressure, blood oxygen, etc.; the basic driving data includes seat front-back data, seat backrest data, seat height data, steering wheel data, rearview mirror data, etc.; among them, the daily data includes the temperature, wind speed and wind direction of the in-vehicle air conditioner; among them, the entertainment data includes Bluetooth connection, navigation tone color, navigation volume, music type and music volume, etc.

[0037] It should be noted that a driving feature database is constructed. Based on the face data of the driver, the facial feature vector is extracted through a convolutional neural network, and the facial feature vector is bound to the unique ID corresponding to the driver; at the same time, the driver's basic biometric data, driving data, daily data and entertainment data are bound to the unique ID corresponding to the driver; and thus the driving feature data storage of each driver is completed, and then the construction of the driving feature database is completed.

[0038] It should be noted that the face data, basic biometric data, basic driving data, daily data and entertainment data in the driver's basic information are associated through the CAN bus to generate a unique ID corresponding to the driver.

[0039] Step 2: Basic information adjustment: Identify the driver's identity according to the face recognition technology, and extract the basic information corresponding to the driver, and then complete the basic adjustment of the vehicle cockpit.

[0040] It should be noted that when the vehicle starts, the built-in camera automatically turns on to capture the driver's facial image, extracts the facial feature vector through the face recognition technology, and matches the unique ID of the driver according to the facial feature vector, and then extracts the driving data, daily data and entertainment data corresponding to the unique ID of the driver in the driving feature database, so as to complete the basic adjustment of the vehicle cockpit.

[0041] Step 3: Preliminary adjustment of the cockpit: When the vehicle starts to drive, obtain the driver's various real-time basic data and the vehicle's various driving data, analyze and obtain the driver's various real-time state evaluation coefficients, and then analyze the driver's real-time state, so as to analyze the vehicle's driving state, and complete the preliminary adjustment of the cockpit according to the driver's selection.

[0042] It should be noted that the driver's real-time basic data includes thigh length, leg length, knee bending angle, knee distance from the center console, natural bending angle of both hands holding the steering wheel, distance from the top of the head to the roof of the car, distance between the head and the headrest, occlusion area of each part of the dashboard from the driver's perspective, and eye height, etc.

[0043] It should be noted that the driving data includes the steering wheel angle, the steering wheel change frequency, the vehicle location and the vehicle speed corresponding to this location (for example, the speed in the urban area shall not exceed 30 mph, and on the highway, it shall not be less than 60 mph, etc.), acceleration and deceleration, hard acceleration, hard braking, the frequency of the vehicle crossing the line and deviating from the lane; the vehicle distance (the distance from the vehicle in front).

[0044] In a specific example, the analysis to obtain the real-time state evaluation coefficients of the driver includes: recording the real-time basic data of the driver as , where represents the number corresponding to each real-time data, , where represents the total number of real-time data, is a natural integer greater than 2.

[0045] According to the calculation formula the real-time state evaluation coefficients of the driver are analyzed and obtained , where represents the standard value corresponding to each real-time basic data, represents the allowable change value of each real-time basic data, represents the correction factor corresponding to each real-time basic data.

[0046] It should be noted that the standard value corresponding to each real-time basic data is the standard numerical value corresponding to each real-time basic data. For example, the distance from the top of the head to the roof of a compact car or SUV is greater than or equal to 70 mm, and for a medium-sized or large-sized vehicle, it is greater than or equal to 100 mm; the allowable change value of each real-time basic data represents the difference between the maximum and minimum values of the allowable change range of the standard numerical value corresponding to each real-time basic data. For example, the allowable change value of the distance from the top of the head to the roof of a compact car or SUV is 40 mm; the correction factor corresponding to each real-time basic data is a numerical factor that is multiplied by the uncorrected measurement result to compensate for the measurement error.

[0047] In a specific example, the analysis of the real-time state of the driver includes: transmitting the real-time state evaluation coefficients of the driver to the in-cockpit adjustment model. When the real-time state of the driver is abnormal, it is determined that the cockpit needs to be adjusted, and the cockpit adjustment instructions corresponding to the real-time basic data of the driver are output, and the driving state of the vehicle is analyzed; when the real-time state of the driver is not abnormal, it is determined that the cockpit does not need to be adjusted, no cockpit adjustment instructions are output, and the driving state of the vehicle is directly analyzed.

[0048] It should be noted that the cockpit adjustment model prepares the data of the real-time state evaluation coefficients and the cockpit adjustment instructions according to the {input, output} paired data mode, divides the data set, uses the recurrent neural network RNN as the model architecture, and completes the training, evaluation and optimization of the model.

[0049] In a specific example, the analysis of the driving state of the vehicle includes: B1. Denote each driving data of the vehicle as , where represents the number corresponding to each driving data, , where represents the total number of driving data, is a natural integer greater than 2; According to the calculation formula Analyze and obtain the evaluation coefficients of each driving state of the vehicle , where represents the standard value corresponding to each driving data, represents the allowable change value of each driving data, represents the correction factor corresponding to each driving data.

[0050] B2. Compare the evaluation coefficients of each driving state of the vehicle with the upper and lower limit values of the evaluation coefficients of each driving state in the database. When the evaluation coefficient of a certain driving state of the vehicle is greater than the lower limit value of the evaluation coefficient of the driving state in the database and less than the upper limit value, it is determined that the driving state of the vehicle is abnormal; otherwise, it is determined that the driving state of the vehicle has not changed abnormally.

[0051] It should be noted that the standard value corresponding to each driving data is the standard numerical value corresponding to each driving data, such as the rapid acceleration frequency or the rapid braking frequency is less than 0.05; the allowable change value of each driving data represents the difference between the maximum and minimum values of the allowable change range of the standard numerical value corresponding to each driving data, such as the allowable change value of the rapid acceleration frequency or the rapid braking frequency is 0.02; the correction factor corresponding to each driving data is a numerical factor that is multiplied by the uncorrected measurement result to compensate for the measurement error.

[0052] In a specific example, the preliminary adjustment of the cockpit according to the driver's selection includes: C1. When the real-time state of the driver has not changed abnormally and the driving state of the vehicle has not changed abnormally, no preliminary adjustment of the cockpit is performed.

[0053] C2. When the real-time state of the driver has not changed abnormally and the driving state of the vehicle is abnormal, record the current state as the first abnormal state and perform step four.

[0054] C3. When the real-time state of the driver has changed abnormally and the driving state of the vehicle is abnormal, record the current state as the second abnormal state and perform step four.

[0055] C4. When the real-time state of the driver is abnormal and the driving state of the vehicle is normal, a corresponding preliminary cockpit adjustment application is sent to the driver. When the driver agrees to perform the preliminary cockpit adjustment, the cockpit is adjusted according to the cockpit adjustment instructions corresponding to the driver's real-time basic data, thus completing the preliminary cockpit adjustment. At the same time, the adjusted data is updated to the driving feature database; when the driver does not agree to perform the preliminary cockpit adjustment, the preliminary cockpit adjustment ends; when the driver does not agree to perform the preliminary cockpit adjustment but makes a manual adjustment, the adjusted data is updated to the driving feature database.

[0056] It should be noted that preference optimization is set, and a reward function is set. And the update rule is set as , it should be noted that, represents the basic posture data of the driver, represents the updated basic posture data of the driver, represents the initial adjustment request, represents the updated adjustment request, represents the learning rate, represents the discount factor.

[0057] This application constructs a driving feature database, performs face recognition on the driver to match the data in the driving feature database, and makes a basic adjustment to the vehicle cockpit before the vehicle enters the driving state. When the vehicle enters the driving state, it monitors the driver's real-time basic data and the vehicle's driving data, and at the same time conducts multi-situation analysis based on the driver's real-time state and the vehicle's driving state, improving the comfort of the driver's cockpit while ensuring vehicle safety, updating the driving feature database in real time, and setting hobby optimization, so as to achieve a significant leap in the cockpit adjustment experience, make driving more in line with personal habits, and significantly improve the intelligence and humanization level of the cockpit.

[0058] Step 4. Abnormal state analysis: Extract the evaluation coefficients of each driving state of the vehicle and the evaluation coefficients of each real-time state of the driver, and obtain each behavior data and each real-time characterization data of the driver, and then analyze the first abnormal state and the second abnormal state.

[0059] It should be noted that the behavior data includes: body tilt angle, head rotation angle, hand position, mobile phone usage duration, mobile phone usage frequency, smoking frequency, and smoking duration.

[0060] It should be noted that the driver's real-time characterization data is obtained according to each biosensor on the driver's seat in the cockpit and the wearable device connected to the driver, and the real-time characterization data includes; heart rate, blood pressure, body temperature, blink frequency, eyes-closed duration, yawning frequency, drowsiness frequency, and the frequency of the line of sight deviating from the road surface, etc.

[0061] It should be noted that the driver's behavior data and biometric data are both acquired by the camera devices inside the vehicle.

[0062] In a specific example, the analysis of the first abnormal state includes: respectively denoting the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient as and , and obtaining the analysis result of the first abnormal state according to the calculation formula , where represents the weight factor corresponding to the behavior data evaluation coefficient, represents the weight factor corresponding to the real-time characterization data evaluation coefficient.

[0063] It should be noted that , , .

[0064] It should be noted that the weight factors corresponding to the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient are obtained through factor analysis. First, the information of the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient is condensed, then the variance explained rate after rotation is obtained, and the weight is obtained by dividing the cumulative variance explained rate.

[0065] It should be noted that factor analysis is a well-known technology. It is a multivariate statistical analysis method that starts from the study of the internal correlation dependencies of variables and reduces a number of variables with intricate relationships to a few comprehensive factors; information condensation is expressed as calculating the median; the variance explained rate is the amount of information extracted by the factor, and the variance explained rate = eigenvalue / total number of analysis items; the variance explained rate after rotation is the variance explained rate of the factor after maximum variance rotation.

[0066] It should be noted that according to the calculation formula the behavior data evaluation coefficient is analyzed and obtained, where represents each behavior data, where represents the number corresponding to each behavior data, , where represents the total number of behavior data, is a natural integer greater than 2; where represents the standard value corresponding to each behavior data, represents the correction factor corresponding to each behavior data.

[0067] It should be noted that according to the calculation formula the real-time characterization evaluation coefficient is analyzed and obtained, where represents each real-time characterization data, where is represented as the number corresponding to each real-time characterization data, , where is represented as the total number of real-time characterization data, is a natural integer greater than 2; where is represented as the standard value corresponding to each real-time characterization data, is represented as the allowable change value corresponding to each real-time characterization data, is represented as the correction factor corresponding to each real-time characterization data.

[0068] In a specific example, the analysis of the second abnormal state includes: obtaining the analysis result of the second abnormal state according to the calculation formula , where is represented as the weight factor corresponding to the real-time state evaluation coefficient, is represented as the weight factor corresponding to the behavior data evaluation coefficient, is represented as the weight factor corresponding to the real-time characterization data evaluation coefficient.

[0069] It should be noted that , , .

[0070] It should be noted that through the weight factor corresponding to the real-time state evaluation coefficient, the weight factor corresponding to the behavior data evaluation coefficient, and the weight factor corresponding to the real-time characterization data evaluation coefficient, the information of the real-time state evaluation coefficient, the behavior data evaluation coefficient, and the real-time characterization data evaluation coefficient is condensed, and then the variance interpretation rate after rotation is obtained, and the weight is obtained by dividing the cumulative variance interpretation rate.

[0071] Step Five: Abnormality handling: Perform abnormality handling according to the analysis results of the first abnormal state and the second abnormal state.

[0072] In a specific example, the performing abnormality handling according to the analysis results of the first abnormal state and the second abnormal state includes: D1. When the vehicle is abnormal, transmit to the abnormality handling model, output and complete the corresponding vehicle abnormality handling plan.

[0073] D2. When the driver is abnormal, transmit , and to the abnormality handling model, output and complete the corresponding driver abnormality handling plan.

[0074] D3. After the abnormality handling is completed, perform Step Three again.

[0075] It should be noted that the anomaly handling model prepares data for the real-time status evaluation coefficient, behavior data evaluation coefficient, real-time representation data evaluation coefficient, and vehicle anomaly handling solution according to the pattern of {input, output} paired data. At the same time, it prepares data for the driving status evaluation coefficient and vehicle anomaly handling solution, divides the dataset, uses the recurrent neural network (RNN) as the model architecture, and completes the training, evaluation, and optimization of the model.

[0076] This application obtains various behavior data and real-time representation data of the driver, and constructs a comprehensive driving state perception ability; it analyzes the first anomaly state and the second anomaly state of the vehicle from multiple angles and with multiple data, and uses different data for analysis according to the different anomaly states. This "state-appropriate" data analysis strategy significantly improves the accuracy of anomaly state recognition and the comprehensiveness of judgment, and effectively reduces the false alarm and missed alarm rates; it triggers a hierarchical intelligent anomaly handling mechanism: for vehicle body anomalies, measures such as warning prompts, performance restrictions, function degradation, or suggestions for emergency docking and repair may be taken; for driver state anomalies, multi-level warnings or active comfort adjustments may be initiated; through this closed-loop processing flow based on precise analysis, on the premise of ensuring driving safety, it realizes an efficient and intelligent response to anomaly states.

[0077] Please refer to Figure 2 As shown, this application provides a vehicle intelligent cockpit adjustment system in a second aspect, including: a driver basic information entry module for the driver to enter basic information, thereby completing the construction of a driving feature database.

[0078] A basic information adjustment module for identifying the driver's identity according to face recognition technology, extracting the corresponding basic information of the driver, and thereby completing the basic adjustment of the vehicle cockpit.

[0079] A cockpit preliminary adjustment module for, when the vehicle starts to drive, obtaining various real-time basic data of the driver and various driving data of the vehicle, analyzing to obtain various real-time status evaluation coefficients of the driver, thereby analyzing the real-time status of the driver, and thus analyzing the driving status of the vehicle, and completing the preliminary adjustment of the cockpit according to the driver's selection.

[0080] An anomaly state analysis module for extracting various driving status evaluation coefficients of the vehicle and various real-time status evaluation coefficients of the driver, and obtaining various behavior data and real-time representation data of the driver, and thereby analyzing the first anomaly state and the second anomaly state.

[0081] An anomaly handling terminal for performing anomaly handling according to the analysis results of the first anomaly state and the second anomaly state.

[0082] A vehicle intelligent cockpit adjustment method and system provided by the present application perform real-time data collection and multi-data analysis on the basis of the basic adjustment of the vehicle cockpit, and complete the preliminary adjustment and data update of the cockpit under specific conditions, solving the limitations existing in the feasibility analysis process of the current development of vehicle intelligent cockpit adjustment. When the vehicle is abnormal, it collects various behavior data and real-time characterization data of the driver, analyzes the cause of the abnormality, and performs abnormality handling according to the analysis results of the first abnormal state and the second abnormal state, and conducts data feedback, realizing a comprehensive and objective analysis of vehicle intelligent cockpit adjustment, and ensuring the reliability and authenticity of the analysis results of vehicle intelligent cockpit adjustment.

[0083] The above content is only an example and illustration of the concept of the present application. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all belong to the protection scope of the present application.

Claims

1. A vehicle intelligent cockpit adjustment method and system, characterized in that Including: Step 1, Driver basic information entry: The driver enters basic information, thereby completing the construction of the driving characteristic database. Step 2, Basic information adjustment: Identify the driver's identity based on face recognition technology, extract the corresponding basic information of the driver, and then complete the basic adjustment of the vehicle cockpit. Step 3, Initial cockpit adjustment: When the vehicle starts to drive, obtain the real-time basic data of the driver and the driving data of the vehicle, analyze and obtain the real-time state evaluation coefficients of the driver, then analyze the real-time state of the driver, thereby analyze the driving state of the vehicle, and complete the initial adjustment of the cockpit according to the driver's selection. Step 4, Abnormal state analysis: Extract the driving state evaluation coefficients of the vehicle and the real-time state evaluation coefficients of the driver, and obtain the behavior data and real-time characterization data of the driver, and then analyze the first abnormal state and the second abnormal state. Step 5, Abnormal handling: Perform abnormal handling according to the analysis results of the first abnormal state and the second abnormal state.

2. The vehicle intelligent cockpit adjustment method according to claim 1, wherein, The basic information includes face data, basic biometric data, basic driving data, daily data, and entertainment data.

3. The vehicle intelligent cockpit adjustment method according to claim 2, characterized in that The analysis and obtaining of the real-time state evaluation coefficients of the driver includes: Record the driver's real-time basic data as , where represents the number corresponding to each real-time data, , where represents the total number of real-time data, is a natural integer greater than 2; According to the calculation formula Analyze and obtain the real-time state evaluation coefficients of the driver , where Represents the standard value corresponding to each real-time basic data, Represents the allowable change value of each real-time basic data, Represents the correction factor corresponding to each real-time basic data.

4. The vehicle intelligent cockpit adjustment method according to claim 3, wherein, The analysis of the real-time state of the driver includes: Transmit the real-time state evaluation coefficients of the driver to the middle cockpit adjustment model. When the real-time state of the driver is abnormal, it is judged that the cockpit needs to be adjusted, output the cockpit adjustment instructions corresponding to the real-time basic data of the driver, and analyze the driving state of the vehicle; when the real-time state of the driver is not abnormal, it is judged that the cockpit does not need to be adjusted, do not output the cockpit adjustment instructions, and directly analyze the driving state of the vehicle.

5. The vehicle intelligent cockpit adjustment method according to claim 4, characterized in that, The analysis of the driving state of the vehicle includes: B1. Record each driving data of the vehicle as , where represents the number corresponding to each driving data, , where represents the total number of driving data, is a natural integer greater than 2; According to the calculation formula Analyze and obtain the evaluation coefficients of each driving state of the vehicle , where Is expressed as the standard value corresponding to each driving data, Is expressed as the allowable change value of each driving data, Is expressed as the correction factor corresponding to each driving data; B2. Compare the driving state evaluation coefficients of the vehicle with the upper and lower limit values of the driving state evaluation coefficients in the database. When a certain driving state evaluation coefficient of the vehicle is greater than the lower limit value of the driving state evaluation coefficient in the database and less than the upper limit value, it is judged that the driving state of the vehicle is abnormal; otherwise, it is judged that the driving state of the vehicle is not abnormal.

6. The vehicle intelligent cockpit adjustment method according to claim 5, wherein The completion of the initial adjustment of the cockpit according to the driver's selection includes: C1. When the real-time state of the driver is not abnormal and the driving state of the vehicle is not abnormal, do not perform the initial adjustment of the cockpit. C2. When the real-time state of the driver is not abnormal and the driving state of the vehicle is abnormal, record the current state as the first abnormal state and perform Step 4. C3. When the real-time state of the driver is abnormal and the driving state of the vehicle is abnormal, record the current state as the second abnormal state and perform Step 4. C4. When the real-time status of the driver is abnormal and the driving status of the vehicle is normal, a corresponding preliminary cockpit adjustment application is sent to the driver. When the driver agrees to perform the preliminary cockpit adjustment, the cockpit is adjusted according to the cockpit adjustment instructions corresponding to the driver's real-time basic data, thereby completing the preliminary cockpit adjustment. At the same time, the adjusted data is updated to the driving feature database; when the driver does not agree to perform the preliminary cockpit adjustment, the preliminary cockpit adjustment ends; when the driver does not agree to perform the preliminary cockpit adjustment but makes a manual adjustment, the adjusted data is updated to the driving feature database.

7. The vehicle intelligent cockpit adjustment method according to claim 6, wherein, The analysis of the first abnormal state includes: Denote the behavior data evaluation coefficient and the real-time representation data evaluation coefficient as and respectively, and obtain the analysis result of the first abnormal state according to the calculation formula , where represents the weight factor corresponding to the behavior data evaluation coefficient, represents the weight factor corresponding to the real-time representation data evaluation coefficient.

8. The vehicle intelligent cockpit adjustment method according to claim 7, wherein, The analysis of the second abnormal state includes: According to the calculation formula obtain the analysis result of the second abnormal state, where represents the weight factor corresponding to the real-time state evaluation coefficient, represents the weight factor corresponding to the behavior data evaluation coefficient, represents the weight factor corresponding to the real-time characterization data evaluation coefficient.

9. The vehicle intelligent cockpit adjustment method according to claim 8, wherein, The abnormal handling based on the analysis results of the first abnormal state and the second abnormal state includes: D1. When the vehicle is abnormal, transfer to the abnormal handling model, output and complete the corresponding vehicle abnormal handling solution; D2. When the driver is abnormal, transmit , and to the abnormal handling model, output and complete the corresponding driver abnormal handling plan; D3. After the abnormal handling is completed, step three is performed again.

10. A vehicle intelligent cockpit adjustment system according to any one of claims 1-9, characterized in that, It includes: A driver basic information entry module, which is used for the driver to enter basic information, thereby completing the construction of the driving feature database; A basic information adjustment module, which is used to identify the driver's identity based on face recognition technology, extract the basic information corresponding to the driver, and then complete the basic adjustment of the vehicle cockpit; A preliminary cockpit adjustment module, which is used to obtain the driver's real-time basic data and the vehicle's driving data when the vehicle starts to drive, analyze the driver's real-time status evaluation coefficients, and then analyze the driver's real-time status, thereby analyzing the vehicle's driving status, and completing the preliminary cockpit adjustment according to the driver's selection; An abnormal state analysis module, which is used to extract the vehicle's driving status evaluation coefficients and the driver's real-time status evaluation coefficients, and obtain the driver's behavior data and real-time characterization data, and then analyze the first abnormal state and the second abnormal state; An abnormal handling terminal, which is used to perform abnormal handling according to the analysis results of the first abnormal state and the second abnormal state.

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