Vehicle intelligent cockpit adjustment method and system

Abstract

The present application discloses a vehicle intelligent cockpit adjustment method and system, which relates to the technical field of vehicle intelligent cockpit adjustment. The present application includes driver basic information input, basic information adjustment, cockpit preliminary adjustment, abnormal state analysis, and abnormality processing. Based on the basic adjustment of the vehicle cockpit, real-time data collection and multi-data analysis are performed, and the preliminary adjustment of the cockpit and data update are completed under specific conditions, which solves the limitations existing in the current feasibility analysis process of vehicle intelligent cockpit adjustment development. When the vehicle is abnormal, the driver's various behavioral data and real-time characterization data are collected, the cause of the abnormality is analyzed, and the abnormality is processed according to the analysis results of the first abnormal state and the second abnormal state, and data feedback is performed, which realizes the comprehensive and objective analysis of the vehicle intelligent cockpit adjustment and ensures the reliability and authenticity of the vehicle intelligent cockpit adjustment analysis results.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle intelligent cockpit adjustment, and in particular to a vehicle intelligent cockpit adjustment method and system. Background Art

[0002] With the development of technology, vehicle cabin adjustment has gradually become more intelligent. Ensuring the driver's comfort and safety during the vehicle cabin adjustment process is of paramount importance.

[0003] Existing technology, such as the invention patent application with publication number CN113353011A, discloses a vehicle comfort adjustment method and device. The method includes: obtaining the current user's anatomy data; matching optimal cabin parameters for the vehicle cabin based on the anatomy data; and, when the optimal cabin parameters are inconsistent with the current cabin parameters, controlling the vehicle cabin to perform adjustments based on the optimal cabin parameters while also controlling the selection of each rearview mirror to achieve the optimal rearview angle corresponding to the optimal cabin parameters. This solves the problem of requiring users to manually adjust cabin parameters and rearview mirror angles in related technologies, resulting in an inability to optimally match these parameters and rearview mirror angles with the user's anatomy data, a poor driving experience, and a lack of intelligent control.

[0004] In view of the above solution, the inventors of this application have found that the above technology has at least the following technical problems:

[0005] 1. Currently, there is a lack of monitoring of the driver's real-time basic data and vehicle driving data when the vehicle enters the driving state. At the same time, there is no multi-situation analysis based on the driver's real-time status and the vehicle's driving status. It is impossible to ensure vehicle safety while improving the driver's cockpit comfort. The driving characteristics database is updated in real time. However, there is a lack of setting up preference optimization, which to a certain extent reduces the cockpit adjustment experience, makes it impossible to make driving more in line with personal habits, and reduces the intelligence and humanization level of the cockpit.

[0006] 2. Currently, there is a lack of multi-angle and multi-data analysis of the first abnormal state and the second abnormal state of the vehicle. Different data are not used for analysis according to different abnormal states, which to a certain extent reduces the accuracy of abnormal state identification and the comprehensiveness of judgment, and cannot effectively reduce the false alarm and missed alarm rates. At the same time, there is a lack of a hierarchical intelligent abnormality handling mechanism: it is impossible to take early warning prompts, performance restrictions, function degradation or emergency stop and maintenance measures for abnormalities of the vehicle itself; it is impossible to activate multi-level warnings or active comfort adjustments for abnormal driver conditions; there is a lack of such a closed-loop processing process based on precise analysis, and it is impossible to achieve efficient and intelligent response to abnormal states under the fundamental premise of ensuring driving safety. Summary of the Invention

[0007] In response to the above-mentioned technical deficiencies, the purpose of this application is to provide a vehicle intelligent cockpit adjustment method and system.

[0008] In order to solve the above technical problems, the present application adopts the following technical solutions: In the first aspect, the present application provides a vehicle intelligent cockpit adjustment method, which includes the following steps: Step 1, driver basic information input: the driver enters basic information, and then completes the construction of the driving characteristics database.

[0009] Step 2: Basic information adjustment: Identify the driver's identity based on facial recognition technology, extract the driver's corresponding basic information, and then complete the basic adjustment of the vehicle cockpit.

[0010] Step 3: Initial cockpit adjustment: When the vehicle starts to move, the driver's real-time basic data and the vehicle's driving data are obtained, and the driver's real-time status evaluation coefficients are analyzed. The driver's real-time status is then analyzed, and thus the vehicle's driving status is analyzed, and initial cockpit adjustments are completed based on the driver's choice.

[0011] Step 4: Abnormal state analysis: extract the vehicle's driving state evaluation coefficients and the driver's real-time state 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.

[0012] Step 5: Exception handling: Perform exception handling based on the analysis results of the first abnormal state and the second abnormal state.

[0013] Preferably, the basic information includes facial data, basic biometric data, basic driving data, daily data and entertainment data.

[0014] Preferably, the analysis of the driver's real-time status includes: transmitting the driver's various real-time status evaluation coefficients to the cockpit adjustment model; when the driver's real-time status is abnormal, it is judged that the cockpit needs to be adjusted, cockpit adjustment instructions corresponding to the driver's various real-time basic data are output, and the vehicle's driving status is analyzed; when the driver's real-time status is not abnormal, it is judged that the cockpit does not need to be adjusted, the cockpit adjustment instructions are not output, and the vehicle's driving status is directly analyzed.

[0015] Preferably, completing the preliminary adjustment of the cockpit according to the driver's selection includes: C1. When there is no abnormality in the real-time state of the driver and the driving state of the vehicle, no preliminary adjustment of the cockpit is performed.

[0016] C2. When the driver's real-time state is normal and the vehicle's driving state is abnormal, the current state is recorded as the first abnormal state and step 4 is performed.

[0017] C3. When the real-time state of the driver is abnormal and the driving state of the vehicle is abnormal, the current state is recorded as the second abnormal state and step 4 is performed.

[0018] C4. When the driver's real-time status is abnormal and the vehicle's driving status is normal, a corresponding cockpit preliminary adjustment request is sent to the driver. When the driver agrees to 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 adjustment of the cockpit, and the adjusted data is updated to the driving characteristic database; when the driver disagrees to the preliminary adjustment of the cockpit, the preliminary adjustment of the cockpit is terminated; when the driver disagrees to the preliminary adjustment of the cockpit but performs manual adjustment, the adjusted data is updated to the driving characteristic database.

[0019] Preferably, the analysis of the first abnormal state includes: recording the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient as and , and according to the calculation formula Obtain analysis results of the first abnormal state, wherein Expressed as the weight factor corresponding to the behavioral data evaluation coefficient, It is expressed as the weight factor corresponding to the real-time characterization data evaluation coefficient.

[0020] Preferably, the analysis of the second abnormal state includes: according to the calculation formula Obtain analysis results of the second abnormal state, wherein Expressed as the weight factor corresponding to the real-time state evaluation coefficient, Expressed as the weight factor corresponding to the behavioral data evaluation coefficient, It is expressed as the weight factor corresponding to the real-time characterization data evaluation coefficient.

[0021] Preferably, the abnormality processing 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 exception handling model, output and complete the corresponding vehicle exception handling plan.

[0022] D2. When the driver is abnormal, 、 and The information is transferred to the exception handling model, and the corresponding driver exception handling plan is output and completed.

[0023] D3. After the exception handling is completed, proceed to step 3 again.

[0024] In a second aspect, the present application provides a system for a vehicle intelligent cockpit adjustment method, comprising: a driver basic information entry module, configured to input basic information of the driver, thereby completing the construction of a driving characteristics database;

[0025] The basic information adjustment module is used to identify the driver's identity based on face recognition technology, extract the driver's corresponding basic information, and then complete the basic adjustment of the vehicle cockpit.

[0026] The cockpit preliminary adjustment module is used to obtain the driver's real-time basic data and the vehicle's driving data when the vehicle starts to move, 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 adjustment of the cockpit according to the driver's choice.

[0027] The abnormal state analysis module is used to extract the vehicle's driving state evaluation coefficients and the driver's real-time state 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.

[0028] The exception handling terminal is used to perform exception handling according to the analysis results of the first abnormal state and the second abnormal state.

[0029] The beneficial effects of the present application are: 1. The present application provides a vehicle intelligent cockpit adjustment method and system, which conducts real-time data collection and multi-data analysis on the basis of basic adjustment of the vehicle cockpit, and completes the preliminary adjustment of the cockpit and data update under specific conditions, solving the limitations of the current feasibility analysis process of vehicle intelligent cockpit adjustment. When the vehicle is abnormal, the driver's various behavioral data and real-time characterization data are collected, the cause of the abnormality is analyzed, and the abnormality is handled according to the analysis results of the first abnormal state and the second abnormal state, and data feedback is performed, which realizes the comprehensiveness and objectivity of the vehicle intelligent cockpit adjustment analysis and ensures the reliability and authenticity of the vehicle intelligent cockpit adjustment analysis results.

[0030] 2. This application constructs a driving feature database, performs facial recognition on the driver and matches the data in the driving feature database, and performs basic adjustments 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 performs multi-situation analysis based on the driver's real-time status and the vehicle's driving status to ensure vehicle safety while improving the comfort of the driver's cockpit. The driving feature database is updated in real time and preference optimization is set, thereby achieving a significant leap in the cockpit adjustment experience, making driving more in line with personal habits, and significantly improving the intelligence and humanization of the cockpit.

[0031] 3. This application obtains various behavioral data and real-time characterization data of the driver to build a comprehensive driving status perception capability; 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 "adaptable to the situation" data analysis strategy significantly improves the accuracy of abnormal state identification and the comprehensiveness of judgment, and effectively reduces the false alarm and missed alarm rates; triggers a hierarchical intelligent abnormality handling mechanism: for vehicle body abnormalities, early warning prompts, performance restrictions, function degradation or emergency stop and maintenance recommendations may be taken; for driver status abnormalities, multi-level early warnings or active comfort adjustments may be activated; through this closed-loop processing process based on precise analysis, efficient and intelligent response to abnormal states is achieved under the fundamental premise of ensuring driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 The figure is a flowchart of the steps for implementing the application method.

[0034] Figure 2 This is a schematic diagram of the system structure connection for this application. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0036] See also Figure 1 As shown, the present application provides a vehicle intelligent cockpit adjustment method in the first aspect, including: step 1, driver basic information input: the driver enters basic information, and then completes the construction of the driving feature database.

[0037] In a specific example, the basic information includes facial data, basic biometric data, basic driving data, daily data and entertainment data.

[0038] It should be noted that facial data includes pictures and videos of the user's face in all directions; basic biometric data includes: heart rate, blood pressure and blood oxygen; basic driving data includes: seat front and back data, seat back data, seat height data, steering wheel data and rearview mirror data; daily data includes air-conditioning temperature, wind speed and wind direction in the car; entertainment data includes Bluetooth connection, navigation tone, navigation volume, music type and music volume, etc.

[0039] It should be noted that, in order to construct a driving feature database, a facial feature vector is extracted based on the driver's facial data through a convolutional neural network, and the facial feature vector is bound to the driver's corresponding unique ID; at the same time, the driver's basic biometric data, driving data, daily data and entertainment data are bound to the driver's corresponding unique ID; thereby completing the storage of each driver's driving feature data, and then completing the construction of the driving feature database.

[0040] It should be noted that the facial 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.

[0041] Step 2: Basic information adjustment: Identify the driver's identity based on facial recognition technology, extract the driver's corresponding basic information, and then complete the basic adjustment of the vehicle cockpit.

[0042] It should be noted that when the vehicle is started, the built-in camera automatically turns on to capture the driver's facial image, extracts the facial feature vector through face recognition technology, and matches the driver's unique ID based on the facial feature vector, and then extracts the driving data, daily data and entertainment data corresponding to the driver's unique ID from the driving feature database, thereby completing the basic adjustment of the vehicle cockpit.

[0043] Step 3: Initial cockpit adjustment: When the vehicle starts to move, the driver's real-time basic data and the vehicle's driving data are obtained, and the driver's real-time status evaluation coefficients are analyzed. The driver's real-time status is then analyzed, and thus the vehicle's driving status is analyzed, and initial cockpit adjustments are completed based on the driver's choice.

[0044] It should be noted that the driver's real-time basic data includes thigh length, leg length, knee bending angle, distance between the knee and the center console, natural bending angle of both hands holding the steering wheel, distance from the top of the head to the roof, distance between the head and the headrest, the blocked area from various parts of the instrument panel to the driver's field of view, and eye height, etc.

[0045] It should be noted that driving data includes steering wheel angle, steering wheel change frequency, vehicle location and corresponding speed (for example, the speed in urban areas must not exceed 30 miles per hour, and the speed on highways must not be less than 60 miles per hour, etc.), acceleration and deceleration, frequency of sudden acceleration, sudden braking, vehicle crossing the line and lane deviation; and vehicle distance (distance from the vehicle in front).

[0046] In a specific example, the analysis to obtain the real-time status evaluation coefficients of the driver includes: recording the real-time basic data of the driver as ,in Indicates the number corresponding to each real-time data. ,in Expressed as the total number of real-time data, is a natural integer greater than 2.

[0047] According to the calculation formula Analyze and obtain the driver's real-time status evaluation coefficients ,in It is expressed as the standard value corresponding to each real-time basic data. Expressed as the allowable change value of each real-time basic data, Expressed as the correction factor corresponding to each real-time basic data.

[0048] It should be noted that the standard value corresponding to each real-time basic data is the standard value corresponding to each real-time basic data, such as the distance from the top of the head to the roof of a compact car or SUV is greater than or equal to 70mm, and greater than or equal to 100mm for a medium or large car; the allowable variation value of each real-time basic data is expressed as the difference between the maximum and minimum values ​​of the variable range of the standard value corresponding to each real-time basic data, such as the allowable variation value of the distance from the top of the head to the roof of a compact car or SUV is 40mm; the correction factor corresponding to each real-time basic data is a digital factor multiplied by the uncorrected measurement result to compensate for the measurement error.

[0049] In a specific example, the analysis of the driver's real-time status includes: transmitting the driver's various real-time status evaluation coefficients to the cockpit adjustment model; when the driver's real-time status is abnormal, it is judged that the cockpit needs to be adjusted, and the cockpit adjustment instructions corresponding to the driver's various real-time basic data are output, and the driving status of the vehicle is analyzed; when the driver's real-time status is not abnormal, it is judged that the cockpit does not need to be adjusted, and the cockpit adjustment instructions are not output, and the driving status of the vehicle is directly analyzed.

[0050] It should be noted that the cockpit adjustment model prepares data for each real-time state evaluation coefficient and cockpit adjustment command according to the {input, output} paired data pattern, divides the data set, uses the recurrent neural network (RNN) as the model architecture, and completes the model training, evaluation, and optimization.

[0051] In a specific example, the analysis of the vehicle's driving state includes: B1, recording each driving data of the vehicle as ,in Indicates the number corresponding to each driving data. ,in Expressed as the total number of driving data, is a natural integer greater than 2;

[0052] According to the calculation formula Analyze and obtain the evaluation coefficients of each driving state of the vehicle ,in It is expressed as the standard value corresponding to each driving data. Expressed as the allowable variation value of each driving data, Expressed as the correction factor corresponding to each driving data.

[0053] B2. Compare each driving state evaluation coefficient of the vehicle with the upper and lower limits of each driving state evaluation coefficient in the database. When a driving state evaluation coefficient of the vehicle is greater than the lower limit of the driving state evaluation coefficient in the database and less than the upper limit, 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.

[0054] It should be noted that the standard value corresponding to each driving data is the standard value corresponding to the standard value corresponding to each driving data, such as the sudden acceleration frequency or the sudden braking frequency is less than 0.05; the allowable variation value of each driving data is expressed as the difference between the maximum value and the minimum value of the variable range of the standard value corresponding to each driving data, such as the allowable variation value of the sudden acceleration frequency or the sudden braking frequency is 0.02; the correction factor corresponding to each driving data is a digital factor multiplied by the uncorrected measurement result to compensate for the measurement error.

[0055] In a specific example, completing the preliminary adjustment of the cockpit according to the driver's selection includes: C1. When there is no abnormality in the real-time state of the driver and the driving state of the vehicle, no preliminary adjustment of the cockpit is performed.

[0056] C2. When the driver's real-time state is normal and the vehicle's driving state is abnormal, the current state is recorded as the first abnormal state and step 4 is performed.

[0057] C3. When the real-time state of the driver is abnormal and the driving state of the vehicle is abnormal, the current state is recorded as the second abnormal state and step 4 is performed.

[0058] C4. When the driver's real-time status is abnormal and the vehicle's driving status is normal, a corresponding cockpit preliminary adjustment request is sent to the driver. When the driver agrees to 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 adjustment of the cockpit, and the adjusted data is updated to the driving characteristic database; when the driver disagrees to the preliminary adjustment of the cockpit, the preliminary adjustment of the cockpit is terminated; when the driver disagrees to the preliminary adjustment of the cockpit but performs manual adjustment, the adjusted data is updated to the driving characteristic database.

[0059] It should be noted that setting preference optimization and setting reward function , and set the update rule to , it should be noted that, Represented as the driver's basic posture data, Represented as the updated driver's basic posture data, Indicates an initial adjustment request, Represents an adjustment request for an update, Denoted as the learning rate, Expressed as a discount factor.

[0060] This application constructs a driving feature database, performs facial recognition on the driver and matches the data in the driving feature database, and performs basic adjustments 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 performs multi-situation analysis based on the driver's real-time status and the vehicle's driving status. This ensures vehicle safety while improving the comfort of the driver's cockpit, updates the driving feature database in real time, and sets preference optimization, thereby achieving a significant leap in the cockpit adjustment experience, making driving more in line with personal habits, and significantly improving the intelligence and humanization of the cockpit.

[0061] Step 4: Abnormal state analysis: extract the vehicle's driving state evaluation coefficients and the driver's real-time state 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.

[0062] It should be noted that behavioral data includes: body inclination, head rotation angle, hand position, duration of mobile phone use, frequency of mobile phone use, smoking frequency and smoking duration.

[0063] It should be noted that the driver's real-time characterization data is obtained based on the various biosensors in the driver's seat of the cockpit and the wearable devices connected to the driver, and the real-time characterization data includes: heart rate, blood pressure, body temperature, blinking frequency, length of time closed eyes, yawning frequency, drowsiness frequency and frequency of vision deviation from the road, etc.

[0064] It should be noted that the driver's behavior data and biometric data are all obtained by the camera equipment in the car.

[0065] In a specific example, the analysis of the first abnormal state includes: recording the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient as and , and according to the calculation formula Obtain analysis results of the first abnormal state, wherein Expressed as the weight factor corresponding to the behavioral data evaluation coefficient, It is expressed as the weight factor corresponding to the real-time characterization data evaluation coefficient.

[0066] It should be noted that 、 、 .

[0067] It should be noted that the weight factors corresponding to the behavioral data evaluation coefficients and the weight factors corresponding to the real-time representation data evaluation coefficients are obtained through factor analysis. First, the information of the behavioral data evaluation coefficients and the real-time representation data evaluation coefficients is condensed, and then the variance explanation rate after rotation is obtained, and the weight is obtained by dividing the cumulative variance explanation rate.

[0068] It should be noted that factor analysis is a well-known technology. It is a multivariate statistical analysis method that starts from studying the internal dependencies of variables and reduces some variables with complex relationships to a few comprehensive factors; information concentration is expressed as calculating the median; the variance explanation rate is the amount of information extracted by the factor, and the variance explanation rate = characteristic root / total number of analysis items; the variance explanation rate after rotation is expressed as the variance explanation rate of the factor after maximum variance rotation.

[0069] It should be noted that according to the calculation formula Analyze the behavioral data evaluation coefficient ,in Represented as each behavior data, where Indicates the number corresponding to each behavior data. ,in Expressed as the total number of behavioral data, is a natural integer greater than 2; It is expressed as the standard value corresponding to each behavior data. Expressed as the correction factor corresponding to each behavioral data.

[0070] It should be noted that according to the calculation formula Real-time characterization evaluation coefficients are obtained through analysis ,in Represented as real-time characterization data, where It represents the number corresponding to each real-time characterization data. ,in Expressed as the total amount of real-time characterization data, is a natural integer greater than 2; It is represented by the standard value corresponding to each real-time characterization data. It is expressed as the allowable change value of each real-time characterization data. It is expressed as the correction factor corresponding to each real-time characterization data.

[0071] In a specific example, the analysis of the second abnormal state includes: according to the calculation formula Obtain analysis results of the second abnormal state, wherein Expressed as the weight factor corresponding to the real-time state evaluation coefficient, Expressed as the weight factor corresponding to the behavioral data evaluation coefficient, It is expressed as the weight factor corresponding to the real-time characterization data evaluation coefficient.

[0072] It should be noted that , , .

[0073] It should be noted that the information of the real-time state evaluation coefficient, the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient is condensed through the weight factors corresponding to the real-time state evaluation coefficient, the behavior data evaluation coefficient and the real-time characterization data evaluation coefficient, and then the variance explanation rate after rotation is obtained, and the weight is obtained by dividing the cumulative variance explanation rate.

[0074] Step 5: Exception handling: Perform exception handling based on the analysis results of the first abnormal state and the second abnormal state.

[0075] In a specific example, the abnormality processing 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 exception handling model, output and complete the corresponding vehicle exception handling plan.

[0076] D2. When the driver is abnormal, 、 and The information is transferred to the exception handling model, and the corresponding driver exception handling plan is output and completed.

[0077] D3. After the exception handling is completed, proceed to step 3 again.

[0078] It should be noted that the exception handling model prepares data for the real-time state evaluation coefficient, behavior data evaluation coefficient, real-time characterization data evaluation coefficient, and vehicle exception handling solution based on the {input, output} paired data pattern. At the same time, it prepares data for the driving state evaluation coefficient and the vehicle exception handling solution, divides the data set, and uses the recurrent neural network (RNN) as the model architecture to complete the model training, evaluation, and optimization.

[0079] This application obtains the driver's various behavioral data and real-time characterization data to build a comprehensive driving status perception capability; 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 "adaptable to the situation" data analysis strategy significantly improves the accuracy of abnormal state identification and the comprehensiveness of judgment, and effectively reduces the false alarm and missed alarm rates; triggers a hierarchical intelligent abnormality handling mechanism: for vehicle body abnormalities, early warning prompts, performance restrictions, function degradation or emergency stop and maintenance recommendations may be taken; for driver status abnormalities, multi-level early warnings or active comfort adjustments may be activated; through this closed-loop processing process based on precise analysis, efficient and intelligent response to abnormal states is achieved under the fundamental premise of ensuring driving safety.

[0080] See also Figure 2 As shown, the present application provides a system for a vehicle intelligent cockpit adjustment method in the second aspect, including: a driver basic information input module, which is used for the driver to input basic information and then complete the construction of a driving feature database.

[0081] The basic information adjustment module is used to identify the driver's identity based on face recognition technology, extract the driver's corresponding basic information, and then complete the basic adjustment of the vehicle cockpit.

[0082] The cockpit preliminary adjustment module is used to obtain the driver's real-time basic data and the vehicle's driving data when the vehicle starts to move, 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 adjustment of the cockpit according to the driver's choice.

[0083] The abnormal state analysis module is used to extract the vehicle's driving state evaluation coefficients and the driver's real-time state 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.

[0084] The exception handling terminal is used to perform exception handling according to the analysis results of the first abnormal state and the second abnormal state.

[0085] The present application provides a vehicle intelligent cockpit adjustment method and system, which, based on the basic adjustment of the vehicle cockpit, collects real-time data and performs multi-data analysis, and completes the preliminary adjustment of the cockpit and data update under specific conditions, thereby solving the limitations existing in the current feasibility analysis process of the development of vehicle intelligent cockpit adjustment. When the vehicle is abnormal, the driver's various behavioral data and real-time characterization data are collected, the cause of the abnormality is analyzed, and the abnormality is handled according to the analysis results of the first abnormal state and the second abnormal state, and data feedback is performed, thereby realizing a comprehensive and objective analysis of the vehicle intelligent cockpit adjustment and ensuring the reliability and authenticity of the vehicle intelligent cockpit adjustment analysis results.

[0086] The above content is merely an example and explanation of the concept of the present application. Technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present application.

Claims

1. A vehicle intelligent cockpit adjustment method, characterized in that: include: Step 1: Input basic driver information: The driver inputs basic information to complete the construction of the driving characteristics database; Step 2: Basic Information Adjustment: Identify the driver's identity using facial recognition technology and extract basic information corresponding to the driver to complete basic adjustments to the vehicle cockpit; Step 3: Initial cockpit adjustment: When the vehicle starts driving, the driver's real-time basic data and the vehicle's driving data are obtained, and the driver's real-time state evaluation coefficients are analyzed. The driver's real-time state is then analyzed, and thus the vehicle's driving state is analyzed. The initial cockpit adjustment is completed according to the driver's selection. The analysis obtains various real-time status evaluation coefficients of the driver, including: The real-time basic data of the driver is recorded as ,in Indicates the number corresponding to each real-time data. ,in Expressed as the total number of real-time data, is a natural integer greater than 2; According to the calculation formula Analyze and obtain the driver's real-time status evaluation coefficients ,in It is expressed as the standard value corresponding to each real-time basic data. Expressed as the allowable change value of each real-time basic data, Expressed as the correction factor corresponding to each real-time basic data; The analysis of the driver's real-time status includes: The driver's real-time state evaluation coefficients are transmitted to the cockpit adjustment model. When the driver's real-time state is abnormal, the cockpit is judged to need adjustment, and cockpit adjustment instructions corresponding to the driver's real-time basic data are output, and the vehicle's driving state is analyzed. When the driver's real-time state is normal, the cockpit is judged to not need adjustment, and no cockpit adjustment instructions are output, and the vehicle's driving state is directly analyzed. The analysis of the driving state of the vehicle includes: B1. Record the vehicle's driving data as ,in Indicates the number corresponding to each driving data. ,in Expressed as 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 ,in It is expressed as the standard value corresponding to each driving data. Expressed as the allowable variation value of each driving data, Expressed as the correction factor corresponding to each driving data; B2. Compare each driving state evaluation coefficient of the vehicle with the upper and lower limits of each driving state evaluation coefficient in the database. When a driving state evaluation coefficient of the vehicle is greater than the lower limit of the driving state evaluation coefficient in the database and less than the upper limit, determine that the driving state of the vehicle is abnormal; otherwise, determine that the driving state of the vehicle is normal; The initial cockpit adjustment is completed according to the driver's selection, including: C1. When the driver's real-time status and the vehicle's driving status are normal, no preliminary cockpit adjustments are performed; C2. When the driver's real-time status is normal and the vehicle's driving status is abnormal, the current status is recorded as the first abnormal status and step 4 is performed; C3. When the driver's real-time state is abnormal and the vehicle's driving state is abnormal, the current state is recorded as a second abnormal state and step 4 is performed; C4. When the driver's real-time status is abnormal and the vehicle's driving status is normal, a corresponding cockpit preliminary adjustment request is issued to the driver. When the driver agrees to 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 and updating the adjusted data to the driving characteristic database. If the driver disagrees with the preliminary cockpit adjustment, the preliminary cockpit adjustment is terminated. If the driver disagrees with the preliminary cockpit adjustment but performs manual adjustment, the adjusted data is updated to the driving characteristic database. Step 4: Abnormal state analysis: extract the vehicle's driving state evaluation coefficients and the driver's real-time state evaluation coefficients, obtain the driver's behavior data and real-time characterization data, and then analyze the first abnormal state and the second abnormal state; Step 5: Exception handling: Perform exception handling based on the analysis results of the first abnormal state and the second abnormal state.

2. The vehicle intelligent cockpit adjustment method according to claim 1, characterized in that: The basic information includes facial 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 analyzing the first abnormal state includes: The behavioral data evaluation coefficient and the real-time representation data evaluation coefficient are respectively denoted as and , and according to the calculation formula Obtain analysis results of the first abnormal state, wherein Expressed as the weight factor corresponding to the behavioral data evaluation coefficient, It is expressed as the weight factor corresponding to the real-time characterization data evaluation coefficient.

4. The vehicle intelligent cockpit adjustment method according to claim 3, characterized in that: The analyzing the second abnormal state includes: According to the calculation formula Obtain analysis results of the second abnormal state, wherein Expressed as the weight factor corresponding to the real-time state evaluation coefficient, Expressed as the weight factor corresponding to the behavioral data evaluation coefficient, It is expressed as the weight factor corresponding to the real-time characterization data evaluation coefficient.

5. The vehicle intelligent cockpit adjustment method according to claim 4, characterized in that: The performing of abnormality processing 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 exception handling model, output and complete the corresponding vehicle exception handling plan; D2. When the driver is abnormal, 、 and Transmit to the exception handling model, output and complete the corresponding driver exception handling plan; D3. After the exception handling is completed, proceed to step 3 again.

6. A system for executing the vehicle intelligent cockpit adjustment method according to any one of claims 1 to 5, characterized in that: include: The driver basic information entry module is used for the driver to enter basic information and then complete the construction of the driving characteristics database; The basic information adjustment module is used to identify the driver's identity based on facial recognition technology and extract the driver's corresponding basic information to complete the basic adjustment of the vehicle cockpit; The cockpit preliminary adjustment module is used to obtain the driver's real-time basic data and the vehicle's driving data when the vehicle starts to move, 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 adjustment of the cockpit according to the driver's selection; An abnormal state analysis module is used to extract the vehicle's driving state evaluation coefficients and the driver's real-time state 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; The exception handling terminal is used to perform exception handling according to the analysis results of the first abnormal state and the second abnormal state.

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