A smart cabin-oriented interactive interface self-adaptive adjustment method

Abstract

This invention discloses an adaptive adjustment method for the interactive interface of a smart cockpit, comprising: acquiring multi-source state information within the smart cockpit, including vehicle driving state information, cockpit environment information, occupant state information, and interactive behavior information; determining the current interactive scenario type of the smart cockpit based on the vehicle driving state information, calculating the interaction load index based on the multi-source state information, and determining that the current interactive interface has a risk of attention interference when the index exceeds a preset threshold; generating an adaptive adjustment strategy for the interactive interface in this case, including at least one of an interface information display priority adjustment strategy, an interface layout simplification strategy, and an interaction mode switching strategy; then completing the mapping and distribution of the interactive interface configuration, and performing consistency and availability checks on the interface after completion; if the checks fail, reverting to the safe default interface configuration or reducing the adjustment range and re-distributing the interface. This invention enables dynamic adaptive adjustment of the interactive interface, enhancing safety and stability.

Description

Technical Field

[0001] This invention belongs to the field of smart cockpit and human-computer interaction technology, specifically relating to an adaptive adjustment method for the interactive interface of a smart cockpit. Background Technology

[0002] With the rapid development of intelligent vehicles and cockpit electronics technology, the smart cockpit has gradually evolved from a traditional information display and entertainment terminal into a comprehensive interactive space integrating driving information perception, human-machine interaction, and service decision-making. However, the smart cockpit still faces significant complexity in practical applications: on the one hand, the vehicle's driving status, road environment, cockpit environment, and the physiological and behavioral states of occupants all exhibit highly dynamic changes, and the requirements for the interactive interface vary significantly in different driving scenarios; on the other hand, current smart cockpits generally adopt a multi-screen collaborative and multi-modal interactive design architecture, with the number of interaction methods and interface elements constantly increasing, resulting in a significant increase in interface organization and interaction load.

[0003] To address the limitations of traditional rule-based switching based on single conditions or coarse-grained interface configuration methods based on driving modes in smart cockpits, researchers have proposed an interactive interface adjustment approach oriented towards multi-source perception and contextual adaptation. This approach dynamically optimizes interface content, layout, and interaction methods by integrating vehicle status, cockpit environment, and occupant interaction characteristics. However, existing adaptive interactive interface solutions still have key shortcomings: most solutions use fixed weights or priorities to integrate multi-source information, making it difficult to dynamically adjust information under different driving loads and risk levels, resulting in delayed or excessive interface adjustments; smart cockpit interaction involves multi-level collaboration among multi-screen display layers, interaction channel layers, and application service layers, and existing layering often leads to cross-screen information conflicts, repeated prompts, or layer mismatches due to state synchronization delays and inconsistent policy distribution; existing evaluation and constraint mechanisms mostly focus on short-term interaction efficiency or local visual effects, lacking systematic verification and closed-loop correction of interface consistency, accessibility of key functions, and safety safeguards. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, this invention provides a [topic name]. The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, embodiments of the present invention provide an adaptive adjustment method for the interactive interface of a smart cockpit, the method comprising: S1, acquire multi-source status information within the smart cockpit, including vehicle driving status information, cockpit environment information, occupant status information, and interactive behavior information; S2, based on the vehicle driving status information, determine the current smart cockpit interaction scenario type, the interaction scenario includes normal driving interaction scenario, high-load driving interaction scenario and non-driving interaction scenario; S3, calculate the interaction load index based on the multi-source state information, and when the interaction load index exceeds a preset threshold, determine that the current interactive interface has a risk of attention interference. S4. If it is determined that there is a risk of attention interference, an adaptive adjustment strategy for the interactive interface is generated based on the interaction load index, the interaction scenario type, the vehicle driving status information and the interaction behavior information. The adaptive adjustment strategy for the interactive interface includes at least one of the following: interface information display priority adjustment strategy, interface layout simplification strategy and interaction mode switching strategy. S5. Based on the adaptive adjustment strategy of the interactive interface, the mapping and distribution of the interactive interface configuration are completed. After the mapping and distribution are completed, the consistency and availability of the interface are checked. If the check fails, the interface configuration is rolled back to the safe default configuration or the adjustment range is reduced and then re-distributed.

[0005] In one embodiment of the present invention, S1 includes: S11, the multi-source state information at time... Unified as a state vector, the constructed expression is: ; in, For a moment The state vector corresponding to multi-source state information; It is a moment The vehicle driving state subvector at that time is used to represent the vehicle driving state information; It is a moment The cockpit environment subvector is used to represent cockpit environment information. It is a moment The crew state subvector at time is used to represent crew state information; It is a moment The interaction behavior sub-vectors are used to represent interaction behavior information; S12, By collecting data, the vehicle driving state information is modeled to obtain a vehicle driving state sub-vector: ; in, It is a moment The vehicle speed at that time; It is a moment acceleration at time; It is a moment The turning angle at that time; It is a moment Driving mode information at the time; S13, By acquiring data, the cockpit environment information is modeled to obtain a cockpit environment sub-vector: ; in, It is a moment The light intensity of the cabin environment at that time; It is a moment The noise level at that time; It is a moment Information on road complexity at that time; S14, By collecting data, the occupant state information is modeled to obtain the occupant state sub-vector: ; in, It is a moment The direction of the occupants' gaze; It is a moment The head posture of the occupants; It is a moment Information on the fatigue status of passengers; S15, By collecting data, the interactive behavior information is modeled to obtain interactive behavior sub-vectors: ; in, It is a moment The current interaction method corresponding to the time; It is a moment Frequency of interface operations , For in the interval Number of operations within, For the length of the statistical time window; S16, normalize each parameter in the state vector to obtain the normalized state vector corresponding to the multi-source state information.

[0006] In one embodiment of the present invention, S2 includes: S21, for each scenario in the preset set of interactive scenarios, construct the scenario discrimination scoring function corresponding to the scenario, and calculate the value of each scenario discrimination scoring function based on the vehicle driving status information; S22, determine the scenario corresponding to the maximum value among the values ​​of the scenario discrimination scoring function as the current smart cockpit interaction scenario type.

[0007] In one embodiment of the present invention, the set of interactive scenarios is represented as: ; in, This indicates a normal driving interaction scenario; This indicates a high-load driving interaction scenario; This indicates non-driving interaction scenarios; The scene discrimination scoring function is expressed as follows: ; in, Indicates the first Scene discrimination and scoring functions for similar scenarios; Indicates the first The weight vector for the class scenario; Indicates the first Bias terms for similar scenarios; The eigenvector represents the time step. Vehicle driving state subvector The normalized quantity includes , , and , respectively time speed at time Normalized quantity, time acceleration at time Normalized quantity, time Steering angle at time Normalized quantity, time Driving mode information at the time The normalized quantity; The formula used to determine the scene corresponding to the maximum value of the scene discrimination scoring function is as follows: ; in, It is a moment The type of interaction scenario in the current smart cockpit is determined in real time.

[0008] In one embodiment of the present invention, S3 includes: S31, based on multi-factor weighting, defines the interaction load index, expressed as: ; in, It is a moment Interactive load index at that time; For a moment Driving load component at time; For a moment Environmental load components at that time; For a moment Crew status components at that time; For a moment Interaction intensity components at time; , For the corresponding weight coefficients, and ; S32, based on time The normalized value of the vehicle driving state subvector is used to calculate the driving load component, and the calculation formula is as follows: ; in, , , , They are time points speed Normalized quantity, time Time acceleration Normalized quantity, time Steering angle Normalized quantity, time Driving mode The normalized quantity; , They are , , , The weighting coefficients, and , ; S33, based on time The environmental load component is calculated using the cabin environment subvector and its normalized value, as follows: ; in, It is a moment Normalized quantity of road complexity information; It is a moment The normalized measure of the noise level at that time; It is a moment Excessive exposure to sunlight The normalized quantity; , They are , , The weighting coefficients, and , ; ; ; It is a moment The light intensity of the cabin environment at that time; and These are the lower and upper limits of the comfortable lighting range, respectively. and These are the lower and upper limits for inappropriate lighting conditions; S34, Based on the normalized value of the occupant state sub-vector, calculate the occupant state components using the following formula: ; in, For a moment Normalized amount of eye direction information of occupants For a moment Normalized values ​​of occupant head posture information It is a moment Normalized measure of occupant fatigue status information; It is a moment When the occupant's line of sight deviates from the driving-related area parameters The normalized quantity; , , , ,and ; ; ; in, It is a moment The unit vector of the line of sight direction; It is a moment The reference unit vector pointing to the area of ​​interest ahead of the road; and These are the lower and upper limits of the deviation angle calibration range, respectively; S35, based on the normalization of the interaction behavior sub-vectors, calculates the interaction intensity components using the following formula: ; in, It is a moment Normalized quantity of time interaction method It is a moment Normalized value of the frequency of interface operations; It is a moment Time interface complexity The normalized quantity; It is a moment Interaction mode load factor; They are respectively , , , The weighting coefficients, and ; ; ; ; in, It is a moment The number of interactive controls currently visible on the interface; It is a moment The depth of the interaction path for completing common tasks in a timely manner; and This is the scaling factor; and These are the lower and upper limits of the interface complexity calibration range, respectively; S36. The calculated interaction load index is judged based on a preset threshold to determine whether there is a risk of attention interference in the current interactive interface. The calculation formula is as follows: ; in, It is a moment Interactive load index at that time; It is a preset threshold; This indicates that the current interactive interface poses a risk of attention distraction. This indicates that there is no risk of attention being distracted by the current interactive interface.

[0009] In one embodiment of the present invention, S4 includes: S41, if it is determined that there is a risk of attention interference in the current interactive interface, an adaptive adjustment strategy for the interactive interface is generated based on the interaction load index, the vehicle driving status information, and the interaction behavior information, and the expression is constructed as follows: ; in, Indicates time Adaptive adjustment strategy for the user interface during operation; Indicates the interaction load index; Indicates vehicle driving status information; Indicates interactive behavior information; The strategy generates the mapping function; The constructed expression is further expanded as follows: ; in, Indicates time Information priority sorting vector at that time; Indicates time The level of interface complexity at that time; Indicates time The optimal layout template at that time; Indicates time The result of the interactive channel selection at that time; The adaptive adjustment strategy for the interactive interface is defined by generating an information display priority adjustment strategy by sorting the importance of candidate information, generating an interface layout simplification strategy by classifying the interface complexity, and generating an interaction mode switching strategy by evaluating the comprehensive cost of each candidate interaction channel. S42, generate an information priority sorting vector in descending order of importance, calculated using the following formula: ; For each candidate information item The overall importance is calculated as follows: ; in, This represents the number of candidate information items. A candidate information item refers to an independent information unit that can be displayed or hidden in the current interface. This indicates that the index sequence should be sorted in descending order and output. For a moment Candidate Information Items The static security level index represents the security priority of the candidate information item itself. It is a preset constant that does not change over time. For a moment Candidate Information Items Scenario relevance metrics; The interaction scenario type; For a known scene mapping function; For a moment Candidate Information Items User demand intensity index; For a moment Candidate information items within the corresponding recent time window Number of visits; This is to count the maximum number of visits within the window; , , ,and ; S43, Based on the interaction load index, generate the interface complexity level, calculated using the following formula: ; in, For a moment Interactive load index at that time; , For the graded threshold and ; It is a moment The interface complexity level at that time This indicates the default. To indicate simplification, Minimalism is indicated; S44, Select the optimal layout template based on the preset layout template set, as follows:

[0010] in, A collection of preset layout templates; For the first One candidate layout template; To use layout templates The deviation in interface complexity afterward; To use layout templates The key information displayed afterward shows the deviation in the area; To use layout templates The deviation in scene adaptation afterward; For the corresponding weight coefficients, and ; S45, the interaction channel selection result is obtained based on the interaction channel comprehensive cost function, as follows: ; Among them, time Time Interaction Channel The overall cost function is: ; in, For a moment Time Interaction Channel The average time cost to complete a task; For a moment Use the interactive channel The average time to complete the current task; The preset maximum reference time; It is a moment The cost of attention occupancy at that time; For interactive channels Inherent cognitive load coefficient; It is a moment Interactive load index at that time; It is a moment The cost of misoperation at that time; ; Interaction channel per unit time Number of erroneous operations; This is the upper limit for counting the number of erroneous operations; For the corresponding weight coefficients, and . In one embodiment of the present invention, S5 includes: S51, the adaptive adjustment strategy of the interactive interface is mapped into a unified form of the interface configuration parameter set, expressed as: ; in, Indicates time Adaptive adjustment strategy for the user interface during operation; This is a parameterized mapping function used to convert strategy parameters into executable UI configuration parameters; The set of interface configuration parameters obtained from the aforementioned strategy mapping is as follows: ; in, Indicates time The display configuration at any time is used to control the amount of information displayed. Indicates time The layout configuration at that time is used to control structural complexity; Indicates time The interaction configuration is used to control the interaction method; , , These represent the display rendering configuration mapping function, the layout configuration mapping function, and the interaction configuration mapping function, respectively. S52, calculate the interface consistency index and interface usability index, using the following formulas: ; in, Indicates time Interface consistency metrics during operation; Indicates the number of candidate information items; , The first The quantified value of the presentation status of the candidate information item on two different display interfaces represents the presentation status of the first candidate information item. The degree of visual presentation of each candidate information item on two different display interfaces is used to measure whether the information is presented completely and prominently on both interfaces; It represents the maximum possible value in the state quantification system, which is a fixed preset value. ; in, Indicates time User interface usability metrics at that time; Indicates time The average number of interaction steps required to complete a key interactive task in a given time; Indicates time This refers to the average interaction time to complete a key interactive task; The maximum number of interaction steps allowed; The maximum allowed interaction time; and Let be the weight coefficient, and satisfy... ; S53, based on the interface consistency index and interface usability index, determine whether the interface configuration passes the verification. The calculation formula is as follows: ; in, This is the consistency threshold; This is the availability threshold; This indicates that the interface configuration has passed validation. This indicates that the interface configuration failed validation; S54, when the interface configuration fails validation, the interface configuration parameters are updated using the following formula: ; in, Indicates time The complexity of the interface at that time; Configure parameter vectors for the safe default interface; The amplitude reduction parameter is used to adjust the intensity of interface changes. This means updating the left side using the right side. Secondly, embodiments of the present invention provide an adaptive adjustment device for the interactive interface of a smart cockpit, the device comprising: The multi-source status information acquisition module is used to acquire multi-source status information within the smart cockpit, including vehicle driving status information, cockpit environment information, occupant status information, and interactive behavior information. The interaction scenario type determination module is used to determine the current interaction scenario type of the smart cockpit based on the vehicle driving status information. The interaction scenarios include normal driving interaction scenarios, high-load driving interaction scenarios, and non-driving interaction scenarios. The current interactive interface has an attention interference risk judgment module, which is used to calculate the interaction load index based on the multi-source state information. When the interaction load index exceeds a preset threshold, it is determined that the current interactive interface has an attention interference risk. The interactive interface adaptive adjustment strategy generation module is used to generate an interactive interface adaptive adjustment strategy based on the interactive load index, the interactive scenario type, the vehicle driving status information and interactive behavior information when it is determined that there is a risk of attention interference. The interactive interface adaptive adjustment strategy includes at least one of the following: an interface information display priority adjustment strategy, an interface layout simplification strategy and an interaction mode switching strategy. The interactive interface configuration mapping, distribution, and verification module is used to complete the mapping and distribution of the interactive interface configuration based on the adaptive adjustment strategy of the interactive interface. After the mapping and distribution are completed, the consistency and availability of the interface are verified. If the verification fails, the configuration is rolled back to the safe default interface configuration or the adjustment range is reduced and then re-distributed.

[0011] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the adaptive adjustment method for the interactive interface of a smart cockpit provided in this embodiment of the invention.

[0012] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the adaptive adjustment method for the interactive interface of a smart cockpit provided in embodiments of the present invention.

[0013] The beneficial effects of this invention are: The present invention addresses the problems of information overload, interface conflicts, and increased driver distraction risks caused by static interface configurations and limited adjustment rules in existing smart cockpits under complex driving scenarios. It creatively proposes an adaptive adjustment method for the smart cockpit's interactive interface. This method constructs a multi-source perception system integrating vehicle driving status, cockpit environment, occupant status, and interactive behavior to quantitatively assess the interactive load, using this as the core trigger for interface adjustment. Furthermore, it introduces an interactive scenario recognition and strategy generation mechanism, incorporating interface information display priority, layout complexity, and interaction methods into a unified adjustment framework, and effectively decoupling strategy and interface execution through parameterized mapping. Simultaneously, consistency and availability checks are set after interface configuration is issued, and a safety closed loop is formed by combining rollback or reduced-amplitude re-issuance mechanisms, ensuring the controllability and stability of the interface adjustment process. Thus, this application achieves a shift from passive interface adjustment relying on fixed rules or static configurations to proactive adaptive adjustment based on interactive load perception and closed-loop verification, effectively reducing driver distraction risks and improving the interactive safety and user experience of smart cockpits in multi-screen, multi-modal collaborative scenarios. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating an adaptive adjustment method for the interactive interface of a smart cockpit, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an adaptive adjustment device for the interactive interface of a smart cockpit provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0016] To address the problems of lagging interface adjustment, difficulty in quantifying interaction load, insufficient interface consistency and usability, and weak stability of multi-screen and multi-modal collaboration in existing smart cockpits under complex driving scenarios, embodiments of the present invention provide an adaptive adjustment method, device, electronic device, and storage medium for the interactive interface of smart cockpits.

[0017] In a first aspect, embodiments of the present invention provide an adaptive adjustment method for the interactive interface of a smart cockpit, such as... Figure 1 As shown, the method may include the following steps S1 to S5: S1, acquire multi-source status information within the smart cockpit, including vehicle driving status information, cockpit environment information, occupant status information, and interactive behavior information; Vehicle driving status information refers to the dynamic operating parameters of the vehicle itself, directly reflecting the vehicle's real-time movement, including vehicle speed, acceleration, steering angle, and driving mode information. Vehicle speed refers to the vehicle's current speed. Acceleration refers to the rate of change of vehicle speed, including acceleration and deceleration. Steering angle refers to the angle of steering wheel rotation, reflecting the vehicle's steering intention and range. For example, the steering wheel is turned 90 degrees to the left. Driving mode refers to the vehicle's currently used preset driving style. For example, Eco mode, Sport mode, or Autopilot mode. Vehicle driving status information is collected and output by existing automotive-grade sensors and control systems. For details on the collection process, please refer to relevant technical explanations; it will not be detailed here. Cockpit environment information refers to the physical state parameters of the vehicle's internal and external environment, affecting cabin comfort and safety. This includes light intensity, noise levels, and road complexity information. Light intensity refers to the brightness of the interior environment. Noise level refers to the level of ambient noise within the vehicle. Road complexity is a quantitative assessment of the current road conditions. For example, navigation maps and camera recognition determine whether the current road is congested, a winding mountain road, or a flat highway, thus predicting the driving load. Cockpit environment information is derived from existing environmental perception systems; for details, please refer to relevant technical explanations, which will not be elaborated here.

[0018] Occupant status information refers to the physiological and attentional state of the driver (and sometimes other passengers), including line orientation, head posture, and fatigue status information. Line orientation refers to the direction the driver's eyes are focused on. For example, whether the gaze is continuously focused on the road ahead, or frequently looking at the central control screen or out the window. Head posture refers to the orientation and angle of the driver's head. For example, whether the head is facing forward directly, or continuously shifting to the left or right. Fatigue status information is inferred from facial features (such as blinking frequency and yawning). Occupant status information is output by the driver monitoring system; for details, please refer to the relevant technical explanations, which will not be elaborated here.

[0019] Interactive behavior information refers to behavioral data generated when users interact with the in-cabin hardware and software systems, including the current interaction method and interface operation frequency. The current interaction method refers to the channel the user is using to interact with the vehicle, such as voice commands, touchscreen operation, physical buttons, or gesture control. Interface operation frequency refers to how frequently a user operates a specific function within a certain period. For example, the number of times the navigation interface is zoomed or dragged, or the frequency of changing music tracks. Interactive behavior information is provided by vehicle software layer behavioral statistics; for details, please refer to relevant technical explanations, which will not be elaborated here.

[0020] Specifically, S1 may include the following steps: S11, the multi-source state information at time... Unified as a state vector, the constructed expression is: ; in, For a moment The state vector corresponding to multi-source state information; It is a moment The vehicle driving state subvector at that time is used to represent the vehicle driving state information; It is a moment The cockpit environment subvector is used to represent cockpit environment information. It is a moment The crew state subvector at time is used to represent crew state information; It is a moment The interaction behavior sub-vectors are used to represent interaction behavior information; S12, By collecting data, the vehicle driving state information is modeled to obtain a vehicle driving state sub-vector: ; in, It is a moment The vehicle speed at that time; It is a moment acceleration at time; It is a moment The turning angle at that time; It is a moment Driving mode information at the time; S13, By acquiring data, the cockpit environment information is modeled to obtain a cockpit environment sub-vector: ; in, It is a moment The light intensity of the cabin environment at that time; It is a moment The noise level at that time; It is a moment Information on road complexity at that time; S14, By collecting data, the occupant state information is modeled to obtain the occupant state sub-vector: ; in, It is a moment The direction of the occupants' gaze; It is a moment The head posture of the occupants; It is a moment Information on the fatigue status of passengers; S15, By collecting data, the interactive behavior information is modeled to obtain interactive behavior sub-vectors: ; in, It is a moment The current interaction method corresponding to the time; It is a moment Frequency of interface operations , For in the interval Number of operations within, For the length of the statistical time window; S16, normalize each parameter in the state vector to obtain the normalized state vector corresponding to the multi-source state information.

[0021] For all physical quantities to be normalized, the following formula is used for normalization: ; in, express The physical quantity that needs to be normalized at any given time is a continuous quantity. express The result after normalizing the physical quantity that needs to be normalized at any given time. and These represent the lower and upper limits of the physical quantity in the system calibration, respectively. It is understandable that the above formula is applied to each parameter in the state vector to perform corresponding normalization processing, and the corresponding normalized quantity is obtained.

[0022] S2, based on the vehicle driving status information, determine the current smart cockpit interaction scenario type, the interaction scenario includes normal driving interaction scenario, high-load driving interaction scenario and non-driving interaction scenario; S2 may include the following steps: S21, for each scenario in the preset set of interactive scenarios, construct the scenario discrimination scoring function corresponding to the scenario, and calculate the value of each scenario discrimination scoring function based on the vehicle driving status information; The set of interactive scenarios is represented as follows: ; in, This indicates a normal driving interaction scenario; This indicates a high-load driving interaction scenario; This indicates non-driving interaction scenarios; The scene discrimination scoring function is expressed as follows: ; in, Indicates the first Scene discrimination and scoring functions for similar scenarios; Indicates the first The weight vector for the class scenario; Indicates the first Bias terms for similar scenarios; The eigenvector represents the time step. Vehicle driving state subvector The normalized quantity includes , , and , respectively time speed at time Normalized quantity, time acceleration at time Normalized quantity, time Steering angle at time Normalized quantity, time Driving mode information at the time The normalized quantity; S22, determine the scenario corresponding to the maximum value among the values ​​of the scenario discrimination scoring function as the current smart cockpit interaction scenario type.

[0023] The formula used in this step is: ; in, It is a moment The interaction scenario type of the current smart cockpit is determined in real time. For simplicity, it will be used in the following... express.

[0024] S3, calculate the interaction load index based on the multi-source state information, and when the interaction load index exceeds a preset threshold, determine that the current interactive interface has a risk of attention interference. S3 may include the following steps: S31, based on multi-factor weighting, defines the interaction load index, expressed as: ; in, It is a moment Interactive load index at that time; For a moment Driving load component at time; For a moment Environmental load components at that time; For a moment Crew status components at that time; For a moment Interaction intensity components at time; , For the corresponding weight coefficients, and ; S32, based on time The normalized value of the vehicle driving state subvector is used to calculate the driving load component, and the calculation formula is as follows: ; in, , , , They are time points speed Normalized quantity, time Time acceleration Normalized quantity, time Steering angle Normalized quantity, time Driving mode The normalized quantity; , They are , , , The weighting coefficients, and , ; S33, based on time The environmental load component is calculated using the cabin environment subvector and its normalized value, as follows: ; in, It is a moment Normalized quantity of road complexity information; It is a moment The normalized measure of the noise level at that time; It is a moment Excessive exposure to sunlight The normalized quantity; , They are , , The weighting coefficients, and , ; ; ; It is a moment The light intensity of the cabin environment at that time; and These are the lower and upper limits of the comfortable lighting range, respectively. and These are the lower and upper limits for inappropriate lighting conditions; S34, Based on the normalized value of the occupant state sub-vector, calculate the occupant state components using the following formula: ; in, For a moment Normalized amount of eye direction information of occupants For a moment Normalized values ​​of occupant head posture information It is a moment Normalized measure of occupant fatigue status information; It is a moment When the occupant's line of sight deviates from the driving-related area parameters The normalized quantity; , , , ,and ; ; ; in, It is a moment The unit vector of the line of sight direction; It is a moment The reference unit vector pointing to the area of ​​interest ahead of the road; and These are the lower and upper limits of the deviation angle calibration range, respectively; S35, based on the normalization of the interaction behavior sub-vectors, calculates the interaction intensity components using the following formula: ; in, It is a moment Normalized quantity of time interaction method It is a moment Normalized value of the frequency of interface operations; It is a moment Time interface complexity The normalized quantity; It is a moment Interaction mode load factor; They are respectively , , , The weighting coefficients, and ; ; ; ; in, It is a moment The number of interactive controls currently visible on the interface; It is a moment The depth of the interaction path for completing common tasks in a timely manner; and This is the scaling factor; and These are the lower and upper limits of the interface complexity calibration range, respectively; S36. The calculated interaction load index is judged based on a preset threshold to determine whether there is a risk of attention interference in the current interactive interface. The calculation formula is as follows: ; in, It is a moment Interactive load index at that time; It is a preset threshold; This indicates that the current interactive interface poses a risk of attention distraction. This indicates that there is no risk of attention being distracted by the current interactive interface, meaning the risk is acceptable.

[0025] S4. If it is determined that there is a risk of attention interference, an adaptive adjustment strategy for the interactive interface is generated based on the interaction load index, the interaction scenario type, the vehicle driving status information and the interaction behavior information. The adaptive adjustment strategy for the interactive interface includes at least one of the following: interface information display priority adjustment strategy, interface layout simplification strategy and interaction mode switching strategy. The set of adaptive adjustment strategies for the interactive interface is represented as follows: ; in, Adjust the strategy for prioritizing the display of interface information. To simplify the interface layout strategy, Switching strategies for interaction methods; S4 may include the following steps: S41, if it is determined that there is a risk of attention interference in the current interactive interface, an adaptive adjustment strategy for the interactive interface is generated based on the interaction load index, the vehicle driving status information, and the interaction behavior information, and the expression is constructed as follows: ; in, Indicates time Adaptive adjustment strategy for the user interface during operation; Indicates the interaction load index; Indicates vehicle driving status information; Indicates interactive behavior information; The strategy generates the mapping function; The constructed expression is further expanded as follows: ; in, Indicates time Information priority sorting vector at that time; Indicates time The level of interface complexity at that time; Indicates time The optimal layout template at that time; Indicates time The result of the interactive channel selection at that time; The adaptive adjustment strategy for the interactive interface is defined by generating an information display priority adjustment strategy by sorting the importance of candidate information, generating an interface layout simplification strategy by classifying the interface complexity, and generating an interaction mode switching strategy by evaluating the comprehensive cost of each candidate interaction channel. The following will be a detailed calculation.

[0026] S42, generate an information priority sorting vector in descending order of importance, calculated using the following formula: ; For each candidate information item The overall importance is calculated as follows: ; in, This represents the number of candidate information items. A candidate information item refers to an independent information unit that can be displayed or hidden in the current interface. This indicates that the index sequence should be sorted in descending order and output. For a moment Candidate Information Items The static security level index represents the security priority of the candidate information item itself. It is a preset constant that does not change over time. For a moment Candidate Information Items Scenario relevance metrics; The interaction scenario type; For a known scene mapping function; For a moment Candidate Information Items User demand intensity index; For a moment Candidate information items within the corresponding recent time window Number of visits; This is to count the maximum number of visits within the window; , , ,and ; S43, Based on the interaction load index, generate the interface complexity level, calculated using the following formula: ; in, For a moment Interactive load index at that time; , For the graded threshold and ; It is a moment The interface complexity level at that time This indicates the default. To indicate simplification, Minimalism is indicated; S44, Select the optimal layout template based on the preset layout template set, as follows:

[0027] in, A collection of preset layout templates; For the first One candidate layout template; To use layout templates The deviation in interface complexity afterward; To use layout templates The key information displayed afterward shows the deviation in the area; To use layout templates The deviation in scene adaptation afterward; For the corresponding weight coefficients, and ; S45, the interaction channel selection result is obtained based on the interaction channel comprehensive cost function, as follows: ; Among them, time Time Interaction Channel The overall cost function is: ; in, For a moment Time Interaction Channel The average time cost to complete a task; For a moment Use the interactive channel The average time to complete the current task; The preset maximum reference time; It is a moment The cost of attention occupancy at that time; For interactive channels Inherent cognitive load coefficient; It is a moment Interactive load index at that time; It is a moment The cost of misoperation at that time; ; Interaction channel per unit time Number of erroneous operations; This is the upper limit for counting the number of erroneous operations; For the corresponding weight coefficients, and . S5. Based on the adaptive adjustment strategy of the interactive interface, the mapping and distribution of the interactive interface configuration are completed. After the mapping and distribution are completed, the consistency and availability of the interface are checked. If the check fails, the interface configuration is rolled back to the safe default configuration or the adjustment range is reduced and then re-distributed.

[0028] S5 may include the following steps: S51, the adaptive adjustment strategy of the interactive interface is mapped into a unified form of the interface configuration parameter set, expressed as: ; in, Indicates time Adaptive adjustment strategy for the user interface during operation; This is a parameterized mapping function used to convert strategy parameters into executable UI configuration parameters; The set of interface configuration parameters obtained from the aforementioned strategy mapping is as follows: ; in, Indicates time The display configuration is used to control the amount of information. It determines the amount of information that can be displayed and generates a visual level mapping based on the overall importance ranking of candidate information items and the current interaction load index. Indicates time The layout configuration is used to control the structural complexity. Based on the interaction load index and the current interaction scenario type, the interface structure parameters are compressed or restored through the load-complexity mapping function. Indicates time The interaction configuration is used to control the interaction mode. By calculating the comprehensive cost function of each candidate interaction channel and minimizing it, the optimal main interaction channel is determined under the constraints of the scenario. , , These represent the display rendering configuration mapping function, the layout configuration mapping function, and the interaction configuration mapping function, respectively. S52, calculate the interface consistency index and interface usability index, using the following formulas: ; in, Indicates time Interface consistency metrics during operation; Indicates the number of candidate information items; , The first The quantified value of the presentation status of the candidate information item on two different display interfaces represents the presentation status of the first candidate information item. The degree to which a candidate information item is visually presented on two different display interfaces is used to measure whether the information is presented completely and prominently on both interfaces. , It is a known quantity that can be obtained; It represents the maximum possible value in the state quantification system, which is a fixed preset value. ; in, Indicates time User interface usability metrics at that time; Indicates time The average number of interaction steps to complete a key interactive task refers to the average number of operations required for a user to complete a predefined "key interactive task" under the current interface configuration, which is obtained in real time from the system interaction log. Indicates time The average interaction time for completing a key interactive task refers to the average time consumed from the start of the task to the confirmation of task completion, which is automatically recorded by the system event timestamp. The maximum number of allowed interaction steps refers to the maximum reasonable number of operations allowed to complete this type of critical task under safety and experience constraints, and is a preset fixed value; The maximum allowed interaction time is the maximum safe time threshold allowed to complete the critical interaction task, and it is a preset fixed value. and Let be the weight coefficient, and satisfy... ; S53, based on the interface consistency index and interface usability index, determine whether the interface configuration passes the verification. The calculation formula is as follows: ; in, This is the consistency threshold; This is the availability threshold; This indicates that the interface configuration has passed validation. This indicates that the interface configuration failed validation; S54, when the interface configuration fails validation, the interface configuration parameters are updated using the following formula: ; in, Indicates time The complexity of the interface at that time; Configure parameter vectors for the safe default interface; The amplitude reduction parameter is used to adjust the intensity of interface changes. This means updating the left side using the right side. The present invention addresses the problems of information overload, interface conflicts, and increased driver distraction risks caused by static interface configurations and limited adjustment rules in existing smart cockpits under complex driving scenarios. It creatively proposes an adaptive adjustment method for the smart cockpit's interactive interface. This method constructs a multi-source perception system integrating vehicle driving status, cockpit environment, occupant status, and interactive behavior to quantitatively assess the interactive load, using this as the core trigger for interface adjustment. Furthermore, it introduces an interactive scenario recognition and strategy generation mechanism, incorporating interface information display priority, layout complexity, and interaction methods into a unified adjustment framework, and effectively decoupling strategy and interface execution through parameterized mapping. Simultaneously, consistency and availability checks are set after interface configuration is issued, and a safety closed loop is formed by combining rollback or reduced-amplitude re-issuance mechanisms, ensuring the controllability and stability of the interface adjustment process. Thus, this application achieves a shift from passive interface adjustment relying on fixed rules or static configurations to proactive adaptive adjustment based on interactive load perception and closed-loop verification, effectively reducing driver distraction risks and improving the interactive safety and user experience of smart cockpits in multi-screen, multi-modal collaborative scenarios.

[0029] Secondly, corresponding to the above method embodiments, this invention also provides an adaptive adjustment device for the interactive interface of a smart cockpit, such as... Figure 2 As shown, the device includes: The multi-source status information acquisition module is used to acquire multi-source status information within the smart cockpit, including vehicle driving status information, cockpit environment information, occupant status information, and interactive behavior information. The interaction scenario type determination module is used to determine the current interaction scenario type of the smart cockpit based on the vehicle driving status information. The interaction scenarios include normal driving interaction scenarios, high-load driving interaction scenarios, and non-driving interaction scenarios. The current interactive interface has an attention interference risk judgment module, which is used to calculate the interaction load index based on the multi-source state information. When the interaction load index exceeds a preset threshold, it is determined that the current interactive interface has an attention interference risk. The interactive interface adaptive adjustment strategy generation module is used to generate an interactive interface adaptive adjustment strategy based on the interactive load index, the interactive scenario type, the vehicle driving status information and interactive behavior information when it is determined that there is a risk of attention interference. The interactive interface adaptive adjustment strategy includes at least one of the following: an interface information display priority adjustment strategy, an interface layout simplification strategy and an interaction mode switching strategy. The interactive interface configuration mapping, distribution, and verification module is used to complete the mapping and distribution of the interactive interface configuration based on the adaptive adjustment strategy of the interactive interface. After the mapping and distribution are completed, the consistency and availability of the interface are verified. If the verification fails, the configuration is rolled back to the safe default interface configuration or the adjustment range is reduced and then re-distributed.

[0030] For details on the specific processing procedures of each module of the device, please refer to the relevant content in the first section, which will not be repeated here.

[0031] This invention, through comprehensive perception of vehicle status, cabin environment, and occupant interaction characteristics, enables dynamic adaptive adjustment of the interactive interface, reduces the risk of driver distraction, improves human-computer interaction efficiency and consistency, and enhances the safety and stability of the smart cockpit in complex driving scenarios.

[0032] Thirdly, embodiments of the present invention also provide an electronic device, which may be a vehicle-mounted device, such as... Figure 3 As shown, it includes a processor 001, a communication interface 002, a memory 003, and a communication bus 004, wherein the processor 001, the communication interface 002, and the memory 003 communicate with each other through the communication bus 004. The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of any of the adaptive adjustment methods for the interactive interface of a smart cockpit provided in the first aspect of the present invention.

[0033] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0034] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0035] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0036] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0037] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0038] Fourthly, corresponding to the adaptive adjustment method for the interactive interface of a smart cockpit provided in the first aspect, this embodiment of the invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the adaptive adjustment methods for the interactive interface of a smart cockpit provided in the first aspect of this invention.

[0039] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments.

[0040] It should be noted that the device, electronic device and storage medium in the embodiments of the present invention are respectively the device, electronic device and storage medium for applying the above-described adaptive adjustment method for the interactive interface of the smart cockpit. Therefore, all embodiments of the above-described adaptive adjustment method for the interactive interface of the smart cockpit are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0041] It should be noted that, in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for adaptive adjustment of the interactive interface for smart cockpits, characterized in that, include: S1, acquire multi-source status information within the smart cockpit, including vehicle driving status information, cockpit environment information, occupant status information, and interactive behavior information; S2, based on the vehicle driving status information, determine the current smart cockpit interaction scenario type, the interaction scenario includes normal driving interaction scenario, high-load driving interaction scenario and non-driving interaction scenario; S3, calculate the interaction load index based on the multi-source state information, and when the interaction load index exceeds a preset threshold, determine that the current interactive interface has a risk of attention interference. S4. If it is determined that there is a risk of attention interference, an adaptive adjustment strategy for the interactive interface is generated based on the interaction load index, the interaction scenario type, the vehicle driving status information and the interaction behavior information. The adaptive adjustment strategy for the interactive interface includes at least one of the following: interface information display priority adjustment strategy, interface layout simplification strategy and interaction mode switching strategy. S5. Based on the adaptive adjustment strategy of the interactive interface, the mapping and distribution of the interactive interface configuration are completed. After the mapping and distribution are completed, the consistency and availability of the interface are checked. If the check fails, the interface configuration is rolled back to the safe default configuration or the adjustment range is reduced and then re-distributed.

2. The adaptive adjustment method for the interactive interface of a smart cockpit according to claim 1, characterized in that, S1 includes: S11, the multi-source state information at time... Unified as a state vector, the constructed expression is: ； in, For a moment The state vector corresponding to multi-source state information; It is a moment The vehicle driving state subvector at that time is used to represent the vehicle driving state information; It is a moment The cockpit environment subvector is used to represent cockpit environment information. It is a moment The crew state subvector at time is used to represent crew state information; It is a moment The interaction behavior sub-vectors are used to represent interaction behavior information; S12, By collecting data, the vehicle driving state information is modeled to obtain a vehicle driving state sub-vector: ； in, It is a moment The vehicle speed at that time; It is a moment acceleration at time; It is a moment The turning angle at that time; It is a moment Driving mode information at the time; S13, By acquiring data, the cockpit environment information is modeled to obtain a cockpit environment sub-vector: ； in, It is a moment The light intensity of the cabin environment at that time; It is a moment The noise level at that time; It is a moment Information on road complexity at that time; S14, By collecting data, the occupant state information is modeled to obtain the occupant state sub-vector: ； in, It is a moment The direction of the occupants' gaze; It is a moment The head posture of the occupants; It is a moment Information on the fatigue status of passengers; S15, By collecting data, the interactive behavior information is modeled to obtain interactive behavior sub-vectors: ； in, It is a moment The current interaction method corresponding to the time; It is a moment Frequency of interface operations , For in the interval Number of operations within, For the length of the statistical time window; S16, normalize each parameter in the state vector to obtain the normalized state vector corresponding to the multi-source state information.

3. The adaptive adjustment method for the interactive interface of a smart cockpit according to claim 2, characterized in that, S2 include: S21, for each scenario in the preset set of interactive scenarios, construct the scenario discrimination scoring function corresponding to the scenario, and calculate the value of each scenario discrimination scoring function based on the vehicle driving status information; S22, determine the scenario corresponding to the maximum value among the values ​​of the scenario discrimination scoring function as the current smart cockpit interaction scenario type.

4. The adaptive adjustment method for the interactive interface of a smart cockpit according to claim 3, characterized in that, The set of interactive scenarios is represented as follows: ； in, This indicates a normal driving interaction scenario; This indicates a high-load driving interaction scenario; This indicates non-driving interaction scenarios; The scene discrimination scoring function is expressed as follows: ； in, Indicates the first Scene discrimination and scoring functions for similar scenarios; Indicates the first The weight vector for the class scenario; Indicates the first Bias terms for similar scenarios; The eigenvector represents the time step. Vehicle driving state subvector The normalized quantity includes , , and , respectively time speed at time Normalized quantity, time acceleration at time Normalized quantity, time Turning angle at time Normalized quantity, time Driving mode information at the time The normalized quantity; The formula used to determine the scene corresponding to the maximum value of the scene discrimination scoring function is as follows: ； in, It is a moment The type of interaction scenario in the current smart cockpit is determined in real time.

5. The adaptive adjustment method for the interactive interface of a smart cockpit according to claim 4, characterized in that, S3 include: S31, based on multi-factor weighting, defines the interaction load index, expressed as: ； in, It is a moment Interactive load index at that time; For a moment Driving load component at time; For a moment Environmental load components at that time; For a moment Crew status components at that time; For a moment Interaction intensity components at time; , For the corresponding weight coefficients, and ; S32, based on time The normalized value of the vehicle driving state subvector is used to calculate the driving load component, and the calculation formula is as follows: ； in, , , , They are time points speed Normalized quantity, time Time acceleration Normalized quantity, time Steering angle Normalized quantity, time Driving mode The normalized quantity; , They are , , , The weighting coefficients, and , ; S33, based on time The environmental load component is calculated using the cabin environment subvector and its normalized value, as follows: ； in, It is a moment Normalized quantity of road complexity information; It is a moment The normalized measure of the noise level at that time; It is a moment Excessive exposure to sunlight The normalized quantity; , They are , , The weighting coefficients, and , ; ； ； It is a moment The light intensity of the cabin environment at that time; and These are the lower and upper limits of the comfortable lighting range, respectively. and These are the lower and upper limits for inappropriate lighting conditions; S34, Based on the normalized value of the occupant state sub-vector, calculate the occupant state components using the following formula: ； in, For a moment Normalized amount of occupant's line of sight information For a moment Normalized values ​​of occupant head posture information It is a moment Normalized measure of occupant fatigue status information; It is a moment When the occupant's line of sight deviates from the driving-related area parameters The normalized quantity; , , , ,and ; ； ； in, It is a moment The unit vector of the line of sight direction; It is a moment The reference unit vector pointing to the area of ​​interest ahead of the road; and These are the lower and upper limits of the deviation angle calibration range, respectively; S35, based on the normalization of the interaction behavior sub-vectors, calculates the interaction intensity components using the following formula: ； in, It is a moment Normalized quantity of time interaction method It is a moment Normalized value of the frequency of interface operations; It is a moment Interface complexity The normalized quantity; It is a moment Interaction mode load factor; They are respectively , , , The weighting coefficients, and ; ； ； ； in, It is a moment The number of interactive controls currently visible on the interface; It is a moment The depth of the interaction path for completing common tasks in a timely manner; and This is the scaling factor; and These are the lower and upper limits of the interface complexity calibration range, respectively; S36. The calculated interaction load index is judged based on a preset threshold to determine whether there is a risk of attention interference in the current interactive interface. The calculation formula is as follows: ； in, It is a moment Interactive load index at that time; It is a preset threshold; This indicates that the current interactive interface poses a risk of attention distraction. This indicates that there is no risk of attention being distracted by the current interactive interface.

6. The adaptive adjustment method for the interactive interface of a smart cockpit according to claim 5, characterized in that, S4 include: S41, if it is determined that there is a risk of attention interference in the current interactive interface, an adaptive adjustment strategy for the interactive interface is generated based on the interaction load index, the vehicle driving status information, and the interaction behavior information, and the expression is constructed as follows: ； in, Indicates time Adaptive adjustment strategy for the user interface during operation; Indicates the interaction load index; Indicates vehicle driving status information; Indicates interactive behavior information; The strategy generates the mapping function; The constructed expression is further expanded as follows: ； in, Indicates time Information priority sorting vector at that time; Indicates time The level of interface complexity at that time; Indicates time The optimal layout template at that time; Indicates time The result of the interactive channel selection at that time; The adaptive adjustment strategy for the interactive interface is defined by generating an information display priority adjustment strategy by sorting the importance of candidate information, generating an interface layout simplification strategy by classifying the interface complexity, and generating an interaction mode switching strategy by evaluating the comprehensive cost of each candidate interaction channel. S42, generate an information priority sorting vector in descending order of importance, calculated using the following formula: ; For each candidate information item The overall importance is calculated as follows: ； in, This represents the number of candidate information items. A candidate information item refers to an independent information unit that can be displayed or hidden in the current interface. This indicates that the index sequence should be sorted in descending order and output. For a moment Candidate Information Items The static security level index represents the security priority of the candidate information item itself. It is a preset constant that does not change over time. For a moment Candidate Information Items Scenario relevance metrics; The interaction scenario type; For a known scene mapping function; For a moment Candidate Information Items User demand intensity index; For a moment Candidate information items within the corresponding recent time window Number of visits; This is to count the maximum number of visits within the window; , , ,and ; S43, Based on the interaction load index, generate the interface complexity level, calculated using the following formula: ； in, For a moment Interactive load index at that time; , For the graded threshold and ; It is a moment The interface complexity level at that time This indicates the default. To indicate simplification, Minimalism is indicated; S44, Select the optimal layout template based on the preset layout template set, as follows: in, A collection of preset layout templates; For the first One candidate layout template; To use layout templates The deviation in interface complexity afterward; To use layout templates The key information displayed afterward shows the deviation in the area; To use layout templates The deviation in scene adaptation afterward; For the corresponding weight coefficients, and ; S45, the interaction channel selection result is obtained based on the interaction channel comprehensive cost function, as follows: ； Among them, time Time Interaction Channel The overall cost function is: ； in, For a moment Time Interaction Channel The average time cost to complete a task; For a moment Use the interactive channel The average time to complete the current task; The preset maximum reference time; It is a moment The cost of attention occupancy at that time; For interactive channels Inherent cognitive load coefficient; It is a moment Interactive load index at that time; It is a moment The cost of misoperation at that time; ; Interaction channel per unit time Number of erroneous operations; This is the upper limit for counting the number of erroneous operations; For the corresponding weight coefficients, and .

7. The adaptive adjustment method for the interactive interface of a smart cockpit according to claim 6, characterized in that, S5 include: S51, the adaptive adjustment strategy of the interactive interface is mapped into a unified form of the interface configuration parameter set, expressed as: ； in, Indicates time Adaptive adjustment strategy for the user interface during operation; This is a parameterized mapping function used to convert strategy parameters into executable UI configuration parameters; The set of interface configuration parameters obtained from the aforementioned strategy mapping is as follows: ； in, Indicates time The display configuration at any time is used to control the amount of information displayed. Indicates time The layout configuration at that time is used to control structural complexity; Indicates time The interaction configuration is used to control the interaction method; , , These represent the display rendering configuration mapping function, the layout configuration mapping function, and the interaction configuration mapping function, respectively. S52, calculate the interface consistency index and interface usability index, using the following formulas: ； in, Indicates time Interface consistency metrics during operation; Indicates the number of candidate information items; , The first The quantified value of the presentation status of the candidate information item on two different display interfaces represents the presentation status of the first candidate information item. The degree of visual presentation of each candidate information item on two different display interfaces is used to measure whether the information is presented completely and prominently on both interfaces; It represents the maximum possible value in the state quantification system, which is a fixed preset value. ； in, Indicates time User interface availability metrics at that time; Indicates time The average number of interaction steps required to complete a key interactive task in a given time; Indicates time This refers to the average interaction time to complete a key interactive task; The maximum number of interaction steps allowed; The maximum allowed interaction time; and Let be the weight coefficient, and satisfy... ; S53, based on the interface consistency index and interface usability index, determine whether the interface configuration passes the verification. The calculation formula is as follows: ； in, This is the consistency threshold; This is the availability threshold; This indicates that the interface configuration has passed validation. This indicates that the interface configuration failed validation; S54, when the interface configuration fails validation, the interface configuration parameters are updated using the following formula: ； in, Indicates time The complexity of the interface at that time; Configure parameter vectors for the safe default interface; The amplitude reduction parameter is used to adjust the intensity of interface changes. This means updating the left side using the right side.

8. An adaptive adjustment device for the interactive interface of a smart cockpit, characterized in that, include: The multi-source status information acquisition module is used to acquire multi-source status information within the smart cockpit, including vehicle driving status information, cockpit environment information, occupant status information, and interactive behavior information. The interaction scenario type determination module is used to determine the current interaction scenario type of the smart cockpit based on the vehicle driving status information. The interaction scenarios include normal driving interaction scenarios, high-load driving interaction scenarios, and non-driving interaction scenarios. The current interactive interface has an attention interference risk judgment module, which is used to calculate the interaction load index based on the multi-source state information. When the interaction load index exceeds a preset threshold, it is determined that the current interactive interface has an attention interference risk. The interactive interface adaptive adjustment strategy generation module is used to generate an interactive interface adaptive adjustment strategy based on the interactive load index, the interactive scenario type, the vehicle driving status information and interactive behavior information when it is determined that there is a risk of attention interference. The interactive interface adaptive adjustment strategy includes at least one of the following: an interface information display priority adjustment strategy, an interface layout simplification strategy and an interaction mode switching strategy. The interactive interface configuration mapping, distribution, and verification module is used to complete the mapping and distribution of the interactive interface configuration based on the adaptive adjustment strategy of the interactive interface. After the mapping and distribution are completed, the consistency and availability of the interface are verified. If the verification fails, the configuration is rolled back to the safe default interface configuration or the adjustment range is reduced and then re-distributed.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-7.

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