A method and system for dynamic adaptation tension control of automotive seat belts

Abstract

This invention relates to a dynamic adaptation tension control method and system for automotive seat belts, specifically in the field of automotive seat belts. The method includes: acquiring seat belt buckle insertion signals; when the buckle insertion signal is not inserted, releasing the initial pulling restriction of the retractor on the seat belt based on a preset mechanical linkage; when the buckle insertion signal is inserted, acquiring seat belt pull-out action data; deriving quantitative parameters of the user's tightening intention based on the pull-out action data; generating an adaptation tightening command based on the quantitative parameters of the user's tightening intention; and controlling the retractor to execute the adaptation tightening command to complete the adaptation tensioning of the seat belt. This invention improves user convenience.

Description

Technical Field

[0001] This invention relates to the field of automotive seat belts, and in particular to a method and system for dynamic adaptation tension control of automotive seat belts. Background Technology

[0002] As a core component of vehicle passive safety, the seat belt's core function is to effectively restrain occupants to their seats in the event of a collision.

[0003] Currently, the retractor contains an inertial pendulum or steel ball. When the vehicle decelerates suddenly or tilts, the inertial force causes the pendulum / steel ball to move, triggering a locking mechanism that locks the retractor spindle, preventing the webbing from being pulled out further.

[0004] This mechanism is also effective when the buckle is not fastened. If the user pulls the webbing quickly, the resulting acceleration is sufficient to simulate an emergency, which may unexpectedly trigger the inertial lock, causing it to jam and reducing user convenience. This needs improvement. Summary of the Invention

[0005] To improve user convenience, this invention provides a method and system for dynamic adaptation tension control of automotive seat belts.

[0006] In a first aspect, the present invention provides a method for dynamic adaptation tension control of automotive seat belts, employing the following technical solution:

[0007] A method for dynamic adaptation tension control of automotive seat belts includes:

[0008] Collect the buckle insertion signal of the seat belt;

[0009] When the buckle insertion signal is not inserted, the retractor releases the initial pulling restriction on the seat belt based on the preset mechanical linkage.

[0010] When the buckle insertion signal is "inserted", the data on the pull-out action of the seat belt is collected;

[0011] Based on the pull-out action data, quantitative parameters of the user's tightening intention are derived;

[0012] Quantify parameters based on user tightening intent to generate adaptive tightening instructions;

[0013] The retractor is controlled to execute adaptive tightening commands to complete the adaptive tensioning of the seat belt.

[0014] By adopting the above technical solution, when the buckle is not inserted, the locking restriction of the retractor is temporarily released through mechanical linkage, allowing the webbing to be pulled out freely and quickly. This fundamentally avoids the jamming problem caused by accidental triggering of inertial locking, significantly improving the smoothness of operation and user experience. After the buckle is inserted, the system dynamically understands the user's tightening intention based on intelligent analysis of the pulling action and automatically generates a matching tightening command. This allows the seat belt to automatically complete the initial tensioning with a force and speed that matches the user's needs, realizing a transformation from passive locking to active intelligent adaptation. While ensuring core safety functions, it greatly improves the convenience of daily use.

[0015] Optionally, it also includes a method for determining the quantification parameters of the user's tightening intent:

[0016] The peak acceleration and maximum instantaneous velocity during the pull-out process are determined based on the pull-out motion data;

[0017] The initial tightening strength level is determined by comparing the peak acceleration with preset multi-level acceleration thresholds.

[0018] The tightening response level is determined based on the maximum instantaneous velocity;

[0019] Collect the average deceleration speed before the end of the pull-out motion;

[0020] Determine whether the user intends to actively slow down based on the average deceleration rate;

[0021] When there is an intention to actively decelerate, the initial tightening strength level is dynamically corrected based on the average deceleration rate to generate the final tightening strength level.

[0022] Combine the final tightening intensity level with the tightening response level to generate quantitative parameters of the user's tightening intention.

[0023] Optionally, methods for determining the quantification parameters of user tightening intent also include:

[0024] Collect the time interval between two consecutive valid pull-out actions;

[0025] The operation time interval is used to determine whether the current operation is a continuous adjustment action;

[0026] When it is a continuous adjustment action, the final tightening strength level corresponding to the previous pull-out action is collected as the historical reference strength.

[0027] A comprehensive strength level is generated by numerically fusing historical reference strength and final tightening strength level.

[0028] The user's tightening intention quantification parameters are updated based on the comprehensive intensity level, and an adaptive tightening instruction is generated based on the updated user tightening intention quantification parameters.

[0029] When it is not a continuous adjustment action, an adaptive tightening command is generated directly based on the user's tightening intention quantification parameters.

[0030] Optionally, seatbelt rebound assist methods may also be included:

[0031] The frequency of frictional vibration between the seat belt and the guide ring and the natural retraction speed were collected during the retraction process.

[0032] The wear level of the webbing surface is determined based on the frequency of frictional vibration.

[0033] The standard retraction speed range is determined based on the wear level of the webbing surface.

[0034] The natural retraction speed is compared with the standard retraction speed range to determine the degree of rebound force attenuation.

[0035] When the degree of rebound force attenuation exceeds the preset standard attenuation degree, a stepped compensation level is generated based on the degree of rebound force attenuation.

[0036] Based on stepped compensation levels to match the auxiliary torque release curve;

[0037] Based on the auxiliary torque release curve, the preset auxiliary rebound device is controlled to output differentiated auxiliary torques at different stages of seat belt retraction.

[0038] Optional, also includes:

[0039] After the seat belt retraction is completed, the retractor is controlled to apply a preset pulse excitation signal to the webbing and the vibration response signal of the webbing to the excitation is collected.

[0040] Based on vibration response signals to extract signal distortion features;

[0041] The type and location of internal damage are determined based on signal distortion characteristics;

[0042] Combine internal damage type and damage location to generate targeted compensation waveform parameters;

[0043] The auxiliary torque waveform is obtained based on the targeted compensation waveform parameters;

[0044] Based on the targeted compensation waveform parameters, the auxiliary rebound device is controlled to output an auxiliary torque waveform when the damaged location passes through during the subsequent retraction process.

[0045] Optionally, specific methods for outputting differentiated auxiliary torques may also be included:

[0046] Collect the current retraction length of the seat belt;

[0047] The current travel interval is determined based on the current retraction travel length;

[0048] The real-time target torque value is obtained by combining the current travel range and the auxiliary torque release curve;

[0049] The driving parameters of the auxiliary rebound device are calculated and generated based on the real-time target torque value.

[0050] The driving parameters are used to control the output of the auxiliary rebound device to provide the differentiated auxiliary torque required at the moment.

[0051] Optional features include comfort adjustment methods:

[0052] Collect seat position parameters and steering wheel position parameters;

[0053] Combine seat position parameters and steering wheel position parameters to generate driving posture data;

[0054] Determine the type of sitting posture tendency based on driving posture data;

[0055] The seatbelt pretension reference value is matched according to the type of sitting posture.

[0056] Collect the preset manual adjustment signal of the seat belt adjustment wheel;

[0057] The target displacement direction and target displacement distance are generated based on the manual adjustment signal and the pre-tension reference value;

[0058] Based on the target displacement direction and target displacement distance, the preset tension locking mechanism is controlled to move, thereby realizing intelligent auxiliary adjustment of the seat belt tension.

[0059] Optionally, adjustment and correction methods may also be included:

[0060] After the tensioning and locking mechanism completes the displacement, real-time pressure distribution data on the seat belt webbing is collected;

[0061] The uniformity of seat belt fit is determined based on real-time pressure distribution data;

[0062] The uniformity of the fit is compared with a preset optimal comfort threshold to obtain an adjustment effect score;

[0063] Dynamic correction coefficients are generated based on the adjustment effect score, and the seat belt pretension reference value is corrected based on the dynamic correction coefficients.

[0064] Optionally, attitude change pre-tuning methods may also be included:

[0065] Collect dynamic change data of seat position parameters and steering wheel position parameters;

[0066] Based on dynamically changing data, determine whether the driver is making significant posture adjustments;

[0067] When a significant posture adjustment is determined, the type of post-adjustment posture tendency is determined based on dynamic change data.

[0068] Based on the type of seat posture after adjustment, the adjusted seat belt pretension reference value is matched in advance;

[0069] Once the posture adjustment is complete and the seatbelt buckle status remains unchanged, a pre-adjustment command is generated based on the adjusted pre-tension reference value.

[0070] Based on the pre-adjustment command, the tension locking mechanism is pre-displaced in advance so that the tension of the seat belt is close to the fit when the posture is stable.

[0071] Secondly, this application provides a dynamic adaptation tension control system for automotive seat belts, employing the following technical solution:

[0072] A dynamic adaptation tension control system for automotive seat belts, comprising:

[0073] The data acquisition module is used to acquire the insertion signal and pull-out action data of the buckle;

[0074] The memory is used to store the program that implements a method for dynamic adaptation tension control of automotive seat belts;

[0075] The processor is used to load and execute programs stored in memory.

[0076] In summary, this application includes at least one of the following beneficial technical effects:

[0077] 1. When the buckle is not inserted, the mechanical linkage temporarily releases the locking restriction of the retractor, allowing the webbing to be pulled out freely and quickly. This fundamentally avoids the jamming problem caused by accidental triggering of inertial locking, significantly improving the smoothness of operation and user experience. After the buckle is inserted, the system dynamically understands the user's tightening intention based on intelligent analysis of the pulling action and automatically generates a matching tightening command. This allows the seat belt to automatically complete the initial tensioning with a force and speed that matches the user's needs, realizing a transformation from passive locking to active intelligent adaptation. While ensuring core safety functions, it greatly improves the convenience of daily use.

[0078] 2. By analyzing the vibration characteristics generated by the friction between the webbing and the guide ring, the system can non-contactly diagnose the wear condition of the webbing surface and set a dynamic standard retraction speed benchmark accordingly. When the system detects a decrease in natural retraction speed due to spring aging, it automatically activates a stepped compensation strategy based on the degree of decrease, controlling the auxiliary rebound device to output differentiated auxiliary torques at different stages of the retraction stroke. This effectively solves the problem of weak rebound caused by component fatigue, improving product durability and user experience.

[0079] 3. By comprehensively analyzing the positional parameters of the seat and steering wheel, the system can accurately identify the driver's real-time seating posture and intelligently match a preset tension reference value, providing personalized initial tension suggestions for different postures. When the user makes manual fine adjustments using the adjustment wheel, the system combines the manual operation intention with the current posture reference value to generate precise adjustment commands, driving the tension locking mechanism to complete the displacement. This significantly shortens the adjustment time, reduces operational complexity, and ultimately enables the seat belt to provide occupants with a comfortable restraint experience tailored to individual needs in different driving scenarios. Attached Figure Description

[0080] Figure 1 This is a flowchart of a method for dynamic adaptation tension control of automotive seat belts.

[0081] Figure 2 This is a flowchart of the attitude change pre-adjustment method. Detailed Implementation

[0082] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0083] Reference Figure 1 This application discloses a dynamic adaptation tension control method for automotive seat belts. Before implementing this method, a detailed mechanical simulation and structural optimization of the seat belt restraint system for the target vehicle model have been completed, providing an engineering basis for the safe and effective execution of the adaptation tensioning command in this method. Specifically, this includes: establishing finite element models of key components including the seat frame, seat belt webbing, B-pillar, slide rail, and retractor connecting plate based on vehicle model data; determining an optimized simulation scheme for quasi-static strength analysis through algorithm and element type comparison; performing strength analysis of the seat belt fixing points, identifying and optimizing the structure of weak points such as the B-pillar mounting point. Based on this optimized model, simulations determine the maximum safe tensioning force, optimal force loading rate, and boundary parameters of the torque stroke that the retractor can apply under different working conditions. The dynamic control in this method is performed within these safety boundaries. The method includes the following steps:

[0084] S10: Collect the buckle insertion signal of the seat belt.

[0085] The buckle insertion signal is an electrical signal used to determine whether the seatbelt buckle is correctly engaged with the buckle holder. In this embodiment, this signal is acquired by a miniature microswitch installed inside the buckle holder. When the buckle is fully inserted, the trigger switch closes, and the sensor immediately generates an "inserted" signal; otherwise, it remains a "not inserted" signal.

[0086] S11: When the buckle insertion signal is not inserted, the initial pulling restriction of the retractor on the seat belt is released based on the preset mechanical linkage.

[0087] The "not inserted" signal refers to the sensor signal state, as described in S10, indicating that the buckle is not engaged.

[0088] Mechanical linkage refers to a purely mechanical transmission device that connects the buckle base and the retractor locking mechanism. Specifically, it is achieved through a push rod physically connected to the buckle base. When the buckle is not inserted, the push rod is in the extended position, and its top inclined surface directly pushes the fulcrum of a limiting lever, forcing the lever to rotate around its axis, thereby completely disengaging it from the locking gear of the retractor and releasing the restriction on the rapid pulling of the webbing.

[0089] When the buckle insertion signal is not inserted, mechanical linkage is required to release the initial pulling restriction of the retractor on the seat belt so that the seat belt can be pulled quickly.

[0090] S12: When the buckle insertion signal is an inserted signal, collect the seat belt pull-out action data.

[0091] The inserted signal refers to the sensor signal that confirms the buckle has been correctly engaged, as described in S10.

[0092] Pull-out action data refers to the set of dynamic parameters during the process of the webbing being pulled out, mainly including pull-out speed and acceleration. In this embodiment, these are acquired by a photoelectric rotary encoder installed on the winding shaft. The encoder converts the angular displacement of the shaft into a pulse sequence. The system calculates the number of pulses per unit time to obtain the instantaneous linear velocity of the webbing, and then calculates the real-time pull-out acceleration by differentiating the velocity.

[0093] When the buckle insertion signal is "inserted", the seat belt pull-out action data must be collected first for subsequent steps.

[0094] S13: Based on the pull-out action data, quantify the user's tightening intention parameters.

[0095] The user tightening intent quantification parameter refers to a set of digital control parameters that can be directly executed by the controller to precisely guide the tightening action of the retractor. The specific method for determining the user tightening intent quantification parameter will be explained in detail in S20 to S26 and S30 to S35, and will not be repeated here.

[0096] S14: Quantize parameters based on the user's tightening intention to generate adaptive tightening instructions.

[0097] Adaptive tightening commands refer to the specific command parameters that control the retractor to perform tightening actions.

[0098] The adaptive tightening command is obtained by querying a preset intent command mapping table. This table predefines parameters such as tightening torque and response speed corresponding to different user tightening intent quantification parameters. This mapping table is established by those skilled in the art within the aforementioned safety boundaries by setting specific parameters and associating records according to the ergonomic requirements of different intents, and will not be elaborated here.

[0099] S15: Control the retractor to execute the adaptive tightening command to complete the adaptive tensioning of the seat belt.

[0100] This step is achieved through the motor controller built into the retractor. The controller receives the adaptive tensioning command generated by S14, and precisely executes the tensioning action by adjusting the current and start-up timing of the drive motor, so that the seat belt automatically fits the occupant's body in a manner that matches the user's intention, thereby completing the adaptive tensioning of the seat belt.

[0101] It also includes methods for determining the quantification parameters of user tightening intent:

[0102] S20: Determine the peak acceleration and maximum instantaneous velocity during the pull-out process based on the pull-out action data.

[0103] Peak acceleration refers to the maximum absolute value of acceleration that can be achieved during a pull-out motion. The maximum value is obtained by comparing the acceleration data stream continuously collected by S12 in real time and recording it.

[0104] Maximum instantaneous speed refers to the maximum speed that can be achieved during a single pull-out motion. This maximum speed is obtained by comparing the continuously acquired speed data stream from S12 in real time, recording and analyzing the data.

[0105] S21: The initial tightening strength level is determined by comparing the peak acceleration with the preset multi-level acceleration threshold.

[0106] Multi-level acceleration thresholds refer to the values ​​used to divide acceleration intervals. These thresholds are pre-set by those skilled in the art and will not be elaborated upon here.

[0107] The initial tightening strength level refers to the tightening strength level initially determined based on the intensity of the pull.

[0108] It is obtained by comparing the peak acceleration value with a multi-level acceleration threshold range. The multi-level acceleration threshold is set by those skilled in the art by dividing the range through analysis of the acceleration statistical characteristics of pull-out actions at different levels of urgency, and will not be elaborated here.

[0109] S22: Determine the tightening response level based on the maximum instantaneous velocity.

[0110] Tightening response level refers to the system response speed level determined by the rate of pull-out.

[0111] The tightening response level is obtained by querying a preset speed level lookup table. This table records the correspondence between the maximum instantaneous speed and the tightening response level. By matching the maximum instantaneous speed value in the table, the corresponding tightening response level can be determined. The speed level lookup table is established by those skilled in the art by analyzing the optimal system response time at different pull-out speeds, dividing speed ranges and associating them with response levels, and will not be elaborated upon here.

[0112] S23: Collect the average deceleration speed before the end of the pull-out action.

[0113] The average deceleration rate refers to the average rate of decrease in speed before the user stops pulling and the webbing speed returns to zero. It is obtained by extracting the speed data sequence within a preset time period before the end of the pulling action (set in advance by those skilled in the art, and not described in detail here) and calculating the absolute value of the linear regression slope (negative value) of the sequence. The specific calculation method is common knowledge in the art and will not be described in detail here.

[0114] S24: Determine whether the user intends to actively decelerate based on the average deceleration speed.

[0115] Active deceleration intention refers to the user consciously and smoothly ending the pull-out action.

[0116] This judgment is achieved by comparing the average deceleration speed calculated in S23 with a preset active deceleration threshold. If the average deceleration speed is greater than or equal to the threshold, it is determined that there is an intention to actively decelerate; otherwise, it is not. The active deceleration threshold is determined by statistically analyzing the difference in deceleration speed between two behaviors: intentional and unintentional release of the device. This threshold is set by those skilled in the art and will not be elaborated upon here.

[0117] S25: When there is an intention to actively decelerate, the initial tightening strength level is dynamically corrected based on the average deceleration speed to generate the final tightening strength level.

[0118] The final tightening strength rating refers to the overall tightening strength rating that combines the force of the pull and the smoothness of the finish.

[0119] This level is generated using a correction function. For example: final level value = initial level value × (1 - k × average deceleration rate), where k is a preset correction coefficient. The corrected final tightening strength level can be generated by substituting the initial level of S21 (already quantified as a value) and the average deceleration rate of S23 into the formula. This correction function and its coefficient k were determined by those skilled in the art through experimental testing of the impact of different deceleration intentions on tightening comfort, and will not be elaborated upon here.

[0120] When there is an intention to actively slow down, the final tightening intensity level must be generated first to facilitate subsequent steps.

[0121] S26: Combine the final tightening intensity level with the tightening response level to generate quantitative parameters of the user's tightening intention.

[0122] The user tightening intent quantization parameter is a set of digital control parameters that can be directly executed by the controller.

[0123] This parameter set is obtained by querying a pre-defined two-dimensional mapping table. The rows and columns of this table correspond to the "final tightening intensity level" and "tightening response level," respectively, and each cell stores a specific set of {target torque, response time} parameters. By querying this table using the two levels obtained from S25 and S22 as a joint index, the corresponding quantitative parameters of the user's tightening intention can be obtained. This two-dimensional mapping table is established by calibrating and testing different combinations of levels within a safe boundary to determine the optimal parameters and associate records; details will not be elaborated here.

[0124] Methods for determining the parameters for quantifying user tightening intent also include:

[0125] S30: Collect the operation time interval between two consecutive effective pull-out actions.

[0126] The operation time interval refers to the time difference between the end of one pull-out action and the start of the next. An effective pull-out action is an action in which the pull-out length exceeds a preset minimum threshold (e.g., 5 cm).

[0127] This interval is obtained by recording the start timestamp of each valid pull-out action using the system clock and calculating the difference between the start times of two consecutive pull-out actions.

[0128] S31: Determine whether the current operation is a continuous adjustment action based on the operation time interval.

[0129] Continuous adjustment refers to repeated fine adjustments made by the user within a short period of time.

[0130] This determination is achieved by comparing the operation time interval with a preset continuity threshold. If the interval is less than the threshold, it is determined to be a continuous adjustment action; otherwise, it is not. This continuity threshold is determined by those skilled in the art through observing the statistical characteristics of the intervals between continuous fine-tuning operations performed by the user, and will not be elaborated upon here.

[0131] S32: When it is a continuous adjustment action, the final tightening strength level corresponding to the previous pull-out action is collected as the historical reference strength.

[0132] Historical reference strength refers to the tightening strength level that was last used in the previous continuous adjustment.

[0133] The final tightening strength level corresponding to each pull-out action is recorded in the system cache, and when S31 determines that it is a continuous action, the level value recorded in the previous time is read from the cache.

[0134] When it is a continuous adjustment action, historical reference intensity must be collected first for subsequent steps.

[0135] S33: Numerical fusion is performed based on historical reference strength and final tightening strength level to generate a comprehensive strength level.

[0136] The comprehensive strength level refers to the new strength level calculated in continuous adjustment to achieve smooth convergence.

[0137] This level is generated using a weighted average algorithm. For example: Overall Level = (Historical Reference Strength × 0.7 + Current Final Strength Level × 0.3). The overall strength level is generated through this calculation. The weighting coefficients of this weighted average algorithm were optimized and determined by those skilled in the art through testing the smoothness and convergence speed of continuous adjustments under different weights, and will not be elaborated upon here.

[0138] S34: Update the user's tightening intention quantification parameters based on the comprehensive intensity level, and generate an adaptive tightening instruction based on the updated user tightening intention quantification parameters.

[0139] This step is achieved by replacing the "final tightening intensity level" in S25 with the "overall intensity level" generated in S33, and then re-executing the process in S26. That is, by using the new "overall intensity level" and the original "tightening response level" as indexes, the two-dimensional mapping table is queried again to obtain the updated quantitative parameters of the user's tightening intention, and then a new adaptive tightening instruction is generated through the process in S14.

[0140] S35: When it is not a continuous adjustment action, generate an adaptive tightening command directly based on the user's tightening intention quantification parameters.

[0141] For independent actions, the original user tightening intent quantization parameters generated in S26 are used directly to generate adaptive tightening instructions through the process in S14.

[0142] It also includes seatbelt rebound assist methods:

[0143] S40: Collect the frequency of frictional vibration between the seat belt and the guide ring and the natural retraction speed during the retraction process.

[0144] Frictional vibration frequency refers to the characteristic frequency of vibration generated by the friction between the webbing and the guide ring. The original vibration signal is collected by a piezoelectric accelerometer attached to the guide ring, and then the dominant frequency is extracted by fast Fourier transform analysis of the signal.

[0145] Natural retraction speed refers to the average retraction speed relying solely on the main spring force. It is measured by the encoder on the retractor shaft during the retraction process, and the ratio of the total retraction length to the time taken is calculated.

[0146] S41: Determine the wear level of the webbing surface based on the frequency of frictional vibration.

[0147] The surface wear rating of webbing is a quantitative classification of the degree of wear on the nylon surface of the webbing.

[0148] The wear level is determined by comparing the real-time friction vibration frequency spectrum obtained from S40 with a pre-stored reference spectrum for the new webbing. Specifically, the energy attenuation ratio of the two in the characteristic high-frequency range (e.g., 3 to 5 kHz) is calculated. By matching this ratio with a preset threshold, the wear level can be determined. The reference spectrum for the new webbing and the wear determination threshold are established and set by those skilled in the art through analysis and statistics of the vibration spectra of new webbing and webbing with known wear levels measured under laboratory conditions, and will not be elaborated here.

[0149] S42: Determine the standard retraction speed range based on the webbing surface wear level.

[0150] The standard retraction speed range refers to the normal range of retraction speed that should exist under a specific wear level.

[0151] The standard retraction speed range is obtained by consulting a preset wear range table. The corresponding standard retraction speed range can be obtained by looking up the table using the wear level determined in S41 as the index. This wear range table was established by those skilled in the art through statistical analysis and record correlation of the retraction speed ranges of webbing with different wear levels under standard conditions, and will not be elaborated upon here.

[0152] S43: Compare the natural retraction speed with the standard retraction speed range to determine the degree of rebound force attenuation.

[0153] The degree of rebound force attenuation refers to a quantitative indicator of the decrease in rebound force caused by the aging of the main spring.

[0154] The natural retraction speed obtained by S40 is determined by whether it is lower than the lower limit of the standard retraction speed range corresponding to its wear level, and the proportion of the lower value is calculated.

[0155] S44: When the degree of rebound force attenuation exceeds the preset standard attenuation degree, a stepped compensation level is generated based on the degree of rebound force attenuation.

[0156] The standard attenuation level is a preset threshold value at which compensation needs to be initiated. The standard attenuation level is set in advance by those skilled in the art and will not be elaborated here.

[0157] The stepped compensation level refers to the auxiliary strength level divided according to the severity of attenuation.

[0158] The standard attenuation level calculated by S43 is generated by matching a set of preset compensation thresholds. The standard attenuation level and compensation thresholds are determined by those skilled in the art by setting the differentiation thresholds based on the evaluation of the impact of different attenuation levels on the rebound experience, and will not be elaborated here.

[0159] When the degree of rebound force decay exceeds the standard decay level, a stepped compensation level needs to be generated first for subsequent steps.

[0160] S45: Based on stepped compensation levels to match the auxiliary torque release curve.

[0161] The auxiliary torque release curve is a function curve that defines how the auxiliary torque changes with the retraction stroke.

[0162] This curve is obtained by querying a pre-defined compensation level curve library. The library contains different torque-stroke curve data for each step-type compensation level. By searching the library using the level generated by S44 as the keyword, the corresponding auxiliary torque release curve can be matched. This compensation level curve library was established by those skilled in the art through retraction auxiliary testing for each compensation level, optimizing and determining the optimal torque release mode, and associating records; details will not be elaborated here.

[0163] S46: Based on the auxiliary torque release curve, control the preset auxiliary rebound device to output differentiated auxiliary torque at different stages of seat belt retraction.

[0164] The auxiliary rebound device refers to a mechanism that can output auxiliary torque in a controlled manner. In this embodiment, it is an auxiliary roller driven by a micro motor.

[0165] Based on the release curve matched in S45 and combined with the real-time acquired retraction stroke position (from the encoder), the controller calculates the target torque value to be output at the current moment. Then, by adjusting the current of the drive motor, it accurately outputs the differentiated auxiliary torque required at that moment. The specific method for outputting differentiated auxiliary torque is explained in detail in S60 to S64, and will not be repeated here.

[0166] Also includes:

[0167] S50: After the seat belt retraction is completed, the retractor is controlled to apply a preset pulse excitation signal to the webbing and the vibration response signal of the webbing to the excitation is collected.

[0168] A pulsed excitation signal refers to a brief mechanical disturbance signal. It is generated by controlling the retractor drive motor to momentarily reverse by a very small angle (pre-set by those skilled in the art, which will not be elaborated here) and then immediately reset.

[0169] The vibration response signal refers to the stress wave response of the webbing caused by the aforementioned excitation. It is acquired using a high-sensitivity MEMS accelerometer mounted on the take-up reel housing.

[0170] S51: Extract signal distortion features based on vibration response signals.

[0171] Signal distortion characteristics refer to the abnormalities in the response signal relative to the reference signal of a healthy webbing.

[0172] The distortion is extracted by comparing the real-time vibration response signal acquired by the S50 with the pre-stored healthy webbing reference response signal in the time domain, and calculating the cross-correlation coefficient or the root mean square (RMS) value of the amplitude difference between the two within a specific time window. A lower correlation coefficient or a higher RMS value indicates a significant distortion feature. The healthy webbing reference response signal is established by applying standard pulse excitation to undamaged webbing in the laboratory and acquiring its response, which will not be elaborated here.

[0173] S52: Determine the type and location of internal damage based on signal distortion characteristics.

[0174] Internal damage type refers to the type of damage to the fibers inside the webbing, such as "fiber bundle breakage" or "local hardening".

[0175] Damage location refers to the distance from the damage point to the retractor.

[0176] The type and location are obtained through a pre-trained neural network model. This model is trained by learning from a large number of distorted feature samples with labels (known lesion type and location). By inputting the distorted features extracted by S51 into this model, the model can output the judgment result of the lesion type and location. This neural network model is trained using a deep learning framework by collecting and labeling sample data of various typical lesions, which will not be elaborated upon here.

[0177] S53: Combine internal damage type and damage location to generate targeted compensation waveform parameters.

[0178] Targeted compensation waveform parameters refer to torque waveform parameters customized to counteract specific damage resistance.

[0179] This parameter is generated by querying a pre-defined damage waveform parameter mapping table. This table predefines optimal compensation waveform parameters based on the mechanical properties of different types of damage. By querying this table using the damage type and location determined in S52 as the joint key, the corresponding targeted compensation waveform parameters can be generated. This damage waveform parameter mapping table is established by analyzing the impedance characteristics of different types of damage and solving an inverse problem, allowing those skilled in the art to determine the optimal compensation waveform parameters and associate them with records; details will not be elaborated here.

[0180] S54: Obtain the auxiliary torque waveform based on the target compensation waveform parameters.

[0181] The auxiliary torque waveform refers to the torque change curve that is ultimately output and precisely designed in the time domain.

[0182] The waveform is generated by a waveform synthesizer. Based on the target compensation waveform parameters obtained from S53 (such as "center frequency 10Hz, pulse width 100ms"), the synthesizer calls the corresponding fundamental function (such as sine wave, Gaussian pulse) to synthesize the waveform, thereby obtaining specific, digital auxiliary torque waveform data.

[0183] S55: Based on the target compensation waveform parameters, the auxiliary rebound device outputs an auxiliary torque waveform when the damaged position passes through during the subsequent retraction process.

[0184] During the next retraction, the system calculates the expected time when the damage point will pass through the auxiliary rebound device, based on the damage location determined in S52 and the real-time retraction stroke. When that time arrives, the system converts the auxiliary torque waveform data generated in S54 into a drive signal through the controller of the auxiliary rebound device. The control device precisely outputs this customized waveform to counteract the additional resistance at the damage point.

[0185] It also includes specific methods for outputting differentiated auxiliary torques:

[0186] S60: Collect the current retraction length of the seat belt.

[0187] The current retraction stroke length refers to the length of webbing that has been retracted from the retraction start point to the current moment.

[0188] The angle rotated by the encoder on the retractor shaft during the retraction process is accumulated and multiplied by the radius of the webbing winding.

[0189] S61: Determine the current travel range based on the current retraction travel length.

[0190] The current travel interval refers to the several stages into which the total retrace travel is divided (such as "initial segment", "middle segment", "final segment").

[0191] The current retraction stroke length obtained in S60 is determined by comparing it with preset interval boundary values ​​(such as 30% or 70% of the total length). For example, if the length is less than 30% of the total length, it is determined to be in the "initial segment". These stroke interval boundary values ​​are set by those skilled in the art by analyzing typical stages of resistance change during the retraction process, and will not be elaborated here.

[0192] S62: Combine the current travel range and the auxiliary torque release curve to obtain the real-time target torque value.

[0193] The real-time target torque value refers to the specific value of the auxiliary torque that should be output at the current moment.

[0194] The target torque is obtained by interpolating the current travel range determined in S61 as input onto the auxiliary torque release curve matched in S45. The release curve itself is a function that defines "travel range - target torque".

[0195] S63: Calculate and generate the driving parameters of the auxiliary rebound device based on the real-time target torque value.

[0196] Driving parameters refer to physical quantities that directly drive the actuator, such as PWM (Pulse Width Modulation) duty cycle or current value.

[0197] The driving parameters are calculated using a torque-driven conversion model. The real-time target torque value obtained from S62 is input into this model to calculate the corresponding driving parameters. This torque-driven conversion model is established by fitting or recording the input-output relationship of the auxiliary rebound device through torque calibration tests, and will not be elaborated upon here.

[0198] S64: Based on the driving parameters, control the auxiliary rebound device to output the differentiated auxiliary torque currently required.

[0199] The system directly controls the device to output a differentiated auxiliary torque that matches the current stroke stage by writing the drive parameters generated by S63 into the corresponding registers of the auxiliary rebound device drive chip in real time.

[0200] It also includes comfort adjustment methods:

[0201] S70: Collects seat position parameters and steering wheel position parameters.

[0202] Seat position parameters include seat fore-aft position, height, and backrest angle. These are obtained by reading data from the CAN bus of the seat control module.

[0203] Steering wheel position parameters include steering wheel extension and tilt angles. These are obtained by reading CAN bus data from the steering wheel adjustment module.

[0204] S71: Combines seat position parameters and steering wheel position parameters to generate driving posture data.

[0205] Driving posture data is an abstract data vector that comprehensively represents the driver's sitting posture.

[0206] Driving posture data is generated using a posture fusion algorithm. This algorithm normalizes and weights multiple raw parameters collected by the S70, outputting a driving posture data vector containing feature values ​​such as "torso tilt angle" and "arm extension." The posture fusion algorithm and its weights were optimized and determined by those skilled in the art by analyzing the contribution of different parameters to the representation of the sitting posture, and will not be elaborated here.

[0207] S72: Determines the type of sitting posture based on driving posture data.

[0208] Posture preference type is a classification label for driving posture, such as "standard sitting posture", "relaxed back", and "focused forward".

[0209] The seating posture type is determined by calculating the Euclidean distance between the current driving posture data vector and multiple pre-stored standard seating posture reference vectors, and selecting the label corresponding to the reference vector with the smallest distance. The standard seating posture reference vectors are established by collecting data from the driver in typical postures such as standard, reclined, and forward lean, and calculating their mean values, which will not be elaborated here.

[0210] S73: Match the seat belt pretension reference value according to the sitting posture type.

[0211] The seat belt pretension reference value is a preset initial tension reference value for different sitting posture types.

[0212] The seatbelt pretension force baseline value is obtained by consulting a preset seating posture baseline value table. For example, the table specifies a baseline value of "3 Newtons" for "relaxed back posture". The corresponding baseline value can be found by looking up the table using the seating posture tendency type determined in S72 as an index. This seating posture baseline value table was established by inviting testers to conduct comfort evaluation tests in each seating posture, and the optimal tension force range was statistically determined and recorded by those skilled in the art; details will not be elaborated here.

[0213] S74: Collects the preset manual adjustment signal of the seat belt adjustment wheel.

[0214] The seatbelt adjustment wheel is a knob for users to manually fine-tune the tightness of the seatbelt and integrates a rotary encoder.

[0215] The manual adjustment signals include the rotation direction and angle. These are obtained by reading the output pulse count and phase of the rotary encoder.

[0216] S75: Generate the target displacement direction and target displacement distance based on the manual adjustment signal and the pre-tension reference value.

[0217] The target displacement direction refers to the direction in which the tensioning and locking mechanism needs to move to make the seat belt tighter or looser, which corresponds to the rotation direction of the adjusting wheel.

[0218] The target displacement distance refers to the physical length that the mechanism needs to move.

[0219] The target displacement distance is calculated as: Target Displacement Distance = Base Displacement + Reference Value Compensation. The base displacement is obtained by multiplying the angle of the manual adjustment signal by a coefficient k1; the reference value compensation is obtained by multiplying the pre-tension reference value obtained from S73 by another coefficient k2. This calculation generates the final target displacement distance. The formula and its coefficients are determined by those skilled in the art through fitting the relationship between the calibrated adjustment wheel angle, the reference value, and the actual tension change; details are omitted here.

[0220] The tensioning and locking mechanism is a mechanical slider device that can be driven by a miniature stepper motor or voice coil motor to change the effective length of the seat belt.

[0221] S76: Based on the target displacement direction and target displacement distance, the preset tension locking mechanism is controlled to move, thereby realizing intelligent auxiliary adjustment of the seat belt tension.

[0222] The control process is achieved through the motor controller of the tensioning and locking mechanism. The controller receives the target displacement direction and distance generated by S75, and drives the motor to rotate by the corresponding angle and direction, thereby driving the mechanical slider to move precisely, thus completing the intelligent auxiliary adjustment of the seat belt tension.

[0223] It also includes adjustment and correction methods:

[0224] S80: After the tensioning and locking mechanism completes the displacement, real-time pressure distribution data on the seat belt webbing is collected.

[0225] Real-time pressure distribution data refers to the set of pressure values ​​at multiple points in the area where the webbing contacts the occupant's body.

[0226] The data was collected by an array of flexible thin-film pressure sensors woven into the inner side of the webbing.

[0227] After the tensioning and locking mechanism completes the displacement, real-time pressure distribution data needs to be collected for subsequent steps.

[0228] S81: Determine the uniformity of seat belt fit based on real-time pressure distribution data.

[0229] Uniformity of fit is an indicator that quantifies the degree of evenness in pressure distribution.

[0230] It is obtained by calculating the coefficient of variation (standard deviation / mean) of all valid pressure sensor readings acquired by the S80. The smaller the coefficient of variation, the higher the uniformity.

[0231] S82: Compare the fit uniformity with a preset optimal comfort threshold to obtain an adjustment effect score.

[0232] The optimal comfort threshold is an empirically preset ideal range of uniformity values. The specific values ​​are set in advance by those skilled in the art and will not be elaborated upon here.

[0233] The adjustment effect score is a quantitative evaluation of the adjustment results.

[0234] The adjustment effect score is obtained through a scoring function. For example, if the uniformity reaches a threshold, the score is 100; if it does not, the score decreases linearly as the uniformity worsens. The optimal comfort threshold and scoring function are determined by those skilled in the art through analyzing the correlation between pressure uniformity and subjective comfort evaluation, and the threshold and function form are set by them; these details will not be elaborated here.

[0235] S83: Generate dynamic correction coefficients based on the adjustment effect score, and correct the seat belt pretension reference value based on the dynamic correction coefficients.

[0236] The dynamic correction factor is a scaling factor used to fine-tune the baseline value.

[0237] This coefficient is generated based on the adjustment effect score of S82 using a mapping function. For example, the higher the score, the closer the coefficient is to 1 (no correction); the lower the score, the further the coefficient deviates from 1 (the larger the correction). This mapping function is determined by those skilled in the art by analyzing the relationship between the adjustment effect and the optimization direction of the benchmark value, and will not be elaborated here.

[0238] The correction process is as follows: New reference value = Original reference value × Dynamic correction coefficient. Through this calculation, the corresponding entries in the sitting posture reference value table used by S73 are dynamically optimized and updated.

[0239] Reference Figure 2 It also includes attitude change pre-tuning methods:

[0240] S90: Collects dynamic change data of seat position parameters and steering wheel position parameters.

[0241] Dynamic change data refers to the sequence of continuous changes in seat position parameters and steering wheel position parameters over time.

[0242] The dynamically changing data is obtained by periodically reading the CAN bus data streams of the seat control module and steering wheel adjustment module at a preset sampling frequency (which is set in advance by those skilled in the art and will not be elaborated here), and recording the parameter values ​​at each sampling moment, thereby forming a time series.

[0243] S91: Based on dynamically changing data, determine whether the driver is making significant posture adjustments.

[0244] This judgment is made by analyzing the rate and magnitude of change in dynamic data. If, within a short period of time (as preset by those skilled in the art, which will not be elaborated here), the change in the seat back angle exceeds a preset threshold (as preset by those skilled in the art, which will not be elaborated here), it is determined that a large-scale posture adjustment is taking place.

[0245] S92: When a large-scale posture adjustment is determined, the type of post-adjustment posture tendency is determined based on dynamic change data.

[0246] Post-adjustment posture tendency type is the predicted posture classification after posture adjustment is completed.

[0247] The type of post-adjustment posture tendency is determined by analyzing the endpoint trend value of dynamic change data. Specifically, after determining that a large posture adjustment has been made, the value of the dynamic change data (such as the seat back angle) at the end of the adjustment is smoothed and its stable value is taken; this stable value is compared with the preset posture judgment threshold range (consistent with the rules used in S72), and the corresponding type of post-adjustment posture tendency is determined according to the range it falls into.

[0248] When a significant posture adjustment is determined, the type of post-adjustment posture tendency needs to be identified first for subsequent steps.

[0249] S93: Match the adjusted seat belt pretension reference value in advance based on the type of seat posture after adjustment.

[0250] This step is achieved by executing the exact same procedure as S73, but using the post-adjustment sitting posture tendency type predicted by S92 as input. By querying the sitting posture reference value table, the applicable pre-tension reference value after posture adjustment can be matched in advance.

[0251] S94: When the posture adjustment is completed and the seat belt buckle status has not changed, generate a pre-adjustment command based on the adjusted pre-tension reference value.

[0252] The pre-adjustment command is used to drive the tensioning and locking mechanism to make a small pre-position.

[0253] The pre-adjustment command is generated by substituting the adjusted pre-tension reference value matched by S93 into a preset pre-adjustment calculation formula. The pre-adjustment formula defines a linear mapping relationship between the pre-displacement amount and the pre-tension reference value, for example: pre-displacement amount = proportional coefficient × (new reference value - current reference value). The pre-adjustment formula and its proportional coefficient are determined by those skilled in the art through testing the required pre-compensation displacement amount under different reference values, and will not be elaborated here.

[0254] S95: Based on the pre-adjustment command, the tension locking mechanism is pre-displaced in advance so that the tension of the seat belt is close to the fit when the posture is stable.

[0255] When the system detects that the posture adjustment action has stopped (parameters are stable) and the buckle has not been pulled out, it executes the pre-adjustment command generated by S94 through the controller of the tension locking mechanism, driving the mechanism to make a small pre-adjustment, so that the tightness of the seat belt approaches the ideal state under the new posture in advance, reducing the amount of manual adjustment required by the user later.

[0256] Based on the same inventive concept, embodiments of the present invention provide a dynamic adaptation tensioning control system for automotive seat belts, comprising:

[0257] The data acquisition module is used to collect buckle insertion signals, pull-out action data, average deceleration speed, operation time interval, final tightening strength level, friction vibration frequency, natural retraction speed, vibration response signal, current retraction stroke length, seat position parameters, steering wheel position parameters, manual adjustment signal, real-time pressure distribution data, and dynamic change data.

[0258] The memory is used to store the program that implements a method for dynamic adaptation tension control of automotive seat belts;

[0259] The processor is used to load and execute programs stored in memory.

[0260] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0261] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for dynamic adaptation and tension control of automotive seat belts, characterized in that, include: Collect the buckle insertion signal of the seat belt; When the buckle insertion signal is not inserted, the retractor releases the initial pulling restriction on the seat belt based on the preset mechanical linkage. When the buckle insertion signal is "inserted", the data on the pull-out action of the seat belt is collected; Based on the pull-out action data, quantitative parameters of the user's tightening intention are derived; Quantify parameters based on user tightening intent to generate adaptive tightening instructions; The retractor is controlled to execute adaptive tightening commands to complete the adaptive tensioning of the seat belt; It also includes seatbelt rebound assist methods: The frequency of frictional vibration between the seat belt and the guide ring and the natural retraction speed were collected during the retraction process. The wear level of the webbing surface is determined based on the frequency of frictional vibration. The standard retraction speed range is determined based on the wear level of the webbing surface. The natural retraction speed is compared with the standard retraction speed range to determine the degree of rebound force attenuation. When the degree of rebound force attenuation exceeds the preset standard attenuation degree, a stepped compensation level is generated based on the degree of rebound force attenuation. Based on stepped compensation levels to match the auxiliary torque release curve; Based on the auxiliary torque release curve, the preset auxiliary rebound device is controlled to output differentiated auxiliary torques at different stages of seat belt retraction.

2. The method for dynamic adaptation and tension control of automotive seat belts according to claim 1, characterized in that, It also includes methods for determining the quantification parameters of user tightening intent: The peak acceleration and maximum instantaneous velocity during the pull-out process are determined based on the pull-out motion data; The initial tightening strength level is determined by comparing the peak acceleration with preset multi-level acceleration thresholds. The tightening response level is determined based on the maximum instantaneous velocity; Collect the average deceleration speed before the pull-out motion ends; Determine whether the user intends to actively slow down based on the average deceleration rate; When there is an intention to actively decelerate, the initial tightening strength level is dynamically corrected based on the average deceleration rate to generate the final tightening strength level. Combine the final tightening intensity level with the tightening response level to generate quantitative parameters of the user's tightening intention.

3. The method for dynamic adaptation and tension control of automotive seat belts according to claim 2, characterized in that, Methods for determining the parameters for quantifying user tightening intent also include: Collect the time interval between two consecutive valid pull-out actions; The operation time interval is used to determine whether the current operation is a continuous adjustment action; When it is a continuous adjustment action, the final tightening strength level corresponding to the previous pull-out action is collected as the historical reference strength. A comprehensive strength level is generated by numerically fusing historical reference strength and final tightening strength level. The user's tightening intention quantification parameters are updated based on the comprehensive intensity level, and an adaptive tightening instruction is generated based on the updated user tightening intention quantification parameters. When it is not a continuous adjustment action, an adaptive tightening command is generated directly based on the user's tightening intention quantification parameters.

4. The method for dynamic adaptation and tension control of automotive seat belts according to claim 1, characterized in that, Also includes: After the seat belt retraction is completed, the retractor is controlled to apply a preset pulse excitation signal to the webbing and the vibration response signal of the webbing to the excitation is collected. Based on vibration response signals to extract signal distortion features; The type and location of internal damage are determined based on signal distortion characteristics; Combine internal damage type and damage location to generate targeted compensation waveform parameters; The auxiliary torque waveform is obtained based on the targeted compensation waveform parameters; Based on the targeted compensation waveform parameters, the auxiliary rebound device is controlled to output an auxiliary torque waveform when the damaged location passes through during the subsequent retraction process.

5. The method for dynamic adaptation tension control of automotive seat belts according to claim 1, characterized in that, It also includes specific methods for outputting differentiated auxiliary torques: Collect the current retraction length of the seat belt; The current travel interval is determined based on the current retraction travel length; The real-time target torque value is obtained by combining the current travel range and the auxiliary torque release curve; The driving parameters of the auxiliary rebound device are calculated and generated based on the real-time target torque value. The driving parameters are used to control the output of the auxiliary rebound device to provide the differentiated auxiliary torque required at the moment.

6. The method for dynamic adaptation tension control of automotive seat belts according to claim 1, characterized in that, It also includes comfort adjustment methods: Collect seat position parameters and steering wheel position parameters; Combine seat position parameters and steering wheel position parameters to generate driving posture data; Determine the type of sitting posture tendency based on driving posture data; The seatbelt pretension reference value is matched according to the type of sitting posture. Collect the preset manual adjustment signal of the seat belt adjustment wheel; The target displacement direction and target displacement distance are generated based on the manual adjustment signal and the pre-tension reference value; Based on the target displacement direction and target displacement distance, the preset tension locking mechanism is controlled to move, thereby realizing intelligent auxiliary adjustment of the seat belt tension.

7. The method for dynamic adaptation tension control of automotive seat belts according to claim 6, characterized in that, It also includes adjustment and correction methods: After the tensioning and locking mechanism completes the displacement, real-time pressure distribution data on the seat belt webbing is collected; The uniformity of seat belt fit is determined based on real-time pressure distribution data; The uniformity of the fit is compared with a preset optimal comfort threshold to obtain an adjustment effect score; Dynamic correction coefficients are generated based on the adjustment effect score, and the seat belt pretension reference value is corrected based on the dynamic correction coefficients.

8. The method for dynamic adaptation and tension control of automotive seat belts according to claim 6, characterized in that, It also includes attitude change pre-tuning methods: Collect dynamic change data of seat position parameters and steering wheel position parameters; Based on dynamically changing data, determine whether the driver is making significant posture adjustments; When a significant posture adjustment is determined, the type of post-adjustment posture tendency is determined based on dynamic change data. Based on the type of seat posture after adjustment, the adjusted seat belt pretension reference value is matched in advance; Once the posture adjustment is complete and the seatbelt buckle status remains unchanged, a pre-adjustment command is generated based on the adjusted pre-tension reference value. Based on the pre-adjustment command, the tension locking mechanism is pre-displaced in advance so that the tension of the seat belt is close to the fit when the posture is stable.

9. A dynamic adaptation tension control system for automotive seat belts, characterized in that, include: The data acquisition module is used to acquire the buckle insertion signal and pull-out action data; A memory for storing a program that implements a dynamic adaptation tension control method for automotive seat belts as described in any one of claims 1 to 8; The processor is used to load and execute programs stored in memory.

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