Multi-pressure sensor data fusion and intelligent compensation method and system of pneumatic massage system

Abstract

The invention belongs to the technical field of automotive electronics, and particularly relates to a multi-pressure sensor data fusion and intelligent compensation method of a pneumatic massage system, which comprises the following steps of: constructing a six-pressure sensor framework, and combining a set of layered progressive intelligent compensation algorithm, real-time and on-line calibration and compensation of inherent errors and individual differences of a conventional-precision pressure sensor are achieved, and finally high-precision and high-consistency air pressure measurement values are output. According to the invention, the air pressure measurement precision is obviously improved, the measurement error of a conventional precision sensor is greatly reduced through on-line compensation, and the output precision can approach or reach the levelness of a higher-level sensor; according to the invention, the cost and complexity are reduced, an expensive high-precision pressure sensor or a pressure sensor with a complex compensation circuit does not need to be selected, high performance can be realized through a software algorithm by using a conventional MEMS sensor, and the BOM cost of the system and the complexity of hardware design are reduced.

Description

Technical Field

[0001] This invention belongs to, but is not limited to, the field of automotive electronics technology, and particularly relates to a method and system for multi-pressure sensor data fusion and intelligent compensation in a pneumatic massage system. Background Technology

[0002] With the rapid development of China's automotive industry, more and more consumers are demanding higher levels of driving and riding comfort. Car seats with lumbar support and massage functions have become an important feature in mid-to-high-end vehicles. Pneumatic lumbar support and massage systems achieve support and massage for the occupant's lower back by precisely controlling the air pressure inside the airbags. Therefore, the accuracy of air pressure detection is the core foundation for achieving comfortable, reliable, and intelligent control in pneumatic lumbar support and massage systems.

[0003] Currently, most mainstream lumbar support massage systems rely on one or a few embedded pressure sensors to monitor the air pressure inside the airbags. These pressure sensors convert the air pressure signals into electrical signals, which the control system uses to determine the airbag status and make corresponding decisions to inflate, maintain pressure, or deflate. However, pressure sensors inevitably have inherent errors during the manufacturing process, such as zero-point offset, sensitivity error, and nonlinearity error. These errors are present when the sensors leave the factory and vary between individual sensors.

[0004] In practical applications, the inherent error of pressure sensors has become a major bottleneck restricting the accuracy of air pressure detection and the improvement of system performance. Inaccurate air pressure detection leads to a large deviation between the actual support force of the airbags and the user's settings, a problem that is particularly prominent in scenarios requiring symmetrical support on both sides. For example, when the system attempts to inflate the side airbags located on both sides of the seat to the same target air pressure value, if the inherent errors between the individual pressure sensors monitoring these two airbags are large (e.g., one is biased positive and the other negative), the control system will misjudge that the air pressure on both sides has reached equilibrium. However, the actual physical support force of the airbags on the occupant's lower back is significantly unbalanced due to sensor errors. This asymmetry in left-right support greatly undermines the stability and comfort of the ride, bringing a significant negative subjective experience to the occupant and severely weakening the core value of the product.

[0005] Traditional solutions primarily focus on selecting pressure sensors with higher accuracy levels. However, high-precision sensors are expensive; a pneumatic lumbar support system with side wings requires at least four pressure sensors. Using high-precision models for all of them would drastically increase the overall system cost, severely hindering its widespread adoption in mainstream vehicles. Therefore, how to effectively overcome the inherent errors of pressure sensors while maintaining cost control, and significantly improve the accuracy, stability, and long-term reliability of air pressure detection in lumbar massage systems, has become a key technical challenge urgently needing to be addressed. A more intelligent and robust air pressure detection error processing mechanism is urgently required.

[0006] Current technology lacks an effective mechanism to handle the error differences between individual sensors. When a system uses multiple sensors (such as one sensor for the main lumbar support airbag and one for each of the side wing airbags), the inherent error characteristics of different individual sensors (such as different zero-bias directions and varying sensitivity) can overlap or even be amplified. The most typical negative manifestation is bilateral support imbalance: even if the control system commands the left and right side wing airbags to inflate to the same target air pressure value, due to the inherent errors of the sensors on both sides (different directions or magnitudes), the feedback air pressure value read by the system may "show" balance, but the actual physical support force applied by the airbags to the occupant's lower back is significantly unbalanced. This support asymmetry directly caused by individual sensor differences greatly undermines the stability and comfort of the ride, becoming a major source of user complaints. Summary of the Invention

[0007] This invention aims to overcome key problems in existing pneumatic lumbar support massage systems, such as insufficient detection accuracy, inaccurate support force, and bilateral support imbalance caused by inherent errors in pressure sensors and individual differences. Specifically, the core technical problem this invention addresses is: how to effectively calibrate and compensate for the inherent errors of multiple conventional precision pressure sensors online through an innovative system architecture and intelligent algorithms, without using high-cost, high-precision pressure sensors, thereby significantly improving the absolute accuracy, consistency, and long-term stability of the air pressure detection in the entire lumbar support massage system.

[0008] This invention is implemented as follows: a method for multi-pressure sensor data fusion and intelligent compensation in a pneumatic massage system, the design of which includes: A six-pressure sensor architecture is constructed and combined with a hierarchical intelligent compensation algorithm to achieve real-time, online calibration and compensation for the inherent errors and individual differences of conventional precision pressure sensors, ultimately outputting high-precision and highly consistent air pressure measurements.

[0009] Furthermore, the design specifically includes: The system is equipped with six pressure sensors S1-S6, forming a unique collaborative monitoring network. Five of these sensors, S1-S5, are deployed in the key pneumatic actuators of the lumbar support massage system, directly sensing and measuring the working pressure value of the corresponding airbag. The sixth sensor, S6, is independently deployed inside or near the car seat, physically isolated from any working airbag. Its pressure-sensing port is directly exposed to the current cabin atmosphere and is dedicated to real-time, synchronous acquisition of the ambient atmospheric pressure value Patm. All six sensors acquire data synchronously through the system's main control unit, the seat control module ECU. Upon receiving the calibration command, the system immediately enters calibration mode. At this time, the microcontroller issues a control command to synchronously and fully open the exhaust valves of all airbags to ensure that the entire air circuit network is equal to the current ambient atmospheric pressure. After a preset short delay, the system will start subsequent data acquisition only after confirming that the balance is stable. The system continuously collects the raw output data of the sensor in a stable atmospheric pressure environment with a specific sampling frequency and duration. After data collection, the collected data is processed by a specific algorithm to accurately determine the sensor's inherent zero-point offset error and store it in non-volatile memory. This ensures that even after the system is completely powered off and restarted, this critical calibration parameter can be completely retained and read without requiring the user to recalibrate each time, greatly improving the system's long-term stability and user convenience. When the system switches to normal operation mode, its core task becomes measuring ambient air pressure in real time and accurately. At this time, the sensor continuously collects raw data reflecting the current air pressure. In order to obtain accurate measurement results, the system will immediately perform real-time compensation operation to "zero-correct" the signal. Only the compensated data represents the air pressure change signal that is closer to the physical reality relative to the calibration zero point. Finally, the system will use a preset sensor conversion model to perform final calculations on the compensated data. Finally, the processed data source is accurately converted into the final, user-readable air pressure value.

[0010] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows: Significantly improves the accuracy of barometric pressure measurement, greatly reducing the measurement error of conventional precision sensors through online compensation, and the output accuracy can approach or reach the level of higher-grade sensors.

[0011] To reduce costs and complexity, there is no need to choose expensive high-precision pressure sensors or those with complex compensation circuits. High performance can be achieved by using conventional MEMS sensors through software algorithms, which reduces system BOM costs and hardware design complexity.

[0012] Enhancing reliability and comfort, high-precision and consistent air pressure measurement is the foundation of precise massage control, providing a more delicate, stable, and predictable side and lumbar support experience, while avoiding the risk of overcharging or undercharging due to pressure detection errors, thus improving system reliability. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of a multi-pressure sensor data fusion and intelligent compensation method for a pneumatic massage system provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0015] like Figure 1 As shown, this embodiment of the invention provides a method for multi-pressure sensor data fusion and intelligent compensation in a pneumatic massage system. This design includes: A six-pressure sensor architecture is constructed and combined with a hierarchical intelligent compensation algorithm to achieve real-time, online calibration and compensation for the inherent errors and individual differences of conventional precision pressure sensors, ultimately outputting high-precision and highly consistent air pressure measurements.

[0016] This invention constructs a collaborative monitoring architecture with six pressure sensors. Five pressure sensors are arranged in the air paths of each airbag in the pneumatic massage system to collect the working pressure signals of the corresponding airbag. The sixth pressure sensor is independently arranged inside or in the vicinity of the system, with its sensing port isolated from the air paths of each airbag, to synchronously collect ambient atmospheric pressure signals. The six pressure signals are synchronously sampled by a control module in a unified timing sequence to ensure the consistency of data across all channels.

[0017] Upon receiving the calibration command, the system enters calibration mode, controlling the airflow to connect all airbags to the external atmosphere, creating a uniform pressure environment. After the pressure stabilizes, the system continuously acquires pressure signals from each channel at a preset sampling frequency, performs outlier removal and steady-state statistical processing on the acquired data, calculates the zero-point reference value of each pressure sensor under the uniform environmental pressure conditions, and uses the environmental pressure signal as a common reference to obtain the zero-point offset parameter of each airbag pressure signal relative to the common reference. This zero-point offset parameter is stored in a non-volatile memory area for later use during system operation.

[0018] After the system enters normal operation, the real-time acquired airbag pressure signals are compensated online based on the zero-point offset parameters, and dynamically corrected in conjunction with real-time environmental pressure signals to eliminate the influence of inherent sensor zero drift and environmental changes. The compensated multi-channel airbag pressure signals are further subjected to consistency constraint fusion processing. Weights are assigned according to the magnitude of the deviation between each pressure signal, and a weighted calculation is performed to form a fused pressure result. The fused pressure result is used for system display and airbag inflation / deflation control, achieving stable and reliable measurement and adjustment of airbag pressure status. The system is equipped with six pressure sensors S1-S6, forming a unique collaborative monitoring network. Five sensors S1-S5 are deployed in the key pneumatic actuators of the lumbar support massage system, directly sensing and measuring the working pressure value of the corresponding airbag. The sixth sensor S6 is independently deployed inside or near the car seat, physically isolated from any working airbag. Its pressure-sensing port is directly exposed to the current cabin atmosphere, dedicated to real-time, synchronous acquisition of the ambient atmospheric pressure value (Patm). All six sensors are synchronously acquired through the system's main control unit, the seat control module (ECU). Upon receiving the calibration command, the system immediately enters calibration mode. At this time, the microcontroller issues a control command to synchronously and fully open the exhaust valves of all airbags to ensure that the entire air circuit network is equal to the current ambient atmospheric pressure. After a preset short delay, the system will start subsequent data acquisition only after confirming that the balance is stable. The system continuously collects the raw output data of the sensor in a stable atmospheric pressure environment with a specific sampling frequency and duration. After data collection, the collected data is processed by a specific algorithm to accurately determine the sensor's inherent zero-point offset error and store it in non-volatile memory. This ensures that even after the system is completely powered off and restarted, this critical calibration parameter can be completely retained and read without requiring the user to recalibrate each time, greatly improving the system's long-term stability and user convenience. When the system switches to normal operation mode, its core task becomes measuring ambient air pressure in real time and accurately. At this time, the sensor continuously collects raw data reflecting the current air pressure. In order to obtain accurate measurement results, the system will immediately perform real-time compensation operation to "zero-correct" the signal. Only the compensated data represents the air pressure change signal that is closer to the physical reality relative to the calibration zero point. Finally, the system will use a preset sensor conversion model to perform final calculations on the compensated data. Finally, the processed data source is accurately converted into the final, user-readable air pressure value.

[0019] Implementation process: The system first enters calibration mode. The controller issues a command to open the air valve, ensuring complete and unobstructed communication between the internal air circuit and the external atmospheric environment. This step is crucial, as it aims to eliminate any residual pressure gradient within the system, bringing the entire air circuit network, including connecting pipes, chambers, and both sides of the sensor diaphragm, into a state of static equilibrium, meaning that the air pressure at all points is theoretically equal to the current ambient atmospheric pressure.

[0020] The system synchronously acquires raw data. After a short delay and confirmation that the gas path balance has been established, the microcontroller initiates the synchronous acquisition command. This command ensures that all six pressure sensors configured in the system read the raw output values ​​of their analog-to-digital converters (ADCs) at the same precise clock tick.

[0021] The system performs 100 consecutive samples. Each sample is taken under balanced gas path conditions, with a single synchronous ADC reading performed on all six sensors. A strict 10-millisecond (ms) interval is set between adjacent samples. After 100 samples, a dataset containing 100 raw ADC values ​​is generated independently for each sensor. For each sensor's 100 data points, the arithmetic mean (μ) and standard deviation (σ) are calculated. Outliers exceeding ±3 standard deviations (±3σ) are automatically identified and removed using a ±3 standard deviation (±3σ) principle. For example, if a sensor's 100 readings contain a value less than (μ - 3σ) or greater than (μ + 3σ), it is considered an outlier and removed. The dataset used to calculate the baseline value (valid readings) is ensured to have high statistical robustness. After removal, each sensor retains a valid reading set of size N (N ≤ 100).

[0022] For each sensor, the arithmetic mean of all valid readings is calculated. This average represents the typical value of the sensor's ADC output under pneumatic equilibrium (atmospheric pressure reference) conditions, including a comprehensive zero-offset reference value that incorporates factors such as the sensor's own zero-offset and circuit bias. After obtaining the independent zero-offset reference values ​​for the six sensors, the arithmetic mean of these six reference values ​​is calculated. This global average can serve as a reference indicator for the overall zero point of the system (ZeroOffset).

[0023] The system securely writes the calculated and verified ZeroOffset value into a pre-allocated specific address region in the microcontroller's non-volatile memory.

[0024] During normal system operation (non-calibration mode), the microcontroller reads the raw ADC output values ​​(ADCRawSensorX) of all six sensors in real time according to the set control cycle. After reading the ZeroOffset stored in the non-volatile memory, it performs the following compensation calculation for each raw ADC value read by each sensor: ADCCompensatedSensorX = ADCRawSensorX - ZeroOffset.

[0025] Finally, the compensated ADC value is converted into a pressure value with physical meaning using the linear formula ADCCompensatedSensorX=(A×P+B)×VDD.

[0026] This pneumatic massage system employs a collaborative monitoring architecture comprised of six pressure sensors. The first five pressure sensors are positioned within the air paths of the five key airbag actuators of the lumbar support massage system, respectively, to collect the raw working pressure signals of the corresponding airbags in real time. The sixth pressure sensor is independently located inside or adjacent to the seat, physically isolated from any working airbag, with its pressure-sensing port directly exposed to the vehicle's environment, to synchronously collect the current atmospheric pressure. All six sensors are synchronously sampled by a unified clock triggered by the seat control module, ensuring the comparability of data from each source at the same time and providing a foundation for subsequent compensation and fusion processing.

[0027] Upon receiving the calibration command, the system enters calibration mode. The seat control module drives the microcontroller to issue control commands, simultaneously and fully opening the exhaust valves of all airbags, connecting the entire air circuit network to the outside atmosphere, thus ensuring that the first five pressure sensors are at the same pressure state as the ambient pressure sensors. The system executes a preset short delay to allow valve response and air circuit pressure to balance, and after determining that the air pressure has stabilized through stability criteria, it initiates the calibration data acquisition process.

[0028] Under stable conditions, the system continuously collects raw pressure data for a certain duration at a preset sampling frequency, forming raw output data sequences from six pressure sensors. In the zero-point offset error estimation layer, the system performs anti-interference statistical processing on each data sequence, including removing abnormal abrupt data points or smoothing using median filtering. Then, it calculates the average or median of the steady-state data as the zero-point reference for that pressure sensor under environmental pressure conditions. Simultaneously, using the steady-state output of the environmental pressure sensor as a common reference, the system calculates the zero-point offset of each airbag pressure sensor relative to this reference. This offset characterizes the sensor's inherent zero drift and individual differences.

[0029] All obtained zero-point offset parameters are written to non-volatile memory, along with a version number and time stamp, so that the system can still directly read the parameters and use them for compensation calculations after a power outage and restart, without requiring the user to repeat the calibration process, thereby improving the long-term stability and ease of use of the system.

[0030] Once the system switches to normal operating mode, it enters the real-time compensation and fusion output stage. First, zero-point compensation is performed on each channel of real-time acquired raw pressure data, that is, the corresponding zero-point offset parameter is subtracted from the raw data to obtain a relative pressure signal with zero drift eliminated. Then, the real-time output of the environmental pressure sensor is introduced as a dynamic reference benchmark to perform environmental compensation processing on the airbag pressure signal, thereby eliminating the systematic drift effect caused by changes in external air pressure (such as changes in altitude, changes in the airtightness of the vehicle compartment, etc.).

[0031] In the multi-sensor fusion output layer, the system performs consistency-constrained fusion processing on the five airbag pressure signals. The system first calculates the deviation between each airbag pressure signal and the median value of the current five pressure signals, and then adaptively assigns weights based on the magnitude of this deviation; channels with smaller deviations have larger weights, and those with larger deviations have smaller weights. The system then performs a weighted summation of the five pressure signals according to the assigned weights to form the fused pressure output result.

[0032] When the system detects that a certain pressure signal deviates from other channels for a long period of time, and the deviation continues to exceed the preset threshold and the duration window, the channel is marked as an abnormal channel and is subjected to weight reduction or elimination, thereby ensuring the stability and reliability of the fusion output results.

[0033] Finally, the seat control module inputs the fused pressure results into the preset sensor conversion model, performs unit conversion and numerical formatting, outputs a pressure value that is readable by the user, and simultaneously provides independent pressure values ​​for each airbag for closed-loop control adjustment.

[0034] This invention belongs to the field of automotive intelligent seat and pneumatic massage control technology, specifically relating to a multi-pressure sensor data fusion and intelligent compensation method and system for automotive seat pneumatic massage systems. It can be embedded in the automotive seat control module (ECU) or seat domain controller, working collaboratively with the airbag drive unit, valve assembly, and human-machine interface module to achieve real-time and accurate sensing and adjustment of the internal air pressure status of multiple pneumatic actuators such as the lumbar support, back support, and side wings. This invention can also be extended to products with multi-airbag collaborative control requirements, such as aircraft seats, medical rehabilitation pneumatic physiotherapy equipment, smart mattresses, and wearable pneumatic physiotherapy devices, to improve the accuracy and consistency of system pressure sensing, and enhance comfort, safety, and control stability.

[0035] In terms of specific product form, this invention can be deployed as a software algorithm module in a microcontroller or embedded processor as a seat control system. It works in conjunction with multiple conventional precision pressure sensors and an environmental pressure reference sensor to form a collaborative monitoring network. Without the need to introduce high-cost, high-precision sensors, it can achieve online calibration, dynamic compensation, and fusion output of airbag pressure, thereby improving the measurement reliability and long-term stability of the entire system while keeping hardware costs under control.

[0036] By implementing the multi-sensor fusion and hierarchical progressive compensation algorithm of this invention, objective evidence of the following technical effects can be obtained. First, by connecting the gas path to the environment in calibration mode and uniformly estimating and storing the zero-point offset of the sensors, all pressure sensors are made consistent under the same reference, thereby significantly reducing the zero-drift differences caused by manufacturing errors, installation deviations, and device aging. This effect can be verified by comparing the dispersion of the outputs of multiple sensors before and after calibration under the same pressure conditions. After calibration, the outputs of each channel are more concentrated and the deviation is smaller, indicating that the consistency is improved.

[0037] Secondly, introducing ambient atmospheric pressure as a dynamic reference source during normal operation can effectively eliminate systematic drift caused by changes in external air pressure (such as changes in altitude, changes in the airtightness of the vehicle compartment, etc.), allowing the airbag pressure to reflect a more realistic relative force, thereby improving the stability and repeatability of massage control. This effect can be verified by comparing the stability of the system output under different ambient air pressure conditions; the output fluctuation is reduced after adopting the method of this invention.

[0038] Furthermore, by applying consistency constraints and adaptive weighted fusion to the multi-channel airbag pressure data, the overall output stability and reliability can be maintained even in the event of anomalies or increased noise in a single sensor, thus improving system robustness. The effectiveness of this technique can be verified through comparative testing with the introduction of single-channel perturbations or offsets; the fused output still maintains continuous, smooth, and reasonable variations.

[0039] Therefore, this invention achieves a simultaneous improvement in the pressure sensing accuracy, consistency, and stability of a pneumatic massage system without increasing hardware complexity, demonstrating clear engineering applicability and verifiable technical effects.

[0040] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for multi-pressure sensor data fusion and intelligent compensation in a pneumatic massage system, characterized in that, This includes the following collaborative working mechanisms: When the system is in calibration, the air passages of multiple airbags are connected to the outside atmosphere by unified control of the air passages, forming a unified pressure environment. Under a uniform pressure environment, multiple airbag pressure signals and ambient pressure signals are collected simultaneously, and the ambient pressure signal is used as a common reference to determine the zero-point offset parameter of each airbag pressure signal relative to the common reference. The zero-point offset parameter is stored in a non-volatile storage area; When the system is in operation, online compensation processing based on the zero-point offset parameter is performed on the real-time collected airbag pressure signals, and environmental compensation processing is performed on the compensated airbag pressure signals using the real-time environmental pressure signal as a dynamic reference. Based on the consistency relationship between multiple airbag pressure signals, the compensated airbag pressure signals are weighted and fused, and the fused pressure result is output as the basis for control and display.

2. The method according to claim 1, characterized in that, When determining the zero-point offset parameters, abnormal data removal is performed on each pressure signal, and the median of the stable data is calculated as the zero-point reference after removing the abnormal data.

3. The method according to claim 1, characterized in that, When performing consistency-weighted fusion processing, weights are assigned based on the deviation between each airbag pressure signal and the median of all airbag pressure signals, with smaller deviations resulting in larger weights.

4. The method according to claim 1, characterized in that, When the deviation of a certain airbag pressure signal continuously exceeds a preset threshold and lasts for a preset time, the signal is marked as an abnormal signal and its fusion weight is reduced.

5. A multi-pressure sensor data fusion and intelligent compensation system for a pneumatic massage system, characterized in that, include: A unified acquisition module is used to simultaneously acquire pressure signals from multiple airbags and environmental pressure signals; The calibration processing module is used to determine the zero-point offset parameter of each airbag pressure signal relative to the ambient pressure signal when the airway is connected to the outside atmosphere. A storage module is used to store the zero-point offset parameters; The compensation processing module is used to perform online compensation of the airbag pressure signal based on the zero-point offset parameter and environmental compensation based on the real-time environmental pressure signal. The fusion processing module is used to perform weighted fusion processing on the compensated airbag pressure signals based on the consistency relationship between the airbag pressure signals.

6. The system according to claim 5, characterized in that, The calibration processing module includes an abnormal data removal unit and a steady-state calculation unit.

7. The system according to claim 5, characterized in that, The fusion processing module includes a deviation calculation unit and a weight allocation unit.

8. A method for pressure measurement and control of a pneumatic massage system, characterized in that, This includes the following closed-loop coordination mechanisms: A unified pressure calibration environment is formed through a unified gas path connection mechanism; Zero-point offset parameters of multiple airbag pressure signals are determined and stored based on a unified pressure calibration environment; During system operation, dual compensation processing based on zero-point offset parameters and environmental pressure signals is performed; A fused pressure result is generated based on the compensated pressure signal, and the airbag inflation and deflation process is controlled based on the fused pressure result.

9. The method according to claim 8, characterized in that, The fusion pressure result is used to adjust the inflation and deflation time of the airbag.

10. The method according to claim 8, characterized in that, When the fusion pressure result deviates from the preset control target, a control adjustment command is triggered to perform pressure correction on the airbag.

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