Automobile digital air suspension system and application method thereof

Abstract

The invention relates to the technical field of automobile suspension, and discloses an automobile digital air suspension system and an application method thereof.The automobile digital air suspension system comprises a suspension controller, a height sensor, an air compression assembly and a valve assembly, the air compression assembly comprises an air compressor, an air spring and an air storage tank, and the valve assembly comprises a plurality of electromagnetic valves; the intelligent control system is composed of a data acquisition module, an edge computing node, a real-time diagnosis unit, a fault early warning module, a cloud analysis center and a collaborative response bus. And the data acquisition module is connected with a vibration sensor arranged on the air compressor, a deformation sensor arranged on the air spring and an air leakage sensor arranged on the air storage tank. According to the invention, through the driving scene classification model generated by the cloud and in combination with the real-time positioning and matching dynamic threshold library, the false alarm rate under a complex road condition is significantly reduced, false alarms are frequently generated by a traditional fixed threshold scheme under different working conditions, and the defect of insufficient early warning reliability caused by poor environmental adaptability in the prior art is overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile suspension, and particularly relates to a digital air suspension system for automobiles and an application method thereof. BACKGROUND

[0002] The air suspension system uses compressed air as an elastic medium to replace the steel coil spring or the steel plate spring in the traditional suspension, and the core is an elastic element composed of rubber air bags, which are filled with air and bear the weight of the vehicle body, control the air suspension of the running vehicle, and thus reduce or raise the ground clearance of the chassis to realize the stability of the vehicle body and the passing ability on complex road conditions.

[0003] The air suspension system is based on the detection of the height sensor and the control of the air spring by the suspension controller, and the air suspension system will also fail or be damaged after long-term use. The existing air suspension system cannot be detected by professionals in time, which causes safety hazards when the vehicle is used, and the air suspension system cannot be warned and failed in time, which is not conducive to driving safety. SUMMARY

[0004] In order to make up for the above shortcomings, the present application provides a digital air suspension system for automobiles and an application method thereof, which aims to improve the problem that the air suspension system cannot be warned and failed in time, which is not conducive to driving safety.

[0005] In the first aspect, the present application provides the following technical scheme, a digital air suspension system for automobiles, characterized in that it comprises: a suspension controller, a height sensor, an air compression assembly and a valve assembly, wherein the air compression assembly comprises an air compressor, an air spring and an air tank, and the valve assembly comprises a plurality of electromagnetic valves; an intelligent control system composed of a data acquisition module, an edge computing node, a real-time diagnosis unit, a fault warning module, a cloud analysis center and a collaborative response bus; The data acquisition module is connected with a vibration sensor arranged on the air compressor, a deformation sensor of the air spring, a gas leakage sensor and a pressure sensor of the air tank, and a position feedback sensor of the electromagnetic valve, and acquires instruction data of the suspension controller in real time; The edge computing node is deployed on a vehicle terminal, receives the original data stream of the data acquisition module, and performs data preprocessing and short-term abnormal fluctuation detection; The real-time diagnosis unit is connected with the edge computing node, and is configured to dynamically calculate the health degree of the component based on the preprocessed data, and synchronously compare the dynamic threshold value in the habitual parameter library; When the real-time diagnosis unit outputs an abnormal signal, the fault warning module generates a hierarchical warning instruction and pushes it to the vehicle display screen and the bound mobile terminal; The cloud analysis center is connected with a real-time diagnosis unit, stores historical data, and trains a long-period fault prediction model, and returns the optimized algorithm parameters to the real-time diagnosis unit; A cooperative response bus receives front road condition preview information of an ADAS system, and activates a high-frequency monitoring mode of the real-time diagnosis unit 200 ms in advance when a bumpy road section is predicted.

[0006] Through the above technical solution: a hardware basic layer is constructed, mechanical states (such as air pressure / deformation / vibration) are captured in real time through a sensor network, physical data sources are provided for digital control, a data acquisition module connects various sensors and obtains controller instructions, full-link data fusion is realized, mechanical signals (vibration / deformation / pressure) and electronic instructions are synchronously collected, and data islands are eliminated.

[0007] Preferably, the real-time diagnosis unit comprises: a health degree calculation subunit that quantifies performance degradation through an air compressor efficiency attenuation formula real-time output performance scores; a dynamic threshold library that stores air spring compression frequency safety intervals [F L ,F C ] and gas tank pressure tolerance bands [P L ,P C ] based on driving habit learning; a millisecond-level response engine that triggers a warning signal within 50 ms when it is detected that the data exceeds the dynamic threshold library or the health degree score is lower than a critical value.

[0008] Through the above technical solution: the health degree calculation subunit quantifies performance degradation through an air compressor efficiency attenuation formula, the dynamic threshold library adaptively adjusts safety intervals based on driving scenarios (city / highway / off-road), and the millisecond-level response engine completes diagnosis and decision-making (such as immediately warning if spring deformation recovery is overdue) within 50 ms, thereby upgrading traditional periodic maintenance to real-time predictive maintenance.

[0009] Preferably, the edge computing node performs: Kalman filter noise reduction on the electromagnetic valve response delay data stream; sliding window algorithm to calculate the gas tank pressure change rate If Q max is a preset leakage rate threshold, the real-time diagnosis unit is directly triggered to intervene.

[0010] Through the above technical solution: Kalman filter noise reduction improves the signal-to-noise ratio of electromagnetic valve delay data, sliding window pressure analysis realizes real-time identification of gas tank leakage (such as triggering diagnosis at a second level when the pressure change rate exceeds the threshold), and localized real-time calculation avoids cloud delay, thereby realizing millisecond-level risk interception.

[0011] Preferably, the fault early warning module establishes bidirectional communication with the suspension controller: When the real-time diagnosis unit detects an emergency fault, a forced instruction is sent to the suspension controller to lock the current air spring pressure state; Receive the feedback state of the suspension controller and include it in the health degree calculation parameter; The fault early warning module pushes the following content: Thermal map display based on fault location; According to the remaining safe mileage Generate a countdown warning, where a p The pressure drop acceleration; Navigation guidance interface associated with repair station location.

[0012] Through the above technical solution: bidirectional control, emergency forced locking of spring pressure (to prevent pressure loss risk), visualization of damage location and safety countdown, calculation of remaining mileage based on pressure drop acceleration, automatic push of the nearest service point, closed-loop management from early warning to disposal, and guarantee of driving safety.

[0013] Preferably, the update mechanism of the dynamic threshold library includes: The cloud analysis center generates a driving scene classification model based on historical data clustering; Set the compression frequency safety interval [F Li ,F Ui ] for urban congestion, high-speed cruising, and off-road modes, respectively, where i is the scene number; The real-time diagnosis unit automatically matches the current scene threshold based on GPS positioning data.

[0014] Through the above technical solution: predict the remaining life of the air spring by adding the stress spectrum and failure feature vector, distribute the optimized model parameters to the vehicle end, continuously improve the diagnosis accuracy, and realize the self-evolution ability of end-to-cloud collaboration.

[0015] Preferably, the real-time diagnosis unit starts the early warning when any of the following conditions is met: Air spring deformation recovery time Where, Is the historical mean; Air compressor current fluctuation standard deviation for 5 consecutive working periods Where K is the material attenuation coefficient.

[0016] Preferably, the cloud analysis center performs: Establish an air spring remaining life prediction model through an LSTM neural network: Where S t is the real-time stress spectrum, is a historical failure feature vector; the prediction result RUL t Real-time issuance to the vehicle terminal.

[0017] In a second aspect, the present application provides the following technical solution, an application method of a digital air suspension of an automobile, the application method comprising the following steps: S1, collecting suspension component operation data and suspension controller instructions in real time through a multi-source sensor group; S2, performing data filtering and short-term risk screening at an edge computing node, and if a pressure mutation rate exceeding a limit is detected, jumping to S4; S3, dynamically calculating a health degree index at a real-time diagnosis unit, and comparing with a scene adaptive threshold library; S4, generating a warning instruction with a positioning code when the health degree is lower than a safety line or data deviates from a threshold; S5, uploading an abnormal data segment through a vehicle-cloud data channel, and receiving prediction model parameters issued by the cloud; S6, displaying a fault heat map and a remaining safety time on a vehicle-mounted interface, and outputting a maintenance strategy topology.

[0018] In a third aspect, the present application provides the following technical solution, a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the above-mentioned application method of a digital air suspension of an automobile when executing the computer program.

[0019] In a fourth aspect, the present application provides the following technical solution, a readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the above-mentioned application method of a digital air suspension of an automobile.

[0020] The present application provides a digital air suspension system for an automobile and an application method thereof. The present application has the following beneficial effects: 1. In the present application, a driving scene classification model generated by the cloud is combined with a dynamic threshold library matched in real time, which significantly reduces the false alarm rate under complex road conditions. The traditional fixed threshold scheme frequently produces false alarms under different working conditions, and overcomes the defects of insufficient warning reliability caused by poor environmental adaptability in the prior art.

[0021] 2. In the present application, data preprocessing and short-term risk assessment are performed by a vehicle-mounted edge node, and a component state is dynamically calculated by a real-time diagnosis unit with hardware acceleration, which realizes a fault response speed much faster than that of a traditional system. The prior art relies on a central processor to process data in batches, and the response has obvious delay. The present application solves the problem of transient fault missed detection caused by insufficient calculation efficiency in the traditional scheme.

[0022] 3、The time series prediction model trained in the cloud forms a parameter iterative closed loop with the vehicle terminal in the application, realizes the progressive prediction ability of the component life, and the traditional single machine algorithm or offline detection cannot effectively predict long-period faults, and solves the limitation that the prior art lacks a prediction means for progressive failure.

[0023] 4、The application establishes a two-way communication protocol between the early warning module and the suspension controller, actively intervenes in the suspension state in an emergency, greatly reduces the driving risk coefficient, and solves the safety hidden danger that the prior art lacks an active protection mechanism in a critical working condition. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 It is a schematic diagram of the architecture of the digital air suspension system of the application; Figure 2 It is a method flowchart of the application method of the digital air suspension of the application. DETAILED DESCRIPTION

[0025] The technical solutions of the application will be described below in conjunction with the drawings of the specification. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the application.

[0026] Embodiment one Reference Figure 1 In the first embodiment of the application, the application provides a digital air suspension system for a vehicle, comprising: a suspension controller, a height sensor, an air compression assembly and a valve assembly, the air compression assembly comprising an air compressor, an air spring and an air tank, the valve assembly comprising a plurality of electromagnetic valves; An intelligent control system, composed of a data acquisition module, an edge computing node, a real-time diagnosis unit, a fault warning module and a cloud analysis center; The data acquisition module is connected to the vibration sensor of the air compressor, the deformation sensor of the air spring, the air leakage sensor and the pressure sensor of the air tank, and the position feedback sensor of the electromagnetic valve, and real-time instruction data of the suspension controller are acquired; The edge computing node is deployed on the vehicle terminal, receives the original data stream of the data acquisition module, and performs data preprocessing and short-term abnormal fluctuation detection; The real-time diagnosis unit is connected to the edge computing node and is configured to dynamically calculate the component health degree based on the preprocessed data, and synchronously compare the dynamic threshold value in the habitual parameter library; When the real-time diagnosis unit outputs an abnormal signal, the fault warning module generates a graded warning instruction and pushes it to the vehicle display screen and the bound mobile terminal. Cloud analysis center connects real-time diagnosis unit, stores historical data and trains long-period fault prediction model, and returns optimized algorithm parameters to real-time diagnosis unit; Synergistic response bus receives front road condition preview information of ADAS system, and activates high-frequency monitoring mode of real-time diagnosis unit 200 ms in advance when bumping road section is predicted.

[0027] The data acquisition module is built-in with a dual-channel data verification circuit to prevent sensor signal loss. The edge computing node is equipped with a real-time operating system with an interrupt delay of <5μs. The real-time diagnosis unit uses hardware-accelerated FPGA for parallel processing of 12 signals. The fault warning module supports 5G-V2X communication protocol. The cloud analysis center uses a distributed time series database. In the data acquisition module, the vibration sensor is a three-axis MEMS accelerometer, the deformation sensor is based on a metal strain gauge, the air leakage sensor uses ultrasonic detection principle, and the position feedback sensor is a linear Hall element. In the synergistic response bus, high-frequency monitoring: sampling rate is increased from 100Hz to 1kHz, analysis window is shortened from 500ms to 50ms, power consumption is increased by 15W, and it is powered by a dedicated power module.

[0028] The real-time diagnosis unit includes: Health degree calculation sub-unit, through air compressor efficiency decay rate formula Real-time output performance score; Dynamic threshold library, storing air spring compression frequency safety interval [F L ,F C ] and gas tank pressure tolerance band [P L ,P C ] based on driving habit learning; Millisecond-level response engine, triggering warning signal within 50ms when detecting data exceeding dynamic threshold library or health score below critical value.

[0029] Health degree calculation, through air compressor efficiency decay rate formula: η=(P nom -P act ) / t op ×100%; Where P nom =8bar, t op is the cumulative operating hours, the dynamic threshold library stores 1024 sets of driving mode parameters, and the response engine uses a hardware comparator array with a delay of ≤2ms.

[0030] The edge computing node performs: Kalman filter noise reduction on electromagnetic valve response delay data stream; Adopt sliding window algorithm to calculate the pressure change rate of gas tank If Where Q max is a preset leakage rate threshold, directly triggering the real-time diagnosis unit to intervene.

[0031] The pretreatment includes: wavelet packet decomposition of the vibration signal, extracting the 0.5-1kHz characteristic frequency band, moving average filter for pressure data, window width 200ms.

[0032] The fault warning module establishes a two-way communication with the suspension controller: When the real-time diagnosis unit detects an emergency fault, it sends a forced command to the suspension controller to lock the current air spring pressure state; Receive the feedback state of the suspension controller and include it in the health degree calculation parameters; The fault warning module push content includes: Thermal map display based on fault positioning; According to the remaining safety mileage Generate countdown warning, where a p is the pressure drop acceleration; navigation guidance interface associated with the location of the repair station.

[0033] Table 1, warning classification Rank Trigger condition Response action Level 1 η < 60% Screen pop-up Level 2 η < 40% Audible and visual alarm Level 3 η < 20% Forced speed reduction The thermal map is generated based on the Delaunay triangulation algorithm to locate the fault area.

[0034] The update mechanism of the dynamic threshold library includes: The cloud analysis center generates a driving scene classification model based on historical data clustering; Set the compression frequency safety interval [F Li ,F Ui ] for city congestion, highway cruising, and off-road modes respectively, where i is the scene number; The real-time diagnosis unit automatically matches the current scene threshold based on GPS positioning data.

[0035] The real-time diagnosis unit starts the warning when any of the following conditions is met: Air spring deformation recovery time Where is the historical mean; Current air compressor current fluctuation standard deviation for 5 consecutive working periods Where K is the material attenuation coefficient.

[0036] The cloud analysis center performs: Establish an air spring residual life prediction model through LSTM neural network: Where S tReal-time stress spectrum, Historical failure feature vector; the prediction result RUL t Real-time to vehicle terminal.

[0037] LSTM network structure: divided into input layer, hidden layer and output layer, where the input layer has 64 neurons, the hidden layer is 3x128 LSTM units, and the output layer is trained through Sigmoid activation Training data set: 100,000 historical failure samples, updated every 24 hours.

[0038] Sensor expansion configuration: vibration sensor increases three-axis acceleration detection dimension, deformation sensor integrates optical fiber strain measurement unit, and air leakage sensor adds sound wave spectrum analysis function; Pretreatment refinement operation: FFT frequency domain decomposition is performed on the air compressor current signal, and the sliding window algorithm is used to calculate the pressure change rate gradient. If the instantaneous fluctuation amplitude exceeds 3 times the standard deviation, trigger the real-time diagnosis unit to interrupt intervention.

[0039] Health degree calculation strengthening: the health degree calculation subunit introduces a weight factor: S health =α2E comp +β2P leak ; Where, E comp is the efficiency of the air compressor, P leak is the leakage rate, and the dynamic threshold library stores the pressure tolerance band according to temperature grading, divided into 5 temperature zones from -20℃ to 80℃; Early warning link enhancement: hierarchical instructions contain sound-light warning intensity gradient (70dB / 90dB / 110dB), mobile terminal push attached emergency operation video guide; Cloud analysis expansion function: historical data are labeled with space-time tags according to fault types, long-period models use transfer learning to optimize cold start problems, and algorithm parameters are encrypted and signed before being returned.

[0040] Collaborative mechanism supplement: high-frequency monitoring mode sampling rate is increased from 100Hz to 1kHz, and ADAS preview information contains road roughness index RQI; Health degree calculation optimization: temperature compensation term is introduced into the efficiency attenuation rate formula: η=η0·[1-k(T-T ref )]; Millisecond-level response engine uses hardware interrupt priority mechanism.

[0041] Kalman filter increases adaptive adjustment of process noise; Leakage judgment increases continuous time constraint: And last>5s; Safety control strengthening: mandatory instructions contain three-level air pressure locking mode (80% / 50% / 30% rated value); Remaining safety mileage calculation: Wherein, a p Is the pressure drop acceleration.

[0042] Early warning condition supplement: increase the composite condition: t recover >1.2μ hist And current fluctuation>3σ, material attenuation coefficient correlation cumulative working hours.

[0043] Model training expansion: add environmental humidity feature channel to the input layer of LSTM, and the failure feature vector contains the rubber hardness degradation trajectory.

[0044] Example two Referring Figure 2 In the second embodiment of the present application, the present application provides an application method of digital air suspension of automobile, the application method comprising the following steps: S1, collecting suspension component operation data and suspension controller instructions in real time through a multi-source sensor group; S2, performing data filtering and short-term risk screening on the edge computing node, and if the pressure mutation rate is detected to be out of limit, jumping to S4; S3, dynamically calculating the health degree index in the real-time diagnosis unit, and comparing with the scene adaptive threshold library; S4, generating a warning instruction with a positioning code when the health degree is lower than the safety line or the data deviates from the threshold; S5, uploading the abnormal data segment through the vehicle-cloud data channel, and receiving the prediction model parameters issued by the cloud; S6, displaying the fault heat map and the remaining safety time on the vehicle-mounted interface, and outputting the maintenance strategy topology.

[0045] Example three In the third embodiment of the present application, based on the same inventive concept, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the application method of digital air suspension of automobile in the above embodiment.

[0046] Example four In the fourth embodiment of the present application, based on the same inventive concept, the present application provides a computer device, which comprises a processor and a memory; the processor and the memory communicate with each other; the memory is used for storing instructions; the processor is used for executing the instructions in the memory, and the application method of digital air suspension of automobile in the above embodiment is executed.

[0047] It should be understood that various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well-known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0048] Finally, it should be noted that the above-mentioned only is the preferred embodiment of the present application, and is not used to limit the present application, although the present application is described in detail with reference to the foregoing embodiments, for those skilled in the art, the technical solution recorded in the foregoing embodiments can be modified, or some technical features can be replaced, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A digital air suspension system for automobiles, characterized in that: include: Suspension controller, height sensor, air compressor assembly and valve assembly, the air compressor assembly includes an air compressor, air spring and air tank, and the valve assembly includes several solenoid valves; The intelligent control system consists of a data acquisition module, edge computing nodes, a real-time diagnosis unit, a fault warning module, a cloud analysis center, and a collaborative response bus; The data acquisition module is connected to the vibration sensor of the air compressor, the deformation sensor of the air spring, the air leakage sensor and pressure sensor of the air tank, and the position feedback sensor of the solenoid valve, and obtains the command data of the suspension controller in real time; The edge computing node is deployed on the vehicle terminal, receives the original data stream from the data acquisition module, and performs data preprocessing and short-term abnormal fluctuation detection; The real-time diagnostic unit is connected to the edge computing node and is configured to dynamically calculate the component health based on the pre-processed data and synchronously compare the dynamic threshold value in the conventional parameter library; When the real-time diagnosis unit outputs an abnormal signal, the fault warning module generates a graded warning instruction and pushes it to the vehicle display screen and the bound mobile terminal; The cloud analysis center is connected to the real-time diagnosis unit, stores historical data and trains long-term fault prediction models, and transmits optimized algorithm parameters back to the real-time diagnosis unit; The collaborative response bus receives the ADAS system's preview information on the road conditions ahead. When a bumpy road section is predicted, the high-frequency monitoring mode of the real-time diagnostic unit is activated 200ms in advance.

2. The automotive digital air suspension system according to claim 1, characterized in that: The real-time diagnosis unit comprises: The health calculation subunit uses the air compressor efficiency attenuation rate formula Output performance scores in real time; Dynamic threshold library, which stores the air spring compression frequency safety interval based on driving habit learning [F L ,F C ] and gas tank pressure tolerance band [P L ,P C ]; The millisecond-level response engine triggers an early warning signal within 50ms when it detects that the data exceeds the dynamic threshold library or the health score is lower than the critical value.

3. The automobile digital air suspension system according to claim 1, characterized in that: The edge computing node performs: Perform Kalman filtering to reduce noise on the solenoid valve response delay data stream; Calculate the rate of change of gas tank pressure using sliding window algorithm like Among them, Q max The preset leakage rate threshold directly triggers the intervention of the real-time diagnostic unit.

4. The automobile digital air suspension system according to claim 1, characterized in that: The fault warning module establishes two-way communication with the suspension controller: When the real-time diagnosis unit detects an emergency fault, it sends a mandatory command to the suspension controller to lock the current air pressure state of the air spring; Receive feedback status from the suspension controller and incorporate it into health calculation parameters; The content pushed by the fault warning module includes: Heat map display based on fault location; Based on the remaining safe mileage The countdown alert generated, where a p is the pressure drop acceleration; A navigation guidance interface associated with the maintenance station location.

5. The automobile digital air suspension system according to claim 1, characterized in that: The update mechanism of the dynamic threshold library includes: The cloud analysis center generates a driving scenario classification model based on historical data clustering; Set compression frequency safety ranges for city congestion, high-speed cruising, and off-road modes [F Li ,F Ui ], where i is the scene number; The real-time diagnosis unit automatically matches the current scene threshold based on GPS positioning data.

6. The automobile digital air suspension system according to claim 1, characterized in that: The real-time diagnosis unit starts an early warning when any of the following conditions are met: Air spring deformation recovery time in, is the historical mean; Standard deviation of current fluctuation of air compressor for 5 consecutive working cycles Where K is the material attenuation coefficient.

7. The automobile digital air suspension system according to claim 1, characterized in that: The cloud analysis center performs: Establish an air spring remaining life prediction model through LSTM neural network: Among them, S t is the real-time stress spectrum, is the historical failure feature vector; the predicted result RUL t Sent to the vehicle terminal in real time.

8. A method for applying digital air suspension to an automobile, characterized in that: For an automobile digital air suspension system according to any one of claims 1 to 7, the application method comprises the following steps: S1, real-time collection of suspension component operating data and suspension controller instructions through a multi-source sensor group; S2: Perform data filtering and short-term risk screening at the edge computing node. If the pressure mutation rate exceeds the limit, jump to S4. S3, dynamically calculate the health index in the real-time diagnosis unit and compare it with the scene adaptive threshold library; S4. When the health level is lower than the safety line or the data deviates from the threshold, an early warning instruction with positioning code is generated; S5. Upload the abnormal data fragment through the vehicle-cloud data channel and receive the prediction model parameters sent by the cloud; S6. Display the fault heat map and remaining safety time on the vehicle interface, and output the maintenance strategy topology map.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the automobile digital air suspension application method according to claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the automobile digital air suspension application method according to claim 8 is implemented.

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