Variable lumen inflatable fixation device and intelligent control method thereof

Abstract

The application relates to the technical field of inflatable fixing control methods, and particularly discloses an inflatable fixing device with a variable inner cavity and an intelligent control method thereof, which is executed based on the inflatable fixing device. The inflatable fixing device comprises a plurality of independent sealed air chambers surrounded by an outer box body and a flexible inner container film. The method comprises the following steps: acquiring multi-dimensional attribute data of an object to be fixed, constructing a full-dimensional digital twin containing the geometry, mechanics and vulnerability characteristics of the object to be fixed, and generating a basic clamping protection parameter baseline corresponding to each independent sealed air chamber based on the full-dimensional digital twin. According to the application, the full-dimensional digital twin of the object is constructed before transportation to establish a safety protection baseline, and a global risk map is generated by fusing space-time data, so that the system can realize accurate space backstepping and time sprinting in the actual transportation process.

Description

Technical Field

[0001] This invention relates to the technical field of inflatable fixation control methods, specifically to an inflatable fixation device with a variable internal cavity and its intelligent control method. Background Technology

[0002] In modern logistics and special transportation, inflatable securing devices are often used to wrap and protect precision instruments or fragile items.

[0003] Existing control methods for inflatable fixing devices generally employ a passive feedback adjustment mechanism. This means that the system uses built-in sensors to monitor pressure changes inside the device or external vibration conditions in real time. Only after detecting an actual abnormal impact will the air pump and valves be triggered to perform inflation or deflation actions, thereby adjusting the clamping state of the object.

[0004] However, in practical applications, this passive control method faces an irreconcilable technical conflict between the physical response delay of pneumatic actuation and the instantaneous suddenness of road condition impacts: since gas compression, pipeline transmission, and the opening and closing of mechanical solenoid valves all require fixed physical response times, when the transport vehicle encounters complex road conditions such as potholes, bumps, or emergency braking, the inflation and deflation compensation actions of the control system are always lagging behind the actual moment of impact.

[0005] This severe spatiotemporal misalignment means that the inflatable securing device cannot provide the matching clamping force or buffering stiffness in time at the exact moment when the item needs the most protection, which can easily cause collisions and damage to the high-value items inside. Summary of the Invention

[0006] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the objective of this invention is to propose an inflatable fixing device with a variable internal cavity and its intelligent control method, to improve the safety and reliability of fragile items under complex transportation conditions.

[0007] To achieve the above objectives, a first aspect of the present invention proposes an intelligent control method for an inflatable fixation device with a variable inner cavity, based on the inflatable fixation device comprising multiple independent sealed air chambers enclosed by an outer casing and a flexible inner liner membrane. The method includes:

[0008] Acquire multi-dimensional attribute data of the item to be fixed, construct a full-dimensional digital twin containing the geometric, mechanical and vulnerable characteristics of the item to be fixed, and generate a baseline of basic clamping and protection parameters corresponding to each independent sealed air chamber based on the full-dimensional digital twin;

[0009] Multi-source heterogeneous working condition data are collected in real time and spatiotemporally fused to generate a standardized full-link working condition spatiotemporal dataset. Based on the spatiotemporal dataset, road segment risk features are extracted to quantify the risk level of the entire road segment. Combining the baseline of the basic clamping protection parameters with the quantified risk level of the entire road segment, a global risk map containing the feedforward protection parameters of each road segment target is generated.

[0010] The location information of the transport vehicle is acquired in real time and matched with the global risk map. Before entering the target risk section, the pressure and buffer stiffness of each independent sealed air chamber are subjected to graded feedforward pre-regulation actions according to the target feedforward protection parameters corresponding to the section.

[0011] During the execution of the graded feedforward pre-regulation action, the multi-dimensional sensing unit collects the status data of the independent sealed air chamber and the object to be fixed in real time, calculates the regulation deviation between the current state and the expected regulation target, and dynamically corrects the air pressure regulation command for each independent sealed air chamber based on the regulation deviation, thus completing the feedback closed-loop fine-tuning control.

[0012] To achieve the above objectives, a second aspect of the present invention provides an inflatable fixing device with a variable inner cavity, comprising: an outer box, a flexible inner liner film, a distributed air path execution unit, a multi-dimensional sensing unit, and an edge intelligent control unit.

[0013] The inner wall of the outer box is provided with multiple independently partitioned sealing grooves;

[0014] The edges of the flexible inner liner film are sealed and fixed to each of the sealing grooves in a full circumferential manner, so that the flexible inner liner film and the inner wall of the outer box form the multiple independent sealed air chambers.

[0015] The distributed pneumatic circuit execution unit includes a charging and discharging pump and miniature pneumatic solenoid valves that are set on the pneumatic circuit branches and connected one-to-one with each independent sealed air chamber.

[0016] The edge intelligent control unit is electrically connected to the distributed pneumatic actuator and the multi-dimensional sensing unit respectively. The edge intelligent control unit includes a processor and a memory. The memory stores a computer program. When the processor executes the computer program, it implements the intelligent control method of the variable cavity inflatable fixing device described above.

[0017] To achieve the above objectives, a third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the intelligent control method for the above-described variable cavity inflatable fixation device.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] The variable-cavity inflatable fixing device and its intelligent control method of this invention establish a safety protection baseline by constructing a full-dimensional digital twin of the item before transportation and generating a global risk map by integrating spatiotemporal data, enabling the system to achieve precise spatial backward calculation and time acceleration during actual transportation.

[0020] Before the transport vehicle actually enters a known high-risk road section such as a bumpy or sharp bend, the system can predict and trigger a graded feedforward pre-control action, reserving a complete physical inflation and deflation time for the pneumatic actuator; at the same time, it combines the sensor status data during the execution process for feedback closed-loop fine adjustment, ensuring that the sealed air chamber can provide the target clamping force and appropriate buffer stiffness in a tight fit at the precise moment when the vehicle encounters road impact.

[0021] This solution effectively resolves the blind spot problem caused by aerodynamic hysteresis, and realizes a fundamental shift from post-event remediation to pre-event adaptive and collaborative protection, greatly improving the safety and reliability of vulnerable items under complex transportation conditions. Attached Figure Description

[0022] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0023] Figure 1 This is a flowchart illustrating the intelligent control method for the variable-cavity inflatable fixing device provided by the present invention.

[0024] Figure 2 This is a schematic diagram illustrating the implementation of the variable internal cavity inflatable fixing device provided by the present invention;

[0025] Figure 3 This is a schematic diagram of the electronic device provided by the present invention. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0027] The following description, with reference to the accompanying drawings, describes an embodiment of the variable-cavity inflatable fixation device, its intelligent control method, and electronic equipment.

[0028] Example 1:

[0029] This embodiment provides an intelligent control method for an inflatable fixation device with a variable internal cavity. The method is based on an inflatable fixation device. Specifically, the inflatable fixation device includes multiple independent sealed air chambers surrounded by a rigid or semi-rigid outer casing and a flexible inner liner membrane. The air circuit system is equipped with a multi-dimensional sensing unit and a distributed air circuit execution unit, and is centrally calculated and commanded by an edge intelligent control unit.

[0030] like Figure 1 As shown, the method in this embodiment includes the following steps:

[0031] Step 1: Multi-dimensional attribute data collection and full-dimensional digital twin construction.

[0032] Before performing any physical clamping action, the system first needs to create an accurate digital image of the object being protected.

[0033] Specifically, the control system acquires multi-dimensional attribute data of the object to be fixed through an external interface or a local input terminal. This multi-dimensional attribute data includes not only static physical parameters such as the object's three-dimensional geometric dimensions, mass distribution, and center of gravity, but also dynamic mechanical and vulnerability characteristic parameters such as the friction coefficient of its surface material, the yield strength of each component, and the resonant frequency.

[0034] For example, if the item to be fixed is a high-precision medical optical instrument, the system will receive the instrument's CAD design model and factory mechanical test report. Based on the multi-dimensional attribute data, the processor in the edge intelligent control unit initiates a digital twin modeling program to construct a 1:1 full-dimensional digital twin containing the geometric, mechanical, and vulnerability characteristics of the item to be fixed. The construction process of this full-dimensional digital twin includes three parallel sub-levels:

[0035] First, a geometric twin model is constructed based on the multi-dimensional attribute data. In this model, the system reconstructs the spatial three-dimensional shape of the object using mesh generation technology and, combined with the material list in the multi-dimensional attribute data, explicitly marks vulnerable parts of the object's surface. For example, the outer frame of the lens of an optical instrument and the precision cable interface area will be specially marked as highly sensitive stress areas.

[0036] Secondly, a mechanical twin model is constructed. The system applies an equivalent virtual physical field to the geometric twin model in virtual space to simulate the stress distribution and deformation transmission path of various parts of the object under impact accelerations of different directions and magnitudes. This mechanical twin model can predict in advance the stress concentration points that may occur inside the object under specific stress states.

[0037] It is also important to note that, to ensure the safety of subsequent inflation control, the system further constructs a vulnerability twin model based on this. In this geometric twin model, for each marked vulnerable part and the overall structure, a three-tiered warning threshold is set, including a safety threshold, a warning threshold, and a limit threshold. Numerically, the system strictly adheres to constraint logic, meaning the safety threshold, warning threshold, and limit threshold increase sequentially. The safety threshold indicates that the item can withstand multiple cyclic loadings within this stress range without significant fatigue damage; the warning threshold indicates that the stress is approaching the material's elastic limit, requiring significant intervention from the control system; and the limit threshold represents the critical failure stress value at which the item undergoes irreversible plastic deformation or structural fracture.

[0038] Based on the constructed full-dimensional digital twin and the three-level hierarchical early warning thresholds, the system generates corresponding basic clamping protection parameter baselines for each of the multiple independent sealed air chambers. These baselines constitute the underlying physical constraint boundaries of this transportation mission, specifically including the upper limit of the safe pressure, the basic clamping pressure ratio, and the inflation rate for each independent sealed air chamber. For example, the upper limit of the safe pressure for the independent sealed air chamber corresponding to the location of the vulnerable part will be strictly limited below the safe threshold, and its basic clamping pressure ratio will account for a small proportion of the overall air chamber ratio to avoid localized stress overload.

[0039] Step 2: Spatiotemporal fusion of multi-source heterogeneous operating condition data and generation of a global risk map.

[0040] After establishing the protection baseline for the items to be secured, the system needs to develop accurate perception and forward-looking mapping of the evolution of the external environment during the departure and travel of the transport vehicle.

[0041] Specifically, the system collects multi-source heterogeneous operating condition data in real time through multi-dimensional sensing units and vehicle-to-everything (V2X) communication interfaces. This multi-source heterogeneous operating condition data includes, but is not limited to: vehicle geographic coordinates transmitted from the onboard GPS / BeiDou terminal, vehicle speed sensor data, curvature and slope information of the road ahead, high-precision map road condition events (such as construction, congestion, and speed bump distribution), and real-time wind speed and rainfall obtained from the meteorological interface. Because the above data originates from different hardware sensors and cloud services, their data sampling frequencies and timestamp formats differ significantly.

[0042] To eliminate this data heterogeneity, the system uses a unified satellite time as the absolute reference to align the multi-source heterogeneous operating condition data with millisecond-level timestamps within a set tolerance range. It also performs spatial coordinate system normalization processing through a coordinate transformation algorithm, uniformly mapping all spatial location information to the WGS84 geodetic coordinate system, thereby generating a standardized full-link operating condition spatiotemporal dataset.

[0043] Optionally, the system then initiates a path risk quantification procedure, dividing the entire transportation path into multiple road segment units based on geographical features or equidistant segmentation rules. Based on the standardized full-link spatiotemporal dataset, the system extracts risk characteristic parameters for each road segment unit. These risk characteristic parameters include road surface smoothness index, road curvature radius, and historical accident incidence rate. The processor performs a weighted summation of the extracted risk characteristic parameters according to a preset weight matrix, calculating a comprehensive risk score for each road segment unit. Based on this comprehensive risk score, each road segment unit is rigorously divided into road segments corresponding to low, medium, high, and extremely high risk levels.

[0044] Next, the control system enters the core parameter mapping and risk map planning stage. The system calls a pre-set 3D coupled mapping model, which is essentially a deep neural network pre-trained with a large amount of logistics condition data. The system inputs the full-dimensional digital twin attribute parameters of the item to be fixed, the risk feature parameters of the road segment unit, and the baseline of the basic clamping protection parameters generated in the first step as a joint input vector into the 3D coupled mapping model. The neural network inside the model pre-calculates and outputs the target feedforward pressure and auxiliary control parameters corresponding to each road segment through feature extraction and cross-attention calculation.

[0045] It is also important to note that the auxiliary control parameters here mainly involve the initial setting value of the PWM duty cycle of the solenoid valve in the distributed pneumatic actuator and the feedforward value of the charging / discharging pump speed. The system collectively refers to the target feedforward pressure and auxiliary control parameters as the target feedforward protection parameters, and firmly binds this parameter set to the spatial coordinate system of each road segment unit, ultimately mapping and generating a global risk map containing the target feedforward protection parameters of each road segment. This global risk map is temporarily stored in the local high-speed cache of the edge intelligent control unit throughout the transportation process, serving as the basis for subsequent feedforward control execution.

[0046] Step 3: Calculation of dynamic response timing and execution of hierarchical feedforward pre-control.

[0047] Traditional inflatable securing devices typically only inflate when sensors detect an impact, a delay that can cause items to become instantly unstable. The core innovation of this solution lies in using a global risk map to achieve both spatial-dimensional backward calculations and temporal-dimensional proactive responses.

[0048] Specifically, during transportation, the edge intelligent control unit acquires the location information of the transport vehicle in real time and continuously tracks and matches it with the trajectory coordinates in the global risk map. Using trajectory prediction algorithms such as Kalman filtering, the system predicts the risk level of the road segment the vehicle is about to enter and calculates the estimated arrival time in real time based on the current travel speed.

[0049] Because the distributed pneumatic actuator of the inflatable stationary device requires a significant amount of physical time for the air pump to build up pressure, the gas to travel through the pipeline, and the volume expansion of the sealed air chamber after receiving an electrical signal, time delay compensation is necessary. The system obtains the actuator response time from the factory calibration or dynamic self-learning of the inflatable stationary device. Subsequently, the processor combines the actuator response time with the estimated arrival time and performs backward calculations along the time axis to accurately calculate the triggering timing of the feedforward command. For example, if the estimated arrival time of the high-risk bumpy road section ahead is 5.0 seconds, and the actuator response time under the current system conditions is estimated to be 1.2 seconds, then the triggering timing of the feedforward command is precisely determined to be at the absolute time node 1.2 seconds before entering the road section.

[0050] Before or at the moment of triggering, the system performs differentiated, graded feedforward pre-regulation actions based on the target feedforward protection parameters corresponding to that road segment, targeting the pressure and buffer stiffness of each independent sealed air chamber:

[0051] For example, when the system anticipates entering a road segment with a medium or high risk level, it extracts the target feedforward protection parameters for that segment before the triggering time arrives. If the target parameters require increased support stiffness, the system controls a miniature pneumatic solenoid valve and an air pump to perform differential compensation on the clamping force ratio and buffer stiffness of each independent sealed air chamber based on the target feedforward protection parameters. That is, a certain amount of gas is added on top of the original pressure-holding base air pressure to harden the air chambers and resist medium- and high-frequency road impacts.

[0052] For example, when the system anticipates entering a road section with an extremely high risk level, such as entering an emergency escape lane or passing through a deep subsidence pothole, conventional differential compensation is insufficient to meet the protection requirements. In this case, the system triggers emergency protection logic. Without exceeding the limit threshold set by the vulnerability twin model described in the first step, the clamping force of each independent sealed air chamber is instantly and forcibly increased to the upper pressure limit corresponding to the safety threshold. Simultaneously, a full-power emergency response mode is activated, causing the inflation and deflation pumps to operate at maximum power, effectively restricting the freedom of movement of the object to be secured.

[0053] Step 4: Feedback closed-loop fine-tuning control and macro pressure clamping trend assessment.

[0054] Feedforward pre-control provides open-loop command prediction based on a risk map. However, in actual execution, due to gas temperature changes, valve mechanical wear, and slight displacement of materials, the actual state of the gas chamber will inevitably deviate from the expectation. Therefore, a feedback closed-loop fine-tuning mechanism must be superimposed during the execution of feedforward actions.

[0055] Specifically, during the execution phase of issuing air pressure control commands for each independent sealed air chamber, multi-dimensional sensing units, such as the main pressure sensor and branch pressure sensors built into the air path, collect real-time data on the actual state of the independent sealed air chamber and the object to be fixed at high frequency (e.g., 100Hz). The edge intelligent control unit calculates the control deviation between the current state and the expected control target (i.e., the feedforward preset target pressure) in real time. Based on the control deviation, the system dynamically corrects the air pressure control commands for each independent sealed air chamber.

[0056] During dynamic inflation, identifying when the flexible inner liner film truly contacts and effectively clamps the item to be secured is a challenge for the control system. Relying solely on absolute pressure thresholds can easily lead to misjudgments due to differences in packaging box dimensions. Therefore, this embodiment initiates a graded adaptive inflation program during the execution phase of the air pressure control command, introducing a macroscopic air pressure clamping trend evaluation mechanism based on statistical fluctuation characteristics.

[0057] The edge intelligent control unit calculates the clamping trend evaluation coefficient within a sliding detection time window based on the real-time acquired pressure data sequence, denoted as... This coefficient does not represent the magnitude of absolute pressure, but rather the convergence and oscillation degree of the pressure change curve.

[0058] The clamping trend evaluation coefficient The specific formula for calculation is defined as follows:

[0059] ;

[0060] This formula performs a comprehensive evaluation by extracting two orthogonal feature dimensions of the pressure sequence.

[0061] The The pressure fluctuation coefficient is mainly used to smooth and suppress large impulse noise in pressure sequences and amplify small trend changes. Its calculation formula is:

[0062] ;

[0063] The The pressure variation density coefficient is mainly used to assess the spatial clustering and dispersion of pressure fluctuations. If the fluctuations are too dense and the variance is large, this coefficient will increase exponentially, penalizing the stability assessment. Its calculation formula is as follows:

[0064] ;

[0065] In the formula, and All are non-zero weight coefficients pre-calibrated by the control system, and the system is subject to mandatory constraints. To ensure The calculation results are normalized to a reasonable numerical range; Strictly defined as the cumulative number of pressure direction changes within the detection time window, that is, the total number of inflection points where the pressure curve shows a peak or a trough. For the first time window of detection The single pressure change corresponding to the directional change (i.e., the absolute value of the pressure difference between adjacent peaks and troughs). To detect the total pressure change from the start time to the end time within the detection time window; Defined as the absolute value of the difference between the measured absolute pressure at the end of the detection time window and the target clamping pressure expected in this control; Specifically, it refers to the maximum single pressure change that has occurred within the aforementioned detection time window; It represents the statistical standard deviation of all single pressure change data included within the detection time window.

[0066] Based on practical application instructions, during the initial inflation phase, the flexible inner liner membrane is in a state of free expansion, the pressure rises gradually, and the cumulative number of directional changes... Minimal, at this time The values ​​are stable. When the membrane surface first comes into contact with the irregular outline of the object to be fixed, the membrane will experience localized nonlinear wrinkles and deformation, which will be hindered. This microscopic physical resistance will be transmitted back to the air passage, causing high-frequency, minute oscillations in the internal air pressure. At this time... Become dense, Increase, and standard deviation Significant changes. Evaluation coefficients based on the described clamping trend. The system can keenly and accurately identify the initial contact feature points between the flexible inner film and the surface of the object to be fixed, based on the significant abrupt change characteristics.

[0067] After capturing the initial contact feature point, the system seamlessly switches the control strategy from rapid pressure build-up to constant pressure approximation. Based on the position coordinates of the initial contact feature point and the control deviation, the opening of the miniature pneumatic solenoid valve is gradually reduced, and the inflation and deflation parameters are smoothly updated. After the inflation reaches the target pressure, a full circumferential fit check is performed. Once the check passes, the power source is cut off, allowing the corresponding independent sealed air chamber to enter a low-power closed-loop depressurization mode.

[0068] Step 5: Dynamic adaptive micro-adjustment of the valve core displacement of the miniature pneumatic solenoid valve.

[0069] Beyond the macroscopic air pressure regulation in step four above, this embodiment delves into the microscopic actuator layer, the lowest level of fluid control. Air pressure regulation ultimately relies on the high-frequency opening and closing and throttling of a miniature pneumatic solenoid valve. However, when driven by a high-frequency PWM electrical signal and subjected to fluid aerodynamic disturbances, the internal mechanical valve core of the solenoid valve is prone to high-frequency mechanical vibration (valve core jitter). This jitter not only significantly shortens the mechanical life of the solenoid valve but also causes pulsating noise in the output airflow, thus interfering with the assessment of the macroscopic air pressure trend in step four.

[0070] Therefore, in the step of dynamically correcting the air pressure control command for each independent sealed air chamber based on the control deviation in this embodiment, an additional layer of micro-feedforward-feedback control mechanism for the opening degree of the micro pneumatic solenoid valve is nested.

[0071] Specifically, the distributed pneumatic actuator of the inflatable stationary device is equipped with miniature pneumatic solenoid valves, and each miniature pneumatic solenoid valve integrates a high-precision valve core displacement sensor. Through the valve core displacement sensor, the edge intelligent control unit collects the feedback displacement direction change information of the solenoid valve core in real time at an extremely high frequency, such as 1000Hz.

[0072] Based on the feedback displacement direction change information, the system uses a similar isomorphic mathematical model to the aforementioned macroscopic air pressure assessment to calculate the displacement direction fluctuation coefficient and displacement direction change density coefficient at the microscopic level. By weighting the displacement direction fluctuation coefficient and the displacement direction change density coefficient, a trend evaluation coefficient specifically for assessing the mechanical stability of the valve core is finally calculated, denoted as... .

[0073] The evaluation coefficient of the change trend The calculation formula is defined as follows:

[0074] ;

[0075] In this microcontrol architecture, the The displacement oscillation coefficient is designed to compress large-amplitude normal displacement commands through a logarithmic function, thereby highlighting minor abnormal flutter caused by fluid disturbances that are not driven by commands. Its calculation formula is as follows:

[0076] ;

[0077] The The displacement direction change density coefficient is used, and the penalty weight for the valve core jitter frequency is also amplified by a square power. The calculation formula is as follows:

[0078] ;

[0079] In the formula, Set as the preset third weighting coefficient. Set as the preset fourth weighting coefficient; It is specifically defined as the cumulative number of times the valve core displacement direction changes within the microscopic detection time window (reflecting the frequency of mechanical vibration). The first time within the microscopic detection time window The actual change in valve core displacement corresponding to the change in the next direction; It is the absolute total change in valve core feedback displacement (i.e., the overall macroscopic action stroke) within the entire cycle from the start to the end of the detection time window. Defined as the absolute value of the position difference between the actual feedback displacement value of the valve core and the target opening displacement value issued by the control system at the end of the detection time window (characterizing steady-state position error). The maximum displacement of the valve core in a single directional change within the detection time window; It is the standard deviation of the displacement change data corresponding to all single directional changes included within the detection time window.

[0080] During actual control execution, the system will calculate the aforementioned trend evaluation coefficient. As an internal correction parameter for the inner depth loop. When When the valve core is currently experiencing severe high-frequency oscillation, the control system immediately performs dynamic adaptive adjustment of the opening degree of the miniature pneumatic solenoid valve. By introducing control damping or dynamically reducing the duty cycle abrupt change rate of the PWM carrier, the rising edge of the drive current is softened, thereby suppressing the fluid-structure interaction chatter phenomenon of the mechanical valve core at its physical source. This microscopic mechanism greatly improves the linearity of the airflow output, ensuring a highly smooth and stable air delivery to the sealed air chamber.

[0081] Step 6: Equipment resonance suppression and full lifecycle self-learning optimization.

[0082] This embodiment not only focuses on real-time control actions, but also includes resonance suppression for complex mechanical systems and incremental iterative optimization capabilities based on historical data.

[0083] It is also important to note that when the air pump is operating continuously, the electromagnetic torque pulsation of its built-in motor will generate mechanical vibration. If this vibration frequency happens to fall within the natural frequency range of the outer casing, internal pipes, or the fixed object, it will cause serious mechanical resonance.

[0084] To mitigate this hazard, this solution innovatively introduces an electronically controlled resonance suppression step. Specifically, the system rapidly acquires the motor current signal output by the air pump driver in the inflatable stationary device and performs a Fast Fourier Transform (FFT) on it in the frequency domain to accurately identify the torque pulsation frequency band caused by higher harmonics at the current speed. Simultaneously, the system combines multi-dimensional sensing units, such as a triaxial accelerometer, to sense the overall structural vibration mode data and constructs a frequency band coupling matrix in memory. Within this matrix, the system compares the overlapping regions of the electromagnetic excitation frequency and the mechanical natural frequency. Upon detecting overlap, the system actively disperses and transfers the spectral energy of the motor's electromagnetic noise by adjusting the drive algorithm, such as implementing carrier frequency randomization and space vector PWM duration redistribution techniques. This successfully avoids the high-risk resonance region marked in the frequency band coupling matrix without altering the output macroscopic air pressure, achieving active noise reduction and vibration damping.

[0085] Furthermore, this control method possesses strong evolutionary capabilities. After each single transport mission, such as the successful completion and unloading of a precision instrument shipment from City A to City B, the edge intelligent control unit initiates a retrospective analysis program. By comparing historical logs, the system extracts a feedforward control matching degree, characterizing the control effect, based on the convergence of pressure deviation data recorded before and after the transport vehicle performs tiered feedforward pre-regulation actions when passing through high-risk road sections. This feedforward control matching degree is used as a high-quality closed-loop feedback label, packaged with the current mission's operational data to form a training sample, and incrementally iteratively optimized (e.g., by updating the neural network weights using gradient descent) of the pre-set three-dimensional coupled mapping model temporarily stored within the system. Through autonomous learning throughout the entire lifecycle, the system will provide more accurate and effective target feedforward protection parameters when facing similar road conditions or similar vulnerable items in the future.

[0086] Because existing inflatable securing devices generally suffer from severe passive delay defects, current systems only begin calculating and driving the air pump to start after the sensor detects a physical impact signal. However, the slow pressurization process of the pneumatic system causes the inflation and expansion of the air chamber to lag behind the shock waves generated by road bumps. This limitation means that the secured object is often in an unprotected state at the precise moment of impact.

[0087] This embodiment establishes the physical boundaries of the protected object using a full-dimensional digital twin and constructs a global risk map through cross-domain spatiotemporal data fusion, enabling the control system to predict future road conditions. Based on this prediction, the system precisely aligns the time lead with the physical response delay, triggering pneumatic components just before the vehicle hits a pothole, thus completing tiered feedforward pre-control. During the feedforward execution, a pioneering macroscopic air pressure trend assessment (CTEC) is used to find the physical contact point, supplemented by microscopic solenoid valve chatter assessment (TCEC) to ensure linear airflow output, forming a rigorous dual-closed-loop flexible clamping system with feedforward as the primary method, feedback as a secondary method, and microscopic self-adaptation. This control method greatly reduces the adverse effects of airflow physical hysteresis, improves the success rate of protecting highly sensitive stressed objects under sudden and severe conditions, and ensures the safety and reliability of every transportation mission.

[0088] Example 2:

[0089] In Example 1, the system heavily relies on spatiotemporally synchronized fusion data based on BeiDou or GPS satellite time for feedforward pre-control of the global risk map. However, in actual logistics and transportation operations, transport vehicles inevitably need to traverse physical structures where radio frequency signals are attenuated or completely blocked. When satellite positioning signals are lost, if the control system continues to rely on the absolute spatial coordinate system, severe spatiotemporal misalignment will occur, causing the inflatable fixing device to fail to trigger air pressure regulation commands at the correct physical location, thus exposing fragile items to significant impact risks.

[0090] To address the aforementioned conflict-related technical problems, this invention proposes a compensatory control strategy that avoids the need for additional, expensive inertial navigation unit (IMU) hardware. Instead, it utilizes a compensatory control strategy based on the existing sensing modules within the device through deep reuse. The following provides a detailed description of this blind zone spatiotemporal sequence compensatory control method:

[0091] Step 1: Real-time monitoring and mode switching in communication blind spots.

[0092] The system has a high-frequency daemon running in the background to continuously monitor the health status of external signals and the timeliness of data streams.

[0093] Specifically, for communication blind zone scenarios where satellite signals are blocked and spatiotemporal reference updates are interrupted, after generating a global risk map containing feedforward protection parameters for each road segment target, the method further includes: when the update delay time of the spatial coordinate system normalized data exceeds the preset spatiotemporal disconnection threshold, triggering the blind zone spatiotemporal sequence compensation mode.

[0094] In practical applications, the communication interface within the edge intelligent control unit continuously receives NMEA (National Marine Electronics Association) format messages from the vehicle-mounted terminal or its built-in satellite antenna. The processor extracts the timestamp from the message in real time and calculates the difference between it and the local high-precision real-time clock (RTC) within the edge intelligent control unit to obtain the update delay time of the spatial coordinate system normalized data. The spatiotemporal disconnection threshold is a time constant pre-calibrated by the system, and its value is set by comprehensively considering the normal driving speed of the transport vehicle and the response tolerance of the inflation actuator.

[0095] For example, the spatiotemporal disconnection threshold can be set to three seconds. If the system does not receive spatial coordinate data containing a valid positioning solution state (such as a fixed solution or a floating-point solution) within three consecutive seconds, the system determines that it has entered a communication dead zone scenario and immediately generates a hardware interrupt, switching the state machine of the global controller from the conventional absolute spatiotemporal feedforward mode to the dead zone spatiotemporal sequence compensation mode to prevent the system from outputting incorrect inflation / deflation commands based on outdated coordinate data.

[0096] Step 2: Establishing topological anchor points and dimensionality reduction transformation of the global risk map.

[0097] After entering the compensation mode, the system's primary task is to establish a local relative reference system and transform the protection task, which was originally planned based on three-dimensional absolute coordinates, into this relative reference system.

[0098] Specifically, the last valid normalized spatial coordinates before the signal link is broken are set as the topological anchor point, and the road segment units to be driven in the global risk map that are located after the topological anchor point are converted into a one-dimensional sequence mapping relationship with relative driving mileage as the first dimension and risk feature parameters as the second dimension.

[0099] Here, the last valid normalized spatial coordinates refer to the WGS84 latitude, longitude, and elevation coordinates received by the system with the highest confidence level before reaching the spatiotemporal chain break threshold. These coordinates are mathematically frozen by the system and defined as the topological anchor point. The topological anchor point is equivalent to the origin of a local relative coordinate system, and its initial value relative to the travel distance is forcibly initialized to 0.

[0100] Since updates to the 3D coordinates cannot be obtained within communication blind spots, continuing to use a 3D global risk map for matching would consume meaningless computing power and lack a comparative benchmark. Therefore, the edge intelligent control unit invokes a path integral algorithm to calculate the 3D curve arc length along the road geometric centerline for each subsequent road segment unit, starting from the topological anchor point, along the system's pre-planned expected driving trajectory. The processor stretches and unfolds this 3D curve arc length as the relative driving distance in the first dimension.

[0101] Simultaneously, the original risk attributes of this road segment, such as speed bumps, road joints, and abrupt slope changes leading to target feedforward pressure requirements, are retained as risk feature parameters for the second dimension. Through this spatial dimensionality reduction, the massive and complex three-dimensional global risk map is simplified into an efficient data structure, namely the single-dimensional sequence mapping relationship. This mapping relationship is represented in memory as a one-dimensional array or linked list, where the array index or key corresponds to the relative driving distance from the topological anchor point, and its corresponding value is the target feedforward protection parameter that the system needs to execute at that mileage node.

[0102] Step 3: Calculate the relative mileage based on the discrete integral of the running vibration signal.

[0103] After reducing the dimensionality of the map, the system needs to solve the problem of how to determine how far the vehicle has traveled in blind spots such as tunnels when there is no GPS speed measurement feedback.

[0104] Optionally, in the blind zone spatiotemporal sequence compensation mode, the running vibration signal transmitted back by the triaxial vibration sensor inside the inflatable fixing device is collected, and the characteristic peak frequency of the wheel rolling cycle in the running vibration signal is extracted to calculate the real-time relative estimated mileage by discrete integration.

[0105] In real-world vehicle dynamics scenarios, as the wheels of a transport vehicle roll on the road, the periodic contact between the tire tread blocks and the road surface, the rotation of the chassis drive shaft, and the operation of the engine all generate mechanical vibrations with specific frequency characteristics. These vibrations are directly transmitted through the vehicle floor to the inflatable mounting device placed on it. A triaxial vibration sensor in the multi-dimensional sensing unit, such as a high-precision MEMS accelerometer, collects this triaxial acceleration data in real time at a high sampling rate, forming the aforementioned operational vibration signal.

[0106] To extract the characteristic frequencies that are strictly linearly related to vehicle speed from the operating vibration signal mixed with environmental noise, the digital signal processor of the edge intelligent control unit performs a sliding window Fast Fourier Transform (FFT) on the operating vibration signal in the time domain. After transforming to the frequency domain, the algorithm identifies the dominant frequency peak with the highest energy spectral density within a specific low-frequency or mid-frequency search band based on a preset range of vehicle wheel diameter parameters and an experienced vehicle speed range. This peak is defined as the characteristic peak frequency of the wheel roll cycle.

[0107] After extracting the frequency, the system converts the frequency domain data into the real-time linear velocity of the vehicle in the physical world using the following linear velocity calculation formula:

[0108] ;

[0109] In the formula: To be in discrete time step The real-time linear velocity of the transport vehicle estimated by the timekeeping system; The dynamic rolling radius constant of the transport vehicle wheels pre-configured for the system; To be in discrete time step The characteristic peak frequency of the wheel rolling cycle extracted by spectrum analysis at any given moment; The constant is the number of dominant excitation pulses generated per revolution of the transport vehicle tire.

[0110] After calculating the linear velocity at each discrete time step, the system uses a numerical integration method to continuously accumulate the minute distances traveled by the vehicle within the blind spot, thereby obtaining the macroscopic cumulative distance. The specific formula for calculating this relative estimated mileage is as follows:

[0111] ;

[0112] In the formula: The time period from the topological anchor point to the current time. Up to this point, the sum of the relative estimated mileages calculated by the system; This is the total number of discrete time steps counted since the start of the blind zone spatiotemporal sequence compensation mode. In the first Real-time linear velocity estimates within a discrete time step; The fixed time interval, i.e., the integration step size, is used for the system to collect and process the operating vibration signal and perform a linear velocity calculation.

[0113] Using the algorithm described above, the inflatable anchoring device successfully and creatively reuses its internal sensor for sensing three-dimensional spatial vibration as a virtual odometer that does not require external signal intervention, thus solving the problem of insufficient underlying perception that vehicle displacement cannot be quantified in blind spots.

[0114] Step 4: Elimination of cumulative drift error based on waveform cross-correlation matching.

[0115] While the third step above provides a method for estimating relative mileage, the objective engineering reality is that any indirect calculation or integration based on sensor data will typically suffer from integral drift error. Slight wheel slippage, fluctuations in rolling radius due to tire pressure changes, and resolution errors in spectral analysis will all be amplified by the integral formula over time. If a tunnel is ten kilometers long, the relative estimated mileage will change significantly when traveling to the middle or later sections of the tunnel. The deviation from the actual physical mileage can be as high as tens of meters. Such an error is difficult to meet the control accuracy requirements of stationary devices that require precise triggering of feedforward pressure regulation at the millisecond or centimeter level.

[0116] To address the persistent problem of integral drift, this embodiment introduces a landmark correction mechanism that matches road surface physical characteristics with a pre-stored model.

[0117] It should also be noted that when the relative estimated mileage reaches the distance window of the mutation risk node marked in the one-dimensional sequence mapping relationship, the abnormal vibration impact envelope caused by road excitation is extracted. By performing waveform cross-correlation matching between the abnormal vibration impact envelope and the pre-stored risk feature parameters corresponding to the mutation risk node, the cumulative drift error of the relative estimated mileage is eliminated and the mileage calibration is completed.

[0118] Specifically, in the single-dimensional sequence mapping relationship, some road segment units are not only smooth straight roads, but also include deceleration and vibration markings inside tunnels, bridge expansion joints, or known road surface settlement potholes. These features, which have fixed absolute locations in geographic space and can cause severe vertical vibrations in vehicles, are defined by the system as the mutation risk nodes. The mutation risk nodes inherently possess a priori distance attribute in the dimensionality reduction model. For example, there is an expansion joint located precisely 1500 meters from the topological anchor point at the tunnel entrance.

[0119] The system sets a tolerance range, or distance window, around this prior distance. When the relative estimated mileage is calculated in the third step... Upon entering this distance window, the system activates a highly sensitive data acquisition mechanism. At this time, the triaxial vibration sensor will capture the strong transient vibration caused by the sudden excitation of the road surface. The edge intelligent control unit sequentially performs squaring and low-pass filtering operations on the raw transient vibration signal to extract its energy profile and generate the abnormal vibration impact envelope.

[0120] To determine whether the measured impact envelope matches the pre-stored landmark features on the map, and specifically how much it has shifted spatially, the system invokes a cross-correlation algorithm from digital signal processing. The system retrieves the pre-stored risk feature parameters (essentially a standard vibration envelope waveform pre-trained and distributed in the cloud) associated with the mutation risk node, and performs waveform cross-correlation matching based on spatial translation between the real-time extracted abnormal vibration impact envelope and the pre-stored waveform.

[0121] The core mathematical formula for waveform cross-correlation matching is defined as follows:

[0122] ;

[0123] In the formula: To compare the measured waveform with the pre-stored waveform by applying a discrete spatial translation amount The numerical values ​​of the mutual relationships at different times; Let be the independent variable representing the relative displacement offset between two sets of discrete signals; The total window length of the discrete data points of the envelope signal participating in the cross-correlation operation; The index number of the discrete data points within the window; For index The normalized amplitude of the abnormal vibration impact envelope is extracted in real time at the location; To take into account spatial translation Then, the normalized amplitude of the standard waveform of the pre-stored risk characteristic parameter at the corresponding index.

[0124] The edge intelligent control unit performs calculations within a set translation range. , search for The optimal spatial translation that yields the global maximum value is denoted as . The optimal spatial translation amount In a physical sense, it directly represents the spatial misalignment between the relative mileage calculated by the current integration and the actual physical landmark mileage.

[0125] In search of The system then performs a forced calibration and correction on the estimated mileage based on this. The mileage calibration update formula is as follows:

[0126] ;

[0127] In the formula: This is the accurate relative mileage calculated after drift error calibration and compensation; The relative estimated mileage, including accumulated errors, is obtained from the third-step integration algorithm; To make the interrelationship values The optimal spatial translation amount at its maximum; Index each discrete data point The actual physical space distance resolution constant it represents.

[0128] Through this matching and calibration process, the system cleverly utilizes the harsh road surface vibrations that could otherwise damage the goods, transforming them into a high-precision spatial absolute coordinate calibration source. Since this calibration is performed every time a sudden risk node is encountered, the cumulative drift error of the relative estimated mileage is periodically cleared to zero, thus ensuring that even in deep tunnels tens of kilometers long, the control system maintains centimeter-level precision in determining the location of the transport vehicle.

[0129] Step 5: Trigger feedforward control commands based on high-precision calibration mileage.

[0130] After obtaining precise location information to eliminate drift errors, the system can restore precise control of the sealed gas chamber within the blind zone.

[0131] Specifically, based on the relative estimated mileage after mileage calibration, the target feedforward protection parameters of the corresponding road segments in the one-dimensional sequence mapping relationship are sequentially triggered.

[0132] The comparator module in the edge intelligent control unit continuously updates the accurate relative estimated mileage. The system performs a high-speed comparison with the activation distances of each risky road segment to be regulated, stored in the single-dimensional sequence mapping relationship linked list. Simultaneously, the system combines the actuator response time mentioned in Embodiment 1 to calculate the timing advance of the command in real time and convert it into a spatial triggering advance.

[0133] When the precisely calculated relative mileage advances to the required spatial trigger advance position for a certain road segment, the system determines that the triggering condition has been met. The control unit immediately retrieves the target feedforward protection parameters corresponding to that mileage node, interprets them into specific electrical control signals, and drives the miniature pneumatic solenoid valves and inflation / deflation pumps within the distributed pneumatic actuator unit. For example, 1.5 seconds before the vehicle's actual physical wheel runs over a deep pothole inside the blind spot, the system accurately knows the existence and precise distance of the pothole, and completes differentiated inflation of the independently sealed air chambers on the left and right sides in advance, so that the buffer stiffness of the flexible inner liner membrane reaches the optimal energy absorption state at the precise moment when the vehicle experiences a substantial bump.

[0134] This process will be executed cyclically within the blind zone until the communication monitoring process captures continuous, stable, and high-confidence satellite positioning signals again. Only then will the system state machine exit the blind zone spatiotemporal sequence compensation mode, destroy the topological anchor point, and switch the control logic back to the conventional feedforward control mode based on absolute three-dimensional latitude and longitude coordinates.

[0135] In the existing field of precision logistics transportation monitoring, conventional solutions for satellite signal blind spots such as long tunnels or underground parking garages either abandon position matching control altogether (degrading active protection devices into passive packaging with indiscriminate constant pressure) or attach extremely expensive high-precision fiber optic gyroscopes and inertial navigation systems (INS) to the transport vehicle to maintain calculation capabilities. However, the cost of expensive INS systems is not economical for large-scale logistics packaging, and even high-precision inertial navigation equipment still faces unavoidable temperature drift and zero-bias errors during long-term operation.

[0136] This embodiment does not introduce any expensive external hardware. Instead, it uses low-level signal processing algorithms to deeply mine the data from the triaxial vibration sensor integrated into the inflatable fixation device.

[0137] On the one hand, frequency domain feature extraction technology is used to convert vibration sensors into virtual linear velocity meters for discrete calculation; on the other hand, environmental features are used to take the pre-marked road bump risk points in the map as natural calibration anchor points for waveform matching, and the inherent cumulative drift error of the integral system is completely eliminated through a rigorous mathematical cross-correlation algorithm.

[0138] This overall solution enables the inflatable fixing device to maintain high-precision spatial awareness even in extreme communication environments where external spatiotemporal reference updates are completely interrupted. This ensures that all pressure feedforward control commands included in the pre-calculated one-dimensional sequence mapping relationship can be accurately triggered. Compared to the existing technology of blindly waiting for signal recovery or tolerating inertial navigation errors, this embodiment significantly reduces the system's dependence on absolute communication links, effectively improving the device's continuous protection and coordination capabilities in complex logistics conditions across all terrains, scenarios, and weather, ensuring that the precision items held in the device still receive seamless adaptive safety protection when traversing blind spots.

[0139] Example 3:

[0140] In real-world cross-regional long-distance logistics transportation scenarios, transport vehicles often need to traverse complex and variable environments such as high-altitude mountainous areas, extremely low-temperature refrigerated trucks, or high-temperature deserts. In these scenarios, the medium handled by pneumatic actuators—air—is a highly compressible fluid that is extremely sensitive to temperature and pressure. Existing technologies or conventional feedforward control systems typically set the response time of the actuators in pneumatic fixed devices to a fixed constant value, or simply establish a simple linear mapping.

[0141] However, when a sudden drop in external atmospheric pressure leads to a decrease in the density of the inhaled gas, or when a significant decrease in ambient temperature causes a shift in the gas state equation, the time required for the inflation / deflation pump to deliver the same mass of gas into the independent sealed chamber will increase non-linearly and significantly. Simultaneously, the thermal decay of the motor and pump body caused by prolonged continuous operation of the inflation / deflation pump will further exacerbate this non-linear gas hysteresis. Without introducing a multi-dimensional environmental field strength compensation mechanism, the preset fixed feedforward triggering timing will result in severe asynchronous protection, meaning the independent sealed chamber cannot reach the target clamping stiffness at the instant the transport vehicle encounters a road impact.

[0142] To overcome the nonlinear aerodynamic hysteresis problem caused by strong coupling interference from multiple environmental fields, this embodiment discloses a step for performing dynamic compensation for aerodynamic nonlinear hysteresis. This step calculates the triggering timing of the feedforward command by combining the actuator response time and the expected arrival time, and achieves dynamic adaptive locking of the feedforward command triggering timing by establishing a dynamic response time calculation model. Specifically, it includes the following:

[0143] Specifically, the system first performs the step of acquiring environmental state data. This acquisition includes obtaining the current absolute pressure, external atmospheric pressure, and ambient temperature of each independent sealed air chamber. At the hardware implementation level, the edge intelligent control unit collects these parameters in real time through distributed multi-dimensional sensing units. Specifically, a miniature MEMS absolute pressure sensor is deployed at the end of the air intake branch or inside each independent sealed air chamber to collect the current absolute pressure in real time, unaffected by fluctuations in external atmospheric pressure. High-precision digital barometers and thermistor temperature sensors are deployed outside the outer casing of the inflatable fixing device or at the air intake manifold of the inflation / deflation pump to collect the external atmospheric pressure and ambient temperature in real time, respectively. The edge intelligent control unit acquires these raw analog signals at a preset high-frequency sampling rate and filters out signal glitches caused by high-frequency electromagnetic noise and mechanical vibration through analog-to-digital conversion and Kalman filtering algorithms, thereby obtaining high-confidence environmental state data that characterizes the current real physical state. The engineering significance of using absolute pressure instead of relative gauge pressure as the basic input data is that the equation of state for an ideal gas and the calculation of mass flow rate must be based on an absolute thermodynamic temperature scale and an absolute pressure system to ensure the physical rigor of subsequent mass transfer calculations.

[0144] For example, after acquiring the aforementioned environmental state data, the system proceeds to the step of calculating the basic pressure compensation span. The processor of the control unit retrieves the target feedforward protection parameters temporarily stored in memory and extracts the target absolute pressure contained therein. Subsequently, the absolute value of the difference between the target absolute pressure contained in the target feedforward protection parameters and the current absolute pressure is calculated as the basic pressure compensation span. The formula for this step is expressed as follows:

[0145] ;

[0146] In the formula: The basic pressure compensation span is calculated by the system. The target absolute pressure is the absolute pressure of the target extracted by the system from the target feedforward protection parameters. The target absolute pressure is the ideal internal pressure value that the air chamber must reach when the impending impact occurs, which is calculated in advance by the system based on the vulnerability twin model of the object to be fixed and the risk level of the road ahead. This refers to the current absolute pressure of the independent sealed air chamber, which is obtained in real time by the system through a micro MEMS absolute pressure sensor.

[0147] By calculating the absolute value of the difference between these two absolute pressures, the system clarifies the net pressure increment that the charging / discharging pump needs to establish or release for the independent sealed chamber during the following control cycle. This basic pressure compensation span directly determines the theoretical basic charging / discharging workload.

[0148] It is also important to note that when converting the theoretical charge / discharge workload into the actual required execution time, the density changes of the intake gas source and the efficiency decline of the actuator itself must be taken into account. Therefore, the system then proceeds to calculate the gas density decay coefficient based on the external atmospheric pressure and ambient temperature, and extract the volumetric efficiency decay factor based on the continuous operating time of the charge / discharge pump.

[0149] In fluid mechanics, the actual mass flow rate of an inflation / deflation pump is positively correlated with the initial density of the gas at the intake port. According to the ideal gas law, gas density is directly proportional to absolute pressure and inversely proportional to absolute temperature. The system quantifies the influence of the external environment on inflation efficiency using the following formula for calculating the gas density decay coefficient:

[0150] ;

[0151] In the formula: The gas density attenuation coefficient is calculated by the system. This coefficient is a dimensionless ratio used to characterize the degree of deviation of the current ambient gas density from the standard operating condition gas density. The external atmospheric pressure is collected in real time by the multi-dimensional sensing unit; The standard operating condition absolute temperature constant preset for the system; The system is preset with standard atmospheric pressure constants. The ambient temperature is collected in real time by the multi-dimensional sensing unit, and this ambient temperature has been converted from Celsius to absolute thermodynamic temperature before being substituted into the formula.

[0152] According to the above formula, when the transport vehicle travels to a high-altitude area, causing a significant decrease in external atmospheric pressure or a significant increase in ambient temperature, the calculated gas density attenuation coefficient will be less than 1. This indicates that the actual mass of gas drawn into the inflation / deflation pump during a single stroke is reduced, which inevitably leads to a longer inflation time required to achieve the same pressure increment.

[0153] Optionally, the system not only considers changes in the properties of the external medium but also simultaneously assesses the performance degradation of the internal hardware. When the charging / discharging pump executes high-frequency control commands, the temperature rise of its motor coil leads to a decrease in torque. Simultaneously, the thermal expansion of the piston rings or diaphragm inside the pump body causes changes in internal mechanical clearances, resulting in an increase in internal leakage. The system records the start-up and stop times of the charging / discharging pump since its current power-on and accumulates these times to obtain the continuous operating time of the pump. Subsequently, the system quantifies this hardware efficiency decline using the following volumetric efficiency degradation factor extraction formula:

[0154] ;

[0155] In the formula: The volumetric efficiency attenuation factor is calculated by the system. The initial calibration volumetric efficiency constant of the air pump in cold operation; The pump body thermal decay rate constant was determined through bench life testing and pre-programmed into the edge intelligent control unit. The continuous operating time of the charging and discharging pump is accumulated and statistically analyzed by the system in real time.

[0156] Through the above calculations, the system obtains a dynamic factor in real time that is less than or equal to the initial calibrated volumetric efficiency constant, which objectively reflects the real and effective conversion rate of mechanical energy into gas pressure energy by the inflation / deflation pump at the current moment.

[0157] Specifically, after obtaining the attenuation coefficients representing both environmental and hardware interference, the system enters the core time-domain mapping stage. The system multiplies the basic pressure compensation span by a preset equivalent volume constant of the gas chamber, then divides by the product of the gas density attenuation coefficient and the volumetric efficiency attenuation factor to calculate the dynamic response time. This dynamic response time is then used to dynamically update the original actuator response time. This step, through a rigorous mathematical model, converts the pressure domain demand span into execution instructions in the time domain. The specific formula for calculating the dynamic response time is as follows:

[0158] ;

[0159] In the formula: The dynamic response time calculated by the system; The basic pressure compensation span obtained in the preceding steps; The preset equivalent volume constant of the air chamber is a constant that integrates the inherent physical volume of the independent sealed air chamber, the flow resistance coefficient of the air passage, and the rated volumetric flow rate of the charging and discharging pump under standard operating conditions. Its physical dimensions allow the final calculation result of the formula to be correctly interpreted into time units. The gas density attenuation coefficient obtained in the preceding steps; The volumetric efficiency attenuation factor is the one obtained in the preceding steps.

[0160] As can be clearly seen from the above formula, the product of the basic pressure compensation span and the equivalent volume constant of the air chamber represents the theoretical response time reference required by the system under ideal and undamped operating conditions. The product of the two attenuation factors in the denominator acts as a dynamic amplifier of this theoretical time. When the external air pressure is extremely low or the air pump experiences thermal attenuation due to prolonged operation, the denominator value decreases, and the calculated dynamic response time increases significantly. The system writes this dynamic response time, output in real time, into the system's underlying control register, dynamically overwriting and updating the original fixed actuator response time. This step allows the control system to transition from fixed timing control to dynamic delay predictive control with a high degree of physical environment adaptability.

[0161] For example, after obtaining the precise dynamic execution time requirements, the system needs to find a matching trigger at the kinematic level. The edge intelligent control unit calculates in real time the remaining distance of the transport vehicle from the upcoming risky section and divides the remaining distance by the real-time approach speed of the transport vehicle to obtain the estimated remaining arrival time.

[0162] The system calculates the remaining distance by reading the starting coordinates of the target risk segment from the global risk map and combining them with the high-precision positioning coordinates of the current transport vehicle. This is achieved using a spherical distance calculation algorithm or the path planning topology distance from the high-precision map. Simultaneously, the system reads wheel speed sensor data via the vehicle's CAN bus interface or performs first-order difference calculations on the position coordinates across multiple consecutive positioning cycles to obtain extremely accurate real-time approach speed. The formula for calculating the estimated remaining arrival time is as follows:

[0163] ;

[0164] In the formula: The estimated remaining time of arrival is calculated by the system. The remaining distance of the transport vehicle from the upcoming risky road segment is calculated in real time by the system. This refers to the real-time approach speed of the transport vehicle, which is acquired by the system in real time.

[0165] The estimated time remaining represents the countdown before the transport vehicle, based on its current motion, actually runs over or enters the high-risk road section. Since the transport vehicle's speed constantly changes during travel—for example, when the driver brakes to slow down upon seeing a pothole—the real-time approach speed fluctuates dynamically. Therefore, this estimated time remaining is also a prediction that fluctuates in real-time with a high refresh rate.

[0166] It is also important to note that the system executes the comparison logic at an extremely high frequency through a hardware interrupt service subroutine driven by a microsecond-level timer. The system compares the estimated remaining time of arrival with the updated dynamic response time. When the estimated remaining time of arrival decreases to be equal to the dynamic response time, the system locks this moment as the triggering time for the feedforward instruction and immediately issues the control instruction.

[0167] In the real-time operating system of the edge intelligent control unit, the comparator continuously monitors these two time variables. When the transport vehicle is far from the risky section, the estimated remaining time of arrival is much longer than the dynamic response time, and the system remains silent. As the vehicle approaches, the estimated remaining time of arrival decreases. Since the issuance of feedforward control commands must be based on the fundamental logic of ensuring that the independent sealed air chamber can pressurize precisely at the moment of impact, the system defines the moment when these two time variables are equal as the control critical point. When the condition is met, the system locks this moment as the triggering time for the feedforward command and immediately issues a power signal control command to the distributed air circuit execution unit. The solenoid valve then opens, and the inflation / deflation pump begins to operate at full load or according to the set duty cycle according to the command. After experiencing this precisely predicted dynamic response time, the pressure inside the independent sealed air chamber just reaches the configuration standard set by the target feedforward protection parameters. At the same absolute point in time in the physical world, the wheels of the transport vehicle make physical contact with the risky section (such as a speed bump or pothole).

[0168] Existing air pressure feedforward control strategies mostly remain at the level of simple distance triggering or fixed-time triggering. For example, the system is uniformly set to start inflation at a distance of 50 meters from an obstacle or two seconds in advance. This static threshold control ignores the extreme nonlinearity of the actuating fluid dynamics under varying environmental fields. When the environment causes a significant decrease in inflation speed, fixed-time triggering can lead to a dangerous situation where the vehicle has already entered a bumpy section of road, but the air chamber has not yet reached a state of sufficient support stiffness, resulting in a significant reduction in the protective effect of the packaging. Conversely, if the fixed triggering time is blindly extended for safety reasons, the air chamber will prematurely reach a high-pressure hardening state, thereby losing its ability to absorb the subtle high-frequency vibrations when the vehicle is driving on a smooth road.

[0169] The technical solution in this embodiment acquires environmental field state data through multi-source sensing, deeply integrating the gas density decay at the thermodynamic level and the volumetric efficiency decay at the mechanical level into the dynamic extrapolation of the response time. The overall effect of this solution is that it makes the lead time of feedforward control no longer a rigid fixed value, but a highly flexible adaptive environmental variable.

[0170] This method effectively eliminates the physical execution hysteresis error faced by pneumatic hardware under complex and variable operating conditions. It ensures that, whether in hot desert low-altitude areas or extremely cold plateau low-pressure zones, the pneumatic actuator can accurately and promptly utilize the final time window as the vehicle approaches a risky section to smoothly and timely reshape the clamping force and buffer stiffness. This truly precise time-space closed-loop synchronization greatly enhances the protection determinism and control reliability of the variable cavity inflatable fixing device under extreme logistics conditions.

[0171] Example 4:

[0172] In the context of modern logistics and special transportation, most traditional inflatable anchoring devices adopt a passive single-bladder structure. When the transport vehicle encounters sudden situations such as road potholes or emergency braking, these traditional devices often fail to provide matching buffer stiffness at the moment of impact due to the delayed physical response of the pneumatic system.

[0173] To address the lag and passivity issues present in the prior art, this embodiment provides an inflatable fixing device with a variable internal cavity. This device serves as the hardware execution carrier for the intelligent control methods described in Embodiments 1 to 3 above. Through independent control of multiple chambers and a high-frequency sensing system, it achieves proactive, adaptive, and collaborative protection for high-value, fragile items.

[0174] like Figure 2 As shown, this embodiment discloses an inflatable fixing device with a variable inner cavity, including: an outer casing, a flexible inner liner membrane, a distributed air path execution unit, a multi-dimensional sensing unit, and an edge intelligent control unit. These components constitute a highly integrated mechatronics system in terms of spatial physical layout and electrical connections.

[0175] Optionally, regarding the structural design of the outer casing, the inner wall of the outer casing is provided with multiple independently partitioned sealing grooves. In actual industrial manufacturing, the outer casing is often integrally molded from high-strength rigid or semi-rigid materials, such as aerospace-grade carbon fiber composite materials or engineering plastics with internal metal reinforcing ribs, such as modified ABS or polycarbonate. This material selection aims to ensure that the outer casing can maintain its structural integrity when subjected to strong external pressure or impact. The sealing grooves are divided into multiple independent partitions according to spatial orientation, including front, back, left, right, and bottom. The edges of the grooves are precision-machined to provide a flat and continuous sealing surface.

[0176] Specifically, regarding the variable internal cavity structure of the device, the edges of the flexible inner liner film are circumferentially sealed and fixed to each of the sealing grooves, forming multiple independent sealed air chambers between the flexible inner liner film and the inner wall of the outer box. In practical applications, the flexible inner liner film is typically made of a composite polymer material with high gas barrier properties, high tear strength, and excellent weather resistance, such as thermoplastic polyurethane elastomer (TPU) or a special silicone material lined with an aramid fiber mesh layer. The edges of the film are seamlessly anchored to the sealing grooves of the inner wall of the outer box using ultrasonic hot-melt welding or by applying aerospace-grade high-strength sealing adhesive. The rationale for this structural design is that when high-pressure gas is filled into the air chambers, the flexible inner liner film can expand towards the center of the outer box, thereby forming multiple independent buffer interfaces that wrap around the items to be fixed from different spatial directions. The physical isolation of the independent air chambers allows the control system to apply differentiated clamping forces and support stiffness according to the vulnerability of different parts of the item.

[0177] It is also important to note that, to achieve precise power input to each independent sealed air chamber, the distributed pneumatic actuator includes a charging / discharging pump and miniature pneumatic solenoid valves connected to each independent sealed air chamber on the pneumatic branch lines. In terms of hardware architecture, the distributed pneumatic actuator is equipped with a main pneumatic circuit block (ManifoldBlock). The exhaust end of the charging / discharging pump, such as a DC brushless permanent magnet synchronous pump with space vector control, is connected to the main intake manifold of the main pneumatic circuit block. The main pneumatic circuit block has multiple flow channels internally, forming the pneumatic branch lines. Each miniature pneumatic solenoid valve, such as a high-frequency response proportional solenoid valve, is connected in series on its corresponding pneumatic branch line. The opening and closing state and flow channel cross-sectional area of ​​this miniature pneumatic solenoid valve are adjusted in real time by a control signal, thereby independently controlling the intake and exhaust volume of the corresponding sealed air chamber. To reduce mechanical noise during pump operation, a progressive vibration-damping base made of multi-layer damping material can be added between the charging / discharging pump and the outer casing.

[0178] For example, the device deploys a multi-dimensional sensing unit for collecting operating condition data. This sensing unit is physically distributed. Miniature absolute pressure sensors are distributed within each independent sealed chamber or at the end of each gas path branch, used to monitor the real absolute pressure of each chamber in real time. High-frequency triaxial accelerometers and gyroscopes (IMUs) are rigidly fixed to the substrate of the outer casing, used to capture the device's overall vibration, tilt, and impact acceleration responses in three-dimensional space. Additionally, a temperature sensor may be included to acquire the fluid environment temperature in real time. The multi-dimensional sensing unit converts the collected continuous physical quantities into a high-precision digital signal sequence and transmits it to the central computing node via an internal high-speed bus.

[0179] Specifically, the edge intelligent control unit is electrically connected to both the distributed pneumatic actuator and the multi-dimensional sensing unit. The edge intelligent control unit includes a processor and a memory. In practical circuit board-level applications, this edge intelligent control unit typically employs an industrial-grade high-performance multi-core microcontroller (such as an ARM Cortex-M series or a dedicated control chip with a DSP instruction set) and possesses abundant CAN, RS485, and Ethernet communication interfaces to ensure low latency in control signal transmission and sensor data upload.

[0180] Furthermore, the memory stores a computer program, and when the processor executes the computer program, it implements the aforementioned intelligent control method. This means that the processor can run core algorithms such as full-dimensional digital twin modeling, spatiotemporal synchronous fusion of multi-source operating condition data, risk map planning, and blind zone spatiotemporal sequence compensation in real time. The processor reads the data matrix of the multi-dimensional sensing unit at high speed, performs complex matrix operations and extracts time-domain and frequency-domain features, and then outputs a precise PWM (pulse width modulation) signal to the distributed pneumatic actuator unit to drive the micro pneumatic solenoid valve and the charging and discharging pump to perform feedforward pre-regulation and feedback closed-loop fine adjustment. In addition, the massive sensing data vectors such as time-series pressure and vibration recorded in the memory can be stored locally or reported to the cloud in accordance with standard structured data formats (such as multidimensional arrays or tensors). This provides an extremely convenient data support foundation for researchers to subsequently export data and use professional mathematical software to generate visualization maps and trend curves of passenger flow fluctuations or control deviations.

[0181] Taking the cross-regional logistics transport of extremely expensive and highly sophisticated life support instruments such as extracorporeal membrane oxygenation (ECMO) devices as an example, this paper examines a practical application scenario. ECMO devices contain fragile centrifugal pump heads, hollow fiber membranes, and highly sensitive precision electronic flow sensors, making them highly susceptible to hidden damage under bumpy conditions. When the ECMO device is loaded using the variable-cavity inflatable fixation device described in this invention, the flexible inner membrane can form a protective layer around the device. Before the vehicle enters a high-risk, bumpy section, the edge intelligent control unit, based on a pre-established vulnerability twin model of the ECMO device and a global risk map, proactively controls the inflation / deflation pumps and miniature pneumatic solenoid valves. For the independent sealed air chamber near the highly sensitive stress area of ​​the ECMO centrifugal pump head, the system appropriately reduces the inflation pressure to increase the flexible energy absorption space; while for the sealed air chamber located near the robust metal base of the ECMO, the inflation pressure is increased to lock its overall degrees of freedom and prevent macroscopic displacement.

[0182] In summary, the variable-cavity inflatable fixation device provided in this embodiment significantly improves upon the shortcomings of traditional passive airbags, such as delayed response and uneven clamping force distribution, through the deep integration of a multi-chamber physical hardware architecture and an edge intelligent control unit equipped with cutting-edge control algorithms. The device features a clear structural design logic, with close coordination between pneumatic actuation and multi-dimensional sensing, effectively enhancing the safety protection threshold for high-precision, high-value, and fragile items under complex working conditions. It possesses outstanding industrial application value and feasibility.

[0183] Example 5:

[0184] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0185] like Figure 3 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, for example, via a bus 102. Optionally, the electronic device 100 may further include a transceiver 104. It should be noted that in practical applications, the transceiver 104 is not limited to one unit, and the structure of this electronic device 100 does not constitute a limitation on the embodiments of the present invention.

[0186] Processor 101 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 101 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0187] Bus 102 may include a pathway for transmitting information between the aforementioned components. Bus 102 may be a PCI bus or an EISA bus, etc. Bus 102 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0188] The memory 103 stores a computer program corresponding to the intelligent control method of the variable cavity inflatable fixation device of the above embodiments of the present invention. This computer program is controlled and executed by the processor 101. The processor 101 executes the computer program stored in the memory 103 to implement the content shown in the foregoing method embodiments.

[0189] Among them, electronic devices 100 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 3The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0190] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A smart control method for a variable-cavity inflatable fixing device, based on the inflatable fixing device, wherein the inflatable fixing device comprises multiple independent sealed air chambers surrounded by an outer casing and a flexible inner liner film, characterized in that, The method includes: Acquire multi-dimensional attribute data of the item to be fixed, construct a full-dimensional digital twin containing the geometric, mechanical and vulnerable characteristics of the item to be fixed, and generate a baseline of basic clamping and protection parameters corresponding to each independent sealed air chamber based on the full-dimensional digital twin; Multi-source heterogeneous working condition data are collected in real time and spatiotemporally fused to generate a standardized full-link working condition spatiotemporal dataset. Based on the spatiotemporal dataset, road segment risk features are extracted to quantify the risk level of the entire road segment. Combining the baseline of the basic clamping protection parameters with the quantified risk level of the entire road segment, a global risk map containing the feedforward protection parameters of each road segment target is generated. The location information of the transport vehicle is acquired in real time and matched with the global risk map. Before entering the target risk section, the pressure and buffer stiffness of each independent sealed air chamber are subjected to graded feedforward pre-regulation actions according to the target feedforward protection parameters corresponding to the section. During the execution of the graded feedforward pre-regulation action, the multi-dimensional sensing unit collects the status data of the independent sealed air chamber and the object to be fixed in real time, calculates the regulation deviation between the current state and the expected regulation target, and dynamically corrects the air pressure regulation command for each independent sealed air chamber based on the regulation deviation, thus completing the feedback closed-loop fine-tuning control.

2. The method according to claim 1, characterized in that, The construction of a full-dimensional digital twin encompassing the geometric, mechanical, and vulnerability characteristics of the object to be secured, and the generation of baseline clamping and protection parameters corresponding to each independent sealed air chamber based on the full-dimensional digital twin, includes: Based on the multi-dimensional attribute data, a geometric twin model is constructed and the vulnerable parts of the item are marked. A mechanical twin model is then constructed to simulate the stress distribution of each part of the item under different impact accelerations. A vulnerability twin model is constructed, and a three-level hierarchical warning threshold, including a safety threshold, a warning threshold, and a limit threshold, is set in the geometric twin model, with the safety threshold, warning threshold, and limit threshold increasing sequentially. The baseline of basic clamping protection parameters is generated based on the three-level graded early warning threshold. The baseline of basic clamping protection parameters includes the upper limit of the safe pressure of each independent sealed air chamber, the basic clamping pressure ratio and the inflation rate.

3. The method according to claim 1, characterized in that, The process of combining the baseline of the basic clamping protection parameters with the quantified risk level of the entire road segment generates a global risk map containing the feedforward protection parameters of each road segment target, including: Using a unified satellite time as a reference, the multi-source heterogeneous operating condition data is aligned with millisecond-level timestamps and normalized to a spatial coordinate system within a set tolerance range to generate the standardized full-link operating condition spatiotemporal dataset. The entire transportation route is divided into multiple road segment units. Based on the spatiotemporal dataset, risk characteristic parameters of each road segment unit are extracted and a comprehensive risk score is calculated. Based on the comprehensive risk score, each road segment unit is divided into road segments corresponding to different risk levels. The full-dimensional digital twin attribute parameters of the object to be fixed, the risk characteristic parameters of the road segment unit, and the baseline of the basic clamping protection parameters are input into a preset three-dimensional coupling mapping model. The target feedforward pressure and auxiliary control parameters corresponding to each road segment are pre-calculated and output. The target feedforward pressure and auxiliary control parameters are used as the target feedforward protection parameters to generate the global risk map.

4. The method according to claim 1, characterized in that, The step of performing graded feedforward pre-regulation actions on the pressure and buffer stiffness of each independent sealed air chamber according to the target feedforward protection parameters corresponding to the road section includes: Predict the risk level of the upcoming road segment and the estimated arrival time, obtain the preset response time of the actuator of the inflatable fixed device, and calculate the triggering time of the feedforward command by combining the response time of the actuator and the estimated arrival time. When approaching a section of road with a medium or high risk level, before the triggering time arrives, differential compensation is performed on the clamping force ratio and buffer stiffness of each independent sealed air chamber according to the target feedforward protection parameters. When approaching a section of road with an extremely high risk level, the clamping force of each independent sealed air chamber is increased to the upper pressure limit corresponding to the safety threshold, provided that it does not exceed the aforementioned limit threshold, and the full-power emergency response mode is activated.

5. The method according to claim 1, characterized in that, The dynamic correction of the pressure control commands for each independent sealed air chamber based on the control deviation, to complete the feedback closed-loop fine-tuning control, includes: During the execution phase of issuing the air pressure control command, a graded adaptive inflation program is initiated, and the clamping trend evaluation coefficient is calculated based on the real-time collected pressure data. Based on the abrupt change characteristics of the clamping trend evaluation coefficient, the initial contact feature points between the flexible inner film and the surface of the item to be fixed are identified; The inflation and deflation parameters are updated based on the initial contact feature point and the control deviation. After inflation is completed, a full circumferential fit verification is performed. Once the verification is passed, the corresponding independent sealed air chamber enters the closed-loop depressurization mode.

6. The method according to claim 5, characterized in that, The clamping trend evaluation coefficient The calculation formula is: ； The The pressure fluctuation coefficient is calculated using the following formula: ； The The pressure variation density coefficient is calculated using the following formula: ； In the formula, and All are non-zero weight coefficients, and ; This is the cumulative number of pressure direction changes within the detection time window; For the first time window of detection The amount of pressure change corresponding to each directional change; This refers to the total pressure change from the start to the end of the detection time window; It is the absolute value of the difference between the measured pressure and the target clamping pressure at the end of the detection time window; The maximum pressure change in a single instance within the detection time window; It is the standard deviation of all single pressure changes within the detection time window.

7. The method according to claim 1, characterized in that, The inflatable fixing device also includes a miniature pneumatic solenoid valve, which has a built-in valve core displacement sensor. The step of dynamically correcting the pressure control command for each independent sealed air chamber based on the control deviation further includes: The valve core displacement sensor collects real-time feedback displacement direction change information of the solenoid valve core. Based on the feedback displacement direction change information, the displacement direction fluctuation coefficient and the displacement direction change density coefficient are calculated, and the change trend evaluation coefficient is calculated by weighting the displacement direction fluctuation coefficient and the displacement direction change density coefficient. The change trend evaluation coefficient is used as an internal correction parameter to dynamically and adaptively adjust the opening size of the miniature pneumatic solenoid valve.

8. The method according to claim 7, characterized in that, The evaluation coefficient of the change trend The calculation formula is: ； The The displacement direction fluctuation coefficient is calculated using the following formula: ； The The displacement direction change density coefficient is calculated using the following formula: ； In the formula, To preset the third weighting coefficient, This is a preset fourth weighting coefficient; To detect the cumulative number of changes in the valve core displacement direction within the detection time window; The first one within the detection time window The change in valve core displacement corresponding to the change in direction; It is the absolute change in the valve core feedback displacement from the start point to the end point within the detection time window; The absolute value of the difference between the valve core feedback displacement value and the target opening displacement value at the end of the detection time window; The maximum displacement of the valve core in a single directional change within the detection time window; It is the standard deviation of the displacement change corresponding to all single directional changes within the detection time window.

9. The method according to claim 1, characterized in that, The method further includes: The motor current signal of the air pump in the inflatable fixing device is collected and Fourier transform is performed to identify the torque pulsation frequency band. The frequency band coupling matrix is ​​constructed by combining the sensed structural vibration mode data. The high-risk resonance region marked in the frequency band coupling matrix is ​​avoided by carrier frequency randomization and vector action time redistribution. After a single transport mission is completed, the feedforward control matching degree is extracted based on the pressure deviation data before and after the execution of hierarchical feedforward pre-control, and the feedforward control matching degree is used as a training sample to perform incremental iterative optimization on the preset three-dimensional coupling mapping model.

10. A variable-cavity inflatable fixing device, characterized in that, include: Outer casing, flexible inner membrane, distributed pneumatic actuator, multi-dimensional sensing unit, and edge intelligent control unit; The inner wall of the outer box is provided with multiple independently partitioned sealing grooves; The edges of the flexible inner liner film are sealed and fixed to each of the sealing grooves in a full circumferential manner, so that the flexible inner liner film and the inner wall of the outer box form the multiple independent sealed air chambers. The distributed pneumatic circuit execution unit includes a charging and discharging pump and miniature pneumatic solenoid valves that are set on the pneumatic circuit branches and connected one-to-one with each independent sealed air chamber. The edge intelligent control unit is electrically connected to the distributed pneumatic path execution unit and the multi-dimensional sensing unit respectively. The edge intelligent control unit includes a processor and a memory. The memory stores a computer program. When the processor executes the computer program, it implements the intelligent control method as described in any one of claims 1 to 9.

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