A method and system for testing the mechanical performance of seats based on intelligent sensor systems

Abstract

This invention relates to the field of seat testing, and more particularly to a method and system for testing the mechanical performance of seats based on an intelligent sensor system. The method includes: acquiring the original pressure and displacement readings of the seat under test during the testing process; calculating the instantaneous acceleration of the tested part based on the displacement readings, and determining the impact intensity characteristic ratio at the current moment based on the instantaneous acceleration; determining the dynamic retention weight for controlling the noise reduction intensity based on the impact intensity characteristic ratio; reconstructing and outputting the pure pressure reading at the current moment using the dynamic retention weight; calculating the standardized chassis fatigue dissipation ratio of the tested seat component based on the displacement reading sequence and the pure pressure reading sequence within a single test cycle; calculating a comprehensive fatigue life warning value based on the cumulative distribution of the standardized chassis fatigue dissipation ratio across multiple historical cycles, and outputting a life warning result based on the comprehensive fatigue life warning value. This invention improves the safety and accuracy of testing.

Description

Technical Field

[0001] This invention relates to the field of seat testing, and more particularly to a method and system for testing the mechanical performance of seats based on an intelligent sensor system. Background Technology

[0002] In modern furniture manufacturing, office chairs, as tools that bear weight and are in contact with the human body for extended periods, have their structural safety and durability directly impacting user safety. To evaluate the mechanical properties of office chairs with tilting mechanisms and gas springs, the industry typically relies on standard mechanical testing machines for repeated impact and static load tests. Existing testing systems primarily collect mechanical feedback data using pressure sensors deployed at the force application end and displacement sensors mounted on the sides. However, in complex real-world engineering testing scenarios, existing testing methods suffer from the following problems.

[0003] Firstly, in dynamic fatigue testing or drop impact testing, the pressure plate of the mechanical testing machine, upon high-speed downward pressure and contact with the seat cushion, triggers high-frequency mechanical resonance throughout the test bench and seat chassis mechanism due to the rigid impact. This high-frequency vibration, transmitted to the sensor, results in a large amount of disordered spike noise superimposed on the acquired raw pressure data sequence. Traditional signal processing methods often employ moving average filtering or low-pass filtering to smooth this noise. However, traditional filtering algorithms have an inherent flaw of blind smoothing; while eliminating high-frequency mechanical white noise, they inevitably flatten the real, highly destructive impact peak values ​​as well. The loss of the real impact peak value renders subsequent safety verification based on peak stress meaningless.

[0004] Secondly, current testing standards often only involve visually inspecting chassis components for macroscopic fractures after testing, or simply comparing pressure curves to see if there is a step drop in the control system. This evaluation dimension is extremely simplistic, ignoring the implicit hysteresis energy loss generated by core cushioning components inside the seat (such as the gas compression damping inside the gas spring and the springs of the chassis tilt mechanism) when subjected to thousands of repeated alternating stresses. In reality, material fatigue often begins with microscopic lattice slip and damping degradation. The lack of in-depth exploration of continuous dynamic energy dissipation means that existing methods cannot provide accurate lifespan warnings before the seat fractures. Summary of the Invention

[0005] To address the data distortion problem caused by filtering and smoothing out the peak value of actual impact damage in existing mechanical testing, this invention provides a method and system for testing the mechanical performance of seats based on an intelligent sensor system.

[0006] In a first aspect, the present invention provides a method for testing the mechanical performance of a seat based on an intelligent sensor system, employing the following technical solution: A method for testing the mechanical performance of a seat based on an intelligent sensor system includes the following steps: acquiring the original pressure and displacement readings of the seat under test during the test; calculating the instantaneous acceleration of the tested part based on the displacement readings, and determining the impact strength characteristic ratio at the current moment based on the instantaneous acceleration, wherein the impact strength characteristic ratio is positively correlated with the instantaneous acceleration; and determining the dynamic retention weight for controlling the noise reduction intensity based on the impact strength characteristic ratio, wherein the dynamic retention weight is positively correlated with the impact strength characteristic ratio. Using dynamic retention weights, an adaptive weight allocation is performed between the original pressure reading and the reconstructed pressure reading at historical moments, and the pure pressure reading at the current moment is reconstructed and output. Based on the displacement reading sequence and the pure pressure reading sequence within a single test cycle, the standardized chassis fatigue dissipation ratio of the tested seat component is calculated. Based on the cumulative distribution of the standardized chassis fatigue dissipation ratio across multiple historical cycles, a comprehensive fatigue life warning value is calculated, and the life warning result is output based on the comprehensive fatigue life warning value.

[0007] Compared to traditional sliding filters or low-pass filters, which blindly flatten out the real, highly destructive impact peaks when eliminating high-frequency mechanical resonance noise, this invention extracts instantaneous acceleration and dynamically calculates the retention weights. In complex dynamic fatigue or drop impact test bench scenarios, it effectively filters out disordered noise while preserving the real stress limit value that causes seat damage without loss. At the same time, it overcomes the single evaluation method of traditional testing that relies solely on visual observation of macroscopic fractures. By analyzing the hysteresis dissipation energy of pure pressure and displacement sequences, it achieves early detection of hidden microscopic fatigue damage inside the seat chassis and gas spring, issuing an early warning before the structure completely fractures, thus improving the reliability of seat safety testing.

[0008] Preferably, after acquiring the original pressure and displacement readings of the test seat during the test, the method further includes: using a bandpass filter to isolate and remove burrs from the original pressure and displacement readings; using a cross-correlation algorithm to calculate the time difference between the pressure sensor sequence and the high-frequency laser displacement sensor sequence, and performing translation compensation based on the time difference to achieve time phase alignment between the original pressure and displacement readings.

[0009] To address the issues of random electromagnetic noise generated by motor operation and hardware response delays between heterogeneous sensors that are easily introduced into actual industrial testing environments, this method effectively eliminates time misalignment errors caused by multimodal acquisition by pre-eliminating isolated glitches and using cross-correlation algorithms to accurately align the time phases of pressure and displacement sequences. This ensures the accuracy of subsequent force and motion state analysis based on the same time dimension and prevents misjudgment of impact intensity due to data delays.

[0010] Preferably, the step of determining the impact strength characteristic ratio at the current moment is as follows: multiply the equivalent mass of the loading plate of the testing machine by the instantaneous acceleration to obtain the dynamic inertial force; divide the dynamic inertial force by the reference value of the static load support force calibrated by the test seat to obtain the impact strength characteristic ratio.

[0011] By introducing the equivalent mass of the loading platen and the calibrated reference value of the static load support force, the abstract displacement acceleration is transformed into a quantifiable dynamic inertial force impact index. This enables the system to establish a unified and standard transient impact measurement criterion when facing extreme test conditions with different weight ratios and different occupant body shapes, and to more sensitively distinguish between destructive and severe impacts and stable and safe support states.

[0012] Preferably, the specific steps for determining the dynamic retention weight for controlling the noise reduction intensity are as follows: calculate the ratio of the maximum downward stroke set by the pressure plate of the testing machine to the initial nominal thickness of the office chair seat cushion to obtain the geometric compression ratio; multiply the geometric compression ratio by the impact strength characteristic ratio, and then normalize it using the hyperbolic tangent function to obtain the dynamic retention weight.

[0013] By comprehensively considering the geometric compression ratio between the maximum downward stroke of the testing machine and the initial physical thickness of the seat cushion, and using the hyperbolic tangent function for nonlinear normalization, the noise reduction weight can quickly and smoothly approach the state of preserving the true peak value under harsh test conditions where the seat cushion cushion is insufficient and the risk of rigid bottoming out increases. Under mild contact conditions where the impact potential energy is fully absorbed by the sponge, meaningless mechanical noise is suppressed to the maximum extent, thus realizing intelligent adaptive adjustment of noise reduction sensitivity.

[0014] Preferably, the steps for reconstructing the pure pressure reading at the current moment are as follows: multiply the dynamically retained weight by the original pressure reading at the current moment to obtain the first weighted component; multiply the difference between the preset constant and the dynamically retained weight by the historical pure pressure reading reconstructed at the previous moment to obtain the second weighted component; and add the first weighted component and the second weighted component to obtain the pure pressure reading reconstructed at the current moment.

[0015] By using dynamic weights to smoothly transition and weight the current original input and the historical reconstructed pure data, the device can fully trust the current instantaneous extreme value to achieve absolute fidelity when encountering a real destructive shock wave, while adaptively relying on historical smoothing values ​​during the non-destructive high-frequency resonance period, thus ensuring that the mechanical feedback curve output by the device has a high degree of smoothness and consistency.

[0016] Preferably, after reconstructing the output of the current pure pressure reading, the method further includes an automated verification step: comparing the pure pressure reading with the multiple of the calibrated static load support force in real time; if the pure pressure reading exceeds the preset multiple threshold of the calibrated static load support force, then activating the amplitude limiting attenuation module to normalize the pure pressure reading to a preset physical reasonable range.

[0017] To prevent systemic shifts in overall data caused by momentary failures of single-point sensors or extreme abnormal electrical signals, an automated verification defense line based on the calibrated static load support force ratio is added. This line can intercept and forcibly attenuate abnormal isolated values ​​that exceed the physical limits of materials in real time, ensuring the feasibility and rationality of the pure pressure data output in the final reconstruction from an engineering physics perspective.

[0018] Preferably, the steps for calculating the standardized chassis fatigue dissipation ratio of the tested seat component are as follows: calculate the integral work done by the pressure on the displacement closed path in a single cycle using the trapezoidal integral method to obtain the area of ​​the pressure-displacement hysteresis loop; calculate the product of the nominal stiffness of the chassis tilting mechanism and the square of the maximum safe stroke of the gas spring under the chassis to obtain the reference elastic potential energy; divide the area of ​​the pressure-displacement hysteresis loop by the reference elastic potential energy to obtain the standardized chassis fatigue dissipation ratio.

[0019] Based on the physical law that the damping degradation of micromaterials is inevitably accompanied by heat dissipation, the irreversible heat loss caused by metal lattice slip and spring plastic deformation in a single compression and rebound cycle is intuitively quantified. This is then integrated with the dimensional indicators of core safety components such as pneumatic rods, enabling accurate and objective measurement of microscopic damage in the early stages of fatigue in complex mechanical structures.

[0020] Preferably, the steps for calculating the comprehensive fatigue life warning value are as follows: obtain the total number of completed cycle tests and retrieve the standardized chassis fatigue dissipation ratio sequence of all historical cycle cycles; sum the standardized chassis fatigue dissipation ratio sequence to obtain the total dissipation energy index; multiply the attenuation coefficient of microcrack propagation inside the material by the total dissipation energy index, use it as the negative exponent of the natural exponential function, and subtract the result of the natural exponential function using a preset constant to obtain the comprehensive fatigue life warning value.

[0021] Based on the principle of nonlinear continuous damage mechanics, an attenuation coefficient for the propagation of microcracks inside the material is introduced. As the number of test cycles increases, when the microcracks inside the seat connect into macrocracks and approach the fracture critical point, the warning value calculated by the system can rise nonlinearly and sharply. Thus, the pre-fuse mechanism can be successfully triggered hundreds of cycles before physical fracture occurs, providing sufficient reaction margin for quality inspection shutdown.

[0022] Preferably, the method for determining the output of the lifespan warning result is as follows: if the comprehensive fatigue lifespan warning value is greater than the set comprehensive fatigue warning threshold, the tested seat is determined to have a safety hazard and a defective warning signal is output; if the comprehensive fatigue lifespan warning value is less than or equal to the comprehensive fatigue warning threshold, the tested seat is determined to be qualified.

[0023] Transforming complex micro-damage accumulation calculations into intuitive assessments of equipment remaining lifespan depletion risks enables automated test benches to have clear criteria for defective product interception and qualified product release. This reduces reliance on subjective human experience in the quality assessment process and improves the efficiency of automated screening during continuous fatigue testing of batch components on production lines.

[0024] Secondly, this invention provides a seat mechanical performance testing system based on an intelligent sensor system, employing the following technical solution: A seat mechanical performance testing system based on an intelligent sensor system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the seat mechanical performance testing method based on the intelligent sensor system described above.

[0025] The aforementioned method for testing the mechanical performance of seats based on intelligent sensor systems is used to generate a computer program, which is then stored in a memory for loading and execution by a processor. This allows for the creation of a system based on the memory and processor, making it convenient to use.

[0026] The present invention has the following technical effects: By dynamically and adaptively adjusting the denoising weights based on instantaneous acceleration, high-frequency noise from mechanical resonance is filtered out while the true destructive impact peak is preserved without any loss. Secondly, it breaks through the traditional single-dimensional observation of macroscopic fracture by relying solely on the naked eye, and accurately quantifies the heat dissipation of microscopic damping degradation within the material by calculating the area of ​​the pressure-displacement hysteresis loop. Finally, by combining a nonlinear damage mechanics model, it achieves accurate early warning hundreds of cycles before macroscopic physical fracture, improving the safety and accuracy of detection. Attached Figure Description

[0027] Figure 1 This is a flowchart of the seat mechanical performance testing method based on an intelligent sensor system according to the present invention.

[0028] Figure 2 This is a diagram illustrating the adaptive peak retention and reconstruction effect of the present invention.

[0029] Figure 3 This is a hysteresis loop diagram of the material hysteresis dissipation quantification of the present invention.

[0030] Figure 4 This is a comparison chart of the overall lifespan early warning trend of this invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] The test scenario corresponding to this invention is as follows: An office chair with a chassis tilting mechanism and a gas spring is used as the test carrier in a seat testing platform. The office chair is fixed to the base platform of a mechanical testing machine, ensuring that the chassis casters are locked or in a limited position. A pressure sensor is rigidly connected in series between the vertical drive shaft of the mechanical testing machine and the loading platen to ensure that the measured force value is completely equivalent to the actual normal load applied by the loading platen to the seat cushion of the office chair. Simultaneously, to avoid displacement errors caused by the deformation of the main shaft of the mechanical testing machine, a high-frequency laser displacement sensor is horizontally deployed on a rigid side support independent of the mechanical testing machine. The laser emission probe of the high-frequency laser displacement sensor is precisely aligned with the upper edge of the metal outer tube of the gas spring under the office chair. The hardware sampling rate of both the pressure sensor and the high-frequency laser displacement sensor is set to 1000Hz, and hardware-level timestamp synchronization is achieved through the external interrupt pin of the same microcontroller (MCU).

[0033] In drop impact testing, the mechanical testing machine uses a suspension release device to release the equivalent mass... A 136kg loading plate was raised to a predetermined height above the unloaded office chair seat. It was then instantly released, allowing the loading plate to fall freely into the seat. The chair rebounded under the action of springs. If the material were perfectly elastic, the force-displacement curves of the compression and rebound should perfectly coincide. However, in reality, with the current total number of completed test cycles… As the pressure increases, the metal springs inside the chassis tilting mechanism begin to experience microscopic lattice slippage, and the friction between the sealing ring inside the pneumatic rod and the cylinder intensifies. These physical-level damping degradations and irreversible heat losses are directly reflected in the data as the force-displacement curves of the current cycle's downward and rebound forces no longer closing in the two-dimensional coordinate system, but instead expanding to form a hysteresis loop area.

[0034] Based on the above background, embodiments of the present invention disclose a method for testing the mechanical performance of a seat based on an intelligent sensor system, referencing... Figure 1 This includes the following steps: S1: Time synchronization and data acquisition preprocessing for multimodal sensors.

[0035] A pressure sensor is deployed on the contact surface of the loading plate of the mechanical testing machine, and a high-frequency laser displacement sensor is deployed on the side of the test bench. The probe is vertically aligned with the upper end of the pressure bar, and the acquisition frequency is set to 1000Hz. Pressure and displacement readings are acquired over time. The data is collected at the current acquisition time. Using the endpoint as the starting point, data points of a preset length are extracted along the historical direction of the timeline, and a data structure of length [length missing] is constructed in system memory. A first-in, first-out (FIFO) queue buffer window is used. Considering the high likelihood of random electromagnetic noise generated by motor operation in actual industrial testing environments, a conventional bandpass filter is used to preliminarily isolate and remove glitches from the sequence before data extraction to prevent noise points from causing ambiguity in alignment determination. Data within the buffer window is extracted, and a cross-correlation algorithm is used to align the time phase of the pressure sensor sequence and the high-frequency laser displacement sensor sequence. The time difference between the two signals is determined by finding the peak point of the cross-correlation function and then shift compensation is performed to output the current time. Raw pressure readings after time synchronization and displacement readings .

[0036] S2: Adaptive peak preservation and signal reconstruction based on kinematic priors.

[0037] Based on displacement readings Displacement data nodes from adjacent sampling periods are extracted, and the displacement is analyzed using the conventional second-order central difference method. By performing second-order difference calculations, the instantaneous acceleration at the test site can be obtained. The test area can be understood as the seat support surface. The impact strength characteristic ratio is then calculated, expressed as:

[0038] in, Indicates at time Impact strength characteristic ratio at the location; This indicates the equivalent mass of the loading platen of the testing machine; Indicates at time The instantaneous acceleration at the test site; This indicates the reference value of the static load support force calibrated for the test seat.

[0039] The equivalent mass of the loading platen of the testing machine The range of values ​​is usually 100. In this embodiment, the value is 136 kg. This value is chosen to strictly align with the industry testing standard's extreme drop weight requirements; the static load support force reference value calibrated for this test seat. The range of values ​​is usually 100. In this embodiment, N is set to 1334N. The value of 1334N is used to simulate the extreme gravity support benchmark for an adult when riding.

[0040] when When the value increases, it reflects the intensified extreme deceleration condition at the moment the loading pressure plate contacts the seat, leading to a significant increase in the impact strength characteristic ratio, thereby achieving quantitative capture of the degree of transient severe impact; when When the value decreases, it reflects that the pressure plate is in a stable working condition of slow downward pressure or static support, which causes the impact strength characteristic ratio to tend to approach zero, thereby achieving reliable identification of the steady-state working range.

[0041] In summary, the output variables Essentially, it quantifies the severity of the dynamic inertial impact force that the current system is subjected to. When the impact is large, it is determined to be a transient and severe impact; when When the value is relatively small, it is determined to be a steady-state support state.

[0042] Based on this, the dynamic retention weight is calculated, and the expression is:

[0043] in, Indicates at time Dynamic retention weights are used to control the denoising intensity. Indicates at time Impact strength characteristic ratio at the location; This indicates the maximum downward stroke set on the pressure plate of the testing machine; Indicates the initial nominal thickness of the office chair seat cushion; This represents the hyperbolic tangent function, used for normalizing the calculation results.

[0044] The maximum downward stroke set on the pressure plate of the testing machine The range of values ​​is usually 100. mm, in this embodiment, in order to cover the safe deformation range of a standard office chair before it experiences ultimate compression, The value is 50mm; the initial nominal thickness of the office chair seat cushion. The range of values ​​is usually 100. mm, in this embodiment, in order to match the physical thickness of mainstream high-density foam seat cushions on the market, The value is 100mm.

[0045] when When the value increases, it reflects the severe working condition where the seat cushion is insufficiently cushioned and subjected to high-intensity impacts, increasing the risk of rigid direct bottoming out. This causes the dynamic retention weight to increase towards 1, thereby achieving non-destructive interception of the actual extreme value of damage; when When the value decreases, it reflects a mild contact condition in which the impact potential energy is fully absorbed by the thick sponge, causing the dynamic retention weight to converge to 0, thereby achieving maximum suppression of meaningless mechanical clutter.

[0046] In summary, the output variables Essentially, it quantifies the urgency of retaining data based on current stress levels during data cleaning. When the peak value is large, it is determined that the actual peak value of the damage must be preserved as is to prevent peak clipping; when When the value is small, it is determined to be a normal state that allows deep smoothing to be performed to eliminate mechanical white noise.

[0047] Finally, the reconstruction calculation is performed during system initialization, i.e. At that time, set It should be noted here that... The moment the pressure plate contacts the seat, the expression for reconstructing the original pressure is:

[0048] in, Indicates at time The clean pressure reading output from the reconstructed circuit; Indicates at time Dynamic retention weights are used to control the denoising intensity. Indicates at time The original pressure readings collected at the original location; Indicates at time The historical pure pressure readings at the reconstructed output.

[0049] when When the signal increases, it reflects that the current signal contains a real and destructive shock wave that cannot be erased, causing the signal reconstruction process to tend to completely accept the original input at the current instant. This achieves absolute fidelity for dangerous stress peak values; when When the value decreases, it reflects that the system is in a high-frequency resonance period without destructive impacts, causing the signal reconstruction process to tend to rely on historical smooth values. This achieves a smooth transition in the feedback curve.

[0050] In summary, the output variables Essentially, it quantifies the optimal approximation of the actual force situation, when When the force is large, it is determined that the ultimate destructive force has been truly captured; when... When the value is small, it is determined that a stable support force has been captured.

[0051] To further prevent systemic shifts caused by momentary failures of single-point sensors, an automated verification defense is added after this reconstruction step: real-time comparison. The relationship with the calibrated static load support force is such that, once an isolated value exceeding the material's physical limits is identified, the amplitude limiting attenuation module is forcibly activated to normalize it to a reasonable range, ensuring the physical feasibility of the reconstructed data. For example, real-time comparison... If the force exceeds 8 times the support force, the amplitude limiting and attenuation module will be forcibly activated to normalize it to the set maximum value. The maximum value can be set according to historical data, i.e., the historical maximum value will be taken.

[0052] Calculation example: Suppose that at a certain instant of impact, the extracted data is... (Approximately 2G gravitational acceleration), substituting the parameter: equivalent mass Support Calculate the characteristic ratio Next, the weights are calculated. Assuming the initial pressure peak suddenly increases to... The smoothed value at the previous time step Then the output is pure pressure. The actual destructive power of up to 2640N is highly preserved.

[0053] S3: Calculate the fatigue dissipation ratio of the standardized chassis.

[0054] Traditional fatigue testing relies solely on surface deformation or fracture as the evaluation criterion, resulting in a single evaluation dimension. Therefore, this step utilizes the physical law that the degradation of micromaterial damping is inevitably accompanied by heat dissipation, and integrates this with the safety dimensional indicators of the chassis tilting mechanism and the pneumatic rod.

[0055] After the seat is pressed down by the loading plate, it rebounds under the action of the spring. After a single complete pressing and rebound cycle, the time series of discrete displacement readings and pure pressure readings corresponding to the current single cycle are extracted. The area enclosed by the pressure-displacement hysteresis loop is calculated using the trapezoidal integral method, and the standardized chassis fatigue dissipation ratio is calculated. The expression is:

[0056] in, This represents the standardized chassis fatigue dissipation ratio in a single cycle; This represents the integral work done by pressure along the closed path of displacement in a single cycle; Indicates the nominal stiffness of the chassis tilting mechanism; This indicates the maximum safe stroke of the air pressure bar under the chassis.

[0057] The nominal stiffness of the chassis tilting mechanism The range of values ​​is usually 100. In this embodiment, the value is 30000 N / m. This value is chosen to align with the tilt spring rebound resistance setting most commonly used by medium-sized users; this also represents the maximum safe stroke of the gas spring beneath the chassis. The range of values ​​is usually 100. In this embodiment, m is taken as 0.1m. A value of 0.1m corresponds to the ultimate extension and retraction safety limit of a standard Class 3 gas spring.

[0058] when When the value increases, it reflects the slippage of the metal lattice inside the chassis, the plastic deformation of the spring, and the increased friction of the cylinder, resulting in a large amount of irreversible heat loss in a single cycle. This leads to a significant increase in the dissipation ratio, thus enabling accurate measurement of microscopic damage in the early stages of fatigue. When the value decreases, it reflects that the component has excellent elasticity and almost no excess frictional heat generation, resulting in a dissipation ratio that tends to be extremely low, thus achieving objective verification of the healthy and highly elastic state of the structure.

[0059] In summary, the output variables Essentially, it quantifies the degree of hidden damping degradation in the core cushioning components of the seat as the number of tests increases. When the value is large, it is determined that severe irreversible fatigue damage has occurred within the system; when When the value is low, the component is considered to be still in a healthy, flexible operating period.

[0060] S4: End-life warning based on nonlinear continuous damage mechanics.

[0061] Continuously record the current cumulative total number of loop tests completed. It retrieves the dissipation ratio data sequence of all historical cycle periods from the memory pool and calculates the comprehensive fatigue life warning value of the seat, expressed as:

[0062] in, This represents the overall fatigue life warning value after C cycles; This represents the attenuation coefficient for the propagation of microcracks within the material. This indicates the total number of loop tests that have been completed so far; Indicates the first Dimensionless standardized chassis fatigue dissipation ratio in the next cycle.

[0063] Attenuation coefficient The range of values ​​is usually 100. In this embodiment, the value is 0.001. The value of 0.001 is based on the early crack propagation rate of low-carbon steel alloy fatigue, which is determined by a large number of destructive fracture experiments.

[0064] when When the value accumulates rapidly and continuously, it indicates that micro-cracks in the metal have coalesced into macro-cracks, and the overall structure is approaching the fracture critical point. This causes the warning value to nonlinearly and rapidly approach 1 under the influence of the natural exponent, thus achieving a pre-fuse alarm for fracture faults. When the accumulation is extremely slow, it reflects that the material is in a stable high fatigue life cycle, causing the warning value to remain at a safe low level far below 1, thereby enabling continuous release of high-quality batch components.

[0065] Output variables Essentially, it quantifies the percentage of risk that the remaining lifespan of the tested device will be exhausted, when When the overall fatigue warning threshold is exceeded, the batch of chassis or gas springs is deemed to have a safety hazard and a defective product warning is issued; when When the value is below the comprehensive fatigue warning threshold, the tested product is deemed qualified. For example, the comprehensive fatigue warning threshold... It is 85%.

[0066] Calculation example: Assume the first 1000 cycles in the initial stage of the test ( Since the spring is in good condition, the average value each time is... The value is only 0.5, and the cumulative total is 500. Substitute these values ​​into the calculation. At this point, the damage was only 39.4%, and the system deemed it safe. However, as the test progressed to 2000 cycles, friction increased sharply due to material aging, and the average damage in the last 1000 cycles... It jumps to 1.5, and the cumulative total reaches 2000. The damage level reached 86.5%, exceeding the 85% safety threshold, and a potential hazard warning signal was successfully issued before the component completely broke.

[0067] The technical effects of this invention can also be illustrated in conjunction with the accompanying drawings. Figure 2 The figure shows the effect of adaptive peak retention and reconstruction, illustrating the change in stress intensity over time. This figure verifies the adaptive filtering mechanism for preventing peak clipping of this invention. In the normal, stable support range, the red curve is extremely smooth, effectively filtering out meaningless mechanical spikes in the gray area. However, at the moment of extreme impact, the retention weight is drastically increased based on the instantaneous acceleration, causing the red curve to instantly adhere to the very top of the gray peak. This process achieves both conventional noise reduction and preservation of the true extreme value of destructive force, overcoming the defect of traditional low-pass filters that blindly flatten dangerous peaks.

[0068] Figure 3This is a hysteresis loop plot for quantifying material hysteresis dissipation. The horizontal axis represents the pressure plate stroke displacement, and the vertical axis represents the reconstructed pure pressure reading. The loading path curve of the testing machine and the unloading path curve of the chassis rebound are closed at both ends, forming an integral region filled with light purple in the middle. This plot provides a macroscopic visualization and quantification of the microscopic fatigue damage within the material. Due to metal lattice slip and spring damping friction, the force during rebound is necessarily less than the force during compression. This physical hysteresis forms the enclosed region, directly representing the irreversible heat energy converted and dissipated in a single cycle. The larger the region, the more severe the microscopic physical damage inside the chassis and the pressure rod.

[0069] Figure 4 The comprehensive lifespan warning trend comparison chart shows that, due to the lack of perception of internal damage to components by the traditional linear prediction method, when the chair breaks, the curve does not reach the edge of the alarm red line, resulting in missed detection and test accidents.

[0070] This invention keenly detects the sharp increase in frictional heat dissipation caused by internal microcracks. Its warning value shows a nonlinear increase in the later stages of the test. When there are still nearly 300 cycles before physical fracture, the red interception mark in the figure is triggered, and the warning is successfully output.

[0071] This invention also discloses a seat mechanical performance testing system based on an intelligent sensor system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the seat mechanical performance testing method based on the intelligent sensor system according to this invention is implemented.

[0072] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0073] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for testing the mechanical properties of a seat based on an intelligent sensor system, characterized in that, Includes the following steps: Acquire the raw pressure and displacement readings of the tested seat during the test process; calculate the instantaneous acceleration of the tested part based on the displacement reading, and determine the impact strength characteristic ratio at the current moment based on the instantaneous acceleration. The impact strength characteristic ratio is positively correlated with the instantaneous acceleration; determine the dynamic retention weight for controlling the noise reduction intensity based on the impact strength characteristic ratio. The dynamic retention weight is positively correlated with the impact strength characteristic ratio. Using dynamic retention weights, an adaptive weight allocation is performed between the original pressure reading and the reconstructed pressure reading at historical moments, and the pure pressure reading at the current moment is reconstructed and output. Based on the displacement reading sequence and the pure pressure reading sequence within a single test cycle, the standardized chassis fatigue dissipation ratio of the tested seat component is calculated. Based on the cumulative distribution of the standardized chassis fatigue dissipation ratio across multiple historical cycles, a comprehensive fatigue life warning value is calculated, and the life warning result is output based on the comprehensive fatigue life warning value.

2. The method for testing the mechanical performance of a seat based on an intelligent sensor system according to claim 1, characterized in that, After acquiring the original pressure and displacement readings of the test seat during the test process, the method further includes: using a bandpass filter to isolate and remove burrs from the original pressure and displacement readings; using a cross-correlation algorithm to calculate the time difference between the pressure sensor sequence and the high-frequency laser displacement sensor sequence, and performing translation compensation based on the time difference to achieve time phase alignment between the original pressure and displacement readings. 3.The smart sensor system based seat mechanical property testing method according to claim 1, wherein, The steps to determine the impact strength characteristic ratio at the current moment are as follows: multiply the equivalent mass of the loading platen of the testing machine by the instantaneous acceleration to obtain the dynamic inertial force; divide the dynamic inertial force by the reference value of the static load support force calibrated by the test seat to obtain the impact strength characteristic ratio. 4.The method of claim 1, wherein, The specific steps for determining the dynamic retention weight for controlling the noise reduction intensity are as follows: calculate the ratio of the maximum downward stroke of the pressure plate of the testing machine to the initial nominal thickness of the office chair seat cushion to obtain the geometric compression ratio; multiply the geometric compression ratio by the impact strength characteristic ratio, and then normalize it using the hyperbolic tangent function to obtain the dynamic retention weight.

5. The smart sensor system-based seat mechanical property testing method according to claim 1, wherein, The steps for reconstructing the pure pressure reading at the current moment are as follows: multiply the dynamically retained weight by the original pressure reading at the current moment to obtain the first weighted component; multiply the difference between the preset constant and the dynamically retained weight by the historical pure pressure reading reconstructed at the previous moment to obtain the second weighted component; add the first weighted component and the second weighted component to obtain the pure pressure reading reconstructed at the current moment. 6.The smart sensor system based seat mechanical property testing method according to claim 1, wherein, After reconstructing the output of the current pure pressure reading, the method also includes an automated verification step: comparing the pure pressure reading with the multiple of the calibrated static load support force in real time; if the pure pressure reading exceeds the preset multiple threshold of the calibrated static load support force, the amplitude limiting attenuation module is activated to normalize the pure pressure reading to the preset physical reasonable range. 7.The smart sensor system based seat mechanical property testing method according to claim 1, wherein, The steps for calculating the standardized chassis fatigue dissipation ratio of the tested seat component are as follows: use the trapezoidal integral method to calculate the integral work done by the pressure on the displacement closed path in a single cycle, and obtain the area of ​​the pressure-displacement hysteresis loop; calculate the product of the nominal stiffness of the chassis pitching mechanism and the square of the maximum safe stroke of the gas bar under the chassis, and obtain the reference elastic potential energy; divide the area of ​​the pressure-displacement hysteresis loop by the reference elastic potential energy to obtain the standardized chassis fatigue dissipation ratio. 8.The smart sensor system based seat mechanical property testing method according to claim 1, wherein, The steps for calculating the comprehensive fatigue life warning value are as follows: obtain the total number of completed cycle tests and retrieve the standardized chassis fatigue dissipation ratio sequence of all historical cycle cycles; sum the standardized chassis fatigue dissipation ratio sequence to obtain the total dissipation energy index; multiply the attenuation coefficient of microcrack propagation inside the material by the total dissipation energy index, use it as the negative exponent of the natural exponential function, and subtract the result of the natural exponential function using a preset constant to obtain the comprehensive fatigue life warning value.

9. The method for testing the mechanical performance of a seat based on an intelligent sensor system according to claim 1, characterized in that, The method for determining the output of the lifespan warning result is as follows: if the comprehensive fatigue lifespan warning value is greater than the set comprehensive fatigue warning threshold, the tested seat is determined to have a safety hazard and a defective warning signal is output. If the comprehensive fatigue life warning value is less than or equal to the comprehensive fatigue warning threshold, the tested seat is deemed qualified.

10. A seat mechanical performance testing system based on an intelligent sensor system, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the seat mechanical performance testing method based on an intelligent sensor system according to any one of claims 1-9.

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