A method and system for automated processing of tire air tightness test data

Abstract

The application provides a kind of tire air tightness test data automatic processing method and system, comprising: collecting the time series air pressure data of tire to be tested in air tightness test process;The time series air pressure data is dynamically stabilized and identified, determines the starting time of air pressure into stable attenuation stage, and eliminates the transition data before the starting time;The data of stable attenuation stage is filtered for abnormal fluctuation, and transient fluctuation caused by sensor noise or external vibration is eliminated;Search the best calculation window in the filtered effective data, calculate the air pressure loss rate of tire based on the air pressure data in the best calculation window, and automatically generate air tightness test report according to the air pressure loss rate, the application realizes the automatic screening and accurate calculation of test data, significantly improves the processing efficiency and result consistency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology for tire air tightness testing, and specifically to an automated processing method and system for tire air tightness test data. Background Technology

[0002] Tire air tightness is one of the key indicators for measuring tire quality and safety performance. In tire air tightness testing, pressure sensors are usually installed at the tire valve to continuously collect tire internal pressure data over a long period of time at a fixed sampling frequency. Since the air tightness test cycle for a single tire is relatively long, generally no less than 90 days, the amount of raw pressure-time series data collected is enormous.

[0003] Several tire air tightness testing devices and methods already exist in the prior art. For example, patent CN103115736B discloses a tire air tightness performance testing device and method. This device includes a tire pressure acquisition unit, an atmospheric pressure acquisition unit, a temperature acquisition unit, and a data processor. The data processor stores tire pressure signals, atmospheric pressure signals, and ambient temperature signals at predetermined time intervals and calculates the tire pressure change rate of the test tire at standard atmospheric pressure and standard reference temperature. However, although this solution can eliminate the influence of ambient temperature and pressure on the test results, its data processing is still limited to simple signal storage and pressure change rate calculation, lacking the ability to automatically screen, clean, and identify anomalies in the test data. Another example is patent CN206601221U, which discloses a tire air tightness testing device and system. This device can perform continuous real-time tire air tightness testing to improve the accuracy of air tightness test data. However, this solution mainly focuses on hardware improvements and continuous data acquisition of the testing device; its data processing still relies on conventional methods and fails to solve the problem of automated processing and analysis of test data. Furthermore, patent US20220063357A1 discloses a tire pressure monitoring system, whose tire pressure model includes a noise filter for filtering sensor noise. However, this noise filtering only processes tire pressure monitoring signals under vehicle conditions, not pressure decay data under long-term, static conditions in tire air tightness testing scenarios. Moreover, it fails to identify the pressure instability stage caused by the relaxation of rubber material stress after tire inflation, and it cannot distinguish between short-term pressure fluctuations caused by sensor noise or external vibrations and continuous pressure drops caused by actual gas leakage.

[0004] After the test is completed, the raw data usually needs to be manually screened, cleaned, and leak rate calculated by the testers, and finally an inspection report is issued. This manual processing is inefficient. For example, it takes at least 30 minutes to manually complete the data screening, cleaning, leak rate calculation and report issuance for a single tire over 90 days. This is time-consuming and cannot meet the needs of large-scale tire testing.

[0005] Furthermore, the manual processing method suffers from a high error rate. During manual processing, issues such as data omissions, incorrect selections, unit conversion errors, and data entry errors frequently occur, making it difficult to guarantee the reliability and consistency of test conclusions. This can even lead to qualified tires being mistakenly judged as unqualified, or vice versa, impacting product quality control. In addition, there is currently a lack of automated processing tools specifically designed for tire air tightness testing scenarios. While existing general-purpose data processing software (such as Microsoft Excel and general data cleaning tools) can perform basic data processing and curve fitting, they lack the ability to understand the tire air tightness testing scenario. They cannot automatically identify the pressure instability phase caused by rubber material stress relaxation after tire inflation, nor can they distinguish between short-term pressure fluctuations caused by sensor noise or external vibrations and continuous pressure drops caused by actual gas leakage. They also cannot automatically determine which data segment best matches the tire pressure diffusion attenuation law. Therefore, general-purpose software cannot replace manual processing of tire air tightness test data.

[0006] In summary, existing methods for processing tire air tightness test data suffer from drawbacks such as low efficiency, high error rate, and lack of dedicated automated tools. There is an urgent need for an automated data processing method and system specifically designed for tire air tightness testing scenarios.

[0007] Therefore, existing technologies still need further development. Summary of the Invention

[0008] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide an automated processing method and system for tire air tightness test data to solve the problems existing in the prior art.

[0009] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides an automated processing method for tire air tightness test data, comprising: S100. Collect time-series air pressure data of the tire under test during the air tightness test; S200. Dynamically identify the stable segment of the time-series air pressure data, determine the starting time when the air pressure enters the stable decay stage, and remove the transitional data before the starting time. S300: Filter out abnormal fluctuations in the data during the stable decay phase to remove transient fluctuations caused by sensor noise or external vibration. S400: Search for the optimal calculation window in the filtered valid data, calculate the tire pressure loss rate based on the air pressure data within the optimal calculation window, and generate an air tightness test report based on the air pressure loss rate.

[0010] Specifically, step S200 includes: The time-series air pressure data is traversed using a sliding window to calculate the air pressure range within each sliding window. The pressure difference of each sliding window is compared with a dynamic stability threshold, which is determined by matching according to the type of tire under test; When the pressure difference of a consecutive preset number of sliding windows is less than the dynamic stability threshold, it is determined that the air pressure has entered the stable decay stage, and the moment of entering the stable decay stage is recorded as the stable start time. Transitional data before the stable start time is discarded.

[0011] Specifically, the dynamic stability threshold is determined by a threshold matching relationship established in advance for each tire type. The threshold matching relationship is calibrated based on the structural characteristics and inflation process parameters of different tire types.

[0012] Specifically, step S300 includes: The amplitude and duration of each pressure change event during the stable decay phase are detected; The amplitude of each pressure change event is compared with a preset amplitude threshold, and its duration is compared with the shortest effective duration. When the amplitude is greater than a preset amplitude threshold and the duration is less than the shortest effective duration, the air pressure change event is determined to be a transient fluctuation caused by sensor noise or external vibration interference and is rejected. When the duration is greater than or equal to the shortest effective duration, the pressure change event is determined to be a real pressure decay change and is retained.

[0013] Specifically, the shortest effective duration is calculated based on the physical parameters of the tire under test, including: The gas leakage time constant is calculated based on the tire volume, the effective air passage cross-sectional area corresponding to the minimum detectable leak, the gas isentropic index, and the ambient temperature. The shortest effective duration is then determined based on the gas leakage time constant and a preset empirical correction coefficient.

[0014] Specifically, the method for searching the optimal computation window in the filtered valid data includes: A sliding window with a fixed window length slides over the valid data. For the air pressure data within each sliding window, an exponential decay model based on the gas diffusion law is used for fitting, and the corresponding goodness of fit is calculated. The sliding window with the highest goodness of fit is selected as the optimal calculation window.

[0015] Specifically, the exponential decay model reflects the law that the gas pressure decreases exponentially over time and tends to the ambient reference pressure as the gas diffuses outward through the tire rubber layer.

[0016] Specifically, the method for calculating the tire pressure loss rate based on the air pressure data within the optimal calculation window includes: Obtain the initial and final air pressures of the optimal calculation window, and calculate the tire pressure loss rate based on the initial and final air pressures.

[0017] Specifically, the method for generating an airtightness test report based on the pressure loss rate includes: The calculated air pressure loss rate is compared with the preset pass threshold, and the tire air tightness is determined based on the comparison result, thereby generating the pass / fail result of tire air tightness. Generate a test report that includes the tire identification information, test duration, tire pressure loss rate, and pass / fail result.

[0018] According to a second aspect of the present invention, an automated processing system for tire air tightness test data is provided, comprising: Data acquisition module: used to collect time-series air pressure data of the tire under test during the air tightness test; Identification module: used to identify the dynamic stable segment of the time-series air pressure data, determine the start time of the air pressure entering the stable decay stage, and remove the transitional data before the start time; Anomaly filtering module: used to filter out abnormal fluctuations in the data during the stable decay phase, eliminating transient fluctuations caused by sensor noise or external vibration; Optimal Window Search Module: Used to search for the optimal calculation window in the filtered valid data; Loss rate calculation module: used to calculate the tire pressure loss rate based on the air pressure data within the optimal calculation window; Report generation module: Used to generate an airtightness test report based on the pressure loss rate.

[0019] Beneficial effects: This invention provides an automated method and system for processing tire air tightness test data. By automatically collecting time-series air pressure data and sequentially performing dynamic stability segment identification, abnormal fluctuation filtering, optimal calculation window search, and air pressure loss rate calculation and report generation, the entire process of tire air tightness test data processing is automated. This overcomes the technical defects of traditional manual processing methods, such as low efficiency and susceptibility to errors. It can automatically remove unstable transitional data caused by stress relaxation after tire inflation, filter transient fluctuations caused by sensor noise and external vibrations, and automatically select the calculation window that best matches the tire pressure decay characteristics based on gas diffusion laws. This improves the accuracy and reliability of air pressure loss rate calculation and ensures the consistency and repeatability of processing results. Attached Figure Description

[0020] Figure 1 This is a flowchart of an automated processing method for tire air tightness test data provided in a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the composition of the automated processing system for tire air tightness test data provided in a specific embodiment of the present invention; Figure 3 This is a schematic diagram of the initial software interface provided in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the data import interface provided in a specific embodiment of the present invention; Figure 5 This is a schematic diagram of the parameter configuration interface provided in a specific embodiment of the present invention; Figure 6 This is a schematic diagram of the automatic processing and analysis interface provided in a specific embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0022] Example 1 Please see Figure 1 This embodiment provides an automated processing method for tire air tightness test data, which relies on Figure 3 The intelligent detection system implemented in this system aims to solve the technical problems of low efficiency and error-proneness in traditional manual processing. For example... Figure 4As shown in the figure, the left side is the main interface of the intelligent detection system, which integrates functions such as data import, parameter configuration, data processing, and report generation. The right side shows the specific execution feedback of the automatic processing and analysis interface. The method of this embodiment mainly includes the following steps, and each step has a strict logical sequence relationship, forming a dedicated automated processing link for tire air tightness testing scenarios, as follows: Step S100: Collect time-series air pressure data of the tire under test during the air tightness test.

[0023] Specifically, this step connects to a high-precision pressure sensor installed at the tire valve via a wired or wireless communication interface. The sensor collects the pressure value inside the tire in real time at a preset fixed sampling frequency (e.g., once per hour or higher). The collected data is organized into a two-dimensional time sequence containing timestamps and corresponding air pressure values ​​and stored in the system database for subsequent retrieval. This provides the original data foundation for the entire automated processing flow.

[0024] Step S200: Dynamically identify the stable segment of the time-series air pressure data, determine the starting time when the air pressure enters the stable decay stage, and remove the transitional data before the starting time.

[0025] Specifically, after tire inflation, the rubber material undergoes a stress relaxation process, resulting in a rapid and unstable initial pressure drop. This data is called transitional data, and directly using it for calculations would lead to an overestimation of the loss rate. By analyzing the fluctuation characteristics of time-series pressure data, the system automatically locates the critical point where the pressure change becomes gradual and enters a stable decay stage dominated by gas permeation and diffusion. All data before this critical point is discarded. After this processing step, the output data only contains the steady-state decay segment that truly reflects the tire's airtightness, thus avoiding interference from the material's viscoelastic behavior on the test results. Figure 6 As shown in the processing progress pop-up, the system will provide real-time feedback on the identification results of the stable segment start point, such as displaying "Stable segment start point: Day 3", intuitively informing the user of the range of transition data to be removed.

[0026] Step S300: Filter the data in the stable decay phase for abnormal fluctuations, and remove transient fluctuations caused by sensor noise or external vibration.

[0027] Specifically, in actual testing environments, although initial unstable data has been eliminated, the test period, which can last for tens of days, may still be affected by environmental vibrations, power fluctuations, or momentary sensor malfunctions, resulting in non-physical pressure jumps. The received steady-state data is then processed using a pre-defined joint judgment mechanism to identify and remove these transient interference points, while retaining the true attenuation trend consistent with the physical laws of gas leakage. This step ensures that the data entering subsequent calculations has a high degree of purity and reliability. For example, in... Figure 6 In the progress feedback, the system may prompt "6 abnormal fluctuations have been detected and all have been removed", which indicates that the data cleaning task has been completed and prevents occasional noise from misleading the final evaluation conclusion.

[0028] Step S400: Search for the optimal calculation window in the filtered valid data, calculate the tire pressure loss rate based on the air pressure data within the optimal calculation window, and automatically generate an air tightness test report based on the air pressure loss rate.

[0029] Specifically, firstly, within the filtered valid data segment, a data interval that best fits the gas diffusion theoretical model is found using a sliding traversal method as the optimal calculation window, thus avoiding potential edge effects or local disturbances at the beginning and end of the data. Then, differential calculations are performed using only the start and end pressure values ​​and time span within this optimal window to obtain a high-precision pressure loss rate index. Finally, the calculated loss rate is compared with a preset pass threshold to automatically determine the test result. Key information such as tire markings, test duration, loss rate value, and pass / fail conclusion are then filled into a standard template to generate a formal inspection report in PDF or Excel format. Figure 6 As shown, the analysis results area clearly displays core information such as "Optimal window: Day 21 to Day 50", "Goodness of fit R² = 0.9991", and "Judgment result: Qualified", realizing a fully automated closed loop from raw data to final deliverables.

[0030] Through the process architecture of S100 to S400 described above, this embodiment constructs a dedicated data processing system that is fully adapted to the physical characteristics of tire air tightness testing. Unlike general office software or simple statistical tools, each step in this process is embedded with targeted processing logic for tire stress relaxation, gas permeation and diffusion, and environmental interference characteristics. This not only avoids subjective errors and inefficiencies caused by manual screening and calculation, but also ensures the traceability of the processing process and the consistency of the results through a modular data flow mechanism. This lays a solid framework foundation for the detailed implementation of specific algorithm parameters in subsequent embodiments.

[0031] As one implementation, step S200 includes: traversing time-series air pressure data using a sliding window and calculating the air pressure range within each sliding window; comparing the air pressure range of each sliding window with a dynamic stability threshold, wherein the dynamic stability threshold is determined by matching the type of tire under test; when the air pressure range of a consecutive preset number of sliding windows is less than the dynamic stability threshold, it is determined that the air pressure has entered a stable decay stage, and the moment of entering the stable decay stage is recorded as the stable start time, and the transitional data before the stable start time is discarded.

[0032] It should be noted that, in performing the above steps, a fixed-length sliding window is used as the basic analysis unit. For example, the length of the sliding window can be set to include 20 sampling points. This length is sufficient to cover short-term noise fluctuations while also being able to sensitively capture changes in macroscopic trends. For each sliding window position, the difference between the maximum and minimum values ​​of all air pressure values ​​within the window is calculated, i.e., the air pressure range, which serves as an indicator to quantify the amplitude of air pressure fluctuations in the current period. In other implementations, statistical measures such as standard deviation and mean absolute deviation can also be used to characterize the degree of dispersion within the window, as long as they can reflect the drastic nature of air pressure changes. By comparing the calculated air pressure range with the dynamic stability threshold one by one, it is possible to monitor in real time whether the data has transitioned from a state of drastic fluctuations after inflation to a state of gradual attenuation.

[0033] To avoid misjudgments due to occasional momentary stability, this embodiment introduces a continuity verification mechanism. Only when the pressure difference of a consecutive preset number (e.g., N≥3) sliding windows is continuously lower than the dynamic stability threshold, does the system determine that the pressure has entered the stable decay stage, and marks the start time of the first window that meets the condition as the stable start time. This effectively filters out the brief plateau period that may occur during stress relaxation, ensuring that the data segments used to calculate the loss rate are indeed in the stable permeation and diffusion stage after thermodynamic equilibrium, thereby improving the accuracy of the test results.

[0034] Furthermore, to accommodate the differences in physical properties of tires of different specifications, the dynamic stability threshold is not a uniform fixed value, but is determined through a threshold matching relationship established in advance for each tire type. This threshold matching relationship is calibrated based on the structural characteristics and inflation process parameters of different tire types. For example... Figure 5In the parameter configuration interface shown, users can select the tire type to trigger the system to automatically load the corresponding internal calculation parameters. Specifically, for passenger car tires (PCR), due to their relatively small volume and inflation pressure typically ranging from 230 kPa to 250 kPa, the stress relaxation process of the rubber material is relatively fast and the amplitude is small, so the matching dynamic stability threshold is set to 2 kPa; for light truck tires (LTR), the inflation pressure increases to 350 kPa to 450 kPa, the tire carcass deformation increases, and the pressure fluctuation amplitude during the stress relaxation period increases accordingly, so the matching dynamic stability threshold is adjusted to 5 kPa; while for heavy truck tires (TBR), due to their huge volume and inflation pressure as high as 700 kPa to 900 kPa, the viscoelastic response inside the material is more significant, resulting in a wider range of initial pressure fluctuations, so the matching dynamic stability threshold is further increased to 8 kPa.

[0035] It is important to note that the aforementioned threshold matching relationship is based on pre-calibration using a large amount of experimental data, rather than being generated through real-time adaptive calculation during testing. This is because the stress relaxation behavior of a tire depends not only on the current pressure and temperature, but also on inherent properties such as the ply structure, rubber compound, and molding process. These factors are difficult to accurately invert using simple real-time mathematical models. By pre-establishing a mapping table between types and thresholds, this application transforms complex physical differences into executable engineering parameters. This ensures the algorithm's adaptability to different tire types while avoiding the computational burden and uncertainty caused by complex online calculations. Compared to general signal smoothing methods, this threshold matching mechanism based on the physical characteristics of the tire can more accurately define the starting point of effective data, preventing unstable relaxation data from being included in the calculation due to an excessively low threshold setting, or erroneously truncating valuable early stable attenuation data due to an excessively high threshold setting. This ensures the scientific rigor and reliability of the airtightness evaluation from the outset.

[0036] As one implementation, step S300 includes: detecting the amplitude and duration of each pressure change event during the stable decay phase; comparing the amplitude of each pressure change event with a preset amplitude threshold and comparing its duration with the shortest effective duration; when the amplitude is greater than the preset amplitude threshold and the duration is less than the shortest effective duration, determining that the pressure change event is a transient fluctuation caused by sensor noise or external vibration interference, and discarding it; when the duration is greater than or equal to the shortest effective duration, determining that the pressure change event is a real pressure decay change, and retaining it.

[0037] It should be noted that during the stable decay phase, although the macroscopic trend is a decrease in air pressure, short-term spikes may still exist at the microscopic level due to power supply ripple, mechanical vibration, or sensor thermal noise. The traditional single amplitude threshold method is prone to misjudging large-amplitude real leaks as noise, or mistaking long-term minor environmental drifts for normal decay. This embodiment introduces a two-dimensional joint amplitude-time judgment mechanism. Its core physical basis is that the real gas leakage process is constrained by the laws of thermodynamics and has a non-negligible time inertia. That is, the rate of change of air pressure is limited by the diffusion rate of gas through the rubber matrix and the thermal capacity effect of the tire volume, and instantaneous step change is impossible. On the contrary, sensor noise or external vibration usually manifests as pulse signals with concentrated energy and extremely short duration. Therefore, only when an air pressure change event simultaneously meets the two conditions of significant amplitude and sufficiently long duration is it recognized as a real decay signal that conforms to physical laws. Conversely, if the amplitude is large but the duration is below the physical limit, it is identified as non-physical transient interference and is eliminated. This ensures the physical authenticity of data cleaning and avoids signal distortion that may be caused by pure mathematical smoothing.

[0038] Furthermore, in order to ensure that the time scale parameters in the above joint judgment mechanism can be accurately adapted to tires of different specifications, the shortest effective duration is calculated based on the physical parameters of the tire to be tested. Specifically, this includes: calculating the gas leakage time constant based on the tire volume, the effective gas path cross-sectional area corresponding to the minimum detectable leakage, the gas isentropic index, and the ambient temperature, and determining the shortest effective duration based on the gas leakage time constant and the preset empirical correction coefficient.

[0039] In practical implementation, the gas leakage time constant The calculation formula is as follows: ; in, This indicates the internal volume of the tire under test, expressed in cubic meters (m³). This parameter directly determines the tire's internal gas storage capacity; the larger the volume, the slower the pressure response at the same leakage rate. This represents the effective gas path cross-sectional area corresponding to the smallest detectable leak, expressed in square meters (m²). Its value is typically based on the minimum leak orifice diameter specified in the test standard. Calculated (i.e.) This represents the smallest physical leakage path that the system needs to identify. The isentropic index represents the gas, and for air, it is usually taken as 1.4, reflecting the thermodynamic state changes of the gas during the leakage process. The absolute temperature of the test environment is expressed in Kelvin (K). The introduction of the temperature term corrects for the influence of the thermal motion speed of gas molecules on the leakage rate. Based on the fundamental principles of gas dynamics, this formula quantifies the inherent response time scale of a specific tire to a leakage event under a specific environment.

[0040] Subsequently, the shortest effective duration was calculated based on the gas leakage time constant τ. The calculation formula is as follows: ; in, The preset threshold for pressure change amplitude, This is the initial inflation pressure of the current tire. This is an empirical correction factor, typically ranging from 0.5 to 2.0, used to compensate for deviations between theoretical models and actual engineering environments, such as sensor sampling frequency limitations and the nonlinear viscoelasticity of rubber materials. This formula indicates the shortest effective duration... It is not a fixed value, but increases linearly with the increase of tire volume V, and is also modulated by the relative amplitude of pressure changes. If a fixed time window is used for filtering, for passenger car tires with small volume (PCR), an excessively long fixed window may misjudge real rapid leaks as noise and reject them (over-filtering), while for heavy-duty truck tires (TBR) with large volume, an excessively short fixed window may not be able to filter out long-period environmental disturbances (missing detection). This embodiment calculates in real time through a physical model. This enables the anomaly filtering algorithm to adapt to different tire specifications, ensuring accurate differentiation between noise and signal in various testing scenarios, such as... Figure 6 The automated processing and analysis interface clearly displays the feedback message "Step 3: Abnormal fluctuation filtering... 6 abnormal fluctuations have been detected and removed" in the processing progress pop-up window. This visual result directly corroborates the actual operating effect of the aforementioned joint filtering mechanism, indicating that the system has successfully identified and removed transient interference points that do not meet the constraints of the gas leakage time constant, thus providing a high signal-to-noise ratio data foundation for subsequent optimal window search. It should be understood that in other implementations, a simplified form based on this physical model or a lookup table method can also be used to determine the shortest effective duration, as long as the core logic is still based on tire physical properties rather than purely statistical laws to set the time threshold.

[0041] As one implementation method, the method for searching for the optimal calculation window in the filtered valid data includes: sliding a sliding window with a fixed window length over the valid data; fitting the air pressure data in each sliding window using an exponential decay model based on the gas diffusion law, and calculating the corresponding goodness of fit; and selecting the sliding window with the largest goodness of fit as the optimal calculation window.

[0042] It should be noted that the search process described above does not simply involve extracting data from a fixed time period. Instead, it actively identifies the interval with the highest data quality through a traversal optimization approach, setting a fixed-length search window. This window length is typically determined based on testing standards or experience, for example, containing 100 to 500 data points, or corresponding to a time span of 30 to 60 days, to ensure sufficient sample size to support statistical significance. Subsequently, the window slides progressively forward over the effective data segment after anomaly filtering with a fixed step size (e.g., 1 day or 1 sampling point). At each sliding position, curve fitting is performed on all the air pressure data points included in the window. The mathematical model used is an exponential decay model, the specific form of which is as follows: ; in, express Predicted tire internal pressure at any given time. This represents the initial effective pressure amplitude, which is the difference between the tire's internal pressure and the ambient pressure at the beginning of the window. It reflects the initial pressure differential driving force for gas permeation and diffusion. The pressure loss coefficient represents the rate at which gas diffuses through the rubber layer, and its value is directly related to the tire's airtightness. This represents a time variable relative to the start of the window. The ambient reference pressure represents the asymptotic value at which gas diffusion reaches thermodynamic equilibrium. It's important to note that this exponential decay model is not a purely mathematical empirical formula, but rather a profound reflection of the physical law that causes gas to diffuse outward through the tire rubber layer, leading to an exponential decay of pressure over time and its tendency to approach the ambient reference pressure. According to Fick's Law, under isothermal and constant-volume conditions, the permeation flux of gas through polymer materials is proportional to the concentration gradient, resulting in a linear relationship between the rate of change of tire internal pressure and the current internal and external pressure difference. Its integral form is the aforementioned exponential decay function. Therefore, this model strongly integrates the data processing algorithm with the inherent physical mechanism of tire airtightness testing, ensuring the scientific validity and accuracy of subsequent calculation results. To quantitatively evaluate the degree of agreement between the data within each sliding window and the theoretical model, the system introduces a goodness-of-fit metric. As an evaluation indicator of window quality, The value ranges from 0 to 1. The closer the value is to 1, the more closely the measured air pressure data points within that window are distributed on the theoretical exponential curve. In other words, the data in that segment better conforms to the physical laws of gas diffusion and is less affected by residual noise, temperature drift, or edge effects. Conversely, if the value of a certain window is less than 1, the measured air pressure data points within that window are more closely distributed on the theoretical exponential curve. A lower value indicates that the data segment may contain interference signals that have not been completely filtered out, or it may be in a non-steady-state transition region, making it unsuitable as a calculation benchmark. This is determined by traversing all possible window positions and comparing their respective values. The system automatically selects the goodness of fit value. Using the largest sliding window as the optimal calculation window, this adaptive optimization mechanism based on the fit of the physical model can effectively avoid the negative impact of edge effects and local disturbances on the overall evaluation, compared with the traditional manual selection or fixed time period interception method, and significantly improve the robustness and accuracy of airtightness determination.

[0043] After determining the optimal calculation window, a method for calculating the tire pressure loss rate based on the air pressure data within the optimal calculation window includes: obtaining the initial air pressure and the final air pressure of the optimal calculation window, and calculating the tire pressure loss rate based on the initial air pressure and the final air pressure.

[0044] Specifically, differencing is performed only on the data segment deemed optimal, rather than using the average or regression slope of the entire segment. Since the data within the optimal window has been validated by the physical model and possesses the highest signal-to-noise ratio and linearity (in logarithmic coordinates), endpoint differencing within this interval can maximally suppress the accumulation and amplification of random errors, thus minimizing pressure loss. The calculation formula is as follows: ; in, This represents the measured air pressure value at the start of the optimal calculation window, in kPa. This represents the measured air pressure value at the end of the optimal calculation window, in kPa. This represents the time span of the optimal calculation window, in hours; multiplying by 24 converts the unit to the commonly used engineering term "kPa / day". Furthermore, to eliminate the influence of different inflation pressures on the loss rate value and facilitate performance comparisons of tires across different specifications, the system can also calculate the relative pressure loss rate. The formula is as follows: ; This formula normalizes the absolute pressure drop to a percentage of the initial effective pressure difference, providing a more intuitive reflection of the barrier properties of the tire rubber material itself.

[0045] like Figure 6The automatic processing and analysis interface clearly presents the concrete results of the above search and calculation in the processing progress pop-up window and the main interface results display area. For example, the interface displays "Optimal window: Day 21 to Day 50", indicating that the system found that the data in this 30-day interval best conforms to the exponential decay law during the traversal process. At the same time, it displays "Goodness of fit R²=0.9991", which is an extremely high value that corroborates the physical authenticity and calculation reliability of the data in the selected window. The final output "Daily loss rate 0.00043kPa / day" is accurately calculated based on the start and end pressures of this optimal window.

[0046] As one implementation method, the method for automatically generating an air tightness test report based on the air pressure loss rate includes: comparing the calculated air pressure loss rate with a preset pass threshold, and determining whether the tire air tightness is qualified based on the comparison result, thereby generating a pass determination result for the tire air tightness; generating a test report that includes the tire identification, test duration, air pressure loss rate and pass determination result.

[0047] It should be noted that, in executing the above steps, the pass / fail judgment logic is first performed. The calculated air pressure loss rate (which can be the absolute air pressure loss rate or the relative air pressure loss rate) is compared with a pre-set pass / fail threshold. The pass / fail threshold is usually determined based on the tire type, specification, and relevant national standards or enterprise internal control standards. For example, for a certain specification of passenger car tire, its daily air pressure loss rate pass / fail limit may be set at 2.0 kPa / day or the monthly loss rate percentage limit. When the calculated air pressure loss rate is less than or equal to the pass / fail threshold, the tire is automatically judged to be airtight. Conversely, if the air pressure loss rate is greater than the pass / fail threshold, it is judged to be unqualified. This judgment process is entirely algorithm-driven, avoiding visual fatigue or subjective misjudgment that may occur when manually consulting standards and comparing values, ensuring the objectivity and consistency of the judgment conclusion. In other implementations, multi-level threshold division (such as excellent, qualified, unqualified) or a judgment model based on multi-parameter comprehensive scoring can also be used, as long as its core logic is based on the qualitative evaluation output of automated calculation results.

[0048] After the pass / fail determination is completed, the test report is automatically assembled. This report, based on a pre-set standardized template, automatically fills in the corresponding fields with key data generated in the preceding steps. The report content includes at least the tire identification (such as barcode number and model number), test duration (the effective span from the stable start time to the optimal window end time), pressure loss rate (including absolute value and / or relative percentage), and the final pass / fail determination result. Furthermore, to provide more comprehensive quality traceability, the report can automatically embed information such as a data processing result summary table and a comparison graph of the original pressure curve and the fitted curve. All this data is directly read from the system memory or database, eliminating the need for manual transcription or copying and pasting. This avoids common problems in traditional manual report generation, such as data transcription errors, unit conversion omissions, or mismatches between text and graphics. For example, in situations like... Figure 6 In the automated processing and analysis interface shown, after the user confirms that the analysis results are correct, they can click the "Export Report" button. The system will then pop up a save dialog box, allowing the user to select the storage path and confirm the file name (which by default includes the tire model, specifications, and test date for archiving). Subsequently, a formal inspection report in PDF or Excel format will be automatically generated. This one-click delivery mode not only reduces the report preparation time for a single tire from tens of minutes to seconds, but also ensures a high degree of consistency in format specifications, data accuracy, and judgment standards for each factory report.

[0049] Example 2 Please see Figure 2 This embodiment provides an automated processing system for tire air tightness test data. The system is specifically designed for data analysis and evaluation in tire air tightness test scenarios. As one implementation method, the automated processing system for tire air tightness test data includes: a data acquisition module 100, an identification module 200, an anomaly filtering module 300, an optimal window search module 400, a loss rate calculation module 500, and a report generation module 600.

[0050] Specifically, the above modules logically constitute a serial data processing pipeline, which can be physically implemented by a processor in a computer device executing program instructions stored in memory, or by constructing dedicated hardware circuits (such as FPGAs and ASICs). Figure 4 The main interface of the intelligent detection system shown integrates the function entry and status display of each module. Users can trigger the operation of the corresponding module through the function buttons on the left, while the display area on the right provides real-time feedback on the processing progress and results of each module. This modular architecture design enables the system to independently complete the entire automated processing from raw data acquisition to final report output without relying on external general office software, thus ensuring a high degree of uniformity in data processing standards and traceability of results.

[0051] The data acquisition module 100 is used to collect time-series air pressure data of the tire under test during the air tightness test. Specifically, the data acquisition module 100 not only supports real-time streaming data acquisition by directly connecting to a pressure sensor via a wired or wireless interface, but also has multi-format file parsing capabilities, and is compatible with various common test equipment data export formats such as txt, csv, and xls. Figure 4 The data import interface shown allows the data acquisition module 100 to automatically identify the file encoding and column structure after the user selects a data file. It extracts the core data from two columns: timestamp and barometric pressure value, and automatically calculates the sampling frequency based on the interval between adjacent time points. Simultaneously, it displays the first few rows of data in the preview area for verification. The module's output is a standardized raw time-series barometric pressure dataset, which is directly transmitted to the identification module 200 as the baseline input for subsequent processing. By encapsulating data access and format parsing functions within an independent module, the system can adapt to different brands and models of testing equipment, enhancing system compatibility and deployment flexibility.

[0052] The identification module 200 is used to identify the dynamic stabilization segment of time-series air pressure data, determine the starting time of the stable decay phase of air pressure, and remove transitional data before the starting time. Specifically, the identification module 200 receives raw time-series air pressure data from the data acquisition module 100 and, in conjunction with the tire type information selected by the user in the parameter configuration interface, automatically loads the corresponding dynamic stabilization threshold. Internally, this module executes sliding window traversal and range calculation logic. When it detects that the range of a consecutive preset number of windows is lower than the dynamic stabilization threshold, it accurately locates the stabilization start time and marks all data before that time as transitional data for removal. The output of the identification module 200 includes two parts: first, a timestamp of the stabilization start time, used to record the starting point of the effective test cycle; second, a steady-state decay dataset after removing transitional data, which is transmitted to the anomaly filtering module 300. This modular design based on physical characteristics matching thresholds ensures that the stress relaxation period of tires of different specifications can be adaptively and accurately identified, avoiding subjective errors caused by manually setting fixed cutoff points.

[0053] The anomaly filtering module 300 is used to filter out abnormal fluctuations in the data during the stable decay phase, eliminating transient fluctuations caused by sensor noise or external vibrations. Specifically, the anomaly filtering module 300 takes the steady-state decay dataset output by the identification module 200 as input and incorporates a joint judgment algorithm based on the gas leakage time constant. This module calculates the shortest effective duration in real time based on parameters such as the current tire volume, inflation pressure, and ambient temperature, and performs dual verification of the amplitude and duration of each pressure change event in the data sequence. Figure 6The progress pop-up window displays "6 abnormal fluctuations detected, all of which have been removed," reflecting the actual operating status of this module. After filtering, the anomaly filtering module 300 outputs a high signal-to-noise ratio effective dataset for downstream modules. Encapsulating complex physical model filtering logic within this module eliminates the need for upstream data acquisition and stable segment identification to concern themselves with specific noise characteristics. It also facilitates subsequent upgrades to the filtering algorithm for novel interference sources without affecting other parts of the system.

[0054] The optimal window search module 400 is used to search for the optimal computation window in the filtered valid data. Specifically, the optimal window search module 400 receives the valid dataset output by the anomaly filtering module 300, and uses a fixed-length sliding window to traverse and search the dataset. At each window position, the module calls the built-in exponential decay model fitting engine to calculate the goodness of fit R² between the data in the current window and the theoretical model. After traversal, the module automatically selects the window with the largest R² value as the optimal computation window and records its start and end time indices. Figure 6 As shown, the system interface clearly displays the search results for "Optimal Window: Day 21 to Day 50" and "Goodness of Fit R² = 0.9991". The output of this module is the location information of the optimal calculation window and the corresponding data subset, which is directly passed to the loss rate calculation module 500. Global optimization is achieved through an independent search module, ensuring that the calculation benchmark is always established on the data segment that best conforms to the gas diffusion law, effectively avoiding the negative impact of local disturbances on the overall evaluation.

[0055] The loss rate calculation module 500 calculates the tire pressure loss rate based on the air pressure data within the optimal calculation window. Specifically, the loss rate calculation module 500 only uses the data within the optimal calculation window specified by the optimal window search module 400 for calculation. This module reads the air pressure values ​​at the beginning and end of the window, obtains the absolute air pressure loss rate through differential calculation, and can further combine the ambient air pressure and the initial pressure difference to calculate the relative air pressure loss rate. Simultaneously, this module also has built-in pass / fail judgment logic, comparing the calculated loss rate with a preset pass / fail threshold and automatically generating a pass / fail judgment conclusion, such as... Figure 6 As shown in the analysis results area, the module outputs key indicators such as "daily loss rate 0.00043 kPa / day" and "judgment result: qualified". This module transmits the calculation results and judgment conclusions to the report generation module 600 together, and encapsulates the calculation and judgment functions independently, ensuring the accuracy of numerical calculation and the strict implementation of judgment standards, avoiding unit conversion errors or threshold comparison errors that may occur in manual calculation.

[0056] The report generation module 600 is used to automatically generate an airtightness test report based on the air pressure loss rate. Specifically, as the terminal output unit of the system, the report generation module 600 receives the calculation results and judgment conclusions from the loss rate calculation module 500, as well as metadata such as tire markings and test duration from the data acquisition module 100. Based on a pre-set standardized report template, the module automatically fills the above information into the corresponding fields and can embed a summary table of data processing results and an air pressure decay curve. Finally, it generates a formal inspection report in PDF or Excel format. After the user clicks the "Export Report" button, the system pops up a save dialog box and completes the file generation. This fully automated report generation mechanism not only compresses the compilation time of a single report to the second level, but more importantly, it ensures a high degree of consistency in the format, data integrity, and judgment of each deliverable, avoiding the error risks introduced by the manual transcription process.

[0057] It should be noted that the automated processing system provided in this embodiment constructs a complete closed loop from data access to report output through the organic collaboration of six functional modules. The data flow between modules is clear, and the interfaces are well-defined, ensuring both the continuity of the processing flow and improving the maintainability and scalability of the system. For example, if a new anomaly filtering algorithm needs to be introduced in the future, only the internal logic of the anomaly filtering module 300 needs to be updated, without modifying the code of other modules. This integrated system design specifically for tire air tightness testing solves the technical shortcomings of general-purpose tools lacking domain knowledge and manual processing being inefficient and prone to errors, providing reliable technical support for tire quality inspection.

[0058] The working principle of this embodiment is illustrated by the following specific example: This example provides an optimal implementation of a complete passenger car tire (PCR) test scenario. In this embodiment, the test object is a PCR tire with a specification of 205 / 55R17, and the test period is set to 90 days. Figure 4 The main interface of the intelligent testing system, as shown, allows users to first execute step S100, which involves collecting time-series air pressure data of the tire under test during the airtightness test. Users click the data import button on the left side of the interface and select the locally stored raw data file named test_data_001.csv. The data acquisition module 100 automatically parses the file, identifying two columns containing timestamps and air pressure values, and confirms the sampling frequency as once per hour based on the interval between adjacent time points. The status bar at the bottom of the interface provides real-time feedback that the data import was successful, with a total of 2160 data points read. This perfectly matches the theoretical number of sampling points for 90 days multiplied by 24 hours, ensuring the integrity of the raw data. Figure 4 The data import interface shown has a preview area displaying the first few rows of data for the user to verify. After confirming that there are no errors, the user can proceed to the next step.

[0059] Subsequently, the user sets the tire parameters in the parameter configuration interface shown. The user selects PCR as the tire type in the drop-down box. The system background then automatically loads the corresponding dynamic stability threshold of 2kPa according to the preset threshold matching relationship. This process reflects the technical feature of matching and determining the dynamic stability threshold according to the type of tire to be tested in step S200. Since 205 / 55R17 is a typical PCR specification, its inflation pressure is usually between 230kPa and 250kPa, and the rubber stress relaxation range is relatively small. Therefore, the threshold of 2kPa can accurately match its physical characteristics. After the user confirms that the test air pressure is 220kPa, he / she clicks the start processing button to trigger the automatic processing engine.

[0060] The system pops up as follows Figure 6 The processing progress pop-up window shows the core algorithm steps being executed sequentially. In the dynamic stable segment identification stage of step S200, the identification module 200 traverses 2160 data points with a sliding window, calculates the air pressure range within each window and compares it with the 2kPa threshold. The progress bar shows that the current sliding window progress has advanced from 15% to 100%. The final feedback indicates that the starting point of the stable segment is the 3rd day, and the unstable data from the first 3 days has been removed. This means that the system accurately identified that the PCR tire completed stress relaxation and entered the stable decay stage approximately 72 hours after inflation, effectively avoiding interference from transitional data on subsequent calculations.

[0061] Next, the system executes step S300, which involves filtering abnormal fluctuations. The abnormal filtering module 300 calculates the shortest effective duration based on the tire's volume, inflation pressure, and ambient temperature, using the gas leakage time constant formula. Based on this, the module performs a joint determination of amplitude and duration on the steady-state data. The progress pop-up window shows that 6 abnormal fluctuations have been detected and removed. Combined with detailed logs, it can be seen that a total of 16 transient interference points were actually removed. Although the amplitude of these points exceeded the preset threshold, the duration was shorter than the calculated value. The leak was identified as sensor noise or external vibration interference rather than a real leak. This result verifies the effectiveness of the physical model-based joint filtering mechanism in the PCR tire testing scenario, which retains the real small attenuation trend while cleaning up non-physical transient spikes.

[0062] After data cleaning, the system executes the optimal calculation window search in step S400. The optimal window search module 400 slides across the filtered effective data with a fixed-length window and performs exponential decay model fitting on the data within each window. Progress feedback shows that it is traversing all data with a 30-day window and a 1-day step size. Finally, the optimal calculation window is determined to be from day 21 to day 50, with a goodness of fit R² = 0.9991. The extremely high R² value indicates that the pressure changes within this 30-day interval are highly consistent with the gas diffusion law, making it the most reliable data segment for calculating the pressure loss rate. Compared to directly using the entire data segment or manually selecting time periods, this adaptive search strategy based on model fit significantly improves the robustness of the evaluation results.

[0063] Based on the determined optimal calculation window, the loss rate calculation module 500 obtains the air pressure values ​​at the start time (day 21) and end time (day 50) of the window, substitutes them into the difference formula to calculate the daily air pressure loss rate as 0.00043 kPa / day. Subsequently, the system compares this calculation result with a preset acceptable threshold and determines the result to be acceptable. Figure 6 As shown in the analysis results area, the system intuitively displays the daily loss rate, monthly loss rate percentage, and qualification judgment conclusion. All values ​​are accurate to multiple decimal places, avoiding rounding errors and unit conversion errors that may be introduced by manual calculation.

[0064] Finally, the report generation module 600 automatically generates an airtightness test report based on the air pressure loss rate. When the user clicks the "Export Report" button, the system automatically fills in key information such as tire specification 205 / 55R17, test duration 90 days, optimal window start and end times, goodness-of-fit R², daily loss rate 0.00043 kPa / day, and pass / fail result into a standard PDF template, and embeds a comparison graph of the original curve and the fitted curve. The generated report file is automatically saved to the specified path; the entire process requires no manual intervention.

[0065] Understandably, this example demonstrates the stability of the collaborative work of each module and the accuracy of the calculation results, providing a practical technical sample for the automation and standardization of tire air tightness testing. It should be understood that although this embodiment uses PCR tires as an example, for other types of tires such as LTR or TBR, only the tire type option in the parameter configuration needs to be adjusted, and the system can automatically adapt the corresponding threshold and physical model parameters to achieve automated processing with the same accuracy.

[0066] Example 3 In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor. The memory stores computer-readable instructions that, when executed by the processor, implement the automated processing method for tire airtightness test data. This computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0067] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0068] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0069] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0070] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. An automated processing method for tire air tightness test data, characterized in that, include: S100. Collect time-series air pressure data of the tire under test during the air tightness test; S200. Dynamically identify the stable segment of the time-series air pressure data, determine the starting time when the air pressure enters the stable decay stage, and remove the transitional data before the starting time. S300: Filter out abnormal fluctuations in the data during the stable decay phase to remove transient fluctuations caused by sensor noise or external vibration. S400: Search for the optimal calculation window in the filtered valid data, calculate the tire pressure loss rate based on the air pressure data within the optimal calculation window, and generate an air tightness test report based on the air pressure loss rate.

2. The automated processing method for tire air tightness test data according to claim 1, characterized in that, Step S200 includes: The time-series air pressure data is traversed using a sliding window to calculate the air pressure range within each sliding window. The pressure difference of each sliding window is compared with a dynamic stability threshold, which is determined by matching according to the type of tire under test; When the pressure difference of a consecutive preset number of sliding windows is less than the dynamic stability threshold, it is determined that the air pressure has entered the stable decay stage, and the moment of entering the stable decay stage is recorded as the stable start time. Transitional data before the stable start time is discarded.

3. The automated processing method for tire air tightness test data according to claim 2, characterized in that, The dynamic stability threshold is determined by a threshold matching relationship established in advance for each tire type. The threshold matching relationship is calibrated based on the structural characteristics and inflation process parameters of different tire types.

4. The automated processing method for tire air tightness test data according to claim 1, characterized in that, Step S300 includes: The amplitude and duration of each pressure change event during the stable decay phase are detected; The amplitude of each pressure change event is compared with a preset amplitude threshold, and its duration is compared with the shortest effective duration. When the amplitude is greater than a preset amplitude threshold and the duration is less than the shortest effective duration, the air pressure change event is determined to be a transient fluctuation caused by sensor noise or external vibration interference and is rejected. When the duration is greater than or equal to the shortest effective duration, the pressure change event is determined to be a real pressure decay change and is retained.

5. The automated processing method for tire air tightness test data according to claim 4, characterized in that, The shortest effective duration is calculated based on the physical parameters of the tire under test, specifically including: The gas leakage time constant is calculated based on the tire volume, the effective air passage cross-sectional area corresponding to the minimum detectable leak, the gas isentropic index, and the ambient temperature. The shortest effective duration is then determined based on the gas leakage time constant and a preset empirical correction coefficient.

6. The automated processing method for tire air tightness test data according to claim 1, characterized in that, The method for searching the optimal computation window in the filtered valid data includes: A sliding window with a fixed window length slides over the valid data. For the air pressure data within each sliding window, an exponential decay model based on the gas diffusion law is used for fitting, and the corresponding goodness of fit is calculated. The sliding window with the highest goodness of fit is selected as the optimal calculation window.

7. The automated processing method for tire air tightness test data according to claim 6, characterized in that, The exponential decay model reflects the law that gas pressure decreases exponentially over time and tends to the ambient reference pressure as gas diffuses outward through the tire rubber layer.

8. The automated processing method for tire air tightness test data according to claim 6, characterized in that, The method for calculating tire pressure loss rate based on tire pressure data within the optimal calculation window includes: Obtain the initial and final air pressures of the optimal calculation window, and calculate the tire pressure loss rate based on the initial and final air pressures.

9. The automated processing method for tire air tightness test data according to claim 8, characterized in that, The method for generating an airtightness test report based on the pressure loss rate includes: The calculated air pressure loss rate is compared with the preset pass threshold, and the tire air tightness is determined based on the comparison result, thereby generating the pass / fail result of tire air tightness. Generate a test report that includes the tire identification information, test duration, tire pressure loss rate, and pass / fail result.

10. An automated processing system for tire air tightness test data, characterized in that, include: Data acquisition module: used to collect time-series air pressure data of the tire under test during the air tightness test; Identification module: used to identify the dynamic stable segment of the time-series air pressure data, determine the start time of the air pressure entering the stable decay stage, and remove the transitional data before the start time; Anomaly filtering module: used to filter out abnormal fluctuations in the data during the stable decay phase, eliminating transient fluctuations caused by sensor noise or external vibration; Optimal Window Search Module: Used to search for the optimal calculation window in the filtered valid data; Loss rate calculation module: used to calculate the tire pressure loss rate based on the air pressure data within the optimal calculation window; Report generation module: Used to generate an airtightness test report based on the pressure loss rate.

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