Tire rigidity automatic test data processing system

Abstract

The invention relates to the technical field of tire testing, in particular to a tire rigidity automatic test data processing system which comprises a data import module, a data preprocessing module, a linear interval intelligent identification module, a rigidity calculation module and a result output module. The data import module is used for acquiring load and displacement original data generated by the tire testing equipment and performing standardization processing on the load and displacement original data; the data preprocessing module improves the data quality through filtering and drift correction; the linear interval intelligent identification module adopts a sliding window method to automatically identify an optimal linear interval; a rigidity calculation module automatically extracts a boundary value and calculates a rigidity value; and the result output module carries out threshold comparison and generates a structured report. All the modules are sequentially connected through standard interfaces to form a closed-loop processing chain, full-process automation from data collection to report generation is achieved, and the problems of subjective deviation, low efficiency and insufficient consistency caused by manual operation are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of tire testing technology, and more particularly to an automated tire stiffness testing data processing system. Background Technology

[0002] Existing tire stiffness test data processing technologies suffer from the following technical challenges: Manual data processing heavily relies on engineers' personal experience to subjectively identify and select points on the linear segments of the load-displacement curve. Different engineers may have different criteria for judging the same linear interval of the test curve, leading to human bias in stiffness calculations and affecting data repeatability and comparability. For example, when processing test data with multiple loading and unloading cycles or containing sensor noise or anomalies introduced by specimen relaxation, manual point selection is cumbersome and error-prone, making it difficult to ensure consistent efficiency in batch data processing. Furthermore, each step from data import and calculation to report generation requires manual operation, which is not only time-consuming and labor-intensive but also fails to establish a standardized, fixed processing workflow, thus hindering the overall efficiency and reliability of tire mechanical performance testing results. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an automated tire stiffness test data processing system, which solves the technical problems of low efficiency, poor accuracy, and insufficient consistency in tire stiffness test data processing caused by reliance on manual operation and subjective judgment.

[0004] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: The automated tire stiffness test data processing system provided by the present invention includes a data import module, a data preprocessing module, a linear interval intelligent identification module, a stiffness calculation module, and a result output module. The data import module is used to acquire the raw data containing load values ​​and displacement values ​​generated by the tire testing equipment, and to transmit the raw data to the data preprocessing module. The data preprocessing module is used to perform smoothing filtering and zero-point drift correction on the original data to obtain preprocessed data, and then transmit the preprocessed data to the linear interval intelligent recognition module. The linear interval intelligent identification module is used to perform loading and unloading loop segmentation on the preprocessed data, and to identify the optimal linear interval using the sliding window method in each loading loop, and then pass the optimal linear interval to the stiffness calculation module. The stiffness calculation module is used to automatically extract load and displacement boundary values ​​within the optimal linear interval, calculate stiffness values, and transmit stiffness values ​​to the result output module. The result output module is used to compare and verify the stiffness value with a preset threshold range and generate a structured test report.

[0005] Furthermore, in the automated tire stiffness test data processing system of the present invention, the data import module is configured as follows: The data file output by the test equipment is parsed by the file reading unit. The data file contains timestamps, load values, and displacement values. The parsed data is then transmitted to the real-time receiving unit. The real-time receiving unit establishes API interface communication with the test equipment to continuously receive data stream packets. The received data stream packets are passed to the data standardization unit; The system converts raw data in different formats into a standard data structure within the system through a data standardization unit. The standard data structure includes a unified time series, load series, and displacement series.

[0006] Furthermore, in the automated tire stiffness test data processing system of the present invention, the data preprocessing module is configured as follows: The load signal and displacement signal are smoothed by a digital low-pass filter in the filtering unit to obtain filtered data. The filtered data is transmitted to the drift correction unit, which analyzes the data offset in the initial no-load stage, calculates the zero-point drift correction value, applies the correction value to the overall data curve for translation compensation, and outputs the preprocessed data.

[0007] Furthermore, in the automated tire stiffness test data processing system of the present invention, the linear interval intelligent identification module is configured as follows: The first-order difference of the load value in the preprocessed data is calculated by the cyclic segmentation unit, and the zero-crossing point and extreme point of the load change rate are identified to obtain the segmented loading and unloading cyclic data. The segmented load and unload loop data is passed to the window analysis unit; The adjustable-width sliding window is initialized in each loading cycle by the window analysis unit and slides along the displacement axis with a fixed step size. The linear regression determination coefficient and slope variance of the load and displacement data in each window are calculated. Transmit the window calculation results to the interval decision unit; The decision coefficient and slope variance of each window are weighted and scored by the interval decision unit. The window with the highest comprehensive score is selected as the optimal linear interval, and the coordinates of the interval boundary are recorded.

[0008] Furthermore, in the automated tire stiffness test data processing system of the present invention, the width of the sliding window in the window analysis unit is dynamically calculated based on the data sampling frequency and the expected linear segment length, the window sliding step size is fixed, and each window sliding position generates the corresponding linear regression determination coefficient and slope variance calculation results.

[0009] Furthermore, in the automated tire stiffness test data processing system of the present invention, the stiffness calculation module is configured as follows: The load values ​​F1 and F2 and displacement values ​​X1 and X2 are automatically extracted from the endpoints of the optimal linear interval using the boundary extraction unit. The extracted load values ​​F1, F2 and displacement values ​​X1, X2 are passed to the calculation execution unit; The stiffness value is calculated by applying the two-point method formula to the computational execution unit, and the linear correlation coefficient is also calculated.

[0010] Furthermore, in the automated tire stiffness test data processing system of the present invention, the result output module is configured as follows: The quality verification unit compares the stiffness value with the user-preset threshold range and marks abnormal results that exceed the range. The marked abnormal results and stiffness values ​​are passed to the report generation unit; The report generation unit automatically generates a structured test report, which includes raw data graphs, linear interval labels, calculation result tables, and quality indicators.

[0011] Furthermore, the automated tire stiffness test data processing system of the present invention also includes a user interaction module, which is configured as follows: The parameter configuration interface receives user-defined parameters such as sliding window size, filtering parameters, and stiffness threshold. The user-defined parameters are passed to the data preprocessing module, the linear interval intelligent recognition module, and the result output module. The progress monitoring interface displays the processing status of each stage of data import, preprocessing, identification, calculation and output in real time. The system receives stiffness values ​​from the output module through a manual verification interface and provides a manual correction function.

[0012] Furthermore, in the automated tire stiffness test data processing system of the present invention, the user interaction module establishes bidirectional data communication with the data preprocessing module, the linear interval intelligent identification module, and the result output module. The parameter configuration interface transmits user parameters to the data preprocessing module, the linear interval intelligent recognition module, and the result output module in real time, while the manual review interface feeds back the stiffness values ​​corrected by the user to the result output module.

[0013] Furthermore, in the tire stiffness automated test data processing system of the present invention, the data import module outputs standardized data to the data preprocessing module, the data preprocessing module outputs preprocessed data to the linear interval intelligent identification module, the linear interval intelligent identification module outputs the optimal linear interval coordinates to the stiffness calculation module, and the stiffness calculation module outputs stiffness values ​​to the result output module; the modules are sequentially connected through a standard data interface, and the data stream is automatically transmitted.

[0014] Beneficial effects of this invention; The automated tire stiffness testing data processing system provided by this invention effectively solves the problems of low efficiency, poor accuracy, and insufficient consistency caused by reliance on manual operation through a modular automated process. The data import module acquires the raw data from the tire testing equipment and performs standardized processing to ensure input consistency. The data preprocessing module uses digital low-pass filtering and zero-point drift correction to eliminate noise and baseline drift, improving data quality. The linear interval intelligent identification module automatically identifies the optimal linear interval through cyclic segmentation and sliding window method, replacing manual subjective point selection. The stiffness calculation module automatically extracts boundary values ​​and applies the two-point method formula to calculate stiffness values ​​to ensure the objectivity of the results. The result output module performs threshold comparison and generates a structured test report to achieve process standardization. All modules are sequentially connected through standard data interfaces to form a closed-loop processing chain. The data flow is automatically transmitted to eliminate manual intervention, thereby improving overall processing efficiency, enhancing calculation accuracy, and improving result consistency. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0016] Figure 1 The system architecture diagram of the automated tire stiffness test data processing system provided by the present invention is shown. Detailed Implementation

[0017] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0018] Please see Figure 1The automated tire stiffness test data processing system provided by the present invention includes a data import module, a data preprocessing module, a linear interval intelligent identification module, a stiffness calculation module, and a result output module. The data import module is used to acquire the raw data containing load values ​​and displacement values ​​generated by the tire testing equipment, and to transmit the raw data to the data preprocessing module. The data preprocessing module is used to perform smoothing filtering and zero-point drift correction on the original data to obtain preprocessed data, and then transmit the preprocessed data to the linear interval intelligent recognition module. The linear interval intelligent identification module is used to perform loading and unloading loop segmentation on the preprocessed data, and to identify the optimal linear interval using the sliding window method in each loading loop, and then pass the optimal linear interval to the stiffness calculation module. The stiffness calculation module is used to automatically extract load and displacement boundary values ​​within the optimal linear interval, calculate stiffness values, and transmit stiffness values ​​to the result output module. The result output module is used to compare and verify the stiffness value with a preset threshold range and generate a structured test report.

[0019] The automated tire stiffness testing data processing system acquires raw data containing load and displacement values ​​generated by tire testing equipment through a data import module. This module then transmits the raw data to a data preprocessing module. Specifically, the data import module parses the data file output by the testing equipment through a file reading unit. This data file contains timestamps, load values, and displacement values. The parsed data is then transmitted to a real-time receiving unit. This unit establishes an API interface with the testing equipment to continuously receive data stream packets. The received data stream packets are then transmitted to a data standardization unit. This unit converts raw data of different formats into a standard data structure within the system. This standard data structure includes unified time series, load series, and displacement series, thereby ensuring data format consistency and processability.

[0020] The data preprocessing module performs smoothing filtering and zero-point drift correction on the received raw data to obtain preprocessed data, which is then transmitted to the linear interval intelligent recognition module. Specifically, the data preprocessing module uses a digital low-pass filter in the filtering unit to smooth the load signal and displacement signal respectively, obtaining filtered data. The filtered data is then transmitted to the drift correction unit, which analyzes the data offset in the initial no-load stage to calculate the zero-point drift correction value and applies the correction value to the overall data curve for translation compensation, outputting preprocessed data to eliminate noise and drift effects and improve data quality.

[0021] The linear interval intelligent identification module performs loading and unloading loop segmentation on the preprocessed data, and identifies the optimal linear interval within each loading loop using the sliding window method, then passes the optimal linear interval to the stiffness calculation module. Specifically, the linear interval intelligent identification module calculates the first-order difference of the load values ​​in the preprocessed data through the loop segmentation unit, identifies the zero-crossing points and extreme points of the load change rate, and obtains the segmented loading and unloading loop data. The segmented data is then passed to the window analysis unit, which initializes an adjustable-width sliding window within each loading loop, slides along the displacement axis with a fixed step size, and calculates the linear regression determination coefficient and slope variance of the load and displacement data within each window. The window calculation results are passed to the interval decision unit, which performs a weighted score on the determination coefficient and slope variance of each window, selects the window with the highest comprehensive score as the optimal linear interval, and records the interval boundary coordinates, thus automatically replacing manual subjective judgment.

[0022] The stiffness calculation module automatically extracts load and displacement boundary values ​​within the optimal linear interval, calculates stiffness values, and transmits these values ​​to the result output module. Specifically, the stiffness calculation module automatically extracts load values ​​F1 and F2 and displacement values ​​X1 and X2 from the endpoints of the optimal linear interval through the boundary extraction unit. The extracted boundary values ​​are then transmitted to the calculation execution unit, which uses the two-point method formula to calculate the stiffness values ​​and simultaneously calculates the linear correlation coefficient as a result reliability index, thereby ensuring the accuracy and reliability of the calculation.

[0023] The results output module compares and verifies the stiffness values ​​with a preset threshold range and generates a structured test report. Specifically, the results output module compares the stiffness values ​​with the user-preset threshold range through a quality verification unit, marks abnormal results that exceed the range, and transmits the marked abnormal results and stiffness values ​​to the report generation unit. The report generation unit automatically generates a structured test report, which includes original data curves, linear interval labels, calculation result tables, and quality indicators, thus providing complete and standardized test output.

[0024] In the automated tire stiffness test data processing system, the data import module is configured to parse the data file output by the test equipment through the file reading unit. The data file contains timestamps, load values, and displacement values. The parsed data is then transmitted to the real-time receiving unit, which establishes an API interface with the test equipment to continuously receive data stream packets. The received data stream packets are then transmitted to the data standardization unit, which converts the raw data in different formats into the system's internal standard data structure. The standard data structure includes a unified time series, load series, and displacement series, thereby achieving standardized integration of multi-source data and providing consistent input for subsequent processing.

[0025] The data preprocessing module is configured to use a digital low-pass filter in the filtering unit to smooth the load signal and displacement signal respectively, and obtain filtered data. The filtered data is then passed to the drift correction unit, which analyzes the data offset in the initial no-load stage to calculate the zero-point drift correction value, and applies the correction value to the overall data curve for translation compensation, and outputs preprocessed data. This process effectively suppresses high-frequency noise and eliminates baseline drift, thereby improving data quality.

[0026] The linear interval intelligent recognition module is configured to calculate the first-order difference of the load values ​​in the preprocessed data through the cyclic segmentation unit, identify the zero-crossing points and extreme points of the load change rate, and obtain the segmented loading and unloading cyclic data. The segmented data is then passed to the window analysis unit, which initializes an adjustable-width sliding window in each loading cycle, slides along the displacement axis with a fixed step size, calculates the linear regression determination coefficient and slope variance of the load and displacement data in each window, and passes the window calculation results to the interval decision unit. The interval decision unit performs a weighted score on the determination coefficient and slope variance of each window, selects the window with the highest comprehensive score as the optimal linear interval, and records the interval boundary coordinates, thereby automatically completing the linear segment recognition.

[0027] In the window analysis unit, the width of the sliding window is dynamically calculated based on the data sampling frequency and the expected linear segment length. The window sliding step size is fixed. Each window sliding position generates the corresponding linear regression determination coefficient and slope variance calculation results. The dynamic width adjustment adapts to the data density under different test conditions, while the fixed step size ensures the calculation coverage and ensures the comprehensiveness of the evaluation results.

[0028] The stiffness calculation module is configured to automatically extract load values ​​F1, F2 and displacement values ​​X1, X2 from the endpoints of the optimal linear interval through the boundary extraction unit. The extracted boundary values ​​are passed to the calculation execution unit, which uses the two-point method formula to calculate the stiffness value and calculates the linear correlation coefficient. The linear correlation coefficient serves as a reliability index to reflect the fitting quality. The entire calculation process requires no manual intervention.

[0029] The results output module is configured to compare stiffness values ​​with user-preset threshold ranges through the quality verification unit, mark abnormal results that exceed the range, and transmit the marked abnormal results and stiffness values ​​to the report generation unit. The report generation unit automatically generates a structured test report, which includes original data curves, linear interval labels, calculation result tables, and quality indicators, thus providing complete visual output.

[0030] The system also includes a user interaction module, which is configured to receive user-set sliding window size, filtering parameters, and stiffness threshold through a parameter configuration interface. The user-set parameters are then passed to the data preprocessing module, the linear interval intelligent recognition module, and the result output module. The processing status of each stage of data import, preprocessing, recognition, calculation, and output is displayed in real time through a progress monitoring interface. The stiffness value of the result output module is received through a manual review interface, and a manual correction function is provided to achieve flexible parameter adjustment and process transparency.

[0031] The user interaction module establishes bidirectional data communication with the data preprocessing module, the linear interval intelligent recognition module, and the result output module. The parameter configuration interface transmits user parameters to the data preprocessing module, the linear interval intelligent recognition module, and the result output module in real time. The manual review interface feeds back the stiffness values ​​corrected by the user to the result output module. The bidirectional communication supports dynamic parameter updates and iterative optimization of results.

[0032] The data import module outputs standardized data to the data preprocessing module, the data preprocessing module outputs preprocessed data to the linear interval intelligent recognition module, the linear interval intelligent recognition module outputs the optimal linear interval coordinates to the stiffness calculation module, and the stiffness calculation module outputs stiffness values ​​to the result output module. Each module is connected sequentially through a standard data interface, and the data flow is automatically transmitted to build a closed-loop processing chain, eliminating manual intervention.

[0033] The automated tire stiffness testing data processing system addresses the inefficiencies, low accuracy, and inconsistent performance associated with manual processing through an automated workflow. The system first acquires raw data generated by the tire testing equipment via a data import module. This raw data includes timestamps, load values, and displacement values. The data import module then parses the data files output by the testing equipment using a file reading unit. The parsed data is transmitted to a real-time receiving unit, which establishes an API interface with the testing equipment to continuously receive data stream packets. These packets are then passed to a data standardization unit, which converts the raw data into a standardized internal data structure. This standard data structure includes unified time series, load series, and displacement series, thus providing standardized data input for subsequent processing.

[0034] The data preprocessing module performs smoothing filtering and zero-point drift correction on the standardized data output from the data import module. The filtering unit uses a digital low-pass filter to smooth the load and displacement signals separately, obtaining filtered data. This filtered data is then passed to the drift correction unit, which analyzes the data offset during the initial no-load phase to calculate the zero-point drift correction value. The unit then applies the correction value to the overall data curve for translation compensation and outputs the preprocessed data. This process effectively suppresses high-frequency noise and baseline drift, improves data quality, and lays the foundation for linear interval identification.

[0035] The intelligent linear interval identification module performs loading and unloading loop segmentation and optimal linear interval identification on the preprocessed data. The loop segmentation unit calculates the first-order difference of the load values ​​in the preprocessed data, identifies the zero-crossing points and extreme points of the load change rate, and obtains the segmented loading and unloading loop data. This segmented data is then passed to the window analysis unit. Within each loading loop, the window analysis unit initializes an adjustable-width sliding window. The width of the sliding window is dynamically calculated based on the data sampling frequency and the expected linear segment length. The window slides along the displacement axis with a fixed step size, calculating the linear regression determination coefficient and slope variance of the load and displacement data within each window. The window calculation results are passed to the interval decision unit, which performs a weighted score on the determination coefficient and slope variance of each window, selecting the window with the highest comprehensive score as the optimal linear interval and recording the interval boundary coordinates. This automated identification replaces manual subjective judgment, ensuring the consistency and accuracy of linear interval selection.

[0036] The stiffness calculation module automatically extracts load and displacement boundary values ​​within the optimal linear interval. The boundary extraction unit automatically extracts load values ​​F1 and F2 and displacement values ​​X1 and X2 from the endpoints of the optimal linear interval. The extracted boundary values ​​are then passed to the calculation execution unit, which uses the two-point method formula to calculate the stiffness value and simultaneously calculates the linear correlation coefficient as a reliability index. The entire calculation process requires no manual intervention, improving efficiency and reliability.

[0037] The results output module compares and verifies the stiffness values ​​with the user-preset threshold range. The quality verification unit compares the stiffness values ​​with the threshold range, marks abnormal results that exceed the range, and transmits the marked abnormal results and stiffness values ​​to the report generation unit. The report generation unit automatically generates a structured test report, which includes raw data graphs, linear interval labels, calculation result tables, and quality indicators, thus providing complete and standardized output.

[0038] The system also includes a user interaction module. This module receives user-defined parameters such as the sliding window size, filtering parameters, and stiffness threshold through a parameter configuration interface. These parameters are then passed to the data preprocessing module, the linear interval intelligent recognition module, and the result output module. A progress monitoring interface displays the processing status of each stage of data import, preprocessing, recognition, calculation, and output in real time. A manual review interface receives stiffness values ​​from the result output module and provides manual correction functionality. The user interaction module establishes bidirectional data communication with the data preprocessing module, the linear interval intelligent recognition module, and the result output module. The parameter configuration interface transmits user parameters to the corresponding modules in real time, while the manual review interface feeds back the corrected stiffness values ​​to the result output module, supporting dynamic adjustment and iterative optimization.

[0039] The data import module outputs standardized data to the data preprocessing module, which in turn outputs preprocessed data to the linear interval intelligent identification module. This module then outputs the optimal linear interval coordinates to the stiffness calculation module, which in turn outputs the stiffness values ​​to the results output module. All modules are sequentially connected via a standard data interface, with data flowing automatically to form a closed-loop processing chain. This eliminates manual intervention and automates the entire process from data acquisition to report generation, resolving issues of subjective bias and efficiency in tire stiffness test data processing.

[0040] This invention addresses the problems of low efficiency, poor accuracy, and insufficient consistency caused by reliance on manual operation and subjective judgment by constructing a fully automated data processing system. The system adopts a modular design, transforming traditional manual processing flows into standardized data streams. Each module performs a specific task and automatically transmits data, eliminating human intervention. The data import module acquires raw data from the test equipment through a file reading unit and a real-time receiving unit, and converts it into a unified format through a data standardization unit, avoiding errors from manual import and inconsistencies in data formats. The data preprocessing module uses a digital low-pass filter for smoothing and a drift correction unit to compensate for zero-point offset, improving data quality and reducing the impact of noise and baseline drift on the results.

[0041] The linear interval intelligent identification module automatically divides the loading-unloading cycle through a loop segmentation unit, replacing the manual curve identification stage. The window analysis unit dynamically calculates the sliding window width, slides along the displacement axis, and calculates the linear regression determination coefficient and slope variance. The interval decision unit automatically selects the optimal linear interval based on weighted scoring, overcoming the subjectivity and inconsistency of manual point selection. The stiffness calculation module automatically extracts boundary values ​​and applies the two-point method formula to calculate stiffness values, while simultaneously calculating the linear correlation coefficient as a reliability indicator to ensure objective and accurate calculations. The results output module compares threshold markings for anomalies through a quality verification unit and automatically generates a structured report, avoiding the tediousness and errors of manual report preparation.

[0042] The user interaction module provides a parameter configuration interface and a progress monitoring interface, allowing users to set processing parameters and view the status in real time. The manual review interface supports manual correction, but the core processing flow is still completed automatically by the system, ensuring operational flexibility and process standardization. All modules are connected sequentially through standard data interfaces, with data flow automatically transmitted to form a closed-loop processing chain. From data acquisition to report generation, the entire process is automated, ultimately improving processing efficiency, enhancing accuracy, and improving result consistency.

[0043] This invention relates to the practical application of an automated tire stiffness testing data processing system. Addressing the issues of subjective bias, low efficiency, and inconsistent performance caused by manual operation in tire testing technology, it processes raw load and displacement data generated by tire testing equipment through a fully automated process. The system adopts a modular design, with modules connected sequentially via standard interfaces, enabling automatic data transfer and forming a closed-loop processing chain.

[0044] The data import module first acquires the raw data output by the tire testing equipment, which includes timestamps, load values, and displacement values. The data import module then parses the data file output by the testing equipment through the file reading unit and transmits the parsed data to the real-time receiving unit. The real-time receiving unit establishes an API interface communication with the testing equipment and continuously receives data stream packets. The received data stream packets are then transmitted to the data standardization unit, which converts the raw data of different formats into a standard data structure within the system. This standard data structure includes unified time series, load series, and displacement series, ensuring the consistency and processability of the input data.

[0045] The data preprocessing module processes the standardized data output from the data import module. The filtering unit uses a digital low-pass filter to smooth the load and displacement signals separately, obtaining filtered data. The filtered data is then passed to the drift correction unit, which analyzes the data offset during the initial no-load phase, calculates the zero-point drift correction value, and applies the correction value to the overall data curve for translation compensation, outputting the preprocessed data. This process eliminates high-frequency noise and baseline drift, improving data quality.

[0046] The linear interval intelligent identification module performs loading and unloading loop segmentation on the preprocessed data. The loop segmentation unit calculates the first-order difference of the load values ​​in the preprocessed data, identifies the zero-crossing points and extreme points of the load change rate, and obtains the segmented loading and unloading loop data. The segmented data is then passed to the window analysis unit, which initializes an adjustable-width sliding window within each loading loop. The width of the sliding window is dynamically calculated based on the data sampling frequency and the expected linear segment length. The window slides along the displacement axis with a fixed step size, calculating the linear regression determination coefficient and slope variance of the load and displacement data within each window. The window calculation results are passed to the interval decision unit, which performs a weighted score on the determination coefficient and slope variance of each window, selects the window with the highest comprehensive score as the optimal linear interval, and records the interval boundary coordinates. This automated identification process replaces manual subjective point selection, ensuring the objectivity and consistency of linear interval selection.

[0047] The stiffness calculation module automatically extracts load and displacement boundary values ​​within the optimal linear interval. The boundary extraction unit extracts load values ​​F1 and F2 and displacement values ​​X1 and X2 from the endpoints of the optimal linear interval. The extracted boundary values ​​are passed to the calculation execution unit, which uses the two-point method formula to calculate the stiffness value and simultaneously calculates the linear correlation coefficient as a reliability index. The calculation process requires no manual intervention, improving accuracy and efficiency.

[0048] The results output module compares and verifies the stiffness values ​​with the user-preset threshold range. The quality verification unit compares the stiffness values ​​with the threshold range and marks abnormal results that exceed the range. The marked abnormal results and stiffness values ​​are then passed to the report generation unit, which automatically generates a structured test report. The report includes original data graphs, linear interval labels, calculation result tables, and quality indicators, achieving output standardization.

[0049] The system also includes a user interaction module, which receives user-defined parameters such as sliding window size, filtering parameters, and stiffness thresholds through a parameter configuration interface. This interface transmits user parameters in real-time to the data preprocessing module, the linear interval intelligent recognition module, and the result output module. The progress monitoring interface displays the processing status of each stage of data import, preprocessing, recognition, calculation, and output in real time. The manual review interface receives stiffness values ​​from the result output module and provides manual correction functionality. The user interaction module establishes bidirectional data communication with each module, and the manual review interface feeds back the user-corrected stiffness values ​​to the result output module, supporting dynamic parameter adjustment and result optimization.

[0050] Each module is connected sequentially via a standard data interface. The data import module outputs standardized data to the data preprocessing module, which in turn outputs preprocessed data to the linear interval intelligent identification module. The linear interval intelligent identification module outputs the optimal linear interval coordinates to the stiffness calculation module, and the stiffness calculation module outputs stiffness values ​​to the result output module. This automatic data flow eliminates manual intervention, achieving full automation from data acquisition to report generation, effectively improving processing efficiency, enhancing calculation accuracy, and improving result consistency.

Claims

1. An automated tire stiffness test data processing system, characterized by, The system comprises a data import module, a data preprocessing module, a linear interval intelligent identification module, a stiffness calculation module and a result output module. The data import module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to:

2. The automated tire stiffness test data processing system of claim 1, wherein, The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to:

3. The automated tire stiffness test data processing system of claim 2, wherein, The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to:

4. The automated tire stiffness test data processing system of claim 3, wherein, The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to:

5. The automated tire stiffness test data processing system of claim 4, wherein, The result output module is configured to:

6. 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module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result output module is configured to: The data import module is configured to: The data preprocessing module is configured to: The linear interval intelligent identification module is configured to: The stiffness calculation module is configured to: The result The load values F1, F2 and displacement values X1, X2 are automatically extracted from the optimal linear interval endpoints by a boundary extraction unit; The extracted load values F1, F2 and displacement values X1, X2 are transmitted to a calculation execution unit; The stiffness values are calculated by the calculation execution unit using the two-point method formula, and the linear correlation coefficient is also calculated.

7. The automated tire stiffness test data processing system of claim 6, wherein, The result output module is configured to: Compare the stiffness values with the user-preset threshold range through a quality verification unit, and mark abnormal results that exceed the range; Transmit the marked abnormal results and the stiffness values to a report generation unit; Automatically generate a structured test report through the report generation unit, which includes an original data curve graph, linear interval annotations, a calculation result table, and quality identification.

8. The automated tire stiffness test data processing system of claim 7, wherein, The user interaction module is configured to: Receive the user-set sliding window size, filtering parameters, and stiffness threshold through a parameter configuration interface; Transmit the user-set parameters to the data preprocessing module, linear interval intelligent identification module, and result output module; Display the processing status of each stage of data import, preprocessing, identification, calculation, and output in real time through a progress monitoring interface; Receive the stiffness values from the result output module through an artificial review interface, and provide a manual correction function.

9. The automated tire stiffness test data processing system of claim 8, wherein, The user interaction module establishes bidirectional data communication with the data preprocessing module, linear interval intelligent identification module, and result output module; The parameter configuration interface transmits user parameters to the data preprocessing module, linear interval intelligent identification module, and result output module in real time, and the artificial review interface feeds back the user-corrected stiffness values to the result output module.

10. The automated tire stiffness test data processing system of claim 9, wherein, The data import module outputs standardized data to the data preprocessing module, the data preprocessing module outputs preprocessed data to the linear interval intelligent identification module, the linear interval intelligent identification module outputs optimal linear interval coordinates to the stiffness calculation module, and the stiffness calculation module outputs stiffness values to the result output module; the modules are sequentially connected through standard data interfaces, and data flow is automatically transmitted.

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