A method and system for calibrating and predicting the chest of an automotive crash dummy

Abstract

The application provides a car crash dummy chest calibration prediction method and system, a plurality of measurement positions are arranged on the chest of the car crash dummy; the displacement is calculated based on the strain of the measurement position in the collision process; the compression amount curve of a single measurement position is generated based on the displacement of the single measurement position at different sampling times in the collision process; the curve state of the compression amount curve of the single measurement position is determined based on the boundary line of the normal curve; if the curve state of the compression amount curve of the single measurement position is an abnormal state, the abnormal type of the compression amount curve of the single measurement position is determined based on the abnormal compression amount curve in the abnormal curve data; the correction scheme of the single measurement position is determined based on the abnormal type, so that whether the chest of the car crash dummy is normal or not can be accurately judged, and the abnormal type can be quickly determined when it is abnormal, so that the car crash dummy can be quickly locally corrected and calibrated to improve the calibration efficiency and the accuracy of the strategy.

Description

Technical Field

[0001] This application relates to the field of automotive crash test dummies, specifically to a method and system for chest calibration and prediction of automotive crash test dummies. Background Technology

[0002] In vehicle crash safety testing and assessment, dummy chest compression is a key biomechanical indicator for evaluating the risk of chest injury to occupants. Currently, the industry-standard method for measuring chest compression is primarily based on optical image analysis technology. Specifically, multiple optical markers are pre-attached to the surface of the area to be measured on the dummy's chest. A crash test is then conducted, during which a pre-calibrated high-speed camera system acquires image sequences of the dummy's chest before and after the collision. These images are then processed and analyzed to identify and track the positional changes of each marker, thereby calculating the displacement of the marker after the collision, which serves as the basis for determining the chest compression.

[0003] However, the aforementioned existing methods have significant limitations. Their measurements rely entirely on optical capture and two-dimensional or three-dimensional coordinate calculation of markers on the dummy's chest surface, essentially making them an external observation method. This approach cannot detect or account for anomalies that may occur within the dummy's internal structure during a collision, such as jamming of the rib linkage mechanism, slight loosening of the sensor mounting base, nonlinear response or local failure of the internal damping material. These internal anomalies directly affect the true biomechanical response of the chest, causing discrepancies between the externally measured displacement and the actual internal mechanical load and deformation. Therefore, the chest compression data obtained by existing methods is not highly accurate, and the measurement results may not accurately and comprehensively reflect the actual biomechanical load on the dummy's chest under collision impact, thus affecting the accuracy and reliability of vehicle safety performance assessments.

[0004] Therefore, there is an urgent need to develop a new measurement method that can improve measurement accuracy and more realistically reflect the overall and internal response of a dummy's chest during a collision. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method and system for predicting the chest calibration of a car crash dummy.

[0006] According to one aspect of this application, a method for chest calibration prediction of a car crash dummy is provided, comprising: arranging multiple measurement positions on the chest of a car crash dummy, and arranging a strain gauge at each of the multiple measurement positions to collect strain at the multiple measurement positions during a collision; calculating the displacement of the multiple measurement positions during the collision based on the strain at the multiple measurement positions during the collision; generating a compression curve for a single measurement position based on the displacement at different sampling times during the collision; determining the curve state of the compression curve of the single measurement position based on the boundary line between the compression curve of the single measurement position and a normal curve; wherein the curve state includes a normal state and an abnormal state, and the boundary line of the normal curve is determined based on all normal compression curves in the normal curve data; if the curve state of the compression curve of the single measurement position is an abnormal state, determining the abnormality type of the compression curve of the single measurement position based on the compression curve of the single measurement position and the abnormal compression curve in the abnormal curve data; and determining a correction scheme for the single measurement position based on the abnormality type of the compression curve of the single measurement position.

[0007] In one embodiment, arranging multiple measurement positions on the chest of the car crash dummy includes: starting from a first distance below the clavicle of the car crash dummy, setting multiple horizontal measurement lines at equal intervals downwards; using the midline of the sternum of the car crash dummy as a reference, setting multiple vertical measurement lines at equal intervals to the left and right sides; and setting the intersection of the horizontal measurement lines and the vertical measurement lines as the measurement position.

[0008] In one embodiment, calculating the displacement of the plurality of measurement locations during the collision process based on the strain of the plurality of measurement locations includes: the formula for calculating the displacement is:

[0009] ;

[0010] in, For displacement, The resistance change rate of the strain gauge. The length of the strain gauge in the measurement direction. denoted as the sensitivity coefficient of the strain gauge.

[0011] In one embodiment, generating the compression curve of a single measurement location based on the displacement of the single measurement location at different sampling times during the collision includes: calculating the compression of the single measurement location at different sampling times during the collision based on the displacement of the single measurement location at different sampling times during the collision; and fitting the compression curve of the single measurement location based on the compression of the single measurement location at different sampling times during the collision.

[0012] In one embodiment, determining the curve state of the compression curve at a single measurement location based on the boundary line between the compression curve at the single measurement location and the normal curve includes: if the compression curve at the single measurement location is located within the boundary line of the normal curve, then the curve state of the compression curve at the single measurement location is determined to be normal.

[0013] In one embodiment, the method for determining the boundary line of the normal curve includes: calculating the mean and standard deviation of all normal compression curves in the normal curve data; and calculating the upper and lower boundaries of the normal curve based on the mean, the standard deviation, and the quantiles corresponding to the confidence level.

[0014] In one embodiment, determining the anomaly type of the compression curve at a single measurement location based on the compression curve at the single measurement location and the anomaly curve in the anomaly curve data includes: if the compression curve at the single measurement location is located within the anomaly boundary line of at least one anomaly curve in the anomaly curve data, then the anomaly type of the at least one anomaly curve is determined to be the anomaly type of the compression curve at the single measurement location.

[0015] In one embodiment, determining the correction scheme for a single measurement location based on the anomaly type of the compression curve at that single measurement location includes: if the anomaly type of the compression curve at that single measurement location is poor sensor wiring harness contact or open circuit, then disassemble the portion of the sensor wiring harness corresponding to that single measurement location for inspection or replacement; if the anomaly type of the compression curve at that single measurement location is unreasonable sensor accuracy parameter settings, then correct the parameters of the sensor corresponding to that single measurement location; if the anomaly type of the compression curve at that single measurement location is deformation or anomaly of the sensor fixed position structure, then replace the internal structural components of the car collision dummy at that single measurement location; if the anomaly type of the compression curve at that single measurement location is an anomaly of the data acquisition device, then inspect the data acquisition structure and parameters corresponding to that single measurement location.

[0016] In one embodiment, the method for predicting the chest calibration of a car crash dummy further includes: generating a compression matrix of the car crash dummy based on the displacement of multiple measurement positions at the same sampling time during the collision; and removing outliers from the compression matrix of the car crash dummy based on the compression matrix of the car crash dummy.

[0017] According to another aspect of this application, a vehicle collision dummy chest calibration and prediction system is provided, comprising: a strain acquisition module, configured to arrange multiple measurement positions on the chest of the vehicle collision dummy, and to arrange a strain gauge at each of the multiple measurement positions to acquire the strain at the multiple measurement positions during the collision process; a displacement calculation module, configured to calculate the displacement of the multiple measurement positions during the collision process based on the strain at the multiple measurement positions; a compression curve generation module, configured to generate a compression curve for a single measurement position based on the displacement at different sampling times during the collision process; and a curve state determination module, configured to determine the compression curve based on the displacement at the single measurement position. The system uses a compression curve and a normal curve boundary line to determine the curve state of the compression curve at a single measurement location; wherein the curve state includes a normal state and an abnormal state, and the boundary line of the normal curve is determined based on all normal compression curves in the normal curve data; an abnormality type determination module is used to determine the abnormality type of the compression curve at a single measurement location based on the compression curve at the single measurement location and the abnormal compression curves in the abnormal curve data if the curve state of the compression curve at the single measurement location is an abnormal state; and a correction scheme determination module is used to determine a correction scheme for the single measurement location based on the abnormality type of the compression curve at the single measurement location.

[0018] This application provides a method and system for chest calibration and prediction of a car crash dummy. Multiple measurement positions are arranged on the chest of the car crash dummy, and a strain gauge is placed at each of these positions to collect strain data during the crash. Based on the strain data, the displacement of each measurement position during the crash is calculated. A compression curve for a single measurement position is generated based on the displacement data at different sampling times during the crash. The curve state of the compression curve at a single measurement position is determined based on the boundary line between the compression curve and a normal curve. The curve state includes a normal state and an abnormal state, and the boundary line of the normal curve is determined based on all normal compression curves in the normal curve data. If the compression of a single measurement position... If the compression curve is in an abnormal state, the abnormality type of the compression curve at a single measurement location is determined based on the compression curve at that location and the abnormal compression curve in the abnormal curve data. Based on the abnormality type of the compression curve at a single measurement location, a correction scheme for that single measurement location is determined. By arranging multiple measurement locations on the chest of the car crash dummy and generating a compression curve for each measurement location based on the measurement results, the normality of the compression curve is determined based on the boundary line of the normal curve. When the compression curve is abnormal, it is compared with the abnormal curve database to determine the abnormality type. This allows for accurate judgment of whether the chest of the car crash dummy is normal and rapid determination of the abnormality type when abnormality occurs. This enables rapid local correction and calibration of the car crash dummy, improving calibration efficiency and the accuracy of the strategy. Attached Figure Description

[0019] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 This is a flowchart illustrating an exemplary embodiment of the method for predicting the chest calibration of a car crash dummy provided in this application.

[0021] Figure 2 This is a schematic diagram of the structure of a car crash dummy chest calibration and prediction system provided in an exemplary embodiment of this application. Detailed Implementation

[0022] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0023] Figure 1 This is a schematic flowchart illustrating a method for predicting the chest calibration of a car crash dummy according to an exemplary embodiment of this application. Figure 1 As shown, the method for predicting the chest calibration of a car crash dummy includes the following steps:

[0024] Step 110: Arrange multiple measurement positions on the chest of the car crash dummy, and place a strain gauge at each of the multiple measurement positions to collect the strain at the multiple measurement positions during the collision process.

[0025] This application involves placing multiple measurement positions on the chest of a car crash dummy and arranging a strain gauge at each measurement position. The strain gauges are used to collect the strain at each measurement position in real time during the collision process, thereby obtaining the compression amount at the corresponding position of the chest of the car crash dummy.

[0026] Step 120: Calculate the displacement of multiple measurement locations during the collision process based on the strain at multiple measurement locations.

[0027] After collecting the strain at each measurement location during the collision process, the displacement at the corresponding measurement location during the collision process is calculated based on the strain.

[0028] Step 130: Generate the compression curve of a single measurement location based on the displacement at different sampling times during the collision process.

[0029] For each measurement location, a curve showing the change in compression during the collision is generated based on the displacement at different sampling times during the collision.

[0030] Step 140: Determine the curve state of the compression curve at a single measurement location based on the boundary line between the compression curve and the normal curve at a single measurement location.

[0031] The curve status includes normal and abnormal states. The boundary line of the normal curve is determined based on all normal compression curves in the normal curve data. This application determines whether the curve status of the compression curve at each measurement location is normal based on the positional relationship between the boundary line of the compression curve at each measurement location and the normal curve.

[0032] Step 150: If the compression curve of a single measurement location is in an abnormal state, then based on the compression curve of the single measurement location and the abnormal compression curve in the abnormal curve data, determine the abnormal type of the compression curve of the single measurement location.

[0033] If the state of the compression curve at a certain measurement location is determined to be abnormal based on the boundary line between the compression curve and the normal curve, then the abnormality type of the compression curve at that measurement location can be determined by comparing the compression curve at that measurement location with the abnormal compression curve in the abnormal curve data.

[0034] Step 160: Based on the anomaly type of the compression curve at a single measurement location, determine the correction scheme for that single measurement location.

[0035] After determining the anomaly type of the compression curve at the measurement location, a correction scheme for a single measurement location is determined based on the anomaly correction method corresponding to that anomaly type.

[0036] This application provides a method for chest calibration and prediction in automotive collision dummy operations. Multiple measurement positions are arranged on the chest of the dummy, and a strain gauge is placed at each of these positions to collect strain data during the collision. Based on the strain data, the displacement of each measurement position during the collision is calculated. A compression curve for a single measurement position is generated based on the displacement data at different sampling times during the collision. The curve state of the compression curve for a single measurement position is determined based on the boundary line between the compression curve and a normal curve. The curve state includes a normal state and an abnormal state, and the boundary line of the normal curve is determined based on all normal compression curves in the normal curve data. If the compression of a single measurement position... If the compression curve is in an abnormal state, the abnormality type of the compression curve at a single measurement location is determined based on the compression curve at that location and the abnormal compression curve in the abnormal curve data. Based on the abnormality type of the compression curve at a single measurement location, a correction scheme for that single measurement location is determined. By arranging multiple measurement locations on the chest of the car crash dummy and generating a compression curve for each measurement location based on the measurement results, the normality of the compression curve is determined based on the boundary line of the normal curve. When the compression curve is abnormal, it is compared with the abnormal curve database to determine the abnormality type. This allows for accurate judgment of whether the chest of the car crash dummy is normal and rapid determination of the abnormality type when abnormality occurs. This enables rapid local correction and calibration of the car crash dummy, improving calibration efficiency and the accuracy of the strategy.

[0037] In one embodiment, step 110 can be implemented as follows: starting from a first distance below the collarbone of the car crash dummy, multiple horizontal measurement lines are set at equal intervals downwards; taking the midline of the sternum of the car crash dummy as a reference, multiple vertical measurement lines are set at equal intervals to the left and right sides; and the intersection of the horizontal and vertical measurement lines is set as the measurement position.

[0038] This application is based on the anatomical structure of the chest of a car crash dummy, and combines key areas of the chest that are prone to deformation during a collision to define measurement locations. Specifically, using the midline of the sternum of the car crash dummy as the axis of symmetry, starting 2cm below the clavicle, a horizontal measurement line is set every 3cm downwards, for a total of 5 horizontal measurement lines. At the same time, using the midline of the sternum of the car crash dummy as the reference, a vertical measurement line is set every 2cm to the left and right, for a total of 7 vertical measurement lines. The intersection of the horizontal and vertical measurement lines is the measurement location, that is, 35 measurement locations are set.

[0039] Optionally, based on the possible displacement range (typically 0-50mm) of the chest of the dummy during a car collision test, a metal foil strain gauge with a range of 0-60mm, a sensitivity coefficient of 2.0±0.05, and an operating temperature range of -40℃ to 120℃ can be selected.

[0040] Before installing the strain gauge, wipe the surface of the measurement location on the chest of the car crash dummy with an alcohol swab to remove oil, dust and other impurities. Then, gently sand the surface with fine sandpaper to achieve an arithmetic mean roughness of 1.6μm-3.2μm, which will enhance the adhesion between the strain gauge and the surface of the car crash dummy's chest. Finally, wipe the surface again with clean degreased cotton soaked in alcohol to ensure that the surface is clean and dry.

[0041] When installing the strain gauge, apply a layer of special strain adhesive (such as cyanoacrylate adhesive) evenly to the bottom surface of the strain gauge, with the adhesive layer thickness controlled at 5μm-10μm; accurately attach the strain gauge to the corresponding measurement position and press gently for 30s-60s to ensure that the strain gauge is tightly adhered to the surface without bubbles or wrinkles; then apply a ring of sealant around the strain gauge to prevent external dust and moisture from entering during the test and affecting the performance of the strain gauge; place the car crash dummy with the strain gauge attached in an environment with a temperature of 25℃-30℃ and a relative humidity of 40%-60% for 24 hours to ensure that the strain adhesive is fully cured and that the strain gauge forms a firm bond with the dummy's chest.

[0042] Preferably, the data acquisition system used in this application mainly consists of strain gauges, a signal conditioning module, a data acquisition card, and a computer. The strain gauges convert the displacement deformation of the chest of the car collision dummy into a resistance change signal. The signal conditioning module amplifies, filters, and performs temperature compensation processing on the resistance change signal, with the amplification factor set to 1000-5000 times, the filtering frequency set to 1kHz, and the temperature compensation range set to -40℃-120℃ to eliminate the influence of temperature changes on the measurement results. The data acquisition card is a high-speed card with 16-bit resolution and a sampling frequency of up to 1MHz, capable of acquiring the conditioned electrical signal in real time and converting it into a digital signal for transmission to the computer. The computer is equipped with dedicated data acquisition software to control the operating parameters of the data acquisition card (such as sampling frequency and sampling duration) and to store the acquired data in real time.

[0043] Preferably, before the collision test, the data acquisition system is debugged. Specifically, firstly, the strain gauge is connected to the signal conditioning module, and a standard displacement signal (such as a standard displacement of 0-50mm applied through a displacement stage) is applied to the system. The deviation between the measured value displayed by the data acquisition software and the standard displacement value is observed. If the deviation exceeds ±0.5%, the amplification factor and filtering parameters of the signal conditioning module are adjusted until the measurement accuracy meets the requirements. Then, the system stability test is performed by continuously acquiring data for 1 hour and observing the data fluctuation. If the data fluctuation range is less than ±0.1mm, the system stability is qualified.

[0044] In one embodiment, step 120 can be specifically implemented as follows: the formula for calculating the displacement is:

[0045] ;

[0046] in, For displacement, The resistance change rate of the strain gauge. The length of the strain gauge in the measurement direction. denoted as the sensitivity coefficient of the strain gauge.

[0047] Based on the working principle of strain gauges: the resistance change rate of strain gauges It is proportional to the strain ε, that is = ×ε The sensitivity coefficient of the strain gauge is taken as 2.0 ± 0.05, and strain is defined as the change in length Δ of an object before and after deformation. L Compared with the original length L The ratio, i.e., ε=Δ L / L Strain gauges are attached to the surface of the chest of a car crash dummy. The displacement of the dummy's chest is approximately equal to the change in length Δ of the strain gauge along the measurement direction. L Due to the original length of the strain gaugeL Given (usually 5mm or 10mm, depending on the strain gauge model selected), we can obtain: Δ L=ε×L=( Δ R / R)×L / K In actual calculations, the resistance change rate of the strain gauge is obtained through a data acquisition system. Substituting these values ​​into the formula above, the displacement at each measurement location can be calculated.

[0048] In one embodiment, step 130 can be implemented as follows: based on the displacement of a single measurement position at different sampling times during the collision, calculate the compression of a single measurement position at different sampling times during the collision; based on the compression of a single measurement position at different sampling times during the collision, fit a compression curve of the single measurement position.

[0049] First, the collected displacement data is filtered to remove obvious abnormal data (such as abrupt changes caused by electromagnetic interference). The chest compression of the dummy is defined as the difference between the maximum displacement and the initial displacement (displacement before the collision) at each measurement position during the collision. For each time-domain point data (i.e., the displacement at each sampling time), the maximum displacement and the initial displacement are found. Then, the chest compression at that time-domain point = maximum displacement - initial displacement.

[0050] Then, using the time domain (sampling time) as the x-axis and the corresponding chest compression amount as the y-axis, the chest compression amount curves of the car crash dummy in each time domain are fitted. During the fitting process, a smoothing algorithm (such as the moving average method) is used to smooth the data points, making the curve smoother and easier to observe the trend of compression amount change over time.

[0051] In one embodiment, step 140 can be implemented as follows: if the compression curve of a single measurement location is within the boundary line of the normal curve, then the curve state of the compression curve of the single measurement location is determined to be normal.

[0052] This application determines the state of the compression curves at each measurement location during a collision. Specifically, it analyzes the difference between the compression curves at each measurement location and the curves in the normal curve data. If the compression curve is within the upper and lower boundaries of the normal curve, the compression curve is normal and the corresponding car collision dummy structure (corresponding measurement location) is fine. If it exceeds the upper and lower boundaries, the compression curve is determined to be abnormal.

[0053] In one embodiment, step 140 can be implemented by: calculating the mean and standard deviation of all normal compression curves in the normal curve data; and calculating the upper and lower boundaries of the normal curves based on the mean, standard deviation, and quantiles corresponding to the confidence level.

[0054] By calculating and processing the normal chest displacement curves from a large number of crash tests, and performing big data processing, the upper and lower boundary curves of the chest displacement curves were obtained using big data learning. The upper and lower boundaries of the normal curves were defined by the central tendency and dispersion of the data. Specifically, the mean μ and standard deviation σ of all samples at each time point were calculated, and confidence intervals were used to determine the upper and lower boundaries, where the upper boundary UB = μ + z × σ, and the lower boundary LB = μ z×σ, where z is the quantile corresponding to the confidence level.

[0055] In one embodiment, the specific implementation of step 150 above may be: if the compression curve of a single measurement location is located within the abnormal boundary line of at least one abnormal compression curve in the abnormal curve data, then the abnormality type of at least one abnormal compression curve is determined to be the abnormality type of the compression curve of the single measurement location.

[0056] Anomaly curves correspond to various anomaly types, mainly including: poor contact or open circuit in the sensor wiring harness, improper setting of sensor accuracy parameters, deformation or abnormality in the sensor's fixed position structure, and malfunction of the data acquisition device. When the compression curve at a single measurement location is determined to be abnormal, its anomaly type is determined based on the characteristics of the anomaly curve, thereby identifying changes in the internal structure of the car crash dummy's chest and the sensor's state. This allows for timely detection and resolution of problems without disassembling the car crash dummy, facilitating its normal use in subsequent car crash dummies. Specifically, abnormal compression curves are imported into an anomaly curve database for anomaly determination. The database contains various anomalies. If the abnormal compression curve falls within the upper and lower boundaries of the first type of anomaly curve, its anomaly type is determined to be the first type. If it exceeds these boundaries, it is determined whether it falls within the upper and lower boundaries of the second type of anomaly curve, and so on. If the abnormal compression curve does not fall within the upper and lower boundaries of any of the anomaly curves, a new anomaly and its corresponding abnormal curve are added, thus improving the anomaly curve database.

[0057] In one embodiment, step 160 can be implemented as follows: if the anomaly type of the compression curve at a single measurement location is poor sensor wiring harness contact or open circuit, then disassemble part of the sensor wiring harness corresponding to the single measurement location for inspection or replacement; if the anomaly type of the compression curve at a single measurement location is unreasonable sensor accuracy parameter settings, then correct the parameters of the sensor corresponding to the single measurement location; if the anomaly type of the compression curve at a single measurement location is deformation or anomaly of the sensor fixed position structure, then replace the internal structural components of the car collision dummy at the single measurement location; if the anomaly type of the compression curve at a single measurement location is an anomaly of the data acquisition device, then check the data acquisition structure and parameters corresponding to the single measurement location.

[0058] Identifying the type of abnormal curves reveals internal problems within the dummy, allowing for different handling methods for different anomalies. For example, if sensor wiring harness contact is poor or open-circuited, the affected part is disassembled for inspection or replacement; if sensor accuracy parameters are not set correctly, the sensor parameters are calibrated; if sensor mounting structure is deformed or abnormal, internal structural components of the dummy are replaced; and if the data acquisition device malfunctions, the data acquisition structure and parameters are inspected. Specific correction methods or remedial measures are available for different anomalies, avoiding frequent dummy disassembly.

[0059] In one embodiment, the above-mentioned method for predicting the chest calibration of a car crash dummy may further include: generating a compression matrix of the car crash dummy based on the displacement of multiple measurement locations at the same sampling time during the collision; and removing outliers from the compression matrix of the car crash dummy based on the compression matrix of the car crash dummy.

[0060] This application can also generate a compression matrix of the car crash dummy based on the displacement of each measurement position corresponding to the same sampling time, and remove outliers from the compression matrix of the car crash dummy. For example, if there is a sudden change in a single compression value in the compression matrix, or if it causes a sudden change in the trend of the compression matrix along a column or row, then the compression value is determined to be abnormal.

[0061] Figure 2 This is a schematic diagram of the structure of a car crash dummy chest calibration and prediction system provided in an exemplary embodiment of this application. Figure 2As shown, the car crash dummy chest calibration and prediction system 20 includes: a strain acquisition module 21, used to arrange multiple measurement positions on the chest of the car crash dummy, and to arrange a strain gauge at each of the multiple measurement positions to acquire the strain at the multiple measurement positions during the collision process; a displacement calculation module 22, used to calculate the displacement at the multiple measurement positions during the collision process based on the strain at the multiple measurement positions; a compression curve generation module 23, used to generate a compression curve for a single measurement position based on the displacement at different sampling times during the collision process; and a curve state determination module 24, used to determine the compression curve based on the displacement at the single measurement position. The boundary line between the compression curve and the normal curve determines the curve state of the compression curve at a single measurement location; wherein, the curve state includes a normal state and an abnormal state, and the boundary line of the normal curve is determined based on all normal compression curves in the normal curve data; the anomaly type determination module 25 is used to determine the anomaly type of the compression curve at a single measurement location based on the compression curve at the single measurement location and the abnormal compression curves in the abnormal curve data if the curve state of the compression curve at the single measurement location is an abnormal state; the correction scheme determination module 26 is used to determine the correction scheme for the single measurement location based on the anomaly type of the compression curve at the single measurement location.

[0062] This application provides a vehicle collision dummy chest calibration and prediction system. A strain variable acquisition module 21 arranges multiple measurement positions on the chest of the vehicle collision dummy, and a strain gauge is placed at each of these positions to collect the strain variables during the collision process. A displacement calculation module 22 calculates the displacement of the multiple measurement positions during the collision process based on the strain variables. A compression curve generation module 23 generates a compression curve for a single measurement position based on the displacement at different sampling times during the collision process. A curve state determination module 24 determines the curve state of the compression curve for a single measurement position based on the boundary line between the compression curve and the normal curve. The curve state includes a normal state and an abnormal state, and the boundary line of the normal curve is determined based on all normal compression curves in the normal curve data. If the compression curve at a single measurement location is in an abnormal state, the anomaly type determination module 25 determines the anomaly type of the compression curve at that single measurement location based on the compression curve at that single measurement location and the abnormal compression curve in the abnormal curve data. The correction scheme determination module 26 determines the correction scheme for that single measurement location based on the anomaly type of the compression curve at that single measurement location. By arranging multiple measurement locations on the chest of the car crash dummy and generating a compression curve for each measurement location based on the measurement results, the normality of the compression curve is determined based on the boundary line of the normal curve. When the compression curve is abnormal, it is compared with the abnormal curve database to determine the anomaly type. This allows for accurate judgment of whether the chest of the car crash dummy is normal and rapid determination of the anomaly type when abnormal. This enables rapid local correction and calibration of the car crash dummy, improving calibration efficiency and the accuracy of the strategy.

[0063] In one embodiment, the strain acquisition module 21 can be further configured to: start from a first distance below the collarbone of the car crash dummy and set multiple horizontal measurement lines at equal intervals downwards; use the midline of the sternum of the car crash dummy as a reference and set multiple vertical measurement lines at equal intervals to the left and right sides; and set the intersection of the horizontal measurement lines and the vertical measurement lines as the measurement position.

[0064] In one embodiment, the displacement calculation module 22 may be further configured such that the displacement calculation formula is:

[0065] ;

[0066] in, For displacement, The resistance change rate of the strain gauge. The length of the strain gauge in the measurement direction. denoted as the sensitivity coefficient of the strain gauge.

[0067] In one embodiment, the compression curve generation module 23 can be further configured to: calculate the compression amount of a single measurement position at different sampling times during the collision based on the displacement amount of a single measurement position at different sampling times during the collision; and fit a compression curve of a single measurement position based on the compression amount of a single measurement position at different sampling times during the collision.

[0068] In one embodiment, the curve state determination module 24 can be further configured to: if the compression curve of a single measurement position is located within the boundary line of the normal curve, then determine the curve state of the compression curve of the single measurement position as the normal state.

[0069] In one embodiment, the curve state determination module 24 may be further configured to: calculate the mean and standard deviation of all normal compression curves in the normal curve data; and calculate the upper and lower boundaries of the normal curves based on the mean, standard deviation, and quantiles corresponding to the confidence levels.

[0070] In one embodiment, the above-mentioned anomaly type determination module 25 can be further configured to: if the compression curve of a single measurement location is located within the anomaly boundary line of at least one abnormal compression curve in the abnormal curve data, then determine the anomaly type of at least one abnormal compression curve as the anomaly type of the compression curve of the single measurement location.

[0071] In one embodiment, the above-mentioned correction scheme determination module 26 can be further configured as follows: if the anomaly type of the compression curve at a single measurement position is poor contact or open circuit of the sensor wiring harness, then disassemble part of the sensor wiring harness corresponding to the single measurement position for inspection or replacement; if the anomaly type of the compression curve at a single measurement position is unreasonable sensor accuracy parameter settings, then perform calibration processing on the parameters of the sensor corresponding to the single measurement position; if the anomaly type of the compression curve at a single measurement position is deformation or abnormality of the sensor fixed position structure, then replace the internal structural components of the car collision dummy at the single measurement position; if the anomaly type of the compression curve at a single measurement position is abnormality of the data acquisition device, then check the data acquisition structure and parameters corresponding to the single measurement position.

[0072] In one embodiment, the above-mentioned car crash dummy chest calibration prediction system 20 can be further configured to: generate a compression matrix of the car crash dummy based on the displacement of multiple measurement positions at the same sampling time during the collision; and remove outliers from the compression matrix of the car crash dummy based on the compression matrix of the car crash dummy.

[0073] In addition to the methods and systems described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0074] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0075] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0076] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0077] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0078] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0079] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for predicting a calibration of a thorax of an automotive crash dummy, characterized in that, The method comprises the following steps: arranging multiple measuring positions on the chest of an automobile crash dummy, and arranging a strain gauge at each of the multiple measuring positions to collect the strain of each of the multiple measuring positions during the crash; calculating the displacement of each of the multiple measuring positions during the crash based on the strain of each of the multiple measuring positions during the crash; generating a compression curve of a single measuring position based on the displacement of the single measuring position at different sampling times during the crash; determining the curve state of the compression curve of the single measuring position based on the compression curve of the single measuring position and the boundary line of a normal curve, wherein the curve state comprises a normal state and an abnormal state, and the boundary line of the normal curve is determined according to all normal compression curves in normal curve data; if the curve state of the compression curve of the single measuring position is the abnormal state, determining the abnormal type of the compression curve of the single measuring position based on the compression curve of the single measuring position and an abnormal compression curve in abnormal curve data; and determining the correction scheme of the single measuring position based on the abnormal type of the compression curve of the single measuring position.

2. The method of calibrating a thorax of an automotive crash dummy according to claim 1, wherein The step of arranging multiple measuring positions on the chest of an automobile crash dummy comprises the following steps: arranging multiple horizontal measuring lines at equal intervals downward from a first distance below the clavicle of the automobile crash dummy; arranging multiple vertical measuring lines at equal intervals to the left and right of the midline of the sternum of the automobile crash dummy; and arranging the intersection of the horizontal measuring lines and the vertical measuring lines as the measuring positions.

3. The method of claim 1, wherein The step of calculating the displacement of each of the multiple measuring positions during the crash based on the strain of each of the multiple measuring positions during the crash comprises the following steps: The calculation formula of the displacement is: ； wherein, is a displacement amount, is a rate of change of resistance of the strain gauge, is a length of the strain gauge in the measurement direction, is a sensitivity coefficient of the strain gauge.

4. The method of claim 1, wherein The step of generating a compression curve of a single measuring position based on the displacement of the single measuring position at different sampling times during the crash comprises the following steps: calculating the compression of the single measuring position at different sampling times during the crash based on the displacement of the single measuring position at different sampling times during the crash; fitting the compression of the single measuring position at different sampling times during the crash to obtain the compression curve of the single measuring position.

5. The method of claim 1, wherein The step of determining the curve state of the compression curve of the single measuring position based on the compression curve of the single measuring position and the boundary line of a normal curve comprises the following steps: if the compression curve of the single measuring position is within the boundary line of the normal curve, determining that the curve state of the compression curve of the single measuring position is the normal state.

6. The method of claim 1, wherein The method for determining the boundary line of the normal curve comprises the following steps: calculating the mean and the standard deviation of all normal compression curves in the normal curve data; calculating the upper boundary and the lower boundary of the normal curve based on the mean, the standard deviation, and the quantile corresponding to the confidence level.

7. The method of claim 1, wherein The step of determining the abnormal type of the compression curve of the single measuring position based on the compression curve of the single measuring position and an abnormal compression curve in abnormal curve data comprises the following steps: If the compression curve of the single measurement position is located within the abnormal boundary line of at least one abnormal compression curve in the abnormal curve data, the abnormal type of the at least one abnormal compression curve is determined as the abnormal type of the compression curve of the single measurement position.

8. The method of claim 1, wherein The correction scheme of the single measurement position is determined based on the abnormal type of the compression curve of the single measurement position. If the abnormal type of the compression curve of the single measurement position is sensor line bundle poor contact or open circuit, the part of the sensor line bundle corresponding to the single measurement position is disassembled for inspection or replacement. If the abnormal type of the compression curve of the single measurement position is unreasonable sensor precision parameter setting, the parameters of the sensor corresponding to the single measurement position are corrected. If the abnormal type of the compression curve of the single measurement position is sensor fixed position structure deformation or abnormality, the internal structure of the automobile crash dummy at the single measurement position is replaced. If the abnormal type of the compression curve of the single measurement position is data acquisition device abnormality, the data acquisition structure and parameters corresponding to the single measurement position are checked.

9. The method of claim 1, wherein The chest calibration prediction method of the automobile crash dummy further comprises: Based on the displacement amounts of the multiple measurement positions at the same sampling time during the collision process, a compression matrix of the automobile crash dummy is generated. Based on the compression matrix of the automobile crash dummy, abnormal values in the compression matrix of the automobile crash dummy are removed.

10. A prediction system for chest calibration of a car crash dummy, characterized in that, Comprise: A strain amount acquisition module is configured to arrange multiple measurement positions on the chest of an automobile crash dummy, and arrange a strain gauge at each of the multiple measurement positions to acquire strain amounts of the multiple measurement positions during a collision process. A displacement amount calculation module is configured to calculate displacement amounts of the multiple measurement positions during the collision process based on the strain amounts of the multiple measurement positions during the collision process. A compression curve generation module is configured to generate a compression curve of a single measurement position based on displacement amounts of the single measurement position at different sampling times during the collision process. A curve state determination module is configured to determine a curve state of the compression curve of the single measurement position based on the compression curve of the single measurement position and a boundary line of a normal curve, wherein the curve state comprises a normal state and an abnormal state, and the boundary line of the normal curve is determined according to all normal compression curves in normal curve data. An abnormal type determination module is configured to, if the curve state of the compression curve of the single measurement position is the abnormal state, determine an abnormal type of the compression curve of the single measurement position based on the compression curve of the single measurement position and abnormal compression curves in abnormal curve data. A correction scheme determination module is configured to determine a correction scheme of the single measurement position based on the abnormal type of the compression curve of the single measurement position.

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