Measurement system and method based on dynamic ranging adjustment

Abstract

The application discloses a measurement system and method based on dynamic ranging adjustment, and belongs to the technical field of three-coordinate measurement, and comprises a three-coordinate machine main body, a dynamic ranging module, a data fusion module, a coordinate system calibration module and a data processing module. The dynamic ranging module divides data and adjusts the probe parameter, the data fusion module outputs accurate data after preprocessing and denoising, the calibration module selects three points to build a system, and the processing module monitors data fluctuation and generates a result; the system is started to collect workpiece initial contour, distance and shaft speed data; the data is classified and analyzed; the sampling parameter is dynamically adapted; multi-source data fusion optimization is carried out; reference points are selected to build a system to calculate the coordinates of the measured points; the data stability is monitored, and a three-dimensional model and measurement result of the workpiece are generated; and the application reduces errors through dynamic adjustment of the parameter, optimization of data fusion and reliable calibration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-coordinate measurement, in particular to a measurement system and method based on dynamic ranging adjustment. BACKGROUND

[0002] As a core equipment for high-precision three-dimensional size measurement, three-coordinate measurement systems have been widely used in fields such as aerospace, automobile manufacturing, and precision mold manufacturing, which have strict requirements on measurement accuracy. The system collects point position information on the surface of a workpiece through laser probes, contact probes, and other devices, and obtains three-dimensional coordinates of the workpiece by combining coordinate system operations, which is a key process to ensure product size accuracy in industrial manufacturing.

[0003] However, the existing three-coordinate measurement system has a key defect when measuring dynamic or complex profile workpieces (such as high-speed precision parts and mold workpieces with irregular surfaces). The ranging-related parameters (including laser emission frequency, sampling interval, laser pulse synchronization timestamp, etc.) are all fixed preset values, which cannot adjust the ranging strategy according to the real-time three-dimensional profile changes of the workpiece and the dynamic adjustment of the measurement distance, nor effectively fuse the laser distance data with the probe rotation angle data and the three-coordinate machine axis movement angle data. This problem directly leads to the generation of point cloud redundancy (fixed parameters and workpiece profile mismatch causing invalid point positions) and dynamic target tracking lag (fixed sampling interval cannot adapt to the workpiece movement speed) in the measurement process, ultimately causing a significant decrease in measurement accuracy (in high-precision measurement scenarios at the millimeter level and below, the error can reach more than 0.1 mm), and manual repeated parameter adjustment is required when measuring different profile workpieces in batches, with poor system adaptability. SUMMARY

[0004] In one aspect, to solve the problems of the prior art, the present application provides a measurement system based on dynamic ranging adjustment, comprising:

[0005] a three-coordinate machine body, comprising an X / Y / Z axis movement module and a laser probe module; the X / Y / Z axis movement module is connected to a workbench; the laser probe module is connected to the X / Y / Z axis movement module;

[0006] A dynamic ranging module, connected to a laser probe module and an X / Y / Z axis motion module, receives the initial three-dimensional contour of the workpiece, collects the raw distance data output by the laser probe module and the axis speed data of the X / Y / Z axis motion module, and divides these data into historical measurement data and current measurement data according to preset program rules. Through a data association calculation mechanism, it obtains the internal association information of the historical measurement data based on the historical data, and combines the historical measurement data with the current measurement data to obtain the association information between the two. Then, it divides the laser sampling points and calculates the initial distance, generates a first synchronization timestamp based on the initial distance and axis speed, determines the calibration factor based on the contour deviation of adjacent sampling points, and simultaneously uses the aforementioned internal association information of the historical data, as well as the association information obtained by combining the historical measurement data with the current measurement data, to predict the distance deviation of the current sampling point. The second synchronization timestamp is obtained through a sampling optimization function, thereby adjusting the emission frequency and pulse intensity of the laser probe.

[0007] The data fusion module is connected to the dynamic ranging module and the laser probe module respectively. It is used to collect laser distance data, IMU angle data, and axis rotation angle data. After preprocessing these data, the data is processed by prediction and noise reduction logic, and finally the accurate distance and angle data are output after fusion processing.

[0008] A coordinate system calibration module, which is connected to a data fusion module, is used to select three reference points to establish a measurement coordinate system and calculate the three-dimensional coordinates of the point to be measured using the three-point measurement method.

[0009] The data processing module is connected to the data fusion module and the coordinate system calibration module respectively. It is used to monitor the fluctuation of distance and angle data after fusion. When the data fluctuation amplitude is less than the rated measurement accuracy threshold, the delay trigger module is activated. After the stable state continues to reach the preset delay time, the preset sampling parameter command is output. At the same time, a three-dimensional model of the workpiece is generated based on the effective data and the measurement results are calculated.

[0010] Furthermore, the preset rules of the program include: a first division method and a second division method;

[0011] The first division method is fixed time window division: the continuously collected data is divided into historical measurement data and current measurement data according to a preset time length;

[0012] The second division method is adaptive division: the division boundary is dynamically adjusted according to the fluctuation range of the distance from the original data or the shaft speed data. When the fluctuation range is greater than the preset threshold, the historical data window is shortened, and when the fluctuation range is less than the threshold, the window is extended.

[0013] The program's preset rules also include: a data integrity verification method after data partitioning;

[0014] The verification method is as follows: calculate the time coverage and data volume ratio of historical measurement data and current measurement data. If the time coverage is less than 90% or the data volume of a single group is less than the preset minimum sampling number, then automatically supplement the corresponding time period data and re-divide it.

[0015] Furthermore, the data association calculation mechanism is as follows: association information is obtained through covariance calculation, wherein the internal association information of historical data is an autocovariance matrix calculated based on historical measurement data, and the association information between historical data and current data is a cross-covariance vector calculated based on historical measurement data and current measurement data.

[0016] The data association calculation mechanism also includes: a covariance matrix regularization processing method;

[0017] The covariance matrix regularization processing method is as follows: calculate the condition number of the self-covariance matrix. If the condition number is greater than a preset threshold, inject a small regularization parameter into the diagonal of the matrix to avoid matrix singularity causing the calculation of related information to fail.

[0018] Furthermore, the data preprocessing in the data fusion module specifically includes the following steps:

[0019] The first step is to remove the mean, which means subtracting the mean of the data sequence to eliminate the constant offset.

[0020] The second step is normalization, which scales the mean-de-meaned data to the 0-1 range to achieve uniformity in scale for different types of data.

[0021] The data preprocessing also includes verifying the validity of the data. The verification method is as follows: calculate the standard deviation of the complete data after the above three steps. If the standard deviation of the distance data is greater than 0.01 mm or the standard deviation of the angle data is greater than 0.05, the data is judged to be abnormal and all preprocessing steps from demeaning to low-pass filtering need to be re-executed until the calculated standard deviation meets the above range.

[0022] Furthermore, the prediction noise reduction logic is as follows: a linear prediction model is established based on historical measurement data, and the theoretical prediction values ​​of the current laser distance data and IMU angle data are calculated through this model. The data preprocessing also includes verifying the validity of the data. The verification method is as follows: the standard deviation of the complete data after the above three steps is calculated. If the standard deviation of the distance data is greater than 0.01mm or the standard deviation of the angle data is greater than 0, the corresponding theoretical prediction values ​​are subtracted from the actual collected laser distance data, IMU angle data, and axis rotation angle data respectively to eliminate trend interference and noise in the data.

[0023] The prediction denoising logic also includes a method for dynamically adjusting the order of the linear prediction model: using the Akaike Information Criterion as the evaluation index, the AIC value of the linear prediction model of different orders is calculated cyclically within a preset order range, and the order with the smallest AIC value is selected as the optimal linear prediction model order for the current measurement scenario. The order is re-evaluated and adjusted every 50 sampling cycles.

[0024] Furthermore, the coordinate system calibration module selects three reference points, which are located in non-collinear regions of the workpiece. These non-collinear regions mean that the three reference points cannot be on the same straight line; they must be distributed in non-collinear spatial positions on the workpiece. These positions can be selected at the edge, center, and corners of the workpiece, and the initial distance between each reference point and the laser probe is not less than 5mm. The positions of the reference points are initially confirmed using distance and angle data output by the data fusion module.

[0025] The coordinate system calibration module also includes a secondary verification method for reference points;

[0026] The secondary verification method for reference points includes controlling the laser probe to perform three repeated measurements around each initially confirmed reference point, calculating the deviation value of the three measurement results, and if the deviation value is >0.002mm, then the reference point is removed and a new one is selected, until the repeated measurement deviation values ​​of all reference points meet the requirements.

[0027] Furthermore, the rated measurement accuracy threshold in the data processing module is in the range of 0.001mm-0.01mm; the delay trigger module is implemented by a software counter or a hardware timer, the preset delay time can be remotely configured through the human-machine interface, and the data fluctuation is continuously monitored during the delay period. If the fluctuation exceeds the threshold, the delay timer is reset.

[0028] The data processing module also includes a dynamic adaptation method for rated measurement accuracy thresholds: the threshold is adjusted according to the material characteristics of the workpiece. For high-rigidity metal workpieces, a threshold of 0.001mm-0.005mm is selected, and for easily deformable plastic workpieces, a threshold of 0.005mm-0.01mm is selected. The threshold adjustment command is automatically called from the system's preset material library or manually entered.

[0029] Furthermore, the sampling optimization function of the dynamic ranging module is a linear function combining the calibration factor and the distance deviation;

[0030] The formula for the linear function is: f = 1 + 0.1 × calibration factor + 0.05 × distance deviation, where f is the optimization coefficient;

[0031] The second synchronization timestamp = the first synchronization timestamp × f, and the optimized laser probe's emission frequency adjustment range is 50kHz-200kHz, and the pulse intensity adjustment range is 0.5W-2W.

[0032] The dynamic ranging module also includes a real-time correction method for sampling optimization coefficients;

[0033] The real-time correction method for the sampling optimization coefficient includes: calculating the measurement error corresponding to the second synchronization timestamp after optimization; if the error is >0.003mm, then fine-tuning the weighting coefficient of the calibration factor and the distance deviation in steps of 0.1-0.12 and 0.05-0.06, correcting once every 20 sampling cycles until the measurement error is ≤0.003mm.

[0034] On the other hand, this application provides a measurement method based on dynamic ranging adjustment, including the following steps:

[0035] S1. Start the measurement system and collect basic data. Control the main body of the coordinate measuring machine to move the laser probe module to the measurement area, and simultaneously collect the initial three-dimensional contour information of the workpiece, and simultaneously collect the original distance data and axis speed data of the motion axis output by the laser probe.

[0036] S2. Data classification and correlation analysis: The collected raw data is divided into historical reference data and current measurement data according to preset rules. The internal correlation characteristics and interactive correlation characteristics of the two types of data are extracted through the data correlation calculation mechanism.

[0037] S3. Dynamic adaptation of sampling parameters: Based on the workpiece contour features, sampling points are divided. Basic synchronization parameters are generated by combining the initial distance and movement speed. Contour deviation calibration factor and distance deviation predicted by data association features are introduced. Dynamic synchronization parameters adapted to the current measurement scenario are generated through optimization algorithm. The working parameters of the laser probe are dynamically adjusted accordingly.

[0038] S4. Multi-source data fusion optimization: Acquire multi-source data such as laser distance, angle, and motion axis rotation angle. After preprocessing and noise suppression, the data is fused to output high-precision measurement data.

[0039] S5. Measurement reference establishment and coordinate calculation: Select feature reference points to establish a measurement coordinate system, and calculate the three-dimensional coordinates of the workpiece to be measured based on the fused high-precision data;

[0040] S6. Intelligent control and result generation of the measurement process: Real-time monitoring of measurement data stability; in a stable state, a delay control mechanism is activated to maintain measurement parameters; Based on the effective coordinate data, a three-dimensional model of the workpiece is generated and the measurement results are calculated.

[0041] The beneficial effects of this invention are:

[0042] This application utilizes a dynamic ranging module that combines the initial contour of the workpiece with data association information to adjust the synchronization timestamp, emission frequency, and pulse intensity of the laser probe in real time, thereby fundamentally avoiding point cloud redundancy and dynamic tracking lag. The data fusion module outputs high-precision distance and angle data after preprocessing, predictive noise reduction, and fusion processing. The coordinate system calibration module ensures the reliability of the measurement benchmark through the selection of non-collinear reference points and repeated verification, further reducing coordinate calculation errors. The data processing module can dynamically adapt the accuracy threshold according to the workpiece material and activate the delay trigger mechanism when the data is stable. Furthermore, the sampling optimization coefficient of the dynamic ranging module can be corrected in real time, eliminating the need for repeated manual parameter adjustments when batch measuring workpieces with different contours. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the principle framework of the measurement system provided by the present invention;

[0044] Figure 2 A schematic diagram illustrating the principle framework of the program preset rules provided by this invention;

[0045] Figure 3 A schematic diagram illustrating the data association calculation mechanism principle framework provided by this invention;

[0046] Figure 4 This is a flowchart illustrating the measurement method based on dynamic ranging adjustment provided by the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0048] Example 1:

[0049] Please see Figures 1-3 The present invention provides a measurement system based on dynamic ranging adjustment, comprising: a coordinate measuring machine body, the coordinate measuring machine body including: an X / Y / Z axis motion module and a laser probe module; the X / Y / Z axis motion module is integrally connected to the worktable; the laser probe module is connected to the X / Y / Z axis motion module; wherein, the X / Y / Z axis motion module is integrally rigidly connected to the worktable by bolts, providing a stable spatial motion frame for measurement; the laser probe module is detachably connected to the Z-axis slider of the X / Y / Z axis motion module through a connector;

[0050] A dynamic ranging module, connected to a laser probe module and an X / Y / Z axis motion module, receives the initial three-dimensional contour of the workpiece, collects the raw distance data output by the laser probe module and the axis speed data of the X / Y / Z axis motion module, and divides these data into historical measurement data and current measurement data according to preset program rules. Through a data association calculation mechanism, it obtains the internal association information of the historical measurement data based on the historical data, and combines the historical measurement data with the current measurement data to obtain the association information between the two. Then, it divides the laser sampling points and calculates the initial distance, generates a first synchronization timestamp based on the initial distance and axis speed, determines the calibration factor based on the contour deviation of adjacent sampling points, and simultaneously uses the aforementioned internal association information of the historical data, as well as the association information obtained by combining the historical measurement data with the current measurement data, to predict the distance deviation of the current sampling point. The second synchronization timestamp is obtained through a sampling optimization function, thereby adjusting the emission frequency and pulse intensity of the laser probe.

[0051] The dynamic ranging module, as the core unit for dynamic parameter adjustment, is connected to the laser probe module via Ethernet communication and to the X / Y / Z axis motion modules via pulse signals. It possesses full-process functions for data reception, processing, and parameter adjustment. First, it receives the initial three-dimensional contour data obtained by the laser probe module scanning the workpiece. Simultaneously, it acquires the raw distance data output by the laser probe module (including laser round-trip time and reflected light intensity at each sampling moment) and the axis speed data of the X / Y / Z axis motion modules. Then, it classifies the above data according to preset program rules, dividing it into historical measurement data and current measurement data. Subsequently, through a data association calculation mechanism, it mines the internal patterns of the historical measurement data to obtain internal correlation information, combining the historical measurement data with the current measurement data... The data analysis process involves identifying the mapping relationship between the two data points to obtain interactive correlation information. Then, laser sampling points are divided based on the curvature changes of the initial three-dimensional contour of the workpiece, and the initial straight-line distance between each sampling point and the laser probe is calculated. A first synchronization timestamp is generated based on the initial distance and axis velocity data. Simultaneously, the contour deviation of adjacent sampling points in three-dimensional space is compared, and this deviation value is determined as a calibration factor to correct the synchronization time. Finally, the internal correlation information and interactive correlation information of historical data are used to predict the possible distance deviation of the current sampling point. The first synchronization timestamp, calibration factor, and distance deviation are substituted into the sampling optimization function to calculate a second synchronization timestamp adapted to the current measurement scenario. The emission frequency and pulse intensity of the laser probe are adjusted in real time based on the second synchronization timestamp to achieve dynamic matching between sampling parameters and workpiece state.

[0052] The data fusion module is connected to the dynamic ranging module and the laser probe module respectively. It is used to collect laser distance data, IMU angle data, and axis rotation angle data. After preprocessing these data, the data is processed by prediction and noise reduction logic, and finally the accurate distance and angle data are output after fusion processing.

[0053] A coordinate system calibration module, which is connected to a data fusion module, is used to select three reference points to establish a measurement coordinate system and calculate the three-dimensional coordinates of the point to be measured using the three-point measurement method.

[0054] The coordinate system calibration module, as the core unit for establishing the measurement benchmark, is connected to the data fusion module via serial communication and is responsible for constructing a unified and accurate measurement coordinate system. First, it selects three points on the workpiece that meet specific conditions from the distance and angle correlation data output by the data fusion module as reference points: these three reference points must be located in non-collinear areas of the workpiece, such as the edge, center, and corner, to ensure the uniqueness of the coordinate system. Furthermore, the initial distance between each reference point and the laser probe must be no less than 5mm, and the initial position of the reference points is determined through distance and angle data conversion. Then, using the first reference point as the origin (0,0,0), the X-axis direction of the coordinate system is determined based on the distance and angle data from the first to the second reference point. Combining the distance data from the third reference point to the first two reference points, the coordinates of the third reference point are solved through spatial geometric operations, ultimately establishing a complete measurement coordinate system. When measuring the point to be measured, the distance and angle correlation data from the point to be measured to the three reference points are collected and substituted into the spatial coordinate calculation formula of the three-point measurement method to accurately calculate the three-dimensional coordinates of the point to be measured in the measurement coordinate system.

[0055] The data processing module is connected to the data fusion module and the coordinate system calibration module respectively. It is used to monitor the fluctuation of distance and angle data after fusion. When the data fluctuation amplitude is less than the rated measurement accuracy threshold, the delay trigger module is activated. After the stable state continues to reach the preset delay time, the preset sampling parameter command is output. At the same time, a three-dimensional model of the workpiece is generated based on the effective data and the measurement results are calculated.

[0056] The data processing module, as the core unit for intelligent control and result output of the system, is connected to the data fusion module via Ethernet and to the coordinate system calibration module via a data interface. It undertakes the functions of data monitoring, parameter adjustment, and result generation. First, it receives distance and angle correlation data output by the data fusion module in real time and monitors data stability by calculating the fluctuation amplitude of the data. When the data fluctuation amplitude is less than the preset rated measurement accuracy threshold, the delay trigger module is automatically activated. If the data stability state continues to reach the preset delay time, the preset sampling parameter command is output to maintain the current stable measurement state. At the same time, it receives the three-dimensional coordinates of the point to be measured output by the coordinate system calibration module, performs point cloud processing on the coordinate data, constructs a three-dimensional model of the workpiece based on the effective coordinate data, and calculates the measurement results such as the dimensional deviation and geometric tolerance of the workpiece according to the preset dimensional tolerance standard, providing a quantitative basis for workpiece quality inspection.

[0057] In some embodiments, the program preset rules include: a first division method and a second division method;

[0058] The first division method is fixed time window division: the continuously collected data is divided into historical measurement data and current measurement data according to a preset time length;

[0059] The first division method is suitable for measurement scenarios where the workpiece motion is stable and the contour changes smoothly, such as batch measurement of regular workpieces of the same specifications. This method divides the data stream continuously collected by the laser probe and the axis motion module into two segments according to a preset fixed time length. The first segment is historical measurement data, such as the data collected in the first 20 seconds, which is used to build a stable data association model. The second segment is the current measurement data, such as the data collected in the last 10 seconds, which is used as the object of real-time optimization. The division boundary is strictly defined by the time node to ensure the consistency and repeatability of the data division.

[0060] The second division method is adaptive division: the division boundary is dynamically adjusted according to the fluctuation range of the distance from the original data or the shaft speed data. When the fluctuation range is greater than the preset threshold, the historical data window is shortened, and when the fluctuation range is less than the threshold, the window is extended.

[0061] The second division method is suitable for measurement scenarios where the workpiece motion state is variable and the contour changes are complex. This method does not rely on a fixed time window, but uses the data fluctuation characteristics as the division basis. By analyzing the fluctuation range of the original data or the fluctuation range of the axis speed data in real time, if the fluctuation range is greater than the preset threshold, it indicates that the workpiece state is changing drastically and the historical data window needs to be shortened; if the fluctuation range is less than the threshold, it indicates that the workpiece state is stable and the historical data window can be extended. By dynamically adjusting the division boundary, it is ensured that the historical data can effectively reflect the current workpiece state pattern.

[0062] The program's preset rules also include: a data integrity verification method after data partitioning;

[0063] The verification method is as follows: calculate the time coverage and data volume ratio of historical measurement data and current measurement data. If the time coverage is less than 90% or the data volume of a single group is less than the preset minimum sampling number, the corresponding time period data is automatically supplemented and re-divided. To avoid incomplete data after division due to data acquisition interruption or signal interference, which would affect subsequent correlation calculations and parameter optimization, this verification method verifies data integrity from two dimensions. First, it calculates the time coverage, which is the proportion of the time interval of historical measurement data and current measurement data in the total acquisition time interval. If the coverage is less than 90%, it indicates that there are time gaps, and the data is determined to be incomplete. Second, it calculates the data volume ratio by counting the actual number of sampling points of historical measurement data and current measurement data. If the data volume of a single group is less than the preset minimum sampling number, the data is also determined to be incomplete. When any incomplete situation occurs, the system automatically triggers the supplementary acquisition mechanism, controlling the laser probe and axis motion module to supplement the corresponding blank time period or insufficient number of data. After the supplementary acquisition is completed, the data division process is re-executed until the data meets the integrity requirements.

[0064] In some embodiments, the data association calculation mechanism is as follows: obtaining association information through covariance calculation, wherein the internal association information of historical data is an autocovariance matrix calculated based on historical measurement data, and the association information between historical data and current data is a cross-covariance vector calculated based on historical measurement data and current measurement data.

[0065] The data association calculation mechanism also includes: a covariance matrix regularization processing method;

[0066] The covariance matrix regularization processing method is as follows: calculate the condition number of the self-covariance matrix. If the condition number is greater than a preset threshold, inject a small regularization parameter into the diagonal of the matrix to avoid matrix singularity causing the calculation of related information to fail.

[0067] Among them, the correlation information extraction uses covariance analysis to mine the correlation characteristics between data, forming two types of key correlation information: First, the correlation information within historical data, which is achieved by calculating the autocovariance matrix of historical measurement data. Using the original distance and axle speed data in the historical measurement data as variables, a variable matrix is ​​constructed, and the covariance value of each variable in the matrix at different times is calculated to form the autocovariance matrix. This matrix can reflect the fluctuation pattern and correlation within the historical data. Second, the correlation information between historical data and current data, which is achieved by calculating the cross-covariance vector between historical measurement data and current measurement data. Using historical data as one variable group and current data as another variable group, the covariance value between the two groups of variables is calculated to form the cross-covariance vector. This vector can reflect the mapping relationship between historical data and current data. Both types of correlation information together provide data support for distance deviation prediction.

[0068] In the calculation of the autocovariance matrix, if the historical measurement data are linearly correlated, the matrix may become singular, for example, the determinant may be 0, causing the calculation of correlation information to fail. This processing method is specifically designed to solve this problem: First, the condition number of the autocovariance matrix is ​​calculated. The larger the condition number, the closer the matrix is ​​to being singular. If the condition number is greater than a preset critical value, it is determined that the matrix has a risk of being singular. At this time, a small regularization parameter is injected into the diagonal elements of the autocovariance matrix. The linear correlation of the matrix is ​​broken by a small perturbation, making the matrix a non-singular matrix, ensuring that the subsequent calculation of correlation information can be carried out normally, and guaranteeing the reliability of data correlation analysis.

[0069] In some embodiments, the data preprocessing in the data fusion module specifically includes the following steps:

[0070] The first step is to remove the mean, which means subtracting the mean of the data sequence to eliminate the constant offset.

[0071] De-meaning processing: For the constant offset present in the collected laser distance data, IMU angle data, and shaft rotation angle data, calculate the average value of each type of data sequence, subtract the average value from each data point, eliminate the DC component in the data, and make the data fluctuate around the zero value.

[0072] The second step is normalization, which scales the mean-de-meaned data to the 0-1 range to achieve uniformity in scale for different types of data.

[0073] Normalization is performed because the dimensions and numerical ranges of laser distance data, IMU angle data, and axis rotation angle data are very different. Direct fusion may lead to the result being dominated by variables with large values. Therefore, the various types of data after the mean is removed are scaled to the 0-1 range according to the formula data value - minimum data value / maximum data value - minimum data value. This achieves uniformity of scale for different types of data and ensures that the weights of each variable are balanced during fusion.

[0074] The third step is low-pass filtering, which uses a low-pass filter with a cutoff frequency of 0.1kHz-1kHz to filter out high-frequency noise in the data caused by environmental interference.

[0075] Low-pass filtering is used to address the issue that environmental vibrations, electromagnetic interference, and other factors can introduce high-frequency noise into the data during measurement. A low-pass filter with a cutoff frequency of 0.1kHz-1kHz is used to filter the normalized data, retaining the effective low-frequency signals and removing high-frequency noise, making the data smoother and closer to the true measurement value.

[0076] The data preprocessing also includes verifying the validity of the data. The verification method is as follows: calculate the standard deviation of the complete data after the above three steps. If the standard deviation of the distance data is greater than 0.01 mm or the standard deviation of the angle data is greater than 0.05, the data is judged to be abnormal and all preprocessing steps from demeaning to low-pass filtering need to be re-executed until the calculated standard deviation meets the above range.

[0077] The data validity verification method is used to avoid the processing results from becoming invalid due to abnormal data during the preprocessing process. The standard deviation is calculated for the complete data set after the above three steps of processing; judgment thresholds are set: the standard deviation of distance data is greater than 0.01mm and the standard deviation of angle data is greater than 0.05°. If the data standard deviation exceeds the corresponding threshold, the data is judged to be abnormal. At this time, the system automatically triggers the re-preprocessing process, and performs the demeaning, normalization and low-pass filtering operations again. If the data is still abnormal after 3 consecutive preprocessings, an alarm signal is issued to prompt manual inspection until the data standard deviation meets the threshold requirements.

[0078] In some embodiments, the prediction noise reduction logic is as follows: a linear prediction model is established based on historical measurement data, and the theoretical prediction values ​​of the current laser distance data and IMU angle data are calculated through the model. The data preprocessing also includes verifying the validity of the data. The verification method is as follows: the standard deviation of the complete data after the above three steps is calculated. If the standard deviation of the distance data is greater than 0.01 mm or the standard deviation of the angle data is greater than 0, the corresponding theoretical prediction values ​​are subtracted from the actual collected laser distance data, IMU angle data, and axis rotation angle data to eliminate trend interference and noise in the data.

[0079] The core logic of predictive noise reduction is to address the trend-based interference in measurement data caused by changes in ambient temperature, component wear, etc. First, a linear prediction model is established based on historical measurement data, with time as the independent variable and distance / angle data as the dependent variable. The linear equation is obtained by fitting the model using the least squares method. Then, the theoretical prediction value corresponding to the current measurement time is calculated using this model. Finally, the theoretical prediction value is subtracted from the actual distance and angle data collected at the moment of measurement to eliminate the trend component in the data and retain only random noise, thereby achieving data noise reduction.

[0080] The prediction noise reduction logic also includes a method for dynamically adjusting the order of the linear prediction model: using the Akaike Information Criterion as the evaluation index, the AIC value of the linear prediction model of different orders is calculated cyclically within a preset order range, and the order with the smallest AIC value is selected as the optimal linear prediction model order for the current measurement scenario. The order is re-evaluated and adjusted every 50 sampling cycles.

[0081] The specific method for dynamically adjusting the order of linear prediction models is as follows: The order of the linear prediction model, such as 1st, 5th, or 10th order, directly affects the prediction accuracy. If the order is too low, it cannot reflect complex trends, while if the order is too high, it is prone to overfitting. Therefore, the Akaike Information Criterion is used as the evaluation standard. First, a range of orders is set, such as 3-10, to cover common trend complexities. A linear prediction model is established for each order, and the AIC value of the model is calculated using the formula: AIC=2k-2ln(L), where k is the order and L is the model likelihood function value. The order with the smallest AIC value is selected as the optimal model order for the current measurement scenario, ensuring that the model can accurately fit the trend without redundancy. To adapt to changes in the workpiece state, the AIC value of each order model is recalculated every 50 sampling periods, and the optimal order is updated to ensure that the prediction model always matches the current measurement scenario and guarantees stable noise reduction effect.

[0082] In some embodiments, the coordinate system calibration module selects three reference points, which are located in non-collinear regions of the workpiece. These non-collinear regions mean that the three reference points cannot be on the same straight line and must be distributed in non-collinear spatial locations on the workpiece. These locations can be selected at the edge, center, and corner of the workpiece, and the initial distance between each reference point and the laser probe must be no less than 5mm. The positions of the reference points are initially confirmed using distance and angle data output by the data fusion module. The three reference points must be located in non-collinear regions of the workpiece. Specifically, edge points of the workpiece, such as the upper left corner, center, and corner of a rectangular workpiece, can be selected. If the three points are collinear, a unique three-dimensional coordinate system cannot be constructed. Non-collinear distribution allows the three points to determine a unique plane, thereby establishing a three-dimensional coordinate system.

[0083] The initial position of the reference point is determined by the distance-angle correlation data output by the data fusion module. The laser probe is aligned with the preset reference point area of ​​the workpiece, and the distance-angle data of the area is collected. Based on the distance data, points that are close to the preset reference point are selected. Combined with the angle data, the position of the point in space is determined, and the initial positioning of the reference point is completed.

[0084] The coordinate system calibration module also includes a secondary verification method for reference points;

[0085] The secondary verification method for reference points includes controlling the laser probe to perform three repeated measurements around each initially confirmed reference point, calculating the deviation value of the three measurement results, and if the deviation value is >0.002mm, then the reference point is removed and a new one is selected, until the repeated measurement deviation values ​​of all reference points meet the requirements.

[0086] The secondary verification method for reference points involves three repeated measurements for each initially confirmed reference point to avoid inaccurate positioning due to workpiece surface defects. The X / Y / Z axis motion module drives the laser probe to independently acquire distance and angle measurements around the initial position of the reference point. The deviation of the three measurements is calculated. If the deviation is greater than 0.002 mm, the reference point is deemed abnormal, discarded, and a new reference point is selected from other areas of the workpiece. This verification process is repeated until the deviation of the three repeated measurements for all three reference points is ≤0.002 mm, ensuring accurate and stable reference point positioning and providing a reliable benchmark for coordinate system establishment.

[0087] In some embodiments, the rated measurement accuracy threshold in the data processing module is in the range of 0.001mm-0.01mm; the delay triggering module is implemented by a software counter or a hardware timer, and the preset delay time can be remotely configured through a human-machine interface. During the delay, data fluctuations are continuously monitored, and if the fluctuations exceed the threshold, the delay timer is reset; the data processing module also includes a dynamic adaptation method for the rated measurement accuracy threshold: the threshold is adjusted according to the material characteristics of the workpiece. For high-rigidity metal workpieces, a threshold of 0.001mm-0.005mm is selected, and for easily deformable plastic workpieces, a threshold of 0.005mm-0.01mm is selected. The threshold adjustment command is automatically called through the system's preset material library or manually input.

[0088] The rated measurement accuracy threshold of the data processing module is set according to the differences in workpiece material. The delay triggering module achieves flexible control and prevention of misjudgment through a combination of hardware and software. At the same time, a dynamic threshold adaptation method is provided to improve the system's adaptability, as detailed below:

[0089] The measurement requirements and stability vary greatly depending on the material of the workpiece. Therefore, the threshold value range is set to 0.001mm-0.01mm. The specific adaptation rules are as follows: For high-rigidity metal workpieces, which have high surface hardness and small deformation, and require strict measurement accuracy, a threshold of 0.001mm-0.005mm is selected. For easily deformable plastic workpieces, which are prone to slight deformation due to temperature and pressure, excessively high accuracy is unrealistic. A threshold of 0.005mm-0.01mm is selected to ensure that the threshold matches the characteristics of the workpiece and avoid misjudgment caused by excessive pursuit of accuracy or measurement failure caused by insufficient accuracy.

[0090] The system employs a dual control approach combining a software counter and a hardware timer. The software counter tracks the duration of continuous data fluctuations below a threshold in real time, while the hardware timer provides a high-precision timing reference, preventing deviations caused by system load affecting the software timing. The preset delay time can be remotely configured via a human-machine interface, with a configuration range of 100ms-10s. During the delay period, the data processing module continuously monitors data fluctuations. If the fluctuation amplitude exceeds the threshold at any moment, the delay timer is immediately reset to prevent false triggering caused by short-term noise.

[0091] The dynamic adaptation method for rated measurement accuracy thresholds includes: a built-in workpiece material library in the system, where users can select workpiece materials through a human-machine interface, and the system automatically retrieves the corresponding threshold from the material library; if the workpiece material is special, users can manually input a custom threshold; simultaneously, after each batch of workpiece measurements is completed, the system automatically calculates the error distribution of the measurement results. If the errors are generally close to the upper limit of the threshold, the system prompts the user whether to appropriately increase the threshold; if the errors are generally much smaller than the threshold, the system prompts the user whether to decrease the threshold to improve measurement sensitivity, thereby achieving dynamic optimization of the threshold and adapting to the subtle differences between different batches of workpieces.

[0092] In some embodiments, the sampling optimization function of the dynamic ranging module is a linear function combining the calibration factor and the distance deviation;

[0093] The formula for the linear function is: f = 1 + 0.1 × calibration factor + 0.05 × distance deviation, where f is the optimization coefficient;

[0094] The second synchronization timestamp = the first synchronization timestamp × f, and the optimized laser probe's emission frequency adjustment range is 50kHz-200kHz, and the pulse intensity adjustment range is 0.5W-2W.

[0095] The dynamic ranging module also includes a real-time correction method for sampling optimization coefficients;

[0096] The real-time correction method for the sampling optimization coefficient includes: calculating the measurement error corresponding to the second synchronization timestamp after optimization; if the error is >0.003mm, then fine-tuning the weighting coefficient of the calibration factor and the distance deviation in steps of 0.1-0.12 and 0.05-0.06, correcting once every 20 sampling cycles until the measurement error is ≤0.003mm.

[0097] The sampling optimization function of the dynamic ranging module quantifies the parameter optimization relationship through a linear formula, clarifies the adjustment range of the laser probe's working parameters, and is equipped with a real-time correction method for the sampling optimization coefficients to ensure optimization accuracy, as detailed below:

[0098] Sampling optimization function and parameter adjustment range: The sampling optimization function is a linear function combining the calibration factor (reflecting the contour deviation of adjacent sampling points) and the distance deviation (reflecting the predicted distance error). The formula is set as f = 1 + 0.1 × calibration factor + 0.05 × distance deviation, where f is the optimization coefficient. The weight of the calibration factor (0.1) is greater than the weight of the distance deviation (0.05) because the contour deviation has a more direct impact on sampling synchronization. The second synchronization timestamp is calculated by "first synchronization timestamp × f". When f > 1, the synchronization time is extended, and when f < 1, the synchronization time is shortened. To ensure the stable operation of the laser probe, the emission frequency adjustment range is limited to 50kHz-200kHz and the pulse intensity adjustment range is limited to 0.5W-2W after optimization to avoid parameters exceeding hardware capabilities and causing failure.

[0099] Real-time correction method for sampling optimization coefficients: To address the issue of insufficient optimization accuracy of the initial weights (0.1, 0.05) in the optimization function due to changes in workpiece material and measurement environment, this method achieves weight optimization through error monitoring, coefficient fine-tuning, and iterative correction. First, the measurement error corresponding to the second synchronization timestamp after optimization is calculated. The distance data collected by the laser probe based on the second synchronization timestamp is compared with the theoretical distance value in the workpiece design drawing to obtain the actual measurement error. If the error is > 0.003 mm, the weight coefficients are fine-tuned in fixed steps: the calibration factor weight increases from 0.1 to 0.12, and the distance deviation weight increases from 0.05 to 0.06. The error calculation and coefficient fine-tuning are repeated every 20 sampling cycles until the measurement error is ≤ 0.003 mm. If the error still does not meet the standard after 10 consecutive fine-tunings, a system self-check is triggered to ensure that the optimization coefficients always match the actual measurement scenario and guarantee the optimization effect of the sampling parameters.

[0100] Example 2:

[0101] See Figure 4 This application provides a measurement method based on dynamic ranging adjustment, including the following steps:

[0102] S1. Start the measurement system and collect basic data. Control the main body of the coordinate measuring machine to move the laser probe module to the measurement area, and simultaneously collect the initial three-dimensional contour information of the workpiece, and simultaneously collect the original distance data and axis speed data of the motion axis output by the laser probe.

[0103] S2. Data classification and correlation analysis: The collected raw data is divided into historical reference data and current measurement data according to preset rules. The internal correlation characteristics and interactive correlation characteristics of the two types of data are extracted through the data correlation calculation mechanism.

[0104] S3. Dynamic adaptation of sampling parameters: Based on the workpiece contour features, sampling points are divided. Basic synchronization parameters are generated by combining the initial distance and movement speed. Contour deviation calibration factor and distance deviation predicted by data association features are introduced. Dynamic synchronization parameters adapted to the current measurement scenario are generated through optimization algorithm. The working parameters of the laser probe are dynamically adjusted accordingly.

[0105] S4. Multi-source data fusion optimization: Acquire multi-source data such as laser distance, angle, and motion axis rotation angle. After preprocessing and noise suppression, the data is fused to output high-precision measurement data.

[0106] S5. Measurement reference establishment and coordinate calculation: Select feature reference points to establish a measurement coordinate system, and calculate the three-dimensional coordinates of the workpiece to be measured based on the fused high-precision data;

[0107] S6. Intelligent control and result generation of the measurement process: Real-time monitoring of measurement data stability; in a stable state, a delay control mechanism is activated to maintain measurement parameters; Based on the effective coordinate data, a three-dimensional model of the workpiece is generated and the measurement results are calculated.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A measurement system based on dynamic ranging adjustment, characterized in that, include: The main body of the coordinate measuring machine includes: an X-axis motion module, a Y-axis motion module, a Z-axis motion module, and a laser probe module; the X-axis motion module, Y-axis motion module, and Z-axis motion module are connected to the worktable; the laser probe module is connected to the X-axis motion module, Y-axis motion module, and Z-axis motion module. The dynamic ranging module is connected to the laser probe module, the X-axis motion module, the Y-axis motion module, and the Z-axis motion module. It is used to receive the initial three-dimensional contour of the workpiece, collect the original distance data output by the laser probe module and the axis speed data of the X-axis motion module, the Y-axis motion module, and the Z-axis motion module, and divide these data into historical measurement data and current measurement data according to the preset rules of the program. Through a data association calculation mechanism, internal correlation information of historical measurement data is obtained based on historical measurement data, and the correlation information between historical measurement data and current measurement data is obtained. Then, laser sampling points are divided and the initial distance is calculated. The first synchronization timestamp is generated according to the initial distance and axis speed. The calibration factor is determined by combining the contour deviation of adjacent sampling points. At the same time, the distance deviation of the current sampling point is predicted by using the above-mentioned internal correlation information of historical data and the correlation information obtained by combining historical measurement data and current measurement data. The second synchronization timestamp is obtained by optimization through a sampling optimization function, and then the emission frequency and pulse intensity of the laser probe are adjusted. The data fusion module is connected to the dynamic ranging module and the laser probe module respectively. It is used to collect laser distance data, IMU angle data, and axis rotation angle data. After preprocessing these data, the data is processed by prediction and noise reduction logic, and finally the accurate distance and angle data are output after fusion processing. A coordinate system calibration module, which is connected to a data fusion module, is used to select three reference points to establish a measurement coordinate system and calculate the three-dimensional coordinates of the point to be measured using the three-point measurement method. The data processing module is connected to the data fusion module and the coordinate system calibration module respectively. It is used to monitor the fluctuation of distance and angle data after fusion. When the data fluctuation amplitude is less than the rated measurement accuracy threshold, the delay trigger module is activated. After the stable state continues to reach the preset delay time, the preset sampling parameter command is output. At the same time, a three-dimensional model of the workpiece is generated based on the effective data and the measurement results are calculated.

2. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The program's preset rules include: a first division method and a second division method; the first division method is a fixed time window division: the continuously collected data is divided into historical measurement data and current measurement data according to a preset time length; the second division method is an adaptive division: the division boundary is dynamically adjusted according to the fluctuation range of the original distance data or shaft speed data, the historical data window is shortened when the fluctuation range is greater than a preset threshold, and the window is extended when the fluctuation range is less than the threshold; the program's preset rules also include: a data integrity verification method after data division; the verification method is: calculating the time coverage and data volume ratio of historical measurement data and current measurement data, if the time coverage is less than 90% or the data volume of a single group is less than the preset minimum sampling number, then the corresponding time period data is automatically supplemented and re-divided.

3. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The data association calculation mechanism is as follows: association information is obtained through covariance calculation, wherein the internal association information of historical data is an autocovariance matrix calculated based on historical measurement data, and the association information between historical data and current data is a cross-covariance vector calculated based on historical measurement data and current measurement data; the data association calculation mechanism further includes a covariance matrix regularization processing method; the covariance matrix regularization processing method is as follows: the condition number of the autocovariance matrix is ​​calculated, and if the condition number is greater than a preset threshold, a small regularization parameter is injected into the diagonal of the matrix to avoid matrix singularity causing the association information calculation to fail.

4. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The data preprocessing in the data fusion module specifically includes the following steps: First, mean reduction processing, which involves subtracting the average value of the data sequence to eliminate constant offsets; Second, normalization processing, which scales the mean-reduced data to the 0-1 range to achieve scale uniformity for different types of data; Third, low-pass filtering processing, which uses a low-pass filter with a cutoff frequency of 0.1kHz-1kHz to filter out high-frequency noise caused by environmental interference. The data preprocessing also includes verifying the validity of the data. The verification method is as follows: calculate the standard deviation of the complete data after the above three steps. If the standard deviation of distance data is greater than 0.01mm or the standard deviation of angle data is greater than 0.05, the data is judged to be abnormal, and all preprocessing steps from mean reduction processing to low-pass filtering processing need to be repeated until the calculated standard deviation meets the above range.

5. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The prediction denoising logic is as follows: a linear prediction model is established based on historical measurement data. The theoretical prediction values ​​of the current laser distance data and IMU angle data are calculated using this model. Then, the corresponding theoretical prediction values ​​are subtracted from the actual collected laser distance data, IMU angle data, and axis rotation angle data to eliminate trend interference and noise in the data. The prediction denoising logic also includes a dynamic adjustment method for the linear prediction model order: using the Akaike Information Criterion as the evaluation index, the AIC value of the linear prediction model of different orders is calculated iteratively within a preset order range. The order with the smallest AIC value is selected as the optimal linear prediction model order for the current measurement scenario. The order is re-evaluated and adjusted every 50 sampling cycles.

6. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The coordinate system calibration module selects three reference points, which are located in non-collinear regions of the workpiece. These non-collinear regions mean that the three reference points cannot be on the same straight line; they must be distributed in non-collinear spatial positions on the workpiece, such as at the edge, center, and corners. The initial distance between each reference point and the laser probe is no less than 5mm. The positions of the reference points are initially confirmed using distance and angle data output by the data fusion module. The coordinate system calibration module also includes a secondary verification method for the reference points. This method involves controlling the laser probe to perform three repeated measurements around each initially confirmed reference point, calculating the deviation value of the three measurements, and discarding the reference point if the deviation value is greater than 0.002mm, and reselecting a new one until the repeated measurement deviation values ​​of all reference points meet the requirements.

7. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The rated measurement accuracy threshold in the data processing module ranges from 0.001mm to 0.01mm. The delay trigger module is implemented through a software counter or a hardware timer. The preset delay time can be remotely configured through a human-machine interface, and data fluctuations are continuously monitored during the delay period. If the fluctuation exceeds the threshold, the delay timer is reset. The data processing module also includes a dynamic adaptation method for the rated measurement accuracy threshold: the threshold is adjusted according to the material characteristics of the workpiece. For high-rigidity metal workpieces, a threshold of 0.001mm-0.005mm is selected, and for easily deformable plastic workpieces, a threshold of 0.005mm-0.01mm is selected. The threshold adjustment command is automatically called from the system's preset material library or manually input.

8. The measurement system based on dynamic ranging adjustment according to claim 1, characterized in that, The sampling optimization function of the dynamic ranging module is a linear function combining the calibration factor and the distance deviation; the formula of the linear function is: f = 1 + 0.1 × calibration factor + 0.05 × distance deviation, where f is the optimization coefficient; The second synchronization timestamp = the first synchronization timestamp × f, and the optimized laser probe's emission frequency adjustment range is 50kHz-200kHz, and the pulse intensity adjustment range is 0.5W-2W. The dynamic ranging module also includes a real-time correction method for sampling optimization coefficients; The real-time correction method for the sampling optimization coefficient includes: calculating the measurement error corresponding to the second synchronization timestamp after optimization; if the error is >0.003mm, then fine-tuning the weighting coefficient of the calibration factor and the distance deviation in steps of 0.1-0.12 and 0.05-0.06, correcting once every 20 sampling cycles until the measurement error is ≤0.003mm.

9. A measurement method based on dynamic ranging adjustment, applied to the measurement system based on dynamic ranging adjustment as described in any one of claims 1-8, characterized in that, Includes the following steps: S1. Start the measurement system and collect basic data. Control the main body of the coordinate measuring machine to move the laser probe module to the measurement area, and simultaneously collect the initial three-dimensional contour information of the workpiece, as well as the original distance data and axis speed data of the motion axis output by the laser probe. S2. Data Classification and Correlation Analysis: The collected raw data is divided into historical reference data and current measurement data according to preset rules. The internal correlation characteristics and interactive correlation characteristics of the two types of data are extracted through data correlation calculation mechanism. S3. Dynamic adaptation of sampling parameters: Based on the workpiece contour features, sampling points are divided, and basic synchronization parameters are generated by combining the initial distance and movement speed. Contour deviation calibration factor and distance deviation predicted by data association features are introduced. Dynamic synchronization parameters adapted to the current measurement scenario are generated through optimization algorithm, and the working parameters of the laser probe are dynamically adjusted accordingly. S4. Multi-source data fusion optimization: Collects laser distance, angle, and motion axis rotation angle from multiple sources. After preprocessing and noise suppression, the data is fused to output high-precision measurement data. S5. Measurement reference establishment and coordinate calculation: Select feature reference points to establish a measurement coordinate system, and calculate the three-dimensional coordinates of the workpiece to be measured based on the fused high-precision data; S6. Intelligent control and result generation of the measurement process: Real-time monitoring of measurement data stability; in a stable state, a delay control mechanism is activated to maintain measurement parameters; Based on the effective coordinate data, a three-dimensional model of the workpiece is generated and the measurement results are calculated.

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