Modular assembly method and system for automobile cabin

Abstract

The invention provides an automobile cabin modular assembly method and system, and the method comprises the steps: synchronizing actual pose data, assembly contact force data and station parameters to a virtual assembly scene of a preset digital twin system, so as to construct a dynamic mapping relation between a physical assembly state of a cabin module and the virtual assembly scene; on the basis of the dynamic mapping relationship, comparing the actual pose data of each cabin module with design reference parameters through a preset error analysis model so as to acquire a corresponding comparison result; performing correlation analysis on the comparison result in combination with the assembly contact force data and the station parameters to identify an error source and a dynamic change trend, and generating a corresponding error compensation instruction; and issuing the error compensation instruction to a robot controller on the assembly line to control a robot to execute assembly adjustment. According to the invention, the assembly of the cabin module can be accurately completed, and the assembly efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle engineering, in particular to a modular assembly method and system for a vehicle cabin. BACKGROUND

[0002] With the progress of science and technology and the rapid development of productivity, automobiles have been popularized in people's daily life and have become one of the indispensable means of transportation for people's daily travel, greatly facilitating people's life.

[0003] Among them, in the process of existing automobile production, the modular assembly of the cabin has become the mainstream production mode, which forms a standardized module by pre-integrating parts, which can improve production efficiency and vehicle compatibility.

[0004] Further, in the process of actual application, the existing part module needs to be detected for error after assembly is completed, and error compensation is performed after error is detected, however, most of the existing technology can only perform error detection after assembly is completed, and cannot perform dynamic error compensation during assembly, which leads to the phenomenon of missed detection and false detection, which reduces the efficiency of vehicle assembly. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a modular assembly method and system for a vehicle cabin to solve the problem that the existing technology cannot perform dynamic error compensation during assembly, which leads to the phenomenon of missed detection and false detection.

[0006] The first aspect of the embodiment of the present application is proposed: A modular assembly method for a vehicle cabin, specifically comprising the following steps: Collecting actual pose data, assembly contact force data and station parameters of each cabin module on an assembly line through a preset sensor group, and synchronizing the actual pose data, the assembly contact force data and the station parameters to a virtual assembly scene of a preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cabin module and the virtual assembly scene; Based on the dynamic mapping relationship, comparing the actual pose data of each cabin module with design reference parameters through a preset error analysis model to collect a corresponding comparison result; Correlating the comparison result with the assembly contact force data and the station parameters for correlation analysis to identify error sources and dynamic change trends and generate corresponding error compensation instructions; The error compensation instructions are issued to a robot controller on the assembly line to control the robot to perform assembly adjustment.

[0007] The beneficial effects of this invention are as follows: By collecting the actual pose data, assembly force data, and workstation parameters of each cockpit module, a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene can be constructed. Based on this, it is immediately compared with the design benchmark parameters, and the corresponding comparison results are collected. Then, combined with the above-mentioned assembly contact force data and workstation parameters, an error compensation instruction adapted to each cockpit module is finally generated. The error compensation instruction is then sent to the robot controller to complete the assembly adjustment. Based on this, the assembly of each cockpit module can be completed dynamically and accurately, avoiding the phenomenon of missed inspection and false inspection, and improving the assembly efficiency.

[0008] Furthermore, the step of synchronizing the actual pose data, the assembly contact force data, and the workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene includes: The collected actual pose data, assembly contact force data, and workstation parameters are spatiotemporally aligned, and the timestamp reference of each data is unified to generate a standardized multi-dimensional data stream. A mapping rule library is constructed between the multi-dimensional data stream and the virtual entity attributes of the virtual assembly scene, and the virtual model motion trajectory and physical assembly path corresponding to the cockpit module are rendered in the virtual assembly scene according to the mapping rule library. The dynamic mapping relationship is constructed based on the motion trajectory of the virtual model and the corresponding physical assembly path.

[0009] Furthermore, the step of constructing the dynamic mapping relationship based on the motion trajectory of the virtual model and the corresponding physical assembly path includes: Extract the keyframe coordinates of the motion trajectory of the virtual model and the measured feature point coordinates of the physical assembly path, and compare the coordinates point by point at preset time intervals to generate the corresponding coordinate deviation matrix. When any deviation value in the coordinate deviation matrix exceeds the preset deviation threshold, the dynamic correction algorithm in the mapping rule base is invoked to adjust the coordinate transformation coefficient of the virtual model motion trajectory and the spatiotemporal scaling ratio of the physical assembly path until the deviation values ​​are all within the range of the preset deviation threshold, so as to form a mapping parameter set. The mapping parameter set is serialized to generate the corresponding dynamic mapping relationship.

[0010] Furthermore, the step of comparing the actual pose data of each cockpit module with the design reference parameters based on the dynamic mapping relationship and using a preset error analysis model to collect the corresponding comparison results includes: Based on the dynamic mapping relationship, the three-dimensional coordinate set corresponding to the actual pose data of each cockpit module is extracted in the virtual assembly scene. At the same time, the three-dimensional reference coordinate set in the design reference parameters is called, and the three-dimensional coordinate set and the three-dimensional reference coordinate set are transformed to the same world coordinate system to obtain standard coordinate data pairs. The standard coordinate data is input into the preset error analysis model, and the feature points in the three-dimensional coordinate set are compared one by one with the corresponding feature points in the three-dimensional reference coordinate set through the feature point matching algorithm, and the linear deviation value and angular deviation value generated by each feature point on the coordinate axis are calculated. The linear deviation value and the angular deviation value are classified and statistically processed to generate corresponding comparison results.

[0011] Furthermore, the step of classifying and statistically processing the linear deviation value and the angular deviation value to generate corresponding comparison results includes: According to the assembly sequence of the cockpit module, the linear deviation values ​​and angular deviation values ​​of all feature points are divided into stages so that the deviation values ​​of feature points within the same time sequence are assigned to the corresponding stage data group. The cumulative deviation and deviation growth rate are calculated based on the linear deviation values ​​in each stage data set, and the rotation axis offset and deviation convergence coefficient are calculated based on the angular deviation values ​​in each stage data set. An animation of deviation evolution is generated according to the assembly sequence stages. At the same time, a three-dimensional scatter plot of the cumulative deviation and rotation axis offset of each stage is generated, as well as a time-series comparison curve of the deviation growth rate and deviation convergence coefficient. The deviation evolution animation, the three-dimensional scatter plot and the time-series comparison curve are superimposed and labeled with the design deviation threshold range corresponding to each stage, and integrated to form the comparison result.

[0012] Furthermore, the step of combining the comparison results with the assembly contact force data and the workstation parameters to perform correlation analysis to identify the source of error and dynamic change trend, and generating corresponding error compensation instructions includes: Based on the spatial coordinate partitions in the workstation parameters, the assembly contact force data is segmented according to a preset time window and mapped to the corresponding spatial coordinate partitions respectively. Feature extraction is performed on the segmented contact force data within each spatial coordinate partition to obtain the contact force amplitude fluctuation and force vector change rate. Time-series correlation calculation is then performed to obtain the error correlation coefficient. The error compensation instruction is generated based on the error correlation coefficient.

[0013] Furthermore, the step of generating the error compensation instruction based on the error correlation coefficient includes: At least three error correlation thresholds are set, and the error correlation coefficients are compared with each error correlation threshold to divide the high correlation interval, medium correlation interval and low correlation interval. For error correlation coefficients falling into the high correlation range, the assembly process of the corresponding spatial coordinate partition is reproduced in the virtual assembly scenario, the segmented contact force data time series curve of the partition is loaded synchronously, and the virtual robot executes the preset multi-dimensional pose fine-tuning scheme to record the fine-tuning parameter combination that meets the fit of the cockpit module in the virtual assembly scenario. Based on the fine-tuning parameter combination, and combined with the robot motion constraint range in the workstation parameters of the corresponding spatial coordinate partition, the pose adjustment amount, contact force control threshold and adjustment execution sequence are calculated. The pose adjustment amount, contact force control threshold and adjustment execution sequence are then encapsulated into error compensation instructions in a preset format.

[0014] The second aspect of the present invention proposes: A modular assembly system for an automotive cockpit, wherein the system comprises: The data acquisition module is used to collect the actual pose data, assembly contact force data and workstation parameters of each cockpit module on the assembly line through a preset sensor group, and synchronize the actual pose data, assembly contact force data and workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene. The comparison module is used to compare the actual pose data of each cockpit module with the design reference parameters based on the dynamic mapping relationship and through a preset error analysis model, so as to collect the corresponding comparison results. The analysis module is used to perform correlation analysis by combining the comparison results with the assembly contact force data and the station parameters, so as to identify the source of error and the dynamic trend of change, and generate corresponding error compensation instructions. The control module is used to send the error compensation command to the robot controller on the assembly line to control the robot to perform assembly adjustments.

[0015] Furthermore, the acquisition module is specifically used for: The collected actual pose data, assembly contact force data, and workstation parameters are spatiotemporally aligned, and the timestamp reference of each data is unified to generate a standardized multi-dimensional data stream. A mapping rule library is constructed between the multi-dimensional data stream and the virtual entity attributes of the virtual assembly scene, and the virtual model motion trajectory and physical assembly path corresponding to the cockpit module are rendered in the virtual assembly scene according to the mapping rule library. The dynamic mapping relationship is constructed based on the motion trajectory of the virtual model and the corresponding physical assembly path.

[0016] Furthermore, the acquisition module is specifically used for: Extract the keyframe coordinates of the motion trajectory of the virtual model and the measured feature point coordinates of the physical assembly path, and compare the coordinates point by point at preset time intervals to generate the corresponding coordinate deviation matrix. When any deviation value in the coordinate deviation matrix exceeds the preset deviation threshold, the dynamic correction algorithm in the mapping rule base is invoked to adjust the coordinate transformation coefficient of the virtual model motion trajectory and the spatiotemporal scaling ratio of the physical assembly path until the deviation values ​​are all within the range of the preset deviation threshold, so as to form a mapping parameter set. The mapping parameter set is serialized to generate the corresponding dynamic mapping relationship.

[0017] Furthermore, the comparison module is specifically used for: Based on the dynamic mapping relationship, the three-dimensional coordinate set corresponding to the actual pose data of each cockpit module is extracted in the virtual assembly scene. At the same time, the three-dimensional reference coordinate set in the design reference parameters is called, and the three-dimensional coordinate set and the three-dimensional reference coordinate set are transformed to the same world coordinate system to obtain standard coordinate data pairs. The standard coordinate data is input into the preset error analysis model, and the feature points in the three-dimensional coordinate set are compared one by one with the corresponding feature points in the three-dimensional reference coordinate set through the feature point matching algorithm, and the linear deviation value and angular deviation value generated by each feature point on the coordinate axis are calculated. The linear deviation value and the angular deviation value are classified and statistically processed to generate corresponding comparison results.

[0018] Furthermore, the comparison module is specifically used for: According to the assembly sequence of the cockpit module, the linear deviation values ​​and angular deviation values ​​of all feature points are divided into stages so that the deviation values ​​of feature points within the same time sequence are assigned to the corresponding stage data group. The cumulative deviation and deviation growth rate are calculated based on the linear deviation values ​​in each stage data set, and the rotation axis offset and deviation convergence coefficient are calculated based on the angular deviation values ​​in each stage data set. An animation of deviation evolution is generated according to the assembly sequence stages. At the same time, a three-dimensional scatter plot of the cumulative deviation and rotation axis offset of each stage is generated, as well as a time-series comparison curve of the deviation growth rate and deviation convergence coefficient. The deviation evolution animation, the three-dimensional scatter plot and the time-series comparison curve are superimposed and labeled with the design deviation threshold range corresponding to each stage, and integrated to form the comparison result.

[0019] Furthermore, the analysis module is specifically used for: Based on the spatial coordinate partitions in the workstation parameters, the assembly contact force data is segmented according to a preset time window and mapped to the corresponding spatial coordinate partitions respectively. Feature extraction is performed on the segmented contact force data within each spatial coordinate partition to obtain the contact force amplitude fluctuation and force vector change rate. Time-series correlation calculation is then performed to obtain the error correlation coefficient. The error compensation instruction is generated based on the error correlation coefficient.

[0020] Furthermore, the analysis module is specifically used for: At least three error correlation thresholds are set, and the error correlation coefficients are compared with each error correlation threshold to divide the high correlation interval, medium correlation interval and low correlation interval. For error correlation coefficients falling into the high correlation range, the assembly process of the corresponding spatial coordinate partition is reproduced in the virtual assembly scenario, the segmented contact force data time series curve of the partition is loaded synchronously, and the virtual robot executes the preset multi-dimensional pose fine-tuning scheme to record the fine-tuning parameter combination that meets the fit of the cockpit module in the virtual assembly scenario. Based on the fine-tuning parameter combination, and combined with the robot motion constraint range in the workstation parameters of the corresponding spatial coordinate partition, the pose adjustment amount, contact force control threshold and adjustment execution sequence are calculated. The pose adjustment amount, contact force control threshold and adjustment execution sequence are then encapsulated into error compensation instructions in a preset format.

[0021] The third aspect of the present invention proposes: A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the modular assembly method for an automotive cockpit as described above.

[0022] The fourth aspect of the present invention proposes: A readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the modular assembly method for an automotive cockpit as described above.

[0023] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0024] Figure 1 A flowchart of a modular assembly method for an automobile cockpit provided in the first embodiment of the present invention; Figure 2This is a structural block diagram of a modular assembly system for an automotive cockpit provided in the sixth embodiment of the present invention.

[0025] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0026] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0027] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] Please see Figure 1 The figure shows a modular assembly method for an automobile cabin provided in the first embodiment of the present invention. The modular assembly method for an automobile cabin provided in this embodiment can objectively and accurately complete the assembly of each cabin module, thereby improving the assembly efficiency.

[0030] Specifically, this embodiment provides: A modular assembly method for an automobile cockpit specifically includes the following steps: Step S10: Collect actual pose data, assembly contact force data and workstation parameters of each cockpit module on the assembly line through a preset sensor group, and synchronize the actual pose data, assembly contact force data and workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene. It's important to note that, firstly, key data is collected through a pre-set sensor group (such as vision sensors, force sensors, and position encoders): actual pose data (reflecting the three-dimensional position and attitude of the cockpit module), assembly contact force data (recording the magnitude and direction of contact forces between modules or between a module and tooling), and workstation parameters (such as the robot model, assembly sequence, and tooling positioning accuracy at the current workstation). This data needs to be synchronized to the virtual assembly scene of the pre-set digital twin system. Specifically, the digital twin system can construct a virtual environment consistent with the physical assembly line, achieving real-time mapping between the physical state and the virtual scene through data synchronization, providing a digital carrier for subsequent analysis and processing.

[0031] Step S20: Based on the dynamic mapping relationship, the actual pose data of each cockpit module is compared with the design reference parameters through a preset error analysis model to collect the corresponding comparison results. It's important to note that, based on this dynamic mapping relationship, the pre-set error analysis model compares the actual pose data with design reference parameters (such as the theoretical position and angle in the CAD model) to accurately capture assembly deviations. Subsequently, it combines assembly contact force data (abnormal fluctuations in force may indicate module interference or positioning deviations) and station parameters (differences in equipment precision at different stations may lead to errors) for correlation analysis: for example, if the contact force at a certain station suddenly increases and the pose deviation increases, it may be due to tooling positioning errors at that station. This facilitates subsequent processing.

[0032] Step S30: Combine the comparison results with the assembly contact force data and the station parameters to perform correlation analysis, so as to identify the source of error and the dynamic trend of change, and generate the corresponding error compensation instruction. It should be noted that by analyzing and identifying error sources (such as tooling wear and robot path deviation) and dynamic trends (such as the cumulative pattern of deviations as assembly progresses), targeted error compensation instructions are generated to facilitate subsequent processing.

[0033] Step S40: The error compensation command is sent to the robot controller on the assembly line to control the robot to perform assembly adjustments.

[0034] It should be noted that, finally, the command is sent to the robot controller to control the robot to adjust parameters such as posture and force in real time, ensuring that the assembly accuracy of the cockpit module meets the design requirements, thus facilitating subsequent processing.

[0035] Second Embodiment Furthermore, the step of synchronizing the actual pose data, the assembly contact force data, and the workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene includes: The collected actual pose data, assembly contact force data, and workstation parameters are spatiotemporally aligned, and the timestamp reference of each data is unified to generate a standardized multi-dimensional data stream. A mapping rule library is constructed between the multi-dimensional data stream and the virtual entity attributes of the virtual assembly scene, and the virtual model motion trajectory and physical assembly path corresponding to the cockpit module are rendered in the virtual assembly scene according to the mapping rule library. The dynamic mapping relationship is constructed based on the motion trajectory of the virtual model and the corresponding physical assembly path.

[0036] It should be noted that the first step is spatiotemporal alignment: the actual pose data, contact force data, and workstation parameters may come from different sensors and have different timestamps (e.g., the sampling frequency of the vision sensor is 20Hz, while that of the force sensor is 50Hz). It is necessary to unify the timestamp benchmark (e.g., to synchronize at the millisecond level) and integrate them into a standardized multi-dimensional data stream according to the time series. Specifically, this step eliminates the mapping deviation caused by data asynchrony and ensures that the virtual scene can reflect the physical state in real time.

[0037] The second step is to construct a mapping rule base: the rule base defines the correspondence between physical data and virtual entity attributes. For example, "actual pose data (X,Y,Z,θ) corresponds to the coordinates and rotation parameters of the virtual model," "contact force data (Fx,Fy,Fz) corresponds to the force simulation parameters of the contact points of the virtual module," and "workstation parameters (such as robot ID) correspond to the robot model identifier in the virtual scene." Based on the rule base, the virtual model motion trajectory (such as the hoisting path of the dashboard module) and physical assembly path (such as the docking path between the seat module and the floor) that are consistent with the physical assembly are rendered in the virtual assembly scene, making the entire physical assembly process visualized in the virtual scene.

[0038] Ultimately, by linking virtual trajectories with physical paths in real time, a dynamic mapping relationship is constructed. Specifically, this relationship ensures that every change in the physical assembly (such as slight module offset or increased contact force) is reflected instantly in the virtual scene, providing a combined virtual and real analytical basis for subsequent error analysis, thus facilitating subsequent processing.

[0039] Furthermore, the step of constructing the dynamic mapping relationship based on the motion trajectory of the virtual model and the corresponding physical assembly path includes: Extract the keyframe coordinates of the motion trajectory of the virtual model and the measured feature point coordinates of the physical assembly path, and compare the coordinates point by point at preset time intervals to generate the corresponding coordinate deviation matrix. When any deviation value in the coordinate deviation matrix exceeds the preset deviation threshold, the dynamic correction algorithm in the mapping rule base is invoked to adjust the coordinate transformation coefficient of the virtual model motion trajectory and the spatiotemporal scaling ratio of the physical assembly path until the deviation values ​​are all within the range of the preset deviation threshold, so as to form a mapping parameter set. The mapping parameter set is serialized to generate the corresponding dynamic mapping relationship.

[0040] It should be noted that, firstly, the key frame coordinates of the virtual model's motion trajectory (such as the three-dimensional coordinates of trajectory turning points and assembly start points) are extracted and compared with the measured feature point coordinates of the physical assembly path (the corresponding position coordinates measured by equipment such as laser trackers). The comparison is performed point by point at preset time intervals (such as 10ms / point) to generate a coordinate deviation matrix. Specifically, each element in the matrix represents the deviation value between the virtual coordinates and the physical coordinates at a certain moment, which directly reflects the degree of consistency between the virtual and physical mapping.

[0041] When any deviation value in the deviation matrix exceeds a preset threshold (e.g., 0.5mm, set according to assembly accuracy requirements), the dynamic correction algorithm in the mapping rule base is invoked: the algorithm iterates repeatedly by adjusting the coordinate transformation coefficients of the virtual model trajectory (e.g., scaling ratio, translation amount) and the spatiotemporal scaling ratio of the physical path (e.g., correcting path misalignment caused by timestamp deviation) until all deviation values ​​are within the threshold range, forming a stable mapping parameter set (including coordinate transformation coefficients, scaling ratio, time synchronization parameters, etc.).

[0042] Finally, the mapping parameter set is serialized (converted into a storable and callable data stream format) to generate dynamic mapping relationships. Specifically, the serialized parameter set can be called by the digital twin system in real time, ensuring that the physical state and the virtual scene maintain high-precision synchronization throughout the assembly process, providing a reliable basis for virtual-real comparison for subsequent error analysis, thus facilitating subsequent processing.

[0043] Furthermore, the step of comparing the actual pose data of each cockpit module with the design reference parameters based on the dynamic mapping relationship and using a preset error analysis model to collect the corresponding comparison results includes: Based on the dynamic mapping relationship, the three-dimensional coordinate set corresponding to the actual pose data of each cockpit module is extracted in the virtual assembly scene. At the same time, the three-dimensional reference coordinate set in the design reference parameters is called, and the three-dimensional coordinate set and the three-dimensional reference coordinate set are transformed to the same world coordinate system to obtain standard coordinate data pairs. The standard coordinate data is input into the preset error analysis model, and the feature points in the three-dimensional coordinate set are compared one by one with the corresponding feature points in the three-dimensional reference coordinate set through the feature point matching algorithm, and the linear deviation value and angular deviation value generated by each feature point on the coordinate axis are calculated. The linear deviation value and the angular deviation value are classified and statistically processed to generate corresponding comparison results.

[0044] It should be noted that, firstly, based on the dynamic mapping relationship, the three-dimensional coordinate set corresponding to the actual pose data is extracted from the virtual assembly scene (since the virtual scene and physical state have been accurately mapped, this coordinate set is equivalent to the actual coordinates of the physical module); at the same time, the three-dimensional reference coordinate set (the theoretical coordinates in the CAD model) in the design reference parameters is called, and the two are transformed to the same world coordinate system (such as a coordinate system with the assembly line origin as the reference) to obtain standard coordinate data pairs. Specifically, a unified coordinate system is a prerequisite for ensuring the accuracy of deviation calculation and avoiding comparison errors caused by different coordinate references.

[0045] The standard coordinate data is input into the preset error analysis model. The model uses a feature point matching algorithm (such as the ICP iterative nearest point algorithm) to match the feature points in the actual 3D coordinate set (such as the center of the mounting hole of the module, the corner vertex) with the corresponding feature points in the reference coordinate set one by one. The model calculates the linear deviation value (position deviation) of each feature point on the X, Y, and Z axes and the angular deviation value (attitude deviation) around each axis. For example, if the actual X coordinate of a mounting hole is 100.2mm and the reference is 100.0mm, then the linear deviation value is +0.2mm; if the actual rotation around the Z axis is 1.5° and the reference is 0°, then the angular deviation value is +1.5°.

[0046] Finally, all linear and angular deviation values ​​are categorized and statistically analyzed (e.g., grouped by module type and feature point type), generating comparison results that include deviation magnitude, distribution range, and the number of out-of-tolerance feature points. Specifically, these results visually present the assembly deviations, providing data support for subsequent error source analysis and facilitating further processing.

[0047] Furthermore, the step of classifying and statistically processing the linear deviation value and the angular deviation value to generate corresponding comparison results includes: According to the assembly sequence of the cockpit module, the linear deviation values ​​and angular deviation values ​​of all feature points are divided into stages so that the deviation values ​​of feature points within the same time sequence are assigned to the corresponding stage data group. The cumulative deviation and deviation growth rate are calculated based on the linear deviation values ​​in each stage data set, and the rotation axis offset and deviation convergence coefficient are calculated based on the angular deviation values ​​in each stage data set. An animation of deviation evolution is generated according to the assembly sequence stages. At the same time, a three-dimensional scatter plot of the cumulative deviation and rotation axis offset of each stage is generated, as well as a time-series comparison curve of the deviation growth rate and deviation convergence coefficient. The deviation evolution animation, the three-dimensional scatter plot and the time-series comparison curve are superimposed and labeled with the design deviation threshold range corresponding to each stage, and integrated to form the comparison result.

[0048] It should be noted that, firstly, according to the assembly sequence of the cockpit module (such as "instrument panel pre-assembly → seat installation → interior panel bonding → wiring harness connection"), the deviation values ​​of all feature points are divided into corresponding stage data groups. Specifically, different stages of assembly operations (such as hoisting, tightening, and plugging) may introduce different types of errors. Dividing by stage can accurately locate the timing nodes where errors occur.

[0049] For each data set at each stage, key deviation parameters are calculated: based on the linear deviation value, the cumulative deviation (the sum of deviations at all feature points in that stage, reflecting the overall degree of deviation) and the deviation growth rate (the increment of deviation between adjacent time points, reflecting whether the deviation is increasing); based on the angular deviation value, the rotation axis offset (the total rotational deviation around a certain axis, reflecting the degree of attitude deviation) and the deviation convergence coefficient (the rate at which the deviation decreases over time, determining whether the deviation is controllable). These parameters characterize the deviation from a dynamic perspective, making them more valuable for analysis than simple static deviation values.

[0050] Finally, the multi-dimensional analysis results are integrated to generate a deviation evolution animation (dynamically displaying the change process of deviation at each stage), a 3D scatter plot (presenting the spatial distribution of cumulative deviation and rotation axis offset), and a time-series comparison curve (intuitively comparing the changing trends of deviation growth rate and convergence coefficient). These visualization results are then overlaid and annotated with the design deviation threshold ranges for each stage (e.g., the linear deviation threshold of ±0.3mm in the pre-assembly stage). Specifically, for example, points exceeding the threshold are marked in red in the scatter plot, and the threshold line is marked with a dashed line in the curve. Through this integration, the final comparison results can both quantify deviation data and intuitively present the dynamic trend of deviation, providing a clear basis for identifying the source of error and facilitating subsequent processing.

[0051] Furthermore, the step of combining the comparison results with the assembly contact force data and the workstation parameters to perform correlation analysis to identify the source of error and dynamic change trend, and generating corresponding error compensation instructions includes: Based on the spatial coordinate partitions in the workstation parameters, the assembly contact force data is segmented according to a preset time window and mapped to the corresponding spatial coordinate partitions respectively. Feature extraction is performed on the segmented contact force data within each spatial coordinate partition to obtain the contact force amplitude fluctuation and force vector change rate. Time-series correlation calculation is then performed to obtain the error correlation coefficient. The error compensation instruction is generated based on the error correlation coefficient.

[0052] It should be noted that, firstly, based on the spatial coordinate partitioning in the workstation parameters (dividing the assembly area into several sub-areas according to the module assembly position, such as "instrument panel area" and "seat area"), the assembly contact force data is segmented according to a preset time window (such as 1 second / window). Specifically, the time window ensures that short-term fluctuations in contact force (such as sudden changes in force at a certain moment) can be captured, while the spatial partitioning can locate the specific assembly part where the fluctuation occurs, thus realizing the "spatiotemporal dual-dimensional" force data division.

[0053] Features are extracted from the segmented contact force data within each spatial coordinate partition: contact force amplitude fluctuation (the difference between the maximum and minimum force values ​​within the window, reflecting force stability) and force vector change rate (the rate at which the force direction changes over time, reflecting whether abnormal sliding of the module has occurred). These features are then correlated with the deviation values ​​in the comparison results using time-series correlation calculations (such as the Pearson correlation coefficient) to obtain the error correlation coefficient. Specifically, the higher the coefficient, the stronger the correlation between contact force fluctuation and deviation in that area (for example, the correlation coefficient between contact force fluctuation and seat installation deviation in the seat area reaches 0.8, indicating that seat assembly deviation is very likely caused by abnormal contact force).

[0054] Based on the error correlation coefficient, the sources of error (such as inaccurate tooling positioning in highly correlated areas or deviations in the force control of the robot's end effector) and their dynamic trends (such as the correlation coefficient increasing with assembly progress, indicating that errors are accumulating) are identified. Targeted error compensation instructions are then generated. Specifically, the instructions must clearly specify the area to be adjusted and the parameter type (pose or force), providing a basis for subsequent robot adjustments and facilitating subsequent processing.

[0055] Furthermore, the step of generating the error compensation instruction based on the error correlation coefficient includes: At least three error correlation thresholds are set, and the error correlation coefficients are compared with each error correlation threshold to divide the high correlation interval, medium correlation interval and low correlation interval. For error correlation coefficients falling into the high correlation range, the assembly process of the corresponding spatial coordinate partition is reproduced in the virtual assembly scenario, the segmented contact force data time series curve of the partition is loaded synchronously, and the virtual robot executes the preset multi-dimensional pose fine-tuning scheme to record the fine-tuning parameter combination that meets the fit of the cockpit module in the virtual assembly scenario. Based on the fine-tuning parameter combination, and combined with the robot motion constraint range in the workstation parameters of the corresponding spatial coordinate partition, the pose adjustment amount, contact force control threshold and adjustment execution sequence are calculated. The pose adjustment amount, contact force control threshold and adjustment execution sequence are then encapsulated into error compensation instructions in a preset format.

[0056] It should be noted that, firstly, at least three error correlation thresholds (such as 0.7, 0.4, and 0.1) are set to divide the error correlation coefficient into high, medium, and low correlation ranges. Specifically, high correlation (>0.7) indicates that contact force fluctuation is the main cause of error, medium correlation (0.4-0.7) indicates that there is a certain influence, and low correlation (<0.4) indicates that the influence is small. This division can focus on the key sources of error.

[0057] For error correlation coefficients in high-correlation intervals, the assembly process of the corresponding spatial coordinate partitions is reproduced in a virtual assembly scenario (calling historical data from the dynamic mapping relationship), and the time-series curves of segmented contact force data for that partition are loaded synchronously (intuitively displaying force fluctuations). A virtual robot executes a preset multi-dimensional pose fine-tuning scheme (such as a combination of ±0.2mm on the X-axis and ±0.5° around the Z-axis), recording the fine-tuning parameter combinations that achieve the required fit for the cockpit module in the virtual scenario (such as X-axis -0.15mm and Z-axis +0.3°, where the fit meets the requirements). Specifically, virtual verification avoids the trial-and-error costs of physical adjustments and ensures the effectiveness of parameter combinations.

[0058] Finally, based on the fine-tuning parameter combination and the robot motion constraint range in the corresponding workstation parameters (such as the robot's maximum adjustment amount ±0.5mm), the specific pose adjustment amount (such as X-axis -0.15mm), contact force control threshold (such as controlling the contact force fluctuation within ±5N), and adjustment execution sequence (such as starting adjustment at the 3rd second of assembly) are calculated. These parameters are encapsulated into error compensation instructions in a preset format (such as XML or JSON). The instructions contain clear adjustment objects, parameters, and timing to ensure that the robot controller can execute accurately, ultimately achieving high-precision assembly of the cockpit module, facilitating subsequent processing.

[0059] Please see Figure 2 The third embodiment of the present invention provides: A modular assembly system for an automotive cockpit, wherein the system comprises: The data acquisition module is used to collect the actual pose data, assembly contact force data and workstation parameters of each cockpit module on the assembly line through a preset sensor group, and synchronize the actual pose data, assembly contact force data and workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene. The comparison module is used to compare the actual pose data of each cockpit module with the design reference parameters based on the dynamic mapping relationship and through a preset error analysis model, so as to collect the corresponding comparison results. The analysis module is used to perform correlation analysis by combining the comparison results with the assembly contact force data and the station parameters, so as to identify the source of error and the dynamic trend of change, and generate corresponding error compensation instructions. The control module is used to send the error compensation command to the robot controller on the assembly line to control the robot to perform assembly adjustments.

[0060] Furthermore, the acquisition module is specifically used for: The collected actual pose data, assembly contact force data, and workstation parameters are spatiotemporally aligned, and the timestamp reference of each data is unified to generate a standardized multi-dimensional data stream. A mapping rule library is constructed between the multi-dimensional data stream and the virtual entity attributes of the virtual assembly scene, and the virtual model motion trajectory and physical assembly path corresponding to the cockpit module are rendered in the virtual assembly scene according to the mapping rule library. The dynamic mapping relationship is constructed based on the motion trajectory of the virtual model and the corresponding physical assembly path.

[0061] Furthermore, the acquisition module is specifically used for: Extract the keyframe coordinates of the motion trajectory of the virtual model and the measured feature point coordinates of the physical assembly path, and compare the coordinates point by point at preset time intervals to generate the corresponding coordinate deviation matrix. When any deviation value in the coordinate deviation matrix exceeds the preset deviation threshold, the dynamic correction algorithm in the mapping rule base is invoked to adjust the coordinate transformation coefficient of the virtual model motion trajectory and the spatiotemporal scaling ratio of the physical assembly path until the deviation values ​​are all within the range of the preset deviation threshold, so as to form a mapping parameter set. The mapping parameter set is serialized to generate the corresponding dynamic mapping relationship.

[0062] Furthermore, the comparison module is specifically used for: Based on the dynamic mapping relationship, the three-dimensional coordinate set corresponding to the actual pose data of each cockpit module is extracted in the virtual assembly scene. At the same time, the three-dimensional reference coordinate set in the design reference parameters is called, and the three-dimensional coordinate set and the three-dimensional reference coordinate set are transformed to the same world coordinate system to obtain standard coordinate data pairs. The standard coordinate data is input into the preset error analysis model, and the feature points in the three-dimensional coordinate set are compared one by one with the corresponding feature points in the three-dimensional reference coordinate set through the feature point matching algorithm, and the linear deviation value and angular deviation value generated by each feature point on the coordinate axis are calculated. The linear deviation value and the angular deviation value are classified and statistically processed to generate corresponding comparison results.

[0063] Furthermore, the comparison module is specifically used for: According to the assembly sequence of the cockpit module, the linear deviation values ​​and angular deviation values ​​of all feature points are divided into stages so that the deviation values ​​of feature points within the same time sequence are assigned to the corresponding stage data group. The cumulative deviation and deviation growth rate are calculated based on the linear deviation values ​​in each stage data set, and the rotation axis offset and deviation convergence coefficient are calculated based on the angular deviation values ​​in each stage data set. An animation of deviation evolution is generated according to the assembly sequence stages. At the same time, a three-dimensional scatter plot of the cumulative deviation and rotation axis offset of each stage is generated, as well as a time-series comparison curve of the deviation growth rate and deviation convergence coefficient. The deviation evolution animation, the three-dimensional scatter plot and the time-series comparison curve are superimposed and labeled with the design deviation threshold range corresponding to each stage, and integrated to form the comparison result.

[0064] Furthermore, the analysis module is specifically used for: Based on the spatial coordinate partitions in the workstation parameters, the assembly contact force data is segmented according to a preset time window and mapped to the corresponding spatial coordinate partitions respectively. Feature extraction is performed on the segmented contact force data within each spatial coordinate partition to obtain the contact force amplitude fluctuation and force vector change rate. Time-series correlation calculation is then performed to obtain the error correlation coefficient. The error compensation instruction is generated based on the error correlation coefficient.

[0065] Furthermore, the analysis module is specifically used for: At least three error correlation thresholds are set, and the error correlation coefficients are compared with each error correlation threshold to divide the high correlation interval, medium correlation interval and low correlation interval. For error correlation coefficients falling into the high correlation range, the assembly process of the corresponding spatial coordinate partition is reproduced in the virtual assembly scenario, the segmented contact force data time series curve of the partition is loaded synchronously, and the virtual robot executes the preset multi-dimensional pose fine-tuning scheme to record the fine-tuning parameter combination that meets the fit of the cockpit module in the virtual assembly scenario. Based on the fine-tuning parameter combination, and combined with the robot motion constraint range in the workstation parameters of the corresponding spatial coordinate partition, the pose adjustment amount, contact force control threshold and adjustment execution sequence are calculated. The pose adjustment amount, contact force control threshold and adjustment execution sequence are then encapsulated into error compensation instructions in a preset format.

[0066] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the modular assembly method for an automotive cockpit as described above.

[0067] The fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the modular assembly method for an automotive cockpit as described above.

[0068] In summary, the modular assembly method and system for automotive cockpits provided in the above embodiments of the present invention can objectively and accurately complete the assembly of each cockpit module, thereby improving assembly efficiency.

[0069] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0072] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0073] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0074] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A modular assembly method for an automobile cockpit, characterized in that, The method includes: The actual posture data, assembly contact force data, and workstation parameters of each cockpit module on the assembly line are collected by a preset sensor group. The actual posture data, assembly contact force data, and workstation parameters are synchronized to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene. Based on the dynamic mapping relationship, the actual pose data of each cockpit module is compared with the design reference parameters through a preset error analysis model to collect the corresponding comparison results. The comparison results are combined with the assembly contact force data and the station parameters for correlation analysis to identify the source of error and dynamic change trend, and generate corresponding error compensation instructions. The error compensation command is sent to the robot controller on the assembly line to control the robot to perform assembly adjustments.

2. The modular assembly method for an automobile cockpit according to claim 1, characterized in that, The step of synchronizing the actual pose data, the assembly contact force data, and the workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene includes: The collected actual pose data, assembly contact force data, and workstation parameters are spatiotemporally aligned, and the timestamp reference of each data is unified to generate a standardized multi-dimensional data stream. A mapping rule library is constructed between the multi-dimensional data stream and the virtual entity attributes of the virtual assembly scene, and the virtual model motion trajectory and physical assembly path corresponding to the cockpit module are rendered in the virtual assembly scene according to the mapping rule library. The dynamic mapping relationship is constructed based on the motion trajectory of the virtual model and the corresponding physical assembly path.

3. The modular assembly method for an automobile cockpit according to claim 2, characterized in that, The step of constructing the dynamic mapping relationship based on the motion trajectory of the virtual model and the corresponding physical assembly path includes: Extract the keyframe coordinates of the motion trajectory of the virtual model and the measured feature point coordinates of the physical assembly path, and compare the coordinates point by point at preset time intervals to generate the corresponding coordinate deviation matrix. When any deviation value in the coordinate deviation matrix exceeds the preset deviation threshold, the dynamic correction algorithm in the mapping rule base is invoked to adjust the coordinate transformation coefficient of the virtual model motion trajectory and the spatiotemporal scaling ratio of the physical assembly path until the deviation values ​​are all within the range of the preset deviation threshold, so as to form a mapping parameter set. The mapping parameter set is serialized to generate the corresponding dynamic mapping relationship.

4. The modular assembly method for an automobile cockpit according to claim 1, characterized in that, The step of comparing the actual pose data of each cockpit module with the design reference parameters based on the dynamic mapping relationship and using a preset error analysis model to collect the corresponding comparison results includes: Based on the dynamic mapping relationship, the three-dimensional coordinate set corresponding to the actual pose data of each cockpit module is extracted in the virtual assembly scene. At the same time, the three-dimensional reference coordinate set in the design reference parameters is called, and the three-dimensional coordinate set and the three-dimensional reference coordinate set are transformed to the same world coordinate system to obtain standard coordinate data pairs. The standard coordinate data is input into the preset error analysis model, and the feature points in the three-dimensional coordinate set are compared one by one with the corresponding feature points in the three-dimensional reference coordinate set through the feature point matching algorithm, and the linear deviation value and angular deviation value generated by each feature point on the coordinate axis are calculated. The linear deviation value and the angular deviation value are classified and statistically processed to generate corresponding comparison results.

5. The modular assembly method for an automobile cockpit according to claim 4, characterized in that, The step of classifying and statistically processing the linear deviation value and the angular deviation value to generate corresponding comparison results includes: According to the assembly sequence of the cockpit module, the linear deviation values ​​and angular deviation values ​​of all feature points are divided into stages so that the deviation values ​​of feature points within the same time sequence are assigned to the corresponding stage data group. The cumulative deviation and deviation growth rate are calculated based on the linear deviation values ​​in each stage data set, and the rotation axis offset and deviation convergence coefficient are calculated based on the angular deviation values ​​in each stage data set. An animation of deviation evolution is generated according to the assembly sequence stages. At the same time, a three-dimensional scatter plot of the cumulative deviation and rotation axis offset of each stage is generated, as well as a time-series comparison curve of the deviation growth rate and deviation convergence coefficient. The deviation evolution animation, the three-dimensional scatter plot and the time-series comparison curve are superimposed and labeled with the design deviation threshold range corresponding to each stage, and integrated to form the comparison result.

6. The modular assembly method for an automobile cockpit according to claim 1, characterized in that, The step of combining the comparison results with the assembly contact force data and the workstation parameters to perform correlation analysis, in order to identify the source of error and dynamic change trend, and generate corresponding error compensation instructions includes: Based on the spatial coordinate partitions in the workstation parameters, the assembly contact force data is segmented according to a preset time window and mapped to the corresponding spatial coordinate partitions respectively. Feature extraction is performed on the segmented contact force data within each spatial coordinate partition to obtain the contact force amplitude fluctuation and force vector change rate. Time-series correlation calculation is then performed to obtain the error correlation coefficient. The error compensation instruction is generated based on the error correlation coefficient.

7. The modular assembly method for an automobile cockpit according to claim 6, characterized in that, The step of generating the error compensation instruction based on the error correlation coefficient includes: At least three error correlation thresholds are set, and the error correlation coefficients are compared with each error correlation threshold to divide the high correlation interval, medium correlation interval and low correlation interval. For error correlation coefficients falling into the high correlation range, the assembly process of the corresponding spatial coordinate partition is reproduced in the virtual assembly scenario, the segmented contact force data time series curve of the partition is loaded synchronously, and the virtual robot executes the preset multi-dimensional pose fine-tuning scheme to record the fine-tuning parameter combination that meets the fit of the cockpit module in the virtual assembly scenario. Based on the fine-tuning parameter combination, and combined with the robot motion constraint range in the workstation parameters of the corresponding spatial coordinate partition, the pose adjustment amount, contact force control threshold and adjustment execution sequence are calculated. The pose adjustment amount, contact force control threshold and adjustment execution sequence are then encapsulated into error compensation instructions in a preset format.

8. A modular assembly system for an automotive cockpit, characterized in that, The system includes: The data acquisition module is used to collect the actual pose data, assembly contact force data and workstation parameters of each cockpit module on the assembly line through a preset sensor group, and synchronize the actual pose data, assembly contact force data and workstation parameters to the virtual assembly scene of the preset digital twin system to construct a dynamic mapping relationship between the physical assembly state of the cockpit module and the virtual assembly scene. The comparison module is used to compare the actual pose data of each cockpit module with the design reference parameters based on the dynamic mapping relationship and through a preset error analysis model, so as to collect the corresponding comparison results. The analysis module is used to perform correlation analysis by combining the comparison results with the assembly contact force data and the station parameters, so as to identify the source of error and the dynamic trend of change, and generate corresponding error compensation instructions. The control module is used to send the error compensation command to the robot controller on the assembly line to control the robot to perform assembly adjustments.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the modular assembly method for automobile cockpit as described in any one of claims 1 to 7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the modular assembly method for the automotive cockpit as described in any one of claims 1 to 7.

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