Multi-source error compensation method, system, electronic device and storage medium

Abstract

The present disclosure provides a multi-source error compensation method, system, electronic device and storage medium, relating to the technical field of cylindrical structure assembly. The method comprises: acquiring multi-type dynamic shape data corresponding to the coaxial assembly of the cylindrical structure; based on the optimal reference axis and the optimal reference plane, processing the multi-type dynamic shape data to obtain an error value corresponding to each type of dynamic shape data in the multi-type dynamic shape data; based on the error value corresponding to each type of dynamic shape data, obtaining a comprehensive dynamic error; performing PID control on the comprehensive dynamic error to obtain a first compensation amount; and sending the first compensation amount to the assembly operation station to adjust the tool adjusting mechanism. The present disclosure can effectively suppress the accumulation of dynamic errors in the assembly process, greatly improve the coaxial assembly forming precision and batch consistency of the cylindrical structure.

Description

Technical Field

[0001] This disclosure relates to the field of coaxial splicing technology for cylindrical structural components, and more particularly to a multi-source error compensation method, system, electronic device, and storage medium. Background Technology

[0002] Cylindrical structural components, such as drying drums in asphalt mixing plants, mixing drums in concrete mixer trucks, and vibratory rollers in road rollers, are common core components in construction machinery. During the coaxial assembly process, these components are prone to multiple sources of dimensional and positional errors, including positioning tilt deviations, coaxiality deviations, radial runout deviations, and hole spacing deviations. These errors fluctuate dynamically in real time and are coupled and superimposed on each other. Related assembly processes often employ a tooling positioning + post-assembly sampling inspection model. Summary of the Invention

[0003] One technical problem this disclosure aims to solve is to provide a multi-source error compensation method, system, electronic device, and storage medium that can effectively suppress the accumulation of dynamic errors in the splicing process and significantly improve the coaxial splicing forming accuracy and batch consistency of cylindrical structural parts.

[0004] According to one aspect of this disclosure, a multi-source error compensation method is proposed, comprising: acquiring multiple types of dynamic shape and position data corresponding to the coaxial splicing points of cylindrical structural components; processing the multiple types of dynamic shape and position data based on the optimal reference axis and the optimal reference plane to obtain the error value corresponding to each type of dynamic shape and position data; obtaining a comprehensive dynamic error based on the error value corresponding to each type of dynamic shape and position data; performing PID proportional-integral-derivative control on the comprehensive dynamic error to obtain a first compensation amount; and sending the first compensation amount to the splicing point operation station to adjust the tooling adjustment mechanism.

[0005] In some embodiments, multiple sets of sampling point data corresponding to the cylindrical structural components are acquired, the sampling point data including contour data, docking end face data, and positioning reference data; based on the multiple sets of sampling point data, a reference axis and a reference plane are fitted using a least squares algorithm; based on the reference axis and the reference plane, a function is constructed, the function being the sum of squared deviations of the deviation values ​​of each set of sampling point data relative to the reference axis and the reference plane and the mean deviation value; with the goal of minimizing the function, the optimal reference axis and the optimal reference plane are obtained.

[0006] In some embodiments, obtaining the comprehensive dynamic error based on the error value corresponding to each type of dynamic shape and position data includes: determining the weight of each type of dynamic shape and position data at the next sampling point; and using the weight of each type of dynamic shape and position data at the next sampling point to perform a weighted calculation on the error values ​​corresponding to multiple types of dynamic shape and position data to obtain the comprehensive dynamic error.

[0007] In some embodiments, determining the weight of each type of dynamic shape and position data at the next sampling point includes: determining the error change of each type of dynamic shape and position data based on the error value corresponding to each type of dynamic shape and position data; and determining the weight of each type of dynamic shape and position data at the next sampling point based on the absolute value of the error change, the sum of the absolute values ​​of the error changes corresponding to multiple types of dynamic shape and position data, the weight learning rate coefficient, and the weight of each type of dynamic shape and position data at the current sampling point.

[0008] In some embodiments, performing PID control on the comprehensive dynamic error to obtain a first compensation amount includes: performing PID control on the comprehensive dynamic error to obtain a second compensation amount; determining a correction coefficient based on the magnitude of the comprehensive dynamic error; and determining the first compensation amount based on the product of the second compensation amount and the correction coefficient.

[0009] In some embodiments, determining the correction coefficient based on the magnitude of the comprehensive dynamic error includes: when the comprehensive dynamic error is greater than or equal to a first threshold, the correction coefficient is a first value; when the comprehensive dynamic error is less than the first threshold and greater than or equal to a second threshold, the correction coefficient is a second value, the second value being less than the first value; when the comprehensive dynamic error is less than the second threshold, the correction coefficient is a third value, the third value being less than the second value.

[0010] In some embodiments, the multiple types of dynamic shape and position data include: tilt, coaxiality, radial runout, and hole spacing.

[0011] In some embodiments, the various types of dynamic shape and position data are preprocessed.

[0012] According to another aspect of this disclosure, a multi-source error compensation system is also proposed, comprising: a multi-sensor collaborative sensing module configured to acquire multiple types of dynamic shape and position data corresponding to the coaxial splicing points of a cylindrical structural component; a dynamic error calculation and evaluation module configured to process the multiple types of dynamic shape and position data based on an optimal reference axis and an optimal reference plane, to obtain an error value corresponding to each type of dynamic shape and position data, and to obtain a comprehensive dynamic error based on the error value corresponding to each type of dynamic shape and position data; and a servo dynamic compensation execution module configured to perform PID proportional-integral-derivative control on the comprehensive dynamic error to obtain a first compensation amount, and to send the first compensation amount to the splicing point operation station module for adjusting the tooling adjustment mechanism.

[0013] In some embodiments, the multi-source error compensation system further includes: a dot-matrix workstation module, configured to receive the first compensation amount and adjust the tooling adjustment mechanism based on the first compensation amount.

[0014] In some embodiments, the multi-source error compensation system further includes at least one of the following: a data preprocessing module configured to preprocess the various types of dynamic shape and position data; and a process visualization and control module configured to interact with at least one of the following modules: the multi-sensor collaborative sensing module, the dynamic error calculation and evaluation module, the servo dynamic compensation execution module, the point-matching workstation module, and the data preprocessing module, and to perform visualization display.

[0015] According to another aspect of this disclosure, an electronic device is also proposed, comprising: at least one memory; and at least one processor coupled to said at least one memory, said at least one processor being configured to perform the multi-source error compensation method as described above based on instructions stored in said at least one memory.

[0016] According to another aspect of this disclosure, a computer-readable storage medium is also proposed, on which computer instructions are stored, which, when executed by a processor, implement the multi-source error compensation method as described above.

[0017] According to another aspect of this disclosure, a computer program product is also proposed that, when run on a computer, causes the computer to implement the multi-source error compensation method as described above.

[0018] In this embodiment, the optimal reference axis and the optimal reference plane are used to process multiple types of dynamic shape and position data to obtain the error value corresponding to each type of dynamic shape and position data, and then to obtain the comprehensive dynamic error. Based on PID control, the compensation amount is obtained to adjust the tooling adjustment mechanism, thereby effectively suppressing the accumulation of dynamic error in the splicing process and significantly improving the coaxial splicing forming accuracy and batch consistency of cylindrical structural parts.

[0019] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0020] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0021] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein: Figure 1 This is a flowchart illustrating some embodiments of the multi-source error compensation method disclosed herein; Figure 2 This is a flowchart illustrating some other embodiments of the multi-source error compensation method disclosed herein; Figure 3 This is a flowchart illustrating some further embodiments of the multi-source error compensation method disclosed herein; Figure 4 This is a block diagram of some embodiments of the multi-source error compensation system disclosed herein; Figure 5 Block diagrams of other embodiments of the multi-source error compensation system disclosed herein; Figure 6 Block diagrams showing some embodiments of the electronic devices disclosed herein. Detailed Implementation

[0022] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0023] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0024] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0025] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0026] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0028] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0029] The existing splicing processes mostly employ a tooling positioning + post-assembly sampling inspection model, which can only achieve static verification before splicing and error detection after completion, but cannot capture dynamic error changes in real time during the splicing process. Furthermore, the lack of a synchronous calculation and dynamic compensation mechanism for multiple types of geometric tolerances makes it impossible to correct instantaneous deviations online, resulting in large cumulative splicing errors, poor forming consistency, and difficulty in meeting the quality control requirements of coaxial splicing processes for such structural components, making rework and repair highly likely.

[0030] This disclosure provides a multi-source error compensation method that can effectively suppress the accumulation of dynamic errors in the splicing process, significantly improve the coaxial splicing forming accuracy and batch consistency of cylindrical structural parts, and upgrade the splicing process from post-event sampling inspection to dynamic and precise process control.

[0031] The present disclosure will now be described in conjunction with specific embodiments.

[0032] like Figure 1 As shown, Figure 1 This is a flowchart illustrating some embodiments of the multi-source error compensation method disclosed herein, which includes steps S11-S15.

[0033] In step S11, multiple types of dynamic shape and position data corresponding to the coaxial splicing points of the cylindrical structural components are obtained.

[0034] In some embodiments, multiple types of dynamic shape and position data corresponding to the coaxial splicing points of cylindrical structural components are collected in real time.

[0035] Various types of dynamic geometric data include tilt, coaxiality, radial runout, and hole spacing.

[0036] In some embodiments, the multi-sensor collaborative sensing module dynamically acquires data throughout the entire splicing operation. This multi-sensor collaborative sensing module can integrate a line laser sensor, a laser displacement sensor, and a tilt sensor. For example, the tilt sensor acquires the tilt angle, the line laser sensor acquires the coaxiality and radial runout, and the laser displacement sensor acquires the hole spacing, thereby capturing the instantaneous attitude disturbances and deviations of the splicing operation in real time.

[0037] This step achieves multi-dimensional error synchronous online perception by capturing real-time tooling micro-offsets, workpiece posture disturbances, and dynamic deviations at the splicing points, replacing the traditional single-point static detection method.

[0038] In step S12, based on the optimal reference axis and the optimal reference plane, multiple types of dynamic shape and position data are processed to obtain the error value corresponding to each type of dynamic shape and position data.

[0039] For example, a real-time dynamic benchmark is constructed by least-squares benchmark fitting, and four types of single-item shape and position errors are obtained by solving.

[0040] In step S13, the comprehensive dynamic error is obtained based on the error value corresponding to each type of dynamic shape and position data.

[0041] In some embodiments, a real-time comprehensive dynamic error is obtained through dynamic weight adaptive iterative coupling calculation. This comprehensive dynamic error is used to determine whether the current splicing accuracy meets the standard. If the accuracy does not meet the standard, subsequent steps are executed; if the accuracy meets the standard, continuous real-time monitoring continues.

[0042] In step S14, the comprehensive dynamic error is subjected to PID (Proportional Integral Differential) control to obtain the first compensation amount.

[0043] The optimal fine-tuning compensation amount is obtained through PID control.

[0044] In step S15, the first compensation amount is sent to the splicing workstation module so as to adjust the tooling adjustment mechanism.

[0045] The assembly workstation serves as the assembly operation platform for structural components. It is equipped with precision positioning fixtures and servo fine-tuning mechanisms, and works in conjunction with multi-sensor acquisition and dynamic compensation actions to complete the entire high-precision assembly operation.

[0046] In this step, for example, the workpiece posture and tooling position are adjusted and compensated in real time by driving a servo mechanism.

[0047] In the above embodiments, the optimal reference axis and the optimal reference plane are used to process multiple types of dynamic shape and position data to obtain the error value corresponding to each type of dynamic shape and position data, and then to obtain the comprehensive dynamic error. Based on PID control, the compensation amount is obtained to adjust the tooling adjustment mechanism, thereby effectively suppressing the accumulation of dynamic error in the splicing process and greatly improving the coaxial splicing forming accuracy and batch consistency of cylindrical structural parts.

[0048] In some embodiments, sampling point data corresponding to multiple sets of cylindrical structural components are acquired. The sampling point data includes contour data, docking end face data, and positioning reference data. Based on the multiple sets of sampling point data, the reference axis and reference plane are fitted using the least squares algorithm. Based on the reference axis and reference plane, a function is constructed, which is the sum of squared deviations of the deviation values ​​of each set of sampling point data relative to the reference axis and reference plane and the mean deviation value. The optimal reference axis and optimal reference plane are obtained by minimizing the function.

[0049] For example, multiple sets of spatial coordinate sampling points of the circumferential contour, mating end face, and positioning datum of a cylindrical structural component are collected. Based on the least squares algorithm, iterative optimization is performed to fit a dynamic datum that matches the real-time splicing state of the workpiece.

[0050] For example, through formula The optimal datum axis and optimal datum plane are calculated, where, The spatial sampling coordinate point number for the contour and docking end face; This represents the total number of sequence numbers for the sampling points; For the first The geometric position deviation of the group of sampling coordinate points relative to the reference axis and the reference plane includes: workpiece machining size deviation, tooling positioning offset, and splicing point tilt. This represents the average deviation of all sampled coordinate points, indicating the deviation of a single measuring point relative to the average deviation of all measuring points. The squaring calculation is to ensure that positive and negative deviations cancel each other out, while amplifying the influence of abnormal measuring points with large deviations. Essentially, this formula aims to minimize the dispersion of deviations across all sampling points, finding a set of optimal reference axes and optimal reference planes, thus minimizing the fluctuation of deviations of all measuring points relative to this reference. The smaller the value, the higher the degree of matching between the current reference axis and plane and all measuring points on site.

[0051] The above method uses minimizing the squared dispersion of all sampling points as the criterion for iterative convergence, and combines iterative solution with the spatial geometric model to output the dynamic optimal reference axis and dynamic optimal reference plane adapted to the field conditions.

[0052] In some embodiments, the dynamic reference axis is used as the solution reference for two types of radial form and position errors: coaxiality and radial runout, and the dynamic reference plane is used as the solution reference for two types of end face pose errors: tilt and hole spacing.

[0053] In some embodiments, obtaining the comprehensive dynamic error based on the error value corresponding to each type of dynamic shape and position data includes: determining the weight of each type of dynamic shape and position data at the next sampling point; and using the weight of each type of dynamic shape and position data at the next sampling point to perform weighted calculation on the error values ​​corresponding to multiple types of dynamic shape and position data to obtain the comprehensive dynamic error.

[0054] For example, dynamic form and position data includes tilt, coaxiality, radial runout, and hole spacing. Tilt error, coaxiality error, radial runout error, and hole spacing error are calculated. Tilt error is the angular deviation of the mating end face of the cylindrical structural component relative to the dynamic reference plane; coaxiality error is the offset of the actual central axis of the cylindrical structural component relative to the dynamic reference axis; radial runout error is the radial distance fluctuation of the sampling point of the outer circle contour of the cylindrical structural component relative to the dynamic reference axis; hole spacing error is the deviation between the measured relative position of the assembly hole and the theoretical standard hole spacing.

[0055] The tilt error, coaxiality error, radial runout error, and hole spacing error are weighted and calculated to obtain the comprehensive dynamic error.

[0056] For example, according to the formula The comprehensive dynamic error was calculated. ,in, These are the real-time detection values ​​for four types of geometrical errors. The value ranges from 1 to 4, corresponding to the sequence numbers of tilt error, coaxiality error, radial runout error, and hole spacing error, respectively. Indicates the next moment after the sampling time of the k-th frame. The weights of the term errors, where, This satisfies the normalization constraint.

[0057] If the overall dynamic error is less than or equal to the preset accuracy convergence threshold, the accuracy is determined to be up to standard, the compensation is completed, and the process continues. If there is still an error exceeding the standard, the system iteratively optimizes the compensation parameters and repeatedly fine-tunes the compensation until the error converges to the standard.

[0058] In some embodiments, determining the weight of each type of dynamic shape and position data at the next sampling point includes: determining the error change of each type of dynamic shape and position data based on the error value corresponding to each type of dynamic shape and position data; and determining the weight of each type of dynamic shape and position data at the next sampling point based on the absolute value of the error change, the sum of the absolute values ​​of the error changes corresponding to multiple types of dynamic shape and position data, the weight learning rate coefficient, and the weight of each type of dynamic shape and position data at the current sampling point.

[0059] For example, tilt error, coaxiality error, radial runout error, and hole spacing error can be used as the raw error data for subsequent dynamic weight iterations. Based on the raw error data, the coupling weights of each error term can be iteratively updated in real time according to the magnitude of error fluctuations. This automatically increases the coupling proportion of instantaneous abrupt errors, weakens the influence of steady-state micro-errors, adapts to instantaneous splicing conditions, dynamically quantifies the coupling relationship of multi-source errors, and outputs a comprehensive dynamic error that truly reflects the overall state of the current splicing.

[0060] For example, the formula for weight iteration and coupling calculation is: ,in, Represents the sampling time of the k-th frame. The original weights of the term errors; Indicates the next moment after the sampling time of the k-th frame. The weight of the term error, i.e. the error weight after iterative update; To represent the current time of the first... The real-time error fluctuation change of the term error; The weight learning rate coefficient is a pre-configured fixed parameter with a value range of 0.05-0.2, which is adapted to splicing scenarios with different accuracy requirements. It can be manually reset when the workpiece specifications are changed.

[0061] In the above embodiments, error evaluation is completed sequentially through least squares benchmark fitting, single-item shape and position error solution, and dynamic weight adaptive iterative coupling. The error coupling weight can be adaptively updated based on real-time sensing data to accurately quantify the dynamic splicing point comprehensive error. This can effectively avoid the accuracy distortion problem caused by fixed benchmark and fixed weight evaluation, and greatly improve the accuracy, real-time performance and working condition adaptability of splicing point error detection and evaluation.

[0062] In some embodiments, performing PID control on the comprehensive dynamic error to obtain a first compensation amount includes: performing PID control on the comprehensive dynamic error to obtain a second compensation amount; determining a correction coefficient based on the magnitude of the comprehensive dynamic error; and determining the first compensation amount based on the product of the second compensation amount and the correction coefficient.

[0063] The second compensation amount is the basic compensation fine-tuning amount of the servo mechanism, which is calculated by combining the real-time error value, cumulative deviation and changing trend.

[0064] For example, according to the formula The basic compensation fine-tuning amount is calculated. ,in, This is the proportional compensation coefficient, used to quickly eliminate instantaneous deviations; These are integral compensation coefficients used to eliminate steady-state residual errors; This is the differential compensation coefficient, used to predict the trend of error changes.

[0065] After calculating the basic compensation fine-tuning amount, the multi-gradient compensation interval is divided according to the real-time comprehensive dynamic error level. Correction coefficients are set to match different error conditions. The basic compensation fine-tuning amount of the variable parameter PID output is adaptively corrected to obtain the actual fine-tuning compensation amount finally executed by the servo mechanism, thereby adapting to the full-range error compensation requirements.

[0066] In some embodiments, when the overall dynamic error is greater than or equal to a first threshold, the correction coefficient is a first value; when the overall dynamic error is less than the first threshold and greater than or equal to a second threshold, the correction coefficient is a second value, which is less than the first value; when the overall dynamic error is less than the second threshold, the correction coefficient is a third value, which is less than the second value.

[0067] For example,

[0068] in, This is to account for the overall dynamic error, which is the actual fine-tuning compensation amount ultimately performed by the servo. Represents the correction coefficient for large error intervals; The correction coefficient represents the mean error interval; This represents the correction coefficient for the small error range; This is a preset compensation allowable threshold; , The preset error grading threshold is used to divide the compensation working condition range, where This is the high error threshold value. The low error threshold is set at a value where both are less than the allowable compensation threshold. .

[0069] In this embodiment, a two-level closed-loop compensation mechanism is constructed, consisting of variable-parameter PID basic compensation and piecewise coefficient adaptive correction. Based on the real-time error state, the servo fine-tuning amount is calculated hierarchically. A PLC (Programmable Logic Controller) servo system drives the tooling micro-adjustment mechanism to complete real-time workpiece posture and position adjustment and correction. This achieves dynamic error closed-loop correction and iterative accuracy convergence without interrupting the assembly operation. This compensation mechanism uses the basic compensation amount output by the PID algorithm as its core, and adaptively adjusts the compensation intensity through piecewise coefficients to achieve hierarchical and precise compensation for rapid convergence of large errors and fine correction of small errors, ensuring a smooth, overshoot-free, and highly efficient convergence process during the assembly and posture adjustment.

[0070] In some embodiments, multiple types of dynamic geometries are preprocessed.

[0071] For example, raw data collected by multiple sensors is processed by filtering, removing abnormal noise, unifying coordinates and aligning data to eliminate acquisition errors caused by sensor jitter and environmental interference, ensuring the stability and validity of input data, and providing a reliable data foundation for subsequent high-precision error calculation.

[0072] The following section will further describe the scheme disclosed herein, with accompanying drawings.

[0073] Figure 2 The following is a flowchart illustrating another embodiment of the multi-source error compensation method disclosed herein, which includes steps S21-S29.

[0074] In step S21, four types of raw shape and position data are collected in real time using multiple sensors.

[0075] For example, real-time data collection of cylindrical structural components' tilt, coaxiality, radial runout, and hole spacing.

[0076] Before this step, the system first performs initialization and parameter configuration. Based on the splicing tolerance requirements, it presets various geometrical error accuracy thresholds, servo compensation coefficients, iterative convergence accuracy and other parameters, completes sensor calibration and timing synchronization, and positions and installs the column structure at the splicing station.

[0077] In step S22, the raw data is preprocessed.

[0078] The system performs filtering, noise reduction, anomaly removal, coordinate calibration, and synchronization processing on multi-source raw data to output stable and effective measured data.

[0079] In step S23, the dynamic reference axis and dynamic reference plane are fitted based on the least squares algorithm.

[0080] In step S24, based on the fitted dynamic reference axis and dynamic reference plane and real-time measured data, the accurate single-item error value is obtained.

[0081] This step completed the quantitative solution of the shape and position errors of the four core splicing points.

[0082] In step S25, the real-time fluctuation of the error is read.

[0083] In step S26, dynamic weight adaptive iterative update is performed.

[0084] In step S27, the real-time comprehensive dynamic error is obtained based on the iteratively updated dynamic weights and the four types of real-time single-item shape and position errors.

[0085] In step S28, it is determined whether the overall dynamic error is less than or equal to the compensation allowable threshold. If so, step S29 is executed; otherwise, step S26 is executed.

[0086] In step S29, the compensation is completed.

[0087] In the above embodiments, the dynamic online perception and calculation system for the entire splicing process can achieve accurate calculation and adaptive quantitative evaluation of multi-source splicing errors, effectively avoid the accuracy distortion problem caused by fixed benchmarks and fixed weight evaluations, and greatly improve the accuracy, real-time performance and adaptability of splicing error detection and evaluation.

[0088] Figure 3 This is a flowchart illustrating some further embodiments of the multi-source error compensation method disclosed herein, which includes steps S31-S38.

[0089] In step S31, the comprehensive dynamic error is acquired in real time.

[0090] If the overall dynamic error exceeds the compensation allowable threshold, compensation instructions and parameters are generated.

[0091] In step S32, the variable parameter PID is called to calculate the basic compensation amount.

[0092] In step S33, a correction coefficient is matched based on the magnitude of the comprehensive dynamic error.

[0093] In step S34, the basic compensation amount is adaptively adjusted based on the correction coefficient to obtain the optimal fine-tuning compensation amount.

[0094] In step S35, the servo mechanism performs attitude compensation.

[0095] In step S36, the error is collected again and iteratively detected.

[0096] In step S37, it is determined whether the obtained comprehensive dynamic error is less than or equal to the compensation allowable threshold. If so, step S38 is executed; otherwise, step S31 is executed.

[0097] In step S38, maintain the operation and continue monitoring.

[0098] In the above embodiments, the two-level closed-loop dynamic compensation mechanism, which combines variable parameter PID with piecewise coefficient adaptive correction, can adaptively adjust the compensation intensity according to the real-time error magnitude, achieving layered and precise compensation with rapid correction of large deviations and fine convergence of small deviations. This effectively ensures that the splicing and attitude adjustment process is stable without overshoot or attitude oscillation, achieving closed-loop convergence of errors under all working conditions, and significantly improving splicing accuracy and operational stability.

[0099] In other embodiments of this disclosure, the dynamic error data of the entire splicing process, the accuracy status of each quality control point, and the compensation execution record can be displayed in real time. It supports real-time data storage, accuracy trend analysis, and compensation parameter traceability. It has out-of-tolerance prompts and manual intervention fine-tuning functions, so as to realize the transparency, closed-loop, and traceable control of the splicing process.

[0100] In this embodiment, the entire process of splicing error data, compensation parameters, iteration count, and convergence results is recorded. A quality control report is generated upon completion of the task, enabling full-process data traceability and continuous accumulation of compensation samples to optimize system parameters. Furthermore, it supports manual parameter fine-tuning and emergency intervention, achieving intelligent closed-loop quality control of the process.

[0101] The multi-source error dynamic compensation method disclosed herein relies on multiple sensors to collect dynamic multi-source errors of the splicing process online. It quantifies the comprehensive error in real time through dynamic benchmark fitting and dynamic weight adaptive iteration mechanism, and combines piecewise variable parameter PID servo control to realize real-time dynamic compensation and iterative convergence of the operation process, thereby achieving full closed-loop control of dynamic errors in the splicing process.

[0102] Those skilled in the art will understand that, in the methods described in the specific embodiments, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0103] The multi-source error compensation system of this disclosure will now be further described in conjunction with the accompanying drawings.

[0104] Figure 4 The diagram shows some embodiments of the multi-source error compensation system disclosed herein, which includes a multi-sensor collaborative sensing module 41, a dynamic error calculation and evaluation module 42, and a servo dynamic compensation execution module 43.

[0105] The multi-sensor collaborative sensing module 41 is configured to acquire multiple types of dynamic shape and position data corresponding to the coaxial splicing points of the cylindrical structural components.

[0106] In some embodiments, the multi-sensor collaborative sensing module integrates a line laser sensor, a laser displacement sensor, and a tilt sensor to synchronously and in real time collect the tilt, coaxiality, radial runout, and hole spacing corresponding to the coaxial splicing points of the cylindrical structural components.

[0107] In some embodiments, it may also be as follows Figure 5 As shown, the multi-source error compensation system includes a data preprocessing module 51, which is configured to preprocess multiple types of dynamic shape and position data.

[0108] For example, the data preprocessing module receives raw data acquired in real time from multiple sensors, performs data filtering, noise removal, coordinate unification, and data synchronization alignment, eliminating acquisition errors caused by sensor jitter and environmental interference, ensuring the stability and validity of the input data, and providing a reliable data foundation for subsequent high-precision error calculation. For example, the data preprocessing module transmits stable and valid measured data to the dynamic error calculation and evaluation module.

[0109] The dynamic error calculation and evaluation module 42 is configured to process multiple types of dynamic shape and position data based on the optimal reference axis and the optimal reference plane, obtain the error value corresponding to each type of dynamic shape and position data, and obtain the comprehensive dynamic error based on the error value corresponding to each type of dynamic shape and position data.

[0110] In some embodiments, the dynamic error calculation and evaluation module 42 is further configured to acquire multiple sets of sampling point data corresponding to cylindrical structural components, including contour data, docking end face data, and positioning reference data; based on the multiple sets of sampling point data, fit the reference axis and reference plane using the least squares algorithm; based on the reference axis and reference plane, construct a function, which is the sum of squared deviations of the deviation values ​​of each set of sampling point data relative to the reference axis and reference plane and the mean deviation value; and obtain the optimal reference axis and optimal reference plane by minimizing the function.

[0111] In some embodiments, the dynamic error calculation and evaluation module 42 is further configured to determine the weight of each type of dynamic shape and position data at the next sampling point; and to use the weight of each type of dynamic shape and position data at the next sampling point to perform weighted calculation on the error values ​​corresponding to multiple types of dynamic shape and position data to obtain the comprehensive dynamic error.

[0112] In some embodiments, the dynamic error calculation and evaluation module 42 is further configured to determine the error change amount of each type of dynamic shape and position data based on the error value corresponding to each type of dynamic shape and position data; and to determine the weight of each type of dynamic shape and position data at the next sampling point based on the absolute value of the error change amount, the sum of the absolute values ​​of the error changes corresponding to multiple types of dynamic shape and position data, the weight learning rate coefficient, and the weight of each type of dynamic shape and position data at the current sampling point.

[0113] The dynamic error calculation and evaluation module is based on real-time preprocessed sensor data. Through a hierarchical algorithm logic of benchmark fitting, single-item error solving, and dynamic weight adaptive iteration, it completes the accurate calculation and dynamic coupling quantization of multi-source form and position errors, and outputs a comprehensive dynamic error that fits the real-time splicing conditions, providing an accurate quantitative basis for subsequent servo compensation calculation.

[0114] The servo dynamic compensation execution module 43 is configured to perform PID proportional-integral-derivative control on the comprehensive dynamic error to obtain the first compensation amount, and send the first compensation amount to the splicing workstation module so as to adjust the tooling adjustment mechanism.

[0115] In some embodiments, the servo dynamic compensation execution module 43 is configured to perform PID control on the comprehensive dynamic error to obtain a second compensation amount; determine a correction coefficient based on the magnitude of the comprehensive dynamic error; and determine a first compensation amount based on the product of the second compensation amount and the correction coefficient.

[0116] In some embodiments, when the overall dynamic error is greater than or equal to a first threshold, the correction coefficient is a first value; when the overall dynamic error is less than the first threshold and greater than or equal to a second threshold, the correction coefficient is a second value, which is less than the first value; when the overall dynamic error is less than the second threshold, the correction coefficient is a third value, which is less than the second value.

[0117] The servo dynamic compensation execution module receives the compensation command, calls the variable parameter PID and the piecewise dynamic convergence model, adaptively calculates the optimal fine-tuning compensation amount, drives the servo mechanism to complete the real-time attitude adjustment compensation of the workpiece posture and tooling position, realizes the overshoot-free closed-loop correction, and does not interrupt the splicing operation process.

[0118] In other embodiments of this disclosure, such as Figure 5 As shown, the multi-source error compensation system may also include a splicing operation station module 52, which is configured to receive a first compensation amount and adjust the tooling adjustment mechanism based on the first compensation amount.

[0119] The assembly workstation module 52 is pre-initialized and configured with parameters. Based on the assembly tolerance requirements, it presets various geometrical error accuracy thresholds, servo compensation coefficients, iterative convergence accuracy, and other parameters. It completes sensor calibration and timing synchronization, and positions and installs the workpiece at the assembly workstation. After receiving the actual fine-tuning compensation amount, it adjusts the tooling adjustment mechanism.

[0120] In other embodiments of this disclosure, such as Figure 5As shown, the multi-source error compensation system may also include a process visualization control module 53, which is configured to interact with at least one of the following modules: multi-sensor collaborative sensing module, dynamic error calculation and evaluation module, servo dynamic compensation execution module, point-to-point operation station module, and data preprocessing module, and to perform visualization display.

[0121] The process visualization and control module displays dynamic error data, accuracy status of each quality control point, and compensation execution records in real time throughout the entire assembly process. It supports real-time data storage, accuracy trend analysis, and compensation parameter traceability. It also features out-of-tolerance alerts and manual intervention for fine-tuning, enabling transparent, closed-loop, and traceable control of the assembly process.

[0122] This multi-source error dynamic compensation system utilizes a multi-sensor collaborative sensing module, a data preprocessing module, a dynamic error calculation and evaluation module, a servo dynamic compensation execution module, a process visualization and control module, and a splicing workstation. These modules work together in real time to achieve intelligent closed-loop control of the entire splicing process, including online detection, synchronous calculation, dynamic evaluation, real-time closed-loop compensation, and data traceability of multi-source dynamic errors. No manual intervention is required, and the system can suppress the accumulation of splicing errors in real time, ensuring uniform and stable coaxial splicing quality of cylindrical structural parts, effectively improving product consistency and production efficiency.

[0123] It should be noted that the above modules are logical modules divided according to their specific functions, and are not used to restrict the specific implementation method. For example, they can be implemented in software, hardware, or a combination of software and hardware. In actual implementation, the above modules can be implemented as independent physical entities, or they can be implemented by a single entity (e.g., a processor (CPU or DSP, etc.), integrated circuit, etc.).

[0124] Multi-source error compensation systems can also be presented in the form of electronic devices. For example... Figure 6 As shown, Figure 6 This is a block diagram of some embodiments of the electronic device disclosed herein. The electronic device 6 includes: at least one memory 61; and at least one processor 62 coupled to the at least one memory 61, the at least one processor 62 being configured to perform the multi-source error compensation method described above based on instructions stored in the at least one memory 61. The memory 61 may be a disk, flash memory, or any other non-volatile storage medium. The memory is used to store the instructions in the above embodiments. The processor 62, coupled to the memory 61, may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 62 is used to execute the instructions stored in the memory.

[0125] In some embodiments, the processor 62 is coupled to the memory 61 via a BUS bus 63. The electronic device 6 can also be connected to an external storage device 65 via a storage interface 64 to access external data, and can also be connected to a network or another computer system (not shown) via a network interface 66. Further details are omitted here.

[0126] In this embodiment, data instructions are stored in a memory and then processed by a processor. A multi-sensor collaborative online sensing system is constructed to collect multi-source form and position errors throughout the entire splicing process in real time. A multi-precision evaluation model based on benchmark fitting and least squares is built to simultaneously solve multiple dynamic deviations of form and position tolerances. A dynamic error compensation algorithm for the splicing process is established, and combined with a PLC servo control system, real-time micro-motion adjustment compensation of tooling posture and workpiece position is realized, forming a closed-loop control of the entire process of "real-time detection - synchronous calculation - dynamic compensation - feedback convergence". This effectively suppresses the accumulation of dynamic errors in the splicing process, significantly improves the coaxial splicing forming accuracy and batch consistency of cylindrical structural parts, and upgrades the splicing process from post-event sampling inspection to dynamic and precise process control.

[0127] In other embodiments, a computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of the methods described above. Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, apparatus, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] In some embodiments, a computer program product is protected, comprising a computer program or instructions that, when executed by a processor, implement the methods described above. The computer program product includes a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from ROM. When the computer program is executed by a CPU, it performs the functions defined in the methods of embodiments of this disclosure.

[0129] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] This concludes the detailed description of the present disclosure. To avoid obscuring the concept of the disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0133] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0134] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A multi-source error compensation method, comprising: Acquire multiple types of dynamic shape and position data corresponding to the coaxial splicing points of cylindrical structural components; Based on the optimal reference axis and the optimal reference plane, the multiple types of dynamic shape and position data are processed to obtain the error value corresponding to each type of dynamic shape and position data. Based on the error value corresponding to each type of dynamic shape and position data, a comprehensive dynamic error is obtained; The comprehensive dynamic error is subjected to PID proportional-integral-derivative control to obtain the first compensation amount; The first compensation amount is sent to the assembly workstation so that the tooling adjustment mechanism can be adjusted.

2. The multi-source error compensation method according to claim 1 further includes: Acquire multiple sets of sampling point data corresponding to the cylindrical structural components, wherein the sampling point data includes contour data, docking end face data, and positioning reference data; Based on multiple sets of sampling point data, the least squares algorithm is used to fit the reference axis and reference plane; Based on the reference axis and the reference plane, a function is constructed, which is the sum of squares of the deviations of each set of sampled data relative to the reference axis and the reference plane and the mean deviation; With the goal of minimizing the function, the optimal reference axis and the optimal reference plane are obtained.

3. The multi-source error compensation method according to claim 1, wherein, Based on the error values ​​corresponding to each type of dynamic shape and position data, the comprehensive dynamic error includes: Determine the weight of each type of dynamic shape and position data at the next sampling point; By using the weights corresponding to each type of dynamic shape and position data at the next sampling point, the error values ​​corresponding to multiple types of dynamic shape and position data are weighted and calculated to obtain the comprehensive dynamic error.

4. The multi-source error compensation method according to claim 3, wherein, Determining the weight of each type of dynamic shape and position data at the next sampling point includes: Based on the error value corresponding to each type of dynamic shape and position data, determine the error change amount of each type of dynamic shape and position data; Based on the absolute value of the error change, the sum of the absolute values ​​of the error changes corresponding to the multiple types of dynamic shape and position data, the weight learning rate coefficient, and the weight of each type of dynamic shape and position data at the current sampling point, the weight of each type of dynamic shape and position data at the next sampling point is determined.

5. The multi-source error compensation method according to claim 1, wherein, The first compensation amount is obtained by applying PID proportional-integral-derivative control to the comprehensive dynamic error, which includes: The comprehensive dynamic error is subjected to PID control to obtain the second compensation amount; The correction coefficient is determined based on the magnitude of the overall dynamic error; The first compensation amount is determined based on the product of the second compensation amount and the correction coefficient.

6. The multi-source error compensation method according to claim 5, wherein, Based on the magnitude of the comprehensive dynamic error, the correction coefficients are determined as follows: If the overall dynamic error is greater than or equal to the first threshold, the correction coefficient is taken as the first value; When the overall dynamic error is less than the first threshold and greater than or equal to the second threshold, the correction coefficient is a second value, which is less than the first value. If the overall dynamic error is less than the second threshold, the correction coefficient is set to a third value, which is less than the second value.

7. The multi-source error compensation method according to any one of claims 1 to 6, wherein, The various types of dynamic geometries include: Inclination, coaxiality, radial runout, and hole spacing.

8. The multi-source error compensation method according to any one of claims 1 to 6, further comprising: The various types of dynamic shape and position data are preprocessed.

9. A multi-source error compensation system, comprising: The multi-sensor collaborative sensing module is configured to acquire multiple types of dynamic shape and position data corresponding to the coaxial splicing points of cylindrical structural components; The dynamic error calculation and evaluation module is configured to process the multiple types of dynamic shape and position data based on the optimal reference axis and the optimal reference plane, obtain the error value corresponding to each type of dynamic shape and position data, and obtain the comprehensive dynamic error based on the error value corresponding to each type of dynamic shape and position data. The servo dynamic compensation execution module is configured to perform PID proportional-integral-derivative control on the comprehensive dynamic error to obtain a first compensation amount, and send the first compensation amount to the splicing workstation module so as to adjust the tooling adjustment mechanism.

10. The multi-source error compensation system according to claim 9, further comprising: The assembly workstation module is configured to receive the first compensation amount and adjust the tooling adjustment mechanism based on the first compensation amount.

11. The multi-source error compensation system according to claim 10, further comprising at least one of the following: The data preprocessing module is configured to preprocess the various types of dynamic shape and position data; The process visualization and control module is configured to interact with at least one of the following modules: the multi-sensor collaborative sensing module, the dynamic error calculation and evaluation module, the servo dynamic compensation execution module, the point-matching workstation module, and the data preprocessing module, and to perform visualization display.

12. An electronic device, comprising: At least one memory; as well as At least one processor coupled to the at least one memory, the at least one processor being configured to execute the multi-source error compensation method as described in any one of claims 1 to 8 based on instructions stored in the at least one memory.

13. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the multi-source error compensation method as described in any one of claims 1 to 8.

14. A computer program product, when run on a computer, causes the computer to implement the multi-source error compensation method as described in any one of claims 1 to 8.

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