Multi-station cooperative control platform of automobile sunroof automation production line

Abstract

The application discloses a multi-station collaborative control platform of an automobile sunroof automatic production line, and relates to the field of intelligent manufacturing. The platform comprises a standard working parameter acquisition module for acquiring standard operation parameters; a sunroof station processing quality evaluation result extraction module for extracting quality evaluation results; a station quality centralized analysis module for centrally analyzing a quality evaluation result set and screening abnormal stations; a quality abnormal station correlation neighborhood construction module for constructing an abnormal station correlation neighborhood; an initial collaborative control scheme generation module for collaborative control; an overall collaborative control scheme analysis module for overall collaborative control analysis; and a multi-station collaborative control module for collaborative control. The platform solves the technical problem of lack of globality in existing sunroof automatic production line control, which leads to insufficient control flexibility and accuracy, and achieves the technical effect of improving control flexibility and accuracy through global collaborative control.

Description

Technical Field

[0001] This application relates to the field of intelligent manufacturing, and in particular to a multi-station collaborative control platform for an automated production line for automotive sunroofs. Background Technology

[0002] With the rapid development of the automotive manufacturing industry, the production quality and efficiency of sunroofs, as an important component for enhancing vehicle comfort and aesthetics, are receiving increasing attention. To meet market demand, automated production lines for automotive sunroofs have emerged, achieving efficient and precise production through highly integrated machinery and advanced control systems. However, in actual production, various factors, such as equipment wear, material differences, and improper process parameter settings, can lead to fluctuations in sunroof processing quality, thus affecting the overall production line efficiency and product quality. Existing automated automotive sunroof production lines typically employ single-station quality control or simple collaborative control of stations based on preset rules, such as adjusting the input parameters of subsequent stations based on the output status of preceding stations. Because existing methods often focus only on single stations or simple collaborative control, lacking a holistic consideration of the entire production line, the overall collaborative effect is poor, making it difficult to adapt to complex and changing production environments and process requirements, resulting in low production efficiency and unstable product quality.

[0003] Currently, the control of automated production lines for automotive sunroofs suffers from a lack of global perspective, resulting in insufficient flexibility and accuracy in control. Summary of the Invention

[0004] This application provides a multi-station collaborative control platform for an automated production line for automotive sunroofs. It employs a method of traversing all stations on the production line, collecting standard operating parameters of the equipment, evaluating the processing quality of stations within a preset window, extracting the evaluation results to form a processing quality evaluation set, and using a quality concentrator to centrally analyze the evaluation results, identifying abnormal stations and constructing a neighborhood association for these abnormal stations. Based on the abnormal stations and their associated neighborhoods, combined with the standard operating parameters of the equipment, an initial collaborative control scheme is generated. Based on the initial collaborative control scheme and the first set of stations (excluding abnormal stations and their associated neighborhoods), an overall collaborative control scheme analysis is performed to obtain a target collaborative control scheme. This target collaborative control scheme is then used to perform multi-station collaborative control of the production line. Through these technical means, the platform achieves the technical effect of improving the flexibility and accuracy of control through global collaborative control.

[0005] This application provides a multi-station collaborative control platform for an automated production line of automotive sunroofs, comprising: a standard working parameter acquisition module, used to traverse R stations of the automated production line to acquire standard working parameters and obtain standard operating parameters for the R station equipment, where R is an integer greater than or equal to 1; a sunroof station processing quality assessment result extraction module, used to extract the sunroof station processing quality assessment results of the R stations within a preset window to obtain a set of R sunroof station processing quality assessments; a station quality centralized analysis module, used to perform station quality centralized analysis on the set of R sunroof station processing quality assessment results using a quality concentrator to obtain a set of R sunroof station processing quality centralized assessment results, and to screen abnormal stations based on the set of R sunroof station processing quality centralized assessment results to obtain L abnormal quality stations and L abnormal sunroof station processing quality centralized assessment results, where L is an integer greater than or equal to 1 and less than or equal to R; and a quality abnormal station association neighborhood structure. The system comprises the following modules: a module for constructing L associated neighborhoods of L quality-abnormal workstations within the automated automotive sunroof production line, wherein each associated neighborhood includes the workstation preceding and following the corresponding quality-abnormal workstation; an initial collaborative control scheme generation module for performing collaborative control on the L quality-abnormal workstations and their associated neighborhoods based on the magnitude of the centralized quality assessment results of the L abnormal sunroof workstations and the standard operating parameters of the R workstations, thereby obtaining an initial collaborative control scheme; an overall collaborative control scheme analysis module for performing overall collaborative control scheme analysis based on the initial collaborative control scheme and the first set of workstations among the R workstations excluding the L quality-abnormal workstations and their associated neighborhoods, thereby obtaining a target collaborative control scheme; and a multi-workstation collaborative control module for performing multi-workstation collaborative control on the automated automotive sunroof production line using the target collaborative control scheme.

[0006] In a possible implementation, the workstation quality centralized analysis module includes: a quality concentrator construction unit for constructing a quality concentrator, wherein the quality concentrator includes a quality centralized analysis function; a traversal calculation unit for traversing and calculating the mean of the set of processing quality evaluation results for the R skylight workstations to obtain the mean of the processing quality evaluation results for the R skylight workstations; and a workstation quality centralized analysis unit for inputting the mean of the processing quality evaluation results for the R skylight workstations and the set of processing quality evaluation results for the R skylight workstations into the quality concentrator for workstation quality centralized analysis, and obtaining the centralized evaluation results of the processing quality for the R skylight workstations when the number of iterations meets a preset number.

[0007] In a possible implementation, the mass concentrator construction unit includes: a mass concentrator analysis function construction subunit, used to construct a mass concentrator analysis function, wherein the mass concentrator analysis function is: Where m(x) represents the centralized evaluation result of the sunroof workstation processing quality, N(x) represents the neighborhood of the sunroof workstation processing quality evaluation results within the preset distance range from the average value of the sunroof workstation processing quality evaluation results, and x represents the average value of the sunroof workstation processing quality evaluation results. i Let K(·) be the processing quality evaluation result of the i-th skylight station in the neighborhood, and K(·) be the Gaussian kernel function.

[0008] In a possible implementation, the workstation quality centralized analysis module includes: a skylight workstation standard processing quality assessment result acquisition unit, used to acquire R skylight workstation standard processing quality assessment results for R workstations; a judgment unit, used to judge whether the R skylight workstation processing quality centralized assessment results meet the R skylight workstation standard processing quality assessment results, and obtain a judgment result; an abnormal skylight processing quality centralized assessment result acquisition unit, used to take the skylight workstation processing quality centralized assessment results with negative judgment results as L abnormal skylight processing quality centralized assessment results; and a quality abnormal workstation acquisition unit, used to take the workstations corresponding to the L abnormal skylight processing quality centralized assessment results as the L quality abnormal workstations.

[0009] In a possible implementation, the initial collaborative control scheme generation module includes: a workstation equipment standard operating parameter matching unit, used to match the L quality-abnormal workstations and the workstation equipment standard operating parameters of the L neighboring workstations based on the R workstation equipment standard operating parameters, to obtain L workstation equipment standard operating parameters and L neighboring workstation equipment standard operating parameter sets; and a first initial collaborative control scheme acquisition unit, used to input the processing quality centralized evaluation results of the L abnormal workstations, the L workstation equipment standard operating parameters, and the L neighboring workstation equipment standard operating parameter sets into an initial collaborative control scheme identifier for analysis, to obtain a first initial collaborative control scheme, wherein the first initial collaborative control scheme includes L first initial workstation equipment operating parameters and L... The system comprises: a first initial generation set of operating parameters for neighboring workstations; a first generation loss time acquisition unit, used to analyze the equipment idling time and semi-processing window product stagnation time of the first initial generation collaborative control scheme, and superimpose the analysis results to obtain the first generation loss time; a first initial generation collaborative control adjustment scheme set acquisition unit, used to randomly adjust at least one of the operating parameters of the first initial generation neighboring workstations in the L sets of operating parameters according to a preset adjustment scale multiple times to obtain a first initial generation collaborative control adjustment scheme set; and a scheme iteration unit, used to iterate the first initial generation collaborative control scheme based on the first initial generation collaborative control adjustment scheme set and the first generation loss time to obtain the initial collaborative control scheme.

[0010] In a possible implementation, the scheme iteration unit includes: a loss time analysis subunit, used to perform loss time analysis on the first set of generated initial cooperative control adjustment schemes to obtain a first set of generated adjustment loss times; a judgment processing subunit, used to determine whether there exists a first generated adjustment loss time less than or equal to the first generated loss time in the first set of generated adjustment loss times; if so, iterating the first generated initial cooperative control scheme according to the first generated initial cooperative control adjustment scheme corresponding to the minimum value in the first set of generated adjustment loss times to obtain a second generated initial cooperative control adjustment scheme; and a scheme iteration subunit, used to iterate the second generated initial cooperative control adjustment scheme until a preset number of iterations is met to obtain the initial cooperative control scheme.

[0011] In a possible implementation, the first unit for generating an initial cooperative control scheme acquisition includes: a training data acquisition subunit, used to acquire the centralized evaluation results of the processing quality of multiple sample abnormal window workstations, the standard operating parameters of multiple sample workstations, and the set of standard operating parameters of multiple sample neighboring workstations as training data; an initial cooperative control scheme recognizer construction subunit, used to construct the generator and adversary of the initial cooperative control scheme recognizer based on a generative adversarial network framework; and an alternating supervised training subunit, used to perform alternating supervised training on the generator and adversary using the training data until the training converges, thereby obtaining the initial cooperative control scheme recognizer.

[0012] In a possible implementation, the overall collaborative control scheme analysis module includes: a first workstation equipment standard operating parameter set acquisition unit, used to obtain the first workstation equipment standard operating parameter set by matching the R workstation equipment standard operating parameters with the workstation equipment standard operating parameters of the first workstation set; and an overall collaborative analysis unit, used to analyze the first workstation equipment standard operating parameter set and the initial collaborative control scheme using the overall collaborative network layer to obtain the target collaborative control scheme.

[0013] This application proposes a multi-station collaborative control platform for an automated automotive sunroof production line. The platform uses a standard operating parameter acquisition module to collect standard operating parameters from R stations along the production line, obtaining standard operating parameters for each station. A sunroof station processing quality assessment result extraction module extracts the processing quality assessment results from each of the R stations within a preset window, resulting in a set of R sunroof station processing quality assessments. A station quality centralized analysis module uses a quality concentrator to perform centralized quality analysis on the set of R sunroof station processing quality assessment results, obtaining a centralized quality assessment result for R sunroof stations. Based on this centralized quality assessment result, abnormal stations are screened, resulting in L abnormal quality stations and L centralized quality assessment results for the abnormal sunroof stations. Finally, a neighboring region construction module for the abnormal quality stations is used. Based on the locations of L quality-abnormal workstations in the automated automotive sunroof production line, a neighborhood association of L quality-abnormal workstations is constructed. The initial collaborative control scheme generation module, based on the magnitude of the centralized quality assessment results of the L abnormal sunroof workstations and combined with the standard operating parameters of R workstations, performs collaborative control on the L quality-abnormal workstations and their associated neighborhoods to obtain an initial collaborative control scheme. The overall collaborative control scheme analysis module, based on the initial collaborative control scheme and the first set of R workstations excluding the L quality-abnormal workstations and their associated neighborhoods, performs overall collaborative control scheme analysis to obtain a target collaborative control scheme. The multi-workstation collaborative control module utilizes this target collaborative control scheme to perform multi-workstation collaborative control on the automated automotive sunroof production line, achieving the technical effect of improving control flexibility and accuracy through global collaborative control. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 This is a schematic diagram of the structure of the multi-station collaborative control platform for the automated production line of automotive sunroofs provided in this application embodiment.

[0016] Figure 2 A schematic diagram of the workstation quality centralized analysis module of the multi-station collaborative control platform for the automated production line of the automotive sunroof provided in this application embodiment.

[0017] Figure labeling: Standard working parameter acquisition module 10, Window station processing quality assessment result extraction module 20, Station quality centralized analysis module 30, Quality anomaly station association neighborhood construction module 40, Initial collaborative control scheme generation module 50, Overall collaborative control scheme analysis module 60, Multi-station collaborative control module 70. Detailed Implementation

[0018] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0021] This application provides a multi-station collaborative control platform for an automated production line of automotive sunroofs, such as... Figure 1 As shown, the platform includes:

[0022] The standard operating parameter acquisition module 10 is used to traverse the R workstations of the automotive sunroof automated production line to acquire standard operating parameters and obtain the standard operating parameters of the R workstation equipment, where R is an integer greater than or equal to 1.

[0023] Specifically, using technologies such as sensor networks, PLCs (Programmable Logic Controllers), or SCADA (Supervisory Control and Data Acquisition) systems, all workstations (R workstations) on the automated automotive sunroof production line are traversed in real-time or periodically. The operating parameters of each workstation, such as equipment speed, temperature, pressure, and time, are collected and stored as standard operating parameters for the workstation equipment. These standard operating parameters refer to the standard operating parameters that each workstation equipment should achieve under normal production conditions. The R workstations refer to the different production stages or equipment on the automated automotive sunroof production line, where R is the number of workstations.

[0024] The skylight workstation processing quality assessment result extraction module 20 is used to extract the skylight workstation processing quality assessment results of the R workstations within the preset window, and obtain the R skylight workstation processing quality assessment set.

[0025] Specifically, the preset window refers to the time range used to extract evaluation results. Using a quality inspection system or data analysis software, the quality of the skylights processed at each workstation is inspected within the set time window, and the inspection results (such as pass rate, defect type, defect quantity, etc.) are summarized into an evaluation set. The skylight workstation processing quality evaluation set is the result of a quantitative evaluation of the skylight processing quality.

[0026] The station quality centralized analysis module 30 is used to perform station quality centralized analysis on the set of processing quality evaluation results of the R skylight stations using a quality concentrator, to obtain the centralized evaluation results of the processing quality of the R skylight stations, and to screen abnormal stations based on the centralized evaluation results of the processing quality of the R skylight stations, to obtain L abnormal quality stations and L abnormal skylight station processing quality centralized evaluation results, where L is an integer greater than or equal to 1 and less than or equal to R.

[0027] Specifically, the quality concentrator refers to a system or model used for centralized analysis and processing of quality assessment results from multiple workstations. Using data analysis algorithms and the quality concentrator, the quality assessment results of each workstation are centrally analyzed. Based on the analysis results, workstations with quality anomalies (L quality anomaly workstations) are identified from all workstations, and the centralized quality assessment results of these workstations are obtained.

[0028] like Figure 2As shown, in one possible implementation, the workstation quality centralized analysis module 30 includes: a quality concentrator construction unit for constructing a quality concentrator, wherein the quality concentrator includes a quality centralized analysis function; a traversal calculation unit for traversing and calculating the mean of the set of processing quality evaluation results for the R skylight workstations to obtain the mean of the processing quality evaluation results for the R skylight workstations; and a workstation quality centralized analysis unit for inputting the mean of the processing quality evaluation results for the R skylight workstations and the set of processing quality evaluation results for the R skylight workstations into the quality concentrator for workstation quality centralized analysis, and obtaining the processing quality centralized evaluation results for the R skylight workstations when the number of iterations meets a preset number.

[0029] Specifically, the quality concentrator is an algorithm-based processor that receives quality assessment data (such as machining accuracy, pass rate, and production speed) from multiple workstations. It processes this data using built-in quality concentrating analysis functions to derive the quality concentrating assessment result for each workstation. The traversal calculation unit is implemented programmatically, using loop statements to iterate through the machining quality assessment result sets of all workstations and calculate the mean for each workstation. The mean machining quality assessment result for each workstation and the original assessment result set are input into the quality concentrator. Iterative analysis is performed using the quality concentrator's quality concentrating analysis functions. Each iteration adjusts the model parameters based on the analysis results. When the number of iterations meets a preset limit, the quality concentrating assessment result for each workstation is output. This implementation, by constructing a quality concentrator and utilizing statistical and machine learning techniques to centrally analyze the machining quality assessment results of multiple workstations, can more accurately identify abnormal workstations and quality problems, improving the accuracy of the analysis.

[0030] In one possible implementation, the mass concentrator construction unit includes: a mass concentrator analysis function construction subunit, used to construct a mass concentrator analysis function, wherein the mass concentrator analysis function is:

[0031]

[0032] Where m(x) represents the centralized evaluation result of the sunroof workstation processing quality, N(x) represents the neighborhood of the sunroof workstation processing quality evaluation results within the preset distance range from the average value of the sunroof workstation processing quality evaluation results, and x represents the average value of the sunroof workstation processing quality evaluation results. i Let K(·) be the processing quality evaluation result of the i-th skylight station in the neighborhood, and K(·) be the Gaussian kernel function.

[0033] Specifically, the Gaussian kernel function (also known as the radial basis function RBF) is the core component of the quality lumped analysis function. For each quality assessment result of the skylight workstation, its distance from the mean of the quality assessment results of that workstation is calculated. Based on a preset distance range (e.g., within one or two standard deviations), assessment results within this range are selected to form a neighborhood. Using the Gaussian kernel function, a weight is assigned to each skylight workstation quality assessment result in the neighborhood, based on the distance between the assessment result and the mean. All weighted assessment results are summed and divided by the sum of their weights to obtain the lumped quality assessment result of the skylight workstation. This implementation uses the Gaussian kernel function to construct the quality lumped analysis function. The Gaussian kernel function can handle nonlinear relationships, and its smoothness helps reduce the impact of noise on the quality assessment results, improving the accuracy and stability of the assessment.

[0034] In one possible implementation, the workstation quality centralized analysis module 30 includes: a skylight workstation standard processing quality assessment result acquisition unit, used to acquire R skylight workstation standard processing quality assessment results for R workstations; a judgment unit, used to judge whether the R skylight workstation processing quality centralized assessment results meet the R skylight workstation standard processing quality assessment results, and obtain a judgment result; an abnormal skylight processing quality centralized assessment result acquisition unit, used to take the skylight workstation processing quality centralized assessment results with negative judgment results as L abnormal skylight processing quality centralized assessment results; and a quality abnormal workstation acquisition unit, used to take the workstations corresponding to the L abnormal skylight processing quality centralized assessment results as the L quality abnormal workstations.

[0035] Specifically, standard processing quality requirements for sunroof workstations are collected from product design documents, production process flows, quality control manuals, and other sources. Historical production data is analyzed to calculate statistical quantities such as the average and standard deviation of processing quality at sunroof workstations, serving as a reference for standard processing quality assessment. Combining the collected standards and historical data analysis results, the standard processing quality assessment result for each sunroof workstation is determined. This standard processing quality assessment result refers to the processing quality level that the sunroof workstation should achieve under the premise of meeting design requirements and production process specifications. The centralized processing quality assessment result for each sunroof workstation is matched with the corresponding standard processing quality assessment result. By setting a certain tolerance range, the actual assessment result is compared with the standard value to determine whether the requirements are met. From the judgment results, all centralized processing quality assessment results of sunroof workstations that do not meet the standards are selected as centralized processing quality assessment results for abnormal sunroofs. The centralized processing quality assessment results for abnormal sunroofs are associated with the workstation information that generated these results. Based on the association information, it is determined which workstations have quality abnormalities, thus identifying the workstations with quality abnormalities. This approach, by setting standard processing quality assessment results, provides a clear basis for judging whether the processing quality of the sunroof station meets the standards. It enables rapid and accurate assessment of the processing quality of the sunroof station, and the identified abnormal quality stations provide targeted guidance for subsequent collaborative control, which helps to reduce product defects and improve product quality.

[0036] The quality anomaly station association neighborhood construction module 40 is used to construct L quality anomaly station association neighborhoods based on the positions of the L quality anomaly stations in the automotive sunroof automated production line, wherein each quality anomaly station association neighborhood includes the station before and the station after the corresponding quality anomaly station.

[0037] Specifically, by using production line layout information and the relationships between workstations, the preceding and following workstations of the workstation with quality abnormality are determined based on its position in the production line, forming the associated neighborhood of the workstation with quality abnormality, that is, the set of workstations that are directly related to the workstation with quality abnormality (previous and subsequent processes).

[0038] The initial collaborative control scheme generation module 50 is used to perform collaborative control on the L abnormal quality workstations and their associated neighborhoods based on the magnitude of the centralized evaluation results of the processing quality of the L abnormal workstations and the standard operating parameters of the R workstations, thereby obtaining an initial collaborative control scheme.

[0039] Specifically, using a control algorithm, based on the magnitude of the centralized evaluation results of the processing quality at the abnormal workstation, and combined with the standard operating parameters of the workstation equipment, the equipment parameters of the workstation with quality abnormalities and its associated neighboring areas are adjusted to improve processing quality and generate an initial collaborative control scheme. Here, collaborative control refers to a control method in which multiple workstations achieve collaborative work through information sharing and parameter adjustment.

[0040] In one possible implementation, the initial collaborative control scheme generation module 50 includes: a workstation equipment standard operating parameter matching unit, used to match the L quality-abnormal workstations and the workstation equipment standard operating parameters of the L neighboring workstations based on the R workstation equipment standard operating parameters, to obtain L workstation equipment standard operating parameters and L neighboring workstation equipment standard operating parameter sets; and a first initial collaborative control scheme acquisition unit, used to input the processing quality centralized evaluation results of the L abnormal workstations, the L workstation equipment standard operating parameters, and the L neighboring workstation equipment standard operating parameter sets into an initial collaborative control scheme identifier for analysis, to obtain a first initial collaborative control scheme, wherein the first initial collaborative control scheme includes L first initial workstation equipment operating parameters. The system includes: a first generation initial neighborhood workstation equipment operating parameter set; a first generation loss time acquisition unit, used to analyze the equipment idling time and semi-processing window product stagnation time of the first generation initial collaborative control scheme, and superimpose the analysis results to obtain the first generation loss time; a first generation initial collaborative control adjustment scheme set acquisition unit, used to randomly adjust at least one of the first generation initial neighborhood workstation equipment operating parameters in the L first generation initial neighborhood workstation equipment operating parameter sets multiple times according to a preset adjustment scale to obtain the first generation initial collaborative control adjustment scheme set; and a scheme iteration unit, used to iterate the first generation initial collaborative control scheme based on the first generation initial collaborative control adjustment scheme set and the first generation loss time to obtain the initial collaborative control scheme.

[0041] Specifically, a matching algorithm is designed and implemented to obtain the standard operating parameters of R workstations and the information of L workstations with quality defects and their associated neighborhoods. This algorithm can retrieve and extract the corresponding standard operating parameters from the standard operating parameters of the R workstations based on the information of the workstations with quality defects and their associated neighborhoods, and output the standard operating parameters of the L workstations with quality defects and the standard operating parameters of the L neighboring workstations.

[0042] The initial collaborative control scheme identifier refers to an algorithm or program capable of generating an initial collaborative control scheme based on input data. The initial collaborative control scheme identifier receives the centralized evaluation results of the processing quality of L abnormal workstations, the standard operating parameters of L workstation equipment, and the standard operating parameter sets of L neighboring workstation equipment. Based on the input data, the initial collaborative control scheme identifier calculates a first generated initial collaborative control scheme using an algorithm, including L first generated initial workstation equipment operating parameters and L first generated initial neighboring workstation equipment operating parameter sets.

[0043] The system acquires relevant information about the initial collaborative control scheme for the first generation, including the operating parameters of the workstation equipment. Utilizing equipment idle time and semi-finished product dwell time analysis techniques, it assesses the lost time of the initial collaborative control scheme. This includes calculating the idle time of equipment waiting for the next process to complete and the dwell time of semi-finished products between workstations, outputting the first generation loss time. Equipment idle time refers to the idle time at a workstation waiting for products from upstream or downstream workstations due to poor coordination between upstream and downstream workstations. Semi-finished product dwell time refers to the dwell time of a product at a workstation after it has been processed to a certain extent, due to the inability of subsequent workstations to receive it in a timely manner.

[0044] Obtain the set of operating parameters of the neighboring workstation equipment in the first generated initial collaborative control scheme, and perform multiple random adjustments on the operating parameters of the neighboring workstation equipment according to the preset adjustment scale (the magnitude or range of parameter changes when making random adjustments), generate multiple adjustment schemes, and output the first generated initial collaborative control adjustment scheme set.

[0045] The process involves obtaining the first generated initial collaborative control scheme and its set of adjustment schemes, along with the corresponding time loss assessment results. Based on the time loss assessment results, the first generated initial collaborative control scheme is iteratively optimized. This includes selecting the adjustment scheme with the minimum time loss as the new initial scheme, further adjusting and optimizing it, and outputting the iteratively optimized initial collaborative control scheme. Iterative optimization refers to the technique of repeatedly adjusting and optimizing the scheme to gradually approach the optimal solution. The optimal scheme is the collaborative control scheme that achieves the minimum time loss or maximum benefit under given conditions. This implementation improves problem-solving efficiency by accurately matching the standard operating parameters of equipment in the abnormal quality workstation and its associated neighborhood, generating collaborative control schemes for specific problems. By selecting the collaborative control scheme with the minimum time loss, production costs and waste are reduced.

[0046] In one possible implementation, the scheme iteration unit includes: a loss time analysis subunit, used to perform loss time analysis on the first set of generated initial cooperative control adjustment schemes to obtain a first set of generated adjustment loss times; a judgment processing subunit, used to determine whether there exists a first generated adjustment loss time less than or equal to the first generated loss time in the first set of generated adjustment loss times; if so, iterating the first generated initial cooperative control scheme according to the first generated initial cooperative control adjustment scheme corresponding to the minimum value in the first set of generated adjustment loss times to obtain a second generated initial cooperative control adjustment scheme; and a scheme iteration subunit, used to iterate the second generated initial cooperative control adjustment scheme until a preset number of iterations is met to obtain the initial cooperative control scheme.

[0047] Specifically, a detailed time loss analysis is performed on each scheme in the first set of initial collaborative control adjustment schemes, including equipment idling time and semi-finished product downtime. To achieve this analysis, the operation of each adjustment scheme on the actual production line can be simulated. The equipment idling time and semi-finished product downtime under each scheme are calculated using algorithms or simulation models, and these two times are added together to obtain the total time loss.

[0048] The first set of generated adjustment loss times provided by the loss time analysis subunit is analyzed to identify whether there are adjustment schemes with a loss time less than or equal to the first generated loss time (i.e., the loss time of the first generated initial collaborative control scheme). If such schemes exist, they are superior to the initial scheme in reducing loss time. Therefore, the first generated initial collaborative control scheme is iterated based on these superior schemes. Specifically, the judgment and processing subunit traverses the first set of generated adjustment loss times, finds the minimum value in the set, selects the adjustment scheme corresponding to the minimum adjustment loss time as the basis for iteration, and iterates the first generated initial collaborative control scheme to further reduce loss time. The iteration process is repeated until the preset number of iterations is met. In each iteration, the scheme iteration subunit fine-tunes the current optimal scheme (i.e., the scheme with the minimum loss time in the previous iteration), generates a new set of adjustment schemes, and performs loss time analysis again. Through continuous iteration and optimization, a relatively optimal initial collaborative control scheme is finally obtained. This implementation method, through continuous iteration and optimization, finds a collaborative control scheme that minimizes the loss time of the automated automotive sunroof production line. Since the various workstations on the production line are interconnected, a quality anomaly or efficiency change at one workstation will affect other workstations. Therefore, by constructing a neighborhood of workstations with quality anomalies and implementing coordinated control over these workstations and their neighborhoods, the overall lost time of the production line is effectively reduced, and the operating efficiency and product quality of the production line are improved.

[0049] In one possible implementation, the first unit for generating an initial cooperative control scheme acquisition includes: a training data acquisition subunit, used to acquire the centralized evaluation results of the processing quality of multiple sample abnormal window workstations, the standard operating parameters of multiple sample workstations, and the set of standard operating parameters of multiple sample neighboring workstations as training data; an initial cooperative control scheme identifier construction subunit, used to construct the generator and adversary of the initial cooperative control scheme identifier based on a generative adversarial network framework; and an alternating supervised training subunit, used to perform alternating supervised training on the generator and adversary using the training data until the training converges, thereby obtaining the initial cooperative control scheme identifier.

[0050] Specifically, relevant production data is extracted from historical production line records, experimental data, or simulation models. This data includes centralized evaluation results of processing quality at multiple sample abnormal workstations, standard operating parameters of equipment at multiple sample workstations, and sets of standard operating parameters for equipment at multiple neighboring workstations. The collected data is cleaned and processed, including removing outliers and filling in missing values, to ensure data accuracy and consistency. Representative samples are selected from the preprocessed data as input data for training the initial collaborative control scheme recognizer.

[0051] Based on the Generative Adversarial Network (GAN) framework, a generator and an adversary are constructed for the initial cooperative control scheme identifier. The generator's task is to generate an initial cooperative control scheme based on the input results of the centralized quality assessment of abnormal workstations, the standard operating parameters of the workstation equipment, and the set of standard operating parameters of neighboring workstation equipment. The generator can capture the complex relationships between the input data and generate reasonable cooperative control schemes. The adversary's task is to evaluate the initial cooperative control scheme generated by the generator, determining whether it meets the expected quality control requirements. The adversary attempts to find deficiencies or errors in the cooperative control scheme generated by the generator and guides the generator to improve it through feedback signals.

[0052] The alternating supervised training subunit sets the initial model parameters for both the generator and the adversary. During training, the adversary's parameters are first fixed, and the generator is trained to produce better cooperative control schemes. Then, the generator's parameters are fixed again, and the adversary is trained to more accurately evaluate the generated cooperative control schemes. This process is repeated alternately until the training convergence criterion is met. After training, the performance of the initial cooperative control scheme recognizer is evaluated using a validation dataset to ensure that it can accurately generate reasonable cooperative control schemes based on the input data. This implementation utilizes Generative Adversarial Networks (GANs) to build an intelligent system capable of automatically generating reasonable cooperative control schemes. The GAN framework efficiently captures the complex relationships between data and generates high-quality cooperative control schemes, thereby achieving the technical effect of improving the efficiency and quality of initial cooperative control scheme generation.

[0053] The overall collaborative control scheme analysis module 60 is used to perform overall collaborative control scheme analysis based on the initial collaborative control scheme and the first set of workstations among the R workstations, excluding the L workstations with quality abnormalities and the associated neighborhoods of the L workstations with quality abnormalities, to obtain the target collaborative control scheme.

[0054] Specifically, the initial collaborative control scheme is applied to other workstations (the first set of workstations) except for the workstation with quality abnormalities and its associated neighborhood. The impact of the initial collaborative control scheme on the overall production line is evaluated using system simulation or optimization algorithms. The initial collaborative control scheme is then analyzed and optimized as a whole to obtain the target collaborative control scheme.

[0055] In one possible implementation, the overall collaborative control scheme analysis module 60 includes: a first workstation equipment standard operating parameter set acquisition unit, used to obtain the first workstation equipment standard operating parameter set by matching the R workstation equipment standard operating parameters with the workstation equipment standard operating parameters of the first workstation set; and an overall collaborative analysis unit, used to analyze the first workstation equipment standard operating parameter set and the initial collaborative control scheme using the overall collaborative network layer to obtain the target collaborative control scheme.

[0056] Specifically, from the standard operating parameters of the R workstations acquired by the standard operating parameter acquisition module 10, the standard operating parameters of the equipment belonging to the first workstation set (i.e., all workstations excluding the L quality-abnormal workstations and their associated neighborhoods) are filtered out based on information such as workstation number or equipment identifier. During execution, the first workstation equipment standard operating parameter set acquisition unit traverses the standard operating parameter database. For each parameter, it checks whether its corresponding workstation number is in the first workstation set. If it is, the parameter is added to the first workstation equipment standard operating parameter set; otherwise, the parameter is ignored. In this way, a set containing only the standard operating parameters of the first workstation set is finally constructed.

[0057] The overall collaborative analysis unit utilizes the overall collaborative network layer (a pre-trained neural network model) to input the standard operating parameter set of the first workstation equipment and the initial collaborative control scheme, and outputs an optimized target collaborative control scheme. During execution, the overall collaborative network layer first receives the input data (including the standard operating parameters of the first workstation equipment and the initial collaborative control scheme), and then processes it layer by layer through multiple hidden layers within the network. Each layer performs feature extraction and transformation on the data, ultimately outputting an optimized collaborative control scheme. This scheme considers the mutual influence and dependencies between various workstations to ensure the overall production line's operational efficiency and product quality. This implementation method, through collaborative control, ensures that the operating parameters of each workstation are matched and coordinated, thereby improving the overall production line's operational efficiency.

[0058] The multi-station collaborative control module 70 is used to perform multi-station collaborative control of the automotive sunroof automated production line using the target collaborative control scheme.

[0059] Specifically, the target collaborative control scheme is converted into specific control commands, which are then sent to each workstation through a control system (equipment or software used to implement automated production line control) or a PLC (Programmable Logic Controller), enabling multi-workstation collaborative control of the automotive sunroof automated production line and achieving collaborative work among multiple workstations. This embodiment of the application involves traversing all workstations on the production line, collecting standard operating parameters of the equipment, evaluating the processing quality of workstations within a preset window, extracting the evaluation results to form a processing quality evaluation set, using a quality concentrator to centrally analyze the evaluation results, filtering out abnormal workstations, and constructing a neighborhood association for these abnormal workstations. Based on the abnormal workstations and their neighborhood associations, combined with the standard operating parameters of the equipment, an initial collaborative control scheme is generated. Based on the initial collaborative control scheme and the first set of workstations (excluding abnormal workstations and their neighborhood associations), an overall collaborative control scheme analysis is performed to obtain the target collaborative control scheme. This target collaborative control scheme is then used to perform multi-workstation collaborative control of the production line, achieving the technical effect of improving the flexibility and accuracy of control through global collaborative control.

[0060] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0061] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A multi-station collaborative control platform for an automated production line of automotive sunroofs, characterized in that, The platform includes: The standard operating parameter acquisition module is used to traverse the R workstations of the automotive sunroof automated production line to acquire standard operating parameters and obtain the standard operating parameters of the R workstation equipment, where R is an integer greater than or equal to 1. The module for extracting the processing quality assessment results of the skylight workstation is used to extract the processing quality assessment results of the R workstations within the preset window, and obtain the processing quality assessment set of the R skylight workstations. The station quality centralized analysis module is used to perform centralized analysis of the processing quality evaluation result set of the R skylight station using a quality concentrator, to obtain the centralized evaluation result of the processing quality of the R skylight station, and to screen abnormal stations based on the centralized evaluation result of the processing quality of the R skylight station, to obtain L abnormal quality stations and L abnormal skylight station processing quality centralized evaluation results, where L is an integer greater than or equal to 1 and less than or equal to R; The quality anomaly station association neighborhood construction module is used to construct L quality anomaly station association neighborhoods based on the positions of the L quality anomaly stations in the automotive sunroof automated production line. Each quality anomaly station association neighborhood includes the station before and the station after the corresponding quality anomaly station. The initial collaborative control scheme generation module is used to perform collaborative control on the L abnormal quality workstations and their associated neighborhoods based on the magnitude of the centralized evaluation results of the processing quality of the L abnormal workstations and the standard operating parameters of the R workstations, thereby obtaining an initial collaborative control scheme. The overall collaborative control scheme analysis module is used to perform overall collaborative control scheme analysis based on the initial collaborative control scheme and the first set of workstations among the R workstations, excluding the L workstations with quality abnormalities and the associated neighborhoods of the L workstations with quality abnormalities, to obtain the target collaborative control scheme. A multi-station collaborative control module is used to perform multi-station collaborative control of the automotive sunroof automated production line using the target collaborative control scheme. The initial collaborative control scheme generation module includes: The standard operating parameter matching unit for workstation equipment is used to match the standard operating parameters of the L quality-abnormal workstations and the standard operating parameters of the workstation equipment in the associated neighborhood of the L quality-abnormal workstations based on the R standard operating parameters of the workstation equipment, so as to obtain the standard operating parameters of the L workstation equipment and the set of standard operating parameters of the L neighborhood workstation equipment. The first initial collaborative control scheme acquisition unit is used to input the centralized evaluation results of the processing quality of the L abnormal skylight workstations, the standard operating parameters of the L workstation equipment, and the set of standard operating parameters of the L neighboring workstation equipment into the initial collaborative control scheme identifier for analysis to obtain the first initial collaborative control scheme. The first initial collaborative control scheme includes the L first initial workstation equipment operating parameters and the L first initial neighboring workstation equipment operating parameter sets. The first generation loss time acquisition unit is used to analyze the equipment idling time and the semi-processed skylight product stagnation time of the first generation initial collaborative control scheme, and to superimpose the analysis results to obtain the first generation loss time. The first initial collaborative control adjustment scheme set acquisition unit is used to randomly adjust at least one of the operating parameters of the first initial neighboring workstation equipment in the L sets of first initial neighboring workstation equipment operating parameters multiple times according to a preset adjustment scale to obtain the first initial collaborative control adjustment scheme set. The scheme iteration unit is used to iterate the first generated initial cooperative control scheme based on the first set of generated initial cooperative control adjustment schemes and the first generated loss time to obtain the initial cooperative control scheme.

2. The multi-station collaborative control platform for the automated production line of automotive sunroofs as described in claim 1, characterized in that, The centralized quality analysis module for the workstation includes: A mass concentrator construction unit is used to construct a mass concentrator, wherein the mass concentrator includes a mass concentrator analysis function; The traversal calculation unit is used to traverse and calculate the mean of the set of processing quality evaluation results of the R skylight workstations to obtain the mean of the processing quality evaluation results of the R skylight workstations. The station quality centralized analysis unit is used to input the average value of the processing quality evaluation results of the R skylight station and the set of processing quality evaluation results of the R skylight station into the quality concentrator for centralized analysis of station quality. When the number of iterations meets the preset number, the centralized evaluation results of the processing quality of the R skylight station are obtained.

3. The multi-station collaborative control platform for the automated production line of automotive sunroofs as described in claim 2, characterized in that, The mass concentrator building unit includes: A sub-unit for constructing the mass lumped analysis function is used to construct the mass lumped analysis function, which is: ； in, The results of the centralized evaluation of the processing quality of the sunroof workstation. The neighborhood of the sunroof workstation processing quality assessment results is defined as the set of sunroof workstation processing quality assessment results whose distance from the mean of the sunroof workstation processing quality assessment results is within a preset distance range. This represents the average value of the processing quality assessment results for the sunroof workstation. This represents the processing quality assessment result for the i-th skylight workstation in the neighborhood. This is the Gaussian kernel function.

4. The multi-station collaborative control platform for the automated production line of automotive sunroofs as described in claim 1, characterized in that, The centralized quality analysis module for the workstation includes: The unit for obtaining the standard processing quality assessment results of the sunroof workstation is used to obtain the standard processing quality assessment results of R sunroof workstations. The judgment unit is used to determine whether the centralized evaluation results of the processing quality of the R skylight workstations meet the standard processing quality evaluation results of the R skylight workstations, and to obtain the judgment result. The abnormal skylight processing quality centralized assessment result acquisition unit is used to take the skylight station processing quality centralized assessment result with the judgment result of "no" as the L abnormal skylight processing quality centralized assessment result. The quality anomaly station acquisition unit is used to identify the stations corresponding to the centralized evaluation results of the processing quality of the L abnormal skylights as the L quality anomaly stations.

5. The multi-station collaborative control platform for the automated production line of automotive sunroofs as described in claim 1, characterized in that, The scheme iteration unit includes: The loss time analysis subunit is used to perform loss time analysis on the first set of generated initial collaborative control adjustment schemes to obtain the first set of generated adjustment loss times. The judgment and processing subunit is used to determine whether there is a first generation adjustment loss time less than or equal to the first generation loss time in the first generation adjustment loss time set. If so, the first generation initial cooperative control scheme is iterated according to the first generation initial cooperative control adjustment scheme corresponding to the minimum value in the first generation adjustment loss time set to obtain a second generation initial cooperative control adjustment scheme. The scheme iteration subunit is used to iterate the second generated initial cooperative control adjustment scheme until a preset number of iterations is met to obtain the initial cooperative control scheme.

6. The multi-station collaborative control platform for the automated production line of automotive sunroofs as described in claim 1, characterized in that, The first unit for generating an initial cooperative control scheme acquisition unit includes: The training data acquisition subunit is used to acquire the centralized evaluation results of the processing quality of multiple sample abnormal skylight workstations, the standard operating parameters of equipment at multiple sample workstations, and the set of standard operating parameters of equipment at multiple sample neighboring workstations as training data. The initial cooperative control scheme identifier construction subunit is used to construct the generator and adversary of the initial cooperative control scheme identifier based on the generative adversarial network framework. An alternating supervised training subunit is used to perform alternating supervised training on the generator and the adversary using the training data until the training converges, thereby obtaining the initial cooperative control scheme recognizer.

7. The multi-station collaborative control platform for the automated production line of automotive sunroofs as described in claim 6, characterized in that, The overall collaborative control scheme analysis module includes: The first workstation equipment standard operating parameter set acquisition unit is used to obtain the first workstation equipment standard operating parameter set by matching the R workstation equipment standard operating parameters with the workstation equipment standard operating parameters of the first workstation set. The overall collaborative analysis unit is used to analyze the standard operating parameter set of the first workstation equipment and the initial collaborative control scheme using the overall collaborative network layer to obtain the target collaborative control scheme.

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