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

Abstract

The invention discloses a multi-station cooperative control platform for an automatic production line of automobile sunroofs, and relates to the related field of intelligent manufacturing, and the platform comprises a standard working parameter collection module which is used for collecting and obtaining standard operation parameters; the skylight station processing quality evaluation result extraction module is used for extracting a quality evaluation result; the station quality centralized analysis module is used for performing centralized analysis on the quality evaluation result set and screening abnormal stations; the quality abnormal station association neighborhood construction module is used for constructing an abnormal station association neighborhood; the initial cooperative control scheme generation module is used for cooperative control; the overall cooperative control scheme analysis module is used for carrying out overall cooperative control analysis; and the multi-station cooperative control module is used for cooperative control. The technical problem of insufficient control flexibility and accuracy caused by lack of globality in existing skylight automatic production line control is solved, and the technical effect of improving the control flexibility and accuracy through globality cooperative control is achieved.

Description

Technical Field

[0001] The present application relates to fields related to intelligent manufacturing, and in particular to a multi-station collaborative control platform for an automated production line for automobile sunroofs. Background Art

[0002] With the rapid development of the automobile manufacturing industry, automobile sunroofs are important components that improve vehicle comfort and aesthetics, and their production quality and efficiency are increasingly concerned. In order to meet market demand, the automobile sunroof automation production line came into being. Through highly integrated mechanical equipment and advanced control systems, efficient and precise production of sunroofs is achieved. However, in the actual production process, due to the influence of various factors, such as equipment wear, material differences, improper process parameter settings, etc., the processing quality of the sunroof may fluctuate, which in turn affects the operating efficiency and product quality of the overall production line. In the existing automobile sunroof automation production line, single-station quality control is usually adopted or simple collaborative control of the stations on the production line is performed according to preset collaborative rules, such as adjusting the input parameters of the subsequent stations according to the output status of the previous stations. Since the existing methods often only focus on a single station or simple collaborative control, lack of global consideration of the entire production line, resulting in poor overall collaborative effect, it is difficult to adapt to the complex and changing production environment and process requirements, resulting in low production efficiency and unstable product quality.

[0003] Among the current related technologies, the control of the automated production line for automobile sunroofs lacks globality, which results in technical problems such as insufficient control flexibility and accuracy. Summary of the invention

[0004] The present application provides a multi-station collaborative control platform for an automated production line for automobile sunroofs, traverses all stations on the production line, collects standard equipment operating parameters, evaluates the processing quality of the stations within a preset window, extracts the evaluation results, forms a processing quality evaluation set, and uses a quality concentrator to centrally analyze the evaluation results, screens out abnormal stations, and constructs associated neighborhoods for stations with abnormal quality. According to the conditions of the abnormal stations and their associated neighborhoods, combined with the standard equipment operating parameters, an initial collaborative control scheme is generated. Based on the initial collaborative control scheme and the first station set (stations excluding the abnormal stations and their associated neighborhoods), an overall collaborative control scheme analysis is performed to obtain a target collaborative control scheme. The target collaborative control scheme is used to perform multi-station collaborative control of the production line, and other technical means are used to achieve the technical effect of improving the flexibility and accuracy of control through global collaborative control.

[0005] The present application provides a multi-station collaborative control platform for an automobile sunroof automation production line, including: a standard working parameter acquisition module, used to traverse R stations of the automobile sunroof automation production line to collect standard working parameters, and obtain standard operating parameters of R station equipment, wherein 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 in a preset window respectively, and obtain R sunroof station processing quality assessment sets; a station quality concentration analysis module, used to use a mass concentrator to perform station quality concentration analysis on the R sunroof station processing quality assessment result sets, obtain R sunroof station processing quality concentration assessment results, and screen abnormal stations based on the R sunroof station processing quality concentration assessment results, to obtain L quality abnormal stations and L abnormal sunroof station processing quality concentration assessment results, wherein L is an integer greater than or equal to 1 and less than or equal to R; quality abnormal station association neighborhood construction A construction module is used to construct L quality abnormality workstations associated neighborhoods based on the positions of the L quality abnormality workstations in the automobile sunroof automation production line, wherein each quality abnormality workstation associated neighborhood includes a previous workstation and a next workstation located at the corresponding quality abnormality workstation; an initial collaborative control scheme generation module is used to collaboratively control the L quality abnormality workstations and the L quality abnormality workstation associated neighborhoods according to the size of the L abnormal sunroof workstation processing quality centralized evaluation results and in combination with the R workstation equipment standard operating parameters to obtain an initial collaborative control scheme; an overall collaborative control scheme analysis module is used to analyze the overall collaborative control scheme based on the initial collaborative control scheme and the first workstation set of the R workstations except the L quality abnormality workstations and the L quality abnormality workstation associated neighborhoods to obtain a target collaborative control scheme; a multi-station collaborative control module is used to perform multi-station collaborative control on the automobile sunroof automation production line using the target collaborative control scheme.

[0006] In a possible implementation, the workstation quality concentration analysis module includes: a mass concentrator construction unit, used to construct a mass concentrator, wherein the mass concentrator includes a mass concentration analysis function; a traversal calculation unit, used to traverse and calculate the mean of the set of processing quality assessment results of the R skylight stations to obtain the mean of the processing quality assessment results of the R skylight stations; a workstation quality concentration analysis unit, used to input the mean of the processing quality assessment results of the R skylight stations and the set of processing quality assessment results of the R skylight stations into the mass concentrator respectively to perform workstation quality concentration analysis, and when the number of iterative analysis meets the preset number of times, the processing quality concentration evaluation results of the R skylight stations are obtained.

[0007] In a possible implementation, the mass concentrator construction unit includes: a mass concentration analysis function construction subunit, which is used to construct a mass concentration analysis function, and the mass concentration analysis function is: Where m(x) is the concentrated evaluation result of the skylight processing quality, N(x) is the neighborhood of the skylight processing quality evaluation results in the skylight processing quality evaluation result set whose distance to the mean of the skylight processing quality evaluation results is within the preset distance range, x is the mean of the skylight processing quality evaluation results, x i is the processing quality evaluation result of the i-th skylight station in the neighborhood, and K(·) is 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, which is used to acquire R skylight workstation standard processing quality assessment results of R workstations; a judgment unit, which is 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, which is used to use the skylight workstation processing quality centralized assessment result with a judgment result of no as L abnormal skylight processing quality centralized assessment results; a quality abnormality workstation acquisition unit, which is used to use the workstation corresponding to the L abnormal skylight processing quality centralized assessment results as the L quality abnormality workstations.

[0009] In a possible implementation, the initial collaborative control scheme generation module includes: a workstation equipment standard operating parameter matching unit, which is used to match the workstation equipment standard operating parameters of the L quality abnormal workstations and the L quality abnormal workstations associated neighborhood based on the R workstation equipment standard operating parameters to obtain L workstation equipment standard operating parameters and L neighborhood workstation equipment standard operating parameter sets; a first generated initial collaborative control scheme acquisition unit, which is used to input the L abnormal skylight workstation processing quality centralized evaluation results, L workstation equipment standard operating parameters and L neighborhood workstation equipment standard operating parameter sets into the initial collaborative control scheme identifier for analysis to obtain a first generated initial collaborative control scheme, wherein the first generated initial collaborative control scheme includes L first generated initial workstation equipment operating parameters and L a first generated initial neighborhood workstation equipment operating parameter set; a first generated loss time acquisition unit, used to analyze the equipment idling time and the stagnation time of the semi-processed skylight product for the first generated initial collaborative control scheme, and superimpose the analysis results to obtain the first generated loss time; a first generated initial collaborative control adjustment scheme set acquisition unit, used to randomly adjust at least one first generated initial neighborhood workstation equipment operating parameter in the L first generated initial neighborhood workstation equipment operating parameter sets according to a preset adjustment scale for multiple times to obtain a first generated initial collaborative control adjustment scheme set; a scheme iteration unit, used to iterate the first generated initial collaborative control scheme based on the first generated initial collaborative control adjustment scheme set and the first generated 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 generated initial collaborative control adjustment scheme set to obtain a first generated adjustment loss time set; a judgment processing subunit, used to judge whether there is a first generated adjustment loss time in the first generated adjustment loss time set that is less than or equal to the first generated loss time. If so, iterating the first generated initial collaborative control scheme according to the first generated initial collaborative control adjustment scheme corresponding to the minimum value in the first generated adjustment loss time set to obtain a second generated initial collaborative control adjustment scheme; a scheme iteration subunit, used to iterate the second generated initial collaborative control adjustment scheme until a preset number of iterations is met to obtain the initial collaborative control scheme.

[0011] In a possible implementation, the first generating initial collaborative control scheme acquisition unit includes: a training data acquisition subunit, used to obtain a plurality of sample abnormal skylight workstation processing quality centralized evaluation results, a plurality of sample workstation equipment standard operating parameters and a plurality of sample neighborhood workstation equipment standard operating parameter sets as training data; an initial collaborative control scheme identifier construction subunit, used to construct a generator and an adversary of the initial collaborative control scheme identifier based on a generative adversarial network as a basic framework; an alternating supervised training subunit, used to perform alternating supervised training on the generator and the adversary using the training data until the training converges to obtain the initial collaborative control scheme identifier.

[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 match the standard operating parameters of the workstation equipment of the first workstation set based on the R workstation equipment standard operating parameters to obtain the first workstation equipment standard operating parameter set; an overall collaborative analysis unit, used to use the overall collaborative network layer to analyze the first workstation equipment standard operating parameter set and the initial collaborative control scheme to obtain the target collaborative control scheme.

[0013] It is planned to use the multi-station collaborative control platform of the automobile sunroof automation production line proposed in this application, traverse the R stations of the automobile sunroof automation production line through the standard working parameter collection module to collect standard working parameters, obtain the standard operating parameters of the R station equipment, extract the sunroof station processing quality assessment results of the R stations in the preset window through the sunroof station processing quality assessment result extraction module, obtain the R sunroof station processing quality assessment set, use the station quality concentration analysis module to use the mass concentrator to perform station quality concentration analysis on the R sunroof station processing quality assessment result set, obtain the R sunroof station processing quality concentration assessment results, and screen abnormal stations according to the R sunroof station processing quality concentration assessment results, obtain L quality abnormal stations and L abnormal sunroof station processing quality concentration assessment results, and use the quality abnormal station association neighborhood construction module to Based on the positions of L quality abnormality workstations in the automobile sunroof automation production line, L quality abnormality workstation associated neighborhoods are constructed, and the initial collaborative control scheme generation module is used to collaboratively control the L quality abnormality workstations and the L quality abnormality workstation associated neighborhoods according to the size of the centralized evaluation results of the processing quality of the L abnormal sunroof workstations and the standard operating parameters of the R workstation equipment to obtain an initial collaborative control scheme, and the overall collaborative control scheme analysis module is used to analyze the overall collaborative control scheme based on the initial collaborative control scheme and the first workstation set of the R workstations except the L quality abnormality workstations and the L quality abnormality workstation associated neighborhoods to obtain a target collaborative control scheme, and the multi-workstation collaborative control module is used to use the target collaborative control scheme to perform multi-workstation collaborative control on the automobile sunroof automation production line, thereby achieving the technical effect of improving the flexibility and accuracy of control through global collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in this application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of a multi-station collaborative control platform for an automated automobile sunroof production line provided in an embodiment of the present application.

[0016] Figure 2 A schematic diagram of the structure of a station quality centralized analysis module of a multi-station collaborative control platform for an automated automobile sunroof production line provided in an embodiment of the present application.

[0017] Explanation of the accompanying drawings: standard working parameter acquisition module 10, skylight workstation processing quality assessment result extraction module 20, workstation quality centralized analysis module 30, quality abnormal workstation associated neighborhood construction module 40, initial collaborative control scheme generation module 50, overall collaborative control scheme analysis module 60, multi-workstation collaborative control module 70. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, 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 that are clearly listed, but may include other steps or modules that are not clearly 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 those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The embodiment of the present application provides a multi-station collaborative control platform for an automobile sunroof automated production line, such as Figure 1 As shown, the platform includes:

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

[0023] Specifically, through technical means such as sensor networks, PLC (programmable logic controller) or SCADA (supervisory control and data acquisition) systems, all workstations (R workstations) on the automobile sunroof automation production line are traversed in real time or regularly, and the working parameters of each workstation are collected, such as equipment speed, temperature, pressure, time, etc., and these parameters are stored as standard operating parameters of the workstation equipment. Among them, the standard operating parameters refer to the operating parameter standards that each workstation equipment should meet under normal production conditions. R workstations refer to different production links or equipment on the automobile sunroof automation production line, and 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 in the preset window respectively, and obtain R skylight workstation processing quality assessment sets.

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

[0026] The workstation quality concentration analysis module 30 is used to use a mass concentrator to perform a workstation quality concentration analysis on the set of R skylight workstation processing quality assessment results, obtain the R skylight workstation processing quality concentration assessment results, and screen abnormal workstations based on the R skylight workstation processing quality concentration assessment results, to obtain L quality-abnormal workstations and L abnormal skylight workstation processing quality concentration assessment 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 for centralized analysis and processing of quality assessment results of multiple workstations. The quality assessment results of each workstation are centralized analyzed using a data analysis algorithm and a quality concentrator. Based on the analysis results, workstations with abnormal quality (L abnormal quality workstations) are identified from all workstations, and the centralized quality assessment results of these workstations are obtained.

[0028] like Figure 2As shown, in a possible implementation, the workstation quality concentration analysis module 30 includes: a mass concentrator construction unit, used to construct a mass concentrator, wherein the mass concentrator includes a mass concentration analysis function; a traversal calculation unit, used to traverse and calculate the mean of the R skylight workstation processing quality assessment result sets to obtain the mean of the R skylight workstation processing quality assessment result sets; a workstation quality concentration analysis unit, used to respectively input the mean of the R skylight workstation processing quality assessment result sets and the R skylight workstation processing quality assessment result sets into the mass concentrator to perform workstation quality concentration analysis, and when the number of iterative analysis times meets the preset number of times, the R skylight workstation processing quality concentration evaluation results are obtained.

[0029] Specifically, the quality concentrator is an algorithm-based processor that can receive quality assessment data (such as processing accuracy, qualified rate, production speed, etc.) of multiple workstations, and process these data through the built-in quality concentration analysis function to obtain the quality concentration evaluation results of each workstation. The traversal calculation unit is implemented through programming, and the processing quality assessment result set of all skylight workstations is traversed using loop statements to calculate the mean of each workstation. The mean of the processing quality assessment results of each workstation and the original assessment result set are input into the quality concentrator, and the quality concentration analysis function in the quality concentrator is used for iterative analysis. Each iteration adjusts the model parameters according to the analysis results. When the number of iterations meets the preset number, the processing quality concentration evaluation results of each workstation are output. This implementation method constructs a quality concentrator and uses statistics and machine learning techniques to conduct centralized analysis on the processing quality assessment results of multiple workstations, which can more accurately identify abnormal workstations and quality problems and improve the accuracy of the analysis.

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

[0031]

[0032] Where m(x) is the concentrated evaluation result of the skylight processing quality, N(x) is the neighborhood of the skylight processing quality evaluation results in the skylight processing quality evaluation result set whose distance to the mean of the skylight processing quality evaluation results is within the preset distance range, x is the mean of the skylight processing quality evaluation results, x i is the processing quality evaluation result of the i-th skylight station in the neighborhood, and K(·) is the Gaussian kernel function.

[0033] Specifically, the Gaussian kernel function (also known as the radial basis function RBF) is the core component of the mass concentration analysis function. For each skylight station processing quality assessment result, the distance between it and the mean of the station processing quality assessment result is calculated. According to a preset distance range (for example, within one standard deviation or two standard deviations), the assessment results with a distance within the range are selected to form a neighborhood. Using the Gaussian kernel function, a weight is assigned to each skylight station processing quality assessment result in the neighborhood, and the weight is based on the distance between the assessment result and the mean. All weighted assessment results are added together and divided by the sum of the weights to obtain the skylight station processing quality concentration assessment result. This implementation method uses the Gaussian kernel function to construct the mass concentration analysis function. The Gaussian kernel function can handle nonlinear relationships, and its smoothness helps to reduce the impact of noise on the quality assessment results, thereby improving the accuracy and stability of the assessment.

[0034] In a possible implementation, the workstation quality centralized analysis module 30 includes: a skylight workstation standard processing quality assessment result acquisition unit, which is used to acquire R skylight workstation standard processing quality assessment results of R workstations; a judgment unit, which is 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, which is used to use the skylight workstation processing quality centralized assessment result with a judgment result of no as L abnormal skylight processing quality centralized assessment results; a quality abnormality workstation acquisition unit, which is used to use the workstations corresponding to the L abnormal skylight processing quality centralized assessment results as the L quality abnormality workstations.

[0035] Specifically, the standard processing quality requirements of the skylight station are collected from sources such as product design documents, production process flow, and quality control manuals. The historical production data is analyzed, and the statistical quantities such as the average value and standard deviation of the processing quality of the skylight station are calculated as a reference for the standard processing quality evaluation. Combined with the collected standards and historical data analysis results, the standard processing quality evaluation results of each skylight station are determined, and the standard processing quality evaluation results refer to the processing quality level that the skylight station should achieve under the premise of meeting the design requirements and production process specifications. The processing quality centralized evaluation results of each skylight station are matched with the corresponding standard processing quality evaluation results, and by setting a certain tolerance range, the actual evaluation results are compared with the standard values ​​to determine whether the requirements are met. From the judgment results, all the skylight station processing quality centralized evaluation results that do not meet the standards are screened out as abnormal skylight processing quality centralized evaluation results. The abnormal skylight processing quality centralized evaluation results are associated with the station information that produces these results, and according to the associated information, it is determined which stations have quality abnormalities, and the quality abnormal stations are obtained. This implementation method provides a clear basis for judging whether the processing quality of the skylight station meets the standards by setting standard processing quality assessment results, realizes a rapid and accurate assessment of the processing quality of the skylight station, and identifies stations with abnormal quality, providing targeted guidance for subsequent collaborative control, which helps to reduce product defects and improve product quality.

[0036] The quality abnormality workstation associated neighborhood construction module 40 is used to construct L quality abnormality workstation associated neighborhoods based on the positions of the L quality abnormality workstations in the automobile sunroof automated production line, wherein each quality abnormality workstation associated neighborhood includes a previous workstation and a next workstation located at the corresponding quality abnormality workstation.

[0037] Specifically, through the production line layout information and the relationship between workstations, the previous and next workstations are determined according to the position of the quality abnormality workstation in the production line, forming the associated neighborhood of the quality abnormality workstation, that is, the set of workstations that are directly related to the quality abnormality workstation (previous and next processes).

[0038] The initial collaborative control scheme generating module 50 is used to collaboratively control the L abnormal quality workstations and the associated neighborhoods of the L abnormal quality workstations according to the size of the concentrated evaluation results of the processing quality of the L abnormal skylight workstations and in combination with the standard operating parameters of the R workstation equipment to obtain an initial collaborative control scheme.

[0039] Specifically, the control algorithm is used to adjust the equipment parameters of the abnormal quality workstation and its associated neighborhood according to the size of the centralized evaluation results of the abnormal skylight workstation processing quality, combined with the standard operating parameters of the workstation equipment, to improve the processing quality and generate an initial collaborative control plan. The collaborative control refers to a control method that realizes collaborative work among multiple workstations through information sharing and parameter adjustment.

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

[0041] Specifically, a set of standard operating parameters of R workstation equipment and information of L quality-abnormal workstations and associated neighborhoods are obtained, and a matching algorithm is designed and implemented. The algorithm can retrieve and extract the corresponding standard operating parameters from the set of R workstation equipment standard operating parameters based on the information of the quality-abnormal workstations and their associated neighborhoods, and output the standard operating parameters of the workstation equipment of the L quality-abnormal workstations and a set of standard operating parameters of the L neighborhood workstation equipment.

[0042] The initial collaborative control scheme identifier refers to an algorithm or program that can generate an initial collaborative control scheme based on input data. The L abnormal skylight station processing quality centralized evaluation results, L station equipment standard operating parameters and L neighboring station equipment standard operating parameter sets are input into the initial collaborative control scheme identifier, and the initial collaborative control scheme identifier calculates the first generated initial collaborative control scheme through an algorithm based on the input data, including L first generated initial station equipment operating parameters and L first generated initial neighboring station equipment operating parameter sets.

[0043] Obtain relevant information of the first-generation initial collaborative control plan, including the operating parameters of the workstation equipment, etc., and use the equipment idle time and semi-processed skylight product stagnation time analysis technology to evaluate the loss time of the first-generation initial collaborative control plan, including calculating the idle time of the equipment while waiting for the next process to be completed and the time when the semi-finished product is stagnant between workstations, and output the first-generation loss time. Among them, the equipment idle time refers to the idle time of a certain workstation while waiting for the upstream workstation to provide products or the downstream workstation to receive products due to the poor coordination between the front and rear workstations. The stagnation time of the semi-processed skylight product refers to the stagnation time caused by the inability of the subsequent workstation to receive the skylight product in time after it has been processed to a certain extent at a certain workstation.

[0044] Obtain a set of operating parameters of the equipment in the neighborhood workstation in the first generated initial collaborative control scheme, perform multiple random adjustments to the operating parameters of the equipment in the neighborhood workstation according to a preset adjustment scale (the amplitude 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] Obtain the first generated initial collaborative control scheme and its set of adjustment schemes, as well as the corresponding loss time evaluation results. Based on the loss time evaluation results, iteratively optimize the first generated initial collaborative control scheme, including selecting the adjustment scheme with the smallest loss time as the new initial scheme, and further adjusting and optimizing it, and outputting the initial collaborative control scheme after iterative optimization. Among them, iterative optimization technology refers to the technology of gradually approaching the optimal solution by repeatedly adjusting and optimizing the scheme. The optimal solution is the collaborative control scheme that can achieve the minimum loss time or maximum benefit under given conditions. This implementation method generates collaborative control solutions for specific problems by accurately matching the standard operating parameters of the equipment in the quality abnormality workstation and its associated neighborhood, thereby improving the efficiency of problem solving. By selecting the collaborative control solution with the smallest loss time, 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 generated initial collaborative control adjustment scheme set to obtain a first generated adjustment loss time set; a judgment processing subunit, used to determine whether there is a first generated adjustment loss time in the first generated adjustment loss time set that is less than or equal to the first generated loss time. If so, iterating the first generated initial collaborative control scheme according to the first generated initial collaborative control adjustment scheme corresponding to the minimum value in the first generated adjustment loss time set to obtain a second generated initial collaborative control adjustment scheme; a scheme iteration subunit, used to iterate the second generated initial collaborative control adjustment scheme until a preset number of iterations is met to obtain the initial collaborative control scheme.

[0047] Specifically, a detailed loss time analysis is performed for each of the first generated initial collaborative control adjustment schemes, including equipment idling time and semi-processed skylight product stagnation time. To achieve this analysis, the operation of each adjustment scheme on the actual production line can be simulated, and the equipment idling time and semi-processed skylight product stagnation time under each scheme can be calculated through an algorithm or simulation model, and the two times are added together to obtain the total loss time.

[0048] The first generation adjustment loss time set provided by the loss time analysis subunit is analyzed to find out whether there is an adjustment scheme that is less than or equal to the first generation loss time (i.e., the loss time of the first generation initial collaborative control scheme). If so, it means that these schemes are better than the initial scheme in reducing the loss time, so the first generation initial collaborative control scheme is iterated according to these better schemes. Specifically, the judgment processing subunit will traverse the first generation adjustment loss time set, find the minimum value in the first generation adjustment loss time set, select the adjustment scheme corresponding to the minimum adjustment loss time as the basis for iteration, and iterate the first generation initial collaborative control scheme to further reduce the loss time. The iterative process will be repeated until the preset number of iterations is met. In each iteration, the scheme iteration subunit will fine-tune based on the current optimal scheme (i.e., the scheme with the smallest loss time in the previous iteration), generate a new set of adjustment schemes, and perform loss time analysis again. Through continuous iteration and optimization, a relatively optimal initial collaborative control scheme is finally obtained. This implementation method finds a collaborative control scheme that can minimize the loss time of the automobile sunroof automation production line through continuous iteration and optimization. Since each workstation on the production line is interrelated, the quality abnormality or efficiency change of one workstation will affect other workstations. Therefore, by constructing the associated neighborhood of the quality abnormality workstation and coordinating the control of these workstations and their associated neighborhoods, the overall loss time of the production line is effectively reduced, and the operation efficiency and product quality of the production line are improved.

[0049] In one possible implementation, the first generating initial collaborative control scheme acquisition unit includes: a training data acquisition subunit, used to obtain a plurality of sample abnormal skylight workstation processing quality centralized evaluation results, a plurality of sample workstation equipment standard operating parameters and a plurality of sample neighborhood workstation equipment standard operating parameter sets as training data; an initial collaborative control scheme identifier construction subunit, used to construct a generator and an adversary of the initial collaborative control scheme identifier based on a generative adversarial network as a basic framework; an alternating supervised training subunit, used to perform alternating supervised training on the generator and the adversary using the training data until the training converges to obtain the initial collaborative control scheme identifier.

[0050] Specifically, relevant production data are extracted from historical production line records, experimental data or simulation models. These data include the centralized evaluation results of the processing quality of multiple sample abnormal skylight stations, multiple sample station equipment standard operating parameters, and multiple sample neighborhood station equipment standard operating parameter sets. The collected data is cleaned and sorted, including removing outliers and filling missing values, to ensure the accuracy and consistency of the data. Representative samples are selected from the preprocessed data as input data for training the initial collaborative control scheme identifier.

[0051] Based on the Generative Adversarial Network (GAN) framework, the generator and adversary of the initial collaborative control scheme identifier are constructed. The task of the generator is to generate an initial collaborative control scheme based on the input abnormal skylight station processing quality centralized evaluation results, station equipment standard operating parameters and neighboring station equipment standard operating parameter sets. The generator can capture the complex relationship between the input data and generate a reasonable collaborative control scheme. The task of the adversary is to judge the initial collaborative control scheme generated by the generator to determine whether it meets the expected quality control requirements. The adversary will try to find deficiencies or errors in the collaborative control scheme generated by the generator and guide the generator to improve through feedback signals.

[0052] The alternating supervised training subunit sets the initial model parameters for the generator and the adversary. During the training process, the adversary parameters are first fixed and the generator is trained to generate a better collaborative control solution; then the generator parameters are fixed and the adversary is trained to more accurately judge the generated collaborative control solution. This process is repeated until the training convergence standard is reached. After the training, the performance of the initial collaborative control solution identifier is evaluated using the validation data set to ensure that it can accurately generate a reasonable collaborative control solution based on the input data. This implementation method uses a generative adversarial network to build an intelligent system that can automatically generate a reasonable collaborative control solution. The GAN framework can efficiently capture the complex relationship between data and generate high-quality collaborative control solutions, thereby achieving the technical effect of improving the efficiency and quality of the generation of the initial collaborative control solution.

[0053] The overall collaborative control scheme analysis module 60 is used to analyze the overall collaborative control scheme 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, so as to obtain a target collaborative control scheme.

[0054] Specifically, the initial collaborative control scheme is applied to other workstations (the first set of workstations) except the workstation with abnormal quality 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 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 match the standard operating parameters of the workstation equipment of the first workstation set based on the R workstation equipment standard operating parameters to obtain the first workstation equipment standard operating parameter set; an overall collaborative analysis unit, used to use the overall collaborative network layer to analyze the first workstation equipment standard operating parameter set and the initial collaborative control scheme to obtain the target collaborative control scheme.

[0056] Specifically, from the R workstation equipment standard operating parameters acquired by the standard working parameter acquisition module 10, the equipment standard operating parameters belonging to the first workstation set (i.e., all workstations except L quality abnormal workstations and their associated neighborhoods) are screened out based on information such as the workstation number or equipment identification. During the execution process, the first workstation equipment standard operating parameter set acquisition unit will traverse the standard operating parameter database, and for each parameter, check whether its corresponding workstation number is in the first workstation set. If so, the parameter is added to the first workstation equipment standard operating parameter set; if not, the parameter is ignored. In this way, a set containing only the first workstation set equipment standard operating parameters is finally constructed.

[0057] The overall collaborative analysis unit uses the overall collaborative network layer (a pre-trained neural network model) to input the standard operating parameter set of the first station equipment and the initial collaborative control plan, and output an optimized target collaborative control plan. During the execution process, the overall collaborative network layer will first receive the input data (including the standard operating parameters of the first station equipment and the initial collaborative control plan), and then process it layer by layer through multiple hidden layers within the network. Each layer will extract and transform the data features, and finally output an optimized collaborative control plan. This plan will take into account the mutual influence and dependency between each station to ensure the operating efficiency and product quality of the overall production line. This implementation method ensures the matching and coordination of the operating parameters between each station through collaborative control, thereby improving the operating efficiency of the overall production line.

[0058] The multi-station collaborative control module 70 is used to perform multi-station collaborative control on the automobile sunroof automated production line by utilizing the target collaborative control scheme.

[0059] Specifically, the target collaborative control scheme is converted into a specific control instruction, which is sent to each workstation through a control system (equipment or software for realizing automated production line control) or a PLC (programmable logic controller) and other equipment, so as to perform multi-station collaborative control on the automated production line of automobile sunroofs and realize collaborative work of multiple workstations. The embodiment of the present application adopts the method of traversing all workstations on the production line, collecting standard operating parameters of the equipment, evaluating the processing quality of the workstations within the preset window, extracting the evaluation results, forming a processing quality evaluation set, and using a quality concentrator to centrally analyze the evaluation results, screen out abnormal workstations, and construct a neighborhood associated with abnormal quality workstations. According to the abnormal workstations 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 workstation set (workstations other than abnormal workstations and their associated neighborhoods), an overall collaborative control scheme analysis is performed to obtain a target collaborative control scheme, and the target collaborative control scheme is used to perform multi-station collaborative control on the production line. Technical means such as the technical effect of improving the flexibility and accuracy of control through global collaborative control is achieved.

[0060] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and 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 implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0061] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art 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 the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. The multi-station collaborative control platform of the automobile sunroof automated production line is characterized by: The platform includes: A standard working parameter collection module, used to traverse the R stations of the automobile sunroof automated production line to collect standard working parameters and obtain standard operating parameters of the R station equipment, where R is an integer greater than or equal to 1; A skylight station processing quality assessment result extraction module is used to extract the skylight station processing quality assessment results of the R stations in the preset window respectively, and obtain a set of R skylight station processing quality assessments; a station quality centralized analysis module, configured to use a mass concentrator to perform station quality centralized analysis on the R skylight station processing quality assessment result sets to obtain R skylight station processing quality centralized assessment results, and to screen abnormal stations based on the R skylight station processing quality centralized assessment results to obtain L stations with abnormal quality and L abnormal skylight station processing quality centralized assessment results, wherein L is an integer greater than or equal to 1 and less than or equal to R; A quality abnormality workstation associated neighborhood construction module is used to construct L quality abnormality workstation associated neighborhoods based on the positions of the L quality abnormality workstations in the automobile sunroof automated production line, wherein each quality abnormality workstation associated neighborhood includes a previous workstation and a next workstation located at the corresponding quality abnormality workstation; An initial collaborative control scheme generating module is used to collaboratively control the L abnormal quality workstations and the L abnormal quality workstations associated neighborhoods according to the size of the concentrated evaluation results of the processing quality of the L abnormal skylight workstations and in combination with the standard operating parameters of the R workstation equipment to obtain an initial collaborative control scheme; An overall collaborative control scheme analysis module, configured to perform overall collaborative control scheme analysis based on the initial collaborative control scheme and a first set of workstations among the R workstations excluding the L workstations with abnormal quality and the associated neighborhoods of the L workstations with abnormal quality, to obtain a target collaborative control scheme; A multi-station collaborative control module is used to perform multi-station collaborative control on the automobile sunroof automated production line using the target collaborative control scheme.

2. The multi-station collaborative control platform for the automotive sunroof automated production line according to claim 1, characterized in that: The workstation quality centralized analysis module includes: A mass concentrator construction unit, used to construct a mass concentrator, wherein the mass concentrator includes a mass concentration analysis function; A traversal calculation unit, used for traversing and calculating the mean of the R skylight workstation processing quality assessment result sets to obtain the mean of the R skylight workstation processing quality assessment result sets; The workstation quality concentration analysis unit is used to input the mean value of the R skylight workstation processing quality assessment results and the set of the R skylight workstation processing quality assessment results into the mass concentrator for workstation quality concentration analysis. When the number of iterative analysis meets the preset number, the R skylight workstation processing quality concentration assessment results are obtained.

3. The multi-station collaborative control platform for the automobile sunroof automated production line according to claim 2, characterized in that: The mass concentrator building unit comprises: The mass concentration analysis function construction subunit is used to construct a mass concentration analysis function, and the mass concentration analysis function is: Where m(x) is the concentrated evaluation result of the skylight processing quality, N(x) is the neighborhood of the skylight processing quality evaluation results in the skylight processing quality evaluation result set whose distance to the mean of the skylight processing quality evaluation results is within the preset distance range, x is the mean of the skylight processing quality evaluation results, x i is the processing quality evaluation result of the i-th skylight station in the neighborhood, and K(·) is the Gaussian kernel function.

4. The multi-station collaborative control platform for the automobile sunroof automated production line according to claim 1, characterized in that: 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 of R workstations; A judgment unit, used to judge whether the centralized evaluation results of the processing quality of the R skylight workstations meet the standard evaluation results of the processing quality of the R skylight workstations, and obtain a judgment result; An abnormal sunroof processing quality centralized evaluation result acquisition unit is used to use the sunroof workstation processing quality centralized evaluation result with a judgment result of "no" as L abnormal sunroof processing quality centralized evaluation results; The quality abnormality workstation acquisition unit is used to take the workstations corresponding to the centralized evaluation results of the processing quality of the L abnormal skylights as the L quality abnormality workstations.

5. The multi-station collaborative control platform for the automobile sunroof automated production line according to claim 1, characterized in that: The initial collaborative control scheme generation module includes: A workstation equipment standard operation parameter matching unit, used for matching the L workstation equipment standard operation parameters of the L quality abnormal workstations and the workstation equipment standard operation parameters of the L quality abnormal workstations associated neighborhoods based on the R workstation equipment standard operation parameters, to obtain the L workstation equipment standard operation parameters and the L neighborhood workstation equipment standard operation parameter sets; A first generated initial collaborative control scheme acquisition unit is used to input the L abnormal skylight workstation processing quality centralized evaluation results, the L workstation equipment standard operating parameters and the L neighborhood workstation equipment standard operating parameter sets into the initial collaborative control scheme identifier for analysis to obtain a first generated initial collaborative control scheme, wherein the first generated initial collaborative control scheme includes the L first generated initial workstation equipment operating parameters and the L first generated initial neighborhood workstation equipment operating parameter sets; A 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 coordinated control scheme, and superimpose the analysis results to obtain the first generation loss time; A first generated initial collaborative control adjustment scheme set acquisition unit is used to perform multiple random adjustments on at least one first generated initial neighboring workstation equipment operating parameter in the L first generated initial neighboring workstation equipment operating parameter sets according to a preset adjustment scale to obtain a first generated initial collaborative control adjustment scheme set; A scheme iteration unit is used to iterate the first generated initial cooperative control scheme based on the first generated initial cooperative control adjustment scheme set and the first generated loss time to obtain the initial cooperative control scheme.

6. The multi-station collaborative control platform for the automobile sunroof automated production line according to claim 5, characterized in that: The scheme iteration unit includes: a loss time analysis subunit, configured to perform a loss time analysis on the first generated initial coordinated control adjustment scheme set to obtain a first generated adjustment loss time set; a judgment processing subunit, configured to judge whether there is a first generated adjustment loss time less than or equal to the first generated loss time in the first generated adjustment loss time set, and if so, iterate the first generated initial coordinated control scheme according to the first generated initial coordinated control adjustment scheme corresponding to the minimum value in the first generated adjustment loss time set to obtain a second generated initial coordinated control adjustment scheme; The scheme iteration subunit is used to iterate the second generated initial collaborative control adjustment scheme until a preset number of iterations is met to obtain the initial collaborative control scheme.

7. The multi-station collaborative control platform for the automobile sunroof automated production line according to claim 5, characterized in that: The first generating initial cooperative control scheme acquisition unit comprises: A training data acquisition subunit is used to acquire a plurality of sample abnormal skylight workstation processing quality centralized evaluation results, a plurality of sample workstation equipment standard operating parameters and a plurality of sample neighborhood workstation equipment standard operating parameter sets as training data; An initial collaborative control scheme identifier construction subunit is used to construct a generator and an adversary of the initial collaborative control scheme identifier based on a generative adversarial network as a basic framework; The 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 to obtain the initial collaborative control scheme identifier.

8. The multi-station collaborative control platform for the automobile sunroof automated production line according to claim 7, characterized in that: The overall collaborative control scheme analysis module includes: A first workstation equipment standard operation parameter set acquisition unit, configured to match the workstation equipment standard operation parameters of the first workstation set based on the R workstation equipment standard operation parameters to obtain the first workstation equipment standard operation parameter set; The overall collaborative analysis unit is used to analyze the first workstation equipment standard operating parameter set and the initial collaborative control scheme by using the overall collaborative network layer to obtain the target collaborative control scheme.

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