A production line optimization method, device and storage medium

Abstract

The present application discloses a production line optimization method, device, and storage medium. The method includes: determining the bottleneck action of the production line; finding the category to which the bottleneck action belongs from a category library; optimizing the bottleneck action according to the category to which the bottleneck action belongs; wherein the bottleneck action is an action that meets a preset condition, and the category library is preset with multiple action categories of bottleneck actions. The present application can accurately and quickly obtain the type of the bottleneck action, and perform corresponding optimization on the bottleneck action according to the type of the bottleneck action, improving the efficiency of line optimization.

Description

Technical Field

[0001] This application relates to intelligent manufacturing, and in particular to a production line optimization method, device, and storage medium. Background Art

[0002] In order to optimize the industrial production process, in addition to collecting line body data, it is inevitable to deeply analyze the line body and optimize the beat. The line body consists of multiple workstations, and each workstation is composed of different actions. The bottleneck action refers to the action that takes the longest time at a workstation. Therefore, it is crucial to find the bottleneck action of the workstation and determine the optimization plan.

[0003] Currently, the methods for line body analysis and optimization in related technologies mainly include the following two: First, through the big data platform for line body analysis and optimization, this method can only analyze the overall operation of multiple line bodies through big data, and cannot determine the bottleneck actions of the production line, and does not have the function of systematically determining bottleneck actions and specific optimization plans; Second, through technicians' on-site inspection and visual observation of the line body operation for line body diagnosis to find bottleneck actions, this method is time-consuming and laborious and is not conducive to forming an automated system analysis and diagnosis plan. Therefore, necessary solutions cannot be obtained in a timely manner after finding bottleneck actions.

[0004] Therefore, the above technical problems existing in related technologies need to be solved urgently. Summary of the Invention

[0005] This application aims to solve one of the technical problems in related technologies. For this purpose, embodiments of this application provide a production line optimization method, device, and storage medium, which can relatively quickly and accurately find the bottleneck actions of the line body and optimize the production line.

[0006] According to one aspect of the embodiments of this application, a production line optimization method is provided. The method includes:

[0007] Determine the bottleneck actions of the production line;

[0008] Find the category to which the bottleneck action belongs from the category library;

[0009] Optimize the bottleneck action according to the category to which the bottleneck action belongs;

[0010] Wherein, the bottleneck action is an action that meets a preset condition, and the category library presets multiple action categories of bottleneck actions.

[0011] In one embodiment, the finding the category to which the bottleneck action belongs from the category library includes:

[0012] Obtain the category of the production line control signal of the bottleneck action;

[0013] If the production line control signal corresponding to the bottleneck action belongs to a robot signal, determine that the category to which the bottleneck action belongs is the robot category;

[0014] If the production line control signal corresponding to the bottleneck action belongs to a motor signal, determine that the category to which the bottleneck action belongs is the motor category;

[0015] If the production line control signal corresponding to the bottleneck action belongs to a fixture signal, determine that the category to which the bottleneck action belongs is the fixture category;

[0016] If there is no corresponding production line control signal for the bottleneck action, determine that the category to which the bottleneck action belongs is the manual category.

[0017] In one embodiment, the determining the bottleneck action of the production line includes:

[0018] Detect the statistical data of the production line according to the condition indicators of the bottleneck action;

[0019] Determine the action that meets the condition indicators of the bottleneck action as the bottleneck action according to the statistical data.

[0020] In one embodiment, detecting the statistical data of the action according to the condition indicators of the bottleneck action, if the statistical data meets the condition indicators of the bottleneck action, determining the action as the bottleneck action includes:

[0021] Detect the start signal and end signal of the action;

[0022] Group the actions with the same start signal generation time;

[0023] Calculate the time difference between the start signal and the end signal, and the time difference is the duration of the action;

[0024] Determine the action with the longest duration in each group as the bottleneck action.

[0025] In one embodiment, the method further includes:

[0026] Determine the occurrence order of the actions according to the generation time of the start signal;

[0027] Arrange all the actions of the production line in the occurrence order.

[0028] In one embodiment, the method includes

[0029] If there is only one action in a group, the determining the action with the longest duration in each group as the bottleneck action includes: determining the only action in the group as the bottleneck action;

[0030] If the durations of multiple actions in the group of actions are the same, determining the action with the longest duration in each group as the bottleneck action includes: determining the multiple actions with the same duration in the group as the bottleneck actions.

[0031] In one embodiment, optimizing the bottleneck action according to the category to which the bottleneck action belongs includes:

[0032] If the category to which the bottleneck action belongs is the robot category, perform at least one of the following operations: increase the pre-working positions of the robot, use circular arc approximation as the robot trajectory; optimize the welding and grinding parameters of the robot; delete the redundant timers in the robot program; if the action involves torch cutting, make the dust-proof cover open during torch cutting; release the robot that has completed the work to return to the origin in advance; transfer the welding points to other robots;

[0033] If the category to which the bottleneck action belongs is the motor category, perform at least one of the following operations: adjust the rotation speed and ramp value of the motor; increase the rotation speed and stability of the motor;

[0034] If the category to which the bottleneck action belongs is the fixture category, perform at least one of the following operations: adjust the throttle valve of the fixture to increase the air intake; delete the repeated actions of the fixture; merge multiple groups of fixture actions; synchronize the actions of the fixture with the movement of the robot, and perform the fixture actions and the movement of the robot simultaneously;

[0035] If the category to which the bottleneck action belongs is the manual category, perform at least one of the following operations: optimize the manual operation steps; increase the operators; synchronize the manual actions with other actions, and perform the manual actions and other actions simultaneously.

[0036] In one embodiment, the method further includes:

[0037] Draw a timing Gantt chart according to the duration of the bottleneck action and the category to which the bottleneck action belongs;

[0038] Send the timing Gantt chart to the front end of the production line.

[0039] According to one aspect of the embodiments of the present application, a production line optimization device is provided, and the device includes:

[0040] A first module for determining the bottleneck actions of the production line;

[0041] A second module for finding the category to which the bottleneck action belongs from the category library;

[0042] A third module for optimizing the bottleneck action according to the action category to which the bottleneck action belongs.

[0043] According to one aspect of the embodiments of the present application, a production line optimization device is provided, and the device includes:

[0044] At least one processor;

[0045] At least one memory for storing at least one program;

[0046] When at least one of the at least one program is executed by at least one of the at least one processor, the production line optimization method described in the previous embodiments is implemented.

[0047] According to one aspect of the embodiments of the present application, a storage medium is provided, characterized in that the storage medium stores a program executable by a processor, and when the program executable by the processor is executed by the processor, the production line optimization method described in the previous embodiments is implemented.

[0048] The beneficial effects of a production line optimization method provided by the embodiments of the present application are as follows: determining the bottleneck actions of the production line; finding the category to which the bottleneck actions belong from the category library; and optimizing the bottleneck actions according to the category to which the bottleneck actions belong. By decomposing the production process of the workstations into as fine actions as possible, converting the production process into production data, automatically analyzing each bottleneck action of the workstations and visually displaying them, automatically generating an optimization plan, quickly and accurately finding the bottleneck actions of the line body, and improving the efficiency of line body optimization.

[0049] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a flowchart of a production line optimization method provided by an embodiment of the present application;

[0052] Figure 2 It is a flowchart of finding the category to which the bottleneck actions belong from the category library in an embodiment of the present application;

[0053] Figure 3 It is a flowchart of arranging all the actions of the production line in the order of occurrence in an embodiment of the present application;

[0054] Figure 4 It is a flowchart of determining that the action with the longest duration in each group of actions is the bottleneck action in an embodiment of the present application;

[0055] Figure 5 Schematic diagram of the method for optimizing bottleneck actions in the embodiments of the present application;

[0056] Figure 6 Schematic diagram of a production line optimization device provided by the embodiments of the present application;

[0057] Figure 7 Another schematic diagram of a production line optimization device provided by the embodiments of the present application. Detailed implementation manners

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0059] The terms "first", "second", "third", "fourth", etc. in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0060] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0061] In order to optimize the industrial production process (such as the vehicle production process), in addition to collecting line body data, it is inevitable to deeply analyze the line body and optimize the beat. The line body is composed of multiple workstations, and the workstations are composed of different actions. The bottleneck action refers to the action that takes the longest time at the workstation. Therefore, it is crucial to find out the bottleneck actions of the workstations and determine the optimization plan.

[0062] Currently, the methods for optimizing line analysis in related technologies mainly include the following two: First, optimizing line analysis through a big data platform. This method can only analyze the overall operation of multiple lines through big data and cannot determine the bottleneck actions of the production line. It does not have the function of systematically determining bottleneck actions and specific optimization plans. Second, diagnosing the line by technical personnel visually inspecting the line operation to find bottleneck actions. This method is time-consuming and laborious and is not conducive to forming an automated system analysis and diagnosis plan. Therefore, necessary solutions cannot be obtained in a timely manner after finding bottleneck actions.

[0063] In view of the above problems, this application proposes a production line optimization method, as Figure 1 shown, which specifically includes the following steps:

[0064] S101. Determine the bottleneck actions of the production line.

[0065] In step S101, determining the bottleneck actions of the production line specifically includes: detecting the statistical data of the production line according to the condition indicators of the bottleneck actions; determining the actions that meet the condition indicators of the bottleneck actions as bottleneck actions based on the statistical data. Among them, the condition indicators of the bottleneck actions refer to the conditions or indicators that meet the definition of the bottleneck actions. For example, when the action with a long consumption time is the bottleneck action, the condition indicator of the bottleneck action is the time consumed by the action; when the defective rate of the action is higher than a certain value and it is the bottleneck action, the condition indicator of the bottleneck action is the defective rate of the action.

[0066] S102. Find the action category to which the bottleneck action belongs from the category library.

[0067] In this embodiment, the bottleneck actions have different categories. At the initial stage of the design of the production line, a table of signal interaction between each device and the PLC is obtained. Therefore, as long as there is an interaction signal between each action and the PLC, the specific device type can be known. And whether the device is a motor, a robot or a fixture can also be easily obtained from the device name. The above operations can be completed by writing a program to analyze the table or by manual classification according to experience. For example, finding the category to which the bottleneck action belongs from the category library can be to determine the specific device type according to the type of control signal, or to find the specific device type by manual observation. This step classifies the bottleneck actions into different categories, which can facilitate researchers to clarify the complex actions and lay a foundation for the subsequent optimization of the bottleneck actions.

[0068] S103. Optimize the bottleneck actions according to the action category to which the bottleneck action belongs.

[0069] In this embodiment, the bottleneck action is an action that meets preset conditions, and multiple action categories of bottleneck actions are preset in the category library. During the production operation of the assembly line, there will be joints that take a long time and reduce the production efficiency of the assembly line. Such link actions that cause a reduction in the production efficiency of the assembly line are called bottleneck actions. The determination of bottleneck actions can set different criteria according to different assembly line types and the requirements of technicians. For example, when the production efficiency of the assembly line is low and the time consumption is long, the assembly line actions that take a long time can be determined as bottleneck actions. When the defective rate of the assembly line production is high, the assembly line actions that cause a high defective rate can be determined as bottleneck actions. This embodiment takes the assembly line actions that take a long time as an example of bottleneck actions for illustration, and does not constitute an improper limitation on the definition of bottleneck actions.

[0070] Specifically, in the above embodiment, finding the category to which the bottleneck action belongs in the category library includes the following steps, as Figure 2 shown:

[0071] S201. Obtain the category of the production line control signal of the bottleneck action.

[0072] S202. If the production line control signal corresponding to the bottleneck action belongs to a robot signal, determine that the category to which the bottleneck action belongs is the robot category.

[0073] S203. If the production line control signal corresponding to the bottleneck action belongs to a motor signal, determine that the category to which the bottleneck action belongs is the motor category.

[0074] S204. If the production line control signal corresponding to the bottleneck action belongs to a fixture signal, determine that the category to which the bottleneck action belongs is the fixture category.

[0075] S205. If there is no corresponding production line control signal for the bottleneck action, determine that the category to which the bottleneck action belongs is the manual category.

[0076] Among them, the manual category is an action performed manually (manual action), which may be accompanied by safety signals and the on / off of buttons. In the actual production process, the manual action is that a person enters the operation area (at this time the safety signal is disconnected), exits the area after completing the operation, and presses the button (at this time the button signal is turned on), and then the PLC (controller) line continues to work automatically.

[0077] Optionally, in this embodiment, the station production process can also be decomposed into as fine actions as possible. In this embodiment, currently, the station production process is decomposed into as fine actions as possible manually according to experience. This is because the program standards of different vehicle manufacturers are different. If a standard is determined, it is possible to automatically identify PLC signals through programming, thereby realizing the automatic decomposition of the process into actions. The role of decomposing the station production process into as fine actions as possible is to improve the accuracy and efficiency of determining bottleneck actions and reduce the misjudgment of bottleneck actions caused by the mixing of different actions.

[0078] In this embodiment, there is a control signal during the interaction of each device. The control signal has been formed into a table at the initial stage of the line body design to record the signal areas where each device interacts with the control system, so as to avoid signal conflicts, etc. Moreover, during the actual installation of the line body, it is strictly implemented according to the design, and the debugging is also strictly in accordance with the table. Therefore, through this table, each action corresponds to a device. When this action is ongoing and there is no other signal interaction between this device and the control system, it can be considered that this action is already the smallest action. When all actions are the smallest actions, the decomposition of the station production process into as fine actions as possible is achieved. After decomposing the production process into as fine actions as possible, it is beneficial to determine bottleneck actions.

[0079] In the above embodiment, the statistical data of the action is detected according to the condition indicators of the bottleneck action. If the statistical data meets the condition indicators of the bottleneck action, then the action is determined as the bottleneck action, as Figure 3 shown, and specifically includes the following steps:

[0080] S301. Detect the start signal and end signal of the action.

[0081] In this embodiment, different actions have start signals and end signals. In the background of the control system, the type and emission time of each action start signal are recorded, and at the same time, the type and emission time of each action end signal are also recorded. Therefore, detecting the start signal and end signal of the action can obtain the start time and end time of each action.

[0082] S302. Group the actions with the same time of generation of the start signal.

[0083] In this embodiment, it is necessary to set the benchmarks of all actions to be the same. The benchmark of an action is the start time. A group of actions with the same start time are comparable, and the action with the longest duration in this group is the bottleneck action. For the case where there is only one action in a group of actions, this action itself is the bottleneck action.

[0084] S303. Calculate the time difference between the start signal and the end signal, and this time difference is the duration of the action.

[0085] In this embodiment, the time difference between the start signal and the end signal is the duration of the action. By means of the types and the emitted times of each action start signal and end signal in the background of the control system, the duration of each action can be obtained.

[0086] S304. Determine the action with the longest duration in each group as the bottleneck action.

[0087] In this embodiment, as described above, the bottleneck action is defined as the action with the longest duration. Similarly, those skilled in the art can define the bottleneck action as different actions according to needs, such as the action that consumes the most raw materials, the action with the highest error rate, etc. For example, when the bottleneck action is defined as the action with the highest defective rate, the corresponding method is as follows:

[0088] Detect the statistical data of the action according to the conditional index of the bottleneck action. If the statistical data meets the conditional index of the bottleneck action, then determine that the action is the bottleneck action. It can also be specifically the following steps: Detect the defective rate of the action; Determine the action with the highest defective rate as the bottleneck action.

[0089] Optionally, when determining the bottleneck action of the production line in the above embodiment, the following steps may further be included, as Figure 4 shown:

[0090] S401. Determine the occurrence order of the action according to the generation time of the start signal.

[0091] S402. Arrange all the actions of the production line in the occurrence order.

[0092] The specific implementation manner of this embodiment is: Sort all the actions according to the start time of each action, and assign a sequence number to different actions. For example, assign the sequence number 001 to the action with the earliest start time, and assign the sequence number 002 to the subsequent actions, and so on, so as to sort different actions in the order of occurrence time.

[0093] The function of arranging all the actions of the production line in the occurrence order in this embodiment is that the sequence of the occurrence of the pipeline actions can be determined through all the actions in the occurrence order, which is convenient for researchers to view the overall operation situation of the pipeline.

[0094] Optionally, when determining the bottleneck action, it is possible that there is only one bottleneck action in a group or there are multiple bottleneck actions with the same duration in a group. For this situation, this embodiment further includes: If there is only one action in a group of actions, the step of determining the action with the longest duration in each group as the bottleneck action includes: determining the only action in the group as the bottleneck action; If multiple actions in the group have the same duration, the step of determining the action with the longest duration in each group as the bottleneck action includes: determining the multiple actions with the same duration in the group as the bottleneck actions.

[0095] In this embodiment, for the case where there is only one action in a group of actions, the bottleneck action is the only action in the group; for the case where multiple actions in a group have the same duration, the bottleneck actions are the multiple actions with the same duration in the group.

[0096] Specifically, optimizing the bottleneck action according to the category to which the bottleneck action belongs specifically includes the following content, as Figure 5 shown:

[0097] If the category to which the bottleneck action belongs is the robot category, then increase the robot's pre-working position, use arc approximation as the robot's trajectory; optimize the welding and grinding parameters of the robot; delete the redundant timers in the robot program; if the action involves torch cutting, make the dust-proof cover open during torch cutting; release the robot that has completed the work to return to the origin in advance;

[0098] If the category to which the bottleneck action belongs is the motor category, then adjust the rotation speed and ramp value of the motor;

[0099] If the category to which the bottleneck action belongs is the fixture category, then adjust the throttle valve of the fixture to increase the air intake; delete the repeated actions of the fixture; merge multiple groups of fixture actions; synchronize the actions of the fixture with the movement of the robot;

[0100] If the category to which the bottleneck action belongs is the manual category, then optimize the manual action steps; increase operators; synchronize the actions of the manual with other actions.

[0101] It should be noted that for different bottleneck action categories, different optimization methods are required. This embodiment only introduces the robot category, the motor category, the fixture category, and the manual category. In actual production applications, the production line has more different categories and different corresponding optimization methods, which are not described one by one in this specification.

[0102] Optionally, this embodiment can also draw a timing Gantt chart according to the duration of the bottleneck action and the category to which the bottleneck action belongs, and send the timing Gantt chart to the front end of the production line. The drawn timing Gantt chart can facilitate technicians to intuitively and clearly see the bottleneck action situation of the production line, and help technicians flexibly adjust the operation of the production line.

[0103] Referring to Figure 6 , an embodiment of the present invention also provides a production line optimization device, including:

[0104] A first module 601, configured to determine the bottleneck action of the production line.

[0105] A second module 602, configured to find the category to which the bottleneck action belongs from a category library.

[0106] A third module 603, configured to optimize the bottleneck action according to the category to which the bottleneck action belongs.

[0107] It can be seen that the content in the above method embodiments is applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0108] Referring to Figure 7 , an embodiment of the present application provides a production line optimization device, including:

[0109] At least one processor 701;

[0110] At least one memory 702, configured to store at least one program;

[0111] When at least one program is executed by at least one processor 701, at least one processor 701 implements the production line optimization method of the foregoing embodiment.

[0112] Similarly, the content in the above method embodiments is applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0113] An embodiment of the present invention also provides a storage medium, and the storage medium stores a program, which is used to implement the production line optimization method of the foregoing embodiment when executed by a processor.

[0114] The content in the above method embodiments is applicable to the storage medium embodiments. The functions specifically implemented by the storage medium embodiments are the same as those of the above method embodiments.

[0115] Similarly, the content in the above method embodiments is applicable to this storage medium embodiment. The functions specifically implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0116] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order mentioned in the operation diagrams. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. Additionally, the embodiments presented and described in the flowcharts of this application are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated where the order of various operations is changed and where sub-operations described as part of a larger operation are executed independently.

[0117] Furthermore, although this application is described in the context of functional modules, it should be understood that unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding this application. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement this application as set forth in the claims without undue experimentation. It can also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0118] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0119] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0120] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0121] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] In the above description of this specification, the description with reference to terms such as "one embodiment / Example", "another embodiment / Example", or "certain embodiments / Examples" etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0123] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application. The scope of the present application is defined by the claims and their equivalents.

[0124] The above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent substitutions for some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing a production line, characterized in that, the method includes: determining the bottleneck actions of the production line; finding the category to which the bottleneck actions belong from a category library; the finding the category to which the bottleneck actions belong from the category library includes: obtaining the category of the production line control signal of the bottleneck actions; if the production line control signal corresponding to the bottleneck actions belongs to a robot signal, determining that the category to which the bottleneck actions belong is the robot category; if the production line control signal corresponding to the bottleneck actions belongs to a motor signal, determining that the category to which the bottleneck actions belong is the motor category; if the production line control signal corresponding to the bottleneck actions belongs to a fixture signal, determining that the category to which the bottleneck actions belong is the fixture category; if there is no corresponding production line control signal for the bottleneck actions, determining that the category to which the bottleneck actions belong is the manual category; optimizing the bottleneck actions according to the category to which the bottleneck actions belong; wherein, different optimization methods are carried out for different bottleneck action categories; wherein, the bottleneck actions are actions that meet preset conditions, and the category library presets multiple action categories of bottleneck actions; the method further includes: drawing a timing Gantt chart according to the duration of the bottleneck actions and the category to which the bottleneck actions belong; sending the timing Gantt chart to the front end of the production line.

2. A method for optimizing a production line according to claim 1, characterized in that, the determining the bottleneck actions of the production line includes: detecting the statistical data of the production line according to the condition indicators of the bottleneck actions; determining the actions that meet the condition indicators of the bottleneck actions as bottleneck actions according to the statistical data.

3. A method for optimizing a production line according to claim 2, characterized in that, detecting the statistical data of the actions according to the condition indicators of the bottleneck actions, and if the statistical data meets the condition indicators of the bottleneck actions, determining the actions as bottleneck actions, includes: detecting the start signal and end signal of the actions; grouping the actions with the same generation time of the start signal into one group; calculating the time difference between the start signal and the end signal, and the time difference is the duration of the actions; determining the action with the longest duration in each group as the bottleneck action.

4. A method for optimizing a production line according to claim 3, characterized in that, the method further includes: determining the occurrence order of the actions according to the generation time of the start signal; arranging all the actions of the production line in the occurrence order.

5. A method for optimizing a production line according to claim 3, characterized in that, the method includes: if there is only one action in a group of actions, the determining the action with the longest duration in each group as the bottleneck action includes: determining the only action in the group as the bottleneck action; if the durations of multiple actions in the group are the same, the determining the action with the longest duration in each group as the bottleneck action includes: determining the multiple actions with the same duration in the group as the bottleneck actions.

6. A method for optimizing a production line according to claim 1, characterized in that, Optimize the bottleneck actions according to the categories to which the bottleneck actions belong, including: If the category to which the bottleneck action belongs is the robot category, perform at least one of the following operations: increase the pre-working position of the robot, use circular arc approximation as the robot trajectory; optimize the welding and grinding parameters of the robot; delete the redundant timers in the robot program; if the action involves torch cutting, make the dust-proof cover open during torch cutting; release the robot that has completed the work to return to the origin in advance; transfer the welding points to other robots; If the category to which the bottleneck action belongs is the motor category, perform at least one of the following operations: adjust the rotation speed and ramp value of the motor; improve the rotation speed and stability of the motor; If the category to which the bottleneck action belongs is the fixture category, perform at least one of the following operations: adjust the throttle valve of the fixture to increase the air intake; delete the repetitive actions of the fixture; combine multiple groups of fixture actions; synchronize the actions of the fixture with the movement of the robot, and perform the fixture actions and the movement of the robot simultaneously; If the category to which the bottleneck action belongs is the manual category, perform at least one of the following operations: optimize the manual action steps; increase the operators; synchronize the manual actions with other actions, and perform the manual actions and other actions simultaneously.

7. A production line optimization device, Characterized in that, The device includes: A first module for determining the bottleneck actions of the production line; wherein, the bottleneck actions are actions that meet preset conditions; A second module for finding the category to which the bottleneck action belongs from a category library; wherein, the category library presets multiple action categories of bottleneck actions; the finding the category to which the bottleneck action belongs from the category library includes: obtaining the category of the production line control signal of the bottleneck action; if the production line control signal corresponding to the bottleneck action belongs to a robot signal, determining that the category to which the bottleneck action belongs is the robot category; if the production line control signal corresponding to the bottleneck action belongs to a motor signal, determining that the category to which the bottleneck action belongs is the motor category; if the production line control signal corresponding to the bottleneck action belongs to a fixture signal, determining that the category to which the bottleneck action belongs is the fixture category; if the bottleneck action has no corresponding production line control signal, determining that the category to which the bottleneck action belongs is the manual category; A third module for optimizing the bottleneck actions according to the action category to which the bottleneck action belongs; wherein, for different bottleneck action categories, different optimization methods are performed; The device is further configured to: Draw a timing Gantt chart according to the duration of the bottleneck action and the category to which the bottleneck action belongs; Send the timing Gantt chart to the front end of the production line.

8. A production line optimization device, Characterized in that, The device includes: At least one processor; At least one memory, and the memory is used to store at least one program; When at least one of the programs is executed by at least one of the processors, the production line optimization method according to any one of claims 1-6 is implemented.

9. A storage medium, Characterized in that, The storage medium stores a program executable by a processor, and when the program executable by the processor is executed by the processor, it implements the production line optimization method described in any one of claims 1-6.

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