Automatic optimization method for process parameters of production line based on digital twinning technology

Abstract

The invention discloses a production line process parameter automatic optimization method based on a digital twinning technology, and relates to the technical field of production line process parameter optimization, and the method comprises the steps: obtaining a production line rotation model based on digital twinning; acquiring transmission correlation parameters based on the production line rotation model; carrying out primary optimization and optimization simulation; acquiring primary optimization equipment and transportation optimization characteristics, and establishing an automatic optimization method for optimization; according to an existing production line process parameter optimization method, when the transmission efficiency is reduced due to repeated use or aging of a transmission device in a production line, the parameters of the transmission device cannot be effectively adjusted in real time only through large model training or existing data; and the optimal transmission efficiency cannot be kept in real time in the goods transmission of a production line process.

Description

Technical Field

[0001] The present invention relates to the technical field of production line process parameter optimization, and specifically to a method for automated optimization of production line process parameters based on digital twin technology. Background Art

[0002] Production line process parameters refer to a series of fundamental data or indicators used in completing a specific task. These fundamental parameters form the basis of process operations or design, and their core role is to ensure product quality, production efficiency, process stability, and economic efficiency. Production line process parameters primarily include basic parameters, material parameters, equipment parameters, environmental parameters, and testing parameters. These parameters must be scientifically designed, monitored in real time, and dynamically adjusted to ensure standardized and optimized process flows, thereby ensuring product consistency and reducing production losses.

[0003] Existing improvements in production line process parameters are usually achieved by collecting production line process parameters, and after data analysis of the production line process parameters, constructing a large model for training, such as by introducing multi-layer time series, hybrid attention mechanism and parameter optimization decision-making to achieve the optimization of production line process parameters. Although this improvement method can improve the accuracy of parameter calculation, the large model training does not have the ability to update in real time. As a result, when the transmission efficiency of the conveying device in the production line decreases due to repeated use or aging, it is impossible to effectively adjust the parameters of the conveying device in real time through large model training or existing data, resulting in the problem of being unable to maintain the optimal transmission efficiency in real time during the transmission of goods in the production line process. For example, in the publication number CN119358785A The patent application discloses a system and method for optimizing production line parameters of a smart factory based on a large model. This solution realizes parameter process optimization, improves the pass rate, and achieves cost reduction and efficiency improvement by collecting production line process parameters, data processing, and large model training. Other improvements for optimizing production line process parameters are usually based on parameter collection during trial production to improve production stability and adaptability. However, they still cannot solve the problem that when the transmission efficiency of the conveying device in the production line decreases due to repeated use or aging, the parameters of the conveying device cannot be effectively adjusted in real time through large model training or existing data, resulting in the inability to maintain the optimal transmission efficiency in real time during the transportation of goods in the production line process. In view of this, it is necessary to improve the existing production line process parameter optimization method. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. By proposing an automated optimization method for production line process parameters based on digital twin technology, it is used to solve the problem in the existing production line process parameter optimization method that when the transmission efficiency of the conveying device in the production line decreases due to repeated use or aging, the parameters of the conveying device cannot be effectively adjusted in real time only through large model training or existing data, resulting in the inability to maintain the optimal transmission efficiency in real time during the cargo transmission of the production line process.

[0005] To achieve the above objectives, this application provides a method for automated optimization of production line process parameters based on digital twin technology, comprising the following steps:

[0006] Obtain the equipment parameters of the conveyor belts and robotic arms in the production line; model the conveyor belts and robotic arms in the same production line based on the digital twin and record them as the production line rotation model; simulate the transportation of goods in the production line based on the production line rotation model, and obtain the transportation-related parameters based on the simulation results;

[0007] Initially optimize the operation of the conveyor belt and robotic arm based on the transmission-related parameters. After the initial optimization, optimize the cargo transportation in the production line again based on the production line rotation model, and obtain the initial optimized equipment based on the optimization simulation results.

[0008] Based on the initial optimization of equipment and transmission-related parameters, transportation optimization characteristics are obtained, and an automatic optimization method is established based on the transportation optimization characteristics; before each time the conveyor belt and robotic arm transport goods, the conveyor belt and robotic arm are optimized based on the automatic optimization method.

[0009] Furthermore, the equipment parameters of the conveyor belt and robotic arm in the production line are obtained. Based on the digital twin, the conveyor belt and robotic arm in the same production line are modeled and recorded as the production line rotation model, which includes:

[0010] Obtain the equipment parameters of the conveyor belt and robotic arm in the production line, where the equipment parameters include the dimensional data of the parts in the conveyor belt and robotic arm, as well as the performance parameters, structural parameters, operating parameters, and environmental parameters of the conveyor belt and robotic arm during operation;

[0011] The conveyor belts and robotic arms in the same production line are recorded as a same-line conveying group; all the same-line conveying groups in the production line are obtained, wherein the same conveyor belt or robotic arm can be in multiple same-line conveying groups.

[0012] Furthermore, the equipment parameters of the conveyor belt and robotic arm in the production line are obtained; based on the digital twin, the conveyor belt and robotic arm in the same production line are modeled and recorded as the production line rotation model, which also includes:

[0013] For any co-line transmission group: the equipment used to connect the conveyor belts and robotic arms in the co-line transmission group is recorded as the transmission connection equipment, and the equipment parameters of the transmission connection equipment are obtained; based on the equipment parameters of the conveyor belts and robotic arms in the co-line transmission group and the equipment parameters of the transmission connection equipment, the co-line transmission group and the transmission connection equipment are modeled using digital twins and simulated. The model obtained is recorded as the production line rotation model;

[0014] Obtain the production line rotation models corresponding to all on-line transmission groups.

[0015] Furthermore, the cargo transportation in the production line is simulated based on the production line rotation model, and the transmission-related parameters are obtained based on the simulation results, including:

[0016] For any production line rotation model: the weight of the conveyor belt corresponding to the production line rotation model and the weight of the goods allowed to be transported in the robot arm are recorded as the transmission weight CZ1 to the transmission weight CZ n and transfer weight CZ1 to transfer weight CZ n The closed interval formed by the maximum and minimum values ​​in is recorded as the transmission allowed interval;

[0017] Based on the digital twin, the production line rotation model is simulated using the multi-weight simulation method, and the transmission-related parameters corresponding to the production line rotation model are obtained based on the simulation results;

[0018] Get the transmission association parameters corresponding to all co-line transmission groups.

[0019] Furthermore, the multi-weight simulation method includes:

[0020] The equipment that first contacts the goods between the conveyor belt and the robot arm in the production line rotation model is recorded as the front transmission equipment, and the equipment that later contacts the goods between the conveyor belt and the robot arm in the production line rotation model is recorded as the rear transmission equipment.

[0021] For any value G within the transmission allowable interval, the cargo with a weight of G is recorded as simulated cargo. In the production line rotation model, the simulated cargo is transported by the front transport device, and the relationship between the speed of the simulated cargo moving in the front transport device and time is recorded as the front displacement data. When the simulated cargo is transported by the rear transport device, the relationship between the speed of the simulated cargo moving in the rear transport device and time is recorded as the rear displacement data. Both the front and rear transport devices are operating at rated power.

[0022] Establish a plane rectangular coordinate system and record it as the simulated transport coordinate system, where the unit of the X axis of the simulated transport coordinate system is min and the unit of the Y axis is m / s; based on the front displacement data, draw a corresponding curve between X = 0 and X = t1 in the simulated transport coordinate system, and record it as the front displacement curve, where t1 is the time the simulated goods are transported in the front transport equipment, that is, the time recorded in the front displacement data;

[0023] Based on the post-displacement data, a corresponding curve is drawn between X=t2 and X=t3 in the simulated transport coordinate system, which is recorded as the post-displacement curve, where the difference between t2 and t1 is the time the simulated goods stay in the conveying connection device; the difference between t3 and t2 is the time the simulated goods are transported in the post-transmission device, that is, the time recorded in the post-displacement data.

[0024] Furthermore, the multi-weight simulation method also includes:

[0025] Use the transport association algorithm to obtain the sub-association parameters corresponding to the simulated goods. The transport association algorithm includes: Among them, F is the sub-association parameter, k min1 is the minimum value of the slope in the front displacement curve, k min2 is the minimum value of the slope in the post-displacement curve, k max1 is the maximum value of the slope in the front displacement curve, k max2 is the maximum value of the slope in the post-displacement curve;

[0026] Obtain sub-association parameters corresponding to all values ​​in the transmission-allowed interval; establish a plane rectangular coordinate system, recorded as the transmission-association coordinate system, where the unit of the X-axis of the transmission-association coordinate system is kg and the Y-axis is a constant axis; punctuate all values ​​in the transmission-allowed interval and the sub-association parameters corresponding to all values ​​in the transmission-association coordinate system, and record the curve obtained by fitting all punctuation points as the transmission-association curve, and record the point with the largest slope and the point with the smallest slope in the transmission-association curve as characteristic parameter point A and characteristic parameter point B, respectively;

[0027] Transfer weight CZ1 to transfer weight CZ n The transmission weight CZ with the smallest difference between the horizontal coordinates of the characteristic parameter point A is recorded as the characteristic weight A; the transmission weight CZ1 to the transmission weight CZ n The transmission weight CZ with the smallest difference between the horizontal coordinates of the center and the characteristic reference point B is recorded as the characteristic weight B;

[0028] The vertical coordinates of the points in the transmission association curve whose horizontal coordinates are characteristic weight A and characteristic weight B are recorded as characteristic association parameters A and characteristic association parameters B respectively; the absolute value of the difference between characteristic association parameters A and characteristic association parameters B is recorded as the transmission association parameter of the production line rotation model.

[0029] Furthermore, the initial optimization of the conveyor belt and the robot arm operation based on the transmission-related parameters includes:

[0030] The production line rotation model with the largest transmission-related parameters is recorded as the initial optimization model, and the transmission-related parameters of the initial optimization model are recorded as the initial related parameters; the operation of the conveyor belt and the robot arm is initially optimized;

[0031] The initial optimization is to adjust the operating power of the conveyor belt and the robotic arm in the initial optimization model until the transmission correlation parameters obtained by the initial optimization model based on the multi-weight simulation method are less than the initial correlation parameters, and reset the operating power of the conveyor belt and the robotic arm in the initial optimization model at this time to the default power of the conveyor belt and the robotic arm in the initial optimization model when they are running.

[0032] Furthermore, the optimization simulation includes:

[0033] When the power of the conveyor belt and the robot arm in any initial optimization model is adjusted to the default power, method V1 is executed; V1 includes: obtaining the transmission-related parameters of all production line rotation models based on the multi-weight simulation method, and re-obtaining the initial optimization model for initial optimization;

[0034] Repeat V1. When the operating power of any conveyor belt or robot arm is reset twice, the conveyor belt or robot arm is recorded as the first optimized device and stop repeating V1.

[0035] Furthermore, obtaining the transport optimization features based on the initial optimization equipment and transport-related parameters includes:

[0036] The first transmission-related parameter corresponding to the initially optimized device is recorded as the transmission optimization feature. When the initially optimized device is in multiple production line rotation models, the first transmission-related parameters of all production line rotation models in which the initially optimized device is located are recorded as the transmission optimization feature.

[0037] Furthermore, the automatic optimization method includes:

[0038] The production line rotation model with the first transmission-related parameters and the same transmission optimization characteristics is recorded as the priority analysis model; the transmission-related parameters of all priority analysis models are obtained based on the multi-weight simulation method, and after the primary optimization equipment is obtained based on the primary optimization and optimization simulation, the primary optimization equipment and the transmission optimization characteristics are updated, and the automatic optimization method is terminated.

[0039] Beneficial effects of the present invention: This application first obtains the equipment parameters of the conveyor belt and the robotic arm in the production line; based on the digital twin, the conveyor belt and the robotic arm in the same production line are modeled and recorded as a production line rotation model; based on the production line rotation model, the cargo transportation in the production line is simulated, and the transmission-related parameters are obtained based on the simulation results; then the operation of the conveyor belt and the robotic arm is initially optimized based on the transmission-related parameters. The advantage of this is that by using the digital twin to obtain the production line rotation model, it is helpful to improve the efficiency of data analysis in subsequent analysis, ensure that analysis errors can be avoided in multiple and repeated data analysis, and save data analysis costs; and by obtaining the transmission-related parameters, the parameters between the transmission rate and time when the conveyor belt and the robotic arm in the production line transport cargo can be obtained, which helps to accurately optimize the operating power of the conveyor belt or the robotic arm based on the transmission-related parameters in the subsequent initial optimization, so that when the transmission efficiency of the transmission device decreases, the power-related parameters are adjusted in time to ensure the transmission efficiency of the transmission device in actual application;

[0040] After the initial optimization, the present application further optimizes and simulates the cargo transportation in the production line based on the production line rotation model, and obtains the initial optimization equipment based on the optimization simulation results; finally, the transportation optimization characteristics are obtained based on the initial optimization equipment and transmission-related parameters, and an automatic optimization method is established based on the transportation optimization characteristics; before each time the conveyor belt and the robotic arm transport the cargo, the conveyor belt and the robotic arm are optimized based on the automatic optimization method. The advantage of this is that by obtaining the initial optimization equipment, obtaining the transportation optimization characteristics and establishing the automatic optimization method, it is helpful to effectively analyze the transmission status of the existing conveying device before each use of the conveying device to transport the cargo, thereby ensuring that after the cargo transmission begins, the conveying device can transport the cargo with the optimal transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the steps of the method of the present invention;

[0042] Figure 2 is a schematic diagram of the simulated transport coordinate system of the present invention;

[0043] Figure 3 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Example 1, please refer to Figure 1 As shown, this application provides a method for automatically optimizing production line process parameters based on digital twin technology, including the following steps:

[0046] Step S1: Obtain the equipment parameters of the conveyor belt and robotic arm in the production line; Model the conveyor belt and robotic arm in the same production line based on the digital twin and record it as the production line rotation model; Simulate the cargo transportation in the production line based on the production line rotation model, and obtain the transmission-related parameters based on the simulation results;

[0047] Step S1 includes: step S101, obtaining equipment parameters of a conveyor belt and a robotic arm in a production line, wherein the equipment parameters include dimensional data of parts in the conveyor belt and the robotic arm, and performance parameters, structural parameters, operating parameters, and environmental parameters of the conveyor belt and the robotic arm when they are working;

[0048] In a specific implementation process, for example, in actual analysis, the performance parameters of the conveyor belt obtained may include bandwidth, belt speed, conveying capacity, conveying length, inclination, motor power, material parameters, and heat resistance. The equipment parameters can be obtained based on the actual parameters of the conveyor belt and the robotic arm analyzed in actual application.

[0049] Step S102: Recording conveyor belts and robotic arms in the same production line as a same-line conveyor group; obtaining all same-line conveyor groups in the production line, wherein the same conveyor belt or robotic arm may be in multiple same-line conveyor groups;

[0050] In a specific implementation process, for example, in a data analysis, the conveyor belt includes conveyor belt A and conveyor belt B, the robot arm includes robot arm A, robot arm B and robot arm C, and it also includes production line A, production line B, production line C, production line D and production line E. Production line A contains conveyor belt A and robot arm A, production line B contains conveyor belt A and robot arm B, production line C contains conveyor belt A and robot arm C, production line D contains conveyor belt B and robot arm A, and production line E contains conveyor belt B and robot arm B. Then conveyor belt A and robot arm A, conveyor belt A and robot arm B, conveyor belt A and robot arm C, conveyor belt B and robot arm A, and conveyor belt B and robot arm B can all be recorded as same-line conveyor groups;

[0051] Step S103: For any co-line transmission group, the equipment used to connect the conveyor belts and robotic arms in the co-line transmission group is recorded as a transmission connection device, and the equipment parameters of the transmission connection device are obtained. Based on the equipment parameters of the conveyor belts and robotic arms in the co-line transmission group and the equipment parameters of the transmission connection device, the co-line transmission group and the transmission connection device are modeled using a digital twin and simulated. The model obtained is recorded as the production line rotation model.

[0052] In the specific implementation process, the purpose of obtaining the production line rotation model through digital twins is to ensure that the obtained production line rotation model helps improve the efficiency of data analysis in subsequent analysis, ensure that analysis errors can be avoided during multiple and repeated data analysis, and save data analysis costs;

[0053] Step S104, obtaining the production line rotation models corresponding to all on-line transmission groups;

[0054] Step S105: for any production line rotation model, the weight of the conveyor belt corresponding to the production line rotation model and the weight of the goods allowed to be transported in the robot arm are recorded as the transmission weight CZ1 to the transmission weight CZ n and transfer weight CZ1 to transfer weight CZ n The closed interval formed by the maximum and minimum values ​​in is recorded as the transmission allowed interval;

[0055] In a specific implementation process, for example, during data analysis, the transmission weights CZ obtained are 10kg, 23kg, 2kg, 50kg, 32kg, and 40kg, respectively. Then, through data analysis, the transmission allowable range can be obtained as [2kg, 50kg].

[0056] Step S106: simulating the production line rotation model using a multi-weight simulation method based on the digital twin, and obtaining transmission-related parameters corresponding to the production line rotation model based on the simulation results;

[0057] Step S107: Acquire transmission association parameters corresponding to all co-line transmission groups.

[0058] Step S108, the multi-weight simulation method includes: step S1081, recording the equipment in the production line rotation model's conveyor belt and the robot arm that first contacts the goods as the front transmission equipment, and recording the equipment in the production line rotation model's conveyor belt and the robot arm that later contacts the goods as the rear transmission equipment;

[0059] Step S1082: For any value G within the permitted transmission interval, a load of weight G is recorded as simulated load. In the production line rotation model, the simulated load is transported by the front transport device, and the relationship between the speed and time of the simulated load in the front transport device is recorded as front displacement data. When the simulated load is transported by the rear transport device, the relationship between the speed and time of the simulated load in the rear transport device is recorded as rear displacement data. Both the front and rear transport devices are operating at rated power.

[0060] Step S1083: Establish a plane rectangular coordinate system and record it as the simulated transport coordinate system, where the unit of the X-axis of the simulated transport coordinate system is min and the unit of the Y-axis is m / s. Based on the front displacement data, draw a corresponding curve between X = 0 and X = t1 in the simulated transport coordinate system, which is recorded as the front displacement curve. Where t1 is the time the simulated goods are transported in the front transport equipment, that is, the time recorded in the front displacement data.

[0061] Step S1083: Based on the rear displacement data, a corresponding curve is drawn between X = t2 and X = t3 in the simulated transport coordinate system, which is recorded as the rear displacement curve. The difference between t2 and t1 is the time the simulated goods stay in the transport connection device; the difference between t3 and t2 is the time the simulated goods are transported in the rear transport device, that is, the time recorded in the rear displacement data.

[0062] In the specific implementation process, for example, during a data analysis, the simulated transport coordinate system is obtained as follows Figure 2 As shown, t1 obtained based on the front displacement data and the rear displacement data is 10 minutes, t2 is 20 minutes, and t3 is 30 minutes, and the front displacement curve and the rear displacement curve obtained are curve QW and curve HW, respectively. Through data analysis, it is found that point QG and point QD are the points with the largest slope and the smallest slope, respectively, in the front displacement curve, and point HG and point HD are the points with the largest slope and the smallest slope, respectively, in the rear displacement curve.

[0063] Step S1084: Use a transmission association algorithm to obtain sub-association parameters corresponding to the simulated goods. The transmission association algorithm includes: Among them, F is the sub-association parameter, k min1 is the minimum value of the slope in the front displacement curve, k min2 is the minimum value of the slope in the post-displacement curve, k max1 is the maximum value of the slope in the front displacement curve, k max2 is the maximum value of the slope in the post-displacement curve;

[0064] In the specific implementation process, for example, in a data analysis, t1 is 10 minutes, t2 is 20 minutes, t3 is 30 minutes, k min1 is 1, k min2 is 0.5, k max1 is 2, k max2 is 1.5; then, through calculation, the sub-correlation parameter is approximately 0.429. By obtaining the sub-correlation parameter and the transmission-correlation parameter in subsequent analysis, the parameters between the transmission rate and time when the conveyor belt and the robotic arm transport goods in the production line can be obtained. This helps to accurately optimize the operating power of the conveyor belt or robotic arm based on the transmission-correlation parameter in the subsequent initial optimization.

[0065] Step S1085: Obtain sub-association parameters corresponding to all values ​​in the transmission-allowed interval; establish a plane rectangular coordinate system, recorded as the transmission-association coordinate system, where the unit of the X-axis of the transmission-association coordinate system is kg and the Y-axis is a constant axis; punctuate all values ​​in the transmission-allowed interval and the sub-association parameters corresponding to all values ​​in the transmission-association coordinate system, and record the curve obtained by fitting all the punctuation points as the transmission-association curve. The point with the maximum slope and the point with the minimum slope in the transmission-association curve are recorded as characteristic parameter point A and characteristic parameter point B, respectively.

[0066] Step S1086, transfer weight CZ1 to transfer weight CZ n The transmission weight CZ with the smallest difference between the horizontal coordinates of the characteristic parameter point A is recorded as the characteristic weight A; the transmission weight CZ1 to the transmission weight CZ n The transmission weight CZ with the smallest difference between the horizontal coordinates of the center and the characteristic reference point B is recorded as the characteristic weight B;

[0067] In the specific implementation process, for example, in a data analysis, the horizontal coordinate of the characteristic parameter point A is 30kg, and the transmission weight CZ1 to the transmission weight CZ n The weights of the conveyor belts and robotic arms are 10kg, 23kg, 2kg, 50kg, 32kg, and 40kg, respectively. Analysis shows that the characteristic weight A is 32kg. By obtaining the characteristic weights A and B, we can determine the weight at which the transmission rate fluctuates when the conveyor belt and robotic arm transport goods on the production line, that is, the weight at which the transmission rate changes significantly. The larger the transmission-related parameters corresponding to the characteristic weights A and B, the greater the range of weight influence on the transmission rate of the corresponding production line rotation model during actual transmission. This helps to accurately optimize the operating power of the conveyor belt or robotic arm through the transmission-related parameters in subsequent analysis, thereby enabling timely adjustment of the power-related parameters when the transmission efficiency of the transmission device decreases, thereby ensuring the transmission efficiency of the transmission device in actual application.

[0068] In step S1087, the vertical coordinates of the points in the transmission association curve whose horizontal coordinates are characteristic weight A and characteristic weight B are recorded as characteristic association parameters A and characteristic association parameters B respectively; the absolute value of the difference between characteristic association parameters A and characteristic association parameters B is recorded as the transmission association parameter of the production line rotation model.

[0069] Step S2: Initially optimize the operation of the conveyor belt and the robotic arm based on the transmission-related parameters; after the initial optimization, optimize the cargo transportation in the production line again based on the production line rotation model, and obtain the initial optimized equipment based on the optimization simulation results;

[0070] Step S2 includes: step S201, recording the production line rotation model with the largest transmission-related parameter as the initial optimization model, and recording the transmission-related parameter of the initial optimization model as the initial related parameter; performing initial optimization on the operation of the conveyor belt and the robot arm;

[0071] Step S202, initial optimization: adjusting the operating power of the conveyor belt and the robotic arm in the initial optimization model until the transmission correlation parameter obtained by the initial optimization model based on the multi-weight simulation method is less than the initial correlation parameter, and resetting the operating power of the conveyor belt and the robotic arm in the initial optimization model at this time to the default power of the conveyor belt and the robotic arm in the initial optimization model when they are in operation;

[0072] In the specific implementation process, when the transmission correlation parameters obtained by the initial optimization model based on the multi-weight simulation method are less than the initial correlation parameters, it means that the transmission power of the conveyor belt and the robot arm corresponding to the initial optimization model has been optimized. Therefore, the operating power of the conveyor belt and the robot arm in the initial optimization model can be reset to the default power of the conveyor belt and the robot arm in the initial optimization model to ensure the efficient operation of the initial optimization model.

[0073] Step S203, optimization simulation includes: Step S2031, when the power of the conveyor belt and the robot arm during operation in any initial optimization model is adjusted to the default power, executing method V1; V1 includes: obtaining transmission-related parameters of all production line rotation models based on the multi-weight simulation method, and re-obtaining the initial optimization model for initial optimization;

[0074] Step S2032, repeating V1. When the operating power of any conveyor belt or robotic arm is reset twice, the conveyor belt or robotic arm is recorded as the first optimized device, and the repetition of V1 is stopped;

[0075] During the specific implementation process, when the operating power of any conveyor belt or robotic arm is reset twice, it means that the conveyor belt or robotic arm needs to be optimized, so it can be recorded as the first optimized equipment and subsequent optimization can be carried out.

[0076] Step S3: obtaining a transport optimization feature based on the initially optimized equipment and transmission-related parameters, and establishing an automatic optimization method based on the transport optimization feature; optimizing the conveyor belt and the robotic arm based on the automatic optimization method before each transport of goods by the conveyor belt and the robotic arm;

[0077] Step S3 includes: step S301, recording the first transmission-related parameter corresponding to the initially optimized device as a transmission optimization feature, wherein, when the initially optimized device is in multiple production line rotation models, the first transmission-related parameters of all production line rotation models in which the initially optimized device is located are recorded as the transmission optimization feature;

[0078] The automatic optimization method includes: recording the production line rotation model with the first transmission-related parameters and the same transmission optimization characteristics as the priority analysis model; obtaining the transmission-related parameters of all priority analysis models based on the multi-weight simulation method, and obtaining the initial optimization equipment based on the initial optimization and optimization simulation, and then updating the initial optimization equipment and the transmission optimization characteristics, and the automatic optimization method ends.

[0079] Example 2, please refer to Figure 3 As shown, Figure 3 An example structural diagram of an electronic device is provided. The electronic device may include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the automatic optimization method of production line process parameters based on digital twin technology are executed to achieve the following functions: first, the equipment parameters of the conveyor belt and the robotic arm in the production line are obtained; based on the digital twin, the conveyor belt and the robotic arm in the same production line are modeled and recorded as the production line rotation model; based on the production line rotation model, the cargo transportation in the production line is simulated, and the transmission-related parameters are obtained based on the simulation results; then, the operation of the conveyor belt and the robotic arm is initially optimized based on the transmission-related parameters; after the initial optimization, the cargo transportation in the production line is optimized again based on the production line rotation model, and the initial optimized equipment is obtained based on the optimization simulation results; finally, the transportation optimization characteristics are obtained based on the initial optimized equipment and the transmission-related parameters, and an automatic optimization method is established based on the transportation optimization characteristics; before each time the conveyor belt and the robotic arm transport goods, the conveyor belt and the robotic arm are optimized based on the automatic optimization method.

[0080] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0081] Example 3. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the automatic optimization method of production line process parameters based on digital twin technology provided by the above methods, the method including: first obtaining the equipment parameters of the conveyor belt and the robotic arm in the production line; modeling the conveyor belt and the robotic arm in the same production line based on the digital twin, and recording it as a production line rotation model; simulating the cargo transportation in the production line based on the production line rotation model, and obtaining transmission-related parameters based on the simulation results; then performing an initial optimization of the operation of the conveyor belt and the robotic arm based on the transmission-related parameters; after the initial optimization, optimizing and simulating the cargo transportation in the production line again based on the production line rotation model, and obtaining the initial optimized equipment based on the optimization simulation results; finally, obtaining transportation optimization characteristics based on the initial optimized equipment and transmission-related parameters, and establishing an automatic optimization method based on the transportation optimization characteristics; before each time the conveyor belt and the robotic arm transport goods, optimizing the conveyor belt and the robotic arm based on the automatic optimization method.

[0082] Example 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by the processor, the steps in the above-mentioned method for automatic optimization of production line process parameters based on digital twin technology are executed to achieve the following functions: first, the equipment parameters of the conveyor belt and the robotic arm in the production line are obtained; based on the digital twin, the conveyor belt and the robotic arm in the same production line are modeled and recorded as a production line rotation model; based on the production line rotation model, the cargo transportation in the production line is simulated, and the transmission-related parameters are obtained based on the simulation results; then, the operation of the conveyor belt and the robotic arm is initially optimized based on the transmission-related parameters; after the initial optimization, the cargo transportation in the production line is optimized again based on the production line rotation model, and the initial optimized equipment is obtained based on the optimization simulation results; finally, the transportation optimization characteristics are obtained based on the initial optimized equipment and the transmission-related parameters, and an automatic optimization method is established based on the transportation optimization characteristics; before each time the conveyor belt and the robotic arm transport goods, the conveyor belt and the robotic arm are optimized based on the automatic optimization method.

[0083] Through the description of the above embodiments, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on this understanding, the above technical solutions, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiment.

[0084] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. The automated optimization method for production line process parameters based on digital twin technology is characterized by: The steps include: Obtain the equipment parameters of the conveyor belts and robotic arms in the production line; model the conveyor belts and robotic arms in the same production line based on the digital twin and record them as the production line rotation model; simulate the transportation of goods in the production line based on the production line rotation model, and obtain the transportation-related parameters based on the simulation results; Initial optimization of the conveyor belt and robot operation based on transmission-related parameters; After the initial optimization, the cargo transportation in the production line is optimized and simulated again based on the production line rotation model, and the initial optimized equipment is obtained based on the optimization simulation results; Obtaining transportation optimization features based on the initial optimization equipment and transmission-related parameters, and establishing an automatic optimization method based on the transportation optimization features; Each time before the conveyor belt and the robotic arm transport goods, the conveyor belt and the robotic arm are optimized based on the automatic optimization method.

2. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 1, characterized in that: Obtain equipment parameters of conveyor belts and robotic arms in the production line; Based on the digital twin, the conveyor belt and robotic arm on the same production line are modeled and recorded as the production line rotation model, which includes: Obtain the equipment parameters of the conveyor belt and robotic arm in the production line, where the equipment parameters include the dimensional data of the parts in the conveyor belt and robotic arm, as well as the performance parameters, structural parameters, operating parameters, and environmental parameters of the conveyor belt and robotic arm during operation; The conveyor belts and robotic arms in the same production line are recorded as a same-line conveying group; all the same-line conveying groups in the production line are obtained, wherein the same conveyor belt or robotic arm can be in multiple same-line conveying groups.

3. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 2, characterized in that: Obtain equipment parameters of conveyor belts and robotic arms in the production line; Based on the digital twin, the conveyor belt and robotic arm on the same production line are modeled and recorded as the production line rotation model. The model also includes: For any co-line transmission group: the equipment used to connect the conveyor belts and robotic arms in the co-line transmission group is recorded as the transmission connection equipment, and the equipment parameters of the transmission connection equipment are obtained; based on the equipment parameters of the conveyor belts and robotic arms in the co-line transmission group and the equipment parameters of the transmission connection equipment, the co-line transmission group and the transmission connection equipment are modeled using digital twins and simulated. The model obtained is recorded as the production line rotation model; Obtain the production line rotation models corresponding to all on-line transmission groups.

4. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 3 is characterized in that: The cargo transportation in the production line is simulated based on the production line rotation model, and the transmission-related parameters are obtained based on the simulation results, including: For any production line rotation model: the weight of the conveyor belt corresponding to the production line rotation model and the weight of the goods allowed to be transported in the robot arm are recorded as the transmission weight CZ1 to the transmission weight CZ n and transfer weight CZ1 to transfer weight CZ n The closed interval formed by the maximum and minimum values ​​in is recorded as the transmission allowed interval; Based on the digital twin, the production line rotation model is simulated using the multi-weight simulation method, and the transmission-related parameters corresponding to the production line rotation model are obtained based on the simulation results; Get the transmission association parameters corresponding to all co-line transmission groups.

5. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 4 is characterized in that: Multi-weight simulation methods include: The equipment that first contacts the goods between the conveyor belt and the robot arm in the production line rotation model is recorded as the front transmission equipment, and the equipment that later contacts the goods between the conveyor belt and the robot arm in the production line rotation model is recorded as the rear transmission equipment. For any value G within the transmission allowable interval, the cargo with a weight of G is recorded as simulated cargo. In the production line rotation model, the simulated cargo is transported by the front transport device, and the relationship between the speed of the simulated cargo moving in the front transport device and time is recorded as the front displacement data. When the simulated cargo is transported by the rear transport device, the relationship between the speed of the simulated cargo moving in the rear transport device and time is recorded as the rear displacement data. Both the front and rear transport devices are operating at rated power. Establish a plane rectangular coordinate system and record it as the simulated transport coordinate system, where the unit of the X axis of the simulated transport coordinate system is min and the unit of the Y axis is m / s; based on the front displacement data, draw a corresponding curve between X = 0 and X = t1 in the simulated transport coordinate system, and record it as the front displacement curve, where t1 is the time the simulated goods are transported in the front transport equipment, that is, the time recorded in the front displacement data; Based on the post-displacement data, a corresponding curve is drawn between X=t2 and X=t3 in the simulated transport coordinate system, which is recorded as the post-displacement curve, where the difference between t2 and t1 is the time the simulated goods stay in the conveying connection device; the difference between t3 and t2 is the time the simulated goods are transported in the post-transmission device, that is, the time recorded in the post-displacement data.

6. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 5 is characterized in that: Multi-weight simulation also includes: Use the transport association algorithm to obtain the sub-association parameters corresponding to the simulated goods. The transport association algorithm includes: Among them, F is the sub-association parameter, k min1 is the minimum value of the slope in the front displacement curve, k min2 is the minimum value of the slope in the post-displacement curve, k max1 is the maximum value of the slope in the front displacement curve, k max2 is the maximum value of the slope in the post-displacement curve; Obtain sub-association parameters corresponding to all values ​​in the transmission-allowed interval; establish a plane rectangular coordinate system, recorded as the transmission-association coordinate system, where the unit of the X-axis of the transmission-association coordinate system is kg and the Y-axis is a constant axis; punctuate all values ​​in the transmission-allowed interval and the sub-association parameters corresponding to all values ​​in the transmission-association coordinate system, and record the curve obtained by fitting all punctuation points as the transmission-association curve, and record the point with the largest slope and the point with the smallest slope in the transmission-association curve as characteristic parameter point A and characteristic parameter point B, respectively; Transfer weight CZ1 to transfer weight CZ n The transmission weight CZ with the smallest difference between the horizontal coordinates of the characteristic parameter point A is recorded as the characteristic weight A; the transmission weight CZ1 to the transmission weight CZ n The transmission weight CZ with the smallest difference between the horizontal coordinates of the center and the characteristic reference point B is recorded as the characteristic weight B; The vertical coordinates of the points in the transmission association curve whose horizontal coordinates are characteristic weight A and characteristic weight B are recorded as characteristic association parameters A and characteristic association parameters B respectively; the absolute value of the difference between characteristic association parameters A and characteristic association parameters B is recorded as the transmission association parameter of the production line rotation model.

7. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 6 is characterized in that: Initial optimization of the conveyor belt and robot operation based on transmission-related parameters includes: The production line rotation model with the largest transmission-related parameters is recorded as the initial optimization model, and the transmission-related parameters of the initial optimization model are recorded as the initial related parameters; the operation of the conveyor belt and the robot arm is initially optimized; The initial optimization is to adjust the operating power of the conveyor belt and the robotic arm in the initial optimization model until the transmission correlation parameters obtained by the initial optimization model based on the multi-weight simulation method are less than the initial correlation parameters, and reset the operating power of the conveyor belt and the robotic arm in the initial optimization model at this time to the default power of the conveyor belt and the robotic arm in the initial optimization model when they are running.

8. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 7, characterized in that: Optimization simulation includes: When the power of the conveyor belt and the robot arm in any initial optimization model is adjusted to the default power, method V1 is executed; V1 includes: obtaining the transmission-related parameters of all production line rotation models based on the multi-weight simulation method, and re-obtaining the initial optimization model for initial optimization; Repeat V1. When the operating power of any conveyor belt or robot arm is reset twice, the conveyor belt or robot arm is recorded as the first optimized device and stop repeating V1.

9. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 8, characterized in that: The transport optimization features obtained based on the initial optimization of equipment and transport-related parameters include: The first transmission-related parameter corresponding to the initially optimized device is recorded as the transmission optimization feature. When the initially optimized device is in multiple production line rotation models, the first transmission-related parameters of all production line rotation models in which the initially optimized device is located are recorded as the transmission optimization feature.

10. The method for automatic optimization of production line process parameters based on digital twin technology according to claim 9, characterized in that: Automatic optimization methods include: The production line rotation model with the first transmission-related parameters and the same transmission optimization characteristics is recorded as the priority analysis model; the transmission-related parameters of all priority analysis models are obtained based on the multi-weight simulation method, and after the primary optimization equipment is obtained based on the primary optimization and optimization simulation, the primary optimization equipment and the transmission optimization characteristics are updated, and the automatic optimization method is terminated.

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