Production line process parameter automatic optimization method based on digital twinning technology

Abstract

The application discloses a production line process parameter automatic optimization method based on digital twin technology and relates to the technical field of production line process parameter optimization.The method comprises the following steps: obtaining a production line rotation model based on digital twin; obtaining transmission correlation parameters based on the production line rotation model; performing initial optimization and optimization simulation; obtaining initial optimization equipment and transmission optimization features, and establishing an automatic optimization method for optimization; and the method is used for solving the problem that in the existing production line process parameter optimization method, when the transmission efficiency of the conveying device in the production line decreases due to multiple uses or aging, the parameters of the conveying device cannot be effectively adjusted in real time through only large model training or existing data, and the optimal transmission efficiency cannot be maintained in real time in the goods transmission of the production line process.

Description

Technical Field

[0001] This invention relates to the field of production line process parameter optimization technology, specifically to an automated optimization method for production line process parameters based on digital twin technology. Background Technology

[0002] Production line process parameters refer to a series of basic data or indicators for completing a certain task. These basic parameters constitute the content of process operation or design, and their core function is to ensure product quality, production efficiency, production process stability, and economy. Production line process parameters mainly include basic parameters, material parameters, equipment parameters, environmental parameters, and testing parameters. These parameters need to be scientifically designed, monitored in real time, and dynamically adjusted to ensure the standardization and optimization of the process flow, thereby ensuring product consistency and reducing production losses.

[0003] Existing improvements to production line process parameters typically involve collecting these parameters, analyzing the data, and then training a large model. This model may incorporate multi-layer time series analysis, hybrid attention mechanisms, and parameter optimization decision-making to optimize the parameters. While this approach improves the accuracy of parameter calculations, the lack of real-time updates in large-scale model training means that when conveyor systems experience reduced efficiency due to repeated use or aging, the parameters cannot be effectively adjusted in real-time using only the large model or existing data. This results in the inability to maintain optimal transport efficiency in the production line, as illustrated in publication number CN119358785A. The patent application discloses a smart factory production line parameter optimization system and method based on a large model. This solution optimizes parameters and improves the pass rate by collecting production line process parameters, processing data, and training a large model, thereby reducing costs and increasing efficiency. Other improvements for production line process parameter optimization 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 conveyor device in the production line decreases due to repeated use or aging, large model training or existing data alone cannot effectively adjust the parameters of the conveyor device in real time. This results in the inability to maintain optimal transmission efficiency in real time during the transport of goods in the production line process. Therefore, it is necessary to improve the existing production line process parameter optimization methods. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art by proposing an automated optimization method for production line process parameters based on digital twin technology. This method addresses the issue that existing production line process parameter optimization methods cannot effectively adjust the parameters of the conveying devices in real time when the transmission efficiency of the conveying devices in the production line decreases due to repeated use or aging. This results in the inability to maintain optimal transmission efficiency in real time during the transport of goods in the production line process.

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

[0006] Obtain the equipment parameters of the conveyor belt and robotic arm in the production line; model the conveyor belt and robotic arm on the same production line based on digital twin and denot it 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 transmission-related parameters based on the simulation results;

[0007] The operation of the conveyor belt and robotic arm is initially optimized based on the transmission correlation parameters; after the initial optimization, the transportation of goods 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.

[0008] Based on the initial optimization of equipment and transmission-related parameters, transportation optimization features are obtained, and an automatic optimization method is established based on these features. Before each transport of goods by the conveyor belt and robotic arm, 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 digital twins, the conveyor belt and robotic arm on the same production line are modeled and denoted as the production line rotation model, including:

[0010] Obtain the equipment parameters of the conveyor belt and robotic arm in the production line. 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 when they are working.

[0011] The conveyor belts and robotic arms that are on the same production line are categorized as co-line conveyor groups; all co-line conveyor groups in the production line are obtained, where the same conveyor belt or robotic arm may be in multiple co-line conveyor groups.

[0012] Furthermore, the equipment parameters of the conveyor belts and robotic arms in the production line are obtained; based on digital twins, the conveyor belts and robotic arms on the same production line are modeled and denoted as the production line rotation model, which also includes:

[0013] For any in-line conveyor group: the equipment used to connect the conveyor belt and the robotic arm in the in-line conveyor group is denoted as the conveyor connection equipment, and the equipment parameters of the conveyor connection equipment are obtained; based on the equipment parameters of the conveyor belt and the robotic arm in the in-line conveyor group and the equipment parameters of the conveyor connection equipment, digital twins are used to model the in-line conveyor group and the conveyor connection equipment and simulate their operation, wherein the model obtained from the modeling is denoted as the production line rotation model;

[0014] Obtain the production line rotation model corresponding to all conveyor groups on the same line.

[0015] Furthermore, the transportation of goods 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 weights of the goods allowed to be transported in the conveyor belt and robotic arm corresponding to the production line rotation model are denoted as conveying weight CZ1 to conveying weight CZ1 respectively. n and transfer weight CZ1 to transfer weight CZ n The closed interval formed by the maximum and minimum values ​​in the interval is called the transmission allowed interval;

[0017] The production line rotation model is simulated using a multi-weight simulation method based on digital twins, and the transmission correlation parameters corresponding to the production line rotation model are obtained based on the simulation results.

[0018] Retrieve transmission association parameters for all transmission groups on the same line.

[0019] Furthermore, multi-weight simulation methods include:

[0020] The equipment in the conveyor belt and robotic arm of the production line rotating model that comes into contact with the goods first is called the front conveyor equipment, and the equipment in the conveyor belt and robotic arm that comes into contact with the goods later is called the rear conveyor equipment.

[0021] For any value G within the allowable transmission range: the goods with weight G are denoted as simulated goods; in the production line rotation model, the simulated goods are transmitted by the front transmission equipment, and the relationship between the moving speed of the simulated goods in the front transmission equipment and time is recorded as the front displacement data; when the simulated goods are no longer transmitted by the rear transmission equipment, the relationship between the moving speed of the simulated goods in the rear transmission equipment and time is recorded as the rear displacement data, wherein both the front transmission equipment and the rear transmission equipment operate at rated power;

[0022] Establish a Cartesian coordinate system and denote it as the simulated transportation coordinate system. The unit of the X-axis of the simulated transportation coordinate system is min, and the unit of the Y-axis is m / s. Based on the forward displacement data, plot the corresponding curve between X=0 and X=t1 in the simulated transportation coordinate system and denote it as the forward displacement curve. Here, t1 is the time when the simulated goods are transported in the forward transmission equipment, that is, the time recorded in the forward displacement data.

[0023] Based on the post-displacement data, the corresponding curve is plotted between X = t2 and X = t3 in the simulated transportation coordinate system, and is denoted as the post-displacement curve. The difference between t2 and t1 is the time when the simulated goods stay in the conveying connection equipment; the difference between t3 and t2 is the time when the simulated goods are transported in the post-transfer equipment, that is, the time recorded in the post-displacement data.

[0024] Furthermore, multi-weight simulation methods also include:

[0025] The sub-association parameters corresponding to the simulated goods are obtained using a transport association algorithm, which includes: Where F is the sub-association parameter, k min1 k is the minimum slope of the forward displacement curve. min2 k is the minimum slope of the back displacement curve. max1 k is the maximum slope of the forward displacement curve. max2 This represents the maximum slope of the rear displacement curve;

[0026] Obtain the sub-association parameters corresponding to all values ​​within the allowed transmission range; establish a Cartesian coordinate system, denoted as the transmission association coordinate system, where the X-axis of the transmission association coordinate system is in kg and the Y-axis is a constant axis; mark all values ​​within the allowed transmission range and their corresponding sub-association parameters in the transmission association coordinate system, and denot the curve obtained by fitting all the marked points as the transmission association curve; mark the point with the largest slope and the point with the smallest slope in the transmission association curve as feature parameter point A and feature parameter point B, respectively.

[0027] Transfer weight CZ1 to transfer weight CZ n The transmission weight CZ with the smallest difference between the x-coordinate of the point and the characteristic parameter A is denoted as the characteristic weight A; transmission weights CZ1 to CZ are then... n The transmission weight CZ with the smallest difference between the x-coordinate of the characteristic parameter point B and the characteristic weight B is denoted as the characteristic weight B.

[0028] The ordinates of the points on the transmission correlation curve whose horizontal coordinates are characteristic weight A and characteristic weight B are denoted as characteristic correlation parameter A and characteristic correlation parameter B, respectively; the absolute value of the difference between characteristic correlation parameter A and characteristic correlation parameter B is denoted as the transmission correlation parameter of the production line rotation model.

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

[0030] The production line rotation model with the largest transmission correlation parameter is designated as the initial optimization model, and the transmission correlation parameter of the initial optimization model is designated as the initial correlation parameter; the operation of the conveyor belt and the robotic arm is initially optimized.

[0031] The initial optimization involves adjusting the operating power of the conveyor belt and robotic arm in the initial optimization model until the transmission correlation parameters obtained from the multi-weight simulation method in the initial optimization model are less than the initial correlation parameters. At this point, the operating power of the conveyor belt and robotic arm in the initial optimization model is reset to the default operating power of the conveyor belt and robotic arm in the initial optimization model.

[0032] Furthermore, the simulation optimization includes:

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

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

[0035] Furthermore, the transportation optimization features obtained based on the initial optimized equipment and transmission-related parameters include:

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

[0037] Furthermore, automatic optimization methods include:

[0038] The production line rotation model with the same transmission correlation parameters and transmission optimization features is designated as the priority analysis model. The transmission correlation parameters of all priority analysis models are obtained based on the multi-weight simulation method. After obtaining the initial optimization equipment based on the initial optimization and optimization simulation, the initial optimization equipment and transmission optimization features are updated, and the automatic optimization method ends.

[0039] The beneficial effects of this invention are as follows: First, the equipment parameters of the conveyor belt and robotic arm in the production line are obtained; the conveyor belt and robotic arm on the same production line are modeled based on digital twins and denoted as the production line rotation model; the transportation of goods 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; then, the operation of the conveyor belt and robotic arm is initially optimized based on the transmission-related parameters. The advantage of this is that by using digital twins to obtain the production line rotation model, the efficiency of data analysis can be improved in subsequent analysis, ensuring that analysis errors can be avoided and data analysis costs can be saved in multiple and repetitive data analyses; and by obtaining the transmission-related parameters, the parameters between the transmission rate and time of the conveyor belt and robotic arm in the production line for transporting goods can be obtained, which helps to accurately optimize the operating power of the conveyor belt or robotic arm based on the transmission-related parameters in the subsequent initial optimization, so that the power-related parameters can be adjusted in a timely manner when the transmission efficiency of the conveying device decreases, so as to ensure the transmission efficiency of the conveying device in actual application.

[0040] This application further optimizes and simulates the transportation of goods in the production line based on the production line rotation model after the initial optimization, and obtains the initial optimized equipment based on the optimization simulation results; finally, it obtains the transportation optimization features based on the initial optimized equipment and transmission-related parameters, and establishes an automatic optimization method based on the transportation optimization features; before each transportation of goods by the conveyor belt and robotic arm, the conveyor belt and robotic arm are optimized based on the automatic optimization method. The advantage of this is that by obtaining the initial optimized equipment, obtaining the transportation optimization features, and establishing the automatic optimization method, it is helpful to effectively analyze the existing transmission status of the conveyor device before each use of the conveyor device to transport goods, thereby ensuring that the conveyor device can transport goods with the optimal transmission efficiency after the start of goods transportation. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

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

[0043] Figure 3 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1, please refer to Figure 1 As shown, this application provides an automated optimization method for 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 on the same production line based on digital twin, and denot it 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 transmission-related parameters based on the simulation results.

[0047] Step S1 includes: Step S101, obtaining equipment parameters of the conveyor belt and robotic arm in the production line, wherein the equipment parameters include the size 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 when they are working;

[0048] In the specific implementation process, such as in actual analysis, the equipment parameters of the conveyor belt obtained may include performance parameters such as bandwidth, belt speed, conveying capacity, conveying length, tilt angle, 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 the actual application.

[0049] Step S102: Record the conveyor belts and robotic arms that are on the same production line as a same-line conveyor group; obtain all the 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 the specific implementation process, for example, in a data analysis, the conveyor belt includes conveyor belt A and conveyor belt B, the robotic arm includes robotic arm A, robotic arm B and robotic arm C, and 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 robotic arm A, production line B contains conveyor belt A and robotic arm B, production line C contains conveyor belt A and robotic arm C, production line D contains conveyor belt B and robotic arm A, and production line E contains conveyor belt B and robotic arm B. Then, conveyor belt A and robotic arm A, conveyor belt A and robotic arm B, conveyor belt A and robotic arm C, conveyor belt B and robotic arm A, and conveyor belt B and robotic arm B can all be recorded as the same line conveyor group.

[0051] Step S103: For any co-line conveyor group: the equipment used to connect the conveyor belt and the robotic arm in the co-line conveyor group is denoted as the conveyor connection equipment, and the equipment parameters of the conveyor connection equipment are obtained; based on the equipment parameters of the conveyor belt and the robotic arm in the co-line conveyor group and the equipment parameters of the conveyor connection equipment, the co-line conveyor group and the conveyor connection equipment are modeled using digital twin and simulated, wherein the model obtained by modeling is denoted as the production line rotation model;

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

[0053] Step S104: Obtain the production line rotation model corresponding to all on-line conveyor groups;

[0054] Step S105: For any production line rotation model: Record the weights of the goods that can be transported in the conveyor belt and robotic arm corresponding to the production line rotation model as conveying weight CZ1 to conveying weight CZ, respectively. n and transfer weight CZ1 to transfer weight CZ n The closed interval formed by the maximum and minimum values ​​in the interval is called the transmission allowed interval;

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

[0056] Step S106: Simulate the production line rotation model using the multi-weight simulation method based on digital twin, and obtain the transmission correlation parameters corresponding to the production line rotation model based on the simulation results.

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

[0058] Step S108, the multi-weight simulation method includes: Step S1081, the equipment that first contacts the goods in the conveyor belt and robotic arm of the production line rotating model is called the front transmission equipment, and the equipment that later contacts the goods in the conveyor belt and robotic arm of the production line rotating model is called the rear transmission equipment.

[0059] Step S1082: For any value G in the allowable transmission range: the goods with weight G are denoted as simulated goods; the simulated goods are transmitted by the front transmission device in the production line rotation model, and the relationship between the moving speed of the simulated goods in the front transmission device and time is recorded as the front displacement data; when the simulated goods are no longer transmitted by the rear transmission device, the relationship between the moving speed of the simulated goods in the rear transmission device and time is recorded as the rear displacement data, wherein both the front transmission device and the rear transmission device operate at rated power;

[0060] Step S1083: Establish a Cartesian coordinate system and denote it as the simulated transportation coordinate system. The unit of the X-axis of the simulated transportation coordinate system is min, and the unit of the Y-axis is m / s. Based on the forward displacement data, plot the corresponding curve between X=0 and X=t1 in the simulated transportation coordinate system and denote it as the forward displacement curve. Here, t1 is the time when the simulated goods are transported in the forward transmission equipment, that is, the time recorded in the forward displacement data.

[0061] Step S1083: Based on the post-displacement data, plot the corresponding curve between X = t2 and X = t3 in the simulated transportation coordinate system, and denot it as the post-displacement curve. The difference between t2 and t1 is the time when the simulated goods stay in the conveying connection equipment; the difference between t3 and t2 is the time when the simulated goods are transported in the post-transmission equipment, that is, the time recorded in the post-displacement data.

[0062] In the specific implementation process, such as during a data analysis, the obtained simulated transportation coordinate system is as follows: Figure 2 As shown, based on the forward and backward displacement data, t1 is 10 min, t2 is 20 min, and t3 is 30 min. The forward and backward displacement curves 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 and smallest slopes in the forward displacement curve, respectively, and point HG and point HD are the points with the largest and smallest slopes in the backward displacement curve, respectively.

[0063] Step S1084: Use a transmission association algorithm to obtain the sub-association parameters corresponding to the simulated goods. The transmission association algorithm includes: Where F is the sub-association parameter, k min1 k is the minimum slope of the forward displacement curve. min2 k is the minimum slope of the back displacement curve. max1 k is the maximum slope of the forward displacement curve. max2 This represents the maximum slope of the rear displacement curve;

[0064] In specific implementation, for example, during a data analysis, t1 is 10 minutes, t2 is 20 minutes, t3 is 30 minutes, and k... min1 =1,k min2 0.5, k max1 For 2, k max2 If the value is 1.5, then the sub-correlation parameter is approximately 0.429. By obtaining the sub-correlation parameter and the transmission correlation parameter in subsequent analysis, we can obtain the parameters between the transmission rate and time when the conveyor belt and the robotic arm transport goods in the production line. 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 the sub-association parameters corresponding to all values ​​in the transmission allowable interval; establish a Cartesian coordinate system, denoted as the transmission association coordinate system, where the X-axis of the transmission association coordinate system is in kg and the Y-axis is a constant axis; mark all values ​​in the transmission allowable 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 marked points as the transmission association curve; mark the point with the largest slope and the point with the smallest slope in the transmission association curve as feature parameter point A and feature 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 x-coordinate of the point and the characteristic parameter A is denoted as the characteristic weight A; transmission weights CZ1 to CZ are then... n The transmission weight CZ with the smallest difference between the x-coordinate of the characteristic parameter point B and the characteristic weight B is denoted as the characteristic weight B.

[0067] In specific implementation, for example, during a data analysis, the abscissa of feature parameter point A is 30kg, and the transmission weight CZ1 to the transmission weight CZ n Given weights of 10kg, 23kg, 2kg, 50kg, 32kg, and 40kg respectively, analysis reveals that characteristic weight A is 32kg. By obtaining characteristic weights A and B, we can determine the weights that cause significant fluctuations in the transmission rate during the transport of goods by the conveyor belt and robotic arm in the production line. The larger the transmission-related parameters corresponding to characteristic weights A and B, the greater the range of influence of weight 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 transmission-related parameters in subsequent analysis, thereby allowing for timely adjustment of power-related parameters when the transmission efficiency of the conveying device decreases, ensuring the transmission efficiency of the conveying device in actual applications.

[0068] Step S1087: The ordinates of the points in the transmission correlation curve whose abscissas are characteristic weight A and characteristic weight B are respectively denoted as characteristic correlation parameter A and characteristic correlation parameter B; the absolute value of the difference between characteristic correlation parameter A and characteristic correlation parameter B is denoted as the transmission correlation parameter of the production line rotation model.

[0069] Step S2: Perform initial optimization of the operation of the conveyor belt and robotic arm based on the transmission correlation parameters; after the initial optimization, perform optimization simulation of the goods transportation in the production line 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 correlation parameter as the initial optimization model, and recording the transmission correlation parameter of the initial optimization model as the initial correlation parameter; performing initial optimization on the operation of the conveyor belt and the robotic arm;

[0071] Step S202, the initial optimization is: adjust 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 then reset the operating power of the conveyor belt and the robotic arm in the initial optimization model to the default power of the conveyor belt and the robotic arm in the initial optimization model.

[0072] In the specific implementation process, if 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 robotic arm corresponding to the initial optimization model has been optimized. Therefore, the operating power of the conveyor belt and the robotic arm in the initial optimization model can be reset to the default power of the conveyor belt and the robotic 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 robotic arm in any initial optimization model is adjusted to the default power, execute method V1; V1 includes: obtaining the transmission correlation parameters of all production line rotation models based on the multi-weight simulation method, and obtaining the initial optimization model again for initial optimization;

[0074] Step S2032, repeat V1. When the operating power of any conveyor belt or robotic arm is reset twice, record that conveyor belt or robotic arm as the first optimization device and stop repeating V1.

[0075] In the specific implementation process, if the operating power of any conveyor belt or robotic arm is reset twice, it indicates that the conveyor belt or robotic arm needs to be optimized. Therefore, it can be recorded as the initial optimization equipment and subsequent optimization can be carried out.

[0076] Step S3: Obtain transportation optimization features based on the initial optimized equipment and transmission-related parameters, and establish an automatic optimization method based on the transportation optimization features; before each transport of goods by the conveyor belt and robotic arm, optimize the conveyor belt and robotic arm based on the automatic optimization method;

[0077] Step S3 includes: Step S301, recording the first transmission association parameter corresponding to the initial optimization equipment as a transmission optimization feature, wherein when the initial optimization equipment is in multiple production line rotation models, the first transmission association parameter of all production line rotation models in which the initial optimization equipment is located is recorded as a transmission optimization feature.

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

[0079] Example 2, please refer to Figure 3 As shown, Figure 3 The example illustrates the structure of an electronic device, which 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 through the communication bus. The memory stores computer-readable instructions, which the processor can call. When the processor executes the computer-readable instructions, it runs steps such as those in the automated optimization method for production line process parameters based on digital twin technology to achieve the following functions: First, acquire the equipment parameters of the conveyor belt and robotic arm in the production line; model the conveyor belt and robotic arm on the same production line based on digital twin technology, and denote it 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 transmission-related parameters based on the simulation results; then perform initial optimization of the operation of the conveyor belt and robotic arm based on the transmission-related parameters; after the initial optimization, perform optimization simulation of the transportation of goods in the production line again based on the production line rotation model, and obtain the initial optimized equipment based on the optimization simulation results; finally, obtain the transportation optimization features based on the initial optimized equipment and transmission-related parameters, and establish an automatic optimization method based on the transportation optimization features; before each transportation of goods by the conveyor belt and robotic arm, optimize the conveyor belt and robotic arm based on the automatic optimization method.

[0080] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and 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 the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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

[0082] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-mentioned automated optimization method for production line process parameters based on digital twin technology to achieve the following functions: First, it obtains the equipment parameters of the conveyor belt and robotic arm in the production line; it models the conveyor belt and robotic arm on the same production line based on digital twin technology and records it as the production line rotation model; it simulates the transportation of goods in the production line based on the production line rotation model and obtains the transmission-related parameters based on the simulation results; then, it performs initial optimization of the operation of the conveyor belt and robotic arm based on the transmission-related parameters; after the initial optimization, it performs optimization simulation of the transportation of goods in the production line again based on the production line rotation model and obtains the initial optimized equipment based on the optimization simulation results; finally, it obtains the transportation optimization features based on the initial optimized equipment and the transmission-related parameters and establishes an automatic optimization method based on the transportation optimization features; before each transportation of goods by the conveyor belt and robotic arm, it optimizes the conveyor belt and robotic arm based on the automatic optimization method.

[0083] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the technical solutions described above, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks, optical disks, etc., and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[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 functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may 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 this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such 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 this application.

Claims

1. An automated optimization method for production line process parameters based on digital twin technology, characterized in that, Includes the following steps: Obtain the equipment parameters of the conveyor belt and robotic arm in the production line; model the conveyor belt and robotic arm on the same production line based on digital twin and denot it 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 transmission-related parameters based on the simulation results; The operation of the conveyor belt and robotic arm was initially optimized based on transmission correlation parameters. After the initial optimization, the transportation of goods in the production line is optimized again based on the production line rotation model, and the equipment optimized in the initial optimization is obtained based on the optimization simulation results. Based on the initial optimization of equipment and transmission-related parameters, transportation optimization features are obtained, and an automatic optimization method is established based on these features. Before each transport of goods by the conveyor belt and robotic arm, the conveyor belt and robotic arm are optimized based on an automatic optimization method. The transportation of goods 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 weights of the goods allowed to be transported in the conveyor belt and robotic arm corresponding to the production line rotation model are denoted as conveying weight CZ1 to conveying weight CZ1 respectively. n and transfer weight CZ1 to transfer weight CZ n The closed interval formed by the maximum and minimum values ​​in the interval is called the transmission allowed interval; The production line rotation model is simulated using a multi-weight simulation method based on digital twins, and the transmission correlation parameters corresponding to the production line rotation model are obtained based on the simulation results. Obtain the transmission association parameters corresponding to all on-line transmission groups; Multi-weight simulation methods include: The equipment in the conveyor belt and robotic arm of the production line rotating model that comes into contact with the goods first is called the front conveyor equipment, and the equipment in the conveyor belt and robotic arm that comes into contact with the goods later is called the rear conveyor equipment. For any value G within the allowable transmission range: the goods with weight G are denoted as simulated goods; in the production line rotation model, the simulated goods are transmitted by the front transmission equipment, and the relationship between the moving speed of the simulated goods in the front transmission equipment and time is recorded as the front displacement data; when the simulated goods are no longer transmitted by the rear transmission equipment, the relationship between the moving speed of the simulated goods in the rear transmission equipment and time is recorded as the rear displacement data, wherein both the front transmission equipment and the rear transmission equipment operate at rated power; Establish a Cartesian coordinate system and denote it as the simulated transportation coordinate system. The unit of the X-axis of the simulated transportation coordinate system is min, and the unit of the Y-axis is m / s. Based on the forward displacement data, plot the corresponding curve between X=0 and X=t1 in the simulated transportation coordinate system and denote it as the forward displacement curve. Here, t1 is the time when the simulated goods are transported in the forward transmission equipment, that is, the time recorded in the forward displacement data. Based on the post-displacement data, the corresponding curve is plotted between X=t2 and X=t3 in the simulated transportation coordinate system, and is denoted as the post-displacement curve. The difference between t2 and t1 is the time when the simulated goods stay in the conveying connection equipment; the difference between t3 and t2 is the time when the simulated goods are transported in the post-transfer equipment, that is, the time recorded in the post-displacement data. The sub-association parameters corresponding to the simulated goods are obtained using a transport association algorithm, which includes: Where F is the sub-association parameter, k min1 k is the minimum slope of the forward displacement curve. min2 k is the minimum slope of the back displacement curve. max1 k is the maximum slope of the forward displacement curve. max2 This represents the maximum slope of the rear displacement curve; Obtain the sub-association parameters corresponding to all values ​​within the allowed transmission range; establish a Cartesian coordinate system, denoted as the transmission association coordinate system, where the X-axis of the transmission association coordinate system is in kg and the Y-axis is a constant axis; mark all values ​​within the allowed transmission range and their corresponding sub-association parameters in the transmission association coordinate system, and denot the curve obtained by fitting all the marked points as the transmission association curve; mark the point with the largest slope and the point with the smallest slope in the transmission association curve as feature parameter point A and feature parameter point B, respectively. Transfer weight CZ1 to transfer weight CZ n The transmission weight CZ with the smallest difference between the x-coordinate of the point and the characteristic parameter A is denoted as the characteristic weight A; transmission weights CZ1 to CZ are then... n The transmission weight CZ with the smallest difference between the x-coordinate of the characteristic parameter point B and the characteristic weight B is denoted as the characteristic weight B. The ordinates of the points in the transmission correlation curve whose abscissas are characteristic weight A and characteristic weight B are respectively denoted as characteristic correlation parameter A and characteristic correlation parameter B; the absolute value of the difference between characteristic correlation parameter A and characteristic correlation parameter B is denoted as the transmission correlation parameter of the production line rotation model. Based on the initial optimized equipment and transmission-related parameters, the transportation optimization features obtained include: The first transmission-related parameter corresponding to the initial optimization equipment is recorded as the transmission optimization feature. When the initial optimization equipment is in multiple production line rotation models, the first transmission-related parameter of all production line rotation models in which the initial optimization equipment is located is recorded as the transmission optimization feature.

2. The automated optimization method for production line process parameters based on digital twin technology according to claim 1, characterized in that, Obtain the equipment parameters of the conveyor belts and robotic arms in the production line; Based on digital twins, a model is created for the conveyor belt and robotic arm on the same production line, denoted as the production line rotation model, which includes: Obtain the equipment parameters of the conveyor belt and robotic arm in the production line. 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 when they are working. The conveyor belts and robotic arms that are on the same production line are categorized as co-line conveyor groups; all co-line conveyor groups in the production line are obtained, where the same conveyor belt or robotic arm may be in multiple co-line conveyor groups.

3. The automated optimization method for production line process parameters based on digital twin technology according to claim 2, characterized in that, Obtain the equipment parameters of the conveyor belts and robotic arms in the production line; Modeling conveyors and robotic arms on the same production line using digital twins, and denoting them as a production line rotation model, also includes: For any in-line conveyor group: the equipment used to connect the conveyor belt and the robotic arm in the in-line conveyor group is denoted as the conveyor connection equipment, and the equipment parameters of the conveyor connection equipment are obtained; based on the equipment parameters of the conveyor belt and the robotic arm in the in-line conveyor group and the equipment parameters of the conveyor connection equipment, digital twins are used to model the in-line conveyor group and the conveyor connection equipment and simulate their operation, wherein the model obtained from the modeling is denoted as the production line rotation model; Obtain the production line rotation model corresponding to all conveyor groups on the same line.

4. The automated optimization method for production line process parameters based on digital twin technology according to claim 3, characterized in that, Initial optimization of the operation of the conveyor belt and robotic arm based on transmission correlation parameters includes: The production line rotation model with the largest transmission correlation parameter is designated as the initial optimization model, and the transmission correlation parameter of the initial optimization model is designated as the initial correlation parameter; the operation of the conveyor belt and the robotic arm is initially optimized. The initial optimization involves adjusting the operating power of the conveyor belt and robotic arm in the initial optimization model until the transmission correlation parameters obtained from the multi-weight simulation method in the initial optimization model are less than the initial correlation parameters. At this point, the operating power of the conveyor belt and robotic arm in the initial optimization model is reset to the default operating power of the conveyor belt and robotic arm in the initial optimization model.

5. The automated optimization method for production line process parameters based on digital twin technology according to claim 4, characterized in that, The optimization simulation includes: When the power of the conveyor belt and robotic arm in any initial optimization model is adjusted to the default power, method V1 is executed; V1 includes: obtaining the transmission correlation parameters of all production line rotation models based on the multi-weight simulation method, and obtaining the initial optimization model again for initial optimization; Repeat V1. When the operating power of any conveyor belt or robotic arm is reset twice, record that conveyor belt or robotic arm as the first optimized device and stop repeating V1.

6. The automated optimization method for production line process parameters based on digital twin technology according to claim 5, characterized in that, Automatic optimization methods include: The production line rotation model with the same transmission correlation parameters and transmission optimization features is designated as the priority analysis model. The transmission correlation parameters of all priority analysis models are obtained based on the multi-weight simulation method. After obtaining the initial optimization equipment based on the initial optimization and optimization simulation, the initial optimization equipment and transmission optimization features are updated, and the automatic optimization method ends.

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