Method for production control of a magnetic drive conveying system and related apparatus

Abstract

The application embodiment proposes a production control method of a magnetic drive conveying system and related equipment. The magnetic drive conveying system comprises a processing conveying line and a mover. The mover runs on the processing conveying line. A plurality of processing procedures are arranged on the processing conveying line. The method comprises the following steps: acquiring conveying line information of the processing conveying line, procedure information of each processing procedure, mover information of the mover, and working position information of each running working position; generating a system processing optimization model based on a mover quantity parameter of the mover, a procedure sequence parameter of the processing procedure, and a working position running parameter of the mover at the running working position; and solving the system processing optimization model based on the conveying line information, the procedure information, the mover information, and the working position information to obtain an optimized mover quantity, an optimized procedure sequence, and an optimized working position running parameter, so as to control the production of the magnetic drive conveying system and improve the overall running efficiency, flexibility, and resource utilization rate of the magnetic drive conveying system.

Description

Technical Field

[0001] This application relates to the field of control technology, and in particular to production control methods and related equipment for magnetic drive conveyor systems. Background Technology

[0002] Magnetic drive conveyor systems, as efficient and flexible automated material handling equipment, are widely used in modern manufacturing, such as for assembling and packaging goods on logistics lines, and for SMT (Surface Mount Technology) of precision electronic components. In these applications, to further improve the operational efficiency of the processing flow in the magnetic drive conveyor system, it is usually necessary to pre-plan a series of suitable production process-related parameters, such as the number of movers in operation and their operating speed.

[0003] In related technologies, when planning production parameters for magnetic drive conveyor systems, multiple production parameter schemes of corresponding scales are typically planned in advance based on the system size. During real-time production in each magnetic drive conveyor system, the production parameter scheme corresponding to the system size is directly adopted for production operation control. However, due to the complex coupling relationships between different real-time data and production parameters in actual production operations, this pre-planned fixed scheme is not flexible enough. This can easily lead to unreasonable resource allocation phenomena such as mover congestion or idle workstations during production, resulting in low production efficiency. Summary of the Invention

[0004] This application provides a production control method and related equipment for a magnetic drive conveyor system, which can improve the production operation efficiency of the magnetic drive conveyor system.

[0005] To achieve the above objectives, a first aspect of this application proposes a production control method for a magnetic drive conveyor system. The magnetic drive conveyor system includes a processing conveyor line and a mover, the mover running on the processing conveyor line, and multiple processing steps arranged on the processing conveyor line. The method includes:

[0006] The conveyor information of the processing conveyor line, the process information of each processing step, the mover information of the mover, and the station information of each running station are obtained. The first running station is the conveying area between the first processing step and the starting point of the processing conveyor line, and the remaining running stations are the conveying areas between every two adjacent processing steps in the processing conveyor line.

[0007] A system processing optimization model is generated based on the number parameters of the mover, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the operating station.

[0008] The system processing optimization model is solved based on the conveyor line information, the process information, the mover information, and the workstation information to obtain the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters.

[0009] The production control of the magnetic drive conveyor system is based on the optimized number of movers, the optimized process sequence, and the optimized station operating parameters.

[0010] In some embodiments, the conveyor line information includes the total length of the conveyor line, and the generation system processing optimization model based on the number of movers, the process sequence parameters of the processing steps, and the station operation parameters of the movers at the operating station includes:

[0011] Based on the number of movers and the process sequence parameters, the total cycle time function of the movers is obtained;

[0012] Based on the ratio of the total length of the conveyor line to the number of movers, the minimum cycle time function of the movers is obtained;

[0013] Based on the number of movers, the processing time variance function of the processing step is obtained;

[0014] Based on the total cycle time function, the minimum cycle time function, and the processing time variance function, the system optimization function is obtained;

[0015] Based on the number of moving parts, the process sequence parameters, the station operation parameters, and the optimization function of the system, the system processing optimization model is obtained.

[0016] In some embodiments, obtaining the total cycle time function of the mover based on the mover quantity parameter and the process sequence parameter includes:

[0017] Based on the difference between the arrival time of the mover and the idle time of the process under the process sequence parameters for each of the processing steps, the process waiting time of the mover in each of the processing steps is obtained;

[0018] Based on the ratio of the station length to the station operation parameters of each operating station, the station operation time of the mover at each operating station is obtained.

[0019] The total cycle time function is obtained by summing the running time of all the workstations and the waiting time of all the processes.

[0020] In some embodiments, obtaining the processing time variance function of the processing step based on the number of movers includes:

[0021] The square of the difference between the actual processing time and the average processing time of the mover in each processing step is obtained to obtain the squared processing difference.

[0022] The average of the squared processing difference based on the number of movers parameter is used to obtain the processing time variance function for each processing step.

[0023] In some embodiments, obtaining the system optimization function based on the total cycle time function, the minimum cycle time function, and the processing time variance function includes:

[0024] The material receiving efficiency of the transfer section of the magnetic drive conveyor system is obtained, and the position deviation of the mover at each operating station is obtained. The average position deviation of the magnetic drive conveyor system is obtained based on the average value of all the position deviations.

[0025] The system optimization function is obtained by weighting and summing the total cycle time function, the minimum cycle time function, the processing time variance function, the material receiving efficiency of the transfer section, and the mean of the system position deviation.

[0026] In some embodiments, solving the system processing optimization model based on the conveyor line information, the process information, the mover information, and the workstation information to obtain the optimized mover quantity, optimized process sequence, and optimized workstation operating parameters includes:

[0027] Based on the number of movers, the process sequence parameters, and the station operation parameters, multiple initial optimization parameters are generated, including continuous initial optimization parameters and discrete initial optimization parameters.

[0028] Based on the current servo cycle information of the conveyor line, the process information, the mover information, the workstation information, and the system optimization function in the system processing optimization model, the fitness value corresponding to each initial optimization parameter is calculated, and the initial optimization parameter with the smallest fitness value is selected from multiple initial optimization parameters as the current optimal parameter.

[0029] Based on the current optimal parameters, the continuous initial optimization parameters in the initial optimization parameters are tracked and updated, and the discrete initial optimization parameters in the initial optimization parameters are searched and updated to obtain the updated optimization parameters;

[0030] When the fitness of the updated optimization parameters is less than the fitness of the current optimal parameters, the current optimal parameters are updated based on the updated optimization parameters, and the iteration continues until a preset number of iterations is reached. The current optimal parameters corresponding to the last iteration are then obtained to obtain the number of optimized actuators, the sequence of optimized processes, and the operating parameters of the optimized workstation.

[0031] In some embodiments, the step of tracking and updating the continuous initial optimization parameters in the initial optimization parameters based on the current optimal parameters includes:

[0032] Obtain acceleration parameters and random parameters;

[0033] Based on the difference between the current optimal parameter and the initial optimized parameter, multiply it by the acceleration parameter and the random parameter to obtain the optimal parameter difference;

[0034] Based on the difference between the optimal parameters and the accumulated value of the continuous initial optimization parameters, the continuous initial optimization parameters in the updated optimization parameters are obtained.

[0035] In some embodiments, the production control of the magnetic drive conveyor system based on the optimized number of movers, the optimized process sequence, and the optimized station operating parameters includes:

[0036] Based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters corresponding to the current servo cycle, the magnetic drive conveyor system is subjected to production control in the current servo cycle.

[0037] Based on the conveyor line information, process information, mover information, and workstation information of the next servo cycle, the optimized mover quantity, optimized process sequence, and optimized workstation operating parameters for the next servo cycle are calculated. Based on the optimized mover quantity, optimized process sequence, and optimized workstation operating parameters of the next servo cycle, the magnetic drive conveyor system is subjected to production control in the next servo cycle.

[0038] To achieve the above objectives, a second aspect of this application provides a production control device for a magnetic drive conveyor system. The magnetic drive conveyor system includes a processing conveyor line and a mover, the mover running on the processing conveyor line, and multiple processing steps arranged on the processing conveyor line. The device includes:

[0039] The information acquisition module is used to acquire the conveyor line information of the processing conveyor line, the process information of each processing process, the mover information of the mover, and the station information of each running station. The first running station is the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining running stations are the conveying areas between every two adjacent processing processes in the processing conveyor line.

[0040] The optimization model generation module is used to generate a system processing optimization model based on the number parameters of the mover, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the operating station.

[0041] The optimization parameter calculation module is used to solve the system processing optimization model based on the conveyor line information, the process information, the mover information and the workstation information to obtain the optimized mover quantity, optimized process sequence and optimized workstation operating parameters;

[0042] The production control module is used to perform production control on the magnetic drive conveyor system based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters.

[0043] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the production control method for the magnetic drive conveyor system as described in the first aspect.

[0044] To achieve the above objectives, a fourth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the production control method for the magnetic drive conveyor system described in the first aspect.

[0045] The production control method and related equipment for a magnetic drive conveyor system proposed in this application include a processing conveyor line and a mover. The mover runs on the processing conveyor line, which is equipped with multiple processing steps. The method includes: First, acquiring the conveyor line information, the process information of each processing step, the mover information, and the station information of each operating station. The first operating station is the conveying area between the first processing step and the starting point of the processing conveyor line, and the remaining operating stations are the conveying areas between every two adjacent processing steps in the processing conveyor line. Then, generating a system processing optimization model based on the mover quantity parameters, the process sequence parameters, and the station operating parameters of the mover at each operating station. Next, solving the system processing optimization model based on the conveyor line information, process information, mover information, and station information to obtain the optimized mover quantity, optimized process sequence, and optimized station operating parameters. Finally, performing production control on the magnetic drive conveyor system based on the optimized mover quantity, optimized process sequence, and optimized station operating parameters. This application embodiment acquires various information from the production line in the magnetic drive conveyor system in real time, and constructs a system processing optimization model based on this information that reflects the complex coupling relationship between the number of movers, the sequence of processes, and the operating speed. By solving the model, the optimal parameter combination under the current production state can be dynamically calculated, and the system can be controlled based on this set of real-time optimized parameters. This allows the production control strategy to proactively adapt to actual changes on the production line, avoiding resource waste such as mover congestion or idle workstations caused by fixed parameters. This significantly improves the overall operating efficiency, flexibility, and resource utilization of the magnetic drive conveyor system.

[0046] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the structure of a magnetic drive conveying system provided in one embodiment of this application.

[0048] Figure 2 This is a flowchart of a production control method for a magnetic drive conveyor system provided in another embodiment of this application.

[0049] Figure 3 yes Figure 2 The flowchart for step 202.

[0050] Figure 4 yes Figure 3 The flowchart for step 301.

[0051] Figure 5 yes Figure 3 The flowchart for step 303.

[0052] Figure 6 yes Figure 3 The flowchart for step 304.

[0053] Figure 7 yes Figure 2 The flowchart for step 203.

[0054] Figure 8 yes Figure 7 The flowchart for step 703.

[0055] Figure 9 This is a schematic diagram of a two-layer framework algorithm for solving a system processing optimization model, provided in another embodiment of this application.

[0056] Figure 10 yes Figure 2 The flowchart for step 204.

[0057] Figure 11 This is a schematic diagram of the structure of the production control device of the magnetic drive conveying system provided in one embodiment of this application.

[0058] Figure 12 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0062] Magnetic drive conveyor systems, as efficient and flexible automated material handling equipment, are widely used in modern manufacturing, such as for assembling and packaging goods on logistics lines, and for SMT (Surface Mount Technology) of precision electronic components. In these applications, to further improve the operational efficiency of the processing flow in the magnetic drive conveyor system, it is usually necessary to pre-plan a series of suitable production process-related parameters, such as the number of movers in operation and their operating speed.

[0063] In related technologies, when planning production parameters for magnetic drive conveyor systems, multiple production parameter schemes of corresponding scales are typically planned in advance based on the system size. During real-time production in each magnetic drive conveyor system, the production parameter scheme corresponding to the system size is directly adopted for production operation control. However, due to the complex coupling relationships between different real-time data and production parameters in actual production operations, this pre-planned fixed scheme is not flexible enough. This can easily lead to unreasonable resource allocation phenomena such as mover congestion or idle workstations during production, resulting in low production efficiency.

[0064] To improve the production efficiency of magnetic drive conveyor systems, this application embodiment acquires various information from the production line in the magnetic drive conveyor system in real time, and constructs a system processing optimization model based on this information that reflects the complex coupling relationship between the number of movers, the sequence of processes, and the operating speed. By solving the model, the optimal parameter combination under the current production state can be dynamically calculated, and the system can be controlled based on this set of real-time optimized parameters. This allows the production control strategy to proactively adapt to actual changes on the production line, avoiding resource waste such as mover congestion or idle workstations caused by fixed parameters, and significantly improving the overall operating efficiency, flexibility, and resource utilization of the magnetic drive conveyor system.

[0065] To better illustrate the mover operation control method for the synchronous transition track provided in this application, this embodiment first describes a maglev transport track applying the mover operation control method. (Refer to...) Figure 1 The diagram shown is a structural schematic of a magnetic drive conveying system provided in an embodiment of this application. Figure 1 As shown, the magnetic drive conveyor system includes a processing conveyor line and multiple movers, with multiple movers operating on the processing conveyor line. To perform workpiece processing, multiple processing steps are set up on the processing conveyor line, such as... Figure 1 The diagram shows two processing steps (typically, there are many processing steps in a magnetic drive conveyor system). Near each processing step, there is at least one movable processing device (such as a robotic arm) used to process the workpiece carried by the mover passing through the processing step.

[0066] In addition, the position between every two adjacent processing steps is defined as the running station, and the first running station is the conveying area between the starting point of the processing conveyor line and the first processing step, while the remaining running stations refer to the conveying areas between every two adjacent processing steps in the processing conveyor line.

[0067] Based on the above-described magnetic drive conveyor system, the production control method of the magnetic drive conveyor system in the embodiments of this application will be described in detail below. (Refer to...) Figure 2 This is an optional flowchart of a production control method for a magnetic drive conveyor system provided in an embodiment of this application. Figure 2 The method may include, but is not limited to, steps 201 to 204. It is also understood that this embodiment... Figure 2 The order of steps 201 to 204 is not specifically limited; the order of steps can be adjusted or certain steps can be added or removed according to actual needs. The production control method for the magnetic drive conveyor system provided in this embodiment can be applied to control systems connected to the magnetic drive conveyor track, such as smart terminals, servers, computers, etc.

[0068] Step 201: Obtain the conveyor information of the processing conveyor line, the process information of each processing operation, the mover information of the mover, and the station information of each running station. The first running station is the conveying area between the first processing operation and the starting point of the processing conveyor line, and the remaining running stations are the conveying areas between every two adjacent processing operations in the processing conveyor line.

[0069] Step 201 will be described in detail below.

[0070] In some embodiments, multiple actuators operate in real time as such Figure 1 When operating on the processing conveyor line shown, the control system needs to collect basic data on the operation of the magnetic drive conveyor system in each servo cycle. This data forms the basis for subsequent optimization decisions. The conveyor line information typically refers to static physical attributes, such as the total length L of the magnetic drive conveyor line. total The system includes information such as the track topology, the number of processing steps M, the number of operating stations K, and the average speed V of all moving parts during operation. The process information for each processing step includes dynamic or semi-dynamic data such as processing time at each processing station and equipment reliability. Moving part information refers to the real-time status of the moving parts that act as the carriers in the system, such as their current position, speed, and identification information, as well as the average speed of all moving parts during operation. Regarding the station information for each operating station, it's important to clarify that an operating station does not refer to the specific site where processing operations are performed. Rather, it refers to the transport area of ​​the mover between different processing steps or from the starting point to the first processing step. Therefore, its information primarily consists of the length of this transport area, i.e., the station length L of each operating station. k And so on. This comprehensive information collectively constitutes the data representing the current state of the entire production system.

[0071] Step 202: Generate a system processing optimization model based on the mover quantity parameters, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the operating station.

[0072] Step 202 will be described in detail below.

[0073] In some embodiments, after obtaining the aforementioned basic information, a mathematical model is further constructed that can describe the inherent laws of the production process. Specifically, this involves three key controllable variables in the production system: the number of movers parameter N (i.e., the number of movers put into the magnetic drive conveyor system at the same time, which determines the material concurrent processing capacity of the production line), and the process sequence parameter S = {s1, s2, ..., s...}. M} (That is, the processing sequence of each processing step on the magnetic drive conveyor line, which determines the material flow path and potential waiting time, such as S=[s1,s2,s3] indicating that the task passes through step 1-->step 2-->step 3 in sequence) and the station operation parameters of the mover at the running station V={v1,v2,…,v K The moving part (i.e., the running speed of the moving part at each conveying station, which determines the running speed of the moving part in different conveying areas) is given a unified mathematical expression. Based on these abstract parameters, the coupling relationship between them is established, ultimately constructing a system processing optimization model that quantifies any combination of these three parameters for the overall performance index of the production system. The following section will further describe in detail how to construct this system processing optimization model.

[0074] Reference Figure 3 A system processing optimization model is generated based on the number parameters of the mover, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the operating station, including the following steps 301 to 305.

[0075] Step 301: Based on the number of movers and the process sequence parameters, obtain the total cycle time function of the movers.

[0076] Step 301 will be described in detail below.

[0077] In some embodiments, in order to construct a system processing optimization model that can be optimized to improve production efficiency in a magnetic drive conveyor system, the following steps are first taken: The number of movers parameter N and the process sequence parameter S = {s1, s2, ..., s...} M}, thus obtaining the total cycle time function of the mover. Where, the process sequence parameter S = {s1, s2, ..., s} M The number of moving parts directly determines the path of the moving parts and the necessary processing and waiting stages, forming the core of the calculated base time. The number of moving parts, N, affects the congestion level within the production line; an increase in the number of moving parts may lead to increased queuing times at certain workstations. Therefore, the total cycle time function is not a fixed value, but rather uses these two parameters as variables to mathematically characterize a key indicator of the overall material flow efficiency of the system, as described below.

[0078] Reference Figure 4 Based on the number of movers and the process sequence parameters, the total cycle time function of the movers is obtained, including the following steps 401 to 403.

[0079] Step 401: Based on the difference between the arrival time of the mover and the idle time of the process under the process sequence parameters for each processing step, obtain the process waiting time of the mover in each processing step.

[0080] Step 402: Based on the ratio of the station length to the station operation parameters of each operating station, obtain the station operation time of the mover at each operating station.

[0081] Step 403: Accumulate the running time of all workstations and the waiting time of all processes to obtain the total cycle time function.

[0082] Steps 401 to 403 are described in detail below.

[0083] In some embodiments, in order to construct a suitable total cycle time function, it is first based on each processing step (e.g., step s). i ArrivalTime(s) under process sequence parameters i ) and process idle time AvailableTime(s i The difference between the two values ​​is used to obtain the process waiting time (Wait(s)) for the mover in each processing step. i As shown in the following formula (1).

[0084] Wait(s i =max(0,ArrivalTime(s) i -AvailableTime(s) i )) (1)

[0085] The process waiting time here refers to the time that must be incurred when a mover has arrived at a designated position in a certain processing step, but the step is processing a previous mover or is not yet ready. This waiting time is calculated by determining the precise moment the mover arrives at the step (i.e., the mover arrival time, ArrivalTime(s)). i The available time (AvailableTime) is the time when the process can begin its next operation. i The difference between the two is used to determine the process, and this process is closely related to the process sequence parameters, because different process sequences will directly change the arrival sequence of the mover and the idle time of the process.

[0086] Furthermore, the arrival time of the moving part, ArrivalTime(s) i It is obtained through the following formula (2).

[0087] ArrivalTime(s i = FinishTime(s) j )+T move (j→i) (2)

[0088] Among them, process s j For process s i The direct preceding workstation, FinishTime(s) j ) is the mover at the preceding station s j Task completion time, T move (j→i) represents the workstation s j Move to s i Transportation time (as opposed to path length L) j→i and speed V k (Related)

[0089] Additionally, the available time (s) for the process idle time i It is obtained through the following formula (3).

[0090] AvailableTime(s i =FinishTime prev (s i +ResetTime(s) i (3)

[0091] Among them, FinishTime prev (s i ) for workstation s i The completion time after processing the previous mover, ResetTime(s) i ) for workstation s iThe preparation time of the interactive external processing equipment, i.e. the station reset time.

[0092] Furthermore, based on the station length L of each operating station k k and workstation operating parameters V k The ratio of the two values ​​gives the station travel time L of the mover at each station. k / V k Then, the running time of all running stations and the waiting time of all processes are summed up to finally obtain the total cycle time function T. cycle As shown in the following formula (4).

[0093]

[0094] Through steps 401 to 403 above, by accurately calculating the running time of each workstation and the waiting time of each process, and then summing these up, a highly accurate total cycle time function with clear physical meaning can be constructed. This allows the model to clearly reveal how decision variables such as process sequence parameters and workstation running parameters affect the total cycle time by influencing the waiting and running phases, thus providing a more accurate and reliable mathematical basis for subsequent system optimization.

[0095] Step 302: Based on the ratio of the total length of the conveyor line to the number of movers, obtain the minimum cycle time function of the movers.

[0096] Step 303: Based on the number of movers, obtain the processing time variance function of the processing steps.

[0097] Steps 302 to 303 are described in detail below.

[0098] Next, based on the total length L of the conveyor line total The ratio of the product of the mover quantity parameter N and the average velocity V yields the minimum beat function T of the mover. beat As shown in the following formula (5).

[0099]

[0100] The minimum cycle time function represents how long the production line can produce a finished product under ideal conditions. This function links the total physical length of the conveyor line with the adjustable number of movers, so that on a line with a fixed total length, the more movers are put in, the smaller the average spacing between the movers will be in theory, thus enabling a shorter production interval.

[0101] Then, based on the number of movers parameter N, the processing steps s are obtained. i The processing time variance function σ 2process,i Because various disturbances in the production process cause actual processing time to fluctuate around an average value, the processing time variance function is a statistical indicator used to measure the severity of this fluctuation. This function calculates the dispersion of actual processing time data under specific mover quantity parameters by collecting and analyzing such data. Generally, a smaller variance value indicates a more stable and predictable production process, while a larger variance suggests potential bottlenecks or unstable processes on the production line. The following section will further describe how to generate this processing time variance function.

[0102] Reference Figure 5 Based on the number of movers, the processing time variance function of the processing steps is obtained, including the following steps 501 to 502.

[0103] Step 501: Obtain the squared difference between the actual processing time and the average processing time of the mover in each processing step, and get the squared processing difference.

[0104] Step 502: Average the squared processing difference based on the mover quantity parameter to obtain the processing time variance function for each processing step.

[0105] Steps 501 to 502 are described in detail below.

[0106] In some embodiments, in order to construct a suitable time variance function First, by obtaining the mover in each processing step s i The actual processing time t process,i,k With average processing time The square of the difference is used to obtain the square of the processing difference. The actual processing time t here process,i,k This refers to the actual time consumed by a mover during the k-th processing step of the i-th operation, from start to finish, while the average processing time... This is an expected or theoretical value of process i under a large amount of production data. Calculating the squared difference between the two aims to quantify the degree to which a single processing event deviates from the normal level. The magnitude of the squared difference directly reflects the severity of the fluctuation in that processing.

[0107] Furthermore, the obtained discrete data representing single fluctuations (i.e., the square of the processing difference) are statistically integrated to form a macroscopic evaluation of the overall stability of the process. That is, the square of the processing difference is averaged based on the number of movers to obtain an index that can represent the overall fluctuation level of the process under a specific number of movers. That is, the processing time variance function of each processing step is shown in the following formula (6).

[0108]

[0109] This function mathematically reveals the intrinsic relationship between the stability of processing time and the number of moving parts in the system, providing a key stability assessment basis for subsequent system optimization.

[0110] Through steps 501 to 502 above, by calculating the square of the processing difference and averaging it, a processing time variance function that can accurately reflect the degree of fluctuation in processing time is successfully constructed. This transforms the originally vague concept of "stability" in the production process into a key performance indicator that can be quantified and optimized. As a result, subsequent system optimization can pursue high efficiency while also taking into account the stability and predictability of the production process. This effectively avoids system bottlenecks and cycle disorder caused by drastic fluctuations in processing time, and ultimately helps to achieve a more robust and reliable production control.

[0111] Step 304: Based on the total cycle time function, minimum cycle time function, and processing time variance function, obtain the system optimization function.

[0112] Step 304 will be described in detail below.

[0113] In some embodiments, after obtaining the total cycle time function (4), the minimum cycle time function (5), and the processing time variance function (6), these functions are further integrated to form a unified optimization objective. Since in actual production, simply pursuing the shortest cycle time may sacrifice stability (i.e., increase variance), and vice versa, a comprehensive evaluation function is needed to balance these potentially conflicting objectives, as described below.

[0114] Reference Figure 6 Based on the total cycle time function, the minimum cycle time function, and the processing time variance function, the system optimization function is obtained, including the following steps 601 to 602.

[0115] Step 601: Obtain the material receiving efficiency of the transfer section of the magnetic drive conveyor system, and obtain the position deviation of the mover at each operating station, and obtain the average system position deviation of the magnetic drive conveyor system based on the average value of all position deviations.

[0116] Step 602: Based on the total cycle time function, minimum cycle time function, processing time variance function, material receiving efficiency of the transfer section, and the mean of system position deviation, a weighted sum is performed to obtain the system optimization function.

[0117] Steps 601 to 602 are described in detail below.

[0118] In some embodiments, in order to expand the evaluation dimensions of system performance to include more key factors affecting actual production results, the material receiving efficiency of the transfer section of the magnetic drive conveyor system is also obtained as shown in the following formula (7).

[0119]

[0120] Among them, v transfer The speed of the transfer section is α, and the equipment reliability coefficient is α (a pre-set fixed parameter). The transfer section here refers to a special mechanism used to transfer the moving parts between different conveying tracks. Its material receiving efficiency quantifies the success rate and smoothness of the transfer process and is an important indicator for measuring the reliability and coordination of the system.

[0121] Simultaneously, the positional deviation |x of the mover at each operating station is also obtained. actual,k -x target,k | This position deviation refers to the actual stopping position x of the mover. actual,k With target position x target,k The error between them is the core parameter for measuring the control accuracy of the system. Finally, the average position deviation of the magnetic drive conveyor system is obtained by the average position deviation of the mover at all operating positions, as shown in the following formula (8).

[0122]

[0123] The goal is to combine the accuracy performance of each workstation into a comprehensive indicator that represents the overall positioning accuracy of the system.

[0124] Finally, all previously defined and independent performance indicators are integrated into a unified and optimizable final goal, which is a weighted sum based on the total cycle time function, minimum cycle time function, processing time variance function, material receiving efficiency of the transfer section, and the mean of system position deviation, resulting in the system optimization function as shown in the following formula (9).

[0125] C = a * T cycle +b*T beat +c*σ 2 process,i +d*(1-η transfer )+e*δ position (9)

[0126] This function transforms a complex multi-objective optimization problem into a single-objective optimization problem by multiplying each function or indicator value with its corresponding weight and then summing the results. The final value of the system optimization function represents the comprehensive score of a set of production parameters under the current weight configuration.

[0127] Through steps 601 to 602 above, by introducing the material receiving efficiency of the transfer section and the average system position deviation, the reliability and control accuracy of the system are also taken into consideration. This makes the optimization model more comprehensive and closer to real industrial application scenarios. Through weighted summation, the system is given great flexibility, allowing users to adjust the importance of each performance index according to different production needs (e.g., prioritizing speed or prioritizing accuracy). Ultimately, this ensures that the obtained system optimization function can serve as a highly comprehensive and customizable optimization objective, guiding the control system to find a combination of production parameters that achieves the best balance in all key performance aspects, thereby achieving true global optimization.

[0128] Step 305: Based on the number of moving parts, process sequence parameters, station operation parameters, and the optimal system optimization function, obtain the system processing optimization model.

[0129] Step 305 will be described in detail below.

[0130] In some embodiments, after obtaining the system optimization function (9), the number of movers parameter N and the process sequence parameter S = {s1, s2, ..., s} are further adjusted. M} and workstation operating parameters V={v1,v2,...,v K} as optimization variables, minimizing the system optimization function (9) as the objective function, and using the station operation parameters V={v1,v2,…,v K Maximum running speed constraint (i.e., v) k ≤v max v max The maximum deviation constraint (i.e., δ) is the maximum speed and the mean deviation of the system position. position ≤δ max δ max The minimum efficiency constraint (i.e., η) is the minimum positional deviation and the material receiving efficiency of the transfer section. transfer ≥η min η min Using the minimum material receiving efficiency as a constraint, the system processing optimization model is constructed as shown in the following formula (10).

[0131]

[0132] Through steps 301 to 305 above, by constructing the total cycle time function, minimum cycle time function, and processing time variance function, the system is comprehensively quantitatively described from three key dimensions: efficiency, cycle time, and stability. By integrating these functions into a single system optimization function, and finally forming a system processing optimization model, a complex, multi-objective production management problem is successfully transformed into a well-structured and solvable mathematical optimization problem. This provides a solid theoretical foundation for subsequent use of algorithms to find the globally optimal combination of production parameters, ensuring the scientific and systematic nature of optimization decisions.

[0133] Step 203: Solve the system processing optimization model based on the conveyor line information, process information, mover information and workstation information to obtain the optimized number of movers, optimized process sequence and optimized workstation operating parameters.

[0134] Step 203 will be described in detail below.

[0135] In some embodiments, after the system processing optimization model (10) is constructed, the real-time and specific information (including conveyor line information, process information, and mover information) obtained in the current servo cycle is used to solve the system processing optimization model (10) to find a set of parameter values ​​that can make the overall performance of the system represented by the model reach the optimal level, thereby obtaining a set of specific and executable optimization results, namely, optimizing the number of movers, optimizing the process sequence, and optimizing the station operation parameters. These together constitute the best production control strategy in the current production state. The following will further describe how to solve the system processing optimization model (10).

[0136] In some embodiments, a solution approach based on the Hybrid Strategy Optimization (HSO) algorithm is adopted. Under this solution framework, a two-layer framework of MPC (Model Predictive Control) + improved CSO (Chicken Optimization) is used.

[0137] In the outer MPC rolling optimization, the input parameters are the current state (including conveyor line information, process information, and mover information, such as station load, trolley position, processing time fluctuation, etc.); the rolling window is to predict the state in the time domain [t, t+H]; the optimization objective is the system processing optimization model (10).

[0138] In the memory-enhanced CSO solution, the cat swarm particle algorithm is used to solve the problem by utilizing the relevant real-time information data of the current servo cycle (including conveyor line information, process information, and mover information), as described below.

[0139] Reference Figure 7The system processing optimization model is solved based on conveyor line information, process information, mover information and workstation information to obtain the optimized number of movers, optimized process sequence and optimized workstation operating parameters, including the following steps 701 to 704.

[0140] Step 701: Generate multiple initial optimization parameters based on the number of movers, process sequence parameters, and station operation parameters.

[0141] Step 702: Based on the current servo cycle's conveyor line information, process information, mover information, workstation information, and the system optimization function in the system processing optimization model, calculate the fitness value corresponding to each initial optimization parameter, and select the initial optimization parameter with the smallest fitness value from multiple initial optimization parameters as the current optimal parameter.

[0142] Step 703: Based on the current optimal parameters, track and update the continuous initial optimization parameters in the initial optimization parameters, and search and update the discrete initial optimization parameters in the initial optimization parameters to obtain the updated optimization parameters.

[0143] Steps 701 to 703 are described in detail below.

[0144] When using the cat swarm particle algorithm to solve for the current servo cycle, the first step is to base the calculation on the mover quantity parameter N and the process sequence parameter S = {s1, s2, ..., s}. M} and the workstation operating parameters V={v1,v2,…,v K}, generate multiple initial optimization parameters X j ={V j N j The multiple initial optimization parameters here can be understood as an initial "population" or "set" containing various potential solutions, serving as the starting point for subsequent iterative optimizations. And in the initial optimization parameters X... j ={V j N j S j The text includes continuous initial optimization parameters V that can take any value within a certain range. j N j And discrete initial optimization parameters S, which can only be selected from a finite set of options. j , such as S j ={1,2,3} represents workstation 2→1→3.

[0145] Next, the initial generated solutions are evaluated and screened. This step calculates the fitness value I corresponding to each initial optimization parameter based on the current servo cycle's conveyor line information, process information, mover information, workstation information, and the system optimization function (9) in the system processing optimization model. Here, the fitness value I is the comprehensive score calculated by the system optimization function after substituting a set of specific initial optimization parameters and real-time production line information; the smaller the value, the better the solution for that parameter. Subsequently, by comparing all fitness values ​​I, the solutions from multiple initial optimization parameters X are selected. j The initial optimization parameter with the smallest fitness value is selected as the current optimal parameter X. best This parameter will serve as the benchmark and learning target for this round of iterations.

[0146] Then, the core iterative update operation is executed to explore better solutions. In this embodiment, a differentiated update strategy is adopted: based on the current optimal parameter X... best For the continuous initial optimization parameters V in the initial optimization parameters j N j Tracking updates typically involve fine-tuning and converging continuous parameters (such as speed) towards the current optimal solution; simultaneously, it searches and updates discrete initial optimization parameters, which usually involves some form of mutation or recombination of discrete parameters (such as process sequence) to explore new, potentially better combinations. Through this hybrid update strategy, the updated optimization parameters are ultimately obtained, representing a new set of potential solutions.

[0147] The following section will further describe how to track and update the continuous initial optimization parameters in the initial optimization parameters.

[0148] Reference Figure 8 The continuous initial optimization parameters in the initial optimization parameters are tracked and updated based on the current optimal parameters, including the following steps 801 to 803.

[0149] Step 801: Obtain acceleration parameters and random parameters.

[0150] Step 802: Based on the difference between the current optimal parameters and the initial optimized parameters, multiply by the acceleration parameters and random parameters to obtain the optimal parameter difference.

[0151] Step 803: Based on the difference between the optimal parameters and the accumulated value of the continuous initial optimization parameters, obtain the continuous initial optimization parameters in the updated optimization parameters.

[0152] Steps 801 to 803 are described in detail below.

[0153] In some embodiments, to finely control the update process, a predetermined acceleration parameter c1 (commonly 2) and a random parameter r1 ∈ [0,1] are first obtained. The acceleration parameter is typically a preset constant that determines the step size or rate at which the current optimized parameter learns from the optimal parameter, thus controlling the convergence speed. The random parameter, on the other hand, is a dynamically generated random number during each update. Its purpose is to introduce uncertainty into the update process to prevent all parameters from moving in the same deterministic way, thereby helping to escape local optima.

[0154] Then, at the current iteration number t, based on the current optimal parameters With initial optimization parameters The difference is then multiplied by the acceleration parameter c1 and the random parameter r1 to obtain the optimal parameter difference. Subsequently, the calculated difference in optimal parameters and the cumulative value of continuous initial optimization parameters are used to obtain the continuous initial optimization parameters in the updated optimization parameters as shown in the following formula (11).

[0155]

[0156] Through steps 801 to 803 above, by calculating the difference between the current optimal parameters and the current optimal parameters, it ensures that the optimization process always "tracks" towards the currently known optimal solution, guaranteeing the convergence of the algorithm. Furthermore, by introducing acceleration and random parameters, it adds controllable perturbations to this tracking process, effectively balancing the algorithm's mining and exploration capabilities. This refined update strategy enables the algorithm to efficiently and robustly search for optimization in a continuous parameter space, thereby increasing the probability of finding the global optimum.

[0157] Furthermore, regarding the discrete initial optimization parameter S in the initial optimization parameters... j During the search and update process, an adaptive mutation strategy is employed, meaning that at the current iteration number t, the mutation probability p is used. mutate Discrete initial optimization parameters Apply random perturbations to generate new discrete initial optimization parameters. The mutation probability p mutate As shown in the following formula (12).

[0158] p mutate =p max e -βt (12)

[0159] Where, p max β represents the maximum mutation probability, and β is a pre-set mutation parameter factor.

[0160] In addition, to improve the applicability of the iteration process, variables that do not meet the relevant constraints are adjusted, such as adjusting the station operation parameters in a certain iteration process. The process sequence parameter S that causes a conflict (detected using the conflict detection function Conflict(S)) j Regenerate.

[0161] Step 704: When the fitness of the updated optimization parameters is less than the fitness of the current optimal parameters, update the current optimal parameters based on the updated optimization parameters, and continue iterating until the preset number of iterations is reached. Obtain the current optimal parameters corresponding to the last iteration to get the number of optimized movers, the optimized process sequence, and the optimized station operation parameters.

[0162] Step 704 will be described in detail below.

[0163] In some embodiments, during an iteration, it is determined whether the newly generated solution is better. That is, if the fitness of the updated optimization parameters is less than the fitness of the current optimal parameters, the better new solution is used to update the current optimal parameters. Then, regardless of whether it is updated, the iteration continues until a preset number of iterations is reached. After the loop ends, the current optimal parameters X* corresponding to the last iteration are obtained. This parameter is the final result obtained from this optimization solution, which includes the number of optimized movers, the optimized process sequence, and the optimized station operation parameters, i.e., {V*, N*, S*}.

[0164] Through steps 701 to 704 above, by generating initial optimization parameters containing both continuous and discrete variables, and employing a hybrid strategy of tracking and searching for updates, the method can efficiently explore a large and complex solution space, effectively avoiding the predicament of getting stuck in local optima. By continuing to iterate and continuously updating the current optimal parameters, this method can gradually approach the global optimal solution. Finally, within a limited computation time, it accurately solves for the optimal number of movers, the optimal sequence of processes, and the optimal station operating parameters that enable the overall system performance to reach the optimal level, greatly improving the quality and reliability of the solution results.

[0165] Reference Figure 9 This is a schematic diagram of a two-layer framework algorithm for solving a system processing optimization model, provided in an embodiment of this application. Figure 9As shown, this process embodies the control concept of "MPC rolling optimization," which dynamically solves for the optimal control parameters in each control cycle. Specifically, when optimization begins, the "state is transmitted," meaning real-time data such as conveyor line information and process information are acquired. Then, the "inner-layer CSO solution" algorithm is initiated. The core step of this algorithm is "variable grouping," which divides the parameters to be optimized into two categories based on their mathematical characteristics: one is "continuous variable optimization - V, N," corresponding to the optimization process of station operation parameters and mover quantity parameters, which uses a tracking update strategy; the other is "discrete variable optimization - S," corresponding to the optimization process of process sequence parameters, which uses a search update strategy. After optimizing these two types of variables respectively, "cooperative constraint processing" ensures that the updated parameter combination still satisfies the overall constraints of the system's processing optimization model. Finally, through continuous iterative optimization, the process "outputs the optimal solution," that is, obtaining the optimized mover quantity, optimized process sequence, and optimized station operation parameters for the current cycle.

[0166] Step 204: Perform production control on the magnetic drive conveyor system based on optimizing the number of movers, optimizing the process sequence, and optimizing the station operating parameters.

[0167] Step 204 will be described in detail below.

[0168] In some embodiments, after obtaining the aforementioned series of optimization parameters (including the number of optimized movers, the sequence of optimized processes, and the operating parameters of optimized workstations, i.e., {V*, N*, S*}), the calculated number of optimized movers, the sequence of optimized processes, and the operating parameters of optimized workstations are sent as instructions to the actuators of the magnetic drive conveyor system. For example, the system will schedule or release a corresponding number of movers for operation based on the number of optimized movers, and plan the specific path and speed curve of each mover based on the sequence of optimized processes and the operating parameters of optimized workstations. By directly applying the optimization results to actual production, the operating state of the entire magnetic drive conveyor system can be dynamically adjusted to the theoretically optimal level, and the above control flow is executed in each service cycle, as described below.

[0169] Reference Figure 10 Production control of the magnetic drive conveyor system is carried out based on optimizing the number of movers, optimizing the process sequence, and optimizing the operating parameters of the workstation, including the following steps 1001 to 1002.

[0170] Step 1001: Based on the optimized number of movers, optimized process sequence, and optimized station operation parameters corresponding to the current servo cycle, perform production control on the magnetic drive conveyor system in the current servo cycle.

[0171] Step 1002: Based on the conveyor line information, process information, mover information, and workstation information of the next servo cycle, calculate the optimized number of movers, optimized process sequence, and optimized workstation operating parameters for the next servo cycle, and perform production control on the magnetic drive conveyor system in the next servo cycle based on the optimized number of movers, optimized process sequence, and optimized workstation operating parameters for the next servo cycle.

[0172] Steps 1001 to 1002 are described in detail below.

[0173] In some embodiments, after obtaining the optimized number of movers, optimized process sequence, and optimized station operating parameters corresponding to the current servo cycle, production control of the magnetic drive conveyor system is performed within the current servo cycle. Here, the servo cycle can be understood as the smallest time unit of system control or a "control tick." Within this brief time slice, the control system translates the previously calculated optimal parameter scheme for the current situation into specific instructions for the physical equipment, such as scheduling a specified number of movers, planning their paths, and setting their speeds. This ensures that at every control instant, the system's operating state is executed based on the latest optimized decisions.

[0174] Then, after acquiring the conveyor line information, process information, mover information, and workstation information for the next servo cycle through direct or predictive techniques, the optimized number of movers, optimized process sequence, and optimized workstation operating parameters for the next servo cycle are calculated based on this information. This means that while the system is busy executing the control commands for the current cycle, it has already begun to plan and calculate for the next upcoming time slice using the latest sensor data and status feedback. Subsequently, based on the optimized number of movers, optimized process sequence, and optimized workstation operating parameters for the next servo cycle, production control of the magnetic drive conveyor system is performed in the next servo cycle, ensuring that the above calculation results are immediately applied when the next servo cycle arrives, thus forming a seamless "execution-prediction-re-execution" rolling cycle.

[0175] Through steps 1001 to 1002 above, by executing optimal control in the current servo cycle while simultaneously performing forward-looking calculations and preparations for the next servo cycle, the entire production control system possesses continuous, real-time self-correction and optimization capabilities. This ensures that the control strategy can quickly respond to any changes or disturbances in the production process, rather than rigidly executing an outdated plan. Consequently, the real-time adaptability and robustness of the magnetic drive conveyor system are greatly enhanced, enabling it to always operate in a dynamic equilibrium state close to global optimum.

[0176] The production control method and related equipment for a magnetic drive conveyor system proposed in this application include a processing conveyor line and a mover. The mover runs on the processing conveyor line, which is configured with multiple processing steps. The method includes: First, acquiring the conveyor line information, the process information of each processing step, the mover information, and the station information of each operating station. The first operating station is the conveying area between the first processing step and the starting point of the processing conveyor line, and the remaining operating stations are the conveying areas between every two adjacent processing steps in the processing conveyor line. Then, based on the difference between the mover arrival time and the process idle time under the process sequence parameters for each processing step, the process waiting time of the mover at each processing step is obtained. Based on the ratio of the station length to the station operating parameters for each operating station, the station running time of the mover at each operating station is obtained. The total station running time is then accumulated. The total cycle time function is obtained by calculating the waiting time of all processes. The minimum cycle time function of the mover is obtained based on the ratio of the total length of the conveyor line to the number of movers. The squared difference between the actual processing time and the average processing time of the mover in each processing process is obtained to obtain the squared processing difference. The squared processing difference is averaged based on the number of movers to obtain the variance function of the processing time of each processing process. The material receiving efficiency of the transfer section of the magnetic drive conveyor system is obtained, as well as the position deviation of the mover at each operating station. The mean of the system position deviation of the magnetic drive conveyor system is obtained based on the average of all position deviations. The system optimization function is obtained by weighted summing based on the total cycle time function, the minimum cycle time function, the variance function of processing time, the material receiving efficiency of the transfer section, and the mean of the system position deviation. The system optimization function is obtained based on the number of movers, the process sequence parameter, the station operation parameter, and the optimized system optimization function. The system processing optimization model is obtained.Next, based on the mover quantity parameters, process sequence parameters, and station operation parameters, multiple initial optimization parameters are generated. These initial optimization parameters include continuous and discrete initial optimization parameters. Based on the current servo cycle's conveyor line information, process information, mover information, station information, and the system optimization function in the system processing optimization model, the fitness value corresponding to each initial optimization parameter is calculated. The initial optimization parameter with the smallest fitness value is selected as the current optimal parameter. Acceleration parameters and random parameters are then obtained. The difference between the current optimal parameter and the initial optimization parameter is multiplied by the acceleration parameters and random parameters to obtain the optimal parameter difference. The system obtains the continuous initial optimized parameters from the updated optimized parameters based on the difference between the optimal parameters and the accumulated value of the continuous initial optimized parameters. It also searches and updates the discrete initial optimized parameters from the initial optimized parameters to obtain the updated optimized parameters. When the fitness of the updated optimized parameters is less than the fitness of the current optimal parameters, the current optimal parameters are updated based on the updated optimized parameters, and the iteration continues until a preset number of iterations is reached. The current optimal parameters corresponding to the last iteration are then obtained to obtain the optimized number of movers, the optimized process sequence, and the optimized station operating parameters. Finally, the magnetic drive conveyor system is used for production control based on the optimized number of movers, the optimized process sequence, and the optimized station operating parameters.

[0177] This application embodiment acquires various information from the production line in the magnetic drive conveyor system in real time, and constructs a system processing optimization model based on this information. This model reflects the complex coupling relationship between the number of movers, the sequence of processes, and the operating speed. By solving the model, the optimal parameter combination under the current production state can be dynamically calculated. Based on this set of real-time optimized parameters, the system is controlled, allowing the production control strategy to proactively adapt to actual changes on the production line. This avoids resource waste such as mover congestion or idle workstations caused by fixed parameters, significantly improving the overall operating efficiency, flexibility, and resource utilization of the magnetic drive conveyor system. Furthermore, by accurately calculating the running time of each workstation and the waiting time of each process, By accumulating these data, a highly accurate total cycle time function with clear physical meaning can be constructed. This allows the model to clearly reveal how decision variables such as process sequence parameters and workstation operating parameters affect the total cycle time through the waiting and running phases, thus providing a more accurate and reliable mathematical basis for subsequent system optimization. Furthermore, by calculating the square of the processing difference and averaging it, a processing time variance function that accurately reflects the degree of processing time fluctuation is successfully constructed. This transforms the originally vague concept of "stability" in the production process into a quantifiable and optimizable key performance indicator, enabling subsequent system optimization to pursue... While achieving high efficiency, it also ensures the stability and predictability of the production process, effectively avoiding system bottlenecks and cycle time disruptions caused by drastic fluctuations in processing time. This ultimately contributes to a more robust and reliable production control. Furthermore, by incorporating the material receiving efficiency of the transfer section and the average system position deviation, system reliability and control accuracy are also taken into consideration. This makes the optimization model more comprehensive and closer to real-world industrial applications. Through weighted summation, the system is given great flexibility, allowing users to adjust the importance of various performance indicators according to different production needs (e.g., prioritizing speed or ensuring accuracy). Ultimately, this ensures that the resulting system optimization function is a highly comprehensive and customizable tool. The optimization objective guides the control system to find a combination of production parameters that achieves the best balance across all key performance aspects, thereby realizing true global optimization. Furthermore, by constructing a total cycle time function, a minimum cycle time function, and a processing time variance function, the system is comprehensively quantitatively described from three key dimensions: efficiency, cycle time, and stability. By integrating these functions into a single system optimization function and ultimately forming a system processing optimization model, a complex, multi-objective production management problem is successfully transformed into a clearly structured and solvable mathematical optimization problem. This provides a solid theoretical foundation for subsequent algorithmic searches for the globally optimal combination of production parameters, ensuring the scientific and systematic nature of optimization decisions.Furthermore, by generating initial optimization parameters containing both continuous and discrete variables, and employing a hybrid strategy of tracking and searching for updates, this method can efficiently explore a large and complex solution space, effectively avoiding the predicament of getting trapped in local optima. Through continued iteration and continuous updating of the current optimal parameters, this method can gradually approach the global optimum. Ultimately, within a limited computation time, it accurately solves for the optimal number of movers, the optimal sequence of processes, and the optimal station operating parameters that maximize the overall system performance, greatly improving the quality and reliability of the solution results.

[0178] This application also provides a production control device for a magnetic drive conveyor system, which can realize the above-described production control method for the magnetic drive conveyor system, as described above. Figure 11 The device 1100 includes:

[0179] The information acquisition module 1110 is used to acquire the conveyor line information of the processing conveyor line, the process information of each processing process, the mover information of the mover, and the station information of each running station. The first running station is the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining running stations are the conveying areas between every two adjacent processing processes in the processing conveyor line.

[0180] The optimization model generation module 1120 is used to generate a system processing optimization model based on the number parameters of the mover, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the running station.

[0181] The optimization parameter calculation module 1130 is used to solve the system processing optimization model based on conveyor line information, process information, mover information and workstation information to obtain the optimized number of movers, optimized process sequence and optimized workstation operating parameters.

[0182] The production control module 1140 is used to control the production of the magnetic drive conveyor system based on optimizing the number of movers, optimizing the process sequence, and optimizing the operating parameters of the workstation.

[0183] In some embodiments, the optimization model generation module 1120 is further configured to:

[0184] Based on the mover quantity parameter and process sequence parameter, the total cycle time function of the mover is obtained;

[0185] Based on the ratio of the total length of the conveyor line to the number of movers, the minimum cycle time function of the movers is obtained;

[0186] Based on the number of movers, the processing time variance function of the processing steps is obtained;

[0187] The system optimization function is obtained based on the total cycle time function, the minimum cycle time function, and the processing time variance function;

[0188] Based on the parameters of the number of movers, the sequence of operations, the operating parameters of the workstations, and the optimization function of the optimal system, a system processing optimization model is obtained.

[0189] In some embodiments, the optimization model generation module 1120 is further configured to:

[0190] Based on the difference between the arrival time of the mover and the idle time of the process under the process sequence parameters for each processing step, the process waiting time of the mover in each processing step is obtained;

[0191] Based on the ratio of the station length to the station operation parameters of each operating station, the station operation time of the mover at each operating station is obtained.

[0192] The total cycle time function is obtained by summing the running time of all workstations and the waiting time of all processes.

[0193] In some embodiments, the optimization model generation module 1120 is further configured to:

[0194] The squared difference between the actual processing time and the average processing time of the mover in each processing step is obtained to obtain the squared processing difference.

[0195] The average of the squared processing differences based on the number of movers is used to obtain the processing time variance function for each processing step.

[0196] In some embodiments, the optimization model generation module 1120 is further configured to:

[0197] The material receiving efficiency of the transfer section of the magnetic drive conveyor system is obtained, as well as the position deviation of the mover at each operating station. The average position deviation of the magnetic drive conveyor system is obtained based on the average of all position deviations.

[0198] The system optimization function is obtained by weighting and summing the total cycle time function, minimum cycle time function, processing time variance function, material receiving efficiency of the transfer section, and the mean of system position deviation.

[0199] In some embodiments, the optimization parameter calculation module 1130 is further configured to:

[0200] Based on the mover quantity parameter, process sequence parameter, and station operation parameter, multiple initial optimization parameters are generated, including continuous initial optimization parameters and discrete initial optimization parameters.

[0201] Based on the current servo cycle's conveyor line information, process information, mover information, workstation information, and the system optimization function in the system processing optimization model, the fitness value corresponding to each initial optimization parameter is calculated, and the initial optimization parameter with the smallest fitness value is selected from multiple initial optimization parameters as the current optimal parameter;

[0202] Based on the current optimal parameters, the continuous initial optimization parameters in the initial optimization parameters are tracked and updated, and the discrete initial optimization parameters in the initial optimization parameters are searched and updated to obtain the updated optimization parameters;

[0203] When the fitness of the updated optimization parameters is less than the fitness of the current optimal parameters, the current optimal parameters are updated based on the updated optimization parameters, and the iteration continues until the preset number of iterations is reached. The current optimal parameters corresponding to the last iteration are then obtained to obtain the number of optimized movers, the optimized process sequence, and the optimized station operation parameters.

[0204] In some embodiments, the optimization parameter calculation module 1130 is further configured to:

[0205] Obtain acceleration parameters and random parameters;

[0206] Based on the difference between the current optimal parameters and the initial optimized parameters, multiply by the acceleration parameter and the random parameter to obtain the optimal parameter difference;

[0207] Based on the difference between the optimal parameters and the accumulated value of the continuous initial optimization parameters, the continuous initial optimization parameters in the updated optimization parameters are obtained.

[0208] In some embodiments, the production control module 1140 is further configured to:

[0209] Based on the current servo cycle, optimize the number of movers, optimize the process sequence, and optimize the station operation parameters to perform production control on the magnetic drive conveyor system in the current servo cycle.

[0210] Based on the conveyor line information, process information, mover information, and workstation information of the next servo cycle, the optimized number of movers, optimized process sequence, and optimized workstation operating parameters for the next servo cycle are calculated. Based on the optimized number of movers, optimized process sequence, and optimized workstation operating parameters for the next servo cycle, the magnetic drive conveyor system is subjected to production control in the next servo cycle.

[0211] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, the specific implementation of the production control device of the magnetic drive conveyor system is basically the same as the specific implementation of the production control method of the magnetic drive conveyor system described above, and will not be repeated here.

[0212] In this embodiment, the production control device of the magnetic drive conveyor system acquires various information from the production line in real time and constructs a system processing optimization model based on this information, which reflects the complex coupling relationship between the number of movers, the sequence of processes, and the operating speed. By solving the model, the optimal parameter combination under the current production state can be dynamically calculated. Based on this set of real-time optimized parameters, the system is controlled, enabling the production control strategy to proactively adapt to actual changes on the production line. This avoids resource waste such as mover congestion or idle workstations caused by fixed parameters, significantly improving the overall operating efficiency, flexibility, and resource utilization of the magnetic drive conveyor system. Furthermore, by accurately calculating the operation of each workstation… By calculating and summing the time and waiting time for each process step, a highly accurate and physically meaningful total cycle time function can be constructed. This allows the model to clearly reveal how decision variables such as process sequence parameters and workstation operating parameters affect the total cycle time through the waiting and running phases, thus providing a more accurate and reliable mathematical basis for subsequent system optimization. Furthermore, by calculating the square of the processing difference and averaging it, a processing time variance function that accurately reflects the degree of processing time fluctuation is successfully constructed. This transforms the originally vague concept of "stability" in the production process into a quantifiable and optimizable key performance indicator, thereby enabling subsequent system optimization... System optimization can pursue high efficiency while also considering the stability and predictability of the production process, effectively avoiding system bottlenecks and cycle time disruptions caused by drastic fluctuations in processing time. This ultimately contributes to a more robust and reliable production control. Furthermore, by introducing the material receiving efficiency of the transfer section and the average system position deviation, system reliability and control accuracy are also taken into consideration, making the optimization model more comprehensive and closer to real-world industrial applications. Through weighted summation, the system is given great flexibility, allowing users to adjust the importance of various performance indicators according to different production needs (e.g., prioritizing speed or accuracy). Ultimately, this ensures that the obtained system optimization function can serve as a highly comprehensive and... Customizable optimization objectives guide the control system to find a combination of production parameters that achieves the best balance across all key performance aspects, thereby realizing true global optimization. Furthermore, by constructing a total cycle time function, a minimum cycle time function, and a processing time variance function, the system is comprehensively quantitatively described from three key dimensions: efficiency, cycle time, and stability. By integrating these functions into a single system optimization function and ultimately forming a system processing optimization model, a complex, multi-objective production management problem is successfully transformed into a clearly structured and solvable mathematical optimization problem. This provides a solid theoretical foundation for subsequent algorithmic searches for the globally optimal combination of production parameters, ensuring the scientific and systematic nature of optimization decisions.Furthermore, by generating initial optimization parameters containing both continuous and discrete variables, and employing a hybrid strategy of tracking and searching for updates, this method can efficiently explore a large and complex solution space, effectively avoiding the predicament of getting trapped in local optima. Through continued iteration and continuous updating of the current optimal parameters, this method can gradually approach the global optimum. Ultimately, within a limited computation time, it accurately solves for the optimal number of movers, the optimal sequence of processes, and the optimal station operating parameters that maximize the overall system performance, greatly improving the quality and reliability of the solution results.

[0213] This application also provides an electronic device, including:

[0214] At least one memory;

[0215] At least one processor;

[0216] At least one program;

[0217] The program is stored in a memory, and the processor executes the at least one program to implement the production control method for the magnetic drive conveyor system described above. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0218] Please see Figure 12 , Figure 12 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0219] The processor 1201 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0220] The memory 1202 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1202 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1202 and is called and executed by the processor 1201 to execute the production control method of the magnetic drive conveyor system of the embodiments of this application.

[0221] The input / output interface 1203 is used to implement information input and output;

[0222] The communication interface 1204 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0223] Bus 1205 transmits information between various components of the device (e.g., processor 1201, memory 1202, input / output interface 1203, and communication interface 1204);

[0224] The processor 1201, memory 1202, input / output interface 1203 and communication interface 1204 are connected to each other within the device via bus 1205.

[0225] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the production control method of the magnetic drive conveyor system described above.

[0226] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0227] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0228] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0229] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0230] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0231] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0232] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0233] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0234] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0235] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0236] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or 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 multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0237] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A production control method for a magnetic drive conveyor system, characterized in that, The magnetic drive conveyor system includes a processing conveyor line and a mover, the mover running on the processing conveyor line, the processing conveyor line being equipped with multiple processing steps, and the method including: The conveyor information of the processing conveyor line, the process information of each processing step, the mover information of the mover, and the station information of each running station are obtained. The first running station is the conveying area between the first processing step and the starting point of the processing conveyor line, and the remaining running stations are the conveying areas between every two adjacent processing steps in the processing conveyor line. A system processing optimization model is generated based on the number parameters of the mover, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the operating station. The system processing optimization model is solved based on the conveyor line information, the process information, the mover information, and the workstation information to obtain the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters. The production control of the magnetic drive conveyor system is based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters. The conveyor line information includes the total length of the conveyor line. The system processing optimization model, generated based on the number of movers, the process sequence parameters of the processing steps, and the station operation parameters of the movers at the operating station, includes: Based on the number of movers and the process sequence parameters, the total cycle time function of the movers is obtained; Based on the ratio of the total length of the conveyor line to the number of movers, the minimum cycle time function of the movers is obtained; Based on the number of movers, the processing time variance function of the processing step is obtained; Based on the total cycle time function, the minimum cycle time function, and the processing time variance function, the system optimization function is obtained; Based on the number of moving parts, the process sequence parameters, the station operation parameters, and the optimization function of the system, the system processing optimization model is obtained; The step of obtaining the total cycle time function of the mover based on the mover quantity parameter and the process sequence parameter includes: Based on the difference between the arrival time of the mover and the idle time of the process under the process sequence parameters for each of the processing steps, the process waiting time of the mover in each of the processing steps is obtained; Based on the ratio of the station length to the station operation parameters of each operating station, the station operation time of the mover at each operating station is obtained. The total cycle time function is obtained by summing the running time of all the workstations and the waiting time of all processes. The step of obtaining the processing time variance function of the processing step based on the number of movers includes: The square of the difference between the actual processing time and the average processing time of the mover in each processing step is obtained to obtain the squared processing difference. The average of the squared processing difference based on the number of movers parameter is used to obtain the processing time variance function for each processing step.

2. The production control method for the magnetic drive conveyor system according to claim 1, characterized in that, The system optimization function is obtained based on the total cycle time function, the minimum cycle time function, and the processing time variance function, including: The material receiving efficiency of the transfer section of the magnetic drive conveyor system is obtained, as is the position deviation of the mover at each operating station. The average position deviation of the magnetic drive conveyor system is obtained based on the average of all the position deviations. The material receiving efficiency of the transfer section is the ratio of the number of successful material receiving to the total number of material receiving. The system optimization function is obtained by weighting and summing the total cycle time function, the minimum cycle time function, the processing time variance function, the material receiving efficiency of the transfer section, and the mean of the system position deviation.

3. The production control method for the magnetic drive conveyor system according to claim 1, characterized in that, The process optimization model of the system is solved based on the conveyor line information, the process information, the mover information, and the workstation information to obtain the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters, including: Based on the number of movers, the process sequence parameters, and the station operation parameters, multiple initial optimization parameters are generated, including continuous initial optimization parameters and discrete initial optimization parameters. Based on the current servo cycle information of the conveyor line, the process information, the mover information, the workstation information, and the system optimization function in the system processing optimization model, the fitness value corresponding to each initial optimization parameter is calculated, and the initial optimization parameter with the smallest fitness value is selected from multiple initial optimization parameters as the current optimal parameter. Based on the current optimal parameters, the continuous initial optimization parameters in the initial optimization parameters are tracked and updated, and the discrete initial optimization parameters in the initial optimization parameters are searched and updated to obtain the updated optimization parameters; When the fitness of the updated optimization parameters is less than the fitness of the current optimal parameters, the current optimal parameters are updated based on the updated optimization parameters, and the iteration continues until a preset number of iterations is reached. The current optimal parameters corresponding to the last iteration are then obtained to obtain the number of optimized actuators, the sequence of optimized processes, and the operating parameters of the optimized workstation.

4. The production control method for the magnetic drive conveyor system according to claim 3, characterized in that, The step of tracking and updating the continuous initial optimization parameters in the initial optimization parameters based on the current optimal parameters includes: Obtain acceleration parameters and random parameters; Based on the difference between the current optimal parameter and the initial optimized parameter, multiply it by the acceleration parameter and the random parameter to obtain the optimal parameter difference; Based on the difference between the optimal parameters and the accumulated value of the continuous initial optimization parameters, the continuous initial optimization parameters in the updated optimization parameters are obtained.

5. The production control method for the magnetic drive conveyor system according to claim 3, characterized in that, The production control of the magnetic drive conveyor system based on the optimized number of movers, the optimized process sequence, and the optimized station operating parameters includes: Based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters corresponding to the current servo cycle, the magnetic drive conveyor system is subjected to production control in the current servo cycle. Based on the conveyor line information, process information, mover information, and workstation information of the next servo cycle, the optimized mover quantity, optimized process sequence, and optimized workstation operating parameters for the next servo cycle are calculated. Based on the optimized mover quantity, optimized process sequence, and optimized workstation operating parameters of the next servo cycle, the magnetic drive conveyor system is subjected to production control in the next servo cycle.

6. A production control device for a magnetic drive conveyor system, characterized in that, The magnetic drive conveyor system includes a processing conveyor line and a mover. The mover runs on the processing conveyor line, and multiple processing steps are arranged on the processing conveyor line. The device includes: The information acquisition module is used to acquire the conveyor line information of the processing conveyor line, the process information of each processing process, the mover information of the mover, and the station information of each running station. The first running station is the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining running stations are the conveying areas between every two adjacent processing processes in the processing conveyor line. The optimization model generation module is used to generate a system processing optimization model based on the number parameters of the mover, the process sequence parameters of the processing steps, and the station operation parameters of the mover at the operating station. The optimization parameter calculation module is used to solve the system processing optimization model based on the conveyor line information, the process information, the mover information and the workstation information to obtain the optimized mover quantity, optimized process sequence and optimized workstation operating parameters; The production control module is used to perform production control on the magnetic drive conveyor system based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters. The conveyor line information includes the total length of the conveyor line. The system processing optimization model, generated based on the number of movers, the process sequence parameters of the processing steps, and the station operation parameters of the movers at the operating station, includes: Based on the number of movers and the process sequence parameters, the total cycle time function of the movers is obtained; Based on the ratio of the total length of the conveyor line to the number of movers, the minimum cycle time function of the movers is obtained; Based on the number of movers, the processing time variance function of the processing step is obtained; Based on the total cycle time function, the minimum cycle time function, and the processing time variance function, the system optimization function is obtained; Based on the number of moving parts, the process sequence parameters, the station operation parameters, and the optimization function of the system, the system processing optimization model is obtained; The step of obtaining the total cycle time function of the mover based on the mover quantity parameter and the process sequence parameter includes: Based on the difference between the arrival time of the mover and the idle time of the process under the process sequence parameters for each of the processing steps, the process waiting time of the mover in each of the processing steps is obtained; Based on the ratio of the station length to the station operation parameters of each operating station, the station operation time of the mover at each operating station is obtained. The total cycle time function is obtained by summing the running time of all the workstations and the waiting time of all processes. The step of obtaining the processing time variance function of the processing step based on the number of movers includes: The square of the difference between the actual processing time and the average processing time of the mover in each processing step is obtained to obtain the squared processing difference. The average of the squared processing difference based on the number of movers parameter is used to obtain the processing time variance function for each processing step.

7. An electronic device, characterized in that, The system includes a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the production control method of the magnetic drive conveyor system according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the production control method for the magnetic drive conveyor system as described in any one of claims 1 to 5.

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