Production control method of magnetic drive conveying system and related equipment

Abstract

According to the production control method of the magnetic drive conveying system and the related equipment, the magnetic drive conveying system comprises the machining conveying line and the rotor, the rotor runs on the machining conveying line, and the machining conveying line is provided with the multiple machining procedures; the method comprises the steps that conveying line information of a machining conveying line, process information of each machining process, rotor information of a rotor and station information of each operation station are obtained; generating a system processing optimization model based on the rotor number parameter of the rotor, the process sequence parameter of the processing process and the station operation parameter of the rotor at the operation station; and solving the system processing optimization model based on the conveying line information, the process information, the rotor information and the station information to obtain an optimized rotor number, an optimized process sequence and optimized station operation parameters, so as to perform production control on the magnetic drive conveying system, and improve the overall operation efficiency, flexibility and resource utilization rate of the magnetic drive conveying system.

Description

Technical Field

[0001] The present application relates to the field of control technology, and in particular to a production control method and related equipment for a magnetic drive conveying system. Background Art

[0002] As a highly efficient and flexible automated material handling device, magnetic conveying systems are widely used in modern manufacturing, for example, in conveying logistics lines for assembly and packaging of goods, and for the surface-mount mounting (SMT) of precision electronic components. To further improve the efficiency of processing within these systems, it is often necessary to pre-plan a series of appropriate production process parameters, such as the number of movers in operation and their operating speed.

[0003] In related technologies, when planning production parameters for a magnetic drive conveying system, multiple production parameter plans of corresponding sizes are usually planned in advance based on the system size of the magnetic drive conveying system. When each magnetic drive conveying system performs real-time production, the production parameter plan corresponding to the system size is directly used to control production operations. However, due to the complex coupling relationship between different real-time data and production parameters in actual production operations, this pre-planned fixed plan is not flexible enough, making it easy for improper resource allocation such as mover congestion or idle workstations to occur during the production process, resulting in low production efficiency. Summary of the Invention

[0004] The embodiments of the present application provide a production control method and related equipment for a magnetic drive conveying system, which can improve the production and operation efficiency of the magnetic drive conveying system.

[0005] To achieve the above-mentioned object, a first aspect of an embodiment of the present application provides a production control method for a magnetic drive conveying system, wherein the magnetic drive conveying system includes a processing conveying line and a mover, wherein the mover runs on the processing conveying line, and a plurality of processing steps are arranged on the processing conveying line. The method includes:

[0006] 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 operating station, wherein the first operating station is the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining operating stations are the conveying areas between every two adjacent processing processes in the processing conveyor line;

[0007] generating a system processing optimization model based on a mover quantity parameter of the mover, a process sequence parameter of the processing process, and a station operation parameter of the mover at the operation station;

[0008] 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 operation parameters;

[0009] Production control of the magnetic drive conveying system is performed based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters.

[0010] In some embodiments, the conveyor line information includes the total length of the conveyor line, and the system processing optimization model is generated based on the mover quantity parameter of the mover, the process sequence parameter of the processing process, and the station operation parameter of the mover at the operating station, including:

[0011] Based on the mover quantity parameter and the process sequence parameter, a total cycle time function of the mover is obtained;

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

[0013] Based on the mover quantity parameter, a processing time variance function of the processing step is obtained;

[0014] Obtaining a system optimization function based on the total cycle time function, the minimum beat function, and the processing time variance function;

[0015] The system processing optimization model is obtained based on the mover quantity parameter, the process sequence parameter, the workstation operation parameter and the optimization function of the system.

[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] Obtaining the process waiting time of the mover in each processing step based on the difference between the mover arrival time and the process idle time of each processing step under the process sequence parameters;

[0018] Obtaining a station running time of the mover at each of the operating stations based on a ratio of the station length of each of the operating stations to the station operating parameter;

[0019] The total cycle time function is obtained by accumulating 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 mover quantity parameter includes:

[0021] Obtaining the square of the difference between the actual processing time and the average processing time of the mover in each processing step to obtain the square of the processing difference;

[0022] The squares of the machining differences are averaged based on the mover quantity parameter to obtain the machining time variance function of each machining process.

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

[0024] Obtaining the material receiving efficiency of the ferry section of the magnetic drive conveying system, and obtaining the position deviation of the mover at each operating position, and obtaining the system position deviation mean of the magnetic drive conveying system based on the average value of all the position deviations;

[0025] The system optimization function is obtained by weighted sum processing based on the total cycle time function, the minimum beat function, the processing time variance function, the ferry section material receiving efficiency and the system position deviation mean.

[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 operation parameters includes:

[0027] Based on the mover quantity parameter, the process sequence parameter, and the workstation operation parameter, a plurality of initial optimization parameters are generated, wherein the initial optimization parameters include continuous initial optimization parameters and discrete initial optimization parameters;

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

[0029] Tracking and updating the continuous initial optimization parameters in the initial optimization parameters based on the current optimal parameters, and searching and updating the discrete initial optimization parameters in the initial optimization parameters to obtain updated optimization parameters;

[0030] When the fitness of the updated optimization parameter is less than the fitness of the current optimal parameter, the current optimal parameter is updated based on the updated optimization parameter, and iteration is continued until the preset number of iterations is reached, and the current optimal parameter corresponding to the last iteration is obtained to obtain the optimized number of movers, the optimized process sequence and the optimized workstation operation parameters.

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

[0032] Get acceleration parameters and random parameters;

[0033] Based on the difference between the current optimal parameter and the initial optimized parameter, multiplying it by the acceleration parameter and the random parameter, an optimal parameter difference is obtained;

[0034] The continuous initial optimization parameters in the updated optimization parameters are obtained based on the optimal parameter difference and the accumulated value of the continuous initial optimization parameters.

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

[0036] Performing production control on the magnetic drive conveying system in the current servo cycle based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters corresponding to the current servo cycle;

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

[0038] To achieve the above-mentioned object, a second aspect of an embodiment of the present application provides a production control device for a magnetic drive conveying system, wherein the magnetic drive conveying system includes a processing conveying line and a mover, wherein the mover runs on the processing conveying line, and a plurality of processing steps are arranged on the processing conveying line. The device includes:

[0039] An information acquisition module is used to obtain 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 operating station, the first operating station being the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining operating stations being the conveying areas between every two adjacent processing processes in the processing conveyor line;

[0040] An optimization model generation module, configured to generate a system processing optimization model based on a mover quantity parameter of the mover, a process sequence parameter of the processing process, and a station operation parameter of the mover at the operation station;

[0041] An 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 number, the optimized process sequence and the optimized workstation operation parameters;

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

[0043] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the production control method of the magnetic drive conveying system as described in the first aspect.

[0044] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, it implements the production control method of the magnetic drive conveying system described in the first aspect above.

[0045] The embodiment of the present application proposes a production control method and related equipment for a magnetic drive conveying system, wherein the magnetic drive conveying system includes a processing conveying line and a mover, the mover runs on the processing conveying line, and multiple processing processes are arranged on the processing conveying line, and the method includes: first, obtaining the conveying line information of the processing conveying line, the process information of each processing process, the mover information of the mover, and the station information of each operating station, the first operating station being the conveying area between the first processing process and the starting point of the processing conveying line, and the remaining operating stations being the conveying areas between every two adjacent processing processes in the processing conveying line; then, a system processing optimization model is generated based on the mover quantity parameter of the mover, the process sequence parameter of the processing process, and the station operating parameters of the mover at the operating station; next, the system processing optimization model is solved based on the conveying line information, the process information, the mover information, and the station information to obtain the optimized mover quantity, the optimized process sequence, and the optimized station operating parameters; finally, production control of the magnetic drive conveying system is performed based on the optimized mover quantity, the optimized process sequence, and the optimized station operating parameters. The embodiment of the present application obtains various information of the production line in the magnetic drive conveying system in real time, and constructs a system processing optimization model based on this information that can reflect the complex coupling relationship between the number of movers, process sequence and operating speed. By solving the model, the optimal parameter combination under the current production status can be dynamically calculated, and the system can be controlled based on this set of real-time optimized parameters, so that the production control strategy can actively adapt to the actual changes on the production line, avoiding resource waste such as mover congestion or idle workstations due to parameter solidification, and significantly improving the overall operating efficiency, flexibility and resource utilization of the magnetic drive conveying system.

[0046] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural schematic diagram of a magnetic drive conveying system provided in one embodiment of the present application.

[0048] Figure 2 This is a flow chart of a production control method for a magnetic drive conveying system provided in another embodiment of the present application.

[0049] Figure 3 yes Figure 2 Flowchart of step 202 in FIG.

[0050] Figure 4 yes Figure 3 Flowchart of step 301 in FIG.

[0051] Figure 5 yes Figure 3 Flowchart of step 303 in FIG.

[0052] Figure 6 yes Figure 3 Flowchart of step 304 in FIG.

[0053] Figure 7 yes Figure 2 Flowchart of step 203 in FIG.

[0054] Figure 8 yes Figure 7 Flowchart of step 703 in FIG.

[0055] Figure 9 This is a schematic diagram of a double-layer framework algorithm flow for solving a system processing optimization model provided by another embodiment of the present application.

[0056] Figure 10 yes Figure 2 Flowchart of step 204 in FIG.

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

[0058] Figure 12 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0060] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can 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 those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0062] As a highly efficient and flexible automated material handling device, magnetic conveying systems are widely used in modern manufacturing, for example, in conveying logistics lines for assembly and packaging of goods, and for the surface-mount mounting (SMT) of precision electronic components. To further improve the efficiency of processing within these systems, it is often necessary to pre-plan a series of appropriate production process parameters, such as the number of movers in operation and their operating speed.

[0063] In related technologies, when planning production parameters for a magnetic drive conveying system, multiple production parameter plans of corresponding sizes are usually planned in advance based on the system size of the magnetic drive conveying system. When each magnetic drive conveying system performs real-time production, the production parameter plan corresponding to the system size is directly used to control production operations. However, due to the complex coupling relationship between different real-time data and production parameters in actual production operations, this pre-planned fixed plan is not flexible enough, making it easy for improper resource allocation such as mover congestion or idle workstations to occur during the production process, resulting in low production efficiency.

[0064] In order to improve the production and operation efficiency in the magnetic drive conveying system, the embodiment of the present application obtains various information of the production line in the magnetic drive conveying system in real time, and constructs a system processing optimization model based on this information that can reflect the complex coupling relationship between the number of movers, process sequence and 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, so that the production control strategy can actively adapt to the actual changes on the production line, avoiding resource waste such as mover congestion or idle workstations due to parameter solidification, and significantly improving the overall operation efficiency, flexibility and resource utilization of the magnetic drive conveying system.

[0065] In order to better illustrate the mover operation control method of the synchronous transition track provided by the embodiment of the present application, this embodiment first describes the magnetic levitation conveyor track to which the mover operation control method is applied. Figure 1 As shown in FIG, it is a structural diagram of a magnetic drive conveying system provided in an embodiment of the present application. Figure 1 As shown, the magnetic drive conveying system includes a processing conveying line and a mover, and multiple movers run on the processing conveying line. In order to realize the processing of the workpiece, multiple processing steps are set on the processing conveying line, such as Figure 1 The two processing steps shown in FIG. 2 (generally, there are many processing steps in a magnetic drive conveying system). At least one movable processing device (such as a processing robot arm, etc.) is provided near each processing step 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 an operating station, and the first operating station is the conveying area between the starting point of the processing conveyor line and the first processing step, and the remaining operating stations refer to the conveying areas between every two adjacent processing steps in the processing conveyor line.

[0067] Based on the above magnetic drive conveying system, the production control method of the magnetic drive conveying system in the embodiment of the present application will be described in detail below. Figure 2 , which is an optional flow chart of the production control method of the magnetic drive conveying system provided in an embodiment of the present application, Figure 2 The method may include but is not limited to steps 201 to 204. It is also understood that this embodiment is for Figure 2 The order of steps 201 to 204 is not specifically limited, and the order of the steps can be adjusted or some steps can be reduced or added according to actual needs. The production control method of the magnetic drive conveying system provided in the embodiment of the present application can be applied to a control system connected to the magnetic drive conveying track, such as an intelligent terminal, a server, a computer, etc.

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

[0069] Step 201 is described in detail below.

[0070] In some embodiments, multiple movers are running in real time as follows Figure 1 When the processing conveyor line shown in , the control system needs to collect basic data of the magnetic drive conveyor system in each servo cycle. These data form the basis for subsequent optimization decisions. Among them, the conveyor line information of the processing conveyor line usually refers to the static properties at the physical level, such as the total conveyor line length L of the magnetic drive conveyor line. total , track topology, number of processing steps M, number of operating stations K, average speed V of all movers during operation, etc.; the process information of each processing step includes dynamic or semi-dynamic data such as processing time and equipment reliability of each processing station; the mover information refers to the real-time status of the mover as a carrier tool in the system, such as its current position, speed and identification information, as well as the average speed of all movers during operation As for the station information of each operating station, it should be noted that the operating station does not refer to the station where the processing operation is performed, but refers to the conveying area between different processing steps or from the starting point to the first processing step. Therefore, its information mainly refers to the length of the conveying area, that is, the station length L of each operating station. k Etc. This comprehensive information together constitutes the data representing the current status of the entire production system.

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

[0072] Step 202 is described in detail below.

[0073] In some embodiments, after obtaining the above basic information, a mathematical model that can describe the internal laws of the production process is further constructed. Specifically, the three key controllable variables in the production system, namely, the mover number parameter N (that is, the number of movers put into the magnetic drive conveying system at the same time, which determines the material concurrent processing capacity in the production line), the process sequence parameter S = {s1, s2, ..., s M} (i.e., the processing order 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] means that the task goes through step 1--> step 2--> step 3 in sequence) and the position operation parameters V = {v1, v2, ..., v K The three parameters are expressed mathematically (i.e., the corresponding operating speed of the mover at each conveying station, which determines the operating speed of the mover in different conveying areas). This allows for the establishment of a coupling relationship between these three parameters based on abstract parameters, ultimately leading to a system processing optimization model that quantifies the effect of any combination of these three parameters on the overall performance of the production system. The following describes how this system processing optimization model is constructed.

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

[0075] Step 301: Based on the mover quantity parameter and the process sequence parameter, obtain the total cycle time function of the mover.

[0076] Step 301 is described in detail below.

[0077] In some embodiments, in order to construct a system processing optimization model for optimizing the production efficiency in the magnetic drive conveying system, first, based on the number of movers parameter N and the process sequence parameter S = {s1, s2, ..., s M}, and the total cycle time function of the mover is obtained. Among them, the process sequence parameter S={s1,s2,...,s M The parameter N directly determines the path of the mover and the required processing and waiting stages, forming the basis for calculating the basic duration. The parameter N, which determines the number of movers, influences the degree of congestion within the line. An increase in the number of movers can lead to increased waiting times at certain workstations. Therefore, the total cycle time function is not a fixed value. Instead, these two parameters are used as variables to mathematically represent a key indicator of the system's overall material flow efficiency, as described below.

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

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

[0080] Step 402: Based on the ratio of the station length of each operating station to the station operating parameter, obtain the station operating 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 a 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, firstly, based on each processing step (such as step s i ) Arrival time of the mover under the process sequence parameters ArrivalTime(s) i ) and process idle time AvailableTime(s i ) to obtain the waiting time Wait (s) of 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 when the mover has arrived at the designated position of a certain processing step, but the process is processing the previous mover or is not yet ready. The waiting time is calculated by calculating the exact time when the mover arrives at the process (i.e. the mover arrival time ArrivalTime (s i )) and the time when the process can start the next operation (i.e. the process idle time AvailableTime (s i )), and this process is closely related to the process sequence parameters, because different process sequences will directly change the arrival sequence of the movers and the idle timing of the process.

[0086] And, the arrival time of the mover is ArrivalTime(s i ) is obtained by the following formula (2).

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

[0088] Among them, process s j Process s i The immediate preceding station, FinishTime(s j ) is the mover in the previous station s j The task completion time, T move (j→i) is from workstation s j Move to s i The transportation time (and the path length L j→i and speed V k Related)

[0089] In addition, the process idle time AvailableTime(s i ) is obtained by the following formula (3).

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

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

[0092] Furthermore, based on the station length L of each operating station k k And station operation parameters V k The ratio of the moving position to the moving position is obtained, and the moving position running time L is obtained. k / V k Then, the station running time of all running stations and the waiting time of all processes are accumulated to finally obtain the total cycle time function T cycle As shown in the following formula (4).

[0093]

[0094] Through the above steps 401 to 403, by accurately calculating the operating time of each workstation and the waiting time of each process and accumulating them, a highly accurate total cycle time function with clear physical meaning can be constructed, so that the model can clearly reveal how decision variables such as process sequence parameters and workstation operating parameters affect the total cycle time by affecting the two links of waiting and running, thereby 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, a minimum beat function of the movers is obtained.

[0096] Step 303: Based on the parameter of the number of movers, a processing time variance function of the processing step is obtained.

[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 number parameter N and the average speed V of the mover is used to obtain the minimum beat function T of the mover. beat , as shown in the following formula (5).

[0099]

[0100] This minimum beat function represents how long it takes for the production line to produce a finished product under ideal circumstances. This function links the total physical length of the conveyor line with the adjustable number of movers. As a result, on a line with a fixed total length, the more movers are used, the smaller the average spacing between the movers will theoretically be, thus achieving a shorter production interval.

[0101] Then, based on the number parameter N of the movers, the processing step s is 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 collects and analyzes actual processing time data when operating under specific actuator quantity parameters and calculates its degree of dispersion. Generally speaking, a smaller variance value indicates a more stable and predictable production process, while a larger variance indicates a potential bottleneck or unstable process in the production line. The following further describes how to generate this processing time variance function.

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

[0103] Step 501: Obtain the square of the difference between the actual processing time and the average processing time of the mover in each processing step to obtain the square of the processing difference.

[0104] Step 502: performing an average processing on the square of the processing difference based on the parameter of the number of movers to obtain the processing time variance function of each processing step.

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

[0106] In some embodiments, to construct a suitable time variance function First, we obtain the mover in each processing step s i The actual processing time t process,i,k Average processing time The square of the difference between The actual processing time here is t process,i,k It refers to the actual time consumed by a mover from the beginning to the end of the i-th processing step for the kth time, and the average processing time is the expected or theoretical value of process i under a large amount of production data. Calculating the square of the difference between the two is intended to quantify the degree to which a single processing event deviates from the normal level. The size of the square of the processing difference intuitively reflects the severity of the fluctuation of the processing.

[0107] Furthermore, the obtained discrete data representing a single fluctuation (i.e., the square of the processing difference) is statistically integrated to form a macro-evaluation of the overall stability of the process. That is, the square of the processing difference is averaged based on the number of movers parameter to obtain an indicator that can represent the overall fluctuation level of the process under a specific number of movers parameter. 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 correlation between the stability of processing time and the number of movers running in the system, providing a key stability evaluation basis for subsequent system optimization.

[0110] Through the above steps 501 to 502, by calculating the square of the processing difference and performing averaging processing, a processing time variance function that can accurately reflect the degree of processing time fluctuation is successfully constructed, so that the originally vague concept of "stability" of the production process is transformed into a key performance indicator that can be quantified and optimized. Therefore, subsequent system optimization can not only pursue high efficiency but also take into account the stability and predictability of the production process, thereby effectively avoiding system bottlenecks and beat disorders caused by drastic fluctuations in processing time, and ultimately helping to achieve more robust and reliable production control.

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

[0112] Step 304 is described in detail below.

[0113] In some embodiments, after obtaining the total cycle time function (4), the minimum beat function (5), and the processing time variance function (6), these functions are further integrated to form a unified optimization goal. 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 required to balance these potentially conflicting goals, as described below.

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

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

[0116] Step 602: Perform weighted sum processing based on the total cycle time function, the minimum beat function, the processing time variance function, the ferry section material receiving efficiency and the system position deviation mean to obtain a system optimization function.

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

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

[0119]

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

[0121] At the same time, the position deviation of the mover at each operating position |x is also obtained actual,k -x target,k |, the position deviation refers to the actual stop position x actual,k and the target position x target,k The error between them is the core parameter for measuring the control accuracy of the system. Finally, the mean value of the system position deviation of the magnetic drive conveying system is obtained by taking the average value of the position deviation of the mover at all operating positions as shown in the following formula (8).

[0122]

[0123] The precision performances scattered across various workstations are aggregated into a comprehensive indicator that can represent the overall positioning accuracy of the system.

[0124] Finally, all previously defined, independent performance indicators are integrated into a unified, optimizable final goal, which is weighted and processed based on the total cycle time function, minimum beat function, processing time variance function, ferry section material receiving efficiency, and system position deviation mean, to obtain 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 converts 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 them up. 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 the above steps 601 to 602, by introducing the ferry section material receiving efficiency and the mean value of the system position deviation, the reliability and control accuracy of the system are also taken into consideration, making the optimization model more comprehensive and close to the real industrial application scenario. Through weighted sum processing, the system is given great flexibility, allowing users to adjust the importance of various performance indicators according to different production needs (for example, prioritizing speed or prioritizing accuracy), and ultimately ensuring that the obtained system optimization function can serve as a highly comprehensive and customizable optimization target, guiding the control system to find a production parameter combination that achieves the best balance in all key performance aspects, thereby achieving true global optimization.

[0128] Step 305: Based on the mover quantity parameters, process sequence parameters, station operation parameters and the optimization function of the optimization system, a system processing optimization model is obtained.

[0129] Step 305 is 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 are further converted into {s1, s2, ..., s M} and station operation parameters V={v1,v2,...,v K} as optimization variables, minimizing the system optimization function (9) as the objective function, and station operation parameters V = {v1, v2, ..., v K}'s maximum operating speed constraint (i.e. v k ≤v max , v max is the maximum speed), the maximum deviation constraint of the system position deviation mean (i.e., δ position ≤δ max , δ max is the maximum position deviation), the minimum efficiency constraint of the ferry section material connection efficiency (i.e. η transfer ≥η min , η min Taking the minimum material connection efficiency as the constraint condition, the system processing optimization model is constructed as shown in the following formula (10).

[0131]

[0132] Through the above steps 301 to 305, by constructing the total cycle time function, the minimum beat function and the processing time variance function, a comprehensive quantitative description of the system is carried out from the three key dimensions of efficiency, beat 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 clearly structured and solvable mathematical optimization problem, which provides a solid theoretical basis for the subsequent use of algorithms to find the globally optimal production parameter combination, ensuring the scientific and systematic nature of the optimization decision.

[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 mover quantity, optimized process sequence and optimized workstation operation parameters.

[0134] Step 203 is 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 value, thereby obtaining a set of specific and executable optimization results, namely, optimizing the number of movers, optimizing the process sequence, and optimizing the workstation operation parameters, which together constitute the optimal production control strategy under the current production status. The following will further describe how to solve the system processing optimization model (10).

[0136] In some embodiments, a solution idea based on a hybrid strategy optimization algorithm (HSO) is adopted. Under this solution framework, a two-layer framework of MPC (model predictive control) + improved CSO (chicken swarm optimization algorithm) is adopted.

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

[0138] In the memory-improved CSO solution, the cat swarm particle algorithm is used to solve 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 7, based on the conveyor line information, process information, mover information and workstation information, the system processing optimization model is solved to obtain the optimized mover number, optimized process sequence and optimized workstation operation parameters, including the following steps 701 to 704.

[0140] Step 701: Generate a plurality of initial optimization parameters based on the mover quantity parameters, process sequence parameters, and workstation operation parameters.

[0141] Step 702: Based on the conveyor line information, process information, actuator information, workstation information of the current servo cycle 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, 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 updated optimization parameters.

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

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

[0145] Next, the multiple solutions initially generated are evaluated and screened for the first time. This step is based on the conveyor line information, process information, actuator information, workstation information of the current servo cycle and the system optimization function (9) in the system processing optimization model to calculate the fitness value I corresponding to each initial optimization parameter. The fitness value I here 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 parameter solution. Subsequently, by comparing all fitness values ​​I, the fitness value I corresponding to the initial optimization parameter X is obtained. j Select the initial optimization parameter with the smallest fitness value as the current optimal parameter X best , which will serve as the benchmark and learning target for this round of iteration.

[0146] Then the core iterative update operation is started to explore a better solution. 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 The algorithm performs tracking updates, which usually means fine-tuning and converging continuous parameters (such as speed) toward the current optimal solution. Simultaneously, it searches for and updates the discrete initial optimization parameters within the initial optimization parameters, which usually means mutating or reorganizing discrete parameters (such as process sequence) in some way to explore new and potentially better combinations. This hybrid update strategy ultimately yields updated optimization parameters, representing a new generation of potential solutions.

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

[0148] Reference Figure 8 , tracking and updating the continuous initial optimization parameters in the initial optimization parameters 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 parameter and the initial optimized parameter, multiply it by the acceleration parameter and the random parameter to obtain the optimal parameter difference.

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

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

[0153] In some embodiments, to fine-tune the update process, a predetermined acceleration parameter c1 (typically 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 optimization parameter learns toward the optimal parameter, thus controlling the convergence rate. The random parameter is a random number dynamically generated during each update. Its purpose is to introduce uncertainty into the update process, preventing all parameters from moving in the same deterministic manner, thereby helping to escape from the local optimal solution.

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

[0155]

[0156] Through steps 801 to 803, the algorithm calculates the difference from the current optimal parameter, ensuring that the optimization process always "tracks" toward the currently known optimal solution, guaranteeing the algorithm's convergence. 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 optimal solutions in the continuous parameter space, increasing the probability of finding the global optimal solution.

[0157] In addition, for the discrete initial optimization parameter S in the initial optimization parameter j When searching and updating, an adaptive mutation strategy is adopted, that is, at the current number of iterations t, the mutation probability p is used. mutate Discrete initial optimization parameters Perform 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] Among them, p max is the maximum mutation probability, and β is a pre-set mutation parameter factor.

[0160] In addition, in order to improve the applicability of the iteration process, the variables that do not meet the relevant constraints are adjusted, such as adjusting the station operation parameters in a certain iteration process to The process sequence parameter S that has conflicts (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, the current optimal parameters are updated based on the updated optimization parameters, and the iteration is continued until the preset number of iterations is reached, and the current optimal parameters corresponding to the last iteration are obtained to obtain the optimized number of movers, optimized process sequence, and optimized workstation operation parameters.

[0162] Step 704 is described in detail below.

[0163] In some embodiments, during a particular iteration, if the newly generated solution is determined to be superior, that is, if the fitness of the updated optimization parameters is less than that of the current optimal parameters, the current optimal parameters are updated with the new, superior solution. Regardless of whether or not an update is performed, the iteration continues until a preset number of iterations is reached. After the loop completes, the current optimal parameter X* corresponding to the last iteration is obtained. This parameter is the final result of the optimization solution, which includes the optimized number of movers, the optimized process sequence, and the optimized station operating parameters, namely {V*, N*, S*}.

[0164] Through the above steps 701 to 704, by generating initial optimization parameters containing continuous and discrete variables, and adopting a hybrid strategy of tracking update and search update, it is possible to efficiently explore in a large and complex solution space, effectively avoiding the dilemma of falling into the local optimal solution. Then, through the mechanism of continuing to iterate and continuously updating the current optimal parameters, this method can gradually approach the global optimal solution, and finally, within a limited computing time, accurately solve the optimized number of movers, optimized process sequence, and optimized workstation operating parameters that can make the overall performance of the system reach the optimal state, greatly improving the quality and reliability of its solution results.

[0165] Reference Figure 9 , is a schematic diagram of a two-layer framework algorithm flow for solving a system processing optimization model provided by an embodiment of the present application. Figure 9As shown in the figure, the overall process embodies the control concept of "MPC rolling optimization," which dynamically solves for optimal control parameters within each control cycle. Specifically, when the optimization begins, the state is first transferred, acquiring real-time data such as conveyor line information and process information. Subsequently, the "inner CSO solver" algorithm is initiated. The core step of this algorithm is "variable grouping," which classifies the parameters to be optimized into two categories based on their mathematical properties. One category, "continuous variable optimization (V, N)," as shown in the figure, optimizes the workstation operating parameters and the number of actuators using a tracking update strategy; the other category, "discrete variable optimization (S)," optimizes the process sequence parameters using a search update strategy. After optimizing these two categories of variables separately, "cooperative constraint processing" is used to ensure that the updated parameter combination still meets the overall constraints of the system's machining optimization model. Finally, through continuous iterative optimization, the process "outputs the optimal solution," namely, the optimized number of actuators, the optimized process sequence, and the optimized workstation operating parameters for the current cycle.

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

[0167] Step 204 is described in detail below.

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

[0169] Reference Figure 10 , based on optimizing the number of movers, optimizing the process sequence and optimizing the workstation operating parameters, the production control of the magnetic drive conveying system includes the following steps 1001 to 1002.

[0170] Step 1001: performing production control on the magnetic drive conveying system in the current servo cycle based on the optimized number of movers, optimized process sequence, and optimized workstation operation parameters corresponding to 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 mover quantity, optimized process sequence and optimized workstation operating parameters of the next servo cycle, and perform production control on the magnetic drive conveying system in the next servo cycle based on the optimized mover quantity, optimized process sequence and optimized workstation operating parameters of the next servo cycle.

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

[0173] In some embodiments, after determining the optimized number of movers, optimized process sequence, and optimized workstation operating parameters corresponding to the current servo cycle, production control of the magnetic drive conveying system is performed during the current servo cycle. The servo cycle here can be understood as the minimum time unit of system control, or a "control beat." During this short time slice, the control system converts the previously calculated set of optimal parameter solutions for the current situation into specific instructions to the physical device, such as scheduling a specified number of movers, planning their paths, and setting their speeds. This ensures that at every control moment, the system's operating state is executed according to the latest optimization decision.

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

[0175] Through the above steps 1001 to 1002, by executing optimal control in the current servo cycle and performing forward-looking calculations and preparations for the next servo cycle, the entire production control system has continuous, real-time self-correction and optimization capabilities, ensuring that the control strategy can quickly respond to any changes or disturbances in the production process rather than rigidly executing an outdated plan, thereby greatly enhancing the real-time adaptability and robustness of the magnetic drive conveying system, so that it always operates in a dynamic equilibrium state close to the global optimal state.

[0176] The production control method and related equipment of the magnetic drive conveying system proposed in the embodiment of the present application, the magnetic drive conveying system includes a processing conveyor line and a mover, the mover runs on the processing conveyor line, and multiple processing processes are arranged on the processing conveyor line. The method includes: first, obtaining 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 operating station, the first operating station is the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining operating stations are the conveying areas between every two adjacent processing processes in the processing conveyor line; then, based on the difference between the mover arrival time and the process idle time of each processing process under the process sequence parameters, the process waiting time of the mover in each processing process is obtained, and based on the ratio of the station length of each operating station and the station operation parameters, the station operation time of the mover in each operating station is obtained, and the operation time of all stations is accumulated. The waiting time of all processes is used to obtain the total cycle time function. Based on the ratio of the total length of the conveyor line to the number of movers, the minimum beat function of the mover is obtained. The square of the difference between the actual processing time and the average processing time of the mover in each processing process is obtained to obtain the processing difference square. The processing difference square is averaged based on the mover number parameter to obtain the processing time variance function of each processing process. The ferry section material receiving efficiency of the magnetic drive conveying system is obtained, and the position deviation of the mover at each operating station is obtained. The system position deviation mean of the magnetic drive conveying system is obtained based on the average value of all position deviations. The total cycle time function, the minimum beat function, the processing time variance function, the ferry section material receiving efficiency and the system position deviation mean are weighted and processed to obtain the system optimization function. Based on the mover number parameter, 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 number parameters of the movers, the process sequence parameters and the workstation operation parameters, multiple initial optimization parameters are generated. The initial optimization parameters include continuous initial optimization parameters and discrete initial optimization parameters. Based on the conveyor line information, process information, mover information, workstation information and the system optimization function in the system processing optimization model of the current servo cycle, the fitness value corresponding to each initial optimization parameter is calculated, and the initial optimization parameter with the smallest fitness value is selected from the multiple initial optimization parameters as the current optimal parameter. The acceleration parameter and the random parameter are obtained. Based on the difference between the current optimal parameter and the initial optimization parameter, the optimal parameter difference is obtained by multiplying the difference by the acceleration parameter and the random parameter. The continuous initial optimization parameters in the updated optimization parameters are obtained based on the optimal parameter difference and the accumulated value of the continuous initial optimization parameters. 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. The iteration is continued until a preset number of iterations is reached. The current optimal parameters corresponding to the last iteration are obtained to obtain the optimized number of movers, the optimized process sequence, and the optimized station operating parameters. Finally, the production control of the magnetic drive conveying system is performed based on the optimized number of movers, the optimized process sequence, and the optimized station operating parameters.

[0177] The embodiment of the present application obtains various information of the production line in the magnetic drive conveying system in real time, and constructs a system processing optimization model based on this information that can reflect the complex coupling relationship between the number of movers, process sequence and operating speed. Then, by solving the model, it can dynamically calculate the optimal parameter combination under the current production state, and control the system based on this set of real-time optimized parameters, so that the production control strategy can actively adapt to the actual changes on the production line, avoiding resource waste such as mover congestion or idle workstations due to parameter solidification, and significantly improving the overall operating efficiency, flexibility and resource utilization of the magnetic drive conveying system; in addition, by accurately calculating the operating time of each workstation and the waiting time of each process, By accumulating on this basis, a highly accurate and physically meaningful total cycle time function can be constructed, which enables the model to clearly reveal how decision variables such as process sequence parameters and workstation operation parameters affect the total cycle time by affecting the two links of waiting and running, thereby providing a more accurate and reliable mathematical basis for subsequent system optimization; and, by calculating the square of the processing difference and averaging it, a processing time variance function that can accurately reflect the degree of processing time fluctuation is successfully constructed, which transforms the originally vague concept of "stability" of the production process into a key performance indicator that can be quantified and optimized, so that subsequent system optimization can be carried out in the pursuit of While achieving high efficiency, it can also take into account the stability and predictability of the production process, thereby effectively avoiding system bottlenecks and rhythm disorders caused by drastic fluctuations in processing time, and ultimately helping to achieve a more robust and reliable production control; and, by introducing the ferry section material receiving efficiency and the system position deviation mean, the reliability and control accuracy of the system are also taken into consideration, making the optimization model more comprehensive and close to the real industrial application scenario, and through weighted sum processing, giving the system great flexibility, allowing users to adjust the importance of various performance indicators according to different production needs (for example, prioritizing speed or prioritizing accuracy), and ultimately ensuring that the obtained system optimization function can be used as a highly comprehensive and customizable The optimization goal guides the control system to find a production parameter combination that achieves the best balance in all key performance aspects, thereby achieving true global optimization. In addition, by constructing the total cycle time function, the minimum beat function, and the processing time variance function, a comprehensive quantitative description of the system from the three key dimensions of efficiency, beat, and stability is carried out. 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 the subsequent use of algorithms to find the globally optimal production parameter combination, ensuring the scientific and systematic nature of the optimization decision.Furthermore, by generating initial optimization parameters containing both continuous and discrete variables and employing a hybrid strategy of tracking and searching updates, the method efficiently explores a vast and complex solution space, effectively avoiding the dilemma of being trapped in a local optimum. By continuously iterating and updating the current optimal parameters, the method gradually approaches the global optimal solution. Ultimately, within a limited computational time, it accurately determines the optimal number of actuators, process sequence, and station operating parameters that maximize overall system performance, significantly improving the quality and reliability of its solution.

[0178] The present application also provides a production control device for a magnetic drive conveying system, which can realize the production control method of the magnetic drive conveying system. Figure 11 , the apparatus 1100 comprises:

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

[0180] An optimization model generation module 1120 is used to generate a system processing optimization model based on the mover quantity parameter of the mover, the process sequence parameter of the processing process, and the station operation parameter of the mover at the operation station;

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

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

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

[0184] Based on the parameters of the number of movers and the process sequence, the total cycle time function of the movers is obtained;

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

[0186] Based on the parameter of the number of movers, the variance function of the machining time of the machining process is obtained;

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

[0188] Based on the parameters of the number of movers, process sequence parameters, workstation operation parameters 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 mover arrival time and the process idle time of each processing step under the process sequence parameters, the process waiting time of the mover in each processing step is obtained;

[0191] Based on the ratio of the station length of each operating station and the station operating parameter, the station operating time of the mover at each operating station is obtained;

[0192] The total cycle time function is obtained by adding up 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] Obtain the square of the difference between the actual processing time and the average processing time of the mover in each processing step to obtain the square of the processing difference;

[0195] The square of the machining difference is averaged based on the number of movers parameter to obtain the machining time variance function of each machining process.

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

[0197] Obtain the material receiving efficiency of the ferry section of the magnetic drive conveying system, as well as the position deviation of the mover at each operating position, and obtain the mean value of the system position deviation of the magnetic drive conveying system based on the average value of all position deviations;

[0198] The system optimization function is obtained by weighted sum processing based on the total cycle time function, minimum beat function, processing time variance function, ferry section material receiving efficiency and system position deviation mean.

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

[0200] Based on the number parameters of the movers, the process sequence parameters and the workstation operation parameters, multiple initial optimization parameters are generated, and the initial optimization parameters include continuous initial optimization parameters and discrete initial optimization parameters;

[0201] Based on the conveyor line information, process information, mover information, workstation information of the current servo cycle 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] Tracking and updating the continuous initial optimization parameters in the initial optimization parameters based on the current optimal parameters, and searching and updating the discrete initial optimization parameters in the initial optimization parameters to obtain 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 is continued until the preset number of iterations is reached. The current optimal parameters corresponding to the last iteration are obtained to obtain the optimized number of movers, optimized process sequence, and optimized workstation operation parameters.

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

[0205] Get acceleration parameters and random parameters;

[0206] 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;

[0207] Based on the optimal parameter difference 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] The production control of the magnetic drive conveying system is performed in the current servo cycle based on the optimized number of movers, optimized process sequence and optimized station operation parameters corresponding to 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 mover quantity, optimized process sequence and optimized workstation operating parameters of the next servo cycle are calculated, and based on the optimized mover quantity, optimized process sequence and optimized workstation operating parameters of the next servo cycle, the magnetic drive conveying system is controlled in the next servo cycle.

[0211] In the above embodiments, the description of each embodiment has its own focus. For the part that is not described in detail in a certain embodiment, the specific implementation method of the production control device of the magnetic drive conveying system is basically the same as the specific implementation method of the production control method of the above magnetic drive conveying system, and will not be repeated here.

[0212] In the embodiment of the present application, the production control device of the magnetic drive conveying system obtains various information of the production line in the magnetic drive conveying system in real time, and constructs a system processing optimization model based on this information that can reflect the complex coupling relationship between the number of movers, process sequence and operating speed. Then, 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, so that the production control strategy can actively adapt to the actual changes on the production line, avoiding resource waste such as mover congestion or idle workstations due to parameter solidification, and significantly improving the overall operation efficiency, flexibility and resource utilization of the magnetic drive conveying system; in addition, by accurately calculating the operation of each workstation Time and the waiting time of each process, and on this basis, accumulation can construct a highly accurate and physically meaningful total cycle time function, so that the model can clearly reveal how decision variables such as process sequence parameters and station operation parameters affect the total cycle time by affecting the two links of waiting and running, thereby providing a more accurate and reliable mathematical basis for subsequent system optimization; and, by calculating the square of the processing difference and averaging it, a processing time variance function that can accurately reflect the degree of processing time fluctuation is successfully constructed, which transforms the originally vague concept of "stability" of the production process into a key performance indicator that can be quantified and optimized, so that the subsequent system System optimization can not only pursue high efficiency, but also take into account the stability and predictability of the production process, thus effectively avoiding system bottlenecks and rhythm disorders caused by drastic fluctuations in processing time, and ultimately helping to achieve a more robust and reliable production control; and, by introducing the ferry section material receiving efficiency and the mean value of the system position deviation, the reliability and control accuracy of the system are also taken into consideration, making the optimization model more comprehensive and close to the real industrial application scenario, and through weighted sum processing, giving the system great flexibility, allowing users to adjust the importance of various performance indicators according to different production needs (for example, giving priority to speed or giving priority to accuracy), and ultimately ensuring that the obtained system optimization function can be used as a highly comprehensive and Customizable optimization objectives guide the control system to find a production parameter combination that achieves the best balance across all key performance aspects, thereby achieving true global optimization. Furthermore, by constructing a total cycle time function, a minimum beat function, and a processing time variance function, a comprehensive quantitative description of the system is provided from the three key dimensions of efficiency, beat, 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, solvable mathematical optimization problem. This provides a solid theoretical foundation for the subsequent use of algorithms to find the globally optimal production parameter combination, 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 updates, the method efficiently explores a vast and complex solution space, effectively avoiding the dilemma of being trapped in a local optimum. By continuously iterating and updating the current optimal parameters, the method gradually approaches the global optimal solution. Ultimately, within a limited computational time, it accurately determines the optimal number of actuators, process sequence, and station operating parameters that maximize overall system performance, significantly improving the quality and reliability of its solution.

[0213] An embodiment of the present application further 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 the memory, and the processor executes the at least one program to implement the production control method of the magnetic drive conveying system implemented in the present application. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0218] See also Figure 12 , Figure 12 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0219] The processor 1201 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an 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 the present 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 an 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 codes are stored in the memory 1202 and are called by the processor 1201 to execute the production control method of the magnetic drive conveying system of the embodiments of this application.

[0221] Input / output interface 1203, used to implement information input and output;

[0222] Communication interface 1204, used to implement communication interaction between this device and other devices, which 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 , which 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 , the memory 1202 , the input / output interface 1203 and the communication interface 1204 are connected to each other in communication within the device via the bus 1205 .

[0225] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the production control method of the magnetic drive conveying system is implemented.

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

[0227] The embodiments described in the embodiments of this application are intended to more clearly illustrate 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. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in 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 the present application, and may include more or fewer steps than shown in the figures, or a combination of 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, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

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

[0231] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0232] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

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

[0234] The units described above as separate components may or may not be physically separate, and 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 these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0235] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0236] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0237] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A production control method for a magnetic drive conveying system, characterized in that: The magnetic drive conveying system includes a processing conveying line and a mover, wherein the mover runs on the processing conveying line, and a plurality of processing steps are arranged on the processing conveying line. The method includes: 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 operating station, wherein the first operating station is the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining operating stations are the conveying areas between every two adjacent processing processes in the processing conveyor line; generating a system processing optimization model based on a mover quantity parameter of the mover, a process sequence parameter of the processing process, and a station operation parameter of the mover at the operation station; 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 operation parameters; Production control of the magnetic drive conveying system is performed based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters.

2. The production control method of the magnetic drive conveying system according to claim 1, characterized in that: The conveyor line information includes the total length of the conveyor line. The system processing optimization model is generated based on the mover quantity parameter of the mover, the process sequence parameter of the processing process, and the station operation parameter of the mover at the operation station, including: Based on the mover quantity parameter and the process sequence parameter, a total cycle time function of the mover is obtained; Based on the ratio of the total length of the conveyor line to the number parameter of the movers, a minimum beat function of the movers is obtained; Based on the mover quantity parameter, a processing time variance function of the processing step is obtained; Obtaining a system optimization function based on the total cycle time function, the minimum beat function, and the processing time variance function; The system processing optimization model is obtained based on the mover quantity parameter, the process sequence parameter, the workstation operation parameter and the optimization function of the system.

3. The production control method of the magnetic drive conveying system according to claim 2, characterized in that: The step of obtaining a total cycle time function of the mover based on the mover quantity parameter and the process sequence parameter includes: Obtaining the process waiting time of the mover in each processing step based on the difference between the mover arrival time and the process idle time of each processing step under the process sequence parameters; Obtaining a station running time of the mover at each of the operating stations based on a ratio of the station length of each of the operating stations to the station operating parameter; The total cycle time function is obtained by accumulating the running time of all the workstations and the waiting time of all the processes.

4. The production control method of the magnetic drive conveying system according to claim 2, characterized in that: The step of obtaining the processing time variance function of the processing step based on the mover quantity parameter includes: Obtaining the square of the difference between the actual processing time and the average processing time of the mover in each processing step to obtain the square of the processing difference; The squares of the machining differences are averaged based on the mover quantity parameter to obtain the machining time variance function of each machining process.

5. The production control method of the magnetic drive conveying system according to claim 2, characterized in that: The system optimization function is obtained based on the total cycle time function, the minimum beat function and the processing time variance function, including: Obtaining the material receiving efficiency of the ferry section of the magnetic drive conveying system, and obtaining the position deviation of the mover at each operating position, and obtaining the system position deviation mean of the magnetic drive conveying system based on the average value of all the position deviations; The system optimization function is obtained by weighted sum processing based on the total cycle time function, the minimum beat function, the processing time variance function, the ferry section material receiving efficiency and the system position deviation mean.

6. The production control method of the magnetic drive conveying system according to claim 2, characterized in that: 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 operation parameters includes: Based on the mover quantity parameter, the process sequence parameter, and the workstation operation parameter, a plurality of initial optimization parameters are generated, wherein the initial optimization parameters include continuous initial optimization parameters and discrete initial optimization parameters; Based on the conveyor line information, the process information, the mover information, the workstation information of the current servo cycle and the system optimization function in the system processing optimization model, the fitness value corresponding to each of the initial optimization parameters is calculated, and the initial optimization parameter with the smallest fitness value is selected from the multiple initial optimization parameters as the current optimal parameter; Tracking and updating the continuous initial optimization parameters in the initial optimization parameters based on the current optimal parameters, and searching and updating the discrete initial optimization parameters in the initial optimization parameters to obtain updated optimization parameters; When the fitness of the updated optimization parameter is less than the fitness of the current optimal parameter, the current optimal parameter is updated based on the updated optimization parameter, and iteration is continued until the preset number of iterations is reached, and the current optimal parameter corresponding to the last iteration is obtained to obtain the optimized number of movers, the optimized process sequence and the optimized workstation operation parameters.

7. The production control method of the magnetic drive conveying system according to claim 6, characterized in that: The tracking and updating of the continuous initial optimization parameters in the initial optimization parameters based on the current optimal parameters includes: Get acceleration parameters and random parameters; Based on the difference between the current optimal parameter and the initial optimized parameter, multiplying it by the acceleration parameter and the random parameter, an optimal parameter difference is obtained; The continuous initial optimization parameters in the updated optimization parameters are obtained based on the optimal parameter difference and the accumulated value of the continuous initial optimization parameters.

8. The production control method of the magnetic drive conveying system according to claim 6, characterized in that: The production control of the magnetic drive conveying system based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters includes: Performing production control on the magnetic drive conveying system in the current servo cycle based on the optimized number of movers, the optimized process sequence, and the optimized workstation operating parameters corresponding to the current servo cycle; Based on the conveying line information, the process information, the mover information and the workstation information of the next servo cycle, the optimized mover quantity, the optimized process sequence and the optimized workstation operating parameters of the next servo cycle are calculated, and based on the optimized mover quantity, the optimized process sequence and the optimized workstation operating parameters of the next servo cycle, production control of the magnetic drive conveying system is performed in the next servo cycle.

9. A production control device for a magnetic drive conveying system, characterized in that: The magnetic drive conveying system includes a processing conveying line and a mover, wherein the mover runs on the processing conveying line, and a plurality of processing steps are arranged on the processing conveying line. The device includes: An information acquisition module is used to obtain 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 operating station, the first operating station being the conveying area between the first processing process and the starting point of the processing conveyor line, and the remaining operating stations being the conveying areas between every two adjacent processing processes in the processing conveyor line; An optimization model generation module, configured to generate a system processing optimization model based on a mover quantity parameter of the mover, a process sequence parameter of the processing process, and a station operation parameter of the mover at the operation station; An 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 number, the optimized process sequence and the optimized workstation operation parameters; A production control module is used to control the production of the magnetic drive conveying system based on the optimized number of movers, the optimized process sequence and the optimized workstation operating parameters.

10. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the production control method of the magnetic drive conveying system according to any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the production control method of the magnetic drive conveying system according to any one of claims 1 to 7 is implemented.

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