An optimization method for selection strategy of multi-process assembly system considering waiting threshold

Abstract

The present disclosure relates to the technical field of product assembly, and in particular, provides a method for optimizing the matching strategy of a multi-process assembly system taking into account a waiting threshold. The above-mentioned method for optimizing the matching strategy of a multi-process assembly system taking into account a waiting threshold comprises: defining the quality grade distribution parameters of the workpieces to be assembled; based on the quality grade distribution parameters of the workpieces to be assembled, evaluating the productivity of the assembly system corresponding to the matching strategies of different waiting thresholds; determining the total revenue of the assembly system corresponding to different productivity; and determining the corresponding waiting threshold as the target waiting threshold when the total revenue meets the preset conditions. The present disclosure comprehensively considers the quality grade of the product under different waiting thresholds and the production efficiency of the assembly system, and then determines the total revenue under different waiting thresholds, and determines the target waiting threshold based on the total revenue. By determining the target waiting threshold, a balance can be found between production efficiency and product quality to maximize economic benefits.

Description

Technical Field

[0001] The present disclosure relates to the technical field of product assembly, and in particular to a method for optimizing a selection strategy for a multi-process assembly system taking into account a waiting threshold. Background Art

[0002] In the process of product manufacturing, processing and assembly are the core parts of product manufacturing. The quality of the product not only comes from the design quality given in the design stage, but also depends on the processing quality and assembly quality of each component in the manufacturing process. With the growing market demand for product diversification and the widespread adoption of multi-process assembly systems, how to ensure that the manufacturing system can operate efficiently and stably while ensuring product quality and maximizing economic benefits has become the focus and difficult problem of modern manufacturing companies. Therefore, it is particularly urgent to conduct in-depth analysis and optimization of multi-process assembly systems.

[0003] At present, in the research on the optimization of manufacturing strategies for multi-process assembly systems, most of the focus is on the scheduling of tasks, while little attention is paid to the optimization of matching strategies in assembly system management. Matching refers to the selection and configuration of corresponding parts or components according to specific requirements or standards during product manufacturing or assembly. The matching strategy involves assigning matching logic to the assembly machines in the system, which determines how the parts are combined to form higher quality components or products. Therefore, the reasonable design and optimization of matching strategies can help achieve a balance and improvement between assembly efficiency and assembly quality. Summary of the invention

[0004] In view of this, an exemplary embodiment of the present disclosure provides a method for optimizing a multi-process assembly system matching strategy taking into account a waiting threshold to solve the problems existing in the related art.

[0005] One aspect of the exemplary embodiments of the present disclosure provides a method for optimizing a multi-process assembly system matching strategy considering a waiting threshold, the method comprising:

[0006] Define the quality grade distribution parameters of the workpieces to be assembled;

[0007] Based on the quality grade distribution parameters of the workpieces to be assembled, the productivity of the assembly system under different waiting threshold selection strategies is evaluated.

[0008] Determine the total revenue of the assembly system at different production rates;

[0009] When the total revenue meets the preset condition, the corresponding waiting threshold is determined as the target waiting threshold.

[0010] Another aspect of the exemplary embodiments of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in the exemplary embodiments of the present disclosure.

[0011] According to another aspect of the exemplary embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0012] According to another aspect of the exemplary embodiments of the present disclosure, a computer program product is provided, including a computer program / instruction. When the computer program / instruction is executed by a processor, the method described in the exemplary embodiments of the present disclosure is implemented.

[0013] As will be described in detail below, according to an embodiment of the present disclosure, a multi-process assembly system matching strategy optimization method considering waiting thresholds is defined by defining the quality grade distribution parameters of the workpieces to be assembled; based on the quality grade distribution parameters of the workpieces to be assembled, the productivity of the assembly system corresponding to the matching strategies of different waiting thresholds is evaluated; the total revenue of the assembly system corresponding to different productivity is determined; when the total revenue meets the preset conditions, the corresponding waiting threshold is determined as the target waiting threshold. Therefore, the multi-process assembly system matching strategy optimization method considering waiting thresholds provided by the present disclosure first defines the quality grade distribution parameters of the workpieces to be assembled. Then, based on these quality grade distribution parameters, the productivity of the assembly system under different waiting thresholds is evaluated, wherein the waiting threshold determines the time or quantity that the assembly machine waits for matching grade accessories before starting assembly. Further, the total revenue under different productivity is determined, thereby comprehensively considering the impact of production efficiency and product quality on revenue under different waiting thresholds. When the total revenue meets the preset conditions, the corresponding waiting threshold is determined as the target waiting threshold, which is the key to optimizing production efficiency while ensuring product quality, thereby maximizing total revenue. By adjusting the waiting threshold, a balance can be found between assembly efficiency and assembly quality, greatly improving production revenue. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1A flow chart of a method for optimizing a selection strategy for a multi-process assembly system taking into account a waiting threshold value provided by an exemplary embodiment of the present disclosure;

[0016] Figure 2 When the input workpiece level provided for an exemplary embodiment of the present disclosure is 1 G i State transition diagram;

[0017] Figure 3 When the input workpiece level provided for an exemplary embodiment of the present disclosure is 2 G i State transition diagram;

[0018] Figure 4 When the input workpiece level provided for an exemplary embodiment of the present disclosure is 3 G i State transition diagram;

[0019] Figure 5 The dual-machine model ( e 1, e 2, N 1) Schematic diagram;

[0020] Figure 6 A schematic diagram of a three-machine assembly system considering component levels provided as an example of the present disclosure;

[0021] Figure 7 A first-level decomposition schematic diagram provided as an example of the present disclosure;

[0022] Figure 8 A second-level decomposition schematic diagram provided as an example of the present disclosure;

[0023] Fig. 9 A virtual machine provided as an example of the present disclosure Schematic diagram of the prerequisites for work;

[0024] Fig.10 A virtual machine provided as an example of the present disclosure Schematic diagram of the working prerequisites;

[0025] Fig.11 A schematic diagram of a multi-process assembly system provided as an example in the present disclosure;

[0026] Fig.12 A schematic block diagram of functional modules of a multi-process assembly system matching strategy optimization device considering a waiting threshold provided by an exemplary embodiment of the present disclosure;

[0027] Fig.13 A structural block diagram of an electronic device provided as an example in the present disclosure;

[0028] Fig.14A schematic diagram of a computer program product provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0031] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0032] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0033] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0034] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0035] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0036] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0037] Before introducing the embodiments of the present disclosure, the following definitions are given for the relevant terms involved in the embodiments of the present disclosure:

[0038] Waiting threshold: Waiting threshold refers to the maximum limit set on the waiting time of parts or components on the assembly line in order to optimize the production process and improve efficiency in a multi-process assembly system. The key to setting this threshold is to balance the smoothness and efficiency of the production line, while reducing downtime and improving overall production efficiency.

[0039] In the process of product manufacturing, processing and assembly are the core parts of product manufacturing. The quality of the product not only comes from the design quality given in the design stage, but also depends on the processing quality and assembly quality of each component in the manufacturing process. With the growing market demand for product diversification and the widespread adoption of multi-process assembly systems, how to ensure that the manufacturing system can operate efficiently and stably while ensuring product quality and maximizing economic benefits has become the focus and difficult problem of modern manufacturing companies. Therefore, it is particularly urgent to conduct in-depth analysis and optimization of multi-process assembly systems.

[0040] At present, in the research on the optimization of manufacturing strategies for multi-process assembly systems, most of the focus is on the scheduling of tasks, while little attention is paid to the optimization of matching strategies in assembly system management. Matching refers to the selection and configuration of corresponding parts or components according to specific requirements or standards during product manufacturing or assembly. The matching strategy involves assigning matching logic to the assembly machines in the system, which determines how the parts are combined to form higher quality components or products. Therefore, the reasonable design and optimization of matching strategies can help achieve a balance and improvement between assembly efficiency and assembly quality.

[0041] Therefore, in order to solve the above problems, an exemplary embodiment of the present invention provides a method for optimizing the matching strategy of a multi-process assembly system considering a waiting threshold, which specifically includes: step one, calculating the quality grade distribution of assembled products; step two, evaluating the productivity of a multi-process assembly system; step three, calculating the total revenue of a multi-process assembly system; and step four, analyzing the target waiting threshold.

[0042] In step one, it is necessary to calculate the probability of the possible quality grade distribution of the product under different assembly options and process conditions. This step involves analyzing each input workpiece and processing state, and simulating the matching strategy of how to assemble the output workpiece into the final product. This process can help determine which combinations may lead to higher or lower product quality, thereby providing data support for subsequent production decisions.

[0043] In step 2, the productivity of the entire assembly system is evaluated, which includes analyzing the production capacity of different processing steps, assembly steps and different matching strategies. The goal is to identify bottlenecks and inefficient links in the production process and evaluate the potential impact of different assembly strategies on productivity.

[0044] In step three, the total revenue of the multi-process assembly system is a comprehensive expression of the system productivity and product quality level. By defining the profit loss coefficient between different quality levels, the total revenue under different selection strategies is determined.

[0045] In step 4, the optimal balance between production efficiency and total revenue is found by simulating and calculating the impact of different waiting time settings on the production process and product quality. The setting of the waiting threshold takes into account the relationship between production efficiency and product quality, with the aim of increasing production efficiency as much as possible without sacrificing product quality.

[0046] For example, Figure 1 The flowchart of the optimization method of multi-process assembly system matching strategy considering the waiting threshold provided by the present disclosure is as follows: Figure 1 As shown, the following steps may be specifically included:

[0047] Step S110: defining quality grade distribution parameters of the workpieces to be assembled.

[0048] For example, Table 1 is a symbolic description of the calculation of the quality grade distribution of the assembled product:

[0049] Table 1 Assembly product quality grade distribution calculation symbols and meanings

[0050]

[0051] In a multi-process serial production system, according to the quality characteristic indicators of the workpieces produced in each process, the products can be divided into different quality levels according to the inspection standards. These quality levels reflect the degree to which the workpieces meet the quality characteristic indicators. Here, the quality level of the workpiece can be defined as u ,in u The value range of is a positive integer from 1 to Y, where Y represents the total number of levels set. In this embodiment, three levels will be used as an example for explanation, namely u =1,2,3; Y=3, the derivation of other quality grades is the same. According to the process flow, the workpiece It is a process The input of the workpiece is two parts. The process Output.

[0052] In step 1, the quality grade distribution parameters of the raw materials for the assembled product are set as follows: Is the input system or the first process Raw materials of different quality grades Before entering When , the probability distribution is stable Here ~ Represents raw materials The probability that the quality level of is 1, 2, and 3, the sum of the three is equal to 1. Similarly, as a process Output artifacts , also with quality grade distribution .

[0053] In step 1, the state transfer matrix of the processing procedure is also parameterized: for a processing procedure, its processing procedure state determines the quality level of the output workpiece. Therefore, the process can be defined The status is ( u =1,2,3). To show how the input workpiece level affects the state of the processing process, the state transition matrix can be set ~ , these matrices correspond to the input workpiece The state transition when the quality level is 1, 2, and 3.

[0054] For example, Figure 2 When the input workpiece level provided for an exemplary embodiment of the present disclosure is 1 State transition diagram, such as Figure 2 As shown, Indicates the processing steps When the input workpiece level is 1, the state Transfer to state The probability of . ( u =1,2,3) indicates the processing steps For example, Indicates the processing steps When the input workpiece level is 1, the state Transfer to state probability. Indicates the processing steps When the input workpiece level is 1, the state Transfer to state probability. Indicates the processing steps When the input workpiece level is 1, the state Transfer to state The probability of other state transitions is the same.

[0055] For example, Figure 3 When the input workpiece level provided for an exemplary embodiment of the present disclosure is 2 State transition diagram, such as Figure 3 As shown, Indicates the processing steps When the input workpiece level is 2, the status Transfer to state The probability of . ( u =1,2,3) indicates the processing steps For example, Indicates the processing steps When the input workpiece level is 2, the status Transfer to state probability. Indicates the processing steps When the input artifact level is 2, the status Transfer to state probability. Indicates the processing steps When the input artifact level is 2, the status Transfer to state The probability of other state transitions is the same.

[0056] For example, Figure 4 When the input workpiece level provided for an exemplary embodiment of the present disclosure is 3 State transition diagram, such as Figure 4 As shown, Indicates the processing steps When the input workpiece level is 3, the status Transfer to state The probability of . ( u =1,2,3) indicates the processing steps For example, Indicates the processing steps When the input workpiece level is 3, the status Transfer to state probability. Indicates the processing steps When the input workpiece level is 3, the status Transfer to state probability. Indicates the processing steps When the input workpiece level is 3, the status Transfer to state The probability of other state transitions is the same.

[0057] Based on this, ~ It is expressed as follows:

[0058] (1)

[0059] in, ; Indicates the processing steps When the input workpiece level is 1, the state Transfer to state The probability of Indicates the processing steps When the input artifact level is 2, the status Transfer to state The probability of Indicates the processing steps When the input workpiece level is 3, the status Transfer to state probability.

[0060] After defining the quality grade distribution of the output workpieces and the probability of processing state transition, the quality grade distribution of the assembly product is calculated.

[0061] For example, Figure 5 The dual-machine model ( ) schematic diagram, such as Figure 5 As shown, The machine representing the first process; Indicates the machine production efficiency; Represents a buffer; Indicates the capacity of the buffer, that is, how many workpieces can be accommodated; The machine representing the second process; Indicates the machine production efficiency.

[0062] by Figure 5 The two processing steps shown and Based on the assumption of three quality levels, the two-process model There are 9 possible processing states in time period t: , , , , , , , , During the processing, the dual-process model will transfer between these nine processing states with a certain probability. t The processing state probability matrix of the dual-process model within a period is:

[0063] (2)

[0064] First, analyze the process The processing state, given the raw materials input into the first process The probability distribution of quality level is: Under the conditions, Depend on Transformed into The probability is:

[0065] (3)

[0066] Similarly, the other 8 state transition probability values ​​can be obtained:

[0067] (4)

[0068] Because the raw materials Probability distribution of quality levels is certain, so the process can be The state transition probability matrix in three cases ~ Combine into a matrix :

[0069] (5)

[0070] in, .

[0071] Based on this, and ~ Redefining the dual-process model .

[0072] On this basis, we continue to analyze the state transfer of the dual-process model during the processing process. Transfer to ( For example, the meaning of the transfer process is:

[0073] ①Process by The probability of t and t +1 Maintained for two consecutive periods processing state, so in t +1 Process at the beginning of the period Input artifacts The quality level is 1;

[0074] ② Due to input workpiece The quality level is 1, by The probability of t Processing status of the time period Transfer to t +1 processing status .

[0075] Therefore, the double process The status changes from ( Transfer to ( The probability of is the product of the above two probability values .

[0076] Similarly, we can get the dual-process model The state transition probability matrix is:

[0077] (6)

[0078] In formula (6), the sum of each column of the matrix is ​​1. The state sequence in the middle is the benchmark (the first state is , the 9th state is ), The i Ledi j The row elements represent the dual process from i The state is transferred to j The probability of a state, for example: express from Transfer to The probability of This means that the system Transfer to The probability of . Define the dual process in t The state probability vector of the time period is:

[0079] (7)

[0080] Then its state transfer formula is:

[0081] (8)

[0082] According to the Markov steady-state condition, the probability of the two-process model being in each state can be calculated. In order to simplify the calculation, the matrix in equation (6) can be processed in blocks. Among them, is the system state probability vector The first three items, the middle three items, and the last three items.

[0083] (9)

[0084] After obtaining the steady-state solution of equation (8), the double process Output product quality grade distribution in steady state Can be considered as a process The probability of being in the corresponding state is obtained, so there is formula (10). The above process can be used to solve the product quality distribution output by multiple serial processing steps.

[0085] (10)

[0086] For example, Figure 6 A schematic diagram of a three-machine assembly system considering component levels is provided as an example of the present disclosure, such as Figure 6 As shown, including processing steps , With an assembly process , Indicates the first process machines; Indicates the machine production efficiency; Indicates the machine Output components; express The grade distribution of ; Represents a buffer; Indicates the capacity of the buffer; Indicates the second process machines; Indicates the machine production efficiency; Indicates the machine Output components; express The grade distribution of ; Represents a buffer; Indicates the capacity of the buffer; Indicates assembly process machines; Indicates the machine production efficiency; Indicates the machine The output component of express The grade distribution of Larger-scale multi-process assembly systems can be simplified into three-machine assembly systems using existing merging methods.

[0087] Among them, for the assembly process, components of the same quality grade can be assembled together to obtain a component of quality grade = 1, components of adjacent quality grades (i.e., one grade difference) can be assembled together to obtain a component of quality grade = 2, and components of two quality grades different (such as grade = 1 and grade = 3) can be assembled together to obtain a component of quality grade = 3. Based on this, It can be expressed as:

[0088] (11)

[0089] Step S120: Based on the quality grade distribution parameters of the workpieces to be assembled, the productivity of the assembly system corresponding to the matching strategies with different waiting thresholds is evaluated.

[0090] In step S120, the productivity evaluation of the multi-process assembly system needs to be performed according to the selection strategy considering the waiting threshold, including:

[0091] Define the matching strategy and set multiple waiting thresholds in the matching buffer;

[0092] Based on the matching strategy, the marginal probabilities and waiting state probabilities corresponding to multiple waiting thresholds are determined respectively; the marginal probabilities include the first marginal probability and the second marginal probability, and the first marginal probability indicates that there is a waiting state probability in the accessory buffer. i indivual u The probability of the second marginal probability indicates that there is i Non u The probability of level accessories, i =0,1, …, , , the waiting state probability indicates that when the main part is level u The probability that the assembly process is in a waiting state when ;

[0093] Based on the quality grade distribution parameters, marginal probability and waiting state probability of the workpieces to be assembled, the productivity corresponding to the plurality of waiting thresholds is determined respectively.

[0094] First, the two processing machines in the three-machine system are defined as the main part processing machine and the accessory processing machine, which process and output "main parts" and "accessories" respectively. In the selection strategy, the main parts follow the first-in-first-out principle, and the accessories will be selected according to the main part level.

[0095] Secondly, the buffer zone where the accessories are located b 2Set the waiting threshold ( wt ) (1≤ wt ≤ ).exist Occupancy reached wt Previously, if there was an accessory of the same grade as the main part, the assembly was performed directly, which was a perfect match, and the quality grade of the component output after assembly = 1; if there was no accessory of the same grade, the assembly machine was in a waiting state until The same level of accessories appear or reach wt Once the waiting threshold is reached or exceeded, the assembly machine selects the component according to the Closest Quality Pair Principle (CQMP).

[0096] To illustrate the meaning of CQMP, two examples are given. After the conditions for reaching the threshold are met: if the level of the main part to be assembled is 3, and If there is no level 3 component in the assembly machine, the assembly machine will follow the order of "first select level 2, then select level 1" Search and extract accessories; if the main part to be assembled is level 2, and If there is no level 2 accessory in the assembly machine, the assembly machine will select level 1 accessories and level 3 accessories with equal probability. Table 2 shows the symbols and their meanings for the productivity evaluation of the multi-process assembly system.

[0097] Table 2 Symbols and meanings for productivity evaluation of multi-process assembly systems

[0098] and is the marginal probability, and is the conditional probability, and there is the following relationship between marginal probability and conditional probability:

[0099] (12)

[0100] The expression is:

[0101] (13)

[0102] Exemplarily, the productivity evaluation of a multi-process assembly system can be performed using a two-level decomposition method. Figure 7 The first level decomposition diagram provided as an example of the present disclosure is as follows: Figure 7 As shown, including processing steps , With an assembly process , Indicates the first process machines; Indicates the machine production efficiency; Represents a buffer; Indicates the capacity of the buffer; Indicates the second process machines; Indicates the machine production efficiency; Represents a buffer; Indicates the capacity of the buffer; Indicates assembly process machines; Indicates the machine To facilitate subsequent calculations, the assembly machine Decomposition into assembly virtual machines and , build a host dual-machine system ( , , ) and accessories dual system ( , , ).

[0103] Under the matching strategy, the buffer The number of a certain level of accessories in the game will not only affect its own quantity at the end of the time period, but may also affect the number of accessories of other levels. The state space cannot be expressed simply by the amount of occupancy, but needs to be divided into levels in more detail. u To solve this problem, we need to perform a second-level decomposition of the dual-machine model based on the first-level decomposition.

[0104] For example, Figure 8 The second level decomposition diagram provided as an example of the present disclosure is as follows: Figure 8 As shown in the figure, the accessory dual-machine model is further decomposed into two relatively independent virtual dual-machine models, so as to u Grade accessories and non u The first-level accessories are placed in these two virtual dual-machine models for analysis. Indicates the second process machines; Represents a buffer; Represents a virtual assembly machine in a dual-machine system for accessories; Indicates the process after secondary decomposition Can only produce u Machines with grade accessories; Indicates that only storage is performed after secondary decomposition u Buffer zone for grade accessories; Indicates the process after secondary decomposition Can only extract u Machines with grade accessories; Indicates the process after secondary decomposition Can only produce non u Machines with grade accessories; Indicates that after the secondary decomposition, non u Buffer zone for grade accessories; Indicates the process after secondary decomposition Only non u Since the number of quality levels is 3, 3 pairs of 6 virtual twin machine models can be obtained.

[0105] Two virtual dual-machine models and The parameters in need to be determined through analysis. The machine efficiency of the two processing machines can be equivalent to:

[0106] (14)

[0107] The capacity of the buffer and is uncertain. For example: Assume that =5, then when If there is only one level 1 accessory in a certain period of time, then , which means that the virtual dual-machine model Therefore, the virtual dual-machine model with different occupancy should be analyzed separately.

[0108] At the same time, for assembly machines and For example, different from and The prerequisite for this work is The output is level u or non-level u Accessories, and More prerequisites need to be met to work, so and Take the example to analyze its prerequisites.

[0109] For example, Fig. 9 A virtual machine provided as an example of the present disclosure Schematic diagram of working prerequisites, Fig.10 A virtual machine provided as an example of the present disclosure Schematic diagram of the working prerequisites.

[0110] like Fig. 9 As shown, in the assembly machine Works fine and buffer When it is not empty, the quality level of the main parts to be assembled is obtained, and the probability of being a level 1 main part is , the probability of being a level 2 master is , the probability of being a level 3 master is . And then judge Is there a level 1 accessory in If there are level 1 accessories in the memory, select the level 1 accessories for assembly. There are no level 2 accessories. The occupancy reaches the waiting threshold wt and When there are level 1 accessories, judge If there is a level 3 accessory, if not, choose a level 1 accessory for assembly. If it exists, there is a 1 / 2 chance to choose a level 1 accessory for assembly. There are no level 3 accessories. The occupancy reaches the waiting threshold wt , Level 1 accessories exist and If there is no level 2 accessory, select level 1 accessory for assembly.

[0111] Fig.10 Similarly, I will not go into details here.

[0112] Depend on Fig. 9 and Fig.10 It can be seen that the efficiency of the assembly machine obtained by the second-level decomposition is and The expression of contains the marginal probability of the occupancy of each level of accessories ( and ), such as according to Fig. 9 Conclusion The expression is as follows:

[0113] (15)

[0114] In formula (15), Means: When the level of the main part to be assembled is 2, The occupancy has reached the waiting threshold, and the probability that the assembly machine will perform the selection according to CQMP. Similarly, in formula (15) The meaning is: when the level of the main part to be assembled is 3, The occupancy has reached the waiting threshold, and the assembly machine will perform the matching probability according to CQMP.

[0115] Productivity of each grade of product The specific solution steps are as follows:

[0116] Step S121: Setting initial conditions: When the system is initially started, both buffers are empty. The initial conditions can be expressed as:

[0117] (16)

[0118] Step S122: Setting the number of iterations n , and first n =0, Marginal probabilities for level 1 and non-level 1 accessories and ( i =0,…, ) solution as an example, the iterative process is as follows:

[0119] Define the assembly efficiency iteration formula of the assembly process and update the marginal probability corresponding to each level of accessories;

[0120] updating the waiting state probability based on the updated marginal probability;

[0121] When the preset convergence condition is met, the updated marginal probability and waiting state probability are determined as the marginal probability and waiting state probability corresponding to the current waiting threshold.

[0122] Step S1221: define the machine efficiency of two virtual processing machines as:

[0123] (17)

[0124] Step S1222: See Fig. 9 Virtual assembly machine available Machine efficiency The iteration formula is:

[0125] (18)

[0126] Step S1223: Analyze based on the dual-machine model and update the corresponding conditional probability:

[0127] (19)

[0128] (20)

[0129] in, For dual-machine model In steady state, the probability that the buffer occupancy is 0 is calculated as:

[0130] (twenty one)

[0131] Among them, the function α The expression expands to:

[0132] (twenty two)

[0133] Step S1224: Further update the marginal probability of level 1 accessories:

[0134] (twenty three)

[0135] Step S1225: See Fig.10 Virtual assembly machine available Machine efficiency for:

[0136] (twenty four)

[0137] Step S1226: Similarly, according to the dual-machine model , update the corresponding conditional probability:

[0138] (25)

[0139] (26)

[0140] Step S1227: Further update the marginal probability of non-level 1 accessories:

[0141] (27)

[0142] The above steps S1221 to S1227 show how to obtain and , and thus the conditional probability is obtained based on the analysis of the two-machine model and , thus obtaining the marginal probability and .

[0143] Step S123: Similarly, the marginal probabilities of the other two pairs of virtual dual-machine models are solved by referring to steps S1221 to S1226.

[0144] Step S124: Update the waiting probability of the assembly system according to the above-obtained marginal probabilities:

[0145] (28)

[0146] Step S125: Further update the buffer and The probability of being empty ( and ):

[0147] (29)

[0148] (30)

[0149] (31)

[0150] (32)

[0151] Step S126: At this point, steps S122 to S125 have completed an iteration process, and it is checked whether the preset convergence condition is met:

[0152] (33)

[0153] in, ε = .

[0154] If the preset convergence condition is met, the iteration is terminated, otherwise n = n +1 and return to step S122 for the next iteration.

[0155] The above steps S121 to S126 are an iterative solution process. After convergence, the productivity of the assembled products of three quality levels is obtained as shown in the following formula (34), where the quality level of the assembled products can be obtained by the above formula (11).

[0156] (34)

[0157] The quality grade distribution of the output products of the three-machine system can also be obtained for:

[0158] (35)

[0159] Step S130: Determine the total revenue corresponding to different production rates of the assembly system.

[0160] Total revenue of multi-process assembly system ( TR) is a comprehensive expression of system productivity and product quality level. In actual production, the level of product grade determines the profit of a single product. Therefore, the higher the grade of the product, the higher the proportion of the overall yield, which means the higher the total profit of the system. Definition d is the profit loss coefficient between levels, and its value range is 0~1. That is, when the level is reduced by one level, the profit becomes (1- d )×100%. Therefore, d The larger the value, the more stringent the requirement on product quality level.

[0161] If the product income of level 1 (the highest level) is set to =1, the total return index can be expressed as:

[0162] (36)

[0163] Step S140: When the total revenue meets the preset condition, the corresponding waiting threshold is determined as the target waiting threshold.

[0164] In step S140, the target waiting threshold analysis is performed based on the total revenue calculation result of the multi-process assembly system. wt A larger waiting threshold means that the assembly machine has a wider range of optional accessories and is more likely to assemble a perfectly matched product, but on the other hand, a larger waiting threshold also means that the probability of the assembly machine being in a waiting state will increase, thereby reducing the productivity of the entire system. wt The existence of has a positive and negative impact on the yield and productivity of the matching system. In addition, due to wt Usually discrete, and the actual assembly process wt It is usually small, so that the optional solution space of the variable can be traversed in an acceptable time. Therefore, the optimal wt The steps to determine are as follows:

[0165] Step S141: You can first preliminarily select wt Optional range.

[0166] Step S142: Calculate each wt Next TR .

[0167] Step S143: Drawing TR - wt curve, TR - wt The curves show the total benefits for different waiting thresholds.

[0168] By analyzing TR - wtCurve to select TR Optimal wt If the selected wt In range TR If not converged, the selectable range is expanded and steps S142 and S143 are repeated.

[0169] Based on the above embodiments, the present disclosure also provides a specific embodiment taking a multi-process assembly system as an example. Fig.11 This is a schematic diagram of a multi-process assembly system provided as an example of the present disclosure. The processing state transfer matrix only acts on each processing process, and the output grade distribution of assembly processes 7 and 13 depends on the grade distribution of input components. At the same time, processes 1, 4 and 9 are the input ends of the entire system, and the quality grade distribution of their raw materials is respectively: =[0.68, 0.24, 0.08], =[0.68, 0.24, 0.08], =[0.68, 0.24, 0.08]. The final output of the complete component C1 will also determine the profit according to the different quality levels. The profit loss coefficient between each level is d =0.3. The basic parameters of the multi-process assembly system are shown in Table 3:

[0170] Table 3 Basic parameters of multi-process assembly system

[0171]

[0172] Then, the above steps S110 to S140 are executed to obtain the target waiting threshold.

[0173] In this embodiment, since the automation level of each process is high and there is a central control platform for unified monitoring and control, the impact of incoming materials is limited. Therefore, in order to simplify the processing, the processing state transfer matrix of each process is unified as follows:

[0174]

[0175] In this embodiment, the indicators of the random assembly strategy and the matching strategy optimization method proposed in this disclosure are compared in Table 4. The total system revenue under the four waiting threshold combinations is TR See Table 5 to Table 8.

[0176] Table 4 Comparison of indicators between the random assembly strategy and the matching strategy optimization method proposed in this disclosure

[0177]

[0178] Table 4 Indicates the process The production efficiency of the machine and the previous production line, such as Indicates the machine ~ The production efficiency of this production line.

[0179] As shown in Table 4, compared with the random assembly strategy, the matching strategy optimization method proposed in this disclosure has good quality distribution. , , and The quality of TR It is also higher than the random assembly strategy. It can be seen that the matching strategy optimization method proposed in the present invention is superior to the random assembly strategy in terms of product quality and total revenue.

[0180] Table 5 Buffer selection and Setting the wait threshold

[0181]

[0182] Table 6 Buffer selection and Setting the wait threshold

[0183]

[0184] Table 7 Buffer selection and Setting the wait threshold

[0185]

[0186] Table 8 Select buffer and Setting the wait threshold

[0187]

[0188] From Tables 5 to 8 above, we can see that when The value range is 3~7. When the value range is 7-8, the total benefit of the system will reach the optimal value of 0.88, which is about 16.25% higher than the random assembly strategy. Therefore, this solution is the target selection strategy of this embodiment.

[0189] One or more technical solutions provided in the exemplary embodiments of the present disclosure first define the quality grade of the workpiece, set the quality grade distribution parameters of the raw material and the workpiece, and describe the transfer relationship of the quality grade between different processes through a state transfer matrix, reflecting how the input workpiece grade affects the quality grade of the output workpiece.

[0190] In the productivity evaluation stage, the productivity of the multi-process assembly system under the selection strategy considering the waiting threshold is evaluated. The productivity of products of different grades is iteratively calculated, the productivity is combined with the product quality grade, the contribution of products of different grades to the total revenue is considered, and the revenue loss coefficient is introduced to measure the impact of grade decline on revenue.

[0191] The target waiting threshold analysis calculates the total benefits under different waiting thresholds and plots TR - wt The optimal waiting threshold can be determined by using the curve. This optimal waiting threshold can find a balance between improving product quality (by increasing the proportion of perfectly matched products) and maintaining production efficiency (reducing the waiting time of assembly machines), thereby improving overall productivity. At the same time, by increasing the waiting time, the assembly machine is more likely to find accessories that match the level of the main part, thereby improving the level of the assembled product and thus increasing the market value of the product.

[0192] Therefore, the multi-process assembly system matching strategy optimization method considering the waiting threshold provided in the exemplary embodiment of the present disclosure can improve the grade of the assembled product by setting the target waiting threshold, thereby improving the total revenue of the product.

[0193] The above mainly introduces the solutions provided by the exemplary embodiments of the present disclosure. It is understandable that in order to implement the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0194] The exemplary embodiments of the present disclosure can divide the functional units of the electronic device according to the above method examples. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the exemplary embodiments of the present disclosure is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0195] In the case of dividing each functional module according to each function, an exemplary embodiment of the present disclosure provides a multi-process assembly system matching strategy optimization device considering a waiting threshold, and the multi-process assembly system matching strategy optimization device considering a waiting threshold can be a server or a chip applied to a server. Fig.12 This is a schematic block diagram of the functional modules of a multi-process assembly system matching strategy optimization device considering waiting thresholds provided by the present disclosure. Fig.12 As shown, the multi-process assembly system matching strategy optimization device 1200 considering the waiting threshold includes:

[0196] The data acquisition module 1210 is used to define the quality grade distribution parameters of the workpiece to be assembled;

[0197] The data processing module 1220 is used to evaluate the productivity of the assembly system under the matching strategies with different waiting thresholds based on the quality grade distribution parameters of the workpieces to be assembled;

[0198] The data processing module 1220 is also used to determine the total revenue of the assembly system corresponding to different production rates;

[0199] The data processing module 1220 is further configured to determine the corresponding waiting threshold as the target waiting threshold when the total revenue meets a preset condition.

[0200] In another embodiment provided by the present disclosure, the data processing module 1220 is also used to define the quality grade distribution parameters of the input workpiece; determine the state transfer matrix based on the quality grade distribution parameters of the input workpiece; the state transfer matrix is ​​used to reflect the transition probability of the quality grade of the workpiece to be assembled with different quality grades of the input workpiece, and the workpiece to be assembled is the output workpiece of the processing procedure; based on the quality grade distribution parameters of the input workpiece and the state transfer matrix, determine the quality grade distribution parameters of the workpiece to be assembled.

[0201] In another embodiment provided by the present disclosure, the data processing module 1220 is further used to obtain i The quality grade distribution parameters corresponding to the workpieces to be assembled are: i is a positive integer greater than or equal to 2; based on i The quality grade distribution parameters corresponding to the workpieces to be assembled are respectively used to determine the quality grade distribution parameters of the output products of the assembly process; the quality grade distribution parameters of the output products are expressed as:

[0202]

[0203] in, Indicates the process The quality grade distribution of the output workpieces, , and Indicates the processing steps, Indicates the assembly process; u Indicates the quality level, .

[0204] In another embodiment provided by the present disclosure, the workpiece to be assembled includes a main part and an accessory; the data processing module 1220 is further used to define a matching strategy and set multiple waiting thresholds in the accessory buffer; based on the matching strategy, the marginal probabilities and waiting state probabilities corresponding to the multiple waiting thresholds are determined respectively; the marginal probabilities include a first marginal probability and a second marginal probability, and the first marginal probability indicates that there is a main part in the accessory buffer. i indivual u The probability of the second marginal probability indicates that there is i Non u The probability of level accessories, i =0,1, …, , , the waiting state probability indicates that when the main part is level u , the probability that the assembly process is in a waiting state when the assembly process is in a waiting state; and based on the quality grade distribution parameters, marginal probability and waiting state probability of the workpiece to be assembled, respectively determining the productivity corresponding to the multiple waiting thresholds.

[0205] In another embodiment provided by the present disclosure, the data processing module 1220 is also used to define an iterative formula for assembly efficiency of the assembly process, and to update the marginal probabilities corresponding to each level of accessories; to update the waiting state probability based on the updated marginal probability; and when the preset convergence conditions are met, to determine the updated marginal probability and waiting state probability as the marginal probability and waiting state probability corresponding to the current waiting threshold.

[0206] In another embodiment provided by the present disclosure, the data processing module 1220 is further used to set a profit loss coefficient, wherein the profit loss coefficient represents the profit loss between each level; based on the profit loss coefficient and different productivity, the total profit corresponding to different productivity is determined; the total profit is expressed as:

[0207]

[0208] in, TR represents the total revenue; Indicates the quality level i Product revenue at the time of Indicates the quality level i productivity at the time of d Represents the profit loss coefficient.

[0209] In another embodiment provided by the present disclosure, the data processing module 1220 is also used to set an initial optional range of the waiting threshold; based on the initial optional range, the total revenue corresponding to each waiting threshold is obtained, and it is determined whether the total revenue converges; when the total revenue converges, the waiting threshold corresponding to the maximum total revenue is determined as the target waiting threshold; when the total revenue does not converge, the initial optional range is expanded, and based on the expanded initial optional range, the total revenue corresponding to each waiting threshold is re-obtained.

[0210] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0211] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to the embodiments of the present disclosure.

[0212] Fig.13 The block diagram of the electronic device provided as an example of the present disclosure, the block diagram of the electronic device 1300 that can be used as the server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0213] like Fig.13 As shown, the electronic device 1300 includes a computing unit 1301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1302 or a computer program loaded from a storage unit 1308 into a random access memory (RAM) 1303. In the RAM 1303, various programs and data required for the operation of the electronic device 1300 can also be stored. The computing unit 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0214] A plurality of components in the electronic device 1300 are connected to the I / O interface 1305, including: an input unit 1306, an output unit 1307, a storage unit 1308, and a communication unit 1309. The input unit 1306 may be any type of device capable of inputting information to the electronic device 1300, and the input unit 1306 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1307 may be any type of device capable of presenting information, and may include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1308 may include but is not limited to a disk, an optical disk. The communication unit 1309 allows the electronic device 1300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0215] The computing unit 1301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1301 performs the various methods and processes described above. Each of the methods described above can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1300 via the ROM 1302 and / or the communication unit 1309.

[0216] Fig.14 This is a schematic diagram of an exemplary computer program product provided by the present disclosure. The exemplary embodiment of the present disclosure also provides a computer program product 1400, including a computer program 1401, wherein the computer program 1401, when executed by a processor of a computer, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0217] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0218] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0219] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0220] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0221] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0222] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0223] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc (DVD); it may also be a semiconductor medium, for example, a solid state drive (SSD).

[0224] Although the present disclosure has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely exemplary illustrations of the present disclosure as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A method for optimizing the selection strategy of a multi-process assembly system considering a waiting threshold, characterized in that: The method comprises: Defining quality grade distribution parameters of workpieces to be assembled; the workpieces to be assembled include main parts and accessories; Based on the quality grade distribution parameters of the workpieces to be assembled, the productivity of the assembly system under different waiting threshold selection strategies is evaluated. Determine the total revenue of the assembly system at different production rates; When the total revenue meets the preset condition, the corresponding waiting threshold is determined as the target waiting threshold; Among them, based on the quality grade distribution parameters of the workpieces to be assembled, the productivity of the assembly system corresponding to the matching strategies of different waiting thresholds is evaluated, including: Define the matching strategy and set multiple waiting thresholds in the matching buffer; Based on the matching strategy, the marginal probabilities and waiting state probabilities corresponding to the multiple waiting thresholds are determined respectively; the marginal probabilities include a first marginal probability and a second marginal probability, the first marginal probability indicating that there is a waiting state probability in the accessory buffer. i’ indivual u The probability of the second marginal probability indicates that there is i’ Non u The probability of level accessories, i’ =0,1, …, , u Indicates the quality level, , the waiting state probability indicates that when the main part is level u The probability that the assembly process is in a waiting state when ; Determining the productivity corresponding to the plurality of waiting thresholds respectively based on the quality grade distribution parameters, the marginal probability and the waiting state probability of the workpieces to be assembled; Respectively determining the marginal probabilities and the waiting state probabilities corresponding to the plurality of waiting thresholds includes: Define the assembly efficiency iteration formula of the assembly process and update the marginal probability corresponding to each level of accessories; updating the waiting state probability based on the updated marginal probability; When the preset convergence condition is met, the updated marginal probability and waiting state probability are determined as the marginal probability and waiting state probability corresponding to the current waiting threshold.

2. The method according to claim 1, characterized in that The method of defining the quality grade distribution parameters of the workpieces to be assembled includes: Define the quality grade distribution parameters of the input workpiece; Determine a state transfer matrix based on the quality grade distribution parameters of the input workpiece; the state transfer matrix is ​​used to reflect the transition probability of the quality grade of the workpiece to be assembled from the input workpieces of different quality grades, and the workpiece to be assembled is the output workpiece of the processing process; Based on the quality grade distribution parameters of the input workpieces and the state transfer matrix, the quality grade distribution parameters of the workpieces to be assembled are determined.

3. The method according to claim 2, characterized in that The method further comprises: Get i The quality grade distribution parameters corresponding to the workpieces to be assembled are: i is a positive integer greater than or equal to 2; based on i The quality grade distribution parameters corresponding to the workpieces to be assembled are respectively used to determine the quality grade distribution parameters of the output products of the assembly process; the quality grade distribution parameters of the output products are expressed as: in, Indicates the process The quality grade distribution of the output workpieces, , and Indicates the processing steps, Indicates the assembly process; u Indicates the quality level, .

4. The method according to claim 1, characterized in that Determining the total revenue corresponding to different production rates of the assembly system includes: Setting a profit loss coefficient, wherein the profit loss coefficient represents the profit loss between different levels; Based on the profit loss coefficient and different production rates, the total profit corresponding to different production rates is determined; the total profit is expressed as: in, TR represents the total revenue; Indicates the quality level u Product revenue at the time of Indicates the quality level u productivity at the time of d Represents the profit loss coefficient.

5. The method according to claim 1, characterized in that When the total revenue satisfies a preset condition, determining the corresponding waiting threshold as the target waiting threshold includes: Set the initial selectable range of the wait threshold; Based on the initial optional range, obtaining the total revenue corresponding to each waiting threshold, and determining whether the total revenue converges; When the total revenue converges, the waiting threshold corresponding to the maximum total revenue is determined as the target waiting threshold; When the total benefit has not converged, the initial optional range is expanded, and the total benefits corresponding to each waiting threshold are re-obtained based on the expanded initial optional range.

6. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method of claim 1.

7. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method of claim 1 is implemented.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method of claim 1 is implemented.

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