An assembly line rapid reconfiguration method based on forward variant network

Abstract

The application discloses an assembly line quick reconfiguration method based on a positive variant network, summarizes a multi-domain conduction principle of "product similarity-system configuration similarity-system execution similarity" of a man-machine cooperation assembly line, describes physical and logical reconfiguration connotation of the man-machine cooperation assembly line in five dimensions of "function-structure-behavior-execution-performance", comprehensively considers each aspect of the assembly line, and improves comprehensiveness and coordination of overall design. In a flexible assembly line design process with the assembly line quick reconfiguration design as a core, a parameterized node network model and set pair theory are adopted, so that an optimal assembly line change scheme can be more scientifically and objectively selected, and a time period from information summarization to scheme implementation is shortened. Depending on actual production conditions of an enterprise, a mathematical model with change propagation risk, change propagation time and change propagation cost as comprehensive targets is constructed, fine management and cost optimization are realized, and cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of assembly line reconstruction, and in particular to a method for rapid assembly line reconstruction based on a forward variant network. Background Art

[0002] In a market environment characterized by rapidly changing demands, companies are faced with frequent product design updates, which places higher demands on flexible production lines. Traditional assembly line upgrades or reorganizations often focus on single-aspect adjustments, such as focusing solely on structural changes while ignoring the combined impact of function, behavior, execution, and performance. This lacks a systematic and comprehensive understanding of production line reconfiguration requirements, resulting in low system efficiency. Furthermore, traditional assembly line upgrade or reorganization technologies often rely on subjective manual evaluation, calculation, and design. Reconfiguring production lines is time-consuming, costly, and error-prone. The lack of refined management in risk assessment, time, and cost forecasting for change propagation makes it difficult to adapt to complex and changing market demands. Furthermore, in traditional assembly line management, each device operates independently, preventing information sharing and reducing decision-making efficiency. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for rapid reconstruction of an assembly line based on a forward variant network.

[0004] To achieve the above objectives, the technical solutions provided by the present invention are:

[0005] A method for rapid assembly line reconfiguration based on a forward variant network, comprising:

[0006] S1. Confirm product change requirements, including changes to product features and expected effects;

[0007] S2. Analyze the impact of the change on the existing assembly line;

[0008] S3. Gradually decompose the functions involved in the change and organize them into different modules;

[0009] S4, characterize the physical and logical reconstruction relationship of the human-machine collaborative assembly line through multi-dimensional information;

[0010] S5. Based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line, a network model reflecting the assembly line structure is preliminarily established;

[0011] S6. Optimizing the established network model reflecting the assembly line structure to obtain a network association relationship model between nodes based on the network topology structure;

[0012] S7. Optimizing the network association relationship model between nodes based on the network topology structure to obtain a multi-dimensional change model;

[0013] S8. Optimize the multi-dimensional change model to obtain a mathematical model that quantifies the change propagation path;

[0014] S9. Rapidly reconfigure assembly lines through a mathematical model that quantifies change propagation paths.

[0015] Furthermore, based on the multi-domain conduction principle of “product similarity-system configuration similarity-system execution similarity”, the impact of the change on the existing assembly line is analyzed.

[0016] Furthermore, the five dimensions of "function-structure-behavior-execution-performance" are used to characterize the physical and logical reconstruction relationship of the human-machine collaborative assembly line, ensuring that the basic functional characteristics of the module, physical structure layout, interactive behavior patterns between modules, execution efficiency and final output performance are taken into account during the change process, thereby achieving compatibility and consistency at the physical and logical levels.

[0017] Furthermore, based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line, a network model reflecting the assembly line structure was preliminarily established, including:

[0018] Analyze the relationship between modules based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line;

[0019] Identify the key components in each module, parameterize the key components, and convert the key attributes of the key components into operational parameters;

[0020] Use parameterized key components as nodes and establish connections between nodes;

[0021] Determine the degree of correlation between nodes and preliminarily establish a network model that reflects the assembly line structure.

[0022] Furthermore, the correlation between modules, the correlation between nodes and the degree of correlation between nodes are analyzed based on performance correlation, function correlation, behavior correlation and structural correlation.

[0023] Furthermore, the established network model reflecting the assembly line structure is optimized, including:

[0024] By simplifying complex multiple dependencies into single or bidirectional dependencies, the coupling and restrictions between various components and their changing characteristics on the assembly line are set.

[0025] Furthermore, the network association relationship model between nodes based on the network topology structure is optimized, including:

[0026] Set pair theory is used to characterize different aspects of the assembly line to ensure the comprehensiveness and accuracy of the model. At the same time, relational equations and mapping matrices are used to define the coupling and constraint relationships between key components to ensure that the model can reflect the complexity of reality.

[0027] Furthermore, the multi-dimensional change model is optimized, including:

[0028] Taking change propagation risk, change propagation time and change propagation cost as key indicators, the change propagation path planning goals are defined. According to the actual situation of the enterprise and the market, the goal priorities and optimization strategies are set to obtain a mathematical model for quantifying the change propagation path.

[0029] Compared with the existing technology, the principles and advantages of this technical solution are as follows:

[0030] 1. The multi-domain conduction principle of "product similarity-system configuration similarity-system execution similarity" of the human-machine collaborative assembly line is summarized, and the five dimensions of "function-structure-behavior-execution-performance" are used to characterize the physical and logical reconstruction connotations of the human-machine collaborative assembly line. This comprehensively considers all aspects of the assembly line and improves the comprehensiveness and coordination of the overall design.

[0031] 2. In the flexible production line design process centered on rapid production line variation design, the use of parametric node network models and set pair theory can more scientifically and objectively select the optimal production line change plan, shortening the time period from information induction to plan implementation.

[0032] 3. Based on the company's actual production situation, a mathematical model is constructed with comprehensive objectives, including change propagation risk, change propagation time, and change propagation cost, to achieve refined management and cost optimization, thereby reducing costs. At the same time, the use of forward variant network models makes production lines more flexible.

[0033] 4. Use high-level semantic models for multi-dimensional change modeling, effectively integrate and share information, break down information silos, strengthen collaboration between modules, and improve decision-making efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for use in the embodiments or the prior art descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 This is a principle flow chart of a method for rapid reconstruction of an assembly line based on a forward variant network according to the present invention;

[0036] Figure 2 Assembly line module analysis schematic diagram;

[0037] Figure 3 Assembly line change feature association modeling schematic diagram;

[0038] Figure 4 Module and feature dependency relationship simplification schematic diagram;

[0039] Figure 5 "Function-structure-behavior-execution-performance" forward variant network model diagram. DETAILED DESCRIPTION

[0040] The application will be further described below in conjunction with specific embodiments:

[0041] As Figure 1 shown, the assembly line rapid reconfiguration method based on the forward variant network described in the embodiment includes the following steps:

[0042] S1, confirming product change requirements, including product feature change points and expected effects;

[0043] S2, based on the multi-domain conduction principle of "product similarity-system configuration similarity-system execution similarity", analyze the impact of the change on the existing assembly line;

[0044] S3, decompose each function involved in the change step by step, and summarize it into different modules;

[0045] As Figure 2 shown, as the module analysis of the mobile phone assembly line, the total function of the assembly line is first decomposed into basic function 1, basic function 2, auxiliary function 1, auxiliary function 2, etc., and further refined into basic module 1, basic module 2, auxiliary module 1, auxiliary module 2, etc. According to the above modules, the specific assembly process of the mobile phone assembly line is constructed, including mechanical hand, two sides with lifting equipment, clamp rotating equipment, double-sided adhesive tape equipment, auxiliary material equipment, shell screw mechanism, feeding machine equipment, pressing equipment, equipment positioning mechanism, and circulating turnover mechanism.

[0046] S4, after careful modular decomposition of the original assembly line, an important step is to conduct in-depth internal relationship analysis on these independent modules. This process not only requires understanding the role and function of each module in the production line from a macro perspective, but also must meticulously explore the complex interaction mechanism between modules.

[0047] This step uses the five dimensions of "function-structure-behavior-execution-performance" to characterize the physical and logical reconstruction relationship of the human-machine collaborative assembly line, ensuring that the basic functional characteristics of the module, physical structure layout, interactive behavior patterns between modules, execution efficiency and final output performance are taken into account during the change process, thereby achieving compatibility and consistency at the physical and logical levels.

[0048] S5. Based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line, a network model reflecting the assembly line structure is preliminarily established;

[0049] This step includes:

[0050] Analyze the relationship between modules based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line;

[0051] Identify the key components in each module, parameterize the key components, and convert the key attributes of the key components into operational parameters;

[0052] Use parameterized key components as nodes and establish connections between nodes;

[0053] Determine the degree of correlation between nodes and preliminarily establish a network model that reflects the assembly line structure.

[0054] like Figure 3 As shown above, the correlation between modules, the correlation between nodes and the degree of correlation between nodes are analyzed based on performance correlation (W1), function correlation (W2), behavior correlation (W3) and structure correlation (W4).

[0055] Through systematic and comprehensive analysis, we can not only deeply understand the internal connections between modules, but also quickly and accurately adjust the configuration of the assembly line when faced with product change requirements, ensuring that the production line can respond efficiently and flexibly to rapid market changes.

[0056] S6. Optimizing the established network model reflecting the assembly line structure to obtain a network association relationship model between nodes based on the network topology structure;

[0057] To more intuitively and comprehensively understand the interactions between modules, a principle of simplicity is followed: when expressing associations, unidirectional associations are preferred due to their inherent clarity and directness. Secondly, if complexity increases slightly, simple bidirectional associations can effectively balance difficulty of understanding with information integrity, and are therefore preferable to more complex, complex bidirectional associations. Therefore, this implementation prefers to simplify complex, multiple dependencies into single or bidirectional dependencies, while using these relationships to describe the coupling and constraint relationships between assembly line components and change features. Building a suitable complex network association model between parameter nodes based on the characteristics of the network topology makes assembly line components easier to track and manage, allowing companies to efficiently identify and analyze the chain reactions caused by component changes, providing a solid foundation for the flexible reconstruction of production lines.

[0058] S7. Optimize the network association relationship model between nodes based on the network topology structure to obtain a multi-dimensional change model;

[0059] In this step, optimization includes:

[0060] Set pair theory is used to characterize different aspects of the assembly line to ensure the comprehensiveness and accuracy of the model. At the same time, relational equations and mapping matrices are used to define the coupling and constraint relationships between key components to ensure that the model can reflect the complexity of reality.

[0061] S8. Optimize the multi-dimensional change model to obtain a mathematical model that quantifies the change propagation path;

[0062] In order to ensure the effective application and implementation of the model, this embodiment relies on the actual production situation of the enterprise, studies the comprehensive goals of designing the change propagation path with change propagation risk, change propagation time and change propagation cost, clarifies the optimization rules of each goal, clarifies and sets the calculation rules of different change goals in the change propagation path planning, establishes the mathematical model of the change propagation path planning problem, and finally establishes the "function-structure-behavior-execution-performance" forward variation network driven by product change requirements, such as Figure 5 As shown in the figure, M1 through M7 represent different functional modules. This network model reflects the interrelationships between function, structure, behavior, execution, and performance, systematically addressing the problem of planning the optimal change propagation path while considering these multiple objective constraints. This model enables production lines to respond quickly and accurately to product change requests, adjusting production lines to keep pace with the rapid pace of market change, and providing powerful tools for complex production line change management.

[0063] S9. Rapidly reconfigure the assembly line using a mathematical model that quantifies the change propagation path. The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes based on the shape and principles of the present invention are intended to fall within the scope of protection of the present invention.

Claims

1. A method for rapid assembly line reconstruction based on forward variant network, characterized in that: include: S1. Confirm product change requirements, including changes to product features and expected effects; S2. Analyze the impact of the change on the existing assembly line; S3. Gradually decompose the functions involved in the change and summarize them into different modules; S4, characterize the physical and logical reconstruction relationship of the human-machine collaborative assembly line through multi-dimensional information; S5. Based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line, a network model reflecting the assembly line structure is preliminarily established; S6. Optimizing the established network model reflecting the assembly line structure to obtain a network association relationship model between nodes based on the network topology structure; S7. Optimize the network association relationship model between nodes based on the network topology structure to obtain a multi-dimensional change model; S8. Optimize the multi-dimensional change model to obtain a mathematical model that quantifies the change propagation path; S9. Rapidly reconfigure assembly lines through a mathematical model that quantifies change propagation paths.

2. The method for rapid assembly line reconstruction based on a forward variant network according to claim 1, characterized in that: Based on the multi-domain conduction principle of "product similarity-system configuration similarity-system execution similarity", the impact of changes on existing assembly lines is analyzed.

3. The method for rapid assembly line reconstruction based on a forward variant network according to claim 1, characterized in that: The five dimensions of "function-structure-behavior-execution-performance" are used to characterize the physical and logical reconstruction relationship of the human-machine collaborative assembly line, ensuring that the basic functional characteristics of the module, physical structure layout, interactive behavior patterns between modules, execution efficiency and final output performance are taken into account during the change process, thereby achieving compatibility and consistency at the physical and logical levels.

4. The method for rapid assembly line reconstruction based on a forward variant network according to claim 1, characterized in that: Based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line, a network model reflecting the assembly line structure was preliminarily established, including: Analyze the relationship between modules based on the physical and logical reconstruction relationship of the human-machine collaborative assembly line; Identify the key components in each module, parameterize the key components, and convert the key attributes of the key components into operational parameters; Use parameterized key components as nodes and establish connections between nodes; Determine the degree of correlation between nodes and preliminarily establish a network model that reflects the assembly line structure.

5. The method for rapid assembly line reconstruction based on forward variant network according to claim 1, characterized in that: The correlation between modules, the correlation between nodes and the degree of correlation between nodes are analyzed based on performance correlation, functional correlation, behavioral correlation and structural correlation.

6. The method for rapid assembly line reconstruction based on forward variant network according to claim 1, characterized in that: Optimize the network model that reflects the assembly line structure, including: By simplifying complex multiple dependencies into single or bidirectional dependencies, the coupling and restrictions between various components and their changing characteristics on the assembly line are set.

7. The method for rapid assembly line reconstruction based on a forward variant network according to claim 1, characterized in that: Optimize the network association relationship model between nodes based on the network topology structure, including: Set pair theory is used to characterize different aspects of the assembly line to ensure the comprehensiveness and accuracy of the model. At the same time, relational equations and mapping matrices are used to define the coupling and constraint relationships between key components to ensure that the model can reflect the complexity of reality.

8. The method for rapid assembly line reconstruction based on a forward variant network according to claim 1, characterized in that: Optimize the multi-dimensional change model, including: Taking change propagation risk, change propagation time and change propagation cost as key indicators, the change propagation path planning goals are defined. According to the actual situation of the enterprise and the market, the goal priorities and optimization strategies are set to obtain a mathematical model for quantifying the change propagation path.

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