A hybrid control method for production line design change propagation

Abstract

The present invention is applicable to a field, and in particular, relates to a hybrid control method for propagating design changes of production lines. Compared with the prior art, the present invention obtains production line information, establishes an initial production line parameter association network according to the production line information; sets the initial load and node capacity of each node in the initial production line parameter association network to obtain a production line parameter association network; uses production line changes as input to the production line parameter association network, and the production line parameter association network performs change risk analysis and regulates the production line to obtain a regulation result; and regulates the production line according to the regulation result. The present invention targets the four design dimensions of production line configuration, dynamic type, control type, and optimization type, and adopts a hybrid control method that improves the performance of the complex network of the production line to perform change risk control, effectively solving the network crash caused by design changes of the production line and maximizing the regulation effect.

Description

Technical Field

[0001] The present invention is applicable to a field, and in particular relates to a hybrid control method for propagating design changes of a production line. Background Art

[0002] The production line is defined as a complex manufacturing system with high cost and intensive engineering. The complexity of the production line is mainly reflected in the relationship between the configuration, dynamic, control and optimal design dimensions, which will be mapped to the complex relationship between the design parameters. Changes are everywhere in production line design. Faced with factors such as enterprise technology upgrades, product replacements, and equipment changes, the production line will have to undergo design changes. Design changes will affect any information generated during the development of the production line. Due to the complex associations between different design dimensions, even minor changes in a single design item or component may cause the design change to propagate among the components of the entire production line. However, uncontrollable design changes may cause the production line to fail to complete the transformation within the specified time, fail to complete the production and processing of the product, and affect the order delivery date, which will cause significant losses to the company and related stakeholders.

[0003] At present, faster product updates and shorter product design cycles have become key factors in corporate market competition. Flexible production lines, with their multi-variety and small-batch production characteristics, can quickly adjust production processes and production tasks to meet the flexibility and rapid changes in market demand. With the continuous updating and replacement of products and equipment, the problem of change risk control brought about by flexible production lines has become increasingly prominent. As one of the existing control methods, risk assessment and analysis has the disadvantage of being overly theoretical and difficult to meet the complexity and variability of actual production conditions. In addition, establishing a good communication mechanism to ensure the coordination of the network during the change propagation process, this method still has limitations and cannot fully deal with the problems and risks caused by design changes.

[0004] Therefore, a new hybrid control method for production line design change propagation is urgently needed to solve the above technical problems. Summary of the invention

[0005] The present invention proposes a hybrid control method for propagating design changes of production lines, aiming to solve the network collapse problem caused by design changes of production lines.

[0006] The following steps are involved:

[0007] S1. Acquire production line information, and establish an initial production line parameter association network according to the production line information;

[0008] S2. Setting the initial load and node capacity of each node in the initial production line parameter association network to obtain a production line parameter association network;

[0009] S3, using the production line change situation as the input of the production line parameter association network, the production line parameter association network performs change risk analysis and regulates the production line to obtain a regulation result;

[0010] S4. Regulating the production line according to the regulation result.

[0011] Preferably, the initial load satisfies the following relationship:

[0012] L i (0) = a*[C D (i)+Bet i ];

[0013] Among them, a represents the adjustment parameter, C D (i) represents degree centrality, Bet i represents the betweenness centerline, L i (0) represents the initial load of node i.

[0014] Preferably, the degree centrality and the betweenness centrality satisfy the following relationship:

[0015]

[0016] Among them, k i represents the number of connected edges of node i, and N represents the total number of nodes in the network to which node i belongs;

[0017]

[0018] in, represents the number of paths passing through node i among all the shortest paths between node s and node t, g st Represents the sum of all shortest paths between node s and node t.

[0019] Preferably, the node capacity satisfies the following relationship:

[0020] C i =L i (0)+αL i (0) β ;

[0021] Among them, α and β represent tolerance parameters, C i represents the node capacity of node i.

[0022] Preferably, step S3 includes the following sub-steps:

[0023] S31. When a node receives a change risk, it divides the change risk according to a preset ratio to obtain a first change risk, and transmits the first change to a child node;

[0024] S32, adjusting the tolerance parameter of the child node, and determining whether the first change risk exceeds the node capacity corresponding to the child node after the tolerance parameter is adjusted, and if so, proceeding to step S33;

[0025] S33: reallocate the first change risk according to a preset change risk allocation ratio to obtain a second change risk, and transfer the second change risk to an adjacent child node of the child node.

[0026] Preferably, the preset change risk allocation ratio satisfies the following relationship:

[0027]

[0028] in, represents adjustable parameters, represents the preset change risk allocation ratio, τ i represents the set of child nodes of node i, ω iq represents the weight of the edge connecting node i and node q, ω ij Represents the weight of the edge connecting node i and node j.

[0029] Preferably, in step S33, the node capacity of the subnode can be increased by additional resource investment; wherein the additional resource investment includes equipment resource investment, personnel resource investment and configuration resource investment.

[0030] Preferably, step S3 further includes the following steps:

[0031] S34, quantifying the sensitivity of each node in the production line parameter association network to obtain a corresponding sensitivity value; when a node fails and the corresponding sensitivity value is higher than a preset value, removing the edge connecting the node to the adjacent node.

[0032] Preferably, the sensitivity value satisfies the following relationship:

[0033]

[0034] Among them, S i represents the sensitivity value of node i, N Ω represents the number of adjacent nodes of a node, and Ω represents the set of adjacent nodes.

[0035] Compared with the prior art, the present invention obtains production line information, establishes an initial production line parameter association network based on the production line information; sets the initial load and node capacity of each node in the initial production line parameter association network to obtain a production line parameter association network; uses production line changes as input to the production line parameter association network, performs change risk analysis on the production line and regulates the production line to obtain a regulation result; regulates the production line based on the regulation result. The present invention targets the four design dimensions of production line configuration, dynamic type, control type, and optimization type, and adopts a hybrid regulation method that improves the performance of the complex network of the production line to perform change risk regulation, effectively solving the network crash caused by design changes of the production line and maximizing the regulation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0037] Figure 1 is a flow chart of a hybrid control method for propagating design changes of a production line provided by an embodiment of the present invention;

[0038] Figure 2 It is a schematic diagram of the change propagation process of the production line design change propagation hybrid control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0040] Please refer to Figure 1-Figure 2 The present invention proposes a hybrid control method for propagating design changes of production lines, comprising the following steps:

[0041] S1. Acquire production line information, and establish an initial production line parameter association network based on the production line information.

[0042] In an embodiment of the present invention, based on complex network theory, the design parameters of the four dimensions of configuration, movement, control and optimization of the production line are abstracted into network nodes, and the mutual correlation between the various design parameters in the production line is abstracted into network edges. These points and edges constitute a complex network model of the production line, which clearly represents the complex relationship between the various design parameters.

[0043] S2. Setting the initial load and node capacity of each node in the initial production line parameter association network to obtain the production line parameter association network.

[0044] In the embodiment of the present invention, the change propagation process is essentially a change in one or more parameters, which uses the association between the parameters to trigger the adaptive change of downstream parameters. If the downstream parameters that carry the design change lack the ability to absorb the response changes, they may not be able to complete the design change and cause failure. Whether the design parameters can absorb the design change depends mainly on the maximum design margin on the basis of ensuring performance or original functional requirements.

[0045] The present invention defines the initial load of a node by combining the degree centrality and betweenness centrality of the node. The greater the importance of the node, the greater the initial load. Degree centrality and betweenness centrality represent the importance of the node in the local network and the overall network respectively. The initial load satisfies the following relationship:

[0046] L i (0) = a*[C D (i)+Bet i ];

[0047] Among them, a represents the adjustment parameter, C D (i) represents degree centrality, Bet i represents the betweenness centerline, L i (0) represents the initial load of node i.

[0048] The degree centrality and the betweenness centrality satisfy the following relationship:

[0049]

[0050] Among them, k i represents the number of connected edges of node i, and N represents the total number of nodes in the network to which node i belongs;

[0051]

[0052] in, represents the number of paths passing through node i among all the shortest paths between node s and node t, g st Represents the sum of all shortest paths between node s and node t.

[0053] In the embodiment of the present invention, any design parameter of the production line has its design margin. It is precisely because of the existence of the design margin that the component can absorb a certain change risk. The design margin of the design parameter is related to the initial load. The node capacity satisfies the following relationship:

[0054] C i =L i (0)+αL i (0) β ;

[0055] Among them, α and β represent tolerance parameters with a range of (0,1). Both are positive numbers. The larger the value, the stronger the node's tolerance is, the stronger the node's ability to resist change risks is, and the stronger the robustness is. However, the greater the network investment cost is, C i represents the node capacity of node i.

[0056] In the embodiment of the present invention, please refer to Figure 2 , Figure 2 It is a schematic diagram of the change propagation process of the hybrid control method for propagating design changes in production line provided by an embodiment of the present invention. In the figure, k, r, p, m, s, i, j, q, and n all represent nodes. Based on the cascading failure theory, for the propagation of change loads, since it is a directed weighted network, it is stipulated that the change risk can only be propagated from the upstream node to the downstream node, and is propagated in a certain proportion, and its propagation proportion is related to the weight.

[0057] The weight of node i is defined as: Among them, μ (μ∈(0,+∞)) is an adjustable parameter that controls the size of the node weight. The larger the μ value, the greater the weight of the node.

[0058] Then the edge E in the network ij The edge weight ω ij Defined as:

[0059] If node i changes at time t, all its adjacent downstream nodes will be affected by the change. Starting from time t+1, the propagation of the change risk can be carried out according to the coupling strength between nodes, that is, the edge weight ratio.

[0060] Then the change risk distribution ratio absorbed by node j is:

[0061]

[0062] Among them, i is the upstream node of j, τ i is the set of downstream nodes of node i.

[0063] Then the change load propagated to node j is:

[0064] △L j (t) = ASS j *L i (0) * ;

[0065] Among them, L i (0) * is the change risk generated when node i changes, which is quantified here.

[0066] L i (0)* =L i (0)*ξ;

[0067] Among them, ξ is an adjustment parameter, which is used to make the quantified change risk consistent with the current fault node's propagation capacity. After the change is propagated, the total load on node j is:

[0068] L j (t+1)=△L j (t)+L j (0);

[0069] At this time, if L j (t+1)≤C j , that is, node j does not exceed its node capacity after absorbing the change risk, and this design parameter will not fail; on the contrary, if L j (t+1)≥C j , then if the design node j fails, it needs to continue to allocate its own change risk to the downstream node set and conduct a new round of change risk allocation process. Cascading failures gradually spread in complex networks.

[0070] S3. Using the production line change situation as the input of the production line parameter association network, the production line parameter association network performs change risk analysis and regulates the production line to obtain a regulation result.

[0071] S4. Regulating the production line according to the regulation result.

[0072] In the embodiment of the present invention, step S3 includes the following sub-steps:

[0073] S31. When a node receives a change risk, it divides the change risk according to a preset ratio to obtain a first change risk, and transmits the first change to a child node; specifically, this step ensures that the change risk will not have a serious impact on the core functions and stability of the network. On the other hand, the network's own attributes can help evaluate the system's robustness and anti-interference ability, providing an objective basis for actual production line processing design changes, thereby minimizing the change risk.

[0074] S32, adjust the tolerance parameters of the child node, determine whether the first change risk exceeds the node capacity corresponding to the child node after adjusting the tolerance parameters, and if so, proceed to step S33. Specifically, the design parameters themselves have the redundant ability to resist the change risk, and the ability of the design parameters to resist the change risk can be enhanced by increasing investment, and the tolerance parameters α, β values ​​can be adjusted in the node capacity model. Of course, in specific cases, it will be constrained by cost, and appropriate tolerance parameter values ​​need to be selected to achieve the ideal control effect.

[0075] S33: reallocate the first change risk according to a preset change risk allocation ratio to obtain a second change risk, and transfer the second change risk to an adjacent child node of the child node.

[0076] Specifically, according to the nonlinear model of node load capacity, if the load of the current node reaches the failure capacity threshold, the risk redistribution principle is adopted to distribute the quantified change risk to adjacent downstream nodes in proportion to the remaining absorption capacity, thereby making the parameter association network flexible and controllable.

[0077] Since the change risk is propagated according to the weight ratio, the risk handling capacity of a node is evaluated from the perspective of the remaining capacity of the node. If the remaining capacity of a node is small, then its handling capacity is relatively weaker, and the allocated change risk is also smaller. In addition, combining the remaining capacity of the node with the weight to formulate the redistribution principle can more comprehensively measure the strength of the node's remaining change risk absorption capacity from a local perspective.

[0078] The preset change risk allocation ratio satisfies the following relationship:

[0079]

[0080] in, represents adjustable parameters, represents the preset change risk allocation ratio, τ i represents the set of child nodes of node i, ω iq represents the weight of the edge connecting node i and node q, ω ij Represents the weight of the edge connecting node i and node j.

[0081] In the embodiment of the present invention, in step S33, the node capacity of the subnode can be increased by additional resource investment; wherein the additional resource investment includes equipment resource investment, personnel resource investment and configuration resource investment.

[0082] Specifically, after the change risk occurs, if the child node j absorbs the change risk and exceeds its node capacity, ΔC is added j The size of the additional resource investment is determined by the importance of the design parameters. The more important the design parameters are, the greater the additional resources invested.

[0083] ΔC j =C j *η;

[0084] Among them, η is an adjustable parameter (η∈(0,1)), which is used to control the input of additional resources and minimize the input cost.

[0085] At this time, the node capacity of child node j is Cj +ΔC j .

[0086] In the embodiment of the present invention, step S3 further includes the following steps:

[0087] S34, quantifying the sensitivity of each node in the production line parameter association network to obtain a corresponding sensitivity value; when a node fails and the corresponding sensitivity value is higher than a preset value, removing the edge connecting the node to the adjacent node.

[0088] In the embodiment of the present invention, the sensitivity value satisfies the following relationship:

[0089]

[0090] Among them, S i represents the sensitivity value of node i, N Ω represents the number of adjacent nodes of a node, and Ω represents the set of adjacent nodes.

[0091] Specifically, step S34 quantifies the sensitivity values ​​of different parameter nodes based on the parameter node sensitivity analysis method, and identifies each parameter node and propagation link. By blocking the parameter change propagation link with high sensitivity, the change propagation impact is blocked. The entire implementation process that can effectively block is that if node i fails and the node sensitivity value is high, its edges with adjacent nodes are removed to achieve the effect of blocking the propagation link, thereby avoiding the large-scale spread of change risks. At the same time, when propagation isolation and increased sensitivity of variable points still cannot control the avalanche-like change propagation of parameters, consider adjusting the system structure. By redefining the extraction granularity of parameters, using generalized encapsulation, modularization and other technologies, a new parameter characterization system is extracted, a new production line parameter association network is constructed, and the parameter change propagation process analysis is re-performed.

[0092] Compared with the prior art, the present invention obtains production line information, establishes an initial production line parameter association network based on the production line information; sets the initial load and node capacity of each node in the initial production line parameter association network to obtain a production line parameter association network; uses production line changes as input to the production line parameter association network, performs change risk analysis on the production line and regulates the production line to obtain a regulation result; regulates the production line based on the regulation result. The present invention targets the four design dimensions of production line configuration, dynamic type, control type, and optimization type, and adopts a hybrid regulation method that improves the performance of the complex network of the production line to perform change risk regulation, effectively solving the network crash caused by design changes of the production line and maximizing the regulation effect.

[0093] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0094] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A hybrid control method for propagating design changes of production lines, characterized in that: The following steps are involved: S1. Acquire production line information, and establish an initial production line parameter association network according to the production line information; S2. Setting the initial load and node capacity of each node in the initial production line parameter association network to obtain a production line parameter association network; S3, using the production line change situation as the input of the production line parameter association network, the production line parameter association network performs change risk analysis and regulates the production line to obtain a regulation result; S4, regulating the production line according to the regulation result; The initial load satisfies the following relationship: ； in, represents the adjustment parameters, represents degree centrality, represents the betweenness centrality, Representation Node said initial load; The degree centrality and the betweenness centrality satisfy the following relationship: ； in, To represent the node The number of connected edges, N represents the node The total number of nodes in the network; ； in, Representation Node s With Node t All shortest paths between nodes i The number of paths, Representation Node s With Node t The sum of all shortest paths between; The node capacity satisfies the following relationship: ； in, and represents the tolerance parameter, Representation Node i The node capacity of Step S3 includes the following sub-steps: S31. When a node receives a change risk, it divides the change risk according to a preset ratio to obtain a first change risk, and transmits the first change risk to a child node; S32, adjusting the tolerance parameter of the child node, and determining whether the first change risk exceeds the node capacity corresponding to the child node after the tolerance parameter is adjusted, and if so, proceeding to step S33; S33: reallocate the first change risk according to a preset change risk allocation ratio to obtain a second change risk, and transfer the second change risk to an adjacent child node of the child node; The preset change risk allocation ratio satisfies the following relationship: ； in, represents adjustable parameters, represents the preset change risk allocation ratio, Representation Node i The collection of child nodes of Representation Node i and nodes q The weight of the edge connecting them, Represents node i and node j The weight of the edge connecting them, and Respectively represent nodes j and nodes q The node capacity.

2. The hybrid control method for propagating design changes of production lines according to claim 1, characterized in that: In step S33, the node capacity of the subnode can be increased by additional resource investment; wherein the additional resource investment includes equipment resource investment, personnel resource investment and configuration resource investment.

3. The hybrid control method for propagating design changes of production lines according to claim 1, characterized in that: Step S3 further includes the following steps: S34, quantifying the sensitivity of each node in the production line parameter association network to obtain a corresponding sensitivity value; when a node fails and the corresponding sensitivity value is higher than a preset value, removing the edge connecting the node to the adjacent node.

4. The hybrid control method for propagating design changes of production lines according to claim 3, characterized in that: The sensitivity value satisfies the following relationship: = *[1 - ]； in, Representation Node i The sensitivity value of Indicates the number of adjacent nodes of a node. represents the set of adjacent nodes, Representation Node i The node capacity.

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