A risk propagation analysis method for production line design changes

Abstract

The present invention is applicable to the technical field of production lines, and in particular, relates to a risk propagation analysis method for production line design changes. The present invention obtains production line information, establishes a first production line parameter association network according to the production line information; sets the initial load and node capacity of each node in the first production line parameter association network; uses the production line change data as the input of the second production line parameter association network, and the second production line parameter association network calculates the change risk of the production line according to the production line change data to obtain the change propagation result; the change propagation result is analyzed by the normal state rate of the nodes in the second production line parameter association network. The robustness of the second production line parameter association network is analyzed to obtain the analysis result. Compared with the prior art, the present invention can effectively analyze the dynamic process of design change propagation, and understand the impact of dynamic change propagation on various parts of the production line, so as to better optimize the production line.

Description

Technical Field

[0001] The present invention is applicable to the technical field of production lines, and in particular relates to a risk propagation analysis method for production line design changes. 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 relationship 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 brings significant losses.

[0003] Different models have been developed to analyze the risk of changes in the design process based on different models, but these models are studied from the perspective of the product, and lack research on the risk propagation of multidimensional design changes in the production line. The design parameters of the production line are higher in dimension, the parameter correlation is more complex, and the parameter change propagation process is richer, which poses a challenge to understanding the propagation of design change risks. At the same time, some studies have shown that design changes are dynamic in nature, and this feature may affect the propagation path and consequences of specific design changes. These studies have qualitatively analyzed the dynamic process of design change propagation in specific situations and cannot explain the general law of dynamic propagation.

[0004] Changes in market demand, technological progress, and cost optimization will lead to changes in product production lines, which will lead to design changes in production lines. In order to clarify the design change process of the production line, it is necessary to mathematically model the production line, but the traditional design change modeling method has the following disadvantages:

[0005] 1. Existing design change modeling is mainly based on products and lacks modeling methods for production lines.

[0006] 2. Lack of modeling methods for various design dimensions of production lines.

[0007] 3. There is a lack of modeling methods for the dynamic propagation process of design changes.

[0008] Therefore, the present invention proposes a new risk propagation analysis method for production line design changes to solve the above problems. Summary of the invention

[0009] The present invention proposes a risk propagation analysis method for production line design changes, aiming to solve the problem of multi-dimensional network modeling of production lines and network establishment for the dynamic propagation of production line design changes. At the same time, a network robustness index measurement standard is established, which can effectively obtain the impact of design changes on the production line.

[0010] The present invention proposes a risk propagation analysis method for production line design changes, comprising the following steps:

[0011] S1. Acquire production line information, and establish a first production line parameter association network according to the production line information;

[0012] S2. Setting the initial load and node capacity of each node in the first production line parameter association network to obtain a second production line parameter association network;

[0013] S3, taking the production line change data as the input of the second production line parameter association network, and the second production line parameter association network calculates the change risk of the production line according to the production line change data to obtain a change propagation result;

[0014] S4. Analyze the robustness of the second production line parameter association network by using the change propagation result and the normal state rate of the nodes in the second production line parameter association network to obtain an analysis result.

[0015] Preferably, the initial load of node i is defined as L i , then the initial load satisfies the following relationship:

[0016]

[0017] in, represents the out-degree of node i, represents the in-degree of node i, represents the set of downstream nodes of node i, Indicates the average influence of the upstream node corresponding to parameter node i on node i.

[0018] Preferably, the node capacity of node i is defined as C i , then the node capacity satisfies the following relationship:

[0019] C i =L i +β(L i ) α ;

[0020] Among them, α and β represent capacity parameters.

[0021] Preferably, step S3 includes the following sub-steps:

[0022] S31, calculating the remaining margin value of the downstream node corresponding to the node where the change occurs in the production line;

[0023] S32. Select a node with the largest remaining margin value among the downstream nodes for change propagation, and calculate the load generated by the change propagation to obtain the change propagation result.

[0024] Preferably, in step S32, the upstream node of node i is defined as node j, and the load generated by change propagation satisfies the following relationship:

[0025]

[0026] Among them, P i (t) indicates that when node j fails, node i is subject to the additional load of node j at time t; γ ji Propagate parameters for dimensions; represents the upstream node set of node j at time t-1; d ji represents the logical relationship between node j and node i, k ij (t) represents the judgment parameter, e ji (t) represents the edge relationship between node j and node i at time t, w ji Indicates the influence of node j on node i.

[0027] Preferably, in step S4, the analysis result satisfies the following relationship:

[0028]

[0029] Wherein, R(t) represents the robustness of the second production line parameter association network at time t, n is the number of nodes in the second production line parameter association network; S i (t) represents the node state function of node i at time t, and the node state function satisfies:

[0030]

[0031] Among them, x i (t) represents the total load of node i at time t.

[0032] Compared with the prior art, the present invention obtains production line information, establishes a first production line parameter association network according to the production line information; sets the initial load and node capacity of each node in the first production line parameter association network to obtain a second production line parameter association network; uses production line change data as input to the second production line parameter association network, and the second production line parameter association network calculates the change risk of the production line according to the production line change data to obtain the change propagation result; the change propagation result analyzes the robustness of the second production line parameter association network through the normal state rate of the nodes in the second production line parameter association network to obtain the analysis result. It can effectively analyze the dynamic process of design change propagation, understand the impact of dynamic change propagation on various parts of the production line, and thus better optimize the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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:

[0034] Figure 1 It is a flowchart of a risk propagation analysis method for production line design changes provided by an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of a second production line parameter association network structure of the risk propagation analysis method for production line design changes provided by an embodiment of the present invention;

[0036] Figure 3 It is a schematic diagram of the change propagation logic relationship of the risk propagation analysis method for production line design changes provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] Please refer to Figure 1-Figure 3 The present invention provides a risk propagation analysis method for production line design changes, comprising the following steps:

[0039] S1. Acquire production line information, and establish a first production line parameter association network according to the production line information.

[0040] In the embodiment of the present invention, based on the multi-layer network theory, the configuration, dynamic, control and optimal models of the production line are abstracted into four-layer networks respectively. Each layer of the network is composed of parameter nodes of the same nature. The relationship between different parameter nodes of the production line is abstracted as edges. The broken line points and edges constitute the multi-layer network model of the production line, which clearly represents the complex relationship between various design parameters. Among them, the configuration refers to the layout network model of each equipment of the production line, the dynamic model refers to the processing action and logistics transportation network model of each equipment of the production line, the control model refers to the network model for controlling each equipment of the production line, and the optimal model refers to the network model for iteratively optimizing the layout structure of the production line.

[0041] S2. Setting the initial load and node capacity of each node in the first production line parameter association network to obtain a second production line parameter association network;

[0042] 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.

[0043] In the parameter association network of the second production line, when a design change occurs and the current parameter node margin is not enough to support this change, this parameter needs to be redesigned. The redesign of the parameter node will definitely lead to changes in the downstream parameters of the network. For example, the redesign of the fixture size under the configuration will definitely lead to changes in the downstream node "dynamic fixture clamping speed". The redesign of the parameter node with a large out-degree will inevitably affect more nodes. Therefore, the initial load of node i is defined as L i , then the initial load satisfies the following relationship:

[0044]

[0045] in, represents the out-degree of node i, represents the in-degree of node i, represents the set of downstream nodes of node i, Indicates the average impact of the upstream node corresponding to the parameter node i on the node i. To avoid the initial load of the network edge node being zero, the initial load starting value L is set s , L s ≥0. is the average influence of the upstream nodes on the parameter node i, and its calculation method is expressed as follows:

[0046]

[0047] Node Set is the upstream parameter node set of parameter node i at time 0, N is the total number of nodes in the upstream parameter node set of parameter node i at time 0, n is the total number of all parameter nodes in the parameter association network of the second production line, e ji (0) indicates whether there is an edge between node j and node i at time 0, w ji Indicates the influence of node j on node i.

[0048] When a production line design change occurs, each parameter has a certain risk resistance. For example, the working range of the robot and the processing beat can be adjusted to meet the requirements of the variant design. The parameter node with a high initial load is more important in the entire production line. In order to avoid design changes at important nodes as much as possible, its risk resistance should be set higher. In order to describe the relationship between risk resistance and load, the node risk resistance is expressed as C i , expressed by the load-capacity nonlinear model, satisfies the following relationship:

[0049] C i =L i +β(L i ) α (3);

[0050] Among them, α and β represent capacity parameters.

[0051] S3, taking the production line change data as the input of the second production line parameter association network, and the second production line parameter association network calculates the change risk of the production line according to the production line change data to obtain a change propagation result;

[0052] In the embodiment of the present invention, there is a logical selection in the propagation of changes in the parameter association network of the second production line. For example, the "movement speed of the robot" in the dynamic type is redesigned due to the change load, which will affect the parameter nodes such as "movement of the robot along the X-axis" and "movement of the robot along the Y-axis". Some parameter changes have a selective effect on the impact of downstream nodes. For example, the failure of the optimal parameter node "work efficiency" due to the change load will affect the "positioning efficiency" and "alignment efficiency", but generally one of them is selected for load propagation. Figure 3 As shown in the figure, “AND” means that the failed node 1 will propagate the change load to both node 2 and node 3, and “OR” means that node 1 will select one node between node 2 and node 3 for propagation.

[0053] Step S3 includes the following sub-steps:

[0054] S31, calculating the remaining margin value of the downstream node corresponding to the node where the change occurs in the production line;

[0055] Specifically, in actual situations, production line staff will give priority to adjusting equipment with more residual margins to ensure that the selected equipment can still work normally without redesign. In order to obtain the change propagation mathematical model formula, first define the residual margin of parameter node i at time t, as expressed in formula (4):

[0056] m i (t) = C i -x i (t) (4);

[0057] x i (t) is the load of parameter node i at time t. In order to determine which node downstream of parameter node i has a larger residual margin value at time t, the judgment parameter k is introduced. ij (t), which can be expressed by formula (5) as follows:

[0058]

[0059] e ji (t) indicates whether there is an edge between node j and node i at time t.

[0060] Node i can have multiple downstream parameter nodes with the same residual margin value, and when encountering or logical association, change propagation will only select one node for propagation. Therefore, it is necessary to calculate how many nodes with the same residual margin value exist at parameter node i at time t. Formula (6) is defined to calculate the number of nodes with the same residual margin at node i at time t. When the number of identical nodes is greater than 1, the downstream node can be randomly selected for propagation.

[0061]

[0062] is the upstream node set of node i at time t, e ji (t) indicates whether there is an edge between node j and node i at time t.

[0063] S32. Select a node with the largest remaining margin value among the downstream nodes for change propagation, and calculate the load generated by the change propagation to obtain the change propagation result.

[0064] In the embodiment of the present invention, when the relationship between node i and upstream node j is an "AND" relationship, when node j fails, the additional load of node j on node i at time t is expressed as follows by formula (7):

[0065]

[0066] Among them, γ ji is the dimension propagation parameter, is the upstream node set of parameter node j at time (t-1). The dimension propagation parameter is a k×k matrix, where k is the number of layers in the network. Dimension propagation transmission indicates the change in propagation load ratio between nodes of different dimensions or the same level of dimensions.

[0067] When the relationship between node i and upstream node j is an "OR" relationship, when node j fails, the additional load on node i at time t due to node j can be expressed by formula (8) as follows:

[0068]

[0069] Combining equations (7) and (8), we can obtain the additional load of node i at time t based on the logical relationship of propagation between nodes. The load generated by change propagation is as shown in equation (9):

[0070]

[0071] Among them, P i (t) indicates that when node j fails, node i is subject to the additional load of node j at time t; γ ji Propagate parameters for dimensions; represents the upstream node set of node j at time t-1; d ji represents the logical relationship between node j and node i, k ij (t) represents the judgment parameter, e ji (t) represents the edge relationship between node j and node i at time t, w ji Indicates the influence of node j on node i.

[0072] The total load at time t is expressed as formula (10):

[0073] x i (t) = P i (t)+x i (t-1) (10);

[0074] Among them, x i (0) = L i .

[0075] S4. Analyze the robustness of the second production line parameter association network by using the change propagation result and the normal state rate of the nodes in the second production line parameter association network to obtain an analysis result.

[0076] In the embodiment of the present invention, when a design change occurs, the designer hopes that as few parameter nodes as possible will be changed to avoid excessive change costs. Therefore, the normal state rate of the node at time t is used to evaluate the robustness of the parameter association network, as shown in formula (11), where n is the number of nodes in the second production line parameter association network. From this formula, it can be seen that R(t) is a number in the range of (0, 1). Within a limited time range, the larger the R(t) value, the better the robustness of the second production line parameter association network. The analysis results satisfy the following relationship:

[0077]

[0078] Wherein, R(t) represents the robustness of the second production line parameter association network at time t, n is the number of nodes in the second production line parameter association network; S i (t) represents the node state function of node i at time t, and the node state function satisfies:

[0079]

[0080] Among them, x i (t) represents the total load of node i at time t. i (t) is the node status function, which is used to describe whether node i fails at time t. When node i fails at time t, S i (t) value is equal to 0 otherwise it is equal to 1.

[0081] Exemplarily, the present invention obtains a process curve of load propagation of design change changes of multi-layer network nodes of a mobile phone production line; obtains the relationship between capacity parameters α and β and the robustness of the production line parameter association network by analyzing the combination of capacity parameters α and β, and provides certain guidance for effectively balancing the initial investment and the cost of subsequent maintenance or replacement; analyzes the influence of dimension parameter γ on the robustness of the parameter association network corresponding to the mobile phone production line; performs single-point attack and multi-point attack on the four layers of the production line parameter association network, namely, configuration, operation, control, and optimization, respectively, and finds that the configuration has the greatest influence on the robustness of the production line parameter association network; finally, performs design changes on the mobile phone line using three methods, namely, out-degree-based attack, initial load-based attack, and random attack, and finds that the out-degree-based attack has the greatest influence on the production line.

[0082] Compared with the prior art, the present invention obtains production line information, establishes a first production line parameter association network according to the production line information; sets the initial load and node capacity of each node in the first production line parameter association network to obtain a second production line parameter association network; uses production line change data as input to the second production line parameter association network, and the second production line parameter association network calculates the change risk of the production line according to the production line change data to obtain the change propagation result; the change propagation result analyzes the robustness of the second production line parameter association network through the normal state rate of the nodes in the second production line parameter association network to obtain the analysis result. It can effectively analyze the dynamic process of design change propagation, understand the impact of dynamic change propagation on various parts of the production line, and thus better optimize the production line.

[0083] 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.

[0084] 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 risk propagation analysis method for production line design changes, characterized in that: The following steps are involved: S1. Acquire production line information, and establish a first production line parameter association network according to the production line information; S2. Setting the initial load and node capacity of each node in the first production line parameter association network to obtain a second production line parameter association network; S3, taking the production line change data as the input of the second production line parameter association network, and the second production line parameter association network calculates the change risk of the production line according to the production line change data to obtain a change propagation result; S4, analyzing the robustness of the second production line parameter association network by using the normal state rate of the nodes in the second production line parameter association network to obtain an analysis result; Defining Nodes i The initial load is , then the initial load satisfies the following relationship: ； in, Representation Node The out-degree, Representation Node The in-degree, Representation Node The set of downstream nodes, Represents a parameter node i The corresponding upstream node pair node i The average effect size of Defining Nodes i The node capacity is , then the node capacity satisfies the following relationship: ； in, and Indicates capacity parameter; Step S3 includes the following sub-steps: S31, calculating the remaining margin value of the downstream node corresponding to the node where the change occurs in the production line; S32, selecting a node with the largest residual margin value among the downstream nodes for change propagation, and calculating the load generated by the change propagation to obtain the change propagation result; In step S32, define the node i The upstream node is node j , the load generated by change propagation satisfies the following relationship: ； ； ； in, Representation Node When a node fails, exist t Node The additional load; Propagate parameters for dimensions; Indicates that at time t-1 the node j The set of upstream nodes; Representation Node With Node The logical relationship between Indicates the judgment parameter, Indicated in t Time Node With Node The connection between Representation Node j For Node i The size of the impact.

2. The risk propagation analysis method for production line design changes according to claim 1, characterized in that: In step S4, the analysis result satisfies the following relationship: ； in, Indicates that the second production line parameter association network is t Robustness at all times, n The number of nodes in the parameter association network for the second production line; Representation Node i exist t The node state function at the time, the node state function satisfies: ； in, Representation Node i exist t The total load at the moment.

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