CN107748935B - Design change propagation risk prediction method and device - Google Patents

Design change propagation risk prediction method and device Download PDF

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CN107748935B
CN107748935B CN201711059956.9A CN201711059956A CN107748935B CN 107748935 B CN107748935 B CN 107748935B CN 201711059956 A CN201711059956 A CN 201711059956A CN 107748935 B CN107748935 B CN 107748935B
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CN107748935A (en
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郭于明
谢世坤
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Jinggangshan University
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Abstract

The invention discloses a design change propagation risk prediction method and device. Aiming at the problem that design resources are excessively consumed due to the avalanche effect of design change propagation in the process of variant design, a design change propagation highest risk path needs to be predicted before a variant design scheme is determined. Firstly, constructing a Product Development (PD) network model, wherein the method considers redundant connection and long-range connection among design nodes in the PD network model and the diffusion characteristic of a design change node set from the aspect that the PD network model follows the inherent characteristics of a small-world network model; and establishing a design change propagation maximum risk quantitative analysis model by using a small world network model analysis technology. The fragile link of the design change propagation path is effectively protected, and the design change propagation avalanche effect is prevented. The method can improve the variant design efficiency and save the design cost, thereby providing a basis for the adaptive dynamic planning of the variant design.

Description

Design change propagation risk prediction method and device
Technical Field
The invention relates to the technical field of product design, in particular to a method and a device for predicting design change propagation risk.
Background
In order to better meet the product customization requirements of customers, the product needs to be modified or changed, the initial product functional structure undergoes evolution, and finally a series of model products are formed. Product variation design is adaptive to new design requirements, and supports large-scale customized production effective means. However, the variant design may cause the design change to propagate in a Product Development (PD) network, and a local change to the product may cause a series of linkage associated changes, even a avalanche effect of the design change propagation may be generated, which causes excessive consumption of design resources (relative to product development resource constraints), so that the efficiency and effect of the variant design are not ideal.
If the highest risk propagation path (here, the risk refers to the possibility of influence of propagation of the design change) is predicted from the possible paths of propagation of the design change before the design change is modified, the vulnerable link of propagation of the design change is protected, and the risk of propagation of the design change is reduced.
In the design change propagation prediction method and system based on the design network disclosed in the prior art, based on the established relationship network model among the design attributes, the optimal propagation path of the design change is analyzed by using a complex network analysis technology, so that the purpose of reducing the influence range of the design change propagation is achieved. CLARKSON P J et al propose a Change Prediction Method (CPM), which utilizes a Design Structure Matrix (DSM) to express direct and indirect associations between Design nodes on the basis of Design experience and acquire all possible propagation paths between the Design nodes from the propagation possibility perspective (refer to CLARKSON P J, SIMONS C, ECKERT C. prediction change in complex Design [ J ]. Journal of Mechanical Design,2004,126(5):788 and 797). BATALLAS D A and the like integrate various network metrics of nodes based on DSM, thereby identifying Hub nodes, i.e., information spreaders, in the design change propagation, protecting the nodes and preventing the avalanche effect of design change propagation (reference: BATALLAS D A, YASSINE AA. information developers in product propagation networks: Social network analysis of the design structure information [ J ]. IEEE Transactions on Engineering Management,2006,53(4):570 information Management 582).
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
(1) the influence of redundant connection and long-range connection between design nodes on design change propagation is not comprehensively considered during modeling, and the design change propagation risk prediction precision is reduced.
(2) Design change propagation risk prediction studies on design change node set diffusion are lacking. This is because acquiring all possible propagation paths between Hub nodes in the whole network or design nodes, and not completely aiming at a variant design node set (design change node set), an avalanche effect of design change propagation is set forth from the inherent characteristics of the PD network model, because the variant design is a design change propagation process under the condition of limited design resources, that is, the design change cannot be completely and sufficiently propagated in this process.
(3) And the research on the adaptive dynamic programming design change node set is not supported. And searching an optimal change propagation path with the minimum change association influence, actually, for the variant design, a plurality of change paths exist, the highest risk of design change propagation needs to be considered, and the specific design change path is searched according to the user requirement by taking the highest risk as guidance, so that the adaptive dynamic planning of the variant design is realized.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting design change propagation risk, which are used for solving the technical problem that the design change propagation is not predicted from inherent characteristics of a product development network in the conventional scheme.
The embodiment of the invention provides a method for predicting design change propagation risk, which comprises the following steps:
establishing a PD network model according to a product design change database, performing modular decomposition on the PD network model, and determining redundant connection and long-range connection between design nodes;
determining direct possibility, direct influence and direct risk of change propagation among design nodes and determining change propagation combined risk among the design nodes according to the PD network model;
determining connection side loads among the design nodes, and determining design change propagation strength among the design nodes according to the change propagation combination risk and the connection side loads;
determining a design change initial node, and determining the diffusion degree and the node load of a design change node set; determining a maximum risk optimization target of the design change node set according to the diffusivity of the design change node set and the node load;
and solving the maximum risk optimization target of the design change node set by using an ant colony algorithm based on the design change initial node, and determining the highest risk of the design change propagation path.
In a possible implementation manner, the determining a risk of a change propagation combination between design nodes includes:
the risk of propagation of the change combination from design node a to design node b is:
Rb,a＝1-∏(1-ρb,u) (ii) a Where u denotes the node at the penultimate level of the propagation tree from a to b, ρb,uRepresents the risk of propagation of a change from node u to node b, and ρb,u＝σu,alb,uib,u(ii) a Wherein σu,aIs the direct possibility of changing the arrival of u from a, lb,uIs the direct possibility of changing the arrival of b from u, ib,uIs the direct effect of a change from u to b.
In a possible implementation manner, the determining a design change propagation strength between design nodes according to the change propagation combination risk and the connection edge load includes:
the strength of the propagation of the design change from design node a to design node b is:
Figure GDA0003116406650000031
wherein R isb,aRepresenting the risk of propagation of a combination of changes from design node a to design node b,
Figure GDA0003116406650000032
representing the connecting edge loads of design nodes a and b, l (e)b,a) Representing the absolute value of the load of the node a and the node b; w is arAnd wlRespectively representing the weight values of the change combination propagation risk and the connection side load.
In a possible implementation manner, the determining a design change node set diffusivity and a node load includes:
the design change node set diffuseness is as follows:
Figure GDA0003116406650000033
wherein d isKqRepresenting the shortest distance from any member in the design change node set K to a design node q, wherein the design node q is a node in the residual network node set, and B is the number of the residual network nodes;
the node load is:
Figure GDA0003116406650000041
wherein, gmn(i) Is a shortest path, g, connecting design node m and design node n and including design node i as an intermediate nodemnAll shortest paths connecting a design node m and a design node n, and a design node i is a node in a design change node set;
in a possible implementation manner, the determining a maximum risk optimization goal of a design change node set according to the design change node set diffusivity and the node load includes:
the maximum risk optimization target and the constraint conditions of the design change node set are as follows:
max(wd×DK+wb×βK)；
Figure GDA0003116406650000042
xhih, i ∈ K, 1 or 0, H ═ 1,2,. H;
wherein, wd、wbRepresenting the diffuseness D of the design Change node set, respectivelyKWith the load mean value betaKThe relative weight of (a) to (b),
Figure GDA0003116406650000043
k represents a design change node set, i belongs to K, and N is the number of elements in the design change node set;
x hi1 denotes that a person h is assigned to a design node i, xhi0 indicates that person H is not assigned to design node i, H indicates the total number of persons; p is a radical ofiNumber of persons, p, required to design node imin,pmaxRespectively representing the minimum and maximum number of people.
Based on the same inventive concept, an embodiment of the present invention further provides a device for predicting risk of propagation of design change, including:
the system comprises a framework module, a product design change database and a design node module, wherein the framework module is used for constructing a PD network model according to the product design change database, modularly decomposing the PD network model and determining redundant connection and long-range connection among design nodes;
the design node dependency analysis module is used for determining the direct possibility, the direct influence and the direct risk of the change propagation among the design nodes and determining the change propagation combined risk among the design nodes according to the PD network model;
the design change propagation strength calculation module is used for determining the connection side load between the design nodes and determining the design change propagation strength between the design nodes according to the change propagation combination risk and the connection side load;
the design change propagation risk prediction module is used for determining a design change initial node and determining the diffusion degree and the node load of a design change node set; determining a maximum risk optimization target of the design change node set according to the diffusivity of the design change node set and the node load; and solving the maximum risk optimization target of the design change node set by using an ant colony algorithm based on the design change initial node, and determining the highest risk of the design change propagation path.
In one possible implementation, the design node dependency analysis module is configured to:
determining the risk of the change combination propagation from the design node a to the design node b as follows:
Rb,a＝1-∏(1-ρb,u) (ii) a Where u denotes the node at the penultimate level of the propagation tree from a to b, ρb,uRepresents the risk of propagation of a change from node u to node b, and ρb,u＝σu,alb,uib,u(ii) a Wherein σu,aIs the direct possibility of changing the arrival of u from a, lb,uIs the direct possibility of changing the arrival of b from u, ib,uIs the direct effect of a change from u to b.
In one possible implementation, the design change propagation strength calculation module is configured to:
determining a design change propagation strength from design node a to design node b as:
Figure GDA0003116406650000051
wherein R isb,aRepresenting the risk of propagation of a combination of changes from design node a to design node b,
Figure GDA0003116406650000052
representing the connecting edge loads of design nodes a and b, l (e)b,a) Representing the absolute value of the load of the node a and the node b; w is arAnd wlRespectively representing the weight values of the change combination propagation risk and the connection side load.
In one possible implementation, the design change propagation risk prediction module is configured to:
determining the degree of diffuseness of the design change node set as follows:
Figure GDA0003116406650000053
wherein d isKqRepresenting the shortest distance from any member in the design change node set K to a design node q, wherein the design node q is a node in the residual network node set, and B is the number of the residual network nodes;
determining the node load as:
Figure GDA0003116406650000061
wherein, gmn(i) Is a shortest path, g, connecting design node m and design node n and including design node i as an intermediate nodemnAll shortest paths connecting a design node m and a design node n, and a design node i is a node in a design change node set;
in one possible implementation, the design change propagation risk prediction module is configured to:
determining the maximum risk optimization target and the constraint conditions of the design change node set as follows:
max(wd×DK+wb×βK)；
Figure GDA0003116406650000062
xhih, i ∈ K, 1 or 0, H ═ 1,2,. H;
wherein, wd、wbRepresenting the diffuseness D of the design Change node set, respectivelyKWith the load mean value betaKThe relative weight of (a) to (b),
Figure GDA0003116406650000063
k represents a design change node set, i belongs to K, and N is the number of elements of the design change node set;
xhi1 denotes that a person h is assigned to a design node i, xhi0 indicates that person H is not assigned to design node i, H indicates the total number of persons; p is a radical ofiNumber of persons, p, required to design node imin,pmaxRespectively representing the minimum and maximum number of people.
According to the method and the device for predicting the risk of design change propagation, provided by the embodiment of the invention, based on the characteristics of the small world network, the strength of the design change propagation is introduced, the diffusivity of a design change node set is provided, the design change propagation range in a PD network model is determined through an ant colony algorithm, and the highest risk of the design change propagation is further determined. The method expands the application of the small-world theory in the PD network model, reflects the local and global properties of the PD real network, and has universality. The traditional design change propagation prediction method focuses on design change nodes, node redundancy connection and long-range connection caused by PD small-world network characteristics are considered, conditions such as variant design resource constraint are considered, the maximum influence risk of a variant node set on a PD residual network is evaluated, and a basis is provided for design change propagation avalanche effect risk prediction and further adaptive dynamic planning of variant design.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for predicting risk of design change propagation in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a design change propagation diffusion model according to an embodiment of the present invention;
FIG. 3 is a DSM diagram of a PD network model in an embodiment of the invention;
FIG. 4 is a diagram illustrating analysis of design node dependencies in an embodiment of the present invention;
FIG. 5 is a diagram of a propagation tree in an embodiment of the present invention;
FIG. 6 is a diagram illustrating an algorithm implementation of a propagation tree according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a network node according to an embodiment of the present invention;
FIG. 8 is a process diagram of a modified design in an embodiment of the invention;
fig. 9 is a block diagram of an apparatus for predicting risk of change propagation according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1, a flow of a method for designing a risk prediction of change propagation according to an embodiment of the present invention includes steps 101-105:
step 101: and constructing a PD network model according to the product design change database, performing modular decomposition on the PD network model, and determining redundant connection and long-range connection between design nodes.
In the embodiment of the invention, the design nodes and the related information among the nodes are stored in the product design change database. Specifically, an information sharing model between a Product Data Management (PDM) system and an Enterprise Resource Planning (ERP) system needs to be constructed, the PDM is a technology for managing all product-related information (including design, process, resources, and the like) and all product-related processes (including process definition and management), and the PDM data management and the process management provide an analysis premise for design change management. On the other hand, the variant design is a design change propagation process under the condition of limited design resources, and the ERP system information is integrated on the basis of the PDM system. The ERP system jumps out of the traditional enterprise boundary and optimizes resources of the enterprise from the supply chain range, so that the acquisition of model product design and manufacture information is the guarantee of resource cost calculation work of the ERP system. The integrated information between the PDM system and the ERP system comprises product structures, parts, documents, CAD files, processes, processing equipment, raw materials, tools, personnel information, department information and the like. As can be seen from the integration information, the product design change information includes product structure change, process change, document change, and design change process resource configuration constraints; therefore, a product design change database facing PDM and ERP information sharing is constructed. The product design change database serves as a data source for building the PD network model.
Meanwhile, a PD network model is described in a module form, modular decomposition is carried out on the model by using a computer-aided modular clustering algorithm according to the PD network model corresponding to the DSM, and nodes which are closely connected with each other are classified into the same module, so that high cohesion in the module is realized, and the connection tightness between mutually independent modules is low. The nodes in the modules are in redundant connection, different modules are connected through long-range connection, and the long-range connection has higher betweenness corresponding to design nodes. According to the DSM, redundant connections, long-range connections between module design nodes can be determined.
The variant design is a large-scale resolving process in a PD network model, and the PD network model belongs to a complex network and follows the characteristics of a small-world network model. The design change propagation process has the characteristics of redundant connection, long-range connection and the like, a mapping relation exists between the design change propagation process and the small-world network model high clustering and small characteristic path length, the small-world network model high clustering causes the redundant connection, the long-range connection causes the small network characteristic path length, the design change propagation process and the small-world network model high clustering and small characteristic path length are inherent characteristics in the PD network model and have important influence on design change propagation risk prediction. Therefore, when the design change propagation risk is predicted, the influence rule of the small world characteristics on the design change propagation risk prediction is deeply excavated so as to guide the solution of the maximum risk optimization problem, and the efficiency of the variant design can be further improved.
The design change propagation diffusion risk originates from the initial change. When a node in the PD network model is initially changed due to design requirements, a neighboring node associated with the node is changed in design, and the change in design is propagated and diffused through the network and gradually propagated to other non-adjacent nodes, thereby causing a change in design association of the PD network model. To describe this process, a small-world analysis of the PD network model is applied to design a change propagation diffusion model, as shown in fig. 2.
In fig. 2, point a is an initial change of the modified design (i.e., a design change initial node), and is propagated in the PD network model, so as to implement step-by-step diffusion. The method comprises the following basic steps: first, design changes are propagated within the module to which they belong, and then, the design changes are propagated to neighboring modules. And repeating the basic steps by the affected design change nodes of the adjacent modules to realize the diffusion of the design change in the PD network model.
Step 102: and determining direct possibility, direct influence and direct risk of change propagation among the design nodes and determining change propagation combined risk among the design nodes according to the PD network model.
In the embodiment of the invention, after the redundant connection and the long-range connection between the design nodes of the modules are determined according to the DSM, the design change node set with the strongest diffusion capability is searched among the modules by further utilizing a path optimization algorithm, so that the whole PD network model can be connected to the maximum extent.
The PD network model can be expressed as S, R, and is represented by the set S ═ S1, S2, …, sn }, whereDesign nodes in the PD network model are shown, and design change propagation relations among the nodes are shown by R. In the embodiment of the invention, DSM is adopted to describe the structural model { S, R } of the PD network model. The matrix M represents the DSM of the PD network model when the element x in the matrix MjkWhen the design node j is 1, the design node j indicates that a direct design change propagation relation exists between the affected design node j and the design node k at the initial change; when element x in matrix MjkWhen 0, it indicates that there is no direct design change propagation relationship between the affected design node j and the design node k at the time of the change initiation. Illustrating M, as shown in fig. 3; wherein, the horizontal direction and the vertical direction of M respectively represent the affected design node and the initial design node of change, the PD network model node corresponding to DSM is a-f, if there is direct design change propagation between them, it is represented by 'x', otherwise it is represented by blank.
Design change propagation diffusion is primarily due to the existence of dependencies between design nodes. In the embodiment of the invention, the design node dependency analysis describes the risk of design change combination propagation caused by redundant connection, namely direct connection and indirect connection between design nodes. Design node dependency analysis is based on direct connection dependencies of design nodes, manifested as direct possibilities, direct effects and direct risks of propagation of changes between design nodes. The direct risk of change propagation is defined as the product of the direct likelihood of a change occurring and the subsequent direct impact, based on the direct likelihood of the change and the direct impact predictive direct risk of change propagation.
In the embodiment of the invention, the direct possibility of change propagation refers to the average probability that the design change of one node causes the change of another design node through the common interface propagation of the design nodes; the common interface represents the coupling of design parameters between nodes, such as the corresponding change of parameters between holes and diameters. Correspondingly, if a design change is propagated, the direct impact of the design change is the average proportion of the design work that needs to be redone. FIG. 4 shows a correlation matrix generated based on the DSM of FIG. 3, in which direct likelihood and direct impact related data are obtained empirically by a designer, based on which the equation r is calculatedj,k＝lj,k×ij,kObtaining a direct risk matrix, rj,k、lj,kAnd ij,kRepresenting elements in a direct risk matrix, a direct likelihood matrix, and a direct impact matrix, respectively.
It should be noted that the relevant data of the immediate possibility and the influence matrix in fig. 4 can be obtained from the design change history and experienced designers, and it is preferable to collect a set of designer opinions rather than solicit individual designer opinions, but at the same time, the designer opinions should be considered in weight, and the designer opinions should be considered comprehensively to obtain a consistency viewpoint, so that the immediate possibility of propagation of the design change and the influence matrix are reasonable. The acquired data is represented as a design node change, and the direct risk of the neighborhood change is caused by the propagation across the public interface, and the data elements in the direct risk matrix are obtained by multiplying the direct possibility by the corresponding data elements in the influence matrix.
Design node dependencies can be modeled as a sequence of connections created by direct dependencies of network nodes. Direct dependency connection here refers to the propagation of changes between neighboring network nodes, e.g. the direct dependency connection between node a and node b in fig. 4. In contrast, an indirect dependency connection needs to involve at least one intermediate network node, e.g., for a change to affect b changes, a causes b changes to be indirect through changes to d and f.
Second, designing node dependencies requires consideration of direct and indirect path effect combinations, which formally appear as a propagation tree. Since the direct possibility matrix, the direct influence matrix and the direct risk matrix only express the direct dependency of the design nodes, the combined effect is that b is changed due to a change of a through the direct path and the indirect path, and the propagation depth of the indirect path can be set but generally does not exceed 4 steps.
In embodiments of the present invention, the combining probability is the final change propagation probability between the design change nodes, regardless of the paths therebetween. The combined impact is the overall impact on the affected nodes with an expected probability of occurrence of l. The combinatorial likelihood algorithm treats the propagation tree as a logical tree, as shown in fig. 5.
In fig. 5, the vertical lines are mathematically expressed as ≡ (and), and the horizontal lines are expressed as coo @. For each tree, the AND/or summing starts at the bottom of the graph, farthest from the change initiating node. The final combined likelihood value may be calculated at the top of the tree by and/or combining the evaluations. Since the design change propagation events are not mutually exclusive, the operation is in the form:
lb,u∩lb,v＝lb,u×lb,v (1)
lb,u∪lb,v＝lb,u+lb,v-(lb,u×lb,v)＝1-((1-lb,u)×(1-lb,v)) (2)
the expression (1) expresses the n function as a probability product, and the expression (2) expresses the u function as a probability sum minus a product term thereof, or a reverse operation of the inverse probability product, thereby ensuring that the combined probability value is less than 1.
In the embodiment of the invention, the combination possibility is defined as the average probability that one node design change leads to another node design change, and the size of the initial change and the subsequent change is not considered. Thus, equations (1) and (2) do not involve altering the impact values, which simplifies the calculation of the combined risk and combined impact values.
Let Rb,aIs the risk of the change propagation combination from a to b, and the risk of the change propagation combination from the design node a to the design node b is:
Rb,a＝1-∏(1-ρb,u) (3)；
where u denotes the node at the penultimate level of the propagation tree from a to b, ρb,uRepresents the risk of propagation of changes from node u to node b, and:
ρb,u＝σu,alb,uib,u (4)；
wherein σu,aIs the direct possibility of changing the arrival of u from a, lb,uIs the direct possibility of changing the arrival of b from u, ib,uIs the direct effect of a change from u to b. The algorithm implementation is shown in fig. 6.
In FIG. 6, the probability σ of reaching the penultimate nodes d and fu,aAre each ld,aAnd lf,aThe direct probability of propagation of changes from d and f to b is lb,dAnd lb,fEach having a corresponding direct influence ib,dAnd ib,f. If the calculation process is top-down, then take FIG. 6 as an example, Rb,aExpressed as:
Figure GDA0003116406650000121
step 103: and determining the connection side load between the design nodes, and determining the design change propagation strength between the design nodes according to the change propagation combination risk and the connection side load.
From the above, the change propagation combined risk between the design nodes is determined according to the redundant connection in the PD network model, and the combined propagation risk between the node pairs only indicates the propagation characteristics of the connecting edges between the design nodes, and cannot express the total number of all the connecting edges of the pairwise connected node pairs passing through the edge in the PD network model. For a PD network model with a small-world characteristic, in addition to considering a combined propagation risk caused by a propagation redundancy characteristic of changes between nodes, it is also necessary to consider the influence of long-range connections of the PD network model on a design change propagation risk, and the nodes adjust the flow direction of design change information therein to expand the design change propagation range. Therefore, considering the above situation comprehensively, the embodiment of the present invention introduces the design change propagation strength, determines the connection edge load between the design nodes from the long-range connection in the PD network model, and determines the design change propagation strength between the design nodes.
Specifically, the design change node adjusts the amount of design change information that can be transmitted, and is substantially a load. In the embodiment of the invention, the number of shortest paths passing through a certain connecting edge is taken as the absolute value of the load of the connecting edge; compared with the design change information quantity which can be adjusted by the PD network model, the larger the absolute value of the load of the connecting edge is, the higher the probability that the shortest path between the node pairs passes through the connecting edge is, and the faster the design change can be diffused through the edge. Therefore, the invention is used in the examples
Figure GDA0003116406650000122
Representing the connection edge load of the nodes a and b; wherein l (e)b,a) Representing the absolute value of the load of node a and node b, i.e. the number of shortest paths through the connecting edge between nodes a and b,
Figure GDA0003116406650000123
representing the number of shortest paths traversed by all connected edges in the PD network model.
High clustering of the small-world network model leads to redundant connection, and long-range connection leads to smaller network characteristic path length, which are inherent characteristics of a product development network and have important influence on design change propagation prediction. The present embodiment determines a design change propagation strength calculation method by comprehensively considering the redundant connection and the long-range connection. Specifically, design change node dependency and connection edge load thereof are comprehensively considered, and design change propagation strength between the PD network model nodes is defined:
Figure GDA0003116406650000131
wherein R isb,aRepresenting the risk of propagation of a combination of changes from design node a to design node b, wrAnd wlRespectively representing the weight values of the change combination propagation risk and the connection side load.
Step 104: determining a design change initial node, and determining the diffusion degree and the node load of a design change node set; and determining the maximum risk optimization target of the design change node set according to the diffusivity and the node load of the design change node set.
In the embodiment of the present invention, the above-mentioned step 101-103 is a step of initial analysis, the step 104 and the following are steps of specific scheme analysis, so that a design change initial node of the modified design needs to be determined according to user requirements, and the design change initial node is a change initiating node. Specifically, product variant design requirements are determined and associated to a module node in the PD network model. In general, it is obvious to design inter-node links, but sometimes it is not clear to determine which design node to initiate a change. Such external changes, which are not clearly defined, are due to unforeseen events, which are triggered by user demand, including estimates of market conditions, future customer demand, and new available technologies, among others. For this reason, for a product with higher complexity, such as an electromechanical product, the corresponding initial PD network model needs to be clearly defined.
And confirming the initial change of the variant design, wherein the design change is propagated in the PD network model, and the risk caused by the propagation of the design change is possibly even brought with the propagation avalanche effect, so that the risk of the propagation of the design change needs to be predicted. The design change propagation risk prediction is to optimize the propagation effect of the design change node set on the basis of the design change propagation strength.
In order to predict the risk of propagation of design change, it is first discovered that the set of design change nodes can maximize the connection with the remaining network nodes of the PD, reaching the highest diffusion risk. Therefore, the design change node set diffusivity is introduced in the embodiment of the invention, and the specific reason is as follows:
first, for the problem of connecting the design change node set with the remaining network, the connection can be confirmed according to the node degree of directly reaching most nodes (i.e. the path length is 1), and a simple node degree is optimal. However, if the problem is defined in terms of reaching most nodes up to m steps, the most suitable measure is node proximity, but this still has disadvantages. In graph theory, node proximity describes the shortest path from a given node to other nodes in the network. For example in fig. 7, node 4 has the best proximity value (24 links from all other nodes, calculating the path length from node 4 to each of the other nodes separately, and then adding them together to 24 links, with duplicate links taken together), however, if we are interested in reaching most nodes along paths of length 2 or less, node 3 is a better choice because it can reach 8 nodes in addition to itself, while node 4 can only reach 6 nodes.
Secondly, the network node proximity index is calculated as
Figure GDA0003116406650000141
In the formula, d (n)i,nj) Is a node n in the networkiAnd njN is the number of network nodes. The proximity definition can only be applied to strongly connected graphs if at niAnd njIf no path exists, d (n)i,nj) Is infinite.
Finally, predicting the maximum risk design change node set is a design change propagation path optimization problem, which is different from finding a plurality of key nodes for independent optimization, and the central positions between the design change nodes (referring to the pivot positions for adjusting the propagation of the design change information in the PD network model) are not isolated from each other.
For the above reasons, in the embodiment of the present invention, the capability of connecting the target node set to other network nodes is represented by the diffuseness of the design change node set, which may be expressed as:
Figure GDA0003116406650000142
in the formula (d)KqRepresenting the shortest distance from any member in the design change node set K to a design node q, wherein the design node q is a node in the residual network node set, and B is the number of the residual network nodes; i.e., q ∈ the shortest distance of V-K, B ═ V-K |, where V denotes the set of nodes for the entire network and V-K denotes the set of remaining network nodes.
In particular, D may beKThe node with the shortest distance of 1 is given the highest weight when the weighted average of all nodes of the modified node set is designed. Thus, when each external node is adjacent to at least one member node of a design change node set, DKReaching a maximum value of 1, this situation undoubtedly produces the greatest risk of avalanche change propagation. When the change node set and the rest network nodes do not belong to the same subnet, namely the change node set and the rest network nodes do not have an adjacency relation, DKTo the minimumWith a value of 0, the design change node set is completely isolated, at which time the variant design is in a lowest risk state.
Meanwhile, the design change propagation risk is directly related to the diffusion of the design change propagation range, the diffusion degree of the design change node set describes the diffusion of the change information of the node set to the peripheral network, and the design change node load also restricts the design change propagation range. Therefore, in the embodiment of the present invention, the maximum risk optimization target of the design change node set is expressed as:
max(wd×DK+wb×βK)；
Figure GDA0003116406650000151
xhih, i ∈ K, 1 or 0, H ═ 1,2,. H; (9)
wherein, wd、wbRepresenting the diffuseness D of the design Change node set, respectivelyKWith the load mean value betaKThe relative weight of (A) can be selected according to actual conditions;
Figure GDA0003116406650000152
k represents a design change node set, i belongs to K, N is the number of elements of the design change node set, biThe load of node i is designed. In the constraint, xhi1 denotes that a person h is assigned to a design node i, xhi0 indicates that person H is not assigned to design node i, H indicates the total number of persons; p is a radical ofiThe number of personnel needed for designing the node i and the resource for completing the variant design task have an upper limit and a lower limit, pmin,pmaxRespectively representing the minimum and maximum number of people.
Beta in the formula (9)KMiddle, node load biCan be expressed as:
Figure GDA0003116406650000153
in the formula, gmn(i) Is connecting design nodesm and design node n and including design node i as the shortest path of the intermediate node, gmnAll shortest paths connecting design node m and design node n, and design node i is a node in the design change node set. "m < n" indicates that the two nodes are not identical.
Step 105: solving a maximum risk optimization target of the design change node set according to an ant colony algorithm, and determining the highest risk of the design change propagation path; and providing basis for adaptive dynamic planning of variant design.
In the embodiment of the invention, the ant colony algorithm is adopted to solve the optimization target, so that the design change propagation risk prediction is realized. Specifically, the ant colony algorithm has the following calculation formula:
Figure GDA0003116406650000161
in the formula (I), the compound is shown in the specification,
Figure GDA0003116406650000162
representing the probability of the ant k propagating from the design node i to the design node j at the moment t, A representing the ant k is positioned in the adjacent movable node set of the design node i, and tauij(t)、ηijRespectively at time tijPheromone and heuristic factor, branch e is set in the embodiment of the inventionijInitial pheromone τ onij(0) 1, heuristic operator ηij＝SbaFrom the physical meaning of the operator object, a is i and b is j. Alpha and beta are weight coefficients of pheromone and heuristic operator, and can be 0.5. The decay of pheromones over time is shown below:
τij(t+1)＝(1-ρ)τij(t)+ρΔτij(t) (12)
in the formula, rho represents the volatilization degree of the pheromone and can be 0.7; delta tauij(t) is a global pheromone, and the updating formula is as follows:
Δtij(t)＝∑TFT (13)
in the formula, FTAnts in the circulationT objective function values of the traversed path. Since the ant colony algorithm is a relatively well-known algorithm, details thereof are not described in this embodiment.
In the embodiment of the invention, the propagation path corresponding to the maximum risk optimization target can be determined according to the ant colony algorithm, namely the propagation path is a design change propagation path capable of causing a high risk state, and then a designer can take certain measures to prevent the propagation path, wherein the measures comprise increasing a change buffer area of a design node, cutting off the connection of certain design change nodes on the premise of ensuring the product performance and the like, so that the probability that a PD network model reaches the high risk state from the product development requirement and the initial change, even the avalanche effect is propagated by the design change is avoided. Guided by this, the designer can quickly plan a cost-effective project and finally derive the required variant design.
After the highest risk is determined in the embodiment of the invention, the process of the variant design is specifically shown in fig. 8, and in fig. 8, a mathematical model of the propagation strength of the design change is established through a small-world network model analysis technology, so that the accuracy of the prediction of the propagation risk of the design change is improved. The method considers the diffusion characteristic of a design change node set, and the design change propagation risk is the result of the joint cumulative effect of propagation strength and propagation range diffusion. As shown in fig. 8, the process includes the steps of:
step (1) initial analysis: the first stage of the design change propagation risk prediction method is to perform initial analysis on the incidence relation between design nodes by using a PD small world network model (product data), a CPM algorithm (redundant connection) and connection edge loads (long-range connection).
Step (2) protocol analysis: and obtaining a PD network model topological structure based on initial analysis, and carrying out scheme analysis before carrying out variant design. The project analysis consists of validating initial changes and design change propagation risk predictions, and finding the maximum propagation risk of a design change node set among a plurality of possible design projects.
Step (3) variant design (redesign process): through the stage, designers seek to find a design change propagation path which can cause a high-risk state, and take certain measures to prevent the design change propagation path, wherein the measures are to increase a change buffer area of a design node, cut off the connection of certain design change nodes and the like on the premise of ensuring the product performance, and further avoid the possibility that a PD network model reaches the high-risk state from the product development requirement and the initial change, even the avalanche effect of the design change propagation. Guided by this, the designer can quickly plan a cost-effective project and finally derive the required variant design. The method provided by the embodiment of the invention discusses the influence rule of the inherent characteristics of the product development network on the risk control of the design change propagation, and explains the maximum risk optimization mechanism of the design change propagation; the method can effectively predict the fragile links of design change propagation and improve the efficiency of variant design.
The design change propagation risk prediction method provided by the embodiment of the invention introduces design change propagation strength based on the characteristics of the small world network, provides the diffusivity of a design change node set, determines the design change propagation range in a PD network model through an ant colony algorithm, and further determines the highest risk of the design change propagation. The method expands the application of the small-world theory in the PD network model, reflects the local and global properties of the PD real network, and has universality. The traditional design change propagation prediction method focuses on design change nodes, node redundancy connection and long-range connection caused by PD small-world network characteristics are considered, conditions such as variant design resource constraint are considered, the maximum influence risk of a variant node set on a PD residual network is evaluated, and a basis is provided for design change propagation avalanche effect risk prediction and further adaptive dynamic planning of variant design.
The above describes in detail the flow of the method for predicting risk of change propagation according to the embodiment of the present invention, and the method may also be implemented by a corresponding apparatus, and the structure and function of the apparatus are described in detail below.
The device for predicting risk of propagation of design change according to the embodiment of the present invention, as shown in fig. 9, includes:
the framework module 81 is used for constructing a PD network model according to a product design change database, performing modular decomposition on the PD network model, and determining redundant connection and long-range connection between design nodes;
a design node dependency analysis module 82, configured to determine, according to the PD network model, direct possibility, direct influence, and direct risk of change propagation among design nodes, and determine a change propagation combination risk among design nodes;
a design change propagation strength calculation module 83, configured to determine a connection edge load between the design nodes, and determine a design change propagation strength between the design nodes according to the change propagation combination risk and the connection edge load;
a design change propagation risk prediction module 84, configured to determine a design change initial node, determine a design change node set diffusivity and a node load; determining a maximum risk optimization target of the design change node set according to the diffusivity and the node load of the design change node set; and solving the maximum risk optimization target of the design change node set according to the ant colony algorithm, and determining the highest risk of the design change propagation path.
In one possible implementation, the design node dependency analysis module 82 is configured to:
determining the risk of the change combination propagation from the design node a to the design node b as follows:
Rb,a＝1-∏(1-ρb,u) (ii) a Where u denotes the node at the penultimate level of the propagation tree from a to b, ρb,uRepresents the risk of propagation of a change from node u to node b, and ρb,u＝σu,alb,uib,u(ii) a Wherein σu,aIs the direct possibility of changing the arrival of u from a, lb,uIs the direct possibility of changing the arrival of b from u, ib,uIs the direct effect of a change from u to b.
In one possible implementation, the design change propagation strength calculation module 83 is configured to:
determining a design change propagation strength from design node a to design node b as:
Figure GDA0003116406650000181
wherein R isb,aRepresenting a set of changes from design node a to design node bIn combination with the risk of transmission,
Figure GDA0003116406650000182
representing the connecting edge loads of design nodes a and b, l (e)b,a) Representing the absolute value of the load of the node a and the node b; w is arAnd wlRespectively representing the weight values of the change combination propagation risk and the connection side load.
In one possible implementation, the design change propagation risk prediction module 84 is configured to:
determining the diffusivity of a design change node set as follows:
Figure GDA0003116406650000191
wherein d isKqRepresenting the shortest distance from any member in the design change node set K to a design node q, wherein the design node q is a node in the residual network node set, and B is the number of the residual network nodes;
determining the node load as follows:
Figure GDA0003116406650000192
wherein, gmn(i) Is a shortest path, g, connecting design node m and design node n and including design node i as an intermediate nodemnAll shortest paths connecting design node m and design node n, and design node i is a node in the design change node set.
In one possible implementation, the design change propagation risk prediction module 84 is configured to:
determining the maximum risk optimization target and the constraint conditions of the design change node set as follows:
max(wd×DK+wb×βK)；
Figure GDA0003116406650000193
xhih, i ∈ K, 1 or 0, H ═ 1,2,. H;
wherein，wd、wbRepresenting the diffuseness D of the design Change node set, respectivelyKWith the load mean value betaKThe relative weight of (a) to (b),
Figure GDA0003116406650000194
k represents a design change node set, i belongs to K, and N is the number of elements of the design change node set;
xhi1 denotes that a person h is assigned to a design node i, xhi0 indicates that person H is not assigned to design node i, H indicates the total number of persons; p is a radical ofiNumber of persons, p, required to design node imin,pmaxRespectively representing the minimum and maximum number of people.
The design change propagation risk prediction device provided by the embodiment of the invention introduces design change propagation strength based on the characteristics of the small world network, provides the diffusivity of a design change node set, determines the design change propagation range in a PD network model through an ant colony algorithm, and further determines the highest risk of the design change propagation. The method expands the application of the small-world theory in the PD network model, reflects the local and global properties of the PD real network, and has universality. The traditional design change propagation prediction method focuses on design change nodes, node redundancy connection and long-range connection caused by PD small-world network characteristics are considered, conditions such as variant design resource constraint are considered, the maximum influence risk of a variant node set on a PD residual network is evaluated, and a basis is provided for design change propagation avalanche effect risk prediction and further adaptive dynamic planning of variant design.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) that contain computer-usable program code.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, it is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.

Claims (2)

1. A method of predicting risk of propagation of a design change, comprising the steps of:
establishing a PD network model according to a product design change database, performing modular decomposition on the PD network model, and determining redundant connection and long-range connection between design nodes;
determining direct possibility, direct influence and direct risk of change propagation among design nodes and determining change propagation combined risk among the design nodes according to the PD network model;
determining connection side loads among the design nodes, and determining design change propagation strength among the design nodes according to the change propagation combination risk and the connection side loads;
determining a design change initial node, and determining the diffusion degree and the node load of a design change node set; determining a maximum risk optimization target of the design change node set according to the diffusivity of the design change node set and the node load;
based on the design change initial node, solving the maximum risk optimization target of the design change node set by using an ant colony algorithm, and determining the highest risk of the design change propagation path;
the determining the design change node set diffuseness and the node load comprises the following steps:
the design change node set diffuseness is as follows:
Figure FDA0003077642170000011
wherein d isKqRepresenting the shortest distance from any member in the design change node set K to a design node q, wherein the design node q is a node in the residual network node set, and B is the number of the residual network nodes;
the node load is:
Figure FDA0003077642170000012
wherein, gmn(i) Is a shortest path, g, connecting design node m and design node n and including design node i as an intermediate nodemnAll shortest paths connecting design node m and design node n, design node i as designCounting nodes in the change node set;
describing a PD network model in a module form, modularizing the model by using a computer-aided modular clustering algorithm according to the PD network model corresponding to DSM, and classifying nodes which are closely connected with each other into the same module, thereby realizing high cohesion in the module, wherein the nodes in the module have redundant connection, different modules are connected through long-range connection, and the redundant connection and the long-range connection between the design nodes of the module can be determined according to the DSM;
the PD network model may be expressed as { S, R }, where the set S ═ S1, S2, …, sn } represents design nodes in the PD network model, and R represents design change propagation relationships between the nodes; the matrix M represents the DSM of the PD network model when the element x in the matrix MjkWhen the design node j is 1, the design node j indicates that a direct design change propagation relation exists between the affected design node j and the design node k at the initial change; when element x in matrix MjkWhen the value is 0, it indicates that no direct design change propagation relation exists between the affected design node j and the design node k at the initial change;
change propagation direct likelihood refers to the average probability of propagating through a common interface of design nodes such that a design change of one node results in a change of another design node;
by calculating the formula rj,k＝lj,k×ij,kObtaining a direct risk matrix, rj,k、lj,kAnd ij,kRespectively representing elements in a direct risk matrix, a direct likelihood matrix, and a direct impact matrix;
the determining of the combined risk of change propagation among design nodes includes:
the risk of propagation of the change combination from design node a to design node b is:
Rb,a＝1-∏(1-ρb,u) (ii) a Where u denotes the node at the penultimate level of the propagation tree from a to b, ρb,uRepresents the risk of propagation of a change from node u to node b, and ρb,u＝σu,alb,uib,u(ii) a Wherein σu,aIs the direct possibility of changing the arrival of u from a, lb,uIs the direct possibility of changing the arrival of b from u, ib,uIs the direct effect of a change from u to b;
the determining of the design change propagation strength between design nodes according to the change propagation combination risk and the connection edge load includes:
the strength of the propagation of the design change from design node a to design node b is:
Figure FDA0003077642170000021
wherein R isb,aRepresenting the risk of propagation of a combination of changes from design node a to design node b,
Figure FDA0003077642170000022
representing the connecting edge loads of design nodes a and b, l (e)b,a) Representing the absolute value of the load of the node a and the node b;
Figure FDA0003077642170000023
representing the number of shortest paths passed by all connected edges in the PD network model; w is arAnd wlRespectively representing weight values of the change combination propagation risk and the connection side load;
determining a maximum risk optimization goal of the design change node set according to the diffusivity of the design change node set and the node load, wherein the maximum risk optimization goal comprises the following steps:
the maximum risk optimization target and the constraint conditions of the design change node set are as follows:
max(wd×DK+wb×βK)；
Figure FDA0003077642170000031
xhih, i ∈ K, 1 or 0, H ═ 1,2,. H;
wherein, wd、wbRepresenting the diffuseness D of the design Change node set, respectivelyKWith the load mean value betaKThe relative weight of (a) to (b),
Figure FDA0003077642170000032
k represents a design change node set, i belongs to K, and N is the number of elements in the design change node set;
xhi1 denotes that a person h is assigned to a design node i, xhi0 indicates that person H is not assigned to design node i, H indicates the total number of persons; p is a radical ofiNumber of persons, p, required to design node imin,pmaxRespectively representing the minimum and maximum number of people.
2. An apparatus for design change propagation risk prediction, comprising:
the system comprises a framework module, a product design change database and a design node module, wherein the framework module is used for constructing a PD network model according to the product design change database, modularly decomposing the PD network model and determining redundant connection and long-range connection among design nodes;
the design node dependency analysis module is used for determining the direct possibility, the direct influence and the direct risk of the change propagation among the design nodes and determining the change propagation combined risk among the design nodes according to the PD network model;
the design change propagation strength calculation module is used for determining the connection side load between the design nodes and determining the design change propagation strength between the design nodes according to the change propagation combination risk and the connection side load;
the design change propagation risk prediction module is used for determining a design change initial node and determining the diffusion degree and the node load of a design change node set; determining a maximum risk optimization target of the design change node set according to the diffusivity of the design change node set and the node load; based on the design change initial node, solving the maximum risk optimization target of the design change node set by using an ant colony algorithm, and determining the highest risk of the design change propagation path;
the design change propagation risk prediction module is to:
determining the degree of diffuseness of the design change node set as follows:
Figure FDA0003077642170000041
wherein d isKqRepresenting the shortest distance from any member in the design change node set K to a design node q, wherein the design node q is a node in the residual network node set, and B is the number of the residual network nodes;
determining the node load as:
Figure FDA0003077642170000042
wherein, gmn(i) Is a shortest path, g, connecting design node m and design node n and including design node i as an intermediate nodemnAll shortest paths connecting a design node m and a design node n, and a design node i is a node in a design change node set;
describing a PD network model in a module form, modularizing the model by using a computer-aided modular clustering algorithm according to the PD network model corresponding to DSM, and classifying nodes which are closely connected with each other into the same module, thereby realizing high cohesion in the module, wherein the nodes in the module have redundant connection, different modules are connected through long-range connection, and the redundant connection and the long-range connection between the design nodes of the module can be determined according to the DSM;
the PD network model may be expressed as { S, R }, where the set S ═ S1, S2, …, sn } represents design nodes in the PD network model, and R represents design change propagation relationships between the nodes; the matrix M represents the DSM of the PD network model when the element x in the matrix MjkWhen the design node j is 1, the design node j indicates that a direct design change propagation relation exists between the affected design node j and the design node k at the initial change; when element x in matrix MjkWhen the value is 0, it indicates that no direct design change propagation relation exists between the affected design node j and the design node k at the initial change;
change propagation direct likelihood refers to the average probability of propagating through a common interface of design nodes such that a design change of one node results in a change of another design node;
by calculating the formula rj,k＝lj,k×ij,kTo obtain a straightMatrix of risk of reception, rj,k、lj,kAnd ij,kRespectively representing elements in a direct risk matrix, a direct likelihood matrix, and a direct impact matrix;
the design node dependency analysis module is configured to:
determining the risk of the change combination propagation from the design node a to the design node b as follows:
Rb,a＝1-∏(1-ρb,u) (ii) a Where u denotes the node at the penultimate level of the propagation tree from a to b, ρb,uRepresents the risk of propagation of a change from node u to node b, and ρb,u＝σu,alb,uib,u(ii) a Wherein σu,aIs the direct possibility of changing the arrival of u from a, lb,uIs the direct possibility of changing the arrival of b from u, ib,uIs the direct effect of a change from u to b;
the design change propagation strength calculation module is to:
determining a design change propagation strength from design node a to design node b as:
Figure FDA0003077642170000051
wherein R isb,aRepresenting the risk of propagation of a combination of changes from design node a to design node b,
Figure FDA0003077642170000052
representing the connecting edge loads of design nodes a and b, l (e)b,a) Representing the absolute value of the load of the node a and the node b;
Figure FDA0003077642170000053
representing the number of shortest paths passed by all connected edges in the PD network; w is arAnd wlRespectively representing weight values of the change combination propagation risk and the connection side load;
the design change propagation risk prediction module is to:
determining the maximum risk optimization target and the constraint conditions of the design change node set as follows:
max(wd×DK+wb×βK)；
Figure FDA0003077642170000054
xhih, i ∈ K, 1 or 0, H ═ 1,2,. H;
wherein, wd、wbRepresenting the diffuseness D of the design Change node set, respectivelyKWith the load mean value betaKThe relative weight of (a) to (b),
Figure FDA0003077642170000055
k represents a design change node set, i belongs to K, and N is the number of elements of the design change node set;
xhi1 denotes that a person h is assigned to a design node i, xhi0 indicates that person H is not assigned to design node i, H indicates the total number of persons; p is a radical ofiNumber of persons, p, required to design node imin,pmaxRespectively representing the minimum and maximum number of people.
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* Cited by examiner, † Cited by third party
Title
"基于小世界网络模型的复杂产品设计变更传播研究";郭于明等;《制造业自动化》;20110131(第01期);第1-5节 *
"基于设计结构矩阵的工程变更影响分析";唐敦兵等;《机械工程学报》;20100131(第01期);第154-161页 *
"复杂产品开发网络中变型设计节点方案评价";郭于明等;《计算机辅助设计与图形学学报》;20140228(第02期);第320-328页 *

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