CN111209941A - Product module type identification method and device - Google Patents
Product module type identification method and device Download PDFInfo
- Publication number
- CN111209941A CN111209941A CN201911397885.2A CN201911397885A CN111209941A CN 111209941 A CN111209941 A CN 111209941A CN 201911397885 A CN201911397885 A CN 201911397885A CN 111209941 A CN111209941 A CN 111209941A
- Authority
- CN
- China
- Prior art keywords
- module
- product
- similarity
- instance
- instances
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 9
- 239000000047 product Substances 0.000 description 135
- 230000006870 function Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003014 reinforcing effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a method and a device for identifying product module types, wherein the method comprises the following steps: acquiring a product instance set and a corresponding module instance set; the set of product instances is a set of product instances of the product, and the set of module instances is a set of module instances of the product containing modules; respectively acquiring module similarity and module use degree according to a module similarity acquisition rule and a module use degree acquisition rule; the module similarity degree represents the similarity degree between the module instances of the same module, and the module utilization degree represents the application popularization degree of the module in the product instance; and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule. According to the product module type identification method and device provided by the embodiment of the invention, the module type is identified through the module similarity and the module use degree by acquiring the product instance set and the corresponding module instance set, so that the automatic module type identification is realized, and the module type identification efficiency and accuracy are improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for identifying a product module type.
Background
At present, with the increase of personalized demands, the product design gradually changes from a uniform mass production mode to a large-scale customization mode. For example, by taking a metro vehicle as an example, with the declaration and opening of more and more metro lines in China, the metro market evolves from the traditional relatively stable type to the dynamic multi-variant type, the current metro vehicle manufacturing industry has changed from a mass production mode to a large-scale customization mode, how to quickly respond to diversified customer demands, how to develop a high-quality product with a lower cost and a shorter design period, and the metro vehicle manufacturing industry becomes a major strategic subject of competitive development of metro vehicle manufacturing enterprises. At present, the large-scale customization is generally realized by adopting a modularized product platform and a product family strategy in the academic world.
Module type identification is the core and key of product platform and product family design. The method is characterized in that a product platform module and a customization module are identified on the basis of module division. The platform module is used for being reused by products in a product family, and helps enterprises to realize large-scale economic benefits; the customization module aims to meet the customization requirements of customers and helps enterprises to realize wide economic benefits. However, the module type identification of the existing product mostly adopts a qualitative analysis method, the subjectivity is strong, and the identification accuracy is difficult to ensure. The problems restrict the identification efficiency of the product module types and influence the construction and application of product platforms and product families. Therefore, how to realize automatic module type identification becomes a problem to be solved urgently by manufacturing enterprises.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a method and an apparatus for identifying a product module type.
In a first aspect, an embodiment of the present invention provides a method for identifying a product module type, including: acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product; for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance; and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
Further, the obtaining the set of product instances and the corresponding set of module instances comprises: and acquiring the product instance set and the corresponding module instance set according to a preset time period.
Further, the module similarity obtaining rule is expressed by the following formula:
wherein S isMi(t) is the module similarity for module i at time t; n (t) is the number of module instances of module i at time t; sMi(r, s) (t) is the similarity between the r-th and s-th module instances of module i at time t.
Further, the similarity S between the r-th module instance and the S-th module instance of the module i at the time tMiThe formula for the calculation of (r, s) (t) is:
wherein j refers to the technical parameter attribute of the module i, and the total number of j is k;
the value is the similarity value between the r-th module instance and the s-th module instance of the module i at the time t and the j-th technical parameter attribute.
Further, the method for calculating the similarity value between the r-th module instance and the s-th module instance of the time t module with respect to the j-th technical parameter attribute comprises the following steps: if the data type of the jth technical parameter attribute is numerical type, the similarity value
The quotient of the minimum value and the maximum value of the jth module instance and the jth module instance related to the jth technical parameter attribute at the moment t is obtained; if it is firstThe data type of the j technical parameter attributes is text type, if the r-th module instance and the s-th module instance at the time t are the same with respect to the j technical parameter attribute, the similarity value is obtained
The value is 1, otherwise the similarity value is
The value is 0.
Further, the module usage acquisition rule is expressed by the following formula:
wherein, UMi(t) is the module usage of module i at time t; q. q.si,pIs a parameter indicating whether the product instance uses module i, if the product instance uses module i, then q isi,pEqual to 1, q if said product instance does not use module ii,pEqual to 0; p represents the serial number of the product instance; m (t) represents the total number of said product instances in said set of product instances at time t.
Further, the preset identification rule is as follows: if ε is not more than SMi(t) is less than or equal to 1, and UMi(t) 1, then the module type of module i is a platform base module; if ε is not more than SMi(t) is less than or equal to 1, and lambda is less than or equal to UMi(t)<1, the module type of the module i is a platform general module; if ε is not more than SMi(t) is not more than 1 and 0 is not less than UMi(t)<λ, the module type of module i is a platform-specific module; if 0 is less than or equal to SMi(t)<Epsilon, the module type of the module i is a non-platform module; wherein epsilon is a preset threshold value of the module similarity, and lambda is a preset threshold value of the module utilization.
In a second aspect, an embodiment of the present invention provides a product module type identification apparatus, including: an instance acquisition module to: acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product; a module usage and module similarity obtaining module for: for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance; a module type identification module to: and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
According to the product module type identification method and device provided by the embodiment of the invention, the module type is identified through the module similarity and the module use degree by acquiring the product instance set and the corresponding module instance set, so that the automatic module type identification is realized, and the module type identification efficiency and accuracy are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a method for identifying types of product modules according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for identifying a type of a product module according to another embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a product module type identification apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a product module type identification device according to another embodiment of the present invention;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a product module type identification method according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step 101, acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances for a product and the set of module instances is a set of module instances for modules included in the product.
The product example refers to a parameterized product, and the module example refers to a parameterized module, and is divided into different examples due to different parameters. A product is made up of a plurality of modules, and therefore, product instances actually contain module instances.
Products are typically stored in the form of a tree of structures to clearly show the hierarchical relationships. The product structure tree describes the composition information of the product in a tree mode, each node in the tree represents a part or an assembly of the product, and the leaf nodes represent parts. In the embodiment of the invention, the parts are uniformly regarded as modules. The level of the product corresponding to the product instance can be determined according to the condition of the platform or the actual analysis requirement, for example, the product corresponding to the product instance can be a certain type of subway vehicle, such as an A type subway vehicle; the products corresponding to the product examples can also be single marshalling vehicles, such as MP1, TC1 vehicles and the like.
Currently, product examples are generally stored in a PDM system of an enterprise, and stored in an Excel table format. The module instance refers to the result of instantiating the features of the module such as size, performance, interface, material, etc. An enterprise PDM system generally only stores technical documents such as a three-dimensional model, a two-dimensional drawing and the like of a module, and rarely stores characteristic information of the module. In recent years, in order to realize rapid configuration and modification design of products, feature information of modules is gradually carded, and a module instance set based on feature expression is stored.
Therefore, to identify the module type, the product module type identification device first needs to obtain a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances for a product and the set of module instances is a set of module instances for modules included in the product.
102, for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module utilization according to a preset module utilization acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance;
the product module type identification means needs to identify the module types of the respective modules included in the product, respectively. In the embodiment of the invention, the module type identification is carried out based on the module similarity and the module use degree, and the module similarity and the module use degree need to be calculated for the obtained module. The module similarity is obtained by obtaining the rule according to the preset module similarity, and the module usage is obtained according to the preset module usage.
The module similarity is used for representing the similarity between the module examples of the same module, for example, the similarity of the module examples in the aspects of performance, size, interface and the like is higher, which indicates that the functional structure design of the two modules is closer, and even meets the interchangeability requirement. The module similarity is obtained according to a preset module similarity obtaining rule, for example, the module similarity can be obtained through module shape similarity comparison.
And acquiring the module utilization according to a preset module utilization acquisition rule. The module usage degree is used for expressing the application popularity of each module in each product instance. The module use degree reflects the frequency of the module adopted by the product, the higher the module use degree is, the more basic the module is, the indispensable for each product is shown, and the module is not changed basically in the subsequent product examples.
And 103, obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
And obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule. For example, weights may be set for the module similarity and the module usage, respectively, and a calculation result may be obtained by a weighted sum method, so as to identify the module type according to the calculation result.
According to the embodiment of the invention, the product instance set and the corresponding module instance set are obtained, and the module type is identified through the module similarity and the module use degree, so that the automatic module type identification is realized, and the module type identification efficiency and accuracy are improved.
Further, based on the above embodiment, the obtaining the product instance set and the corresponding module instance set includes: and acquiring the product instance set and the corresponding module instance set according to a preset time period.
In the prior art, module type identification is usually carried out in a subjective mode, and the method mostly belongs to one-time identification and is difficult to continuously and automatically carry out module type identification. For example, the module type of the subway vehicle module will change with time as the number of the subway vehicle module instances increases with the increase of orders. Therefore, the existing module type identification method cannot realize continuous and real-time automatic module identification.
Therefore, to solve the problem, on the basis of the above embodiments, in the embodiments of the present invention, the product instance set and the corresponding module instance set are obtained through a preset time period, and then module similarity and module usage are obtained, and module type identification is performed based on the module similarity and the module usage, so that continuous, real-time and automatic module identification can be achieved.
Further, based on the above embodiment, the module similarity obtaining rule is expressed by the following formula:
wherein S isMi(t) is the module similarity for module i at time t; n (t) is the number of module instances of module i at time t; sMi(r, s) (t) is the similarity between the r-th and s-th module instances of module i at time t. The upper and lower indices of the summation of the above formula mean that r and s are at least 1 and at most n (t), but r and s are not the same.
On the basis of the embodiment, the module similarity is calculated based on the similarity between the module examples, so that the accuracy of the module similarity result is improved, and the accuracy of module type identification is further improved.
Further, based on the above embodiment, the similarity S between the r-th module instance and the S-th module instance of the module i at the time tMiThe formula for the calculation of (r, s) (t) is:
wherein j refers to the technical parameter attribute of the module i, and the total number of j is k;
the value is the similarity value between the r-th module instance and the s-th module instance of the module i at the time t and the j-th technical parameter attribute. In the above equation, the subscript and subscript of the summation symbol indicate that j varies from 1 to k for summation.
The technical parameter attributes are used for describing the technical parameter characteristics of the module, for example, for the side beam body module, the side beam length, the upper cover plate thickness, the lower cover plate thickness, the outer vertical plate thickness, the inner vertical plate thickness, the reinforcing rib plate thickness, the side beam central circular hole diameter and the side beam central distance all belong to the technical parameter attributes of the side beam body module.
On the basis of the embodiment, the similarity between the module examples is calculated based on the similarity values of the two module examples about the technical parameter attributes, so that the accuracy of the similarity result between the module examples is improved, and the accuracy of the module type identification is further improved.
Further, based on the above embodiment, the method for calculating the similarity value between the r-th module instance and the s-th module instance of the time t module i with respect to the j-th technical parameter attribute includes: if the data type of the jth technical parameter attribute is numerical type, the similarity value
The quotient of the minimum value and the maximum value of the jth module instance and the jth module instance related to the jth technical parameter attribute at the moment t is obtained; if the data type of the jth technical parameter attribute is a text type, if the jth module instance and the sth module instance are the same with respect to the jth technical parameter attribute at the moment t, the similarity value
The value is 1, otherwise the similarity value is
The value is 0.
If the data type of the jth attribute is numerical, the calculation formula of the similarity value of the jth technical parameter attribute of the nth module instance and the jth module instance of the module i at the time t is as follows:
min (r, s) (t) refers to the minimum value of the r-th module instance and the s-th module instance at the time t relative to the jth technical parameter attribute; max (r, s) (t) refers to the maximum value of the r-th module instance and the s-th module instance at the time t relative to the j-th technical parameter attribute.
If the data type of the jth attribute is a text type, the calculation formula of the similarity is as follows:
wherein, r ═ s represents that the r-th module instance is the same as the s-th module instance about the j-th technical parameter attribute at the time t; r ≠ s denotes that the r-th module instance differs from the s-th module instance at time t with respect to the jth technical parameter attribute.
On the basis of the above embodiment, the similarity values between the two modules with respect to the technical parameter attribute are obtained in different manners according to the numerical value and the text attribute of the technical parameter attribute, so that the calculation accuracy of the similarity values between the two modules with respect to the technical parameter attribute is improved, and the accuracy of the module type identification is further improved.
Further, based on the above embodiment, the module usage degree acquisition rule is expressed by the following formula:
wherein, UMi(t) is the module usage of module i at time t; q. q.si,pIs a parameter indicating whether the product instance uses module i, if the product instance uses module i, then q isi,pEqual to 1, q if said product instance does not use module ii,pEqual to 0; p represents the serial number of the product instance; m (t) represents time tA total number of the product instances in a product instance set.
On the basis of the embodiment, the calculation formula of the module utilization degree is reasonably set, so that the accuracy of module utilization degree calculation is improved, and the accuracy of module type identification is further improved.
Further, based on the above embodiment, for the module i, the preset identification rule is: if ε is not more than SMi(t) is less than or equal to 1, and UMi(t) 1, then the module type of module i is a platform base module; if ε is not more than SMi(t) is less than or equal to 1, and lambda is less than or equal to UMi(t)<1, the module type of the module i is a platform general module; if ε is not more than SMi(t) is not more than 1 and 0 is not less than UMi(t)<λ, the module type of module i is a platform-specific module; if 0 is less than or equal to SMi(t)<Epsilon, the module type of the module i is a non-platform module; wherein epsilon is a preset threshold value of the module similarity, and lambda is a preset threshold value of the module utilization.
The product module type identification device can automatically and dynamically identify the type of the module through the preset identification rule. Based on GB/T31982-. A non-platform module has multiple module instances whose shapes and characteristics are not identical in a product family (which can be considered as a collection of product instances). The platform module comprises a basic module, a general module and a special module, and is defined as follows:
a) a basic module: a module, whose shape and characteristics are identical in all product instances, is used in a product family.
b) A general module: a module employed by a plurality of product instances in a product family whose shape and characteristics are identical in those product instances.
c) A special module: modules, which are used by a small number of product instances in a product family, have identical shapes and characteristics in these product instances.
And completing module type identification according to the formulated module identification rule. The identification process is as follows: when the module similarity of a module is greater than or equal to epsilon and less than or equal to 1 and the module use degree is equal to 1, namely each product instance uses the module, the module is identified as a basic module; when the module similarity of a module is larger than or equal to epsilon and smaller than or equal to 1, and the module use degree is larger than or equal to lambda and smaller than 1, namely the module is used by most product examples, the module is identified as a universal module; when the module similarity of a module is greater than or equal to epsilon and less than or equal to 1 and the module use degree is greater than or equal to 0 and less than lambda, namely the module is used by only a small part of product instances, the module is identified as a special module. A module is identified as a non-platform module when its similarity is greater than or equal to 0 and less than epsilon, i.e., each instance of the module is nearly different in size, performance, interface, etc.
And epsilon is a preset threshold value of the module similarity, lambda is a preset threshold value of the module utilization, epsilon can be set to 0.7, lambda can be set to 0.3, and both can be dynamically adjusted.
On the basis of the above embodiment, the embodiment of the invention improves the accuracy of module type identification by setting the identification rule according to the module similarity preset threshold and the module use degree preset threshold.
Fig. 2 is a flowchart of a product module type identification method according to another embodiment of the present invention. As shown in fig. 2, the method includes: acquiring a product instance set and a module instance set based on feature expression; based on the module similarity and the usage degree are calculated; and further, a module identification rule is formulated based on the module similarity and the using degree, and then the subway vehicle module type dynamic identification is completed. The embodiment of the invention solves the technical problems of strong subjectivity, difficulty in realizing dynamic identification and the like in the traditional module type identification method.
In order to make the technical field of the embodiment of the invention better understand, t is given belownThe construction of the A-type subway bogie platform is taken as an example, and the technical scheme in the embodiment of the invention is clearly and completely described. Subsequent tn+1,tn+2… Module type identification and tnThe time is similar, and will not be described in detail herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Step 1: acquiring a subway vehicle product structure tree instance set (product instance set) and a module instance set based on feature expression. Based on enterprise PDM system, t is obtainednA set of temporal product structure tree instances and a set of feature-based module instances. Wherein the module of the side beam body is at tnThree instances (Sidebeam1-ZME80, Sidebeam2-ZMA100 and Sidebeam3-ZMA-120) are published in total at the time, detailed instance information is shown in Table 1, and the rest of the modules are similar.
TABLE 1
| Serial number | Name of technical parameter | Unit of | Sidebeam1-ZME80 | Sidebeam2-ZMA100 | Sidebeam3-ZMA-120 | |
| 1 | Side beam length | mm | 2660 | 3090 | 3570 | |
| 2 | Upper coverThickness of board | mm | 18 | 8 | 10 | |
| 3 | Lower cover | mm | 20 | 12 | 10 | |
| 4 | Outer side riser thickness | mm | 12 | 8 | 10 | |
| 5 | Thickness of inner vertical plate | mm | 12 | 8 | 10 | |
| 6 | Thickness of reinforcing plate | mm | 6 | 6 | 6 | |
| 7 | Diameter of central circular hole of side beam | mm | 110 | 60 | 100 | |
| 8 | Center distance of side beam | mm | 1940 | 2100 | 2100 | |
| 9 | … |
Step 2: for the acquired tnAnd the module at the moment calculates the similarity and the use degree. With tnThe module of the side beam body at the moment is taken as an example, and the calculation process of the usage degree and the similarity of the module is explained in detail.
Based on tnThe side beam body example table 1 and the formula (1) at the moment are used for obtaining tnSimilarity value of side member body at time: 0.78; next, based on the set of product structure tree instances for type a subways, all structure nodes are traversed, labeled 1 if the child nodes of the vehicles (TC1, MP1, M1, etc.) contain "side sill bodies", and 0 otherwise. Based on the marking result, the usage value of the side sill body can be calculated based on the formula (5): 1.
the similarity and the use degree calculation method and the process of the rest modules of the A-type subway bogie are similar to those of a side beam body, and the platform module t of the A-type subway bogie isnThe results of calculating the module similarity and the module usage at the time are shown in table 2.
TABLE 2
And step 3: and formulating a module identification rule based on the module similarity and the usage degree. Here, we set the similarity threshold epsilon and the usage threshold lambda to 0.7 and 0.3, respectively. The rules are as follows: if ε is not more than SMi(t) is less than or equal to 1, and UMi(t) 1, then the module type of module i is a platform base module; if ε is not more than SMi(t) is less than or equal to 1, and lambda is less than or equal to UMi(t)<1, the module type of the module i is a platform general module; if ε is not more than SMi(t) is not more than 1 and 0 is not less than UMi(t)<λ, the module type of module i is a platform-specific module; if 0 is less than or equal to SMi(t)<Epsilon, the module type of the module i is a non-platform module; wherein epsilon is a preset threshold value of the module similarity, and lambda is a preset threshold value of the module utilization.
And 4, step 4: and finishing the identification of the subway vehicle module type according to the formulated module identification rule.
Taking the side beam body as an example, the module tnThe module similarity at the moment is 0.78, the module use degree is 1, and the module similarity of the side beam body module is 0.78 according to the identification rule>0.7 and the module degree of use is equal to 1, the side sill body module is identified as the base module.
Taking the positioning arm mounting base as an example, the module tnThe module similarity at the moment is 0.73, the module use degree is 0.78, and the module similarity of the positioning arm mounting seat module is 0.73 according to the identification rule>0.7 and module utilization 0.78>0.3, the arm mount module is identified as a universal module.
Taking a collector shoe mounting base as an example, the module tnThe module similarity at the moment is 0.89, the module use degree is 0.04, and the module similarity of the collector shoe mounting seat module is 0.89 according to the identification rule>0.7, and the module use degree is more than 0 and less than 0.3, the collector shoe installation seat module is identified as a special module.
Taking the motor hanging seat as an example, the module tnThe module similarity at the moment is 0.61, the module use degree is 0.69, and according to the identification rule, the module similarity of the motor hanging seat module is 0.61<0.7, the motor mount module is identified as a non-platform module.
The other module identification process is similar to that described above, and table 3 shows a type a metro bogie tnAnd identifying the module type of each module at the moment.
TABLE 3
The embodiment of the invention solves the technical problems of strong subjectivity, difficulty in realizing dynamic identification and the like in the subway vehicle module type identification method.
Fig. 3 is a schematic structural diagram of a product module type identification device according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes an instance obtaining module 10, a module usage degree and module similarity degree obtaining module 20, and a module type identifying module 30, wherein: the example acquisition module 10 is to: acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product; the module usage degree and module similarity degree obtaining module 20 is configured to: for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance; the module type identification module 30 is configured to: and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
According to the embodiment of the invention, the product instance set and the corresponding module instance set are obtained, and the module type is identified through the module similarity and the module use degree, so that the automatic module type identification is realized, and the module type identification efficiency and accuracy are improved.
Fig. 4 is a schematic structural diagram of a product module type identification device according to another embodiment of the present invention. As shown in fig. 4, the apparatus includes:
(1) and the data acquisition function module is used for acquiring the subway product structure tree instance set and the module instance set based on the feature expression. Product instances in the product structure tree instance set and module instances in the module instance set may be characterized by a list of technical parameters. The function module forms a basic data set by reading a product structure tree instance and a module instance in the enterprise PDM system.
(2) And the module similarity and utilization calculation function module is used for calculating the similarity and the utilization of the acquired modules. The function module obtains the similarity and the use degree value of the module through a module similarity and module use degree calculation formula.
(3) And the rule making function module is used for making a rule for identifying the type of the module. The functional module formulates a rule for identifying the type of the module according to the similarity and the use degree of the module. Wherein the similarity and the use degree threshold can be dynamically set.
(4) And the module type identification function module is used for identifying which type module the input product module belongs to. The functional module identifies the type of the module based on the established identification rule and the threshold value: basic module, general module, special module, non-platform module.
The apparatus provided in the embodiment of the present invention is used for the method, and specific functions may refer to the method flow described above, which is not described herein again.
Fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 5, the electronic device may include: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. Processor 510 may call logic instructions in memory 530 to perform the following method: acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product; for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance; and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
Furthermore, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to perform the method provided by the foregoing embodiments, for example, including: acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product; for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance; and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for identifying a type of a product module, comprising:
acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product;
for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance;
and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
2. The method of claim 1, wherein obtaining a set of product instances and a corresponding set of module instances comprises:
and acquiring the product instance set and the corresponding module instance set according to a preset time period.
3. The product module type identification method according to claim 1 or 2, wherein the module similarity acquisition rule is represented by the following formula:
wherein S isMi(t) is the module similarity for module i at time t; n (t) is the number of module instances of module i at time t; sMi(r, s) (t) is the similarity between the r-th and s-th module instances of module i at time t.
4. The product module type identification method of claim 3, wherein the similarity S between the r-th module instance and the S-th module instance of the module i at time tMiThe formula for the calculation of (r, s) (t) is:
wherein j refers to the technical parameter attribute of the module i, and the total number of j is k;
the value is the similarity value between the r-th module instance and the s-th module instance of the module i at the time t and the j-th technical parameter attribute.
5. The product module type identification method according to claim 4, wherein the similarity value between the r-th module instance and the s-th module instance of the t-time module i with respect to the j-th technical parameter attribute is calculated by:
if the data type of the jth technical parameter attribute is numerical type, the similarity value
The quotient of the minimum value and the maximum value of the jth module instance and the jth module instance related to the jth technical parameter attribute at the moment t is obtained;
if the jth data type of the technical parameter attribute is a text type, the tth module instance and the tth module instance at the moment tThe s-th module instance has the same attribute with respect to the j-th technical parameter, the similarity value
The value is 1, otherwise the similarity value is
The value is 0.
6. The product module type identification method according to claim 1 or 2, wherein the module usage degree acquisition rule is represented by the following formula:
wherein, UMi(t) is the module usage of module i at time t; q. q.si,pIs a parameter indicating whether the product instance uses module i, if the product instance uses module i, then q isi,pEqual to 1, q if said product instance does not use module ii,pEqual to 0; p represents the serial number of the product instance; m (t) represents the total number of said product instances in said set of product instances at time t.
7. The product module type identification method according to claim 1 or 2, characterized in that for module i, the preset identification rules are:
if ε is not more than SMi(t) is less than or equal to 1, and UMi(t) 1, then the module type of module i is a platform base module; if ε is not more than SMi(t) is less than or equal to 1, and lambda is less than or equal to UMi(y)<1, the module type of the module i is a platform general module; if ε is not more than SMi(t) is not more than 1 and 0 is not less than UMi(t)<λ, the module type of module i is a platform-specific module; if 0 is less than or equal to SMi(t)<Epsilon, the module type of the module i is a non-platform module; wherein epsilon is a preset threshold value of the module similarity, and lambda is a preset threshold value of the module utilization.
8. A product module type identification device, comprising:
an instance acquisition module to: acquiring a product instance set and a corresponding module instance set; wherein the set of product instances is a set of product instances of a product, and the set of module instances is a set of module instances of modules included in the product;
a module usage and module similarity obtaining module for: for each module, acquiring module similarity according to a preset module similarity acquisition rule, and acquiring module use degree according to a preset module use degree acquisition rule; the module similarity is used for representing the similarity between the module instances of the same module, and the module usage is used for representing the application popularity of each module in each product instance;
a module type identification module to: and obtaining the module type of each module according to the module similarity and the module use degree based on a preset identification rule.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the product module type identification method according to any one of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the steps of the product module type identification method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911397885.2A CN111209941B (en) | 2019-12-30 | 2019-12-30 | Product module type identification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911397885.2A CN111209941B (en) | 2019-12-30 | 2019-12-30 | Product module type identification method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111209941A true CN111209941A (en) | 2020-05-29 |
| CN111209941B CN111209941B (en) | 2024-04-26 |
Family
ID=70788479
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911397885.2A Active CN111209941B (en) | 2019-12-30 | 2019-12-30 | Product module type identification method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111209941B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112633994A (en) * | 2020-11-20 | 2021-04-09 | 西南交通大学 | Rail transit vehicle product platform planning method |
| CN112818593A (en) * | 2021-01-22 | 2021-05-18 | 中车工业研究院有限公司 | Product configuration method and device based on modular design |
| CN113450194A (en) * | 2020-11-20 | 2021-09-28 | 西南交通大学 | Rail transit vehicle product platform construction method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005114495A1 (en) * | 2004-05-20 | 2005-12-01 | Chi Kuen Wong | A system and method of a scalable and rule-based building chassis platform for planning, developing and implementing product family architecture solutions in multi-storey buildings |
| CN103778304A (en) * | 2014-02-25 | 2014-05-07 | 青岛理工大学 | Motor vehicle drive axle design method |
| CN105550393A (en) * | 2015-11-30 | 2016-05-04 | 北京理工大学 | Firearm variant design method supporting rapid generation of schemes |
| DE102016207768A1 (en) * | 2016-05-04 | 2017-11-09 | Siemens Aktiengesellschaft | Apparatus and method for providing a set of module types |
| US20180121979A1 (en) * | 2016-10-28 | 2018-05-03 | Wal-Mart Stores, Inc. | Systems and methods for optimizing normalization of product attributes for a webpage of an online retailer |
| CN108595763A (en) * | 2018-03-26 | 2018-09-28 | 武汉理工大学 | Die assembly design method based on model index technology |
| CN109800465A (en) * | 2018-12-21 | 2019-05-24 | 中车工业研究院有限公司 | Classification method, device and the electronic equipment of rail traffic vehicles products configuration module |
| CN109815541A (en) * | 2018-12-21 | 2019-05-28 | 中车工业研究院有限公司 | Rail traffic vehicles product component module partition method, device and electronic equipment |
| CN109816474A (en) * | 2018-12-21 | 2019-05-28 | 中车工业研究院有限公司 | The modular arrangements method, apparatus and electronic equipment of rail traffic product |
| CN109858114A (en) * | 2019-01-17 | 2019-06-07 | 中车青岛四方机车车辆股份有限公司 | The recognition methods of module type and device |
| CN110348747A (en) * | 2019-07-15 | 2019-10-18 | 齐鲁工业大学 | A kind of Intensive Design method and system of product design |
-
2019
- 2019-12-30 CN CN201911397885.2A patent/CN111209941B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005114495A1 (en) * | 2004-05-20 | 2005-12-01 | Chi Kuen Wong | A system and method of a scalable and rule-based building chassis platform for planning, developing and implementing product family architecture solutions in multi-storey buildings |
| CN103778304A (en) * | 2014-02-25 | 2014-05-07 | 青岛理工大学 | Motor vehicle drive axle design method |
| CN105550393A (en) * | 2015-11-30 | 2016-05-04 | 北京理工大学 | Firearm variant design method supporting rapid generation of schemes |
| DE102016207768A1 (en) * | 2016-05-04 | 2017-11-09 | Siemens Aktiengesellschaft | Apparatus and method for providing a set of module types |
| US20180121979A1 (en) * | 2016-10-28 | 2018-05-03 | Wal-Mart Stores, Inc. | Systems and methods for optimizing normalization of product attributes for a webpage of an online retailer |
| CN108595763A (en) * | 2018-03-26 | 2018-09-28 | 武汉理工大学 | Die assembly design method based on model index technology |
| CN109800465A (en) * | 2018-12-21 | 2019-05-24 | 中车工业研究院有限公司 | Classification method, device and the electronic equipment of rail traffic vehicles products configuration module |
| CN109815541A (en) * | 2018-12-21 | 2019-05-28 | 中车工业研究院有限公司 | Rail traffic vehicles product component module partition method, device and electronic equipment |
| CN109816474A (en) * | 2018-12-21 | 2019-05-28 | 中车工业研究院有限公司 | The modular arrangements method, apparatus and electronic equipment of rail traffic product |
| CN109858114A (en) * | 2019-01-17 | 2019-06-07 | 中车青岛四方机车车辆股份有限公司 | The recognition methods of module type and device |
| CN110348747A (en) * | 2019-07-15 | 2019-10-18 | 齐鲁工业大学 | A kind of Intensive Design method and system of product design |
Non-Patent Citations (1)
| Title |
|---|
| 董春兰等: "基于产品平台的机械产品模块化设计方法", vol. 29, no. 6, pages 3 - 4 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112633994A (en) * | 2020-11-20 | 2021-04-09 | 西南交通大学 | Rail transit vehicle product platform planning method |
| CN113450194A (en) * | 2020-11-20 | 2021-09-28 | 西南交通大学 | Rail transit vehicle product platform construction method |
| CN112818593A (en) * | 2021-01-22 | 2021-05-18 | 中车工业研究院有限公司 | Product configuration method and device based on modular design |
| CN112818593B (en) * | 2021-01-22 | 2023-07-14 | 中车工业研究院有限公司 | Product configuration method and device based on modularized design |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111209941B (en) | 2024-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111209941A (en) | Product module type identification method and device | |
| CN110083623B (en) | Business rule generation method and device | |
| CN106844407B (en) | Tag network generation method and system based on data set correlation | |
| CN111506559B (en) | Data storage method, device, electronic equipment and storage medium | |
| CN109948710B (en) | Micro-service identification method based on API similarity | |
| CN103294817A (en) | Text feature extraction method based on categorical distribution probability | |
| WO2020034880A1 (en) | Logistics object information processing method, device and computer system | |
| CN108520471A (en) | It is overlapped community discovery method, device, equipment and storage medium | |
| CN107247728B (en) | Text processing method and device and computer storage medium | |
| CN111522968A (en) | Knowledge graph fusion method and device | |
| US20110184956A1 (en) | Accessing digitally published content using re-indexing of search results | |
| CN109918678A (en) | A kind of field meanings recognition methods and device | |
| CN114584601A (en) | User loss identification and intervention method, system, terminal and medium | |
| CN110378569A (en) | Industrial relations chain building method, apparatus, equipment and storage medium | |
| CN117194907B (en) | Low-value data theme identification method for data center | |
| CN104965846B (en) | Visual human's method for building up in MapReduce platform | |
| CN112148880A (en) | Customer service dialogue corpus clustering method, system, equipment and storage medium | |
| CN113760521A (en) | Virtual resource allocation method and device | |
| CN111143511A (en) | Emerging technology prediction method, emerging technology prediction device, electronic equipment and medium | |
| CN108959289B (en) | Website category acquisition method and device | |
| CN114860362A (en) | Interface updating method and device | |
| CN107506398B (en) | Method for adding label attribute to book | |
| CN114446425A (en) | Information pushing method and device, electronic equipment and storage medium | |
| CN112580348A (en) | Policy text relevance analysis method and system | |
| CN112308419A (en) | Data processing method, device, equipment and computer storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |