CN109858114A - The recognition methods of module type and device - Google Patents
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Abstract
The invention discloses a kind of recognition methods of module type and devices.Wherein, this method comprises: obtaining the requirements set and module to be identified of bogie；Based on requirements set and module to be identified, the general geological coodinate system of module index of variability, the spread index of module to be identified and module to be identified is obtained；Based on module index of variability, spread index and general geological coodinate system, the module type of module to be identified is obtained.The technical issues of recognition methods that the present invention solves module type in the related technology changes the higher cost for responding insufficient and module design to customer demand.
Description
Technical Field
The invention relates to the field of rail transit product design, in particular to a module type identification method and device.
Background
With the development of urban economy, the number of metro operation cities and operation lines is increased year by year, the metro vehicle market is evolved from the traditional relatively stable type to the dynamic multi-variant type, so that metro vehicle manufacturing enterprises are changed from a mass production mode to a large-scale customization mode, how to quickly respond to diversified customer demands, how to develop high-quality products with low cost and short design period, and the metro vehicle market is a major strategic subject of enterprise competitive development.
The modular design is an advanced design idea and design method, and can combine and exchange modules with different functions or the same function but different performances to form various products with universal variation, so that the product series has great adaptability and meets the diversified requirements of customers. The module type identification is a key link of modular design, and the module is subdivided into a basic module, a configuration module, a parameterization module and a personality module on the basis of module division. Enabling "modular design" to respond to dynamically changing customer needs by configuration module selection, parameterization module adaptive modification, and personality module addition or removal, while keeping the base modules common. The basic module, the configuration module and the parameterization module are the core and key of the modular design. At present, a plurality of subway main engine plants in China develop the 'modular design' work of the bogie, but the existing identification method of the module type is difficult to simultaneously consider two aspects of response of customer requirements and reuse of existing resources of enterprises, so that the problems of insufficient response to the change of the customer requirements, high cost of module design and the like are caused.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a module type identification method and a module type identification device, which at least solve the technical problems that the module type identification method in the related art has insufficient response to the change of customer requirements and the cost of module design is high.
According to an aspect of the embodiments of the present invention, there is provided a method for identifying a module type, including: acquiring a demand set and a module to be identified of a bogie; obtaining a module variation index, a propagation index of the module to be identified and a general degree of the module to be identified based on the demand set and the module to be identified; and obtaining the module type of the module to be identified based on the module variation index, the propagation index and the general degree.
Further, based on the demand set and the module to be identified, obtaining a module variation index, including: classifying each demand in the demand set to obtain a demand type of each demand, wherein the demand type includes one of the following: general requirements, adaptability requirements and individual requirements; and obtaining a module variation index based on the mapping relation between the adaptability requirement and the module to be identified.
Further, obtaining a module variation index based on a mapping relation between the adaptive demand and the module to be identified, including: determining technical indexes based on the adaptability requirement and the module to be identified; obtaining a first correlation matrix based on the influence strength of the adaptability requirement on the technical index; obtaining a second correlation matrix based on the influence strength of the technical index on the module to be identified; and obtaining a module variation index based on the first correlation matrix and the second correlation matrix.
Further, obtaining a module variation index based on the first correlation matrix and the second correlation matrix, including: normalizing the first correlation matrix to obtain a normalized matrix; obtaining an index weight of the technical index based on the normalized matrix and the corresponding demand weight, wherein the demand weight is obtained by adopting an analytic hierarchy process; and obtaining a module variation index based on the index weight and the second correlation matrix.
Further, based on the demand set and the module to be identified, obtaining a propagation index of the module to be identified, including: obtaining a third correlation matrix based on the change influence degree between the modules to be identified; obtaining a module sending propagation index and a module absorption propagation index based on the third correlation matrix; and obtaining the difference between the transmission index sent by the module and the absorption transmission index of the module to obtain the transmission index of the module to be identified.
Further, obtaining a third correlation matrix based on the influence degree of the change between the modules to be identified, including: and obtaining the weighted sum of the change influence degrees among the modules to be identified to obtain a third correlation matrix.
Further, altering the degree of influence includes: structure influence degree, interface influence degree, material influence degree and performance influence degree.
Further, based on the demand set and the module to be identified, the generality of the module to be identified is obtained, and the method comprises the following steps: obtaining a plurality of sample examples included by a module to be identified; classifying a plurality of sample instances to obtain at least one sample instance set, wherein each sample instance set comprises: at least one sample instance; obtaining a first depth index and a first breadth index of each sample instance; obtaining a second depth index and a second breadth index of each sample instance set based on the first depth index and the first breadth index of all sample instances contained in each sample instance set; the popularity is derived based on the second depth index and the second breadth index of the at least one sample instance set.
Further, classifying the plurality of sample instances to obtain at least one sample instance set, including: acquiring characteristic parameters of a module to be identified; obtaining the similarity between the characteristic parameters by using a K mean value clustering algorithm; and classifying the plurality of sample examples based on the similarity to obtain at least one sample example set.
Further, obtaining a first depth index and a first breadth index for each sample instance includes: acquiring a first total amount of each sample instance and a second total amount of a sample instance set to which each sample instance belongs; acquiring the occurrence number of each sample instance in the item and the total number of the items; obtaining a first depth index based on the first total usage and the second total usage; and obtaining the ratio of the occurrence times to the total number of the items to obtain a first breadth index.
Further, obtaining a second depth index and a second breadth index of each sample instance set based on the first depth index and the first breadth index of all sample instances contained in each sample instance set comprises: obtaining the sum of the first depth indexes of all sample examples to obtain a second depth index; and acquiring the maximum first breadth index in the first breadth indexes of all the sample examples to obtain a second breadth index.
Further, deriving a popularity based on the second depth index and the second breadth index of the at least one sample instance set, comprises: obtaining a maximum second depth index in the second depth indexes of at least one sample instance set to obtain a third depth index; obtaining a maximum second breadth index in the second breadth indexes of at least one sample example set to obtain a third breadth index; and obtaining the average value of the third depth index and the third breadth index to obtain the universal degree.
Further, obtaining the module type of the module to be identified based on the module variation index, the propagation index and the general degree, including: comparing the module variation index with a first threshold, comparing the propagation index with a second threshold, and comparing the common degree with a third threshold to obtain a comparison result; based on the comparison, a module type is obtained.
According to another aspect of the embodiments of the present invention, there is also provided an apparatus for identifying a module type, including: the acquisition module is used for acquiring a demand set of the bogie and the module to be identified; the first processing module is used for obtaining a module variation index, a propagation index of the module to be identified and the general degree of the module to be identified based on the demand set and the module to be identified; and the second processing module is used for obtaining the module type of the module to be identified based on the module variation index, the propagation index and the general degree.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the above method for identifying a module type.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes the method for identifying a module type as described above.
In the embodiment of the invention, after the demand set and the module to be identified of the bogie are obtained, the module variation index, the propagation index of the module to be identified and the general degree of the module to be identified can be obtained based on the demand set and the module to be identified, and the module type of the module to be identified can be further obtained based on the module variation index, the propagation index and the general degree. Compared with the prior art, in the module type identification process, the basic module, the configurable module and the parameterized module are identified by comprehensively considering the module variation index, the propagation index and the universality, so that the existing technical resources of an enterprise can be fully reused in the design of the metro bogie, the quick response of a product to the customer requirements can be ensured, the customer requirements can be better met, the research and development efficiency and quality can be improved, the development resources can be saved, and the technical problems that the identification method of the module type in the related technology is insufficient in response to the change of the customer requirements and the cost of module design is high are solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method of identifying a module type according to an embodiment of the present invention;
FIG. 2 is a diagram of an alternative requirement-technical indicator correlation matrix according to an embodiment of the present invention; and
fig. 3 is a schematic diagram of a module type identification apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for identifying a type of module, it being noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
Fig. 1 is a flowchart of a module type identification method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
and S102, acquiring a demand set and a module to be identified of the bogie.
Specifically, the above-mentioned module to be identified may be a subway bogie module, specifically including: the system comprises a suspension module (m1), a traction module (m2), a wheel pair module (m3), a cross beam module (m4), a driving module (m5), a braking module (m6), an air spring module (m7) and a side beam module (m 8). But is not limited thereto.
In an alternative scheme, the existing project bidding of the subway train can be analyzedA condition book, collecting and classifying the bogie requirements to obtain a requirement set { CR1，CR2，…，CRm}。
And step S104, obtaining a module variation index, a propagation index of the module to be identified and the general degree of the module to be identified based on the demand set and the module to be identified.
In an optional scheme, the mapping relationship between the demand and the module can be analyzed to obtain a module variation index VI, and on the basis of analyzing the mapping relationship between the demand and the module, the change influence relationship between the module and the module is further quantitatively analyzed to obtain a propagation index CPI of the module; analysis module example calculation module general degree f (m).
And step S106, obtaining the module type of the module to be identified based on the module variation index, the propagation index and the general degree.
Specifically, the above-mentioned module types may include: the system comprises a basic module, a configuration module, a parameterization module and a personality module, wherein the basic module is shared by all products in a product family, and only one predefined module instance is provided; the configuration module is shared by all products in the product family, has a plurality of predefined instances, and each derived product variant selects one module instance; the parameterized module is shared by all products in the product family and is provided with a parameterized structure model of an adaptable variant; the personality module is shared by partial products in the product family, has no predefined instance, and needs to be redesigned according to the personalized requirements of customers.
In an alternative scheme, the module type can be analyzed and identified by comprehensively considering three indexes of the module variation index VI, the propagation index CPI and the general degree f (M), so that the module type can be identified. Wherein, for the base module: VI is smaller, CPI is positive, f (M) is larger. The module has higher universality and less variants in the existing project example; modules are less affected by customization needs and generally do not change, but once changed, will have a greater impact on other modules. It is therefore advisable to solidify it into a basic module.
For the configuration module: VI is greater, CPI is positive, f (M) is less. The generality of the module in the existing project example is low, and the number of variants is large; modules are greatly affected by customization requirements, and changes generally occur, and the changes have a large impact on other modules. Therefore, it is proposed to study the influence rule of the change of the demand on the module and the change of the demand on other modules, and to respond to the change of the customer demand by designing the configurable module so that the influence of the change of the demand on other modules can be controlled.
For parameterized modules: VI is greater, CPI is negative, and f (M) is less. The generality of the module in the existing project example is low, and the number of variants is large; modules are greatly influenced by customization requirements and generally changed, but the change has little influence on other modules and is greatly influenced by the change of other modules. Therefore, the proposal is to research the influence rule of the demand change on the parameterized module and the other module changes on the parameterized module, and design the parameterized module capable of adaptively changing to dynamically respond to the performance demand of the customer change by solidifying the influence of the other module changes on the parameterized module.
According to the embodiment of the invention, after the demand set and the module to be identified of the bogie are obtained, the module variation index, the propagation index of the module to be identified and the general degree of the module to be identified can be obtained based on the demand set and the module to be identified, and the module type of the module to be identified can be further obtained based on the module variation index, the propagation index and the general degree. Compared with the prior art, in the module type identification process, the basic module, the configurable module and the parameterized module are identified by comprehensively considering the module variation index, the propagation index and the universality, so that the existing technical resources of an enterprise can be fully reused in the design of the metro bogie, the quick response of a product to the customer requirements can be ensured, the customer requirements can be better met, the research and development efficiency and quality can be improved, the development resources can be saved, and the technical problems that the identification method of the module type in the related technology is insufficient in response to the change of the customer requirements and the cost of module design is high are solved.
Optionally, in the above embodiment of the present invention, obtaining a module variation index based on the demand set and the module to be identified includes: classifying each demand in the demand set to obtain a demand type of each demand, wherein the demand type includes one of the following: general requirements, adaptability requirements and individual requirements; and obtaining a module variation index based on the mapping relation between the adaptability requirement and the module to be identified.
In an alternative approach, after the set of requirements is obtained, the customer requirements may be classified based on the design experience of the engineer, and the requirements may be subdivided into general requirements, adaptive requirements, and individual requirements, as shown in table 1 below. The general requirements represent requirements with the same attribute and the same level value in a family of products; the adaptive demand represents the demand that the attributes are the same and the level values are different in a family of products; the individual requirements represent requirements with different attributes and different level values in a family of products, and are individual 'sounds' of customers. The general requirements are responded by the basic module, the personalized requirements are responded by the personalized module, the mapping relation between the adaptive requirements and the modules is complex, the adaptive requirements can be responded by the configuration module and the parameterized module, and the basic module can respond to the adaptive requirements with small changes.
TABLE 1
| Name of requirement item | Range of values of parameters | Type of need |
| Operation speed (km/h) | 80、100、120… | Adaptive requirements |
| Axle weight (t) | 16、17 | Adaptive requirements |
| … | … | … |
| Safety feature | 95J01-M | General requirements |
| Comfort feature | GB5599-1985 | General requirements |
| Gauge (mm) | 1435 | General requirements |
The basic module is mainly affected by general requirements (this relationship is a simpler direct mapping relationship, which is not described in detail in the present invention), and also affected by adaptive requirements, and the configuration module and the parameterized module are mainly affected by adaptive requirements. Therefore, the mapping relation between the adaptability requirement and the modules is mainly analyzed to obtain the module variation index VI, so that the basic module, the configuration module and the parameterization module mapped by the adaptability requirement are accurately identified.
Optionally, in the above embodiment of the present invention, obtaining a module variation index based on a mapping relationship between the adaptive requirement and the module to be identified includes: determining technical indexes based on the adaptability requirement and the module to be identified; obtaining a first correlation matrix based on the influence strength of the adaptability requirement on the technical index; obtaining a second correlation matrix based on the influence strength of the technical index on the module to be identified; and obtaining a module variation index based on the first correlation matrix and the second correlation matrix.
In an alternative, since the operating speed and axle weight are key adaptive requirements of the bogie design and have certain differences in different projects, the index mapping is performed on the different items, and a requirement-technical index correlation matrix (i.e. the first correlation matrix described above) is constructed, as shown in fig. 2. First correlation matrixWherein r isij(i-1, 2, …, m, j-1, 2, …, n) represents the intensity of the impact of the ith demand on the jth technical index, which is defined as: strong correlation-9, large correlation-7, medium correlation-5, small correlation-3, no correlation-0, as shown in fig. 2, strong correlation is represented by a solid circle, large correlation is identified by a hollow circle, medium correlation is represented by a square, small correlation is represented by a triangle, and no correlation is null.
Establishing a technical index-module correlation matrix (i.e. the second correlation matrix mentioned above), and establishing a second correlation matrixWherein bij (i ═ 1,2, …, n;. j ═ 1,2, …, l) represents the influence strength of the ith technical index on the jth module, and the strength is defined as: strong correlation-9, large correlation-7, medium correlation-5, small correlation-3, no correlation-0.
Further based on the first correlation matrix R and the second correlation matrix B, obtaining a module variation index VIj。
Optionally, in the above embodiment of the present invention, obtaining the module variation index based on the first correlation matrix and the second correlation matrix includes: normalizing the first correlation matrix to obtain a normalized matrix; obtaining an index weight of the technical index based on the normalized matrix and the corresponding demand weight, wherein the demand weight is obtained by adopting an analytic hierarchy process; and obtaining a module variation index based on the index weight and the second correlation matrix.
In an alternative scheme, after obtaining the first correlation matrix R and the second correlation matrix B as shown in fig. 2, R may be normalized to obtain a normalized matrix R', which is calculated as follows:
index weight r for a technical index whose technical index j is influenced by demandjAnd (3) calculating according to the following formula:
wherein, ω isiIs a demand weight, andis obtained by adopting an analytic hierarchy process.
Module variation index VI of module j influenced by technical indexjAnd (3) calculating according to the following formula:
according to the attached FIG. 2, the module variation index VI is calculated and the result is shown in Table 2:
TABLE 2
Optionally, in the above embodiment of the present invention, obtaining the propagation index of the module to be identified based on the demand set and the module to be identified includes: obtaining a third correlation matrix based on the change influence degree between the modules to be identified; obtaining a module sending propagation index and a module absorption propagation index based on the third correlation matrix; and obtaining the difference between the transmission index sent by the module and the absorption transmission index of the module to obtain the transmission index of the module to be identified.
Optionally, the changing the degree of influence includes: structure influence degree, interface influence degree, material influence degree and performance influence degree.
In an alternative, the modules are adapted to respond to customer compliance needs, and these adaptations cause the propagation of changes between modules. Therefore, determining the type of a module further requires analyzing the change influence relationship between modules, and the change influence factors between modules include: structure, interface, material, performance.
Establishing a module-to-module propagation impact correlation matrix (i.e., the third correlation matrix described above), the third correlation matrixWhere pij (i, j ═ 1,2, …, l) represents the propagation influence of module i on module j. Send out propagation index P to modulej inAnd (3) calculating according to the following formula:absorption propagation index P for modulej inAnd (3) calculating according to the following formula:pair module comprehensive propagation index CPIiAnd (3) calculating according to the following formula:
on the basis of the mapping relationship between the analysis requirements and the modules, the change influence relationship between the modules is further analyzed to construct a propagation influence correlation matrix between the bogie modules, and the result is shown in table 3:
TABLE 3
Optionally, in the above embodiment of the present invention, obtaining the third correlation matrix based on the influence degree of the change between the modules to be identified includes: and obtaining the weighted sum of the change influence degrees among the modules to be identified to obtain a third correlation matrix.
In an alternative scheme, pijThe calculation formula of (2) is as follows:
pij＝WS×PSij+WI×PIij+WM×PMij+WP×PPij，
wherein, WS、WI、WM、WPRespectively representing structure, interface, material, performance change influence weight, and WS+WI+WM+WPObtained by analytic hierarchy process as 1, PSij、PIij、PMij、PPijRespectively representing the structure influence degree, the interface influence degree, the material influence degree and the performance influence degree of the module i change pair module j.
Optionally, in the above embodiment of the present invention, obtaining the popularity of the module to be identified based on the requirement set and the module to be identified includes: obtaining a plurality of sample examples included by a module to be identified; classifying a plurality of sample instances to obtain at least one sample instance set, wherein each sample instance set comprises: at least one sample instance; obtaining a first depth index and a first breadth index of each sample instance; obtaining a second depth index and a second breadth index of each sample instance set based on the first depth index and the first breadth index of all sample instances contained in each sample instance set; the popularity is derived based on the second depth index and the second breadth index of the at least one sample instance set.
In an alternative scheme, the general use degree of the module is to evaluate the specific gravity of the module adopted by n products in the product family, and the specific gravity is mainly represented as: the total amount of modules, also called depth index; the number of products using the module is also called the breadth index.
Assume that the module M to be analyzed has M sample instances, the set of elements is { c }1,c2,…,cm}. The M sample instances are divided into j classes (i.e., the sample instance set described above) (j ≦ M), M ═ M1,M2,…,Mj}. The g-th module (g is less than or equal to j) MgIncluding k instances (k ≦ M), Mg＝{c1,c2,…,ck}。
Depth index for the h (h ≦ k) module instance for the g-th class (g ≦ j) module(i.e., the first depth index described above) and breadth index(i.e. the second breadth index) is calculated to further obtain the universality of the h module example of the g moduleThe calculation formula is as follows:
depth index f of Mg for the g-th class module on the basis of general use degree of class module instance1(Mg) (i.e., the second depth index described above) and breadth index f2(Mg) (i.e., the second breadth index) is calculated to obtain the general degree f (M) of the g-th module Mgg) The calculation formula is as follows:
and on the basis of the general degree of the module class, calculating the general degree f (M) of the module M to be analyzed.
For example, data of a type a metro bogie instances such as shanghai metro line No. 2, line No. 11 and line No. 16, shenzhen metro line No. 5 and line No. 11, cantonese metro line No. 13 are collected, the universal degree of the bogie module is calculated according to the formula, and the calculation result is shown in table 4:
TABLE 4
Optionally, in the above embodiment of the present invention, classifying the plurality of sample instances to obtain at least one sample instance set includes: acquiring characteristic parameters of a module to be identified; obtaining the similarity between the characteristic parameters by using a K mean value clustering algorithm; and classifying the plurality of sample examples based on the similarity to obtain at least one sample example set.
In an optional scheme, similarity among characteristic parameters is calculated by adopting K-means clustering based on the characteristic parameters of the modules, and m module instances are divided into j classes.
Optionally, in the foregoing embodiment of the present invention, acquiring the first depth index and the first breadth index of each sample instance includes: acquiring a first total amount of each sample instance and a second total amount of a sample instance set to which each sample instance belongs; acquiring the occurrence number of each sample instance in the item and the total number of the items; obtaining a first depth index based on the first total usage and the second total usage; and obtaining the ratio of the occurrence times to the total number of the items to obtain a first breadth index.
In an alternative arrangement, the first and second electrodes may be,andthe calculation formula of (a) is as follows:
wherein,the total amount of the h module instance in the g module (namely the first total amount); j is the total number of module clusters; size (M)g) Representing the total usage of the class g module (i.e. the second total usage mentioned above),can be regarded as the total amount of the module M to be analyzed;it is determined whether the h-th module instance in the g-th class module is present in the existing item i, and if so,otherwiseThenThe number of occurrences of the h-th module instance in the existing project can be considered; n is the total number of items in the product family.
Optionally, in the foregoing embodiment of the present invention, obtaining the second depth index and the second extent index of each sample instance set based on the first depth index and the first extent index of all sample instances included in each sample instance set includes: obtaining the sum of the first depth indexes of all sample examples to obtain a second depth index; and acquiring the maximum first breadth index in the first breadth indexes of all the sample examples to obtain a second breadth index.
In an alternative, f1(Mg) And f2(Mg) The calculation formula of (a) is as follows:
optionally, in the foregoing embodiment of the present invention, obtaining the popularity based on the second depth index and the second breadth index of the at least one sample instance set includes: obtaining a maximum second depth index in the second depth indexes of at least one sample instance set to obtain a third depth index; obtaining a maximum second breadth index in the second breadth indexes of at least one sample example set to obtain a third breadth index; and obtaining the average value of the third depth index and the third breadth index to obtain the universal degree.
In an alternative, the third depth index may be a depth index f of the module to be analyzed1(M), the third breadth index may be the breadth index f of the module to be analyzed2(M)。
In an alternative, the calculation formula of the generality degree f (m) is:
wherein,
optionally, in the above embodiment of the present invention, obtaining the module type of the module to be identified based on the module variation index, the propagation index, and the popularity includes: comparing the module variation index with a first threshold, comparing the propagation index with a second threshold, and comparing the common degree with a third threshold to obtain a comparison result; based on the comparison, a module type is obtained.
Specifically, the first threshold, the second threshold and the third threshold can be determined through experiments so as to identify the module type, for example, the first threshold is 035, the second threshold is 0 and the third threshold is 0.6.
In an alternative scheme, the module variation index VI is 0.35, the propagation index CPI is 0, and the commonality f (m) is 0.6, which are used as thresholds to identify the type of the module, and the identification results are shown in table 5:
TABLE 5
| Serial number | Type of module | Module name |
| 1 | Base module | Traction module, side beam module, cross beam module and air spring module |
| 2 | Configuration module | Drive module, suspension module, and brake module |
| 3 | Parameterization module | Wheel pair module |
Example 2
According to an embodiment of the present invention, an embodiment of an apparatus for identifying a module type is provided.
Fig. 3 is a schematic diagram of a module type identification apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus including:
and the acquisition module 32 is used for acquiring the requirement set of the bogie and the module to be identified.
Specifically, the above-mentioned module to be identified may be a subway bogie module, specifically including: the system comprises a suspension module (m1), a traction module (m2), a wheel pair module (m3), a cross beam module (m4), a driving module (m5), a braking module (m6), an air spring module (m7) and a side beam module (m 8). But is not limited thereto.
In an optional scheme, the existing project bidding condition books of the subway train can be analyzed, the bogie requirements are collected and classified, and a requirement set { CR (demand set) is obtained1，CR2，…，CRm}。
The first processing module 34 is configured to obtain a module variation index, a propagation index of the module to be identified, and a popularity of the module to be identified based on the demand set and the module to be identified.
In an optional scheme, the mapping relationship between the demand and the module can be analyzed to obtain a module variation index VI, and on the basis of analyzing the mapping relationship between the demand and the module, the change influence relationship between the module and the module is further quantitatively analyzed to obtain a propagation index CPI of the module; analysis module example calculation module general degree f (m).
And the second processing module 36 is configured to obtain a module type of the module to be identified based on the module variation index, the propagation index, and the popularity.
Specifically, the above-mentioned module types may include: the system comprises a basic module, a configuration module, a parameterization module and a personality module, wherein the basic module is shared by all products in a product family, and only one predefined module instance is provided; the configuration module is shared by all products in the product family, has a plurality of predefined instances, and each derived product variant selects one module instance; the parameterized module is shared by all products in the product family and is provided with a parameterized structure model of an adaptable variant; the personality module is shared by partial products in the product family, has no predefined instance, and needs to be redesigned according to the personalized requirements of customers.
In an alternative scheme, the module type can be analyzed and identified by comprehensively considering three indexes of the module variation index VI, the propagation index CPI and the general degree f (M), so that the module type can be identified. Wherein, for the base module: VI is smaller, CPI is positive, f (M) is larger. The module has higher universality and less variants in the existing project example; modules are less affected by customization needs and generally do not change, but once changed, will have a greater impact on other modules. It is therefore advisable to solidify it into a basic module.
For the configuration module: VI is greater, CPI is positive, f (M) is less. The generality of the module in the existing project example is low, and the number of variants is large; modules are greatly affected by customization requirements, and changes generally occur, and the changes have a large impact on other modules. Therefore, it is proposed to study the influence rule of the change of the demand on the module and the change of the demand on other modules, and to respond to the change of the customer demand by designing the configurable module so that the influence of the change of the demand on other modules can be controlled.
For parameterized modules: VI is greater, CPI is negative, and f (M) is less. The generality of the module in the existing project example is low, and the number of variants is large; modules are greatly influenced by customization requirements and generally changed, but the change has little influence on other modules and is greatly influenced by the change of other modules. Therefore, the proposal is to research the influence rule of the demand change on the parameterized module and the other module changes on the parameterized module, and design the parameterized module capable of adaptively changing to dynamically respond to the performance demand of the customer change by solidifying the influence of the other module changes on the parameterized module.
According to the embodiment of the invention, after the demand set and the module to be identified of the bogie are obtained, the module variation index, the propagation index of the module to be identified and the general degree of the module to be identified can be obtained based on the demand set and the module to be identified, and the module type of the module to be identified can be further obtained based on the module variation index, the propagation index and the general degree. Compared with the prior art, in the module type identification process, the basic module, the configurable module and the parameterized module are identified by comprehensively considering the module variation index, the propagation index and the universality, so that the existing technical resources of an enterprise can be fully reused in the design of the metro bogie, the quick response of a product to the customer requirements can be ensured, the customer requirements can be better met, the research and development efficiency and quality can be improved, the development resources can be saved, and the technical problems that the identification method of the module type in the related technology is insufficient in response to the change of the customer requirements and the cost of module design is high are solved.
Optionally, in the foregoing embodiment of the present invention, the first processing module includes: the first classification submodule is used for classifying each demand in the demand set to obtain a demand type of each demand, wherein the demand type comprises one of the following: general requirements, adaptability requirements and individual requirements; and the first processing submodule is used for obtaining a module variation index based on the mapping relation between the adaptability requirement and the module to be identified.
Optionally, in the foregoing embodiment of the present invention, the first processing sub-module includes: the determining unit is used for determining technical indexes based on the adaptability requirement and the module to be identified; the first processing unit is used for obtaining a first correlation matrix based on the influence strength of the adaptability requirement on the technical index; the second processing unit is used for obtaining a second correlation matrix based on the influence strength of the technical indexes on the module to be identified; and the third processing unit is used for obtaining the module variation index based on the first correlation matrix and the second correlation matrix.
Optionally, in the foregoing embodiment of the present invention, the third processing unit includes: the normalization subunit is used for performing normalization processing on the first correlation matrix to obtain a normalization matrix; the first processing subunit is used for obtaining the index weight of the technical index based on the normalized matrix and the corresponding demand weight, wherein the demand weight is obtained by adopting an analytic hierarchy process; and the second processing subunit is used for obtaining the module variation index based on the index weight and the second correlation matrix.
Optionally, in the foregoing embodiment of the present invention, the first processing module includes: the second processing submodule is used for obtaining a third correlation matrix based on the change influence degree between the modules to be identified; the third processing submodule is used for obtaining a module transmission propagation index and a module absorption propagation index based on the third correlation matrix; and the first acquisition submodule is used for acquiring the difference between the module transmission propagation index and the module absorption propagation index to obtain the propagation index of the module to be identified.
Optionally, the changing the degree of influence includes: structure influence degree, interface influence degree, material influence degree and performance influence degree.
Optionally, in the above embodiment of the present invention, the second processing sub-module is further configured to obtain a weighted sum of the alteration influence degrees between the modules to be identified, so as to obtain a third correlation matrix.
Optionally, in the foregoing embodiment of the present invention, the first processing module includes: the second obtaining submodule is used for obtaining a plurality of sample examples included by the module to be identified; the second classification submodule is used for classifying the multiple sample instances to obtain at least one sample instance set, wherein each sample instance set comprises: at least one sample instance; the third obtaining submodule is used for obtaining the first depth index and the first breadth index of each sample instance; the fourth processing submodule is used for obtaining a second depth index and a second breadth index of each sample instance set based on the first depth index and the first breadth index of all sample instances contained in each sample instance set; and the fifth processing submodule is used for obtaining the popularity based on the second depth index and the second breadth index of the at least one sample instance set.
Optionally, in the foregoing embodiment of the present invention, the second classification submodule includes: the first acquisition unit is used for acquiring the characteristic parameters of the module to be identified; the second acquisition unit is used for acquiring the similarity between the characteristic parameters by using a K-means clustering algorithm; and the classification unit is used for classifying the multiple sample examples based on the similarity to obtain at least one sample example set.
Optionally, in the above embodiment of the present invention, the third obtaining sub-module includes: the third obtaining unit is used for obtaining the first total usage of each sample instance and the second total usage of the sample instance set to which each sample instance belongs; the fourth acquisition unit is used for acquiring the occurrence number of each sample instance in the item and the total number of the items; the fourth processing unit is used for obtaining a first depth index based on the first total usage and the second total usage; and the fifth acquisition unit is used for acquiring the ratio of the occurrence times to the total number of the items to obtain a first breadth index.
Optionally, in the foregoing embodiment of the present invention, the fourth processing sub-module includes: a sixth obtaining unit, configured to obtain a sum of the first depth indexes of all the sample instances to obtain a second depth index; and the seventh acquisition unit is used for acquiring the maximum first breadth index in the first breadth indexes of all the sample instances to obtain a second breadth index.
Optionally, in the foregoing embodiment of the present invention, the fifth processing sub-module includes: the eighth acquiring unit is configured to acquire a largest second depth index of the second depth indexes of the at least one sample instance set, and obtain a third depth index; a ninth obtaining unit, configured to obtain a maximum second breadth index in the second breadth indexes of the at least one sample instance set, to obtain a third breadth index; and the tenth acquiring unit is used for acquiring the average value of the third depth index and the third breadth index to obtain the popularity.
Optionally, in the foregoing embodiment of the present invention, the second processing module includes: the comparison submodule is used for comparing the module variation index with a first threshold, comparing the propagation index with a second threshold and comparing the popularity with a third threshold to obtain a comparison result; and the sixth processing submodule is used for obtaining the module type based on the comparison result.
Example 3
According to an embodiment of the present invention, an embodiment of a storage medium is provided, where the storage medium includes a stored program, and when the program runs, a device in which the storage medium is controlled to execute the module type identification method in embodiment 1.
Example 4
According to an embodiment of the present invention, an embodiment of a processor for running a program is provided, where the program executes the method for identifying a module type in embodiment 1.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
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 units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. 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 Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (16)
1. A method for identifying a module type, comprising:
acquiring a demand set and a module to be identified of a bogie;
obtaining a module variation index, a propagation index of the module to be identified and the universality of the module to be identified based on the demand set and the module to be identified;
and obtaining the module type of the module to be identified based on the module variation index, the propagation index and the general degree.
2. The method of claim 1, wherein obtaining a module variation index based on the demand set and the module to be identified comprises:
classifying each demand in the demand set to obtain a demand type of each demand, wherein the demand type includes one of the following: general requirements, adaptability requirements and individual requirements;
and obtaining the module variation index based on the mapping relation between the adaptability requirement and the module to be identified.
3. The method of claim 2, wherein obtaining the module variation index based on a mapping relationship between adaptive requirements and the module to be identified comprises:
determining a technical index based on the adaptability requirement and the module to be identified;
obtaining a first correlation matrix based on the influence strength of the adaptability requirement on the technical index;
obtaining a second correlation matrix based on the influence strength of the technical index on the module to be identified;
and obtaining the module variation index based on the first correlation matrix and the second correlation matrix.
4. The method of claim 3, wherein obtaining the module variation index based on the first correlation matrix and the second correlation matrix comprises:
normalizing the first correlation matrix to obtain a normalized matrix;
obtaining an index weight of the technical index based on the normalization matrix and the corresponding demand weight, wherein the demand weight is obtained by adopting an analytic hierarchy process;
and obtaining the module variation index based on the index weight and the second correlation matrix.
5. The method of claim 1, wherein obtaining the propagation index of the module to be identified based on the demand set and the module to be identified comprises:
obtaining a third correlation matrix based on the change influence degree between the modules to be identified;
obtaining a module emission propagation index and a module absorption propagation index based on the third correlation matrix;
and obtaining the difference between the module transmission propagation index and the module absorption propagation index to obtain the propagation index of the module to be identified.
6. The method of claim 5, wherein deriving a third correlation matrix based on a degree of influence of changes between the modules to be identified comprises:
and obtaining the weighted sum of the change influence degrees among the modules to be identified to obtain the third correlation matrix.
7. The method of claim 5, wherein altering the degree of influence comprises: structure influence degree, interface influence degree, material influence degree and performance influence degree.
8. The method of claim 1, wherein obtaining the commonality of the module to be identified based on the requirement set and the module to be identified comprises:
obtaining a plurality of sample instances included by the module to be identified;
classifying the plurality of sample instances to obtain at least one sample instance set, wherein each sample instance set comprises: at least one sample instance;
obtaining a first depth index and a first breadth index of each sample instance;
obtaining a second depth index and a second breadth index of each sample instance set based on the first depth index and the first breadth index of all sample instances contained in each sample instance set;
obtaining the popularity based on a second depth index and a second breadth index of the at least one sample instance set.
9. The method of claim 8, wherein classifying the plurality of sample instances to obtain at least one sample instance set comprises:
acquiring characteristic parameters of the module to be identified;
obtaining the similarity between the characteristic parameters by using a K mean value clustering algorithm;
classifying the plurality of sample instances based on the similarity to obtain the at least one sample instance set.
10. The method of claim 8, wherein obtaining the first depth index and the first breadth index for each sample instance comprises:
acquiring a first total amount of the each sample instance and a second total amount of the sample instance set to which the each sample instance belongs;
acquiring the occurrence number of each sample instance in the item and the total number of the items;
obtaining the first depth index based on the first total usage and the second total usage;
and obtaining the ratio of the occurrence times to the total number of the items to obtain the first breadth index.
11. The method according to claim 8, wherein obtaining the second depth index and the second breadth index of each sample instance set based on the first depth index and the first breadth index of all sample instances contained in each sample instance set comprises:
obtaining the sum of the first depth indexes of all sample examples to obtain the second depth index;
and acquiring the maximum first breadth index in the first breadth indexes of all the sample examples to obtain the second breadth index.
12. The method of claim 8, wherein deriving the popularity based on a second depth index and a second breadth index of the at least one sample instance set comprises:
obtaining a maximum second depth index in the second depth indexes of the at least one sample instance set to obtain a third depth index;
obtaining a maximum second breadth index in the second breadth indexes of the at least one sample instance set to obtain a third breadth index;
and obtaining the average value of the third depth index and the third breadth index to obtain the universal degree.
13. The method according to claim 1, wherein obtaining the module type of the module to be identified based on the module variation index, the propagation index and the popularity comprises:
comparing the module variation index with a first threshold, comparing the propagation index with a second threshold, and comparing the popularity with a third threshold to obtain a comparison result;
and obtaining the module type based on the comparison result.
14. An apparatus for identifying a module type, comprising:
the acquisition module is used for acquiring a demand set of the bogie and the module to be identified;
the first processing module is used for obtaining a module variation index, a propagation index of the module to be identified and the universality of the module to be identified based on the demand set and the module to be identified;
and the second processing module is used for obtaining the module type of the module to be identified based on the module variation index, the propagation index and the general degree.
15. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the module type identification method according to any one of claims 1 to 13.
16. A processor, configured to execute a program, wherein the program executes the method for identifying a module type according to any one of claims 1 to 13.
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