CN109815541A - Rail traffic vehicles product component module partition method, device and electronic equipment - Google Patents
Rail traffic vehicles product component module partition method, device and electronic equipment Download PDFInfo
- Publication number
- CN109815541A CN109815541A CN201811569994.3A CN201811569994A CN109815541A CN 109815541 A CN109815541 A CN 109815541A CN 201811569994 A CN201811569994 A CN 201811569994A CN 109815541 A CN109815541 A CN 109815541A
- Authority
- CN
- China
- Prior art keywords
- module
- degree
- parts
- correlation
- matrix
- 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 83
- 238000005192 partition Methods 0.000 title abstract description 3
- 239000011159 matrix material Substances 0.000 claims abstract description 80
- 230000002068 genetic effect Effects 0.000 claims abstract description 56
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 51
- 230000008878 coupling Effects 0.000 claims abstract description 43
- 238000010168 coupling process Methods 0.000 claims abstract description 43
- 238000005859 coupling reaction Methods 0.000 claims abstract description 43
- 238000006116 polymerization reaction Methods 0.000 claims abstract description 40
- 238000010219 correlation analysis Methods 0.000 claims abstract description 26
- 230000006870 function Effects 0.000 claims description 50
- 210000000349 chromosome Anatomy 0.000 claims description 36
- 238000007621 cluster analysis Methods 0.000 claims description 35
- 238000000638 solvent extraction Methods 0.000 claims description 26
- 238000004364 calculation method Methods 0.000 claims description 24
- 238000004891 communication Methods 0.000 claims description 19
- 230000035772 mutation Effects 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000010353 genetic engineering Methods 0.000 claims description 4
- 238000013486 operation strategy Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 description 26
- 238000010586 diagram Methods 0.000 description 11
- 238000010276 construction Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 238000012827 research and development Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 3
- 238000012356 Product development Methods 0.000 description 2
- 210000004027 cell Anatomy 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004904 shortening Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009401 outcrossing Methods 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 230000005070 ripening Effects 0.000 description 1
- 238000012916 structural analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the present invention provides a kind of rail traffic vehicles product component module partition method, device and electronic equipment, the method comprise the steps that the components to rail traffic vehicles product example carry out correlation analysis, obtains components correlation matrix；All components are divided by disparate modules unit by the genetic algorithm clustering calculated based on the inside modules degree of polymerization and the intermodule degree of coupling based on the components correlation matrix.The embodiment of the present invention carries out correlation analysis by the components to rail traffic vehicles product example, and the genetic algorithm clustering calculated based on the inside modules degree of polymerization and the intermodule degree of coupling is carried out to all components on this basis, realize that the module of all components divides, the execution cycle that the module that can effectively shorten divides, operation efficiency is improved, and further increases operation accuracy.
Description
Technical Field
The embodiment of the invention relates to the technical field of rail transit product design, in particular to a rail transit vehicle product part module dividing method and device and electronic equipment.
Background
The rail transit is gradually a hot spot in urban traffic construction due to the advantages of land saving, large transportation capacity, stable operation time, safety, environmental protection and the like. With the diversified development of rail transit, rail transit presents more and more types, not only extends over long-distance land transportation, but also is widely applied to urban public transit at medium and short distances.
With the increasing application requirements, the research and development of rail transit vehicle products are also enhanced by rail transit related enterprises. In the development process of rail transit vehicle products, the improvement of research and development efficiency can be realized by constructing a base type of a modular product, and in the construction process of the base type of the modular product, the modular analysis is the core for effectively improving the research and development efficiency and shortening the research and development period.
The modular analysis is that on the basis of the parts of the rail transit product, all the parts are clustered and divided into modules by analyzing the characteristics of the parts, so that different module units can be called according to different customization requirements, and the rail transit vehicle product related to the corresponding parts is configured. The quality of the module division can directly determine the quality of the product and the research and development efficiency. At present, module division of parts still stays in division according to design experience and traditional habits, various considerations and division methods of the parts combined into modules are not researched, and incidence relations among the modules after module division are ignored, so that the product development efficiency is low, the period is long, and the reliability of the product quality is possibly further influenced.
Disclosure of Invention
In order to overcome the above problems or at least partially solve the above problems, embodiments of the present invention provide a method, an apparatus, and an electronic device for partitioning components and modules of a rail transit vehicle product, so as to effectively shorten an operation period of module partitioning, improve operation efficiency, and further improve operation accuracy.
In a first aspect, an embodiment of the present invention provides a method for partitioning a rail transit vehicle product component module, including:
performing correlation analysis on parts of a rail transit vehicle product example to obtain a part correlation matrix;
and based on the correlation matrix of the parts, dividing all the parts into different module units through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree.
In a second aspect, an embodiment of the present invention provides a rail transit vehicle product component module dividing apparatus, including:
the correlation analysis module is used for carrying out correlation analysis on the parts of the rail transit vehicle product example to obtain a part correlation matrix;
and the cluster partitioning module is used for partitioning all the parts into different module units through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree based on the part correlation matrix.
In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one memory, at least one processor, a communication interface, and a bus; the memory, the processor and the communication interface complete mutual communication through the bus, and the communication interface is used for information transmission between the electronic equipment and the rail transit vehicle product instance information equipment; the memory is stored with a computer program operable on the processor, and the processor executes the computer program to implement the rail transit vehicle product component module division method according to the first aspect.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the rail transit vehicle product part module division method according to the first aspect.
According to the rail transit vehicle product part module division method, device and electronic equipment provided by the embodiment of the invention, correlation analysis is carried out on parts of a rail transit vehicle product example, and on the basis, genetic algorithm cluster analysis based on module internal polymerization degree and inter-module coupling degree calculation is carried out on all parts, so that module division of all parts is realized, the operation period of module division can be effectively shortened, the operation efficiency is improved, and the operation accuracy is further improved, so that module division in product development is guided, the workload of designers is reduced, the product design quality is improved, and the enterprise competitiveness is 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 described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for partitioning a rail transit vehicle product component module according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a chromosome coding matrix in a rail transit vehicle product component module division method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of genetic cross operation in a rail transit vehicle product part module division method according to an embodiment of the invention;
FIG. 4 is a schematic diagram illustrating an operation of a genetic uniform variation method in a rail transit vehicle product component module division method according to an embodiment of the present invention;
fig. 5 is a schematic flow chart of a method for partitioning modules of parts of a rail transit vehicle product according to another embodiment of the present invention;
FIG. 6 is a schematic algorithm flow chart of the component clustering analysis based on the genetic algorithm in the rail transit vehicle product component module division method according to the embodiment of the invention;
fig. 7 is a schematic structural diagram of a rail transit vehicle product component module dividing device according to an embodiment of the present invention;
fig. 8 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 obtained by persons of ordinary skill in the art based on the embodiments of the present invention without any creative efforts belong to the protection scope of the embodiments of the present invention.
Aiming at the problems of low operation efficiency, long period and possibility of further influencing the operation accuracy when the components are divided in the prior art, the embodiment of the invention realizes the module division of all the components by performing correlation analysis on the components of the rail transit vehicle product example and performing genetic algorithm cluster analysis on all the components based on the calculation of the module internal polymerization degree and the coupling degree between the modules on the basis, thereby effectively shortening the operation period of the module division, improving the operation efficiency and further improving the operation accuracy. Embodiments of the present invention will be described and illustrated with reference to various embodiments.
Fig. 1 is a schematic flow chart of a method for partitioning a rail transit vehicle product component module according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s101, carrying out correlation analysis on parts of the rail transit vehicle product example to obtain a part correlation matrix.
The embodiment of the invention aims to reasonably cluster the parts of the rail transit vehicle product, thereby providing convenience for developing the rail transit vehicle product. Therefore, the embodiment of the invention can firstly identify all key parts according to the rail transit vehicle product example. Thereafter, the correlation between these parts is analyzed. For example, the independent feature correlations between the components may be analyzed from a plurality of aspects such as functions, structures, and physics of the components, and then the comprehensive correlation result may be obtained by performing comprehensive analysis based on the independent feature correlations of the aspects, and may be expressed as a component correlation matrix in a matrix form.
And S102, based on the correlation matrix of the parts, dividing all the parts into different module units through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree.
And on the basis of obtaining a part correlation matrix corresponding to the rail transit vehicle product through analysis according to the steps, performing part clustering analysis based on a genetic algorithm on all parts based on the part correlation matrix. Specifically, initial clustering setting is performed according to the component correlation matrix to obtain a plurality of clustered initial modules. And then, carrying out chromosome coding, and carrying out initialization setting on the population according to the number of the parts and the number of the initial modules. Meanwhile, the expressions of the target criterion function and the fitness function in the genetic algorithm can be determined by determining the calculation form of the intra-module polymerization degree and the inter-module coupling degree. Finally, on the basis of the setting, the correlation matrix of the parts is used as input, clustering analysis based on a genetic algorithm is carried out, and the maximum fitness value and the corresponding optimal individual are output, namely the final clustering result of all the parts is obtained. That is, all parts are divided into a plurality of different module units by clustering analysis based on genetic algorithm.
According to the rail transit vehicle product part module division method provided by the embodiment of the invention, correlation analysis is carried out on parts of a rail transit vehicle product example, and on the basis, genetic algorithm cluster analysis based on module internal polymerization degree and module coupling degree calculation is carried out on all the parts, so that module division of all the parts is realized, the operation period of module division can be effectively shortened, the operation efficiency is improved, and the operation accuracy is further improved.
It is understood that, before the step of cluster analysis by a genetic algorithm based on the calculation of the intra-module degree of polymerization and the inter-module degree of coupling, the method of the embodiment of the present invention may further include: and respectively constructing a target criterion function and a fitness function of the genetic algorithm in the cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree, respectively carrying out chromosome coding and chromosome population initialization of the genetic algorithm based on the component cluster analysis matrix, and determining a genetic operation strategy.
The embodiment of the invention carries out the cluster analysis of the rail transit vehicle product parts based on the genetic algorithm, so before the specific application, basic parameters of the genetic algorithm are set, and specifically, the method comprises the steps of target criterion function construction, chromosome coding and population initialization, fitness function construction and determination of a genetic operation strategy.
In the aspect of target criterion function construction, the embodiment of the invention constructs a target criterion function of component clustering analysis according to the internal polymerization degree of the module and the coupling degree between the modules; in the aspect of constructing the fitness function, the fitness function is a standard for distinguishing the quality of individuals in a group determined according to an objective function, and the larger the value of the fitness function is, the better the fitness function is, so that the fitness function can be constructed according to the internal polymerization degree of a module and the coupling degree between the modules; in the aspects of chromosome coding and population initialization, the component clustering analysis matrix a may be used as a chromosome coding matrix, or the component clustering analysis matrix a may be used as a chromosome to initialize a population.
In practical application, when the fitness function of the target population meets the convergence rule, the algorithm is stopped, otherwise, further selection, crossing and mutation operations are required to generate a new chromosome. Therefore, the genetic operation strategy of the genetic algorithm needs to be set.
Optionally, according to the above embodiments, the step of performing correlation analysis on the parts of the rail transit vehicle product example and acquiring the correlation matrix of the parts specifically includes:
respectively establishing a functional correlation matrix based on the correlation of functional characteristics among different parts, establishing a structural correlation matrix based on the correlation of structural characteristics among the different parts, and establishing a physical correlation matrix based on the correlation of physical characteristics among the different parts;
determining weights respectively corresponding to functional characteristics, structural characteristics and physical characteristics in correlation among different parts by adopting an analytic hierarchy process based on a product example;
and calculating the correlation matrix of the part based on the functional correlation matrix, the structural correlation matrix, the physical correlation matrix and the weight.
It is understood that the embodiment of the present invention first performs correlation analysis on the components separately from the aspects of functional characteristics, structural characteristics and physical characteristics. Specifically, the method comprises the following steps:
the functional correlation analysis of the parts means that the parts realizing the same function are aggregated to form a module, and the principle that the functions of the modules are independent is satisfied as much as possible, and the functional correlation strength of the parts is defined as shown in table 1, which is an example table for the functional correlation definition of the parts according to an embodiment of the present invention. Thereafter, a functional dependency matrix may be generated from the table.
TABLE 1 exemplary table of part functional dependency definitions according to an embodiment of the present invention
The analysis of the structural correlation of the parts refers to the physical connection strength of the parts in space and structural relationship, and the structural positioning precision of perpendicularity, parallelism, coaxiality and the like, and meets the principle that a module interface is easy to separate and connect, the structural correlation strength of the parts is defined as shown in table 2, and an example table is defined for the structural correlation of the parts according to an embodiment of the invention. Thereafter, a structural correlation matrix can be generated from the table.
Table 2 example table for defining the structural dependency of components according to an embodiment of the present invention
The physical correlation analysis of the parts means that energy flow, information flow and material flow exist among the parts, and the definition of the physical correlation strength is shown in table 3, which is an example table of the physical correlation definition of the parts according to an embodiment of the invention. Thereafter, a physical correlation matrix may be generated from the table.
TABLE 3 example table of physical dependency definition of parts according to an embodiment of the present invention
Then, because the customization degrees of different products are different, the weights occupied by the products in different types are different due to the correlation among three factors, namely the function, the structure and the physics. The reasonability of weight distribution of each factor influences the accuracy of a module division result, so that the embodiment of the invention determines the weight of each correlation factor by adopting an analytic hierarchy process aiming at the characteristics of different types of products and comprehensively analyzes the three correlations. The comprehensive analysis makes the determined modular structure tree more accurate, wherein the related weight symbol is defined as shown in table 4, and an example table is allocated for the function, structure and physical relevance weights according to an embodiment of the invention.
TABLE 4 example table of function, structure, physical dependency weight assignment according to an embodiment of the present invention
By constructing respective correlation matrixes of three factors of functions, physics and structures among parts of a product, a comprehensive correlation strength matrix R obtained after weighting is as follows:
in the formula, rijThe direct correlation between any two parts in the product example can be seen through the matrix for the correlation values of any two parts i and j. Wherein r isijThe calculation formula of (2) is as follows:
rij＝ω1RFij+ω2(ω21RCij+ω22RLij)+ω3(ω31REij+ω32RMij+ω33RSij)；
in the formula, RFijFor function-related strength, RCijAnd RLijRespectively representing the coupling and form-position-dependent strength, RE, in structural dependenceij，RMijAnd RSijRespectively representing the energy flow, material flow and information flow correlation strength in the physical correlation.
Optionally, according to the above embodiments, when constructing the target criterion function, assuming that the rail transit vehicle product example is composed of n key components, it may be expressed as P ═ { P ═ P1，P2，…，PnN parts are initially clustered into l initial modules M ═ M1，M2，…，Ml}. The number of parts in the module i is NiSince different parts cannot be present in the same module, P ═ M1∪M2∪...∪Ml，Then, the initial module after initial clustering by the parts can be formally expressed as:
wherein A ═ aij]l×nRepresenting a component clustering analysis matrix, wherein aij1 or 0, aij1 denotes a part PjBelonging to an initial module Mi，aij0 denotes a part PjNot belonging to the initial module Mi。
Wherein,
according to the embodiment of the invention, a target criterion function of component clustering analysis is constructed according to the internal polymerization degree of the module and the coupling degree between the modules, wherein the step of calculating the internal polymerization degree of the module specifically comprises the following steps:
suppose module MuThe corresponding module cluster analysis vector is au＝[au1,au2,…,aui,…,aun]Then, may be based on the initial module MuCorresponding module cluster analysis vector, calculating initial module M according to the following formulauInternal polymerization degree of (2):
on this basis, the total degree of polymerization of the total of l initial modules can be calculated according to the following formula:
in the formula (f)uRepresentation module MuInternal degree of polymerization of (a)uiThe value r of the ith row and ith column in the component clustering matrix AijRepresenting the correlation value, F, of any two parts i and j1Represents the total degree of polymerization of the l modules.
For the calculation of the degree of coupling between the modules, the degree of coupling between any two modules can be measured by the total degree of association between all components in one module and all components in another module. For example, assume module MpAnd MqThe corresponding module division vectors are respectively ap＝[ap1,ap2,…,api,…,apn]And aq＝[aq1,aq2,…,aqi,…,aqn]Then, the step of calculating the coupling degree between the modules may specifically include:
based on the initial module MpCorresponding module cluster analysis vector ap＝[ap1,ap2,…,api,…,apn]And an initial module MqCorresponding module cluster analysis vector aq＝[aq1,aq2,…,aqi,…,aqn]The initial module M can be calculated as followsuAnd an initial module MqDegree of coupling therebetween:
on this basis, the total degree of coupling between a total of l initial modules can be calculated as follows:
in the formula (f)pqRepresentation module MpAnd a module MqDegree of coupling between apiRepresentation module MpThe value of the ith column, a, in the p-th row of the cluster analysis matrix AqjRepresentation module MqThe value r of the jth row and jth column in the cluster analysis matrix AijRepresenting the correlation value, F, of any two parts i and j2Representing the total degree of coupling between the i modules.
Therefore, according to the above embodiments, according to the basic principle of "internal high polymerization degree and external low coupling degree" of component clustering analysis, the step of constructing the target criterion function based on the calculation of the internal polymerization degree and the coupling degree between the modules may specifically include:
based on the total polymerization degree and the total coupling degree of the total l initial modules, an objective criterion function is constructed as follows:
in the aspects of chromosome coding and population initialization, a matrix A ═ a is analyzed according to component clusteringij]l×nIt can be seen that the matrix is composed of n elements, where aij1 or 0, which represents PjWhether it belongs to Mi。aij1 represents PjBelong to Mi，aijTable (0)Show PjNot belonging to MiAnd matrix a must satisfy:
wherein N isiIndicating the number of components in module i.
According to the above conditions, the embodiment of the present invention uses the component clustering analysis matrix a as a chromosome coding matrix. Fig. 2 is a schematic diagram of a chromosome coding matrix in the rail transit vehicle product component module partitioning method according to the embodiment of the present invention, as shown in fig. 2, each row in the matrix represents a clustering module, each column includes a component, the arrangement sequence of the components is kept consistent with the arrangement sequence of elements in the partitioning matrix, and a component clustering analysis matrix in a genetic algorithm is equivalent to a chromosome.
Initializing population by using the component clustering analysis matrix A as chromosome, and initializing any chromosome AiN columns are included, each column containing l elements, where i ═ 1,2, …, m, m denotes the population number. According to the condition that the matrix A must meet, assigning values to n columns in the matrix in sequence randomly so that one element in each column is 1 and the rest are 0, and obtaining the chromosome Ai. Since too small a population may cause premature ripening, and too large a population may reduce computational efficiency, the number of chromosome populations is usually selected to be m-50-300.
In the aspect of constructing the fitness function, it can be understood that in the genetic algorithm, the fitness is a main index for describing individual performance, and the individuals are subjected to high-quality elimination according to the fitness. Fitness is the driving force for genetic algorithms. From the biological point of view, the fitness is equivalent to the living ability of living competition and survival of the fittest, and has important significance in the genetic process.
In the genetic algorithm, the target criterion function composed of the internal polymerization degree of the module and the coupling degree between the modules is the minimum value, and the target of the fitness function is the maximum value. Thus, the fitness function of the target chromosome can be expressed as:
F(i)＝1/F0(i)；
wherein F (i) is a fitness function value of the ith chromosome in the target population, F0(i) The criterion function value of the ith chromosome in the target population is represented by i, 1,2, …, m and m, which represents the population number.
The fitness function is a standard for distinguishing the quality of individuals in a population, which is determined according to an objective function, and is always non-negative, and in any case, the larger the value of the fitness function, the better the fitness function is. When the fitness function of the target population meets the convergence rule, stopping the algorithm and outputting an optimal modular processing result; otherwise, further carrying out operations such as selection, crossing and mutation, and generating a new chromosome.
Common genetic manipulations are selection, crossover and variation in the determination of genetic manipulation strategies. And selecting the superior individuals from the population according to the fitness function value of the individuals in the population, and eliminating the inferior individuals, wherein the higher the individual fitness is, the higher the probability of selection is. Therefore, optionally according to the above embodiments, the step of determining the genetic manipulation strategy may specifically include: the selection probability in the genetic algorithm is calculated as follows:
wherein p (i) represents the selection probability of the i-th target chromosome, f (k) represents the fitness value of the k-th chromosome in the target population, and i and k are 1,2, …, m, m represents the population number;
in order to select mating individuals, the selection probability of each individual in the population is obtained according to the formula P (i), multiple rounds of selection are carried out, each round generates a uniform random number between [0,1], the individuals enter the next generation according to the random number, the individuals of the next generation carry out crossing and mutation operations, common genetic operations comprise a single-point crossing method and a random uniform mutation method, and the specific method comprises the following steps:
the single-point crossing method comprises the following steps: fig. 3 is a schematic diagram illustrating genetic crossover operation in a rail transit vehicle product component module partitioning method according to an embodiment of the present invention, and as shown in fig. 3, a crossover point is arbitrarily selected from two parent chromosomes, and codes located on the right side of the crossover point in the two parent chromosomes are exchanged, so as to generate two child chromosomes.
Random homogeneous variation method: fig. 4 is an operation diagram of a genetic uniform mutation method in the rail transit vehicle product component module division method according to the embodiment of the present invention, and as shown in fig. 4, a parent chromosome is randomly selected according to a mutation rate, then a mutation point is randomly selected to produce a mutation code, remaining row elements are kept unchanged, a child chromosome is generated, and the parent chromosome is replaced.
To further illustrate the technical solutions of the embodiments of the present invention, the embodiments of the present invention provide the following processing flows of the embodiments according to the above embodiments, but do not limit the scope of the embodiments of the present invention.
Fig. 5 is a schematic flow chart of a method for partitioning parts and modules of a rail transit vehicle product according to another embodiment of the present invention, and as shown in fig. 5, in the embodiment of the present invention, a design BOM of a type a subway product in a typical city is used as an input, and a modular partitioning of parts and components of an a type subway bogie is performed through a part correlation analysis and a part clustering analysis based on a genetic algorithm.
Specifically, 26 parts of the bogie are identified according to the design BOM of the bogie, and as shown in table 5, the list of parts of the bogie identified according to the embodiment of the present invention is shown.
TABLE 5 list of truck parts identified according to embodiments of the present invention
And then, performing function, physical and structural analysis on the parts, and respectively constructing a function, physical and structural correlation matrix of the bogie parts. Meanwhile, the weight value of each correlation is determined by an analytic hierarchy process, as shown in table 6, which is a weight value table of functional, physical and structural correlations according to an embodiment of the present invention.
TABLE 6 weight value table for functional, physical and structural dependencies according to an embodiment of the present invention
| Correlation | Weight of | Weighted value |
| Functional relevance | ω1 | 0.537 |
| Physical correlation | ω2 | 0.358 |
| Structural correlation | ω3 | 0.105 |
The functional, physical and structural correlation matrices may then be weighted and summed to obtain a composite correlation matrix.
Thereafter, a bogie component clustering analysis may be performed based on the genetic algorithm. As shown in fig. 6, the schematic algorithm flow diagram of the component clustering analysis based on the genetic algorithm in the rail transit vehicle component module division method provided by the embodiment of the present invention includes the steps of:
(1) genetic algorithm initial condition determination
The number range of the divided bogie modules obtained by analyzing the relevant bogie data and the practical application condition is reasonably determined to be 4-8.
The number of population individuals is 200, the cross probability is 0.99, the mutation probability is 0.05, and the iteration times are 2000 times (result convergence) determined by the related data of the genetic algorithm.
(2) Establishing an initial population
And establishing a matrix of m x n by the number of modules and the number of parts, wherein m is the number of modules, and n is the number of parts. And randomly assigning and generating individuals according to the requirement that the sum of each column is equal to 1, and randomly generating a random matrix with the total number of 200 individuals in the population by taking m as an example and n as an example. With cell array A1 as the initial population, A1 contains 200 random matrices.
(3) Calculating fitness
And solving an objective function for 200 individuals in the initial population A1, substituting the corresponding objective function values into a fitness calculation formula to obtain fitness values of 200 individuals, and storing the fitness values by using a cell array F0.
(4) Selecting excellent individuals
And selecting the individuals with higher fitness from the population A1 by a roulette algorithm to form a new population A2.
(5) The individuals are crossed pairwise
Two individuals are grouped in pairs, namely, an individual 1 and an individual 2 are grouped, an individual 3 and an individual 4 are grouped in … …, an individual 199 and an individual 200 are grouped. Randomly generating 1 (0-1) numerical value, and comparing with the cross probability to judge whether the cross action occurs. And (3) crossing, namely randomly selecting a crossing point for two individuals in the same group, and exchanging matrixes after the crossing point to generate 2 new individuals. All new individuals constitute population a 3.
(6) Variation of individuals
Randomly generating 1 (0-1) numerical value, and comparing with the mutation probability to judge whether mutation behavior occurs. When the variation occurs, a certain column is randomly selected, and the random variation of the position of the row is carried out on the row where the column number is 1. The newly generated individuals and the individuals without variation form a new population A4.
(7) The number of iterations is judged to be satisfied
And judging the size relationship between the number of times of iteration and the number of times of iteration. And (5) continuing iteration until 2000 times, and repeating the steps (3) to (6).
(8) Outputting maximum fitness value and optimal individual
The number of modules is taken to be 4-8 and the genetic algorithm is substituted, and the operation result is shown in table 7, which is a maximum fitness value corresponding table when the number of modules is 4-8 according to an embodiment of the invention.
Table 7 shows a maximum fitness value table when the number of modules is 4 to 8 according to an embodiment of the present invention
| Serial number | Number of modules | Maximum fitness value |
| 1 | 4 | 2.4762*103 |
| 2 | 5 | 3.8529*103 |
| 3 | 6 | 4.2576*103 |
| 4 | 7 | 4.6481*103 |
| 5 | 8 | 4.3648*103 |
By comparison, when the number m of modules is 7, the individual fitness value is the largest. Therefore, the optimal result of the genetic algorithm is obtained from the optimal individual with m being 7, as shown in table 8, which is the result of the cluster analysis with the number of modules being 7 according to an embodiment of the present invention.
TABLE 8 clustering results for a module count of 7 according to an embodiment of the present invention
| Module | The module containing part numbers |
| 1 | 4,5,6 |
| 2 | 22,23 |
| 3 | 24,25,26 |
| 4 | 11,12,13,14,15 |
| 5 | 16,17,18,19,20,21 |
| 6 | 7,8,9,10 |
| 7 | 1,2,3 |
Table 8 shows the result of clustering analysis performed on the parts of the bogie of the type a subway product by using the method of the embodiment of the present invention, where all 26 parts are divided into 7 modules, and the whole processing process has a short operation period and high efficiency.
As another aspect of the embodiments of the present invention, the embodiments of the present invention provide a rail transit vehicle product part module dividing apparatus according to the above embodiments, which is used for implementing the module division of the rail transit vehicle product part in the above embodiments. Therefore, the description and definition in the method for dividing the rail transit vehicle product component module according to the above embodiments may be used for understanding each execution module in the embodiments of the present invention, and specific reference may be made to the above embodiments, which are not repeated herein.
According to an embodiment of the present invention, a structure of a rail transit vehicle product component module dividing apparatus is shown in fig. 7, which is a schematic structural diagram of the rail transit vehicle product component module dividing apparatus provided in the embodiment of the present invention, and the apparatus can be used for implementing the module division of the rail transit vehicle product component in the above method embodiments, and the apparatus includes: a relevance analysis module 701 and a cluster partitioning module 702. Wherein:
the correlation analysis module 701 is used for performing correlation analysis on parts of a rail transit vehicle product example to obtain a part correlation matrix; the cluster partitioning module 702 is configured to partition all the components into different module units based on the component correlation matrix through a genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree.
Specifically, the correlation analysis module 701 may first identify all critical components from the rail transit vehicle product instance. Then, the correlation analysis module 701 analyzes the correlation between these components. For example, the independent feature correlations between the components may be analyzed from a plurality of aspects such as functions, structures, and physics of the components, and then the comprehensive correlation result may be obtained by performing comprehensive analysis based on the independent feature correlations of the aspects, and may be expressed as a component correlation matrix in a matrix form.
Thereafter, the cluster partitioning module 702 can perform a component cluster analysis based on a genetic algorithm on all the components based on the component correlation matrix. Specifically, first, the cluster partitioning module 702 performs initial cluster setting according to the component correlation matrix, to obtain a plurality of initial modules of clusters. Then, the cluster partitioning module 702 performs chromosome coding, and performs initialization setting of the population according to the number of the parts and the number of the initial modules. Meanwhile, the expressions of the target criterion function and the fitness function in the genetic algorithm can be determined by determining the calculation form of the intra-module polymerization degree and the inter-module coupling degree. Finally, on the basis of the above setting, the cluster partitioning module 702 performs cluster analysis based on the genetic algorithm with the component correlation matrix as input, and outputs the maximum fitness value and the corresponding optimal individual, that is, the final clustering result for all the components. That is, all parts are divided into a plurality of different module units by clustering analysis based on genetic algorithm.
According to the rail transit vehicle product part module division device provided by the embodiment of the invention, the corresponding execution module is arranged to perform correlation analysis on parts of a rail transit vehicle product example, and on the basis, genetic algorithm cluster analysis based on module internal polymerization degree and module coupling degree calculation is performed on all parts, so that module division of all parts is realized, the operation period of module division can be effectively shortened, the operation efficiency is improved, and the operation accuracy is further improved.
It is understood that, in the embodiment of the present invention, each relevant program module in the apparatus of each of the above embodiments may be implemented by a hardware processor (hardware processor). Moreover, the rail transit vehicle product part module division device according to the embodiment of the present invention can implement the rail transit vehicle product part module division process according to the above-mentioned method embodiments by using the above-mentioned program modules, and when the device according to the embodiment of the present invention is used for implementing rail transit vehicle product part module division according to the above-mentioned method embodiments, the beneficial effects produced by the device according to the embodiment of the present invention are the same as those produced by the corresponding above-mentioned method embodiments, and the above-mentioned method embodiments may be referred to, and details thereof are not repeated.
As another aspect of the embodiment of the present invention, in this embodiment, an electronic device is provided according to the above embodiments, and with reference to fig. 8, an entity structure diagram of the electronic device provided in the embodiment of the present invention includes: at least one memory 801, at least one processor 802, a communication interface 803, and a bus 804.
The memory 801, the processor 802 and the communication interface 803 complete mutual communication through the bus 804, and the communication interface 803 is used for information transmission between the electronic equipment and the rail transit vehicle product example information equipment; the memory 801 stores a computer program operable on the processor 802, and when the processor 802 executes the computer program, the method for partitioning the parts module of the rail transit vehicle product according to the embodiments described above is implemented.
It is understood that the electronic device at least comprises a memory 801, a processor 802, a communication interface 803 and a bus 804, and the memory 801, the processor 802 and the communication interface 803 form a mutual communication connection through the bus 804, and can complete mutual communication, for example, the processor 802 reads program instructions of the rail transit vehicle product component module division method from the memory 801. In addition, the communication interface 803 can also realize communication connection between the electronic device and the information device of the rail transit vehicle product example, and can complete mutual information transmission, such as module division of parts of the rail transit vehicle product and the like through the communication interface 803.
When the electronic device is running, the processor 802 invokes the program instructions in the memory 801 to perform the methods provided by the above-described method embodiments, including for example: performing correlation analysis on parts of a rail transit vehicle product example to obtain a part correlation matrix; and based on the correlation matrix of the parts, dividing all the parts into different module units and the like through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree.
The program instructions in the memory 801 may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Alternatively, all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, where the program may be stored in a computer-readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Embodiments of the present invention also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for partitioning a rail transit vehicle product component module according to the above embodiments, for example, the method includes: performing correlation analysis on parts of a rail transit vehicle product example to obtain a part correlation matrix; and based on the correlation matrix of the parts, dividing all the parts into different module units and the like through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree.
According to the electronic device and the non-transitory computer readable storage medium provided by the embodiments of the present invention, by executing the method for partitioning a module of a component of a rail transit vehicle product described in each of the embodiments, correlation analysis is performed on the component of the rail transit vehicle product example, and on this basis, genetic algorithm cluster analysis based on calculation of the intra-module degree of polymerization and the inter-module degree of coupling is performed on all the components, so that module partitioning of all the components is realized, the operation cycle of module partitioning can be effectively shortened, the operation efficiency is improved, and the operation accuracy is further improved.
It is to be understood that the above-described embodiments of the apparatus, the electronic device and the storage medium are merely illustrative, and that elements described as separate components may or may not be physically separate, may be located in one place, or may be distributed on different network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the 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. Based on such understanding, the technical solutions mentioned above may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a usb disk, a removable hard disk, a ROM, a RAM, a magnetic or optical disk, etc., and includes several instructions for causing a computer device (such as a personal computer, a server, or a network device, etc.) to execute the methods described in the method embodiments or some parts of the method embodiments.
In addition, it should be understood by those skilled in the art that in the specification of the embodiments of the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects.
However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and not to limit the same; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, it should be understood by those skilled 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 the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A rail transit vehicle product part module dividing method is characterized by comprising the following steps:
performing correlation analysis on parts of a rail transit vehicle product example to obtain a part correlation matrix;
and based on the correlation matrix of the parts, dividing all the parts into different module units through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree.
2. The method of claim 1, further comprising, prior to the step of cluster analysis by a genetic algorithm based on a calculation of a degree of intra-module polymerization and a degree of inter-module coupling:
and respectively constructing a target criterion function and a fitness function of the genetic algorithm in the cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree, respectively carrying out chromosome coding and chromosome population initialization of the genetic algorithm based on the component cluster analysis matrix, and determining a genetic operation strategy.
3. The method of claim 2, wherein the rail transit vehicle product instance is assumed to be composed of n critical components, denoted as P ═ P1，P2，…，PnN parts are initially clustered into l initial modules M ═ M1，M2，…，MlAnd expressing an initial module after initial clustering of the parts as follows:
wherein A ═ aij]l×nRepresenting the component cluster analysis matrix, wherein aij1 or 0, aij1 denotes a part PjBelonging to an initial module Mi，aij0 denotes a part PjNot belonging to the initial module Mi；
Then, the step of calculating the intra-module polymerization degree specifically includes:
based on the initial module MuCorresponding module cluster analysis vector au＝[au1,au2,…,aui,…,aun]The initial module M is calculated as followsuInternal polymerization degree of (2):
and the total degree of polymerization of the i initial modules was calculated as follows:
in the formula (f)uRepresentation module MuInternal degree of polymerization of (a)uiThe value r of the ith row and ith column in the component clustering matrix AijRepresenting the correlation value, F, of any two parts i and j1Represents the total degree of polymerization of the l modules.
4. The method of claim 3, wherein the step of calculating the degree of coupling between modules specifically comprises:
based on the initial module MpCorresponding module cluster analysis vector ap＝[ap1,ap2,…,api,…,apn]And an initial module MqCorresponding module cluster analysis vector aq＝[aq1,aq2,…,aqi,…,aqn]The initial module M is calculated as followspAnd an initial module MqDegree of coupling therebetween:
and calculating the total coupling between the l initial modules according to the following formula:
in the formula (f)pqRepresentation module MpAnd a module MqDegree of coupling between apiRepresentation module MpThe value of the ith column, a, in the p-th row of the cluster analysis matrix AqjRepresentation module MqThe value r of the jth row and jth column in the cluster analysis matrix AijRepresenting the correlation value, F, of any two parts i and j2Representing the total degree of coupling between the i modules.
5. The method according to claim 4, wherein the step of constructing the objective criterion function based on the calculation of the intra-module degree of polymerization and the inter-module degree of coupling specifically comprises:
based on the total polymerization degree and the total coupling degree, the target criterion function is constructed as follows:
6. the method according to claim 2, characterized in that the step of determining a genetic manipulation strategy comprises in particular:
the selection probability in the genetic algorithm is calculated as follows:
wherein p (i) represents the selection probability of the i-th target chromosome, f (k) represents the fitness value of the k-th chromosome in the target population, and i and k are 1,2, …, m, m represents the population number;
and determining to adopt a single-point crossing method to carry out chromosome crossing operation, and adopting a random uniform mutation method to carry out chromosome mutation operation.
7. The method according to any one of claims 1 to 6, wherein the step of performing correlation analysis on the parts of the rail transit vehicle product instance to obtain the parts correlation matrix specifically comprises:
respectively establishing a functional correlation matrix based on the correlation of functional characteristics among different parts, establishing a structural correlation matrix based on the correlation of structural characteristics among the different parts, and establishing a physical correlation matrix based on the correlation of physical characteristics among the different parts;
determining weights respectively corresponding to the functional characteristics, the structural characteristics and the physical characteristics in the correlation among different parts by adopting an analytic hierarchy process based on the product example;
calculating the part correlation matrix based on the functional correlation matrix, the structural correlation matrix, the physical correlation matrix, and the weights.
8. A rail transit vehicle product spare part module divides device which characterized in that includes:
the correlation analysis module is used for carrying out correlation analysis on the parts of the rail transit vehicle product example to obtain a part correlation matrix;
and the cluster partitioning module is used for partitioning all the parts into different module units through the genetic algorithm cluster analysis based on the calculation of the intra-module polymerization degree and the inter-module coupling degree based on the part correlation matrix.
9. An electronic device, comprising: at least one memory, at least one processor, a communication interface, and a bus;
the memory, the processor and the communication interface complete mutual communication through the bus, and the communication interface is also used for information transmission between the electronic equipment and the rail transit vehicle product instance information equipment;
the memory has stored therein a computer program operable on the processor, which when executed by the processor, implements the method of any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811569994.3A CN109815541B (en) | 2018-12-21 | 2018-12-21 | Method and device for dividing rail transit vehicle product parts and modules and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811569994.3A CN109815541B (en) | 2018-12-21 | 2018-12-21 | Method and device for dividing rail transit vehicle product parts and modules and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109815541A true CN109815541A (en) | 2019-05-28 |
| CN109815541B CN109815541B (en) | 2023-11-28 |
Family
ID=66602191
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811569994.3A Active CN109815541B (en) | 2018-12-21 | 2018-12-21 | Method and device for dividing rail transit vehicle product parts and modules and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109815541B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111209941A (en) * | 2019-12-30 | 2020-05-29 | 中车工业研究院有限公司 | Product module type identification method and device |
| CN112612926A (en) * | 2020-11-20 | 2021-04-06 | 西南交通大学 | Method for constructing modular structure tree of rail transit vehicle product |
| CN112633994A (en) * | 2020-11-20 | 2021-04-09 | 西南交通大学 | Rail transit vehicle product platform planning method |
| CN113191457A (en) * | 2021-05-26 | 2021-07-30 | 重庆高新技术产业研究院有限责任公司 | Production data and BOM automatic classification gathering method applied to manufacturing enterprises |
| CN113450194A (en) * | 2020-11-20 | 2021-09-28 | 西南交通大学 | Rail transit vehicle product platform construction method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106779078A (en) * | 2016-12-21 | 2017-05-31 | 武汉科技大学 | A kind of intelligent modularized method of product structure |
| CN107992717A (en) * | 2018-01-16 | 2018-05-04 | 华东交通大学 | A kind of suspension hook group module partition method for considering any intermodule incidence relation two-by-two |
-
2018
- 2018-12-21 CN CN201811569994.3A patent/CN109815541B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106779078A (en) * | 2016-12-21 | 2017-05-31 | 武汉科技大学 | A kind of intelligent modularized method of product structure |
| CN107992717A (en) * | 2018-01-16 | 2018-05-04 | 华东交通大学 | A kind of suspension hook group module partition method for considering any intermodule incidence relation two-by-two |
Non-Patent Citations (3)
| Title |
|---|
| JUNYOU SHI ET AL.: "Research on a Genetic Algorithm for Solving Module Partition Problem", 《2010 SIXTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION》 * |
| 丁力平 等: "基于解析结构模型的产品模块构建及其优化", 《计算机集成制造系统》 * |
| 刘电霆: "绿色设计中产品模块划分的不确定优化及GA求解", 《桂林理工大学学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111209941A (en) * | 2019-12-30 | 2020-05-29 | 中车工业研究院有限公司 | Product module type identification method and device |
| CN111209941B (en) * | 2019-12-30 | 2024-04-26 | 中车工业研究院有限公司 | Product module type identification method and device |
| CN112612926A (en) * | 2020-11-20 | 2021-04-06 | 西南交通大学 | Method for constructing modular structure tree of rail transit vehicle product |
| 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 |
| CN112612926B (en) * | 2020-11-20 | 2022-07-12 | 西南交通大学 | Method for constructing modular structure tree of rail transit vehicle product |
| CN113191457A (en) * | 2021-05-26 | 2021-07-30 | 重庆高新技术产业研究院有限责任公司 | Production data and BOM automatic classification gathering method applied to manufacturing enterprises |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109815541B (en) | 2023-11-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109815541B (en) | Method and device for dividing rail transit vehicle product parts and modules and electronic equipment | |
| CN113411369B (en) | Cloud service resource collaborative optimization scheduling method, system, medium and equipment | |
| US20230394280A9 (en) | Organizing Neural Networks | |
| Scrucca | GA: A package for genetic algorithms in R | |
| CN102063339B (en) | Resource load balancing method and equipment based on cloud computing system | |
| US20170330078A1 (en) | Method and system for automated model building | |
| CN104978612A (en) | Distributed big data system risk predicating method based on AHP-RBF | |
| CN109816474B (en) | Modular configuration method and device of rail transit product and electronic equipment | |
| US10402728B2 (en) | Systems and methods for multi-objective heuristics with conditional genes | |
| CN111626545B (en) | Emergency rescue material high-dimensional multi-target distribution method and system | |
| CN115755954B (en) | Routing inspection path planning method, system, computer equipment and storage medium | |
| CN114066104B (en) | Cloud manufacturing task change-oriented resource dynamic scheduling method | |
| Goel et al. | Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations | |
| CN110189230B (en) | Construction method of analytic model of dynamic partition | |
| Templier et al. | A geometric encoding for neural network evolution | |
| Yu | [Retracted] Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment | |
| CN115593264B (en) | Charging optimization control method and device based on edge calculation and computer equipment | |
| CN111831955A (en) | Lithium ion battery residual life prediction method and system | |
| CN104809098A (en) | Method and device for determining statistical model parameter based on expectation-maximization algorithm | |
| CN114741996B (en) | Circuit dividing method based on reverse search and genetic algorithm | |
| Tian et al. | A fault tree analysis based software system reliability allocation using genetic algorithm optimization | |
| Li et al. | Elucidating genome-wide protein-RNA interactions using differential evolution | |
| CN110689320A (en) | Large-scale multi-target project scheduling method based on co-evolution algorithm | |
| Ramírez-Chavez et al. | A GPU-based implementation of differential evolution for solving the gene regulatory network model inference problem | |
| CN110162704B (en) | Multi-scale key user extraction method based on multi-factor genetic algorithm |
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 |