LU501417B1

LU501417B1 - Intelligent manufacturing industry visual service platform for opm mode

Info

Publication number: LU501417B1
Application number: LU501417A
Authority: LU
Inventors: Xiaolan Xie
Original assignee: Univ Guilin Technology
Priority date: 2022-02-09
Filing date: 2022-02-09
Publication date: 2022-08-11

Abstract

The disclosure discloses an intelligent manufacturing industry visual service platform for an OPM mode. The intelligent manufacturing industry visual service platform comprises a client visual interaction module achieving the interaction function between enterprises and clients; construction of a visual display knowledge base module for increasing the internal module reuse rate; construction of a client customized demand obtaining module for obtaining user customized demands and recommending optional lists to the clients; construction of a client customized demand classification module carrying out order summary by a clustering algorithm; and a product visual scheme and quote module for feeding back a generated scheme and quote to users. By means of the intelligent manufacturing industry visual service platform, the enterprises and the clients are assisted in achieving interactivity, product production is conducted in combination with client customized demands, an intelligent enterprise ecological chain system is established, the problems that in a traditional mode of the enterprises, market analysis is insufficient, products are single, and resources cannot be efficiently utilized; and intelligent production is assisted to be achieved to a certain degree, and the production efficiency of the products is improved.

Description

INTELLIGENT MANUFACTURING INDUSTRY VISUAL SERVICE PLATFORM | LV501417

FOR OPM MODE TECHNICAL FIELD

[01] The disclosure belongs to the field of the intelligent manufacturing industry, and particularly relates to an intelligent manufacturing industry visual service platform for an OPM mode.

BACKGROUND ART

[02] Since the reform and opening-up, the manufacturing industry in China rapidly develops, China has become a world first manufacturer, the yield of many products is the world first, but the manufacturing industry in China similarly has many problems:

[03] (1) the labor cost is increased, the China demographic dividend gradually disappears, and many foreign enterprises transfer production plants to Southeast Asia or other nations with lower labor cost;

[04] (2) aging of population is serious, the core technical capacity is in shortage, although many manufacturing enterprises in China are in world leading position, most of manufacturing enterprises have the problems of lag in production technology and equipment, insufficient in productivity and the like and hardly cope with the challenge brought by constant changing of technological development;

[05] (3) meanwhile, the yield of the manufacturing industry in China can meet national needs, but due to shortage of the high technology key core technology, industrial structure adjustment is arduous, and some defects exist in product quality;

[06] and (4) traditional manufacturing enterprises cannot meet complexity of client demands, the interaction capability between the enterprises and the clients is weak, and the personal customized demands of the clients cannot be met.

SUMMARY

[07] The disclosure aims at helping electronic product enterprises to be transformed to the OPM mode for the original OEM mode and the ODM mode and helping traditional enterprises to get through an industry chain and expand a value chain in combination with artificial intelligence, web crawler, machine learning, data visualization technologies and the like, visual interaction between the enterprises and clients is achieved, the enterprises establish own intelligent ecological chain systems, accordingly the problems that in the traditional manufacturing industry, the client demands are improperly known, products are single, productivity is insufficient, and resources cannot be effectively controlled are solved, and an intelligent manufacturing industry visual service platform for an OPM mode is designed.

[08] The present disclosure is achieved in the manner that the intelligent manufacturing industry visual service platform for the OPM mode comprises a client visual interaction module, a visual display knowledge base module, a client customized demand obtaining module, a client customized demand classification module and a product visual scheme and quote module. The client visual interaction module is connected with the client customized demand obtaining 1 module, the client customized demand obtaining module is connected with the client customized demand classification module, and the visual display knowledge base module and LU501417 the client customized demand classification module are connected with the product visual scheme and quote module.

[09] The client visual interaction module constructs a visual interaction platform in a time order event visual interaction mode, a condition filtering based dimension degradation method is used, the time-order character of data is extracted by a visual tool, and accordingly data visual display is conducted.

[10] The visual display knowledge base module adopts a distributed web crawler technology to obtain various electronics manufacturing data, and artificial intelligence, machine learning, statistics and other related methods are adopted for data statistics and analysis.

[11] The client customized demand obtaining module obtains different customized demands of different clients in combination with the client visual interaction module, and on the basis of QoS constraints and in combination with enterprise inventory, process flow, whole machine compatibility and other factors, optional lists are automatically generated for the clients to be selected and deployed by the clients at will, and QoS constraints and compatibility of the client optional lists are verified.

[12] The client customized demand classification module collects client customized demand CTO (Configure To Oder, customization production) small-batch orders according to a clustering algorithm, and forms certain-rule batched orders.

[13] The product visual scheme and quote module adopts a relevancy rule to conduct function and structure analysis and optimization on products, is combined with the visual display knowledge base module and utilizes a module matching relation library, a module relation adjacent matrix, a matching algorithm and a data reconstitution algorithm to improve modularization reconstitution efficiency and feeds back a visual scheme and order finally generated by the platform to the clients.

[14] Compared with traditional OEM and ODM mode electronic product enterprises, the intelligent manufacturing industry visual service platform, provided by the present disclosure, for the OPM mode has the following advantages that

[15] (1) complex electronics manufacturing data are richly displayed through a client visual interaction mode, and interactivity between the enterprises and the clients is improved,

[16] (2) the visual display knowledge base module effectively integrates and classifies various collected data, and through a module matching relation library, a matching algorithm and a data reconstitution algorithm, the internal module reuse rate is increased, resource waste is reduced, and the development cost is saved;

[17] and (3) through QoS and compatibility assessment on client customized demand data, the problems of the clients and the enterprises in the aspects of brand demands, product accessories, product inventory and the like are known, and lists capable of being selected by the clients are produced and recommended to better meet the client customized demands.

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BRIEF DESCRIPTION OF THE DRAWINGS LU501417

[18] FIG.1 is a platform structure diagram of the embodiment of the present disclosure;

[19] FIG.2 is a flow chart of a visual display knowledge base module in the embodiment of the present disclosure; and

[20] FIG.3 is a flow chart of client customized demand obtaining and classification modules in the embodiment of the present disclosure.

[21] In the drawings, components represented by mark numbers are listed as follows:

[22] 1. Client visual interaction module 2. Visual display knowledge base module, 3. Client customized demand obtaining module 4. Client customized demand classification module 5. Product visual scheme and quote module

DETAILED DESCRIPTION OF THE EMBODIMENTS Embodiment:

[23] To enable technical personnel in the related field can better understand the technical scheme in the embodiment of the present disclosure and make the purpose, the method and advantages in the embodiment of the present disclosure more clear, explanation is further conducted in combination with figures in drawing instructions.

[24] As shown in FIG. 1, an intelligent manufacturing industry visual service platform for an OPM mode totally comprises five functional modules including a client visual interaction module 1, a visual display knowledge base module 2, a client customized demand obtaining module 3, a client customized demand classification module 4 and a product visual scheme and quote module 5. The client visual interaction module 1 is connected with the client customized demand obtaining module 3, the client customized demand obtaining module 3 is connected with the client customized demand classification module 4, and the visual display knowledge base module 2 and the client customized demand classification module 4 are connected with the product visual scheme and quote module 5.

[25] The client visual interaction module 1 extracts a data time-order character through a time order event visual interaction mode, for different data display forms, a condition filtering based dimension degradation method is used, complex electronic intelligent manufacturing industry data are displayed from multiple angles, and client data can be visually displayed according to the time sequence, wherein the condition filtering based dimension degradation method utilizes digraphs, HighCharts, Qlik View, Tableau and other visual tools.

[26] A flow chart of the visual display knowledge base module 2 is shown in FIG.2, through a distributed web crawler technology, connection of multi-source intelligent manufacturing industry data with solid as a center is achieved, a dynamic community detection algorithm for node correlation analysis is studied, effective predictive analysis can be conducted for community dynamic evolutionary characters and data subject evolutionary characters, from the two aspects of service logic and data logic, variables needing to be used are considered, and a proper modeling algorithm is selected for integration and classification; artificial intelligence, machine learning, statistics and other related methods are adopted for achieving category subdivision, and the electronic intelligent manufacturing industry data are divided into a 3 professional standard library, a digital library, a product design case library, a product design experience library, a product design problem library and a network consult library; and the LU501417 visual display knowledge base provides personally-recommended marketing modes for different types of clients. The knowledge base further collects and tides data of other modules of the platform for knowledge reusing, and the utilization rate of the platform is increased.

[27] A flow chart of the client customized demand obtaining module 3 is shown in FIG.3, client customized demand data such as specific software and hardware equipment, material brands and accessories needed by the clients are obtained according to the client visual interaction module 1; and in combination with enterprise inventory, process flow, whole machine compatibility and other factors, optional lists are automatically generated for the clients to be selected by users at will, and QoS constraints and compatibility of the client optional lists are verified,

[28] When the optional lists are recommended to the clients, the material demand, the accessory demand and the quality demand proposed by the clients need to be considered, and more optimization objectives need to be combined; and universal accessories are selected as much as possible, high compatibility is kept as much as possible, existing process flow is approached as much as possible, inventory is balanced as much as possible, production cost is reduced as much as possible, and then a multi-objective optimization problem with constraints is formed. Disperse particle swarm optimization and other evolutionary optimization algorithms are adopted, a PSO algorithm is initialized as a cluster of random particles (random solution), and an optimal solution is found through iteration. In each time of iteration, the particles are updated by tracking two extreme values, one is the optimal solution found by the particles and is called as individual pbest 1, and the other extreme value is the optimal solution found in the whole population at present and is called as the global extreme value gbest i. After the two above extreme values are found, the speed and the position of the particles are updated through two following formulas: Vi=wx Vi+cr(pbest, — Xi) + c,r, (gbest, — X;) formula (1) X1 = X1 + Vi formula (2)

[29] In the formulas, Xi and Vi represent the speed and the position of the ith particle correspondingly, pbest i is the optimal value of the ith particle, omega is inertia weight, cl and c2 are accelerated factors, rl and r2 are two random numbers in the range [0,1], and a constant Vmax is usually used for limiting the speed of the particles, and the search result is improved.

[30] In addition, an electronic product commonly comprises a series of electronic accessories, each accessory and material have many brands and types, the compatibility collision problem exists between some brands and types, it is too strict for requiring the clients to completely avoid the conflicts during self selection, and therefore the platform needs to assess the compatibility of the optional lists of the clients and feeds back the assessment result, and accordingly, Bayesian inference is adopted for quantitatively achieving compatibility of the optional lists. Bayesian inference is one of methods of inferential statistics, and by using the Bayes theorem, when more evidences and information exist, the specific hypothesis probability is updated. Bayes statistical inference is one of important techniques in statistics (especially in mathematical statistics), and the formula is as follows: 4

_ PxeG[P)pe(#) p@|X(0|x) a De Pxle («on Pp (69 Pyle (19) Po (9) formula (3) LU501417

[31] By solving the multi-objective QoS constraint decomposition problem for CTO orders, on the basis of meeting the client QoS constraints, universal and high-compatibility accessories tending to existing process flow are selected, and the purposes of balancing inventory and reducing the production cost are achieved.

[32] The client customized demand classification module 4 is shown in FIG.3, a clustering method is adopted for conducting product CTO order classification on client customized demand data in the client customized demand obtaining module 3, and the core problem is about measuring the similarity of CTO orders based on BOM (Bill of Material, material order) and process flow.

[33] The solution is as follows:

[34] BOM of one CTO order is assumed to be related to n materials including M1, M2, …, Mn, when the order is generated, each material can be selected from a plurality of brands, and each brand has a plurality of types. For the order i and the order j, the brands and the types selected by the order 1 and the order j for the material decide the similarity of the same material, and the similarity of the material is defined as: brand;;, dif ferent brands selected MaterialSim; (Mk) = formula (4) type;;» same brands selected

[35] Wherein, brand is used for describing a similarity matrix between different brands for the same material, the value of each element is in the section [0,1], the value is decided by the alternative cost of different brands on the procedure, and the higher the alternative cost is, the lower the similarity is; and type is used for describing a similarity matrix between different types of the same brand for the same material, and the value rule is similar to brand.

[36] The similarity of two CTO orders defines the average value of the similarity of each material, namely OrderSim,, = TE) K=1 formula (5)

[37] The product visual scheme and quote module 5 adopts a relevancy rule to conduct function and structure analysis on products, a relevancy degree divided by a representation module and a function contribution degree for representing the client customized demand serve as an optimized objective function to optimize a module division result, and product module division scheme optimization is achieved; and on the basis of product module division and in combination with the visual display knowledge base module 2, a corresponding module matching relation library and a module relation adjacent matrix are established, a matching algorithm and a data reconstitution algorithm are utilized for improving the reuse efficiency of internal module reconstitution, and a product scheme and quote most conforming to the client customized demand are shown to the clients.

[38] In conclusion, a client interaction module is designed in the intelligent manufacturing industry visual service platform for the OPM mode, and the clients can well interact with the enterprises, the distributed crawler technology, artificial intelligence, data statistic analysis, machine learning and the like are utilized for constructing a visual display product knowledge LU501417 base, and by utilizing data reconstitution, the internal module reuse rate is increased, and the enterprise development is saved; the clustering algorithm, the PSO algorithm and the Bayes algorithm are utilized for data classification and processing, a customized model conforming to the client personal products can be designed, and the model division result is conveniently optimized; and through the intelligent manufacturing industry visual service platform for the OPM mode, a novel intelligent technology can be effectively utilized for changing a traditional operation pattern for the enterprises, the interactivity between the enterprises and the clients is improved by utilizing the Internet and the intelligent technology, the development efficiency and the product quality are improved, the capability of the enterprises for resisting market changing risks is effectively improved, and future enterprise development is facilitated.

[39] The intelligent manufacturing industry visual service platform, provided by the present disclosure, for the OPM mode is introduced in detail above. Same and similar parts between various embodiments of the specification can be in reference for one another. It needs to be indicated that for common technical personnel in the technical field, the present disclosure can be improved and modified in several aspects without departing from the principle of the present disclosure, and the improvements and modifications fall into the protection scope of the claims of the present disclosure.

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Claims

WHAT IS CLAIMED IS: LUS01417

1. An intelligent manufacturing industry visual service platform for an OPM mode, characterized by comprising a client visual interaction module, a visual display knowledge base module, a client customized demand obtaining module, a client customized demand classification module and a product visual scheme and quote module; the client visual interaction module is connected with the client customized demand obtaining module, the client customized demand obtaining module is connected with the client customized demand classification module, and the visual display knowledge base module and the client customized demand classification module are connected with the product visual scheme and quote module; the client visual interaction module constructs a visual interaction platform in a time order event visual interaction mode, a condition filtering based dimension degradation method is used, the time-order character of data is extracted by a visual tool, and accordingly data visual display is conducted; the visual display knowledge base module adopts a distributed web crawler technology to obtain various electronics manufacturing data, and artificial intelligence, machine learning and statistics are adopted for data statistics and analysis; the client customized demand obtaining module obtains different customized demands of different clients in combination with the client visual interaction module, and on the basis of QoS constraints and in combination with enterprise inventory, process flow and whole machine compatibility factors, optional lists are automatically generated for the clients to be selected and deployed by users at will, and QoS constraints and compatibility of the client optional lists are verified; the client customized demand classification module collects client customized demand CTO small-batch orders according to a clustering algorithm; and the product visual scheme and quote module adopts a relevancy rule to conduct function and structure analysis and optimization on products, is combined with the visual display knowledge base module and utilizes a module matching relation library, a module relation adjacent matrix, a matching algorithm and a data reconstitution algorithm to improve modularization reconstitution efficiency and feeds back a visual scheme and order finally generated by the platform to the clients.

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