Complex equipment full life cycle value chain optimization system and method

Abstract

The invention discloses a complex equipment full life cycle value chain optimization system and method. A data management module in the system stores and manages various management information generated in the equipment production and service process; the project management module plans and configures internal and external value links of an equipment manufacturing enterprise based on the equipment process information to obtain a value chain flow network oriented to a plurality of equipment manufacturing projects; the business process management and control module provides a business process optimization component for each business in a complex application environment; and the value chain optimization module performs multi-objective optimization on the full-life-cycle value chain of the complex equipment to obtain a Pareto solution set of a value chain optimization scheme. According to the method, full-life-cycle value chain optimization is carried out for multiple complex equipment parallel manufacturing projects based on the system, the defect that existing complex equipment only pays attention to single business optimization can be overcome, and dynamic and optimal configuration is carried out on the full-life-cycle value structure of the equipment from the perspective of value improvement.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing, and specifically relates to a system and method for optimizing the value chain of complex equipment throughout its life cycle. Background Art

[0002] Complex equipment manufacturing companies master the core technologies and resources of the entire value chain, are at the core of the value chain system, and dominate the strategic links of the value chain. In order to maintain their competitive advantage in the value chain, equipment manufacturing companies must make comprehensive use of various business data on the value chain, analyze them through big data technology, optimize the equipment value chain links using data mining, machine learning and other technologies, and sort and reconstruct the internal operation value chain, so as to fully and effectively utilize their own resources and those on the chain, concentrate resources on value-added links such as design, manufacturing, operation and maintenance, organize the company more efficiently, and improve the overall efficiency of the value chain.

[0003] As the division of labor and cooperation within and between complex equipment manufacturing enterprises becomes more and more refined, the enterprise value chain continues to expand and extend, the difficulty of managing and controlling the manufacturing process increases accordingly, and customers' demands for product value enhancement are getting higher and higher. However, at present, manufacturing enterprises pay more attention to the daily business management and local optimization of each link, and rarely analyze the business management problems of the enterprise from the perspective of product value-added. Most of the existing value chain optimization methods are qualitative analysis methods, which do not make full use of the business data in each link of the value chain. For example, the literature [Li Ming et al. Research on the value-added mechanism of infrastructure PPP projects based on system dynamics. Soft Science. 37.1 (2023): 10] analyzes and identifies the value-added principles and influencing factors of PPP projects from the perspective of the whole life cycle, and uses the system dynamics method to explore the value-added mechanism and path of PPP projects. The value-added model is mainly intended to analyze the impact of different external conditions on the project value, without analyzing the internal business of the enterprise. The literature [Yang Qing. Research on Value Chain Cost Management of GX Company [D]. North China University of Technology, 2024] analyzes the problems existing in the company's cost management, identifies the internal and external value chains of the company, and proposes cost management optimization measures using the value chain cost management method, which is a qualitative method.

[0004] It can be seen that studying the general methods and technologies for value-added and optimization of complex equipment throughout its life cycle from a value chain perspective will provide useful reference and guidance for improving the overall efficiency of the equipment value chain, promoting service-oriented transformation, and pushing the value chain towards the high end. Summary of the invention

[0005] In view of the above, the present invention provides a value chain optimization system and method for the entire life cycle of complex equipment. The system clusters equipment attributes of value chain operation data, constructs value link node objects, and configures and connects them based on the value link decomposition to obtain a value chain process network configuration template. The system also performs multi-objective optimization of the entire life cycle of complex equipment based on the mined data and business process network structure. Global configuration and optimization functions are added on the basis of the existing complex equipment business management functions to improve the overall efficiency of the equipment value chain.

[0006] A complex equipment full life cycle value chain optimization system, including:

[0007] The data governance unit uses cloud computing and big data technologies, with equipment manufacturing projects as the primary key, to store and manage manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data, and product static data in separate databases, thus obtaining an integrated data base that adapts to the data characteristics of the industrial field;

[0008] The project management unit is used to construct the value link objects (especially the key value links) that constitute the value chain, visualize the value link objects, and perform business logic arrangement on the value link objects based on the value chain operation data to obtain the value chain process network configuration template;

[0009] The business process control unit is used to provide multi-objective optimization scheduling planning components corresponding to each business process in the entire life cycle of complex equipment, and manage these scheduling planning components;

[0010] The value chain optimization scheduling unit is a scheduling planning component based on multiple intelligent optimization algorithms. It performs multi-objective optimization on the entire life cycle of complex equipment according to the value chain process network configuration template, obtains the Pareto optimal solution set of the value chain optimization plan, and manages and configures the business process of the entire life cycle of complex equipment based on this optimal solution set.

[0011] The parameters contained in each solution in the Pareto optimal solution set involve many decisions made during the design, manufacturing, and maintenance stages of the equipment, such as the selection of optional modules and the optimal design of key modules during the design stage, the optimal scheduling of processing sequence, processing resources, processing personnel, etc. during the manufacturing stage, and the optimal decision-making on operation and maintenance teams, spare parts, and operation and maintenance timing during the maintenance stage, etc.

[0012] The system of the present invention connects to the existing business system of the equipment manufacturing enterprise through the data governance unit, obtains various types of management information generated in the equipment production and service process, and realizes a number of data sources divided according to the equipment attributes as the clustering basis. The data source is used as the value chain operation data to provide data support for the project management unit to construct the process network node object; the project management unit plans, configures, warns, and adjusts the internal and external value links of the equipment manufacturing enterprise based on the equipment process information, and visually manages the value link objects to obtain a value chain process network for multiple equipment manufacturing projects, and can provide business process network configuration templates for the full cycle value chain of product services for different enterprises; through the business process control unit, the multi-objective optimization scheduling planning components required for value chain optimization iteration are provided for different enterprises for demand / design / supply chain / manufacturing / operation and maintenance key businesses in complex application environments; the scheduling planning components and value chain process network configuration templates selected according to the actual needs of the enterprise are used to perform multi-objective optimization and value-added on the entire life cycle of the enterprise's complex equipment through the value chain optimization unit, and the Pareto solution set of the value chain optimization plan is obtained, so as to realize universal value-added optimization of the entire life cycle of the enterprise's complex equipment. Therefore, the present invention carries out full life cycle value chain optimization for multiple complex equipment parallel manufacturing projects based on the above system, which can solve the drawback of existing complex equipment that only focuses on single business optimization, and dynamically and optimally configure the value structure of the equipment's entire life cycle from the perspective of value enhancement.

[0013] Furthermore, the data governance unit uses cloud computing architecture to aggregate and govern multi-source heterogeneous data involving manufacturing projects, requirements, design processes, production and manufacturing, supply chains, operation and maintenance services, and products, including:

[0014] Data classification: using technologies including distributed file storage, object storage, time series database, relational database, NOSQL database, and column storage to classify project-related data;

[0015] Data collection, through the functional modules including offline synchronization, real-time synchronization, file collection and interface collection, and based on these functional modules, realize the collection and aggregation of multi-source heterogeneous data;

[0016] Data storage, for structured, semi-structured and unstructured data, under the guidance of data high-speed integration interface, incremental storage specifications and data integration security specifications, through different protocol adaptation and content parsing, the data is stored in the middleware and storage system.

[0017] The integrated big data management function of the data governance unit has high-performance and feature-rich real-time data processing capabilities, which can meet the real-time logical calculation and result output of equipment data and business data of equipment manufacturing enterprises, support data calculation and processing through the stream computing engine, and write it to the database in real time, which can achieve the goals of high throughput, low latency, and high availability, and can be dynamically expanded according to the amount of data processed. In addition, the data governance unit uses big data collection technology to support the synchronization of multi-source heterogeneous data through different protocols, and also facilitates the flexible construction of data business associations, providing a basis for the optimization of value chains such as design business processes, production business processes, logistics business processes, and operation and maintenance business processes in the product service process.

[0018] Furthermore, the project management unit includes:

[0019] The network node construction module builds value link objects based on the business process of the entire life cycle of complex equipment;

[0020] Visual control module, which displays value link objects through controls composed of components, templates, data flows, and human-computer interaction pages;

[0021] The network node operation module arranges the business logic of the value link objects based on the value chain operation data to obtain the value chain process network configuration template.

[0022] The project management unit constructs value link objects based on the business process requirements of each enterprise's complex equipment throughout the life cycle, visualizes the value link objects, and edits the value link objects based on data associations established on an integrated data base. Ultimately, a value chain process network configuration template is obtained that meets the actual needs of the enterprise, allowing all enterprises to configure a value chain process network that meets their actual needs and situations.

[0023] Furthermore, the value link objects are static data and processing records related to the business process and cover the entire life cycle of complex equipment, and their types include:

[0024] Design value link objects, including equipment operating environment, project management requirements, product special requirements, design batches, static data and processing records of design changes related to product design;

[0025] Manufacturing value link objects, including static data and processing records related to raw material procurement, outsourcing production, material distribution, internal production, unloading, assembly, and product transportation related to product manufacturing;

[0026] Logistics value link objects, including static data and processing records of transportation, warehousing and distribution of raw materials during transportation and equipment finished products during disassembly, transportation and reassembly;

[0027] Objects of the operation and maintenance value chain include static data and processing records of the operation and maintenance services and value-added services provided to customers by enterprises after they are officially put into use during equipment and production operations.

[0028] The value link objects provided by the project management unit cover the entire life cycle of complex equipment, involving design business processes, production business processes, logistics business processes and operation and maintenance business processes. They can operate on value link objects from an all-round perspective according to the company's product and service needs, providing a basis for obtaining a suitable value chain process network configuration template.

[0029] Furthermore, the multi-objective optimization scheduling planning component is one or any combination of detection algorithms, classification algorithms, prediction algorithms, scheduling algorithms, and multi-objective optimization algorithms applied to the design, manufacturing, and operation and maintenance processes; the management of these scheduling planning components includes: data model construction, component construction, data transformation and transmission, parameter configuration, dynamic plug-in and performance analysis.

[0030] The multi-objective optimization scheduling planning components provided by the business process control unit can be scheduling algorithm components developed according to the characteristics of the enterprise's production scenarios, problem objectives, and constraints, or existing components in the cloud platform algorithm component library, or a combination of the two; these components can match the enterprise's actual production scenarios and value chain collaboration status, meet the full-cycle value chain optimization needs of actual equipment, optimize at both the overall and local levels, and provide a rich and complete algorithm system for multi-objective optimization of complex equipment throughout its life cycle.

[0031] Furthermore, the value chain optimization unit includes:

[0032] Cost assessment module, used to collect statistics on the cost indicators of the entire life cycle of all complex equipment in each production project within a fixed period (year or quarter);

[0033] The quality statistics module collects statistics on the quality indicators of all complex equipment based on the number and severity of defects reported during equipment operation and maintenance;

[0034] Time delay assessment module, when the equipment product exceeds the delivery time specified in its contract, calculates the equipment delay index;

[0035] Functional satisfaction evaluation module, which calculates the functional satisfaction index of each complex equipment based on the user's satisfaction evaluation results on each function of the equipment;

[0036] The optimization scheduling module optimizes the entire life cycle of complex equipment based on cost indicators, quality indicators, delay indicators, and functional satisfaction indicators using a multi-objective optimization model to obtain the Pareto optimal solution set of the value chain optimization plan, and manages and configures the business processes of the entire life cycle of complex equipment based on this optimal solution set.

[0037] Single equipment x i The calculation expression of the cost index of the whole life cycle is as follows:

[0038] C(x i )=c1(x i )+c2(x i )+c3(x i )

[0039] Where: C(x i ) indicates equipment x i The cost indicator statistics result, c1(x i ) indicates equipment x i The design cost, c2(x i ) indicates equipment x i The manufacturing cost, c3(x i ) indicates equipment x i The operation and maintenance costs;

[0040] Single equipment x i The quality index is defined as the overall defect rate level represented by the number of defective parts during equipment operation, and its calculation expression is as follows:

[0041]

[0042] Where: Q(x i ) indicates equipment x i The statistical results of the quality indicators are as follows: n is the number of defective parts reported, r j represents the importance of the jth defective component, d j represents the defect severity of the j-th defective component;

[0043] Single equipment x i The delay index is defined as the time delay caused by various factors (such as design changes, supply delays, logistics blockages, etc.) during the equipment production process. Its calculation expression is as follows:

[0044] T(x i )=t1(x i )+t2(x i )+t3(x i )-t s (x i )

[0045] Where: T(x i ) indicates equipment x i The latency statistic results, t1(x i ) indicates equipment x i The time spent in the design phase, t2(x i ) indicates equipment x i The time spent in the manufacturing process, t3(x i ) indicates equipment x i Time spent in logistics, t s (x i ) indicates equipment x i The delivery time stipulated in the contract;

[0046] Single equipment x i The calculation expression of the functional satisfaction index is as follows:

[0047]

[0048] Where: S(x i ) indicates equipment x i The statistical results of the functional satisfaction index, m is the number of functional items, r k is the importance of the kth function, s k represents the user's satisfaction evaluation on the kth function of the equipment. If this function is improved, s k The value is greater than 1. If this function is weakened for some reason, s k The value is less than 1, if the function meets the equipment operation requirements unchanged s k The value is 1.

[0049] Furthermore, the functional expression of the multi-objective optimization model is as follows:

[0050]

[0051] Where: w s 、w c 、w q 、w t are the weights of function satisfaction, cost, quality, and time delay, respectively, and N is the number of complex equipment. Due to the large number of specific parameters of equipment manufacturing projects, the present invention integrates various parameters related to many decisions in various stages such as design, manufacturing, and operation and maintenance into the equipment x i Among them, with equipment x i As variables, its functional satisfaction, cost, quality, time delay and other indicators are calculated.

[0052] In response to the problems of changeable states and frequent disturbances in various links of the complex equipment value chain, the value chain optimization and scheduling unit established a spatiotemporal behavior relationship network based on the dynamic scheduling coupling mechanism of multi-dimensional collaborative value chain, constructed a multi-professional field multi-objective optimization model, and obtained the mapping relationship between the state of the value link and the scheduling strategy. The application of complex production flexible dynamic scheduling optimization technology can not only respond to emergencies under various real-time conditions, but also realize the added value of the overall equipment value chain.

[0053] A method for optimizing the value chain of complex equipment throughout its life cycle includes the following steps:

[0054] S1. Obtain manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data and product static data, and use the acquired data as value chain operation data;

[0055] S2. Use big data collection technology to store and manage value chain operation data with equipment manufacturing projects as the primary key, and obtain an integrated data base that adapts to the data characteristics of the industrial field;

[0056] S3. Construct value link objects based on the business process of the entire life cycle of complex equipment manufacturing;

[0057] S4. Visualize the value link objects, perform business logic arrangement on the value link objects based on the value chain operation data, and obtain the value chain process network configuration template;

[0058] S5. Select multi-objective optimization scheduling planning components corresponding to each business process in the entire life cycle of complex equipment;

[0059] S6. Based on the scheduling planning component of multiple intelligent optimization algorithms, multi-objective optimization is performed on the entire life cycle of complex equipment according to the value chain process network configuration template to obtain the Pareto optimal solution set of the value chain optimization solution;

[0060] S7. Based on the Pareto optimal solution set of the value chain optimization scheme, manage and configure the business processes of the entire life cycle of complex equipment to achieve optimization of the value chain of the entire life cycle of complex equipment.

[0061] The method of the present invention uses cloud computing architecture to aggregate and manage the data involved in the value chain links, and uses model-based equipment unified data hybrid modeling technology to perform business logic arrangement and visual display of value link objects, forming a value chain process network configuration template that runs through the product life cycle. It uses the scheduling planning components of multiple intelligent optimization algorithms to perform local optimization, and can further carry out equipment full life cycle value chain optimization. Compared with other methods, the present invention can make full use of various types of data, information and industrial knowledge on a unified platform, strike a balance between multiple goals to construct a global optimization plan, and provide decision-making guidance for value chain collaboration in an uncertain environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a structural block diagram of the complex equipment full life cycle value chain optimization system of the present invention.

[0063] Figure 2 The present invention is a flowchart of the steps of the method for optimizing the value chain of complex equipment throughout its life cycle.

[0064] Figure 3 A schematic diagram of the complex equipment value chain optimization process handled by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to describe the present invention more specifically, the technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0066] like Figure 1 As shown, the complex equipment full life cycle value chain optimization system of the present invention includes: a data governance module, a project management module, a business process control module and a value chain optimization module, wherein:

[0067] The data governance module uses big data collection technology, with equipment manufacturing projects as the primary key, to store and manage manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data, and product static data, to obtain an integrated data base that adapts to the data characteristics of the industrial field.

[0068] The data governance module uses cloud computing architecture to aggregate and manage multi-source heterogeneous data such as manufacturing projects, requirements, design processes, production and manufacturing, supply chains, operation and maintenance services, and products involved. Data management content includes:

[0069] Data classification: classify the data involved in the project by using technologies such as distributed file storage, object storage, time series database, relational database, NOSQL database, and column storage;

[0070] Data collection, through offline synchronization module, real-time synchronization module, file collection module and interface collection module, and based on these modules, realize the collection and aggregation of multi-source heterogeneous data;

[0071] Data storage, for structured data, semi-structured data, and unstructured data, is stored in middleware and storage systems through different protocol adaptation and content parsing under the guidance of data high-speed integration interfaces, incremental storage specifications, and data integration security specifications.

[0072] The project management module includes a value link node construction submodule, a visualization control submodule, and a network node operation submodule, among which:

[0073] The value link node construction submodule is used to construct value link objects according to the business process of the entire life cycle of complex equipment; value link objects cover the entire life cycle of complex equipment, and their types include:

[0074] Design value link objects, including static data and processing records of operating environment, project management requirements, special requirements, design batches, and design changes related to product design;

[0075] Manufacturing value link objects, including static data and processing records related to raw material procurement, outsourcing production, material distribution, internal production, unloading, assembly, and product transportation related to product manufacturing;

[0076] Logistics value link objects, including static data and processing records of transportation, warehousing and distribution of raw materials during transportation, equipment finished products during disassembly, transportation and reassembly;

[0077] Objects of the operation and maintenance value chain include static data and processing records of the operation and maintenance services and value-added services provided by the enterprise to customers after the equipment is officially used in the production operation process.

[0078] The visualization control submodule is used to display value link objects through controls composed of components, templates, data flows, and human-computer interaction pages, where value link objects are static data and processing records related to business processes.

[0079] The network node operation submodule is used to perform business logic arrangement on value link objects based on value chain operation data to obtain a value chain process network configuration template.

[0080] The business process control module is used to provide multi-objective optimization scheduling planning components corresponding to each business process in the entire life cycle of complex equipment, and to manage the scheduling planning components; the multi-objective optimization scheduling planning components are one or any combination of detection algorithms, classification algorithms, prediction algorithms, scheduling algorithms, and multi-objective optimization algorithms applied to the design, manufacturing, and operation and maintenance processes; the management content of the multi-objective optimization scheduling planning components includes data model construction, component construction, data transformation and transmission, parameter configuration, dynamic plug-in and performance analysis.

[0081] The value chain optimization module is used for scheduling planning components based on a variety of intelligent optimization algorithms. According to the value chain process network configuration template, multi-objective optimization is performed on the entire life cycle of complex equipment to obtain the Pareto optimal solution set of the value chain optimization solution.

[0082] The value chain optimization module includes: cost evaluation module, quality statistics module, time delay evaluation module and function satisfaction evaluation module, among which:

[0083] The cost evaluation module collects statistics on the cost of the entire life cycle of all complex equipment in N production projects within a fixed period (year or quarter) to obtain cost indicators. The calculation expression of the cost indicator statistical model of a single piece of equipment x is as follows:

[0084] C(x)=c1(x)+c2(x)+c3(x)+c4(x)

[0085] Among them: C(x) represents the statistical results of cost indicators, p1(x) represents the cost of the design link, p2(x) represents the cost of the manufacturing link, p3(x) represents the cost of the logistics link, and p4(x) represents the cost of the operation and maintenance link.

[0086] The quality statistics module defines the equipment quality by the overall defect rate level represented by the number of defects of each component during the equipment operation. Therefore, the quality index is estimated by the number of defects reported during the equipment operation and maintenance and the severity of the defects. The calculation expression of the quality index statistical model of a single equipment x is as follows:

[0087]

[0088] Where: Q(x) represents the statistical results of quality indicators, n is the number of defects reported, r j Indicates the importance of the reported component, d j Indicates the severity of the defect reported.

[0089] The time delay assessment module defines the time index based on the time delay caused by various factors in the equipment production process, such as design changes, supply delays, logistics blockages, etc. The time standard is defined by the production time of key components of complex equipment. Each equipment product stipulates the delivery time in the contract. If the delivery time is exceeded, the delay cost is calculated; the calculation expression of the delay index statistical model of a single equipment x is as follows:

[0090] T(x)=t1(x)+t2(x)+t3(x)-t s (x)

[0091] Where: T(x) represents the statistical result of the delay index, t1(x) represents the time spent in the design phase, t2(x) represents the time spent in the manufacturing phase, t3(x) represents the time spent in the logistics phase, t s (x) means the time of delivery stipulated in the contract;

[0092] The function satisfaction evaluation module is defined according to the user's satisfaction with the functions contained in the equipment. Assuming that a certain standard function of the equipment that meets the operation requirements is defined as 1, if the function is improved, the value is >1; if a certain function is weakened due to various reasons, the value is <1; therefore, experts or users are required to evaluate the equipment functions; the calculation expression of the function satisfaction index statistical model of a single equipment x is as follows:

[0093]

[0094] Where: S(x) represents the statistical results of the functional satisfaction index, m is the number of functional items, r k is the importance of the component, s k Indicates the satisfaction of a single function.

[0095] In this embodiment, it is assumed that there are N complex equipment under construction at a certain point in time, and different equipment is in different stages of its life cycle. Based on the value indicators of feasible solutions for each stage such as known demand, design, manufacturing, and operation and maintenance of each equipment, all indicators of these equipment are counted, and the whole life cycle of complex equipment is optimized using a multi-objective optimization model to obtain the Pareto optimal solution set of the value chain solution. The calculation expression of the multi-objective optimization model is as follows:

[0096]

[0097] Where: min F(x) represents the multi-objective optimization model, w s 、w c 、w q 、w tThey are the weights of functional satisfaction, cost, quality, time delay and other indicators when calculating the overall index. Assuming that the current demand for a certain shield machine under construction is known, a variety of design, manufacturing, operation and maintenance plans can be initially formed based on the demand, and various value indicators are collected based on different plans and integrated into the above formula; similarly, assuming that the current demand and design of a certain rock drilling rig have been basically determined, a variety of manufacturing and operation and maintenance plans can be formed, and various value indicators are collected and integrated into the above formula.

[0098] like Figure 2 As shown, the optimization method of the present invention based on the value chain optimization system of the whole life cycle of complex equipment includes the following steps:

[0099] S1. Obtain manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data and product static data, and use the acquired data as product data.

[0100] S2. Using cloud computing and big data technology, with equipment manufacturing projects as the primary key, store and manage manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data, and product static data, and obtain an integrated data base that adapts to the data characteristics of the industrial field; specifically:

[0101] S21. Based on the collaborative needs of the equipment's entire life cycle value chain, use the model-based systems engineering approach (MBSE) to establish a SysML model, including use case diagrams, module definition diagrams, internal module diagrams, requirement diagrams, etc.

[0102] S22. Design a data logic model based on the SysML model, which involves the design of the model's entities and attributes, determining the entity's primary key and association relationships, and determining the entity's loading mode.

[0103] S23. Physicalize the logical model, such as table naming, field naming, field type, length, precision, etc., and select the appropriate storage mode considering performance factors, such as relational database, NoSQL database, file storage, etc.

[0104] S24. Use big data collection technology to store product data in a relational database with equipment manufacturing project as the primary key, and use the relational database to manage product data to obtain an integrated data base that adapts to the data characteristics of the industrial field.

[0105] S3. According to the business process of the entire life cycle of complex equipment, construct the value link objects that constitute the value chain; the value chain process network formed by the value link objects is hierarchical, guiding the operation process of the value chain at different levels of the enterprise manufacturing process.

[0106] Figure 3The value chain corresponding to this embodiment and some of the hierarchical value link objects included are shown. The top-level value link objects include design, manufacturing, and operation and maintenance. In the design stage, it can be further divided into overall design, detailed design, key component optimization design, design change, design verification and other links; the above links can be further subdivided; the design stage is to find the optimal design solution among many optional design modules on the premise of meeting customer needs. This embodiment takes the value chain of 4 equipment as the optimization object, Figure 3 The left side shows the value link objects in the design stage, and the optional design schemes of each equipment are obtained according to user needs; the middle is the manufacturing stage, and the value link can be subdivided into supply, cutting, machining, welding, assembly and other stages. In the supply stage, the supply scheme is selected from many suppliers according to the design scheme, which can form S 11 , S 12 , S 13 Equal supply scheme, based on S 12 The supply plan can form the subsequent cutting, machining, assembly and other plans. In the cutting process, production scheduling should be carried out according to factors such as supply conditions, cutting equipment, and transportation costs. For example, the cutting plan O 11 There are 3 unloading devices to choose from, and the other links are similar; the value chain optimization plan is to select the best path among all the optional paths.

[0107] The value link object configures the static parameters, input and output types, and data storage format of the link, such as how many processing machines are available in a certain processing link, which materials are input, which parts are output, how to record the processing information, etc.

[0108] S4. Display the value link objects with visual controls, perform business logic arrangement on the value link objects based on the value chain operation data, and obtain the value chain process network configuration template; extract the information required for this link from each database of the data base, evaluate the current status and needs of the enterprise, mobilize the corresponding value link objects according to the actual needs of the equipment manufacturing project, and configure a new template for the value chain process network; the value chain process network configuration template establishes the relationship between the business process and the data base.

[0109] S5. Select the multi-objective optimization scheduling and planning components required for multi-objective optimization of the entire life cycle of complex equipment; the multi-objective optimization scheduling and planning components can be scheduling algorithm components developed according to the characteristics of the enterprise's production scenarios, problem objectives, and constraints, or existing components in the cloud platform algorithm component library, or a combination of the two. In the design stage, you can use components such as neural network-based equipment key design parameter optimization and Gaussian process-based equipment optimization for design optimization; in the manufacturing stage, you can use welding workshop scheduling, distributed heterogeneous machining workshop scheduling, dynamic (machine damage) flexible job shop scheduling, and flexible job shop scheduling with resource constraints for manufacturing process scheduling and optimization; in the operation and maintenance stage, you can use components such as intelligent scheduling of support material organization, key component life prediction, and key component health status monitoring for operation and maintenance optimization.

[0110] It is possible to establish associations between data and components, integrate and drive unified components as the core, cover the data flow of the entire life cycle of equipment, and form a series of rich component sets; enterprises can connect components into algorithm models according to the needs of specific equipment manufacturing projects, input data for training, and the trained models can be published as services for online calls or edge deployment applications.

[0111] S6. Based on the scheduling planning components of multiple intelligent optimization algorithms, multi-objective optimization is performed on the entire life cycle of complex equipment according to the value chain process network configuration template to obtain the Pareto frontier solution set; the Pareto frontier solution set is the most economically beneficial solution set expected for the enterprise's business processes.

[0112] S7. Combined with the needs, capabilities, preferences and constraints of equipment customers, enterprises and external collaborative units, first determine the priority of decision parameters. For example, if some equipment is urgently needed, the time delay indicator will have a higher priority; if some equipment is used in large-scale key projects and needs to be used continuously for a long period of time, the quality indicator will have a higher priority; secondly, other heuristic knowledge should be considered as constraints, and then the swarm intelligence algorithm can be used to select the best solution from the Pareto frontier solution set.

[0113] S8. Based on the Pareto frontier solution set, manage and configure the business processes of the entire life cycle of complex equipment, guide equipment manufacturing companies to carry out customized agile design, efficient manufacturing, and low-cost operation and maintenance to achieve optimization goals.

[0114] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

Claims

1. A complex equipment full life cycle value chain optimization system, characterized by: include: The data governance unit uses cloud computing and big data technologies, with equipment manufacturing projects as the primary key, to store and manage manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data, and product static data in separate databases, thus obtaining an integrated data base that adapts to the data characteristics of the industrial field; The project management unit is used to construct the value link objects that constitute the value chain, visualize the value link objects, perform business logic arrangement on the value link objects based on the value chain operation data, and obtain the value chain process network configuration template; The business process control unit is used to provide multi-objective optimization scheduling planning components corresponding to each business process in the entire life cycle of complex equipment, and manage these scheduling planning components; The value chain optimization scheduling unit is a scheduling planning component based on multiple intelligent optimization algorithms. It performs multi-objective optimization on the entire life cycle of complex equipment according to the value chain process network configuration template, obtains the Pareto optimal solution set of the value chain optimization plan, and manages and configures the business process of the entire life cycle of complex equipment based on this optimal solution set.

2. The complex equipment full life cycle value chain optimization system according to claim 1 is characterized by: The data governance unit uses cloud computing architecture to aggregate and govern multi-source heterogeneous data involving manufacturing projects, requirements, design processes, production and manufacturing, supply chains, operation and maintenance services, and products, including: Data classification: using technologies including distributed file storage, object storage, time series database, relational database, NOSQL database, and column storage to classify project-related data; Data collection, through the functional modules including offline synchronization, real-time synchronization, file collection and interface collection, and based on these functional modules, realize the collection and aggregation of multi-source heterogeneous data; Data storage, for structured, semi-structured and unstructured data, under the guidance of data high-speed integration interface, incremental storage specifications and data integration security specifications, through different protocol adaptation and content analysis, the data is stored in the middleware and storage system.

3. The complex equipment full life cycle value chain optimization system according to claim 1 is characterized by: The project management unit includes: The network node construction module builds value link objects based on the business process of the entire life cycle of complex equipment; Visual control module, which displays value link objects through controls composed of components, templates, data flows, and human-computer interaction pages; The network node operation module arranges the business logic of the value link objects based on the value chain operation data to obtain the value chain process network configuration template.

4. The complex equipment full life cycle value chain optimization system according to claim 1 is characterized by: The value link objects are static data and processing records related to the business process and cover the entire life cycle of complex equipment. The types include: Design value link objects, including equipment operating environment, project management requirements, product special requirements, design batches, static data and processing records of design changes related to product design; Manufacturing value link objects, including static data and processing records related to raw material procurement, outsourcing production, material distribution, internal production, unloading, assembly, and product transportation related to product manufacturing; Logistics value link objects, including static data and processing records of transportation, warehousing and distribution of raw materials during transportation and equipment finished products during disassembly, transportation and reassembly; Objects of the operation and maintenance value chain include static data and processing records of the operation and maintenance services and value-added services provided to customers by enterprises after they are officially put into use during equipment and production operations.

5. The complex equipment full life cycle value chain optimization system according to claim 1 is characterized by: The multi-objective optimization scheduling planning component is one or any combination of a detection algorithm, a classification algorithm, a prediction algorithm, a scheduling algorithm, and a multi-objective optimization algorithm applied to the design, manufacturing, and operation and maintenance processes; The management of these scheduling planning components includes: data model construction, component construction, data transformation and transmission, parameter configuration, dynamic plug-in and performance analysis.

6. The complex equipment full life cycle value chain optimization system according to claim 1 is characterized by: The value chain optimization unit includes: Cost assessment module, used to collect statistics on the cost indicators of the entire life cycle of all complex equipment in each production project within a fixed period; The quality statistics module collects statistics on the quality indicators of all complex equipment based on the number and severity of defects reported during equipment operation and maintenance; Time delay assessment module, when the equipment product exceeds the delivery time specified in its contract, calculates the equipment delay index; Functional satisfaction evaluation module, which calculates the functional satisfaction index of each complex equipment based on the user's satisfaction evaluation results on each function of the equipment; The optimization scheduling module optimizes the entire life cycle of complex equipment based on cost indicators, quality indicators, delay indicators, and functional satisfaction indicators using a multi-objective optimization model to obtain the Pareto optimal solution set of the value chain optimization plan, and manages and configures the business processes of the entire life cycle of complex equipment based on this optimal solution set.

7. The complex equipment full life cycle value chain optimization system according to claim 6 is characterized by: Single equipment x i The calculation expression of the cost index of the whole life cycle is as follows: C(x i )=c1(x i )+c2(x i )+c3(x i ) Where: C(x i ) indicates equipment x i The cost indicator statistics result, c1(x i ) indicates equipment x i The design cost, c2(x i ) indicates equipment x i The manufacturing cost, c3(x i ) indicates equipment x i The operation and maintenance costs; Single equipment x i The quality index is defined as the overall defect rate level represented by the number of defective parts during equipment operation, and its calculation expression is as follows: Where: Q(x i ) indicates equipment x i The statistical results of the quality indicators are as follows: n is the number of defective parts reported, r j Indicates the importance of the ,th defective component, d j Indicates the defect severity of the ,th defective component; Single equipment x i The delay index is defined as the time delay caused by various factors in the equipment production process, and its calculation expression is as follows: T(x i )=t1(x i )+t2(x i )+t3(x i )-t s (x i ) Where: T(x i ) indicates equipment x i The latency statistic results, t1(x i ) indicates equipment x i The time spent in the design phase, t2(x i ) indicates equipment x i The time spent in the manufacturing process, t3(x i ) indicates equipment x i Time spent in logistics, t s (x i ) indicates equipment x i The delivery time stipulated in the contract; Single equipment x i The calculation expression of the functional satisfaction index is as follows: Where: S(x i ) indicates equipment x i The statistical results of the functional satisfaction index, m is the number of functional items, r k is the importance of the kth function, s k represents the user's satisfaction evaluation on the kth function of the equipment. If this function is improved, s k The value is greater than 1. If this function is weakened for some reason, s k The value is less than 1, if the function meets the equipment operation requirements unchanged s k The value is 1.

8. The complex equipment full life cycle value chain optimization system according to claim 7 is characterized by: The functional expression of the multi-objective optimization model is as follows: Where: w s 、w c 、w q 、w t are the indicator weights of functional satisfaction, cost, quality, and time delay, and N is the number of complex equipment.

9. A method for optimizing the value chain of complex equipment throughout its life cycle, comprising the following steps: S1. Obtain manufacturing project data, demand data, design process data, production data, supply chain data, operation and maintenance service data and product static data, and use the acquired data as value chain operation data; S2. Use big data collection technology to store and manage value chain operation data with equipment manufacturing projects as the primary key, and obtain an integrated data base that adapts to the data characteristics of the industrial field; S3. Construct value link objects based on the business process of the entire life cycle of complex equipment manufacturing; S4. Visualize the value link objects, perform business logic arrangement on the value link objects based on the value chain operation data, and obtain the value chain process network configuration template; S5. Select multi-objective optimization scheduling planning components corresponding to each business process in the entire life cycle of complex equipment; S6. Based on the scheduling planning component of multiple intelligent optimization algorithms, multi-objective optimization is performed on the entire life cycle of complex equipment according to the value chain process network configuration template to obtain the Pareto optimal solution set of the value chain optimization solution; S7. Based on the Pareto optimal solution set of the value chain optimization scheme, manage and configure the business processes of the entire life cycle of complex equipment to achieve optimization of the value chain of the entire life cycle of complex equipment.

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