CN117172410A - Product service full life cycle value chain optimization system and method - Google Patents
Product service full life cycle value chain optimization system and method Download PDFInfo
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Abstract
The invention discloses a system and a method for optimizing a value chain of a full life cycle of product service, belonging to the technical field of intelligent manufacturing, wherein the system comprises a data management module, a flow planning module, an algorithm supporting module and a value chain optimizing module; obtaining a plurality of data sources based on the region space as a clustering basis through a data management module; obtaining a preconfigured service flow network structure based on adding and deleting, editing and storing operation of each data source on the service flow network node object through a flow planning module; the data mining algorithm component required by multi-objective optimization in the whole life cycle of product service is provided through the algorithm supporting module; and carrying out multi-objective optimization on the whole life cycle of the product service through a value chain optimization module to obtain a pareto solution set of the business process optimization scheme. The method is based on the system for optimizing the full life cycle value chain of the product service. The invention solves the problem of low universality of the full life cycle value chain optimization scheme of the existing product service.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a system and a method for optimizing a full life cycle value chain of product service.
Background
The iterative upgrading of the product service full life cycle value chain is a main source of value increment of service type manufacturing enterprises, business flow data are obtained from the product service value chain by utilizing intelligent perception and big data technology, and are optimized by a data mining technology, so that an optimization result is fed back to a corresponding value increment link of the product service full life cycle, the optimization and value increment of the product service value chain are realized, and the service transformation of the traditional manufacturing enterprises is promoted.
The existing optimization method of the product service value chain has the following limitations: at present, a product service value chain optimization method combining big data thinking and related technologies is hardly used, most of the methods are mainly qualitative analysis, and are lack of combination with a large amount of business data generated by a product full life cycle value chain, and research of a quantitative method is carried out; most of the current methods focus on partial links of the value chain, and each link of the whole life cycle of the product service has the problem of data island, so that a large amount of data of the whole life cycle of the product service cannot be deeply analyzed and mined in aspects of business flow, time, space and the like, and the whole optimization of the value chain is difficult to realize; the existing optimization method is a product full life cycle value chain development optimization research aiming at a specific enterprise, and a general optimization method and a general optimization scheme applicable to the product full life cycle value chains of all manufacturing enterprises are not formed yet.
Disclosure of Invention
In order to overcome the defects in the prior art, the system and the method for optimizing the full life cycle value chain of the product service provided by the invention are used for constructing business process network nodes by carrying out regional spatial clustering on product service data, obtaining a pre-configured business process network structure by operating on the basis of the business network process nodes, carrying out multi-objective optimization on the full life cycle of the product service based on the mined data and the business process network structure, and solving the problem of low universality of the prior full life cycle value chain optimizing scheme of the product service.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
in one aspect, the invention provides a product service full life cycle value chain optimization system, comprising:
the data management module is used for storing and managing user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data by taking a geographic position as a slicing key to obtain a plurality of data sources by taking a regional space as a clustering basis;
the flow planning module is used for constructing a business flow network node object, carrying out visual control display on the business flow network node object, and adding, deleting, editing and storing the business flow network node object based on each data source to obtain a preconfigured business flow network structure;
the algorithm supporting module is used for providing a data mining algorithm component required by multi-objective optimization of the whole life cycle of the product service and managing the data mining algorithm component;
the value chain optimization module is used for carrying out multi-objective optimization on the whole life cycle of the product service according to the pre-configured business process network structure based on the selected data mining algorithm component to obtain the pareto solution set of the business process optimization scheme.
The beneficial effects of the invention are as follows: according to the full life cycle value chain optimizing system for product service, the data management module is used for realizing a plurality of data sources divided by taking regional space as a clustering basis, and the data sources are used as product service data to provide data support for a process planning module to construct a process network node object; the method comprises the steps of performing visual control, adding and deleting, editing and storing operations on business process network node objects to obtain a pre-configured business process network structure, and constructing a business process network capable of providing a full-period value chain of product service for different enterprises; the algorithm support module is used for providing data mining algorithm components required by value chain optimization iteration for different enterprises according to actual conditions; and through the value chain optimization module, the multi-objective optimization and increment are carried out on the whole life cycle of the enterprise product service according to the data mining algorithm component and the preconfigured service flow network structure selected by the actual demands of the enterprise, so that the general value chain optimization and increment of the whole life cycle of the enterprise product service are realized.
Further, the data management module adopts a relational database management system and stores and manages the user environment data, the equipment sensing data, the man-machine interaction data, the operation and maintenance service data and the product static data in a distributed architecture.
The beneficial effects of adopting the further scheme are as follows: the distributed relational database is more suitable for local control of data with geographic distribution characteristics, can reduce the communication cost of the related data of the whole life cycle value chain of product service, improves the response speed, greatly reduces the information transmission quantity on a network, ensures better safety of local data and is easier to expand.
Further, the data management module uses the geographic position as a slicing key to perform database separation on user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data, and a plurality of data sources based on regional space as clustering basis are obtained.
The beneficial effects of adopting the further scheme are as follows: the geographic position is used as a slicing key to separate user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data into libraries, namely, the method is suitable for a using mechanism of a distributed relational database, and a basis is provided for optimizing value chains such as a production service flow, a sales service flow, a logistics service flow, an operation and maintenance service flow, a recovery service flow and the like in the product service process.
Further, the flow planning module includes:
the network node construction sub-module is used for constructing a business process network node object according to the business process of the product service full life cycle;
the visual control sub-module is used for displaying the business process network node object through the drag-type visual control, wherein the business process network node object is a static parameter and a data processing record related to the business process;
and the network node operation submodule is used for adding and deleting, editing and storing the business process network node objects based on each data source to obtain a preconfigured business process network structure.
The beneficial effects of adopting the further scheme are as follows: the method comprises the steps of constructing business process network node objects based on the requirements of business processes in the product service full life cycle of each enterprise, performing visual control display on the business process network node objects, increasing, decreasing, editing and storing the business process network node objects on the basis of establishing association with data sources based on regional space as clustering basis, and finally obtaining a preconfigured business process network structure adapting to the actual requirements of the enterprises, so that all enterprises can construct the preconfigured business process network structure meeting the actual requirements and conditions of the enterprises.
Further, the business process network node object covers a product service full life cycle, and the categories include:
a production business process network node object unit for storing static parameters and data processing records including raw material purchase, part production and product assembly related to product manufacture;
the sales business process network node object unit is used for storing static parameters and data processing records including price calibration, sales volume prediction, marketing and advertisement delivery in the process of providing products and services for clients by enterprises;
the logistics business process network node object unit is used for storing static parameters and data processing records including transportation, storage and distribution of the put-in products in the flowing process of the regional spaces;
the operation and maintenance business process network node object unit is used for storing static parameters and data processing records of operation and maintenance services and value-added services provided for customers by enterprises after products are sold;
and the recovery business process network node object unit is used for storing static parameters and data processing records including purchase, disassembly and processing in the recovery process of the waste products by enterprises.
The beneficial effects of adopting the further scheme are as follows: the business process network node object provided by the invention covers the whole life cycle of product service, and respectively relates to a production business process, a sales business process, a logistics business process, an operation and maintenance business process and a recovery business process, so that the business process network node object can be operated from all directions aiming at the product service requirement of enterprises, and a foundation is provided for obtaining a proper pre-configuration business process network structure.
Further, the data mining algorithm component is one or any combination of an optimizing algorithm, a predicting algorithm, a feature extraction algorithm and a clustering algorithm; the management content of the data mining algorithm component comprises component uploading, component editing, remote calling, local calling, parameter configuration, dynamic plug-in, principle description and performance analysis.
The beneficial effects of adopting the further scheme are as follows: the data mining algorithm component provided by the invention can be used for developing matched algorithm components according to business requirements of enterprises, can be used for remotely calling existing algorithm components in a public algorithm component library, can adapt to the optimization requirements of the full-period value chain of the actual product service of different enterprises, provides a data mining basis for multi-objective optimization of the full life cycle of the product service, and performs overall and local double optimization.
Further, the value chain optimization module includes:
the economic benefit optimization module is used for counting the economic benefit of the whole life cycle of the product service through the economic benefit target model to obtain an economic benefit counting result;
the calculation expression of the economic benefit target model is as follows:
f 1 (x)＝n·P 1max -(p 1 (x)+p 2 (x)+p 3 (x)+p 4 (x))
wherein f 1 (x) Represents the statistical result of economic benefit, n represents the sales of the product, and P 1max Representing the highest sales price of the product, p 1 (x) Representing the cost of the sales link, p 2 (x) Represents the cost of a logistics link, p 3 (x) Representing the cost of operation and maintenance links, p 4 (x) Representing the cost of the recovery link;
the service duration optimization module is used for counting the service duration of the product service full life cycle through the service duration target model to obtain a service duration counting result;
the calculation expression of the service duration target model is as follows:
f 2 (x)＝T 1 (x)+t 2 (x)+t 3 (x)+t 4 (x)+t 5 (x)
wherein f 2 (x) Representing the statistical result of service duration, t 1 (x) Time length, t, of delivery service 2 (x) Indicating the duration of installation of the service, t 3 (x) Indicating the duration of maintenance service, t 4 (x) Representing the duration of the operation and maintenance service, t 5 (x represents the duration of the reclamation service;
the environment protection optimizing module is used for carrying out statistics on the environment protection of the whole life cycle of the product service through the environment protection target model to obtain an environment protection statistical result;
the calculation expression of the environment protection target model is as follows:
wherein f a (x) Representing environmental protection statistics, g 1 (x) Represents the emission amount g of the traffic greenhouse gases generated in the transportation process from the production place to the sales place 2 (x) Represents the emission quantity g of traffic greenhouse gases generated in the process of providing service for enterprises in operation and maintenance links 3 (x) Indicating that the product was sold but not recovered effectivelyGreenhouse gas emission when the product is scrapped;
the multi-objective optimization module is used for optimizing the full life cycle of the product service by utilizing the multi-objective optimization model based on the economic benefit statistical result, the service duration statistical result and the environmental protection statistical result to obtain a pareto solution set of the business process optimization scheme;
the computational expression of the multi-objective optimization model is as follows:
minF(x)＝{f 1 (x),f 2 (x),f a (x)}
wherein min represents the minimum, F (x) represents the multi-objective optimization set, and minF (x) represents the multi-objective optimization model.
The beneficial effects of adopting the further scheme are as follows: the multi-objective optimization model constructed by the value chain optimization module provided by the invention takes into account the time dependency relationship and the space restriction relationship of the business process, the pareto solution set of the business process optimization scheme can be obtained after solving, and the value increment of the business process of each link and the value increment of the whole product service value chain can be realized by adopting the optimization scheme in the solution set.
On the other hand, the invention also provides an optimization method based on the product service full life cycle value chain optimization system, which is characterized by comprising the following steps of;
s1, acquiring user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data, and taking the acquired data as product service data;
s2, storing and managing product service data by taking the geographic position as a slicing key to obtain a plurality of data sources taking the regional space as a clustering basis;
s3, constructing a business process network node object according to the business process of the product service full life cycle;
s4, performing visual control display on the business process network node object, and performing adding, deleting, editing and storing operations on the business process network node object based on each data source to obtain a preconfigured business process network structure;
s5, selecting a data mining algorithm component required by multi-objective optimization in a product service full life cycle;
s6, based on the selected data mining algorithm component, carrying out multi-objective optimization on the whole life cycle of the product service according to a pre-configured business process network structure to obtain a pareto solution set of a business process optimization scheme;
s7, managing and configuring the product service full life cycle business process based on the pareto solution set of the business process optimization scheme, and optimizing the product service full life cycle value chain.
The beneficial effects of the invention are as follows: the optimization method based on the product service full life cycle value chain optimization system provided by the invention has the advantages that the distributed database is used for storing product service data of different areas, the draggable space is used for realizing the visualization of the product service network node object, the data mining algorithm is used for carrying out integral and local optimization on the product service value chain, and the invention can carry out iterative optimization on the product service value chain of a manufacturing enterprise; compared with other methods, the method can carry out integral optimization on each link of the full life cycle of the product service, the optimization process considers a plurality of conflict targets, and the method can be adapted to various manufacturing enterprises by adopting an object-oriented thought, so that the enterprises can complete value chain optimization iteration according to the actual demands of the enterprises.
Other advantages that are also present with respect to the present invention will be more detailed in the following examples.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a full lifecycle value chain optimization system for product services in accordance with embodiment 1 of the present invention.
FIG. 2 is a flow chart of steps of an optimization method based on a full life cycle value chain optimization system for product services according to embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Example 1
As shown in fig. 1, in one embodiment of the present invention, the present invention provides a product service full lifecycle value chain optimization system, comprising:
the data management module is used for storing and managing user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data by taking a geographic position as a slicing key to obtain a plurality of data sources by taking a regional space as a clustering basis;
the data management module adopts a relational database management system and stores and manages the user environment data, the equipment sensing data, the man-machine interaction data, the operation and maintenance service data and the product static data in a distributed architecture.
The data management module takes the geographic position as a slicing key to perform database separation on user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data, and a plurality of data sources taking regional space as clustering basis are obtained.
The flow planning module is used for constructing a business flow network node object, carrying out visual control display on the business flow network node object, and adding, deleting, editing and storing the business flow network node object based on each data source to obtain a preconfigured business flow network structure;
the flow planning module comprises:
the network node construction sub-module is used for constructing a business process network node object according to the business process of the product service full life cycle;
the business process network node object covers the product service full life cycle, and the categories comprise:
a production business process network node object unit for storing static parameters and data processing records including raw material purchase, part production and product assembly related to product manufacture;
the sales business process network node object unit is used for storing static parameters and data processing records including price calibration, sales volume prediction, marketing and advertisement delivery in the process of providing products and services for clients by enterprises;
the logistics business process network node object unit is used for storing static parameters and data processing records including transportation, storage and distribution of the put-in products in the flowing process of the regional spaces;
the operation and maintenance business process network node object unit is used for storing static parameters and data processing records of operation and maintenance services and value-added services provided for customers by enterprises after products are sold;
and the recovery business process network node object unit is used for storing static parameters and data processing records including purchase, disassembly and processing in the recovery process of the waste products by enterprises.
The visual control sub-module is used for displaying the business process network node object through the drag-type visual control, wherein the business process network node object is a static parameter and a data processing record related to the business process;
and the network node operation submodule is used for adding and deleting, editing and storing the business process network node objects based on each data source to obtain a preconfigured business process network structure.
The algorithm supporting module is used for providing a data mining algorithm component required by multi-objective optimization of the whole life cycle of the product service and managing the data mining algorithm component;
the data mining algorithm component is one or any combination of an optimizing algorithm, a predicting algorithm, a feature extraction algorithm and a clustering algorithm; the management content of the data mining algorithm component comprises component uploading, component editing, remote calling, local calling, parameter configuration, dynamic plug-in, principle description and performance analysis.
The value chain optimization module is used for carrying out multi-objective optimization on the whole life cycle of the product service according to the pre-configured business process network structure based on the selected data mining algorithm component to obtain the pareto solution set of the business process optimization scheme.
The value chain optimization module comprises:
the economic benefit optimization module is used for counting the economic benefit of the whole life cycle of the product service through the economic benefit target model to obtain an economic benefit counting result;
the calculation expression of the economic benefit target model is as follows:
wherein f 1 (x) Represents the statistical result of economic benefit, n represents the sales of the product, and P 1max Representing the highest sales price of the product, p 1 (x) Representing the cost of the sales link, p 2 (x) Represents the cost of a logistics link, p 3 (x) Representing the cost of operation and maintenance links, p 4 (x) Representing the cost of the recovery link;
the service duration optimization module is used for counting the service duration of the product service full life cycle through the service duration target model to obtain a service duration counting result;
the calculation expression of the service duration target model is as follows:
f 2 (x)＝t 1 (x)+t 2 (x)+t 3 (x)+t 4 (x)+t 5 (x)
wherein f 2 (x) Representing the statistical result of service duration, t 1 (x) Time length, t, of delivery service 2 (x) Indicating the duration of installation of the service, t 3 (x) Indicating the duration of maintenance service, t 4 (x) Representing the duration of the operation and maintenance service, t 5 (x) Indicating a duration of the recovery service;
the environment protection optimizing module is used for carrying out statistics on the environment protection of the whole life cycle of the product service through the environment protection target model to obtain an environment protection statistical result;
the calculation expression of the environment protection target model is as follows:
wherein f a (x) Representing environmental protection statistics, g 1 (x) Represents the emission amount g of the traffic greenhouse gases generated in the transportation process from the production place to the sales place 2 (x) Represents the emission quantity g of traffic greenhouse gases generated in the process of providing service for enterprises in operation and maintenance links 3 (x) Indicating greenhouse gas emissions when sold but not effectively recovered products are scrapped;
the multi-objective optimization module is used for optimizing the full life cycle of the product service by utilizing the multi-objective optimization model based on the economic benefit statistical result, the service duration statistical result and the environmental protection statistical result to obtain a pareto solution set of the business process optimization scheme;
the computational expression of the multi-objective optimization model is as follows:
minF(x)＝{f 1 (x),f 2 (x),f a (x)}
wherein min represents the minimum, F (x) represents the multi-objective optimization set, and minF (x) represents the multi-objective optimization model.
Example 2
As shown in fig. 2, in another embodiment of the present invention, on the basis of embodiment 1, the present invention further provides an optimization method based on a product service full life cycle value chain optimization system, which includes the following steps:
s1, acquiring user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data, and taking the acquired data as product service data;
s2, storing and managing product service data by taking the geographic position as a slicing key to obtain a plurality of data sources taking the regional space as a clustering basis;
the step S2 comprises the following steps:
s21, analyzing software engineering requirements according to service information, establishing a UML model diagram, and establishing a data table according to a software engineering analysis result and the constructed model diagram, wherein the model diagram comprises a use diagram, an ER diagram, a class diagram and the like;
s22, establishing a correlation database based on the data table, and setting a database management system for a server of the correlation database; the database management system is used for performing operations such as adding, deleting, changing, checking and the like on the product service data in the associated database;
s23, storing the product service data into a related database by taking the geographic position information as a slicing key, and managing the product service data by utilizing the related database to obtain a plurality of data sources based on the regional space as a clustering basis.
S3, constructing a business process network node object according to the business process of the product service full life cycle; the process of the constructed business process network node object determines the static parameters, input and output types and data processing records of the corresponding product service links;
s4, performing visual control display on the business process network node object, and performing adding, deleting, editing and storing operations on the business process network node object based on each data source to obtain a preconfigured business process network structure; the method comprises the steps that the actual requirements of enterprises can be obtained based on various data sources, operation is carried out on business process network data node objects according to the actual requirements of the enterprises, and a pre-configured business process network structure is obtained; the pre-configured business process network structure establishes the association between the business process and the data space;
s5, selecting a data mining algorithm component required by multi-objective optimization in a product service full life cycle; the data mining algorithm component can be a private algorithm component formed by the data mining algorithm which is developed and written by an enterprise, or can be a public algorithm component which is called remotely in a public algorithm library; enterprises can configure the business process network structure and the data mining algorithm according to the actual business by themselves, and the business process network structure and the data mining algorithm are respectively presented on a front-end interface in the form of visualized business process network node objects and data mining algorithm components; the front end of the system related to the method adopts a Web architecture, and the interface is friendly and attractive; the rear end adopts a Springboot framework, and Java language is used to realize information management, data processing, algorithm calling and reliable operation in the whole life cycle value chain optimization process of the product service.
S6, based on the selected data mining algorithm component, carrying out multi-objective optimization on the whole life cycle of the product service according to a pre-configured business process network structure to obtain a pareto solution set of a business process optimization scheme; the pareto solution set of the business process optimization scheme is the closest optimization scheme of the business process expectation of the enterprise.
S7, managing and configuring the product service full life cycle business process based on the pareto solution set of the business process optimization scheme, and optimizing the product service full life cycle value chain.
The pareto based on the business process optimization scheme solves the problem of configuring various resource activities in the whole life cycle business process of the product service, generates business work orders and integrated online management of each stage of the product service, and realizes the value-added of the product service value chain.
Although the present invention is not limited to the specific embodiments, the protection scope of the present invention may be further refined in terms of business process division, or differently configured according to the products of the enterprises; when more advanced algorithms are needed to realize enterprise data analysis, the algorithm components can be updated, so that the algorithm components more meet the actual demands of enterprises. Any person skilled in the art can easily think of changes or substitutions within the technical scope of the present disclosure, and the present disclosure is intended to be covered by the present disclosure.
Claims (8)
1. A product service full lifecycle value chain optimization system, comprising:
the data management module is used for storing and managing user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data by taking a geographic position as a slicing key to obtain a plurality of data sources by taking a regional space as a clustering basis;
the flow planning module is used for constructing a business flow network node object, carrying out visual control display on the business flow network node object, and adding, deleting, editing and storing the business flow network node object based on each data source to obtain a preconfigured business flow network structure;
the algorithm supporting module is used for providing a data mining algorithm component required by multi-objective optimization of the whole life cycle of the product service and managing the data mining algorithm component;
the value chain optimization module is used for carrying out multi-objective optimization on the whole life cycle of the product service according to the pre-configured business process network structure based on the selected data mining algorithm component to obtain the pareto solution set of the business process optimization scheme.
2. The full life cycle value chain optimization system of claim 1, wherein the data management module employs a relational database management system and stores and manages the user environment data, device sensor data, human-machine interaction data, operation and maintenance service data, and product static data in a distributed architecture.
3. The full life cycle value chain optimization system of claim 2, wherein the data management module uses geographic locations as slicing keys to perform database separation on user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data to obtain a plurality of data sources based on regional space as clustering basis.
4. The product service full lifecycle value chain optimization system of claim 1, wherein the flow planning module comprises:
the network node construction sub-module is used for constructing a business process network node object according to the business process of the product service full life cycle;
the visual control sub-module is used for displaying the business process network node object through the drag-type visual control, wherein the business process network node object is a static parameter and a data processing record related to the business process;
and the network node operation submodule is used for adding and deleting, editing and storing the business process network node objects based on each data source to obtain a preconfigured business process network structure.
5. The product service full life cycle value chain optimization system of claim 4, wherein the business process network node object covers a product service full life cycle, the categories of which include:
a production business process network node object unit for storing static parameters and data processing records including raw material purchase, part production and product assembly related to product manufacture;
the sales business process network node object unit is used for storing static parameters and data processing records including price calibration, sales volume prediction, marketing and advertisement delivery in the process of providing products and services for clients by enterprises;
the logistics business process network node object unit is used for storing static parameters and data processing records including transportation, storage and distribution of the put-in products in the flowing process of the regional spaces;
the operation and maintenance business process network node object unit is used for storing static parameters and data processing records of operation and maintenance services and value-added services provided for customers by enterprises after products are sold;
and the recovery business process network node object unit is used for storing static parameters and data processing records including purchase, disassembly and processing in the recovery process of the waste products by enterprises.
6. The full life cycle value chain optimization system of claim 5, wherein the data mining algorithm component is one or any combination of a optimizing algorithm, a predicting algorithm, a feature extraction algorithm and a clustering algorithm; the management content of the data mining algorithm component comprises component uploading, component editing, remote calling, local calling, parameter configuration, dynamic plug-in, principle description and performance analysis.
7. The product service full lifecycle value chain optimization system of claim 6, wherein the value chain optimization module comprises:
the economic benefit optimization module is used for counting the economic benefit of the whole life cycle of the product service through the economic benefit target model to obtain an economic benefit counting result;
the calculation expression of the economic benefit target model is as follows:
wherein f 1 (x) Represents the statistical result of economic benefit, n represents the sales of the product,representing the highest sales price of the product, p 1 (x) Representing the cost of the sales link, p 2 (x) Represents the cost of a logistics link, p 3 (x) Representing the cost of operation and maintenance links, p 4 (x) Representing the cost of the recovery link;
the service duration optimization module is used for counting the service duration of the product service full life cycle through the service duration target model to obtain a service duration counting result;
the calculation expression of the service duration target model is as follows:
f 2 (x)＝t 1 (x)+t 2 (x)+t 3 (x)+t 4 (x)+t 5 (x)
wherein f 2 (x) Representing the statistical result of service duration, t 1 (x) Time length, t, of delivery service 2 (x) Indicating the duration of installation of the service, t 3 (x) Indicating the duration of maintenance service, t 4 (x) Representing the duration of the operation and maintenance service, t 5 (x) Indicating a duration of the recovery service;
the environment protection optimizing module is used for carrying out statistics on the environment protection of the whole life cycle of the product service through the environment protection target model to obtain an environment protection statistical result;
the calculation expression of the environment protection target model is as follows:
wherein f a (x) Representing environmental protection statistics, g 1 (x) Represents the emission amount g of the traffic greenhouse gases generated in the transportation process from the production place to the sales place 2 (x) Represents the emission quantity g of traffic greenhouse gases generated in the process of providing service for enterprises in operation and maintenance links 3 (x) Indicating greenhouse gas emissions when sold but not effectively recovered products are scrapped;
the multi-objective optimization module is used for optimizing the full life cycle of the product service by utilizing the multi-objective optimization model based on the economic benefit statistical result, the service duration statistical result and the environmental protection statistical result to obtain a pareto solution set of the business process optimization scheme;
the computational expression of the multi-objective optimization model is as follows:
minF(x)＝{f 1 (x)，f 2 (x)，f a (x)}
wherein min represents the minimum, F (x) represents the multi-objective optimization set, and minF (x) represents the multi-objective optimization model.
8. An optimization method based on the product service full life cycle value chain optimization system of any one of claims 1-7, comprising the following steps:
s1, acquiring user environment data, equipment sensing data, man-machine interaction data, operation and maintenance service data and product static data, and taking the acquired data as product service data;
s2, storing and managing product service data by taking the geographic position as a slicing key to obtain a plurality of data sources taking the regional space as a clustering basis;
s3, constructing a business process network node object according to the business process of the product service full life cycle;
s4, performing visual control display on the business process network node object, and performing adding, deleting, editing and storing operations on the business process network node object based on each data source to obtain a preconfigured business process network structure;
s5, selecting a data mining algorithm component required by multi-objective optimization in a product service full life cycle;
s6, based on the selected data mining algorithm component, carrying out multi-objective optimization on the whole life cycle of the product service according to a pre-configured business process network structure to obtain a pareto solution set of a business process optimization scheme;
s7, managing and configuring the product service full life cycle business process based on the pareto solution set of the business process optimization scheme, and optimizing the product service full life cycle value chain.
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