CN114692486B - Product optimization design method based on user feedback knowledge graph - Google Patents
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Abstract
The invention discloses a product optimization design method based on a user feedback knowledge graph, which belongs to the field of automatic product design and comprises the following steps: collecting design information of a target product, and constructing and forming a knowledge graph for product design; generating various design schemes according to the knowledge graph and the user requirements; fusing the scheme entity nodes converted from the design scheme into a knowledge graph; creating a grading entity node which is mutually related for each scheme entity node, and adding an average score attribute for each scheme entity node; the user scores the design scheme; if the scheme entity node corresponding to the regenerated design scheme exists in the knowledge graph and the average score attribute value corresponding to the design scheme is greater than 0, the design scheme is displayed differently from other design schemes; and sending the design schemes which are scored by the user and have the average score attribute value less than or equal to M to a designer for improvement or deletion or improving the design method.
Description
Technical Field
The invention relates to the field of automatic product design, in particular to a product optimization design method based on a user feedback knowledge graph.
Background
Traditional product design methods include manual design methods and database-based automated product design methods. The method directly designed by the product designer is too dependent on manpower, so that the labor cost is high, the time is long, and the knowledge reserve of the designer may be insufficient or unpaired, so that the design scheme is not strict or wrong. The database automation or semi-automation product design method based on SQL query language has the defects of poor data intuitiveness and visibility, poor relativity among data, poor interpretability of the data to users during data query and low query speed of deep relational data.
(1) In personalized design of clothing, there is a case of producing design scheme by using knowledge patterns, firstly, obtaining fashion trend and clothing engineering technical information of clothing from different data sources, generating clothing design knowledge patterns from public knowledge patterns, then fusing the fashion trend and engineering technical information of clothing with the clothing design knowledge patterns, constructing dynamic clothing design knowledge patterns capable of implementing dynamic changes reflecting fashion trend of clothing and latest development of engineering technology, and finally importing user requirements into the dynamic clothing design knowledge patterns to generate design scheme. At present, the product design method based on the knowledge graph lacks investigation and reflection of user experience information and user satisfaction, the product design process is a unidirectional process of generating a design scheme according to user requirements, user requirements to product design is not formed, a user generates evaluation information aiming at the design scheme, user evaluation is used as feedback information to act on a closed loop of the product design in a reverse direction, and the satisfaction degree of the user to the design scheme is not fully considered, so that the design principle and method cannot be improved or updated according to user feedback. Thus, a more fixed number of designs may be generated for the same user needs, and user dissatisfaction may exist among these designs.
(2) The existing knowledge graph-based product design method generates various design schemes according to user requirements, wherein the design schemes are not ranked in priority or are ranked only according to a certain performance index specified by a product design system, the actual application effect of the design schemes and the satisfaction degree of users are difficult to reflect, and the design schemes are not ranked and screened by combining with historical feedback data of user experience.
Disclosure of Invention
In order to solve the technical problems, the invention provides a product optimization design method based on a user feedback knowledge graph.
In order to solve the technical problems, the invention adopts the following technical scheme:
a product optimization design method based on user feedback knowledge graph, which utilizes a knowledge graph system to generate a design scheme, comprises the following steps:
step one: collecting design information of a target product, and constructing a knowledge graph for product design;
step two: importing the requirements of a user on a target product into a knowledge graph system, and generating various design schemes by the knowledge graph system according to the knowledge graph and the requirements of the user by utilizing a multi-target optimization method based on a genetic algorithm;
step three: the design scheme is converted into scheme entity nodes, and the scheme entity nodes are fused into a knowledge graph, wherein the names of the scheme entity nodes are target product names; adding at least one attribute A for the scheme entity node, wherein the name of the attribute A is the name of a performance index a of a target product, and the value of the attribute A is the requirement value of a user on the performance index a; creating a scoring entity node which is associated with each other for each scheme entity node, and adding an average score attribute for the scoring entity node, wherein the initial value of the average score attribute is 0;
step four: the user scores the design scheme, and the value obtained by arithmetic averaging the scores of the design scheme is used as the attribute value of the average score attribute of the design scheme; the higher the score is, the more satisfied the user is with the design;
step five: when the knowledge graph system generates a design scheme according to the knowledge graph again, if scheme entity nodes corresponding to the regenerated design scheme exist in the knowledge graph and the average score attribute value corresponding to the design scheme is greater than 0, the design scheme is displayed different from other design schemes;
step six: the knowledge graph system sends the design proposal which is scored by the user and has the average score attribute value less than or equal to M to a designer for improvement or deletion, or improves the design method: m is a set qualification score.
Specifically, the product information includes structure information, material information, parameter information, design rules, engineering technology and production process of the target product and the target product component.
Specifically, the design schemes for the differential display are sequentially arranged according to the attribute value of the average score attribute.
Specifically, in the third step, a historical scoring frequency attribute is added for a scoring entity node, the initial value is 0, and when a user scores a design scheme once, the attribute value of the historical scoring frequency attribute of the design scheme is added with 1; step five, if the scheme entity node corresponding to the regenerated design scheme does not exist in the knowledge graph, or the average score attribute value corresponding to the design scheme is 0, or the historical score number attribute value corresponding to the design scheme is 0, the arrangement is performed according to the coincidence degree of the primary performance index of the design scheme; the primary performance index is the most important performance index preset for the design scheme.
In the fifth step, if the scheme entity node corresponding to the regenerated design scheme does not exist in the knowledge graph, adding the scheme entity node and the scoring entity node to the design scheme in the third step.
Compared with the prior art, the invention has the beneficial technical effects that:
1. the invention adopts an automatic product design method based on the knowledge graph, is oriented to intelligent design of industrial products, can extract relevant information of target products in real time from the Internet, a public knowledge base and an enterprise knowledge base, and forms the knowledge graph for product design through data processing, thereby ensuring completeness, real-time performance, visibility, relevance, data query and rapidity of design scheme generation of knowledge; the method is characterized in that a closed loop of 'user requirements to product design, user generation of evaluation information aiming at design schemes and user evaluation as feedback information reversely acting on the product design' is constructed, a link of evaluation feedback of the user on the design schemes generated by the knowledge graph system is added in a product design method, the user can score the corresponding design schemes according to satisfaction degree of the design schemes, the design schemes and the corresponding scoring information are added into the knowledge graph, the scoring information of the schemes is presented to a designer, and the designer can adjust or optimize design principles and methods of the system according to the scoring information.
2. Aiming at the problem (2) in the background technology, the invention sorts the design schemes according to the historical average score from high to low, the design schemes with high average score are presented preferentially, the design schemes with low average score are presented in a later sequence, and the design schemes with lower average score are presented to a designer to decide whether to modify or delete the design schemes or optimize the design method.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
The product design of the manufacturing industry at the present stage is mainly designed by a designer in a semi-automatic way by combining database technology, and the mode of acquiring data information in product design and production is mainly in the mode of inquiring in a traditional database by adopting manual structured inquiry language (Structured Query Language, SQL). The traditional database has no data form forming a graph structure, and has the defects of poor data intuitiveness and visibility, poor relativity among data, poor interpretability of the data to a user during data query and low query speed of deep relation data.
The knowledge graph is taken as one of important foundations for realizing the intelligent machine cognition, a large knowledge network for describing concepts, entities and relations thereof in a structured and graphical form is adopted, the information is expressed into a form which is closer to human cognition, the capability of better organizing, managing and understanding mass information is provided, and a new method is provided for intelligent design of manufacturing industry products. At present, the knowledge graph has realized preliminary application practice in the fields of intelligent manufacture, epidemic prevention and control, intelligent finance and the like, has gradually achieved effects, and has important significance for promoting transformation and upgrading under 'knowledge driving' of manufacturing industry and other related industries and enterprises. The data in the knowledge graph generally has a definite relation chain, so that the mutual relation among the entities can be used for inquiring more conveniently based on the knowledge graph, and the speed of inquiring by using the knowledge graph is faster than that of the traditional database when inquiring the data with the deep relation.
The invention is mainly oriented to intelligent design and optimization of industrial products, such as the design of lithium batteries, transformers and the like. The main innovation point is the construction of a closed loop design flow from user requirements to product design, evaluation information generated by a user aiming at a design scheme, and user evaluation as feedback information which is reversely acted on the product design, and the product optimization design method specifically comprises the following scheme.
(1) Information such as a building component of a target product, a product and a structure, a material, a parameter, a design rule, an engineering technology, a production process and the like of the product are obtained in real time from the Internet, a public knowledge base and an enterprise knowledge base, and are converted into a knowledge representation form of a triplet through data processing steps such as entity identification, semantic annotation, entity set expansion, relation extraction and the like, so that a knowledge graph is constructed and formed, and the detailed process can refer to documents:
[1] yang Yuji, xu, hu Guwei, tongmei, zhang Peng, zheng Li an accurate and efficient method for constructing a domain knowledge map [ J ]. Software journal, 2018,29 (10): 2931-2947;
[2] hu Fanghuai researching [ D ]. Huadong university of 3, 2015 based on Chinese knowledge graph construction method of various data sources;
the constructed knowledge graph can acquire the design requirement of a user on the product, select proper component members, structure and process of the designed product and the like according to the user requirement and the design method, and finally generate the design scheme of the target product.
(2) The method is characterized in that requirements of a user on products are led into a knowledge graph system, according to the performance requirements of the user on the products and the established design principle and method, the knowledge graph system takes the performance requirements as optimization targets in combination with national standards, industry standards and the like, the design principle and the standards as constraint conditions, various design schemes are generated through a multi-target optimization method based on a genetic algorithm, and the specific process of the multi-target optimization method based on the genetic algorithm can refer to the literature: [3] zhang Shuang knowledge engineering for product design several key problem studies [ D ]. University of northeast, 2014.
And converting each generated design scheme into a new scheme entity node with the type of design scheme, and fusing the new scheme entity node into the original knowledge graph, wherein the name of the scheme entity node is the name of the target product, and the attribute name and the attribute value of the scheme entity node are the performance index name of the target product and the requirement value of the user on the performance index respectively.
And then creating scoring entity nodes with types and names of 'user history scoring' for each scheme entity node, and creating association relations between the scheme entity nodes and the scoring entity nodes. And creating an average score attribute and a historical score number attribute for the scoring entity node, wherein the initial attribute values of the average score attribute and the historical score number attribute are 0.
(3) The knowledge-graph system provides the user with an opportunity to score the design, and the user can score the design according to the high-to-low selection of 10 points to 1 point for satisfaction with the design.
The knowledge graph system obtains the scores of the users, obtains new average scores by adopting a method of calculating arithmetic average values, and updates the attribute values of the corresponding average score attributes into the new average scores. And the user scores the design proposal once every time, and the attribute value of the corresponding historical scoring times attribute is increased by 1.
(4) When the knowledge graph system operates again to generate multiple designs, if the generated design is the design generated by the system, namely, the scheme entity node of the design already exists in the knowledge graph, the attribute value of the average score attribute corresponding to the design is queried, if the attribute value is greater than 0, the designs are marked as 'scheme has history score' to be highlighted, and the designs are orderly arranged from large to small according to the attribute value of the average score attribute. If the generated design scheme is a design scheme which is not generated by the system, or the average score attribute value corresponding to the design scheme is 0, or the attribute value of the historical scoring frequency attribute is 0, the design scheme is marked as 'scheme no historical scoring', and the design schemes are arranged according to the coincidence degree of the primary performance index values. And (3) creating corresponding entity nodes and attributes of the design scheme in the knowledge graph according to the method in the step (2) for the initially generated design scheme. Finally, the knowledge-graph system provides scoring opportunities for the design scheme for the user again.
The conformity refers to the ratio of the primary performance index in the design scheme to the primary performance index required by the user.
In the knowledge graph, if a design scheme is scored by a user and the attribute value of the average score attribute is less than or equal to 5 scores, the design scheme is sent to a designer, and the designer can improve or delete the scheme or improve and optimize the design method of the system according to specific information of the design scheme.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a single embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to specific embodiments, and that the embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
Claims (5)
1. A product optimization design method based on user feedback knowledge graph, which utilizes a knowledge graph system to generate a design scheme, comprises the following steps:
step one: collecting design information of a target product, and constructing a knowledge graph for product design;
step two: importing the requirements of a user on a target product into a knowledge graph system, and generating various design schemes by the knowledge graph system according to the knowledge graph and the requirements of the user by utilizing a multi-target optimization method based on a genetic algorithm;
step three: the design scheme is converted into scheme entity nodes, and the scheme entity nodes are fused into a knowledge graph, wherein the names of the scheme entity nodes are target product names; adding at least one attribute A for the scheme entity node, wherein the name of the attribute A is the name of a performance index a of a target product, and the value of the attribute A is the requirement value of a user on the performance index a; creating a scoring entity node which is associated with each other for each scheme entity node, and adding an average score attribute for the scoring entity node, wherein the initial value of the average score attribute is 0;
step four: the user scores the design scheme, and the value obtained by arithmetic averaging the scores of the design scheme is used as the attribute value of the average score attribute of the design scheme; the higher the score is, the more satisfied the user is with the design;
step five: when the knowledge graph system generates a design scheme according to the knowledge graph again, if scheme entity nodes corresponding to the regenerated design scheme exist in the knowledge graph and the average score attribute value corresponding to the design scheme is greater than 0, the design scheme is displayed different from other design schemes;
step six: the knowledge graph system sends the design proposal which is scored by the user and has the average score attribute value less than or equal to M to a designer for improvement or deletion, or improves the design method: m is a set qualification score.
2. The method for optimizing product design based on user feedback knowledge graph according to claim 1, wherein the method is characterized in that: the product information comprises structure information, material information, parameter information, design rules, engineering technology and production technology of the target product and the target product component.
3. The method for optimizing product design based on user feedback knowledge graph according to claim 1, wherein the method is characterized in that: and sequentially arranging the design schemes for distinguishing display according to the attribute value of the average score attribute.
4. The method for optimizing product design based on user feedback knowledge graph according to claim 1, wherein the method is characterized in that: step three, adding a historical scoring frequency attribute for the scoring entity node, wherein the initial value is 0, and the attribute value of the historical scoring frequency attribute of the design scheme is added with 1 when the user scores the design scheme once; step five, if the scheme entity node corresponding to the regenerated design scheme does not exist in the knowledge graph, or the average score attribute value corresponding to the design scheme is 0, or the historical score number attribute value corresponding to the design scheme is 0, the arrangement is performed according to the coincidence degree of the primary performance index of the design scheme; the primary performance index is the most important performance index preset for the design scheme.
5. The method for optimizing product design based on user feedback knowledge graph according to claim 4, wherein the method comprises the following steps: and step five, if the scheme entity node corresponding to the regenerated design scheme does not exist in the knowledge graph, adding the scheme entity node and the grading entity node to the design scheme in a mode of step three.
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