WO2023227012A1 - Procédé et appareil de traitement de données de produit, et support de stockage - Google Patents

Procédé et appareil de traitement de données de produit, et support de stockage Download PDF

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Publication number
WO2023227012A1
WO2023227012A1 PCT/CN2023/095967 CN2023095967W WO2023227012A1 WO 2023227012 A1 WO2023227012 A1 WO 2023227012A1 CN 2023095967 W CN2023095967 W CN 2023095967W WO 2023227012 A1 WO2023227012 A1 WO 2023227012A1
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Prior art keywords
data
product
feature information
recommended content
processing
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PCT/CN2023/095967
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English (en)
Chinese (zh)
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鲁效平
陈录城
高亚琼
高尚
景大智
王超
王玉梅
于晓义
Original Assignee
卡奥斯工业智能研究院(青岛)有限公司
卡奥斯物联科技股份有限公司
海尔数字科技(青岛)有限公司
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Publication of WO2023227012A1 publication Critical patent/WO2023227012A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/904—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/906—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present application relates to the field of data processing technology, and in particular to a product data processing method, device and storage medium.
  • This application provides a product data processing method, device and storage medium to solve the problems existing in the existing technology.
  • this application provides a product data processing method, including:
  • the fusion processing method includes: sequential decision-making multi-modal deep neural network or distributed parallel computing;
  • it also includes:
  • the user requirements are proposed by the user based on the visually output product data feature information.
  • determining recommended content according to the user needs and visually outputting the recommended content includes:
  • the plurality of recommended contents are sorted to obtain sorted recommended content, including:
  • the plurality of recommended contents are sorted in order from high to low matching degrees to obtain sorted recommended contents.
  • it also includes:
  • the product-related links include at least one of product design link, product supply link, product marketing link, product manufacturing link, product logistics link and product service design link.
  • the product element data includes at least one of interactive customization data, precision marketing data, collaborative research and development data, collaborative procurement data, supply chain data, smart logistics data, and smart service data.
  • this application provides a product data processing device, including:
  • the acquisition module is used to obtain the product element data to be processed
  • the scattered point data set in the data set is obtained to obtain a data set composed of different types of data; data fusion processing is performed according to the data set to obtain product data feature information; the fusion processing method includes: sequential decision-making multi-modal deep neural network or distributed parallel computing;
  • An output module is used to construct object particles of data streams based on the product data feature information; extract frequent patterns between the data streams and obtain a data set model based on data resource objects to visually output the product data feature information.
  • it also includes:
  • An iterative module used to iteratively execute the steps of obtaining user needs, determining recommended content according to the user needs, and visually outputting the recommended content until the iteration stop condition is met; wherein the user needs are the user's based on the visual output.
  • Product data feature information is proposed.
  • the iteration module is specifically configured to: determine multiple recommended content according to the user needs; sort the multiple recommended content to obtain sorted recommended content; and sort the sorted recommended content. Perform visual output.
  • the iteration module is specifically configured to: determine the matching degree of each recommended content with the user's needs; sort the multiple recommended contents in order of matching degree from high to low, to obtain Recommended content after sorting.
  • the present application provides a computer-readable storage medium in which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are used to implement the above product data processing method.
  • the product data processing method, device and storage medium provided by this application include: obtaining product element data to be processed; performing dimensionality reduction processing on the product element data to obtain dimensionally reduced data; performing dimensionality reduction on the dimensionally reduced data.
  • Perform data clustering processing to obtain multiple clustering results; remove scattered point data sets in the multiple clustering results to obtain a data set composed of different types of data; perform data fusion processing based on the data set to obtain product data Feature information;
  • the fusion processing method includes: sequential decision-making multi-modal deep neural network or distributed parallel computing; based on the product data feature information, construct object particles of the data stream; extract frequent patterns between the data streams,
  • a data set model based on the data resource object is obtained to perform dimensionality reduction and aggregation processing on the product element data to obtain product data feature information; and the product data feature information is visually output.
  • This application proposes a product data processing method that uses modeling and visualization technology to break through resource management, heterogeneous data fusion, manufacturing big data governance and intelligent analysis, and data and business parallel drive for all-element and multi-dimensional heterogeneous data.
  • Full-chain intelligent collaboration and integrated technology control can help users quickly target the required product data, assist users in decision-making, and promote the intelligent manufacturing of home appliances and other products.
  • Figure 1 is a schematic diagram of a product data processing method provided by an embodiment of the present application.
  • Figure 2 is a schematic diagram of the full-element multi-source heterogeneous data modeling and integration process in the embodiment of the present application;
  • Figure 3 is a schematic diagram of a product data processing device provided by an embodiment of the present application.
  • Figure 4 is a schematic structural diagram of a computer device provided by an embodiment of the present application.
  • the product data processing method provided by this application is intended to solve the above technical problems of the existing technology.
  • This application proposes a product data processing method, aiming at all-factor and multi-dimensional heterogeneous data, through modeling and visualization technology, to break through resource management, heterogeneous data fusion, manufacturing big data governance and intelligence
  • the full-chain intelligent collaboration and integrated technical constraints driven by the parallel connection of analysis, data and business can help users quickly target the required product data, assist users in decision-making, and promote the intelligent manufacturing of home appliances and other products.
  • a product data processing method is provided.
  • Figure 1 is a schematic diagram of a product data processing method provided by an embodiment of the present application. As shown in Figure 1, the method mainly includes the following steps:
  • product element data refers to data related to the production and manufacturing process of the product
  • product element data is the data source of the full life cycle data of mass customized production products.
  • the product element data includes at least one of interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, smart logistics data and smart service data. That is, the product element data can specifically be any of the above.
  • An item in one kind of data can also contain multiple items at the same time.
  • Product element data can also include interactive customization data, precision marketing data, and collaborative data.
  • R&D data collaborative procurement data, supply chain data, smart logistics data, smart service data, etc.
  • S300 Perform data clustering processing on the dimensionally reduced data to obtain multiple clustering results.
  • the fusion processing method includes: sequential decision-making multi-modal deep neural network or distributed parallel computing.
  • non-uniform data types mean that product element data may contain multiple types of data such as structured data, semi-structured data, and unstructured data.
  • structured data refers to data managed in the form of relational database tables. The data storage and arrangement of structured data are regular and support functions such as addition, deletion, modification, and query. Structured data specifically includes forms, etc.
  • Semi-structured data refers to data that is non-relational and has a basic fixed structure pattern. Semi-structured data can be understood as intermediate data, such as log files, XML documents, JSON documents, etc.
  • Unstructured data refers to data without a fixed pattern, such as WORD, PDF, PPT, EXL, and pictures and videos in various formats.
  • Dimension inconsistency means that product element data is data from multiple sources and angles. It can be divided into one-dimensional data, two-dimensional data, multi-dimensional data, etc.
  • one-dimensional data can be questionnaires, research discussion data, etc.
  • the clustering process In the clustering process, the principle of similarity of different types of data is followed, representative high-dimensional data are clustered, and the fuzzy clustering (FCM) algorithm is used to mine representative content in the data, that is, product data is obtained Feature information.
  • FCM fuzzy clustering
  • the clustering process also includes the processing step of removing scattered data sets, that is, aggregating adjacent data from the center for later use of multidimensional data.
  • the cluster analysis method can be used to perform training-free learning on the aggregation.
  • d(i, j) represents a multi-dimensional data set with similarity
  • j represents the data representative content, which can be interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, and smart logistics data. , smart service data and any other content
  • i represents the data dimension
  • q represents the data clustering process.
  • mean represents the similarity of multi-dimensional data customized for home appliances
  • V k represents multi-dimensional data attributes
  • V i represents the standard deviation of multi-dimensional data
  • a ik represents the dimension orientation range
  • N represents the number of output data permutations.
  • sequential decision-making multi-modal deep neural network can be used to fuse multiple features of the data in multiple rounds to form a multi-feature table of the data set and select as needed Extract to obtain accurate product data feature information.
  • S700 Extract frequent patterns between data streams and obtain a data set model based on data resource objects to visually output product data feature information.
  • a data set model based on data resource objects can be formed, and a multi-dimensional panoramic view of the product life cycle data space can be constructed.
  • Python language's Flask framework ECharts and other technologies to complete data visualization.
  • the backend completes data extraction and encapsulation, and uses Ajax technology to complete data interaction between the front and back ends.
  • ECharts technology and Jinja2 template engine and other technologies realize data visualization.
  • users can directly view the product data feature information output in a visual form.
  • the object particles of the data flow are constructed. According to the attributes of frequent itemsets, the object particles of the data flow are established. According to the attributes of the object particles, the domain objects containing the same attribute values are searched in the data stream, and the mining of the maximum frequent itemsets is converted into the mining of object particles with the same attributes. calculate. Second, frequent patterns among data streams are extracted. Object granular computing is introduced into the home appliance manufacturing data flow (interaction, design, supply, marketing, manufacturing, logistics, service), multi-granularity correlation analysis is performed, and frequent patterns between multi-source heterogeneous data are extracted.
  • This embodiment proposes a product data processing method that uses modeling and visualization technology to achieve breakthroughs in resource management, heterogeneous data fusion, manufacturing big data governance and intelligent analysis, and data and business parallel drive for all-element and multi-dimensional heterogeneous data.
  • the full-chain intelligent collaboration and integrated technology constraints can help users quickly target the required product data, assist users in decision-making, and promote the intelligent manufacturing of home appliances and other products.
  • the method further includes: iteratively executing the steps of obtaining user needs, determining recommended content according to user needs, and visually outputting the recommended content until the iteration stop condition is met; wherein the user needs are products based on the user's visual output Data feature information is proposed.
  • the user after viewing the product data feature information of the visual output, the user can put forward corresponding user needs based on the product data feature information of the visual output. Therefore, by obtaining the user needs, the recommended content corresponding to the user can be further determined, and visualization can be continued. Output the recommended content; thus, the user can further propose corresponding user needs based on the visually output recommended content, thereby improving the user experience of participating in product customization.
  • determining recommended content based on user needs and visually outputting the recommended content includes: determining multiple recommended content based on user needs; sorting the multiple recommended content to obtain sorted recommended content; The recommended content is visually output.
  • sort multiple recommended contents to obtain the sorted recommended content including: determining the degree of matching between each recommended content and user needs; sorting the multiple recommended contents in order from high to low matching degree, Get sorted recommended content.
  • the recommended content that best matches the user's needs can be output first, thereby improving the accuracy of the recommendation results. accuracy and improve user experience.
  • the method further includes: outputting product data feature information to product-related links; wherein the product-related links include product design links, product supply links, product marketing links, product manufacturing links, product logistics links, and product service design At least one of the steps, so that the obtained product data feature information can be applied to the entire product process of interaction/design/supply/marketing/manufacturing/logistics/services in a home appliance mass customization environment.
  • product-related links include product design links, product supply links, product marketing links, product manufacturing links, product logistics links, and product service design At least one of the steps, so that the obtained product data feature information can be applied to the entire product process of interaction/design/supply/marketing/manufacturing/logistics/services in a home appliance mass customization environment.
  • Figure 2 is a schematic diagram of the full-element multi-source heterogeneous data modeling and integration process in the embodiment of the present application.
  • the technical solution proposed by the embodiment of the present application is oriented towards interaction/design/in the environment of mass customization of home appliances.
  • Provide full-process product data such as supply/marketing/manufacturing/logistics/services, etc., establish a data set based on data resource objects, and construct a multi-dimensional product life cycle data set model and dynamic panoramic view.
  • this application innovatively proposes to use methods such as multi-modal deep neural network technology based on sequential decision-making and distributed parallel computing models to achieve multi-dimensional heterogeneity.
  • granular computing and frequent item set correlation analysis methods are comprehensively applied to analyze the composite correlation and causality between data sets at multiple granularities and multi-angles, and extract Frequent patterns among multi-source heterogeneous data can be used to mine the evolution rules of the entire product life cycle and the interdependence between each stage in the mass customization platform, providing a basis for decision-making for mass customization production.
  • each step in the flow chart in the above embodiment is shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, the execution of these steps is not strictly limited in order, and they can be executed in other orders. Moreover, at least some of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and their execution order is not necessarily sequential. may be performed in turn or alternately with other steps or sub-steps of other steps or at least part of stages.
  • a product data processing apparatus is provided.
  • Figure 3 is a schematic diagram of a product data processing device provided by an embodiment of the present application. As shown in Figure 3, the device includes:
  • the acquisition module 100 is used to acquire product element data to be processed.
  • product element data refers to data related to the production and manufacturing process of the product
  • product element data is the data source of the full life cycle data of mass customized production products.
  • the product element data includes at least one of interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, smart logistics data and smart service data. That is, the product element data can specifically be any of the above.
  • An item in one kind of data can also contain multiple items at the same time.
  • Product element data can include interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, and smart logistics. Data, smart service data, etc.
  • the processing module 200 is used to perform dimensionality reduction and aggregation processing on product element data to obtain product data feature information.
  • non-uniform data types mean that product element data may contain multiple types of data such as structured data, semi-structured data, and unstructured data.
  • structured data refers to data managed in the form of relational database tables. The data storage and arrangement of structured data are regular and support functions such as addition, deletion, modification, and query. Structured data specifically includes forms, etc.
  • Semi-structured data refers to data that is non-relational and has a basic fixed structure pattern. Semi-structured data can be understood as intermediate data, such as log files, XML documents, JSON documents, etc.
  • Unstructured data refers to data without a fixed pattern, such as WORD, PDF, PPT, EXL, and pictures and videos in various formats.
  • Dimension inconsistency means that product element data is data from multiple sources and angles. It can be divided into one-dimensional data, two-dimensional data, multi-dimensional data, etc.
  • one-dimensional data can be questionnaires, research discussion data, etc.
  • the processing module 200 is specifically used to: perform dimensionality reduction processing on product element data to obtain dimensionally reduced data; perform data clustering processing on the dimensionally reduced data to obtain a data set composed of different types of data; according to the data set Perform data fusion processing to obtain product data feature information.
  • the processing module 200 is specifically used to: perform data clustering processing on the dimensionally reduced data to obtain multiple clustering results; remove scattered point data sets in multiple clustering results to obtain data composed of different types of data. set.
  • the clustering process In the clustering process, the principle of similarity of different types of data is followed, representative high-dimensional data are clustered, and the fuzzy clustering (FCM) algorithm is used to mine representative content in the data, that is, product data is obtained Feature information.
  • FCM fuzzy clustering
  • the clustering process also includes the processing step of removing scattered data sets, that is, aggregating adjacent data from the center for later use of multidimensional data.
  • the cluster analysis method can be used to perform training-free learning on the aggregation.
  • d(i, j) represents a multi-dimensional data set with similarity
  • j represents the data representative content, which can be interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, and smart logistics data. , smart service data and any other content
  • i represents the data dimension
  • q represents the data clustering process.
  • mean represents the similarity of multi-dimensional data customized for home appliances;
  • V k represents multi-dimensional data attributes;
  • V i represents multi-dimensional data standard deviation;
  • a ik represents the dimension orientation range;
  • N represents the number of output data permutations.
  • sequential decision-making multi-modal deep neural network can be used to fuse multiple features of the data in multiple rounds to form a multi-feature table of the data set and select as needed Extract to obtain accurate product data feature information.
  • the output module 300 is used to construct object particles of the data flow based on the product data feature information; Frequent patterns between the data streams are extracted to obtain a data set model based on data resource objects to visually output product data feature information.
  • a data set model based on data resource objects can be formed, and a multi-dimensional panoramic view of the product life cycle data space can be constructed.
  • Python language's Flask framework ECharts and other technologies to complete data visualization.
  • the backend completes data extraction and encapsulation, and uses Ajax technology to complete data interaction between the front and back ends.
  • ECharts technology and Jinja2 template engine and other technologies realize data visualization.
  • users can directly view the product data feature information output in a visual form.
  • the object particles of the data flow are constructed. According to the attributes of frequent itemsets, the object particles of the data flow are established. According to the attributes of the object particles, the domain objects containing the same attribute values are searched in the data stream, and the mining of the maximum frequent itemsets is converted into the mining of object particles with the same attributes. calculate. Second, frequent patterns between data streams are extracted. Object granular computing is introduced into the home appliance manufacturing data flow (interaction, design, supply, marketing, manufacturing, logistics, service), multi-granularity correlation analysis is performed, and frequent patterns between multi-source heterogeneous data are extracted.
  • the processing module 200 is also configured to: iteratively execute the steps of obtaining user needs, determining recommended content according to user needs, and visually outputting the recommended content until the iteration stop condition is met; wherein the user needs are based on the visual The output product data feature information is proposed.
  • the user after viewing the product data feature information of the visual output, the user can put forward corresponding user needs based on the product data feature information of the visual output. Therefore, by obtaining the user needs, the recommended content corresponding to the user can be further determined, and visualization can be continued. Output the recommended content; thus, the user can further propose corresponding user needs based on the visually output recommended content, thereby improving the user experience of participating in product customization.
  • determining recommended content based on user needs and visually outputting the recommended content includes: determining multiple recommended content based on user needs; sorting the multiple recommended content to obtain sorted recommended content; The recommended content is visually output.
  • sort multiple recommended content to obtain the sorted recommended content including: OK The degree of matching between each recommended content and the user's needs; sort multiple recommended contents in order of matching degree from high to low to obtain the sorted recommended content.
  • the recommended content that best matches the user's needs can be output first, thereby improving the accuracy of the recommendation results and improving the user experience.
  • the output module 300 is also used to: output product data feature information to product-related links; product-related links include product design links, product supply links, product marketing links, product manufacturing links, product logistics links, and product services. At least one item in the design process, so that the obtained product data feature information can be applied to the entire product process of interaction/design/supply/marketing/manufacturing/logistics/services in a mass customization environment for home appliances.
  • the product element data includes at least one of interactive customization data, precision marketing data, collaborative research and development data, collaborative procurement data, supply chain data, smart logistics data, and smart service data.
  • Each module in the above product data processing device can be implemented in whole or in part by software, hardware and combinations thereof.
  • Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
  • a computer device is provided.
  • FIG. 4 is a schematic structural diagram of a computer device provided by an embodiment of the present application. As shown in FIG. 4 , the computer device includes a processor 11 , a memory 12 and a display 13 .
  • the memory 12 is used to store programs and data, and the processor 11 calls the programs stored in the memory to execute the following technical solutions:
  • product element data refers to data related to the production and manufacturing process of the product
  • product element data is the data source of the full life cycle data of mass customized production products.
  • the product element data includes at least one of interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, smart logistics data and smart service data. That is, the product element data can specifically be any of the above.
  • An item in one kind of data can also contain multiple items at the same time.
  • Product element data can include interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, and smart logistics. Data, smart service data, etc.
  • non-uniform data types mean that product element data may contain multiple types of data such as structured data, semi-structured data, and unstructured data.
  • structured data refers to data managed in the form of relational database tables. The data storage and arrangement of structured data are regular and support functions such as addition, deletion, modification, and query. Structured data specifically includes forms, etc.
  • Semi-structured data refers to data that is non-relational and has a basic fixed structure pattern. Semi-structured data can be understood as intermediate data, such as log files, XML documents, JSON documents, etc.
  • Unstructured data refers to data without fixed patterns, such as WORD, PDF, PPT, EXL, and pictures and videos in various formats.
  • Dimension inconsistency means that product element data is data from multiple sources and angles. It can be divided into one-dimensional data, two-dimensional data, multi-dimensional data, etc.
  • one-dimensional data can be questionnaires, research discussion data, etc.
  • perform dimensionality reduction and aggregation processing on product element data to obtain product data feature information including:
  • the clustering process In the clustering process, the principle of similarity of different types of data is followed, representative high-dimensional data are clustered, and the fuzzy clustering (FCM) algorithm is used to mine representative content in the data, that is, product data is obtained Feature information.
  • FCM fuzzy clustering
  • the clustering process also includes the processing step of removing scattered data sets, that is, aggregating adjacent data from the center for later use of multidimensional data.
  • the cluster analysis method can be used to perform training-free learning on the aggregation.
  • d(i, j) represents a multi-dimensional data set with similarity
  • j represents the data representative content, which can be interactive customization data, precision marketing data, collaborative R&D data, collaborative procurement data, supply chain data, and smart logistics data. , smart service data and any other content
  • i represents the data dimension
  • q represents the data clustering process.
  • mean represents the similarity of multi-dimensional data customized for home appliances;
  • V k represents multi-dimensional data attributes;
  • V i represents multi-dimensional data standard deviation;
  • a ik represents the dimension orientation range;
  • N represents the number of output data permutations.
  • sequential decision-making multi-modal deep neural network can be used to fuse multiple features of the data in multiple rounds to form a multi-feature table of the data set and select as needed Extract to obtain accurate product data feature information.
  • a data resource-based Data set model of objects After obtaining the product data feature information based on the product element data, a data resource-based Data set model of objects and build a multi-dimensional panoramic view of the product life cycle data space.
  • Python language's Flask framework ECharts and other technologies to complete data visualization.
  • the backend completes data extraction and encapsulation, and uses Ajax technology to complete data interaction between the front and back ends.
  • ECharts technology and Jinja2 template engine and other technologies realize data visualization.
  • users can directly view the product data feature information output in a visual form.
  • the product data feature information is visually output, for example, it may be displayed through the display 13, so that the user can intuitively view the product data feature information.
  • the object particles of the data flow are constructed. According to the attributes of frequent itemsets, the object particles of the data flow are established. According to the attributes of the object particles, the universe objects containing the same attribute values are searched in the data stream, and the mining of the maximum frequent itemsets is converted into the mining of object particles with the same attributes. calculate. Second, frequent patterns among data streams are extracted. Object granular computing is introduced into the home appliance manufacturing data flow (interaction, design, supply, marketing, manufacturing, logistics, service), multi-granularity correlation analysis is performed, and frequent patterns between multi-source heterogeneous data are extracted.
  • the technical solution also includes:
  • the user after viewing the product data feature information of the visual output, the user can put forward corresponding user needs based on the product data feature information of the visual output. Therefore, by obtaining the user needs, the recommended content corresponding to the user can be further determined, and visualization can be continued. Output the recommended content; thus, the user can further propose corresponding user needs based on the visually output recommended content, thereby improving the user experience of participating in product customization.
  • determining recommended content based on user needs and visually outputting the recommended content includes: determining multiple recommended content based on user needs; sorting the multiple recommended content to obtain sorted recommended content; The recommended content is visually output.
  • sort multiple recommended content to obtain the sorted recommended content including: determining the degree of matching between each recommended content and user needs; and sorting multiple recommended contents in order from high to low matching degree.
  • the recommended content is sorted to obtain the sorted recommended content.
  • the recommended content that best matches the user's needs can be output first, thereby improving the accuracy of the recommendation results and improving the user experience.
  • the method further includes: outputting product data feature information to product-related links; wherein the product-related links include product design links, product supply links, product marketing links, product manufacturing links, product logistics links, and product service design At least one of the steps, so that the obtained product data feature information can be applied to the entire product process of interaction/design/supply/marketing/manufacturing/logistics/services in a home appliance mass customization environment.
  • product-related links include product design links, product supply links, product marketing links, product manufacturing links, product logistics links, and product service design At least one of the steps, so that the obtained product data feature information can be applied to the entire product process of interaction/design/supply/marketing/manufacturing/logistics/services in a home appliance mass customization environment.
  • the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction.
  • these components can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus.
  • the memory stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the memory in the form of software or firmware.
  • the processor executes various software programs and modules by running the software programs and modules stored in the memory. Functional applications and data processing.
  • the memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Only Memory Read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
  • RAM Random Access Memory
  • ROM Read Only Memory
  • PROM Programmable Read-Only Memory
  • EPROM Erasable Only Memory Read-only memory
  • EPROM Erasable Programmable Read-Only Memory
  • EEPROM Electrically erasable read-only memory
  • the software programs and modules in the above-mentioned memory may also include an operating system, which may include various software components and/or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may Communicates with various hardware or software components to provide a running environment for other software components.
  • the processor can be an integrated circuit chip with signal processing capabilities.
  • the above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.
  • CPU Central Processing Unit
  • NP Network Processor
  • Each method, step and logical block diagram disclosed in the embodiment of this application can be implemented or executed.
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
  • a computer-readable storage medium is provided, the computer-readable storage medium
  • Computer execution instructions are stored in, and when the computer execution instructions are executed by the processor, they are used to implement the steps of each method embodiment of the present application.
  • a computer program product including a computer program that implements the steps of each method embodiment of the present application when executed by a processor.
  • Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • SRAM static RAM
  • DRAM dynamic RAM
  • SDRAM synchronous DRAM
  • DDRSDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous chain Synchlink DRAM
  • Rambus direct RAM
  • DRAM direct memory bus dynamic RAM
  • RDRAM memory bus dynamic RAM

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Abstract

La présente demande concerne un procédé et un appareil de traitement de données de produit, ainsi qu'un support de stockage. Le procédé consiste à : acquérir des données d'élément de produit à traiter ; effectuer un traitement de réduction de dimension sur lesdites données d'élément de produit ; effectuer un traitement de regroupement de données sur les données qui ont été soumises à un traitement de réduction de dimension, de façon à obtenir une pluralité de résultats de regroupement ; supprimer un ensemble de données de diffusion de la pluralité de résultats de regroupement, de façon à obtenir un ensemble de données composé de données de différents types ; effectuer un traitement de fusion de données selon l'ensemble de données, de façon à obtenir des informations de caractéristique de données de produit ; construire un granule d'objet d'un flux de données sur la base des informations ; et extraire un motif fréquent entre des flux de données, de façon à obtenir un modèle d'ensemble de données sur la base d'un objet de ressource de données pour une sortie visuelle. Pour des données hétérogènes multidimensionnelles tout-élément, au moyen d'une technologie de modélisation et de visualisation, une avancée est effectuée dans les contraintes de gestion de ressources, de fusion de données hétérogènes, de gouvernance et d'analyse intelligente de mégadonnées de fabrication, et une technologie de coopération et d'intégration intelligente à chaîne complète dictée par des données et un service en parallèle, de telle sorte qu'un utilisateur peut être aidé à trouver rapidement des données de produit requises, de façon à aider à la prise de décision d'utilisateur et à favoriser la fabrication intelligente de produits tels que des appareils électroménagers.
PCT/CN2023/095967 2022-05-26 2023-05-24 Procédé et appareil de traitement de données de produit, et support de stockage WO2023227012A1 (fr)

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