CN114661968A - Product data processing method, device and storage medium - Google Patents
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Abstract
The application provides a product data processing method, a device and a storage medium, wherein the method comprises the following steps: acquiring element data of a product to be processed; performing dimension reduction polymerization processing on the product element data to obtain product data characteristic information; and visually outputting the product data characteristic information. The application provides a product data processing method, aiming at full-element multi-dimensional heterogeneous data, and through modeling and visualization technologies, the full-chain intelligent cooperation and integration technology restriction of resource management, heterogeneous data fusion, manufacture big data management and intelligent analysis, and data and service parallel driving is broken through, so that a user can be helped to quickly target required product data, a user decision is assisted, and intelligent manufacture of products such as household appliances is promoted.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing product data, and a storage medium.
Background
With the improvement of the social living standard, it is a trend that users participate in product customization, for example, users can participate in large-scale customization of home appliances to meet different requirements of users.
Due to the fact that links related to the manufacturing process of products such as household appliances are more, the corresponding product data types are more, and the problem that data dimensions are inconsistent exists, a user can possibly not understand part of product data, and the experience effect of the user participating in product customization is influenced.
The above information disclosed in the background section is only for enhancement of understanding of the background of the present application and therefore it may contain information that does not form the prior art that is known to those of ordinary skill in the art.
Disclosure of Invention
The application provides a product data processing method, a product data processing device and a storage medium, which are used for solving the problems in the prior art.
In a first aspect, the present application provides a product data processing method, including:
acquiring element data of a product to be processed;
performing dimension reduction polymerization processing on the product element data to obtain product data characteristic information;
and visually outputting the product data characteristic information.
In some embodiments, performing dimension reduction and aggregation processing on the product element data to obtain product data feature information includes:
performing dimensionality reduction on the product element data to obtain dimensionality reduced data;
carrying out data clustering processing on the data after dimensionality reduction to obtain a data set consisting of different types of data;
and performing data fusion processing according to the data set to obtain the product data characteristic information.
In some embodiments, performing data clustering on the dimensionality reduced data to obtain a data set composed of different types of data includes:
carrying out data clustering processing on the data subjected to dimensionality reduction to obtain a plurality of clustering results;
and removing the scattered point data set in the plurality of clustering results to obtain a data set consisting of different types of data.
In some embodiments, further comprising:
iteratively executing to obtain a user demand, determining recommended content according to the user demand, and performing visual output on the recommended content until an iteration stop condition is met;
wherein the user requirement is provided by the user based on the visually output product data characteristic information.
In some embodiments, determining recommended content according to the user requirement, and visually outputting the recommended content includes:
determining a plurality of recommended contents according to the user requirements;
sequencing the plurality of recommended contents to obtain sequenced recommended contents;
and performing visual output on the sorted recommended content.
In some embodiments, ranking the plurality of recommended contents to obtain ranked recommended contents includes:
determining the matching degree of each recommended content and the user requirement;
and sequencing the plurality of recommended contents according to the sequence of the matching degree from high to low to obtain the sequenced recommended contents.
In some embodiments, further comprising:
outputting the product data characteristic information to a product related link;
the product related links comprise at least one of a product design link, a product supply link, a product marketing link, a product manufacturing link, a product logistics link and a product service design link.
In some embodiments, the product element data includes at least one of interaction customization data, precision marketing data, collaborative development data, collaborative procurement data, supply chain data, intelligent logistics data, and intelligent service data.
In a second aspect, the present application provides a product data processing apparatus comprising:
the acquisition module is used for acquiring element data of a product to be processed;
the processing module is used for carrying out dimension reduction and aggregation processing on the product element data to obtain product data characteristic information;
and the output module is used for visually outputting the characteristic information of the product data.
In some embodiments, the processing module is specifically configured to: performing dimensionality reduction on the product element data to obtain dimensionality reduced data; carrying out data clustering processing on the data after dimensionality reduction to obtain a data set consisting of different types of data; and performing data fusion processing according to the data set to obtain the product data characteristic information.
In some embodiments, the processing module is specifically configured to: performing data clustering processing on the dimensionality reduced data to obtain a plurality of clustering results; and removing the scattered point data set in the plurality of clustering results to obtain a data set consisting of different types of data.
In some embodiments, further comprising:
the iteration module is used for iteratively executing the steps of acquiring user requirements, determining recommended contents according to the user requirements and visually outputting the recommended contents until an iteration stop condition is met; wherein the user requirement is provided by the user based on the visually output product data characteristic information.
In some embodiments, the iteration module is specifically configured to: determining a plurality of recommended contents according to the user requirements; sequencing the plurality of recommended contents to obtain sequenced recommended contents; and performing visual output on the sorted recommended content.
In some embodiments, the iteration module is specifically configured to: determining the matching degree of each recommended content and the user requirement; and sequencing the plurality of recommended contents according to the sequence of the matching degree from high to low to obtain the sequenced recommended contents.
In a third aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the above-mentioned product data processing method when executed by a processor.
The application provides a product data processing method, a device and a storage medium, wherein the method comprises the following steps: acquiring element data of a product to be processed; performing dimension reduction polymerization processing on the product element data to obtain product data characteristic information; and visually outputting the product data characteristic information. The application provides a product data processing method, aiming at full-element multi-dimensional heterogeneous data, through modeling and visualization technologies, the full-chain intelligent cooperation and integration technology restriction of resource management, heterogeneous data fusion, manufacture big data management and intelligent analysis, and data and service parallel driving is broken through, so that a user can be helped to quickly target required product data, the user decision is assisted, and the intelligent manufacture of products such as household appliances is promoted.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic diagram of a product data processing method provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of a full-factor multi-source heterogeneous data modeling and integration process in an embodiment of the present application;
FIG. 3 is a schematic diagram of a product data processing apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. The drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the embodiments of the present application, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a commodity or system that includes the element.
With the improvement of the social living standard, it is a trend that users participate in product customization, for example, users can participate in large-scale customization of home appliances to meet different requirements of users.
Due to the fact that links involved in the manufacturing process of products such as household appliances are more, the corresponding product data types are more, and the problem that data dimensions are inconsistent exists, so that a user may not understand part of product data, and the experience effect of the user participating in product customization is influenced.
For example, in the process of large-scale customization of products such as home appliances, most of demand data, production data and resource data related to the products are distributed in a scattered manner, and data interconnection transmission is obstructed, so that a user cannot acquire all data in time.
In addition, for data processing spaces for the same problem, different granularity selection brings different complexity of calculation work, so whether the granulation standard is correct or not and whether the granulation standard is appropriate or not before the problem is analyzed and calculated based on the granularity calculation is the key for influencing the efficient calculation and solution of the data.
In addition, when data application is visualized, a common technology can only provide a static view of data of a certain node, and the dynamic view of the customized full-flow data of the household appliance in which a user participates is difficult to draw.
The product data processing method provided by the application aims to solve the technical problems in the prior art.
The main conception of the scheme of the application is as follows: the application provides a product data processing method, aiming at full-element multi-dimensional heterogeneous data, through modeling and visualization technologies, the full-chain intelligent cooperation and integration technology restriction of resource management, heterogeneous data fusion, manufacture big data management and intelligent analysis, and data and service parallel driving is broken through, so that a user can be helped to quickly target required product data, the user decision is assisted, and the intelligent manufacture of products such as household appliances is promoted.
It is understood that the processing steps of the product data processing method in the present application can be implemented by the relevant platform/software for product data management.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
In some embodiments, a product data processing method is provided.
Fig. 1 is a schematic diagram of a product data processing method provided in an embodiment of the present application, and as shown in fig. 1, the method mainly includes the following steps:
s100, acquiring element data of a product to be processed;
the product element data refers to data related to the production and manufacturing process of the product, and the product element data is a data source of full life cycle data of a large-scale customized product.
Optionally, the product element data includes at least one of interaction customization data, precision marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, and intelligent service data, that is, the product element data may be one of the above data, or may include multiple items at the same time.
For example, the full-element data of the household appliance can be accessed on a large scale based on the karos COSMOPlat platform engine, and the product element data can simultaneously comprise interactive customization data, accurate marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, intelligent service data and the like.
S200, performing dimension reduction and aggregation processing on the product element data to obtain product data characteristic information;
because the acquired product element data has the problems of non-uniform data types, non-uniform dimensions and the like, data unification and dimension reduction processing are firstly required.
Specifically, the data type is not uniform, which means that the product element data may include multiple types of data, such as structured data, semi-structured data, and unstructured data. The structured data refers to data managed in a relational database table form, the data storage and arrangement of the structured data have regularity, the functions of adding, deleting, modifying, checking and the like are supported, and the structured data specifically includes forms and the like. The semi-structured data refers to data of a non-relational model and having a basic fixed structure mode, and the semi-structured data can be understood as intermediate data, such as a log file, an XML document, a JSON document and the like. Unstructured data refers to data without fixed patterns, such as WORD, PDF, PPT, EXL, and pictures, videos, etc. in various formats.
The inconsistent dimensionality means that the product element data is data with multiple sources and multiple angles, and can be specifically divided into one-dimensional data, two-dimensional data, multi-dimensional data and the like, for example, the one-dimensional data can be questionnaires, investigation and discussion data and the like.
Optionally, the dimension reduction and aggregation processing is performed on the product element data to obtain product data feature information, including:
s210, performing dimensionality reduction on the product element data to obtain dimensionality-reduced data;
s220, carrying out data clustering processing on the data subjected to dimensionality reduction to obtain a data set consisting of different types of data;
and S230, performing data fusion processing according to the data set to obtain product data characteristic information.
Optionally, performing data clustering processing on the data after the dimensionality reduction to obtain a data set composed of different types of data, including:
s221, carrying out data clustering processing on the data subjected to dimensionality reduction to obtain a plurality of clustering results;
s222, removing the scattered point data set in the clustering results to obtain a data set consisting of different types of data.
Specifically, considering that most product element data in large-scale customization of the home appliance are distributed in a scattered manner, before the product element data is processed, the data sampling selection mode can be adopted to select high-altitude representative data for customization manufacturing of the home appliance, and then dimension reduction clustering processing is performed on the representative data.
In the clustering process, according to the principle that different types of data have similarity, clustering representative high-dimensional data, and mining representative contents in the data by using a Fuzzy Clustering (FCM) algorithm to obtain product data characteristic information. The clustering process also comprises a processing step of removing the scatter data set, namely aggregating adjacent data from the center for later calling of the multidimensional data.
In the process of obtaining the characteristic information of the product data according to the data set obtained by clustering, a clustering analysis method can be adopted to perform untrained learning on the aggregation.
For example, assuming that the data type included in the data set is n, similar data existing in the home appliance mass customization can be represented by the following calculation formula.
Wherein the content of the first and second substances,
expressed as a multi-dimensional data set with similarities; j represents data representing content, and can be any content such as interactive customization data, accurate marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, intelligent service data and the like; i is represented as a data dimension; q is denoted as a data clustering process. The above-mentioned meterIn the formula, i and j are considered to have a certain relation, and when the ratio of i to j is close to 0,
the larger the value of (c), the smaller the opposite.
On this basis, the derived data sets are ranked using a front-end engine tool. In the arrangement process, all data are used as research objects, Euclidean distances among different dimensionality data are used as an arrangement basis, and qualitative expression of a data internal recessive rule is carried out based on a global angle, so that a multidimensional data arrangement algorithm formula is as follows:
wherein mean can be represented as
，
Representing the similarity of the customized multidimensional data for the household appliance;
expressed as a multidimensional data attribute;
expressed as the multidimensional data standard deviation;
expressed as a range of dimensional orientations; n represents the number of times the output data is arranged. And combining the calculation formula with the output data to complete the research of the arrangement algorithm of the multidimensional data.
In the process of carrying out data fusion processing according to the data set, the data multi-feature multi-turn fusion can be carried out by adopting a sequential decision multi-mode deep neural network, a distributed parallel computation mode and other modes to form a data set multi-feature table, and the data set multi-feature table is selectively extracted as required to obtain accurate product data feature information.
And S300, visually outputting the characteristic information of the product data.
After the characteristic information of the product data is obtained according to the product element data, a data set model based on the data resource object can be formed, and a multi-dimensional panoramic view of the product life cycle data space is constructed.
Specifically, the data visualization can be completed by using technologies such as flash framework and ECharts of Python language. The back end extracts and encapsulates the data, and the data interaction of the front end and the back end is completed by utilizing the Ajax technology. The ECharts technology, the Jinja2 template engine and the like realize data visualization. Thus, the user can directly view the product data characteristic information output in a visualized form.
In modeling a data set, first, object particles of a data stream are constructed. According to the attributes of the frequent item sets, object particles of the data stream are established, domain objects containing the same attribute values are searched in the data stream according to the attributes of the object particles, and mining of the maximum frequent item set is converted into calculation of the object particles with the same attributes. Second, frequent patterns between data streams are extracted. And (3) introducing object particle calculation into household appliance manufacturing data flow (interaction, design, supply, marketing, manufacturing, logistics and service), performing multi-granularity association analysis, and extracting frequent patterns among multi-source heterogeneous data.
The embodiment provides a product data processing method, aiming at full-element multi-dimensional heterogeneous data, through modeling and visualization technologies, the full-chain intelligent cooperation and integration technology restriction of resource management, heterogeneous data fusion, manufacturing big data management and intelligent analysis, and data and service parallel driving is broken through, so that a user can be helped to quickly target required product data, the user decision is assisted, and the intelligent manufacturing of products such as household appliances is promoted.
In some embodiments, the method further comprises: s400, iteratively executing to obtain user requirements, determining recommended contents according to the user requirements, and visually outputting the recommended contents until iteration stop conditions are met; the user requirements are provided for the user based on the visually output product data characteristic information.
Specifically, after the user views the visually output product data characteristic information, the user can put forward a corresponding user demand according to the visually output product data characteristic information, so that the recommended content corresponding to the user can be further determined by acquiring the user demand, and the recommended content is continuously visually output; therefore, the user can further put forward corresponding user requirements based on the visually output recommended content, and the experience effect of the user participating in product customization can be improved.
In some embodiments, determining recommended content according to user requirements and visually outputting the recommended content includes: determining a plurality of recommended contents according to user requirements; sequencing the plurality of recommended contents to obtain sequenced recommended contents; and visually outputting the sorted recommended content.
Specifically, after user requirements are obtained, a plurality of selectable recommended contents are determined according to the user requirements, the recommended contents are sorted according to a preset sorting rule, and finally the sorted recommended contents are visually output.
Optionally, the sorting the plurality of recommended contents to obtain sorted recommended contents includes: determining the matching degree of each recommended content and the user requirement; and sequencing the plurality of recommended contents according to the sequence of the matching degree from high to low to obtain the sequenced recommended contents.
Therefore, the recommended contents are sorted according to the matching degree of the recommended contents and the user requirements, and the recommended contents with the highest matching degree with the user requirements can be preferentially output, so that the accuracy of the recommendation result is improved, and the user experience is improved.
In some embodiments, the method further comprises: outputting the characteristic information of the product data to a relevant link of the product; the product related links comprise at least one of a product design link, a product supply link, a product marketing link, a product manufacturing link, a product logistics link and a product service design link, so that the obtained product data characteristic information can be applied to the whole product flow such as interaction/design/supply/marketing/manufacturing/logistics/service and the like in a large-scale household appliance customization environment.
Fig. 2 is a schematic diagram of a full-factor multi-source heterogeneous data modeling and integration process in the embodiment of the present application, and as shown in fig. 2, the technical solution proposed in the embodiment of the present application is oriented to full-process data of products such as interaction, design, supply, marketing, manufacturing, logistics, service, and the like in a large-scale household appliance customization environment, establishes a data set based on a data resource object, and establishes a multi-dimensional product lifecycle data set model and a dynamic panoramic view. Aiming at the problems of high generation speed, large magnitude, multi-dimensional isomerism and the like of large-scale customized full-period data, the method for carrying out fusion, modeling and visualization on the multi-dimensional isomerism data set by using a sequential decision-based multi-mode deep neural network technology, a distributed parallel computing mode and the like is innovatively provided. In addition, a particle calculation and frequent item set association analysis method is comprehensively applied based on multi-dimensional multi-modal data of the product life cycle in the large-scale customization environment, the composite association relationship and the causal relationship among data sets are analyzed in a multi-granularity and multi-angle mode, frequent patterns among multi-source heterogeneous data are extracted, the evolution rule of the whole life cycle of the product in a large-scale customization platform and the interdependence relationship among all stages are mined, and a decision basis is provided for large-scale customization production.
It should be understood that, although the steps in the flowcharts in the above embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, in different orders, and may be performed alternately or at least partially with respect to other steps or sub-steps of other steps.
In some embodiments, a product data processing apparatus is provided.
Fig. 3 is a schematic diagram of a product data processing apparatus according to an embodiment of the present application, and as shown in fig. 3, the apparatus includes:
an obtaining module 100, configured to obtain product element data to be processed;
the product element data refers to data related to the production and manufacturing process of the product, and the product element data is a data source of full life cycle data of a large-scale customized product.
Optionally, the product element data includes at least one of interaction customization data, precision marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, and intelligent service data, that is, the product element data may be one of the above data, or may include multiple items at the same time.
For example, the full-element data of the household appliance can be accessed on a large scale based on the karos COSMOPlat platform engine, and the product element data can simultaneously comprise interactive customization data, accurate marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, intelligent service data and the like.
The processing module 200 is configured to perform dimension reduction and aggregation processing on the product element data to obtain product data feature information;
because the acquired product element data has the problems of non-uniform data types, non-uniform dimensions and the like, data unification and dimension reduction processing are firstly required.
Specifically, the data type is not uniform, which means that the product element data may include multiple types of data, such as structured data, semi-structured data, and unstructured data. The structured data refers to data managed in a relational database table form, the data storage and arrangement of the structured data have regularity, the functions of adding, deleting, modifying, checking and the like are supported, and the structured data specifically includes forms and the like. The semi-structured data refers to data of a non-relational model and having a basic fixed structure mode, and the semi-structured data can be understood as intermediate data, such as log files, XML documents, JSON documents and the like. Unstructured data refers to data without fixed patterns, such as WORD, PDF, PPT, EXL, and pictures, videos, etc. in various formats.
The inconsistent dimensionality means that the product element data is data with multiple sources and multiple angles, and can be specifically divided into one-dimensional data, two-dimensional data, multi-dimensional data and the like, for example, the one-dimensional data can be questionnaires, investigation and discussion data and the like.
Optionally, the processing module 200 is specifically configured to: performing dimensionality reduction on the product element data to obtain dimensionality reduced data; performing data clustering processing on the data subjected to dimensionality reduction to obtain a data set consisting of different types of data; and performing data fusion processing according to the data set to obtain the characteristic information of the product data.
Optionally, the processing module 200 is specifically configured to: performing data clustering processing on the data after dimensionality reduction to obtain a plurality of clustering results; and removing the scattered point data set in the clustering results to obtain a data set consisting of different types of data.
Specifically, considering that most of product element data in large-scale customization of the home appliance are distributed in a scattered manner, before processing the product element data, a data sampling selection manner can be adopted to select high-altitude representative data for customization manufacturing of the home appliance, and then dimension reduction clustering processing is performed on the representative data.
In the clustering process, according to the principle that different types of data have similarity, clustering representative high-dimensional data, and mining representative contents in the data by using a Fuzzy Clustering (FCM) algorithm to obtain product data characteristic information. The clustering process also comprises a processing step of removing the scatter data set, namely aggregating adjacent data from the center for later calling of the multidimensional data.
In the process of obtaining the characteristic information of the product data according to the data set obtained by clustering, a clustering analysis method can be adopted to perform untrained learning on the aggregation.
For example, assuming that the data type contained in the data set is n, similar data existing in the home appliance mass customization can be represented by the following calculation formula.
Wherein the content of the first and second substances,
expressed as a multi-dimensional data set with similarities; j represents data representing content, and can be any content such as interactive customization data, accurate marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, intelligent service data and the like; i is represented as a data dimension; q is denoted as a data clustering process. In the above calculation formula, i and j are considered to have a certain relation, and when the ratio of i to j is close to 0,
the larger the value of (a) and the smaller the vice versa.
On this basis, the derived data sets are ranked using a front-end engine tool. In the arrangement process, all data are used as research objects, Euclidean distances among different dimensionality data are used as an arrangement basis, and qualitative expression of a data internal recessive rule is carried out based on a global angle, so that a multidimensional data arrangement algorithm formula is as follows:
wherein mean can be represented as
，
Representing the similarity of the customized multidimensional data for the household appliance;
expressed as a multidimensional data attribute;
expressed as the multidimensional data standard deviation;
expressed as a range of dimensional orientations; n is expressed as the number of times the output data is arranged. And combining the calculation formula with the output data to complete the research of the arrangement algorithm of the multidimensional data.
In the process of carrying out data fusion processing according to the data set, the data multi-feature multi-turn fusion can be carried out by adopting a sequential decision multi-mode deep neural network, a distributed parallel computation mode and other modes to form a data set multi-feature table, and the data set multi-feature table is selectively extracted as required to obtain accurate product data feature information.
And the output module 300 is used for visually outputting the characteristic information of the product data.
After the characteristic information of the product data is obtained according to the product element data, a data set model based on a data resource object can be formed, and a multi-dimensional panoramic view of the product life cycle data space is constructed.
Specifically, the data visualization can be completed by using technologies such as flash framework and ECharts of Python language. The back end extracts and encapsulates the data, and the data interaction of the front end and the back end is completed by utilizing the Ajax technology. The ECharts technology, the Jinja2 template engine and the like realize data visualization. Thus, the user can directly view the product data characteristic information output in a visualized form.
In modeling a data set, first, object particles of a data stream are constructed. According to the attributes of the frequent item sets, object particles of the data stream are established, domain objects containing the same attribute values are searched in the data stream according to the attributes of the object particles, and mining of the maximum frequent item set is converted into calculation of the object particles with the same attributes. Second, frequent patterns between data streams are extracted. And (3) introducing object particle calculation into household appliance manufacturing data flow (interaction, design, supply, marketing, manufacturing, logistics and service), performing multi-granularity association analysis, and extracting frequent patterns among multi-source heterogeneous data.
In some embodiments, the processing module 200 is further configured to: iteratively executing the steps of acquiring user requirements, determining recommended contents according to the user requirements, and performing visual output on the recommended contents until iteration stop conditions are met; the user requirements are provided for the user based on the visually output product data characteristic information.
Specifically, after the user views the visually output product data characteristic information, the user can put forward a corresponding user demand according to the visually output product data characteristic information, so that the recommended content corresponding to the user can be further determined by acquiring the user demand, and the recommended content is continuously visually output; therefore, the user can further put forward corresponding user requirements based on the visually output recommended content, and the experience effect of the user participating in product customization can be improved.
In some embodiments, determining recommended content according to user requirements and visually outputting the recommended content includes: determining a plurality of recommended contents according to user requirements; sequencing the plurality of recommended contents to obtain sequenced recommended contents; and visually outputting the sorted recommended content.
Specifically, after user requirements are obtained, a plurality of selectable recommended contents are determined according to the user requirements, the recommended contents are sorted according to a preset sorting rule, and finally the sorted recommended contents are visually output.
Optionally, the sorting the plurality of recommended contents to obtain sorted recommended contents includes: determining the matching degree of each recommended content and the user requirement; and sequencing the plurality of recommended contents according to the sequence from high matching degree to low matching degree to obtain the sequenced recommended contents.
Therefore, the recommended contents are sorted according to the matching degree of the recommended contents and the user requirements, and the recommended contents with the highest matching degree with the user requirements can be preferentially output, so that the accuracy of the recommendation result is improved, and the user experience is improved.
In some embodiments, the output module 300 is further configured to: outputting the characteristic information of the product data to a relevant link of the product; the product related links comprise at least one of a product design link, a product supply link, a product marketing link, a product manufacturing link, a product logistics link and a product service design link, so that the obtained product data characteristic information can be applied to the whole product flow such as interaction/design/supply/marketing/manufacturing/logistics/service and the like in a large-scale household appliance customization environment.
In some embodiments, the product element data includes at least one of interaction customization data, precision marketing data, collaborative development data, collaborative procurement data, supply chain data, intelligent logistics data, and intelligent service data.
For specific limitations of the product data processing apparatus, reference may be made to the above limitations of the product data processing method, which are not described herein again. The various modules in the product data processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In some embodiments, a computer device is provided.
Fig. 4 is a schematic structural diagram of a computer device provided in an embodiment of the present application, and as shown in fig. 4, the computer device includes: a processor 11, a memory 12, and a display 13.
The memory 12 is used for storing programs and data, and the processor 11 calls the programs stored in the memory to execute the following technical scheme:
(1) acquiring element data of a product to be processed;
the product element data refers to data related to the production and manufacturing process of the product, and the product element data is a data source of full life cycle data of a large-scale customized product.
Optionally, the product element data includes at least one of interaction customization data, precision marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, and intelligent service data, that is, the product element data may be one of the above data, or may include multiple items at the same time.
For example, the full-element data of the household appliance can be accessed on a large scale based on the karos COSMOPlat platform engine, and the product element data can simultaneously comprise interactive customization data, accurate marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, intelligent service data and the like.
(2) Performing dimension reduction polymerization processing on the product element data to obtain product data characteristic information;
because the acquired product element data has the problems of non-uniform data types, non-uniform dimensions and the like, data unification and dimension reduction processing are firstly required.
Specifically, the data type is not uniform, which means that the product element data may include multiple types of data, such as structured data, semi-structured data, and unstructured data. The structured data refers to data managed in a relational database table form, the data storage and arrangement of the structured data have regularity, the functions of adding, deleting, modifying, checking and the like are supported, and the structured data specifically includes forms and the like. The semi-structured data refers to data of a non-relational model and having a basic fixed structure mode, and the semi-structured data can be understood as intermediate data, such as log files, XML documents, JSON documents and the like. Unstructured data refers to data without fixed patterns, such as WORD, PDF, PPT, EXL, and pictures, videos, etc. in various formats.
The inconsistent dimensionality means that the product element data is data with multiple sources and multiple angles, and can be specifically divided into one-dimensional data, two-dimensional data, multi-dimensional data and the like, for example, the one-dimensional data can be questionnaires, investigation and discussion data and the like.
Optionally, performing dimension reduction and aggregation processing on the product element data to obtain product data characteristic information, including:
performing dimensionality reduction on the product element data to obtain dimensionality reduced data;
carrying out data clustering processing on the data subjected to dimensionality reduction to obtain a data set consisting of different types of data;
and performing data fusion processing according to the data set to obtain product data characteristic information.
Optionally, performing data clustering processing on the data after the dimensionality reduction to obtain a data set composed of different types of data, including:
carrying out data clustering processing on the data subjected to dimensionality reduction to obtain a plurality of clustering results;
and removing the scattered point data set in the clustering results to obtain a data set consisting of different types of data.
Specifically, considering that most product element data in large-scale customization of the home appliance are distributed in a scattered manner, before the product element data is processed, the data sampling selection mode can be adopted to select high-altitude representative data for customization manufacturing of the home appliance, and then dimension reduction clustering processing is performed on the representative data.
In the clustering process, according to the principle that different types of data have similarity, clustering representative high-dimensional data, and mining representative contents in the data by using a Fuzzy Clustering (FCM) algorithm to obtain product data characteristic information. The clustering process also comprises a processing step of removing the scatter data set, namely aggregating adjacent data from the center for later calling of the multidimensional data.
In the process of obtaining the characteristic information of the product data according to the data set obtained by clustering, a clustering analysis method can be adopted to perform untrained learning on the aggregation.
For example, assuming that the data type included in the data set is n, similar data existing in the home appliance mass customization can be represented by the following calculation formula.
Wherein the content of the first and second substances,
expressed as a multi-dimensional data set with similarities; j represents data representing content, and can be any content such as interactive customization data, accurate marketing data, collaborative research and development data, collaborative purchase data, supply chain data, intelligent logistics data, intelligent service data and the like; i is represented as a data dimension; q is denoted as a data clustering process. The above calculationIn the formula, i and j are considered to have a certain relation, and when the ratio of i to j is close to 0,
the larger the value of (a) and the smaller the vice versa.
On this basis, the derived data sets are ranked using a front-end engine tool. In the arrangement process, all data are used as research objects, Euclidean distances among different dimensionality data are used as an arrangement basis, and qualitative expression of a data internal recessive rule is carried out based on a global angle, so that a multidimensional data arrangement algorithm formula is as follows:
wherein mean can be represented as
，
Representing the similarity of the customized multidimensional data for the household appliance;
expressed as a multidimensional data attribute;
expressed as the multidimensional data standard deviation;
expressed as a range of dimensional orientations; n is expressed as the number of times the output data is arranged. And combining the calculation formula with the output data to complete the research of the arrangement algorithm of the multidimensional data.
In the process of carrying out data fusion processing according to the data set, the data multi-feature multi-turn fusion can be carried out by adopting a sequential decision multi-mode deep neural network, a distributed parallel computation mode and other modes to form a data set multi-feature table, and the data set multi-feature table is selectively extracted as required to obtain accurate product data feature information.
(3) And visually outputting the characteristic information of the product data.
After the characteristic information of the product data is obtained according to the product element data, a data set model based on the data resource object can be formed, and a multi-dimensional panoramic view of the product life cycle data space is constructed.
Specifically, the data visualization can be completed by using technologies such as flash framework and ECharts of Python language. The back end extracts and encapsulates the data, and the data interaction of the front end and the back end is completed by utilizing the Ajax technology. The ECharts technology, the Jinja2 template engine and the like realize data visualization. Thus, the user can directly view the product data characteristic information output in a visualized form.
Specifically, the product data feature information may be displayed, for example, by the display 13, so that the user can visually view the product data feature information.
In modeling a data set, first, object particles of a data stream are constructed. According to the attributes of the frequent item sets, object particles of the data stream are established, domain objects containing the same attribute values are searched in the data stream according to the attributes of the object particles, and mining of the maximum frequent item set is converted into calculation of the object particles with the same attributes. Second, frequent patterns between data streams are extracted. And (3) introducing object particle calculation into household appliance manufacturing data flow (interaction, design, supply, marketing, manufacturing, logistics and service), performing multi-granularity association analysis, and extracting frequent patterns among multi-source heterogeneous data.
In some embodiments, the technical solution further comprises:
(4) iteratively executing to obtain a user requirement, determining recommended content according to the user requirement, and performing visual output on the recommended content until an iteration stop condition is met; the user requirements are provided for the user based on the visually output product data characteristic information.
Specifically, after the user views the visually output product data characteristic information, the user can put forward a corresponding user demand according to the visually output product data characteristic information, so that the recommended content corresponding to the user can be further determined by acquiring the user demand, and the recommended content is continuously visually output; therefore, the user can further put forward corresponding user requirements based on the visually output recommended content, and the experience effect of the user participating in product customization can be improved.
In some embodiments, determining recommended content according to user requirements and visually outputting the recommended content includes: determining a plurality of recommended contents according to user requirements; sequencing the plurality of recommended contents to obtain sequenced recommended contents; and visually outputting the sorted recommended content.
Specifically, after user requirements are obtained, a plurality of selectable recommended contents are determined according to the user requirements, sequencing is performed according to a preset sequencing rule, and finally, the sequenced recommended contents are visually output.
Optionally, the sorting the plurality of recommended contents to obtain sorted recommended contents includes: determining the matching degree of each recommended content and the user requirement; and sequencing the plurality of recommended contents according to the sequence from high matching degree to low matching degree to obtain the sequenced recommended contents.
Therefore, the recommended contents are sequenced according to the matching degree of the recommended contents and the user requirements, and the recommended contents with the highest matching degree with the user requirements can be preferentially output, so that the accuracy of the recommendation result is improved, and the user experience is improved.
In some embodiments, the method further comprises: outputting the characteristic information of the product data to a relevant link of the product; the product related links comprise at least one of a product design link, a product supply link, a product marketing link, a product manufacturing link, a product logistics link and a product service design link, so that the obtained product data characteristic information can be applied to the whole product flow such as interaction/design/supply/marketing/manufacturing/logistics/service and the like in a large-scale household appliance customization environment.
In the above computer devices, the memory and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines, such as may be provided via a bus. The memory stores computer-executable instructions for implementing the data access control method, and includes at least one software functional module which can be stored in the memory in the form of software or firmware, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory.
The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like. The memory is used for storing programs, and the processor executes the programs after receiving the execution instructions. Further, the software programs and modules within the aforementioned memories may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.
The processor may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In some embodiments, a computer-readable storage medium having stored thereon computer-executable instructions for performing the steps of the method embodiments of the present application when executed by a processor is provided.
In some embodiments, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of the method embodiments of the present application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (15)
1. A method of processing product data, comprising:
acquiring element data of a product to be processed;
performing dimension reduction polymerization processing on the product element data to obtain product data characteristic information;
and visually outputting the characteristic information of the product data.
2. The method according to claim 1, wherein performing dimension reduction aggregation processing on the product element data to obtain product data feature information comprises:
performing dimensionality reduction on the product element data to obtain dimensionality reduced data;
carrying out data clustering processing on the data after dimensionality reduction to obtain a data set consisting of different types of data;
and performing data fusion processing according to the data set to obtain the product data characteristic information.
3. The method of claim 2, wherein performing data clustering on the dimensionality reduced data to obtain a data set composed of different types of data comprises:
performing data clustering processing on the dimensionality reduced data to obtain a plurality of clustering results;
and removing the scattered point data set in the plurality of clustering results to obtain a data set consisting of different types of data.
4. The method of claim 1, further comprising:
iteratively executing to obtain a user demand, determining recommended content according to the user demand, and performing visual output on the recommended content until an iteration stop condition is met;
wherein the user requirement is provided by the user based on the visually output product data characteristic information.
5. The method according to claim 4, wherein determining recommended content according to the user requirement and visually outputting the recommended content comprises:
determining a plurality of recommended contents according to the user requirements;
sequencing the plurality of recommended contents to obtain sequenced recommended contents;
and performing visual output on the sorted recommended content.
6. The method of claim 5, wherein ranking the plurality of recommended content to obtain ranked recommended content comprises:
determining the matching degree of each recommended content and the user requirement;
and sequencing the plurality of recommended contents according to the sequence from high matching degree to low matching degree to obtain the sequenced recommended contents.
7. The method of any one of claims 1-6, further comprising:
outputting the product data characteristic information to a product related link;
the product related links comprise at least one of a product design link, a product supply link, a product marketing link, a product manufacturing link, a product logistics link and a product service design link.
8. The method of any one of claims 1-6, wherein the product element data includes at least one of interaction customization data, precision marketing data, collaborative development data, collaborative procurement data, supply chain data, intelligent logistics data, and intelligent service data.
9. A product data processing apparatus, comprising:
the acquisition module is used for acquiring element data of a product to be processed;
the processing module is used for carrying out dimension reduction and aggregation processing on the product element data to obtain product data characteristic information;
and the output module is used for visually outputting the characteristic information of the product data.
10. The apparatus of claim 9, wherein the processing module is specifically configured to: performing dimensionality reduction on the product element data to obtain dimensionality reduced data; carrying out data clustering processing on the data subjected to dimensionality reduction to obtain a data set consisting of different types of data; and performing data fusion processing according to the data set to obtain the characteristic information of the product data.
11. The apparatus of claim 10, wherein the processing module is specifically configured to: performing data clustering processing on the dimensionality reduced data to obtain a plurality of clustering results; and removing the scattered point data set in the plurality of clustering results to obtain a data set consisting of different types of data.
12. The apparatus of claim 10, further comprising:
the iteration module is used for iteratively executing the steps of obtaining user requirements, determining recommended content according to the user requirements and visually outputting the recommended content until an iteration stop condition is met; wherein the user requirement is provided by the user based on the visually output product data characteristic information.
13. The apparatus of claim 12, wherein the iteration module is specifically configured to: determining a plurality of recommended contents according to the user requirements; sequencing the plurality of recommended contents to obtain sequenced recommended contents; and performing visual output on the sorted recommended content.
14. The apparatus of claim 13, wherein the iteration module is specifically configured to: determining the matching degree of each recommended content and the user requirement; and sequencing the plurality of recommended contents according to the sequence from high matching degree to low matching degree to obtain the sequenced recommended contents.
15. A computer-readable storage medium, having stored thereon computer-executable instructions, which when executed by a processor, are configured to implement the product data processing method of any one of claims 1 to 8.
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