Disclosure of Invention
The technical problem solved by the invention is to provide a commodity classification management and purchasing method of a cosmetic sample vending machine, so that a customer can quickly find a sample product to be purchased.
The technical scheme adopted for solving the technical problems is that the commodity classification management and purchasing method of the cosmetic sample vending machine comprises the following steps:
The commodity information storage step is to store unique identification and related labels of each commodity in the vending machine;
A voice data acquisition step of acquiring voice input data of a user;
a voice recognition step of converting the collected voice input data into text data;
a natural language processing step, namely carrying out language processing and semantic understanding on the text data;
Matching the semantic words processed by the natural language processing step with the labels in the commodity database of the vending machine;
and a sequencing display step, namely sequencing and displaying the commodities matched with the labels by adopting a sequencing algorithm.
Further, in the commodity information storage step, unique identifiers and relevant labels of all commodities in the vending machine are stored;
the labels comprise a primary classification and a secondary classification, wherein the primary classification is a commodity type label, and the secondary classification is a commodity characteristic label.
Further, in the natural language processing step, language processing is carried out on the text data and semantic understanding is carried out, specifically:
Text preprocessing, namely performing word segmentation, word filtering stopping and word shape normalization operation on text data;
Extracting a keyword, namely extracting a high-frequency keyword through word frequency statistics and named entity recognition;
and a semantic understanding step, namely analyzing the grammar structure and semantic relation of the sentence.
Further, in the keyword mapping and tag matching step, the semantic words processed in the natural language processing step are matched with tags in a commodity database of the vending machine, specifically:
A synonym step of mapping synonyms of different expression modes to a unified tag;
the label matching step, namely rapidly matching the label mapped by the synonym to the label in the commodity database by using the inverted index or the hash map;
and a similarity evaluation step, namely calculating the similarity between the user demand and the commodity label.
And further, in the sorting and displaying step, a weighted scoring method is adopted to sort and display the candidate commodities.
The invention also discloses a commodity classification management and purchasing system of the small cosmetic sample vending machine, which comprises a commodity information storage module, a voice data acquisition module, a voice recognition module, a natural language processing module, a keyword mapping and tag matching module and a sequencing display module;
the commodity information storage module is used for storing unique identifiers and related labels of each commodity in the vending machine;
The voice data acquisition module is used for acquiring voice input data of a user;
The voice recognition module is used for converting the collected voice input data into text data;
The natural language processing module is used for carrying out language processing and semantic understanding on the text data;
the keyword mapping and tag matching module is used for matching the semantic words processed by the natural language processing step with tags in a commodity database of the vending machine;
the sorting and displaying module is used for sorting and displaying the commodities with the matched labels by adopting a sorting algorithm.
Further, the voice recognition module comprises a microphone and an audio processing chip.
The commodity information storage module is used for storing unique identifiers and related labels of each commodity in the vending machine;
the labels comprise a primary classification and a secondary classification, wherein the primary classification is a commodity type label, and the secondary classification is a commodity characteristic label.
The invention also discloses a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program realizes the steps of the commodity classification management and the purchasing method of the cosmetic sample vending machine when being executed by a processor.
The invention also discloses a computer device, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are in communication with each other through the communication bus, and the computer device comprises:
The memory is used for storing a computer program;
the processor is used for executing the steps of the commodity classification management and purchasing method of the cosmetic hand-sample vending machine by running the program stored in the memory.
The method has the beneficial effects that through commodity classification management and setting of the purchasing function, the deleting time of a consumer on required product functions and the like is reduced, so that the consumer can quickly purchase the cosmetic sample of the heart instrument.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings.
As shown in fig. 1, the present application discloses a first embodiment, which is a method for classifying, managing and referring to a commodity of a small cosmetic sample vending machine, comprising:
The commodity information storage step is to store unique identification and related labels of each commodity in the vending machine;
A voice data acquisition step of acquiring voice input data of a user;
a voice recognition step of converting the collected voice input data into text data;
a natural language processing step, namely carrying out language processing and semantic understanding on the text data;
Matching the semantic words processed by the natural language processing step with the labels in the commodity database of the vending machine;
and a sequencing display step, namely sequencing and displaying the commodities matched with the labels by adopting a sequencing algorithm.
According to the method, through commodity classification management and setting of the purchasing function, the deleting time of a consumer to required product functions and the like is reduced, so that the consumer can rapidly purchase the cosmetic sample of the self-centering instrument.
In the embodiment, in the commodity information storage step, unique identification and related labels of each commodity in the vending machine are stored, wherein the labels comprise primary classification and secondary classification, the primary classification is a commodity type label, and the secondary classification is a commodity characteristic label.
Specific examples may be shown in the following table:
| Field name | Field English name | Remarks |
| Commodity ID | PRODUCT_ID | Main key |
| Commodity name | PRODUCT_NAME | |
| Commodity sales | PRODUCT_SALE | |
| Branding | PRODUCT_BRAND | |
| Net content of | PRODUCT_CONTENT | |
| Price of | PRODUCT_PRICE | |
| Primary classification label | PRODUCT_TYPE | |
| One of the two-stage classification labels | | |
| Two-level classification label | | |
| ...... | | |
The first class is classified into commodity types, such as water emulsion and face cream of skin care products, foundation makeup foundation and concealer, etc., and the second class is classified into the same commodity type according to its characteristics, such as color system, texture, efficacy, etc.
The uniqueness of each commodity can be guaranteed through the classification, so that follow-up accurate recommendation is facilitated.
Specifically, the device needs to be equipped with a voice data collection module and a natural language processing module (NLP, natuarl Language Processing) to collect the voice of the user, extract keywords for recognizing the input voice, and locate the purchase demand of the user.
The voice data acquisition module carried by the equipment is used for recording voice input of a user. The hardware of the acquisition module comprises a microphone and an audio processing chip. The collected speech data is converted to text data by speech recognition technology (ASR, automatic Speech Recognition) in preparation for further processing.
In this embodiment, in the step of processing natural language, language processing is performed on the text data and semantic understanding is performed, which specifically includes:
Text preprocessing, namely performing word segmentation, word filtering stopping and word shape normalization operation on text data;
Extracting a keyword, namely extracting a high-frequency keyword through word frequency statistics and named entity recognition;
and a semantic understanding step, namely analyzing the grammar structure and semantic relation of the sentence.
Specifically, in the text preprocessing step:
the word segmentation is to perform word segmentation processing on the text after voice recognition. For example, the user inputs "I want a control cream for oily skin. "to be divided into" i/want/one/money/fit/oily skin/oil control/face cream ".
Stop word filtering is to remove common but not practical stop words such as "me", etc.
The morphology normalization is to normalize synonyms, paraphrasing, such as "oily skin" and "oily skin" to "oily skin".
In the keyword extraction step, word frequency statistics is that high-frequency keywords are extracted by counting word frequencies, for example, in the text, oily skin, oil control and face cream are high-frequency keywords. Extracting keywords with higher distinction degree from a large amount of corpus;
Named entities are identified as identifying particular entities in the user input, such as brand names, product categories, and the like.
In the semantic understanding step, the grammar structure and the semantic relation of sentences are analyzed, for example, the fact that "face cream" is a commodity intended by a user, the fact that "oily skin" and "oil control" are demand conditions and "I want" input by the user are identified as buying intention.
The method can accurately extract the requirements of customers, thereby facilitating the subsequent label matching.
In this embodiment, in the step of mapping the keyword and matching the tag, the matching is performed between the semantic word processed in the step of processing the natural language and the tag in the commodity database of the vending machine, specifically:
A synonym step of mapping synonyms of different expression modes to a unified tag;
the label matching step, namely rapidly matching the label mapped by the synonym to the label in the commodity database by using the inverted index or the hash map;
and a similarity evaluation step, namely calculating the similarity between the user demand and the commodity label.
And in the sorting and displaying step, a weighted scoring method is adopted to sort and display the candidate commodities.
Specifically, in the above steps, synonyms of different expression modes are mapped to a unified label, for example, "oil control" and "oil resistance" are mapped to the same label, and when label matching is performed, the keyword is quickly matched to the label in the commodity database by using inverted index or hash mapping. For example, the "oil control face cream" is mapped to the "oil control" label in the "face cream" category, then the similarity between the user demand and the commodity label is calculated, the commodity with high matching degree is screened out, and then the candidate commodity is ranked by using a comprehensive ranking algorithm such as a weighted scoring method. For example, considering commodity grading, sales volume, matching degree and other factors, the commodity grading, sales volume, matching degree and other factors are displayed to the user in sequence.
Through the steps, accurate recommendation of the cosmetic sample can be realized according to the demands of customers, so that the screening time of purchasers is shortened.
The invention also discloses a second embodiment, which is a commodity classification management and purchasing system of the small cosmetic sample vending machine, and comprises a commodity information storage module, a voice data acquisition module, a voice recognition module, a natural language processing module, a keyword mapping and tag matching module and a sequencing display module;
the commodity information storage module is used for storing unique identifiers and related labels of each commodity in the vending machine;
The voice data acquisition module is used for acquiring voice input data of a user;
The voice recognition module is used for converting the collected voice input data into text data;
The natural language processing module is used for carrying out language processing and semantic understanding on the text data;
the keyword mapping and tag matching module is used for matching the semantic words processed by the natural language processing step with tags in a commodity database of the vending machine;
the sorting and displaying module is used for sorting and displaying the commodities with the matched labels by adopting a sorting algorithm.
Specifically, the voice recognition module includes a microphone and an audio processing chip, and the collected voice data is converted into text data by a voice recognition technology (ASR, automatic Speech Recognition) to be ready for the next processing.
Specifically, the commodity information storage module is used for storing unique identifiers and related labels of each commodity in the vending machine;
the labels comprise a primary classification and a secondary classification, wherein the primary classification is a commodity type label, and the secondary classification is a commodity characteristic label.
The first class is classified into commodity types, such as water emulsion and face cream of skin care products, foundation makeup foundation and concealer, etc., and the second class is classified into the same commodity type according to its characteristics, such as color system, texture, efficacy, etc.
The invention also discloses a third embodiment, which is a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the computer program realizes the steps of the commodity classification management and the recommendation method of the cosmetic sample vending machine when being executed by a processor.
Further, the computer readable storage medium provided in the present embodiment may be any combination of one or more readable storage media, where the readable storage media includes an electric, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
The invention also discloses a fourth embodiment, which is a computer device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are used for completing the communication between the processor and the memory through the communication bus, and the invention further comprises the following steps:
The memory is used for storing a computer program;
the processor is used for executing the steps of the commodity classification management and purchasing method of the cosmetic hand-sample vending machine by running the program stored in the memory.
As an embodiment of the present invention, the communication bus mentioned by the above-mentioned terminal may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, abbreviated as PCI) bus or an extended industry standard architecture (ExtendedIndustry StandardArchitecture, abbreviated as EISA) bus, or the like. The communication bus may be classified as an address bus, a data bus, a control bus, or the like.
As an embodiment of the present invention, a communication interface is used for communication between the terminal and other devices.
The memory may include random access memory (RandomAc cessMemory, simply RAM) or non-volatile memory (non-volatile memory), such as at least one disk memory, as an embodiment of the present invention. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
As an embodiment of the present invention, the processor may be a general-purpose processor, including a central Processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a digital signal processor (DIG I TA L SIG NA L Processing, DSP), an Application Specific Integrated Circuit (ASIC), a Field-programmable gate array (Field-Programmable GateArray, FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
Compared with the prior art, the method reduces the deleting time of consumers to the required product functions and the like through commodity classification management and setting of the purchasing function, so that the consumers can rapidly purchase the cosmetic sample of the self-centering instrument.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications are within the spirit and principles of the invention.