CN112199451A - Commodity identification method and device, computer equipment and storage medium - Google Patents
Commodity identification method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN112199451A CN112199451A CN202011069153.3A CN202011069153A CN112199451A CN 112199451 A CN112199451 A CN 112199451A CN 202011069153 A CN202011069153 A CN 202011069153A CN 112199451 A CN112199451 A CN 112199451A
- Authority
- CN
- China
- Prior art keywords
- commodity
- identified
- matching
- commodities
- specification model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 58
- 230000015654 memory Effects 0.000 claims description 40
- 239000013598 vector Substances 0.000 claims description 26
- 238000010586 diagram Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000001360 synchronised effect Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 244000062793 Sorghum vulgare Species 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 235000019713 millet Nutrition 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Cash Registers Or Receiving Machines (AREA)
Abstract
The embodiment of the invention relates to a commodity identification method, a commodity identification device, computer equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining the categories, brands and at least one specification model parameter of a plurality of commodities to be identified; classifying a plurality of the commodities to be identified; for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified, wherein the commodity matching template is provided with a corresponding standard specification model parameter; and dividing the commodities to be identified into the commodity set corresponding to the target commodity matching template, so that the commodities can be accurately identified by the method, and a standard commodity library is established.
Description
Technical Field
The embodiment of the invention relates to the field of commodity identification, in particular to a commodity identification method, a commodity identification device, computer equipment and a storage medium.
Background
The dynamic escort business derived based on the internet e-commerce mode needs to establish and manage a set of standard commodity library, maintain the parameters of description, specification models or price and the like of standard commodities and is used for evaluating the pledge commodities of a financing party in the financing business of e-commerce. When a standard commodity library is established, the identification of the same type of commodities needs to be solved firstly.
In the prior art, keyword search can be performed through a traditional relational database, and fuzzy matching of commodities is realized. However, the above method is to implement fuzzy matching, that is, the recognition accuracy is low, so that it is impossible to implement the above method to establish an accurate standard commodity library.
Disclosure of Invention
In view of this, in order to solve the above technical problem that the product cannot be accurately identified, embodiments of the present invention provide a product identification method, a product identification device, a computer device, and a storage medium.
In a first aspect, an embodiment of the present invention provides a method for identifying a commodity, including:
the method comprises the steps of obtaining the categories, brands and at least one specification model parameter of a plurality of commodities to be identified;
classifying the commodities to be identified, wherein the commodities to be identified in the same category are the same in category and brand;
for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified, wherein the commodity matching template is provided with a corresponding standard specification model parameter;
and dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
In one possible embodiment, the method further comprises:
searching a plurality of commodity matching templates corresponding to the categories by taking the first specification model parameters of the commodities to be identified as keywords;
and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
In one possible embodiment, the method further comprises:
if the commodity matching template containing the keywords is not found, determining the matching degree of the commodity to be identified and a plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodity to be identified;
and determining the commodity matching template with the matching degree exceeding a preset threshold value as the target commodity matching template.
In one possible embodiment, the method further comprises:
extracting a characteristic vector of the commodity to be identified according to at least one specification model parameter of the commodity to be identified;
calculating the similarity between the feature vector and the standard feature vectors of the plurality of commodity matching templates corresponding to the categories;
and determining the matching degree of the commodities to be identified and the plurality of commodity matching templates corresponding to the categories based on the similarity.
In one possible embodiment, the method further comprises:
searching each specification model parameter of the commodity to be identified in the commodity matching template aiming at each commodity matching template corresponding to the category;
if the specification model parameters are found in the commodity matching template, determining a first matching degree of the specification model parameters and the commodity matching template based on a preset weight value corresponding to the specification model parameters;
and determining the matching degree of the commodity to be identified and the commodity matching template based on the first matching degree of each specification model parameter and the commodity matching template.
In one possible embodiment, the method further comprises:
if the commodity matching template containing the keywords is found, setting the matching degree corresponding to the commodity to be identified as a preset value;
for each commodity set, determining a standard commodity from the commodity set according to the matching degree of the commodity to be identified and a commodity matching template corresponding to the commodity set and historical sales information of the commodity to be identified to obtain a plurality of standard commodities, wherein the historical sales information at least comprises sales volume and sales price;
and storing a plurality of standard commodities to a standard commodity library.
In a second aspect, an embodiment of the present invention provides a product identification apparatus, including:
the system comprises an acquisition module, a recognition module and a recognition module, wherein the acquisition module is used for acquiring the categories, brands and at least one specification model parameter of a plurality of commodities to be recognized;
the classification module is used for classifying a plurality of commodities to be identified;
the matching module is used for determining the number of target commodity matching templates matched with the commodities to be identified from a plurality of preset commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodities to be identified aiming at each commodity to be identified in each category;
the matching module is further used for dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
In a possible implementation manner, the matching module is specifically configured to search a plurality of commodity matching templates corresponding to the categories by using the first specification model parameter of the commodity to be identified as a keyword; and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
In a third aspect, an embodiment of the present invention provides a computer device, including: a processor and a memory, the processor being configured to execute a product identification program stored in the memory to implement the product identification method described in the above first aspect.
In a fourth aspect, an embodiment of the present invention provides a storage medium, including: the storage medium stores one or more programs, which are executable by one or more processors, to implement the article identification method described in the above first aspect.
According to the commodity identification scheme provided by the embodiment of the invention, the types, brands and at least one specification model parameter of a plurality of commodities to be identified are obtained; classifying a plurality of the commodities to be identified; for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified; will treat discernment commodity divide extremely in the commodity set that target commodity match template corresponds, because the precision of traditional commodity discernment and matching mode is lower, can not be according to the same money commodity of the accurate discernment different trade companies of demand, through this scheme to the multilayer matching recognition of commodity, can realize the same money commodity of accurate discernment all trade companies, and then for the business is deposited to dynamic goods establish one set of standard commodity storehouse.
Drawings
Fig. 1 is a schematic flow chart of a method for identifying a commodity according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an embodiment of step S13 according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S23 according to the present invention;
FIG. 4 is a flowchart illustrating another step S23 according to an embodiment of the present invention;
fig. 5 is a flowchart of a method for establishing a standard commodity library according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a product identification device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
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.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained with reference to specific embodiments, which are not to be construed as limiting the embodiments of the present invention.
Fig. 1 is a schematic flow chart of a method for identifying a commodity according to an embodiment of the present invention, and as shown in fig. 1, the method specifically includes:
and S11, acquiring the categories, brands and at least one specification model parameter of the commodities to be identified.
In the embodiment of the invention, a plurality of commodities are obtained from the database of the online shopping mall, and the commodities are identified by using the commodity identification method provided by the embodiment of the invention so as to identify the same commodity. Hereinafter, for convenience of description, the plurality of commodities will be referred to as commodities to be identified.
Further, the commodity information of each commodity to be identified can be obtained from the database, and the commodity information includes but is not limited to: a category, a brand, and at least one specification model parameter.
Taking a certain mobile phone as an example, the categories refer to: first class, digital, second class, communication, third class, mobile phone; the brand refers to Hua Shi; the rule model parameters include, but are not limited to: 256G and 8G.
And S12, classifying the commodities to be identified.
As an embodiment, in step S12, the obtained multiple commodities to be identified are first classified according to categories and brands, so that commodities of the same category and brand are classified into the same category, and multiple categories are obtained.
And S13, aiming at each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the categories on the basis of at least one specification model parameter of the commodity to be identified.
In step S13, the to-be-identified commodities in each category are further precisely identified, so as to identify the commodities of the same type.
As an embodiment, commodity matching templates corresponding to a plurality of commodities can be preset manually according to experience, each commodity matching template includes standard rule parameters of a commodity, which means that the commodity matching template can be used to describe a commodity.
For example, taking a commodity as a mobile phone as an example, the main classes in the matching template include: first class, digital, second class, communication, third class, mobile phone; brand name: huashi, millet or apple, etc.; the main attributes of the specification and the model are as follows: and commodity information such as internal memory, transportation or camera.
Based on the commodity matching template, at least one specification model parameter of the commodity to be identified and a standard specification model parameter in each commodity matching template can be matched for each commodity to be identified in each category, if the commodity to be identified is matched with the standard specification model parameter, the commodity to be identified belongs to the matched commodity matching template, and the commodity matching template successfully matched with the commodity to be identified is referred to as a target commodity matching template for convenience in description.
How to determine a target product matching template matching the product to be identified from a plurality of preset product matching templates corresponding to the category based on at least one specification model parameter of the product to be identified is exemplified by different embodiments hereinafter, and will not be described in detail here.
And S14, dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
As can be seen from the above description, if the to-be-identified product matches with the target product matching template, it means that the to-be-identified product belongs to the product described by the target product matching template, and therefore, all the to-be-identified products can be divided into sets corresponding to the target product matching templates corresponding thereto, so as to obtain a plurality of product sets. The commodities in the same commodity set are the same type of commodities. For example, one of the obtained sets of items includes a certain model of mobile phone of a brand. As another example, a certain collection of goods includes a certain model of notebook computer of millet brand.
The commodity identification method provided by the embodiment of the invention comprises the steps of obtaining the types, brands and at least one specification model parameter of a plurality of commodities to be identified; classifying a plurality of the commodities to be identified; for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified; the commodities to be identified are divided into commodity sets corresponding to the target commodity matching templates, and the commodities and the commodity matching templates are matched through the scheme, so that the same type of commodities of all merchants can be accurately identified.
Fig. 2 is a flowchart of an embodiment of step S13 according to an embodiment of the present invention, and as shown in fig. 2, the method specifically includes:
s21, searching a plurality of commodity matching templates corresponding to the categories by taking the first specification model parameters of the commodities to be identified as keywords.
Generally, a certain type of commodity can be identified through a small number of key specification model parameters, and therefore, as an embodiment, when matching is performed, preset key specification model parameters (hereinafter referred to as first specification model parameters for convenience of description), first, matching of the commodity and a commodity matching template is performed through the first specification model parameters.
Specifically, as an optional implementation manner, the first specification model parameter is used as a keyword, and a plurality of commodity matching templates corresponding to the category where the commodity is located are searched from a plurality of preset commodity matching templates. And the first specification model parameters of the plurality of commodity matching templates corresponding to the category of the commodity are the same.
And S22, if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
And if the commodity matching template with the same parameters as the first specification model is found from the plurality of commodity matching templates, determining the commodity matching template as a target commodity matching template. And the parameters of the first specification model are the same, so that the matching result is accurate.
Further, a matching degree (for example, 100%) is set for the to-be-identified product successfully matched with the target product matching template, the to-be-identified product and the product recorded by the target product matching template are considered to be the same product, and at this time, the matched multiple products are all products accurately matched.
And S23, if the commodity matching template containing the keyword is not found, determining the matching degree of the commodity to be identified and the plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodity to be identified.
And S24, determining the commodity matching template with the matching degree exceeding a preset threshold value as the target commodity matching template.
Here, fuzzy matching is performed, because descriptions of the same commodity by each merchant may be different, there may be some commodities which cannot be accurately matched after the accurate matching, and in this case, fuzzy matching is continued
How this is achieved is illustrated below by means of two embodiments, fig. 3 and fig. 4, which are not described in detail here.
According to the commodity identification method provided by the embodiment of the invention, the specification and model parameter information of the commodities is firstly accurately matched, and then fuzzy matching is carried out, so that most commodities can be identified.
Fig. 3 is a flowchart of an embodiment of step S23 according to an embodiment of the present invention, and as shown in fig. 3, the method specifically includes:
s31, extracting the feature vector of the commodity to be identified according to at least one specification model parameter of the commodity to be identified.
In an optional embodiment of the present invention, since descriptions of the same product by each merchant may differ, there may be some products that cannot be accurately matched after the accurate matching, and the product needs to be identified according to the description of the product by the merchant. Through analysis of the commodity description information, a plurality of characteristics corresponding to the commodity are obtained from the description information of the commodity, and a characteristic vector of the commodity is calculated according to the obtained plurality of characteristics.
For example, the natural language recognition NLP algorithm is used to perform overall language semantic recognition and calculate feature vectors according to the information described in the product or the specification and model parameters described in the product page.
And S32, calculating the similarity between the characteristic vector and the standard characteristic vectors of the plurality of commodity matching templates corresponding to the categories.
And comparing the similarity of the feature vector of the to-be-identified commodity obtained by the calculation with the feature vectors of the plurality of commodity matching templates corresponding to the category of the commodity.
And S33, determining the matching degree of the to-be-identified commodities and the plurality of commodity matching templates corresponding to the categories based on the similarity.
And representing the matching degree of the commodity to be identified and the commodity matching template by using the similarity of the feature vector of the commodity to be identified and the feature vectors of the plurality of commodity matching templates corresponding to the category of the commodity.
And setting the commodity matching template with the similarity between the feature vector of the commodity to be identified and the feature vectors of the commodity matching templates corresponding to the category of the commodity exceeding a preset threshold value (for example, 95%) as a target commodity matching template, and further obtaining a plurality of commodities which are the same commodity as the target commodity matching template.
Fig. 4 is a flowchart of another embodiment of step S23 provided in the embodiment of the present invention, and as shown in fig. 4, the method specifically includes:
and S41, aiming at each commodity set, determining a standard commodity from the commodity set according to the matching degree of the to-be-identified commodity and the commodity matching template corresponding to the commodity set and the historical sales information of the to-be-identified commodity, and obtaining a plurality of standard commodities.
And in the commodity set which is matched with the target commodity matching template and identified as the same commodity, determining a standard commodity under the condition of the matching degree, the historical sales volume and the historical sales price of each commodity and the commodity matching template corresponding to the commodity set, and further obtaining a plurality of standard commodities of a plurality of commodity sets.
For example, the matching degree of a certain commodity and the commodity matching template corresponding to the commodity set where the commodity is located is 100%, the historical sales volume of the commodity is the highest compared with the rest of the commodities in the commodity set, the historical sales price is stable, and the difference between the historical sales volume of the commodity and the manufacturer guide price or the average sales price of the commodity on the market is within a set range, then the commodity is taken as the standard commodity.
S42, if the specification model parameters are found in the commodity matching template, determining a first matching degree of the specification model parameters and the commodity matching template based on a preset weight value corresponding to the specification model parameters.
If the corresponding specification and model parameters are found in the commodity matching template, taking a preset weight value (for example, 1) corresponding to the specification and model parameters as a first matching degree of the commodity and the commodity matching template.
Optionally, if the corresponding specification model parameter is found, the matching degree obtained by the specification model parameter is set to 0.
For example, the memory of a certain mobile phone is 256G, if a product matching template with the memory of 256G is found, the matching degree is recorded as 1, further, the shipping storage is 8G, if a product matching template with the shipping storage of 8G is found, the matching degree is recorded as 1, and then, the matching degree obtained by the mobile phone is 2.
And S43, determining the matching degree of the commodity to be recognized and the commodity matching template based on the first matching degree of each specification model parameter and the commodity matching template.
And obtaining the matching degree of the commodity to be identified and the commodity matching template according to the sum of the first matching degrees obtained by matching each specification model parameter and the commodity matching template, determining the commodity with the matching degree exceeding a set value (for example, 10) as the same commodity as the commodity matching template, and obtaining a commodity set corresponding to each commodity matching template.
It should be noted that the two flows shown in fig. 3 and fig. 4 are only two possible implementations of fuzzy matching, and in practical applications, other implementations may also exist. For example, the flow shown in fig. 3 is executed first, and then the flow shown in fig. 4 is executed for the commodity of the unmatched upper template, or the flow shown in fig. 4 is executed first, and then the flow shown in fig. 3 is executed for the commodity of the unmatched upper template, which can realize multi-layer matching.
Fig. 5 is a flowchart of a method for establishing a standard commodity library according to an embodiment of the present invention, and as shown in fig. 5, the method specifically includes:
and S51, if the commodity matching template containing the keywords is found, setting the matching degree corresponding to the commodity to be identified as a preset value.
If the commodity matching template containing the keywords is found, the matching degree of the to-be-identified commodity and the commodity matching template is set as a preset value (for example, 1).
For example, the memory of a certain mobile phone is 256G, the shipping number is 8G, if a matching template of the memory 256G and the shipping number 8G is found, the matching degree is recorded as 1, and then the matching degree obtained by the mobile phone is 1.
And S52, aiming at each commodity set, determining a standard commodity from the commodity set according to the matching degree of the to-be-identified commodity and the commodity matching template corresponding to the commodity set and the historical sales information of the to-be-identified commodity, and obtaining a plurality of standard commodities.
And S53, storing a plurality of standard commodities in a standard commodity library.
In the commodity set which is matched with the target commodity matching template and identified as the same commodity, determining a standard commodity under the condition of the matching degree, the historical sales volume and the historical sales price of each commodity and the commodity matching template corresponding to the commodity set, further obtaining a plurality of standard commodities of a plurality of commodity sets, storing the obtained plurality of standard commodities into a standard commodity library, and recording the incidence relation of the rest commodities in the commodity set and the standard commodities as the same commodity.
For example, the matching degree of a certain commodity and the commodity matching template corresponding to the commodity set in which the commodity is located is 100%, the historical sales volume of the commodity is the highest compared with the rest of the commodities in the commodity set, the historical sales price is stable, and the difference between the historical sales volume of the commodity and the manufacturer guide price or the average sales price of the commodity on the market is within a set range, so that the commodity is used as a standard commodity and is stored in a standard commodity library.
According to the commodity identification scheme provided by the embodiment of the invention, the types, brands and at least one specification model parameter of a plurality of commodities to be identified are obtained; classifying a plurality of the commodities to be identified; for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified; the commodities to be identified are divided into the commodity sets corresponding to the target commodity matching templates, and the commodities are identified according to the scheme, so that the same type of commodities of all merchants can be accurately identified, a marked commodity library is established, and the value evaluation of the quality-guaranteed commodities during enterprise financing business is assisted.
Fig. 6 is a schematic structural diagram of a product identification device according to an embodiment of the present invention, which specifically includes:
an obtaining module 601, configured to obtain categories, brands, and at least one specification model parameter of multiple commodities to be identified;
a classification module 602, configured to classify a plurality of the commodities to be identified;
a matching module 603, configured to determine, for each to-be-identified commodity in each category, a number of target commodity matching templates that match the to-be-identified commodity from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the to-be-identified commodity;
the matching module 603 is further configured to divide the to-be-identified commodities into commodity sets corresponding to the target commodity matching templates.
In a possible implementation manner, the matching module 603 is specifically configured to search a plurality of product matching templates corresponding to the categories by using the first specification model parameter of the product to be identified as a keyword; and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
In a possible embodiment, the matching module 603 is further configured to determine, if a product matching template including the keyword is not found, matching degrees between the product to be identified and a plurality of product matching templates corresponding to the categories based on at least one specification model parameter of the product to be identified; and determining the commodity matching template with the matching degree exceeding a preset threshold value as the target commodity matching template.
In a possible embodiment, the matching module 603 is further configured to extract a feature vector of the to-be-identified commodity according to at least one specification model parameter of the to-be-identified commodity; calculating the similarity between the feature vector and the standard feature vectors of the plurality of commodity matching templates corresponding to the categories; and determining the matching degree of the commodities to be identified and the plurality of commodity matching templates corresponding to the categories based on the similarity.
In a possible embodiment, the matching module 603 is further configured to, for each of the product matching templates corresponding to the categories, search each of the specification and model parameters of the product to be identified in the product matching template; if the specification model parameters are found in the commodity matching template, determining a first matching degree of the specification model parameters and the commodity matching template based on a preset weight value corresponding to the specification model parameters; and determining the matching degree of the commodity to be identified and the commodity matching template based on the first matching degree of each specification model parameter and the commodity matching template.
In a possible implementation manner, the matching module 603 is further configured to set, if a matching template of the product including the keyword is found, the matching degree corresponding to the product to be identified as a preset value; for each commodity set, determining a standard commodity from the commodity set according to the matching degree of the commodity to be identified and a commodity matching template corresponding to the commodity set and historical sales information of the commodity to be identified to obtain a plurality of standard commodities, wherein the historical sales information at least comprises sales volume and sales price; and storing a plurality of standard commodities to a standard commodity library.
The product identification device provided in this embodiment may be the product identification device shown in fig. 6, and may perform all the steps of the product identification method shown in fig. 1 to 5, so as to achieve the technical effects of the product identification method shown in fig. 1 to 5, which please refer to the related descriptions of fig. 1 to 5 for brevity, which is not described herein again.
Fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention, where the computer device 700 shown in fig. 7 includes: at least one processor 701, memory 702, at least one network interface 704, and other user interfaces 703. The various components in the computer device 700 are coupled together by a bus system 705. It is understood that the bus system 705 is used to enable communications among the components. The bus system 705 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration the various busses are labeled in figure 7 as the bus system 705.
The user interface 703 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, trackball, touch pad, or touch screen, among others.
It is to be understood that the memory 702 in embodiments of the present invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a programmable Read-only memory (PROM), an erasable programmable Read-only memory (erasabprom, EPROM), an electrically erasable programmable Read-only memory (EEPROM), or a flash memory. The volatile memory may be a Random Access Memory (RAM) which functions as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (staticiram, SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous dynamic random access memory (syncronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM ), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). The memory 702 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 702 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system 7021 and application programs 7022.
The operating system 7021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 7022 includes various applications, such as a media player (MediaPlayer), a Browser (Browser), and the like, for implementing various application services. Programs that implement methods in accordance with embodiments of the present invention can be included within application program 7022.
In the embodiment of the present invention, the processor 701 is configured to execute the method steps provided by the method embodiments by calling a program or an instruction stored in the memory 702, specifically, a program or an instruction stored in the application 7022, for example, and includes:
the method comprises the steps of obtaining the categories, brands and at least one specification model parameter of a plurality of commodities to be identified; classifying the commodities to be identified, wherein the commodities to be identified in the same category are the same in category and brand; for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified, wherein the commodity matching template is provided with a corresponding standard specification model parameter; and dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
In one possible implementation manner, a plurality of commodity matching templates corresponding to the categories are searched by taking the first specification model parameters of the commodities to be identified as keywords; and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
In a possible implementation manner, if a commodity matching template containing the keyword is not found, determining the matching degree of the commodity to be identified and a plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodity to be identified; and determining the commodity matching template with the matching degree exceeding a preset threshold value as the target commodity matching template.
In one possible implementation mode, extracting a feature vector of the commodity to be identified according to at least one specification model parameter of the commodity to be identified; calculating the similarity between the feature vector and the standard feature vectors of the plurality of commodity matching templates corresponding to the categories; and determining the matching degree of the commodities to be identified and the plurality of commodity matching templates corresponding to the categories based on the similarity.
In a possible implementation manner, for each commodity matching template corresponding to the category, each specification model parameter of the commodity to be identified is searched in the commodity matching template; if the specification model parameters are found in the commodity matching template, determining a first matching degree of the specification model parameters and the commodity matching template based on a preset weight value corresponding to the specification model parameters; and determining the matching degree of the commodity to be identified and the commodity matching template based on the first matching degree of each specification model parameter and the commodity matching template.
In a possible implementation manner, if a commodity matching template containing the keyword is found, setting the matching degree corresponding to the commodity to be identified as a preset value; for each commodity set, determining a standard commodity from the commodity set according to the matching degree of the commodity to be identified and a commodity matching template corresponding to the commodity set and historical sales information of the commodity to be identified to obtain a plurality of standard commodities, wherein the historical sales information at least comprises sales volume and sales price; and storing a plurality of standard commodities to a standard commodity library.
The method disclosed in the above embodiments of the present invention may be applied to the processor 701, or implemented by the processor 701. The processor 701 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be implemented by integrated logic circuits of hardware or instructions in the form of software in the processor 701. The processor 701 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 702, and the processor 701 reads the information in the memory 702 and performs the steps of the above method in combination with the hardware thereof.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
The computer device provided in this embodiment may be a computer device as shown in fig. 7, and may perform all the steps of the product identification method shown in fig. 1 to 5, so as to achieve the technical effect of the product identification method shown in fig. 1 to 5, and for brevity, it is described with reference to fig. 1 to 5, which is not described herein again.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium herein stores one or more programs. Among others, the storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
When one or more programs in the storage medium are executable by one or more processors, the above-described article identification method executed on the computer device side is realized.
The processor is used for executing the commodity identification program stored in the memory so as to realize the following steps of the commodity identification method executed on the computer equipment side:
the method comprises the steps of obtaining the categories, brands and at least one specification model parameter of a plurality of commodities to be identified; classifying the commodities to be identified, wherein the commodities to be identified in the same category are the same in category and brand; for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified, wherein the commodity matching template is provided with a corresponding standard specification model parameter; and dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
In one possible implementation manner, a plurality of commodity matching templates corresponding to the categories are searched by taking the first specification model parameters of the commodities to be identified as keywords; and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
In a possible implementation manner, if a commodity matching template containing the keyword is not found, determining the matching degree of the commodity to be identified and a plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodity to be identified; and determining the commodity matching template with the matching degree exceeding a preset threshold value as the target commodity matching template.
In one possible implementation mode, extracting a feature vector of the commodity to be identified according to at least one specification model parameter of the commodity to be identified; calculating the similarity between the feature vector and the standard feature vectors of the plurality of commodity matching templates corresponding to the categories; and determining the matching degree of the commodities to be identified and the plurality of commodity matching templates corresponding to the categories based on the similarity.
In a possible implementation manner, for each commodity matching template corresponding to the category, each specification model parameter of the commodity to be identified is searched in the commodity matching template; if the specification model parameters are found in the commodity matching template, determining a first matching degree of the specification model parameters and the commodity matching template based on a preset weight value corresponding to the specification model parameters; and determining the matching degree of the commodity to be identified and the commodity matching template based on the first matching degree of each specification model parameter and the commodity matching template.
In a possible implementation manner, if a commodity matching template containing the keyword is found, setting the matching degree corresponding to the commodity to be identified as a preset value; for each commodity set, determining a standard commodity from the commodity set according to the matching degree of the commodity to be identified and a commodity matching template corresponding to the commodity set and historical sales information of the commodity to be identified to obtain a plurality of standard commodities, wherein the historical sales information at least comprises sales volume and sales price; and storing a plurality of standard commodities to a standard commodity library.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for identifying an article, comprising:
the method comprises the steps of obtaining the categories, brands and at least one specification model parameter of a plurality of commodities to be identified;
classifying the commodities to be identified, wherein the commodities to be identified in the same category are the same in category and brand;
for each commodity to be identified in each category, determining a target commodity matching template matched with the commodity to be identified from a plurality of preset commodity matching templates corresponding to the category based on at least one specification model parameter of the commodity to be identified, wherein the commodity matching template is provided with a corresponding standard specification model parameter;
and dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
2. The method of claim 1, wherein the specification model parameter comprises a first specification model parameter;
the determining, from a plurality of preset commodity matching templates corresponding to the categories, a target commodity matching template matching the commodity to be identified based on at least one specification model parameter of the commodity to be identified includes:
searching a plurality of commodity matching templates corresponding to the categories by taking the first specification model parameters of the commodities to be identified as keywords;
and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
3. The method of claim 2, further comprising:
if the commodity matching template containing the keywords is not found, determining the matching degree of the commodity to be identified and a plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodity to be identified;
and determining the commodity matching template with the matching degree exceeding a preset threshold value as the target commodity matching template.
4. The method according to claim 3, wherein the determining the matching degree of the to-be-identified commodity and a plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the to-be-identified commodity comprises:
extracting a characteristic vector of the commodity to be identified according to at least one specification model parameter of the commodity to be identified;
calculating the similarity between the feature vector and the standard feature vectors of the plurality of commodity matching templates corresponding to the categories;
and determining the matching degree of the commodities to be identified and the plurality of commodity matching templates corresponding to the categories based on the similarity.
5. The method according to claim 3, wherein the determining the matching degree of the to-be-identified commodity and a plurality of commodity matching templates corresponding to the categories based on at least one specification model parameter of the to-be-identified commodity comprises:
searching each specification model parameter of the commodity to be identified in the commodity matching template aiming at each commodity matching template corresponding to the category;
if the specification model parameters are found in the commodity matching template, determining a first matching degree of the specification model parameters and the commodity matching template based on a preset weight value corresponding to the specification model parameters;
and determining the matching degree of the commodity to be identified and the commodity matching template based on the first matching degree of each specification model parameter and the commodity matching template.
6. The method of claim 3, further comprising:
if the commodity matching template containing the keywords is found, setting the matching degree corresponding to the commodity to be identified as a preset value;
for each commodity set, determining a standard commodity from the commodity set according to the matching degree of the commodity to be identified and a commodity matching template corresponding to the commodity set and historical sales information of the commodity to be identified to obtain a plurality of standard commodities, wherein the historical sales information at least comprises sales volume and sales price;
and storing a plurality of standard commodities to a standard commodity library.
7. An article identification device, comprising:
the system comprises an acquisition module, a recognition module and a recognition module, wherein the acquisition module is used for acquiring the categories, brands and at least one specification model parameter of a plurality of commodities to be recognized;
the classification module is used for classifying a plurality of commodities to be identified;
the matching module is used for determining the number of target commodity matching templates matched with the commodities to be identified from a plurality of preset commodity matching templates corresponding to the categories based on at least one specification model parameter of the commodities to be identified aiming at each commodity to be identified in each category;
the matching module is further used for dividing the commodities to be identified into commodity sets corresponding to the target commodity matching templates.
8. The device according to claim 7, wherein the matching module is specifically configured to search a plurality of product matching templates corresponding to the categories by using the first specification model parameter of the product to be identified as a keyword; and if the commodity matching template containing the keywords is found, determining the found commodity matching template as the target commodity matching template.
9. A computer device, comprising: a processor and a memory, the processor being configured to execute a product identification program stored in the memory to implement the product identification method according to any one of claims 1 to 6.
10. A storage medium storing one or more programs executable by one or more processors to implement the article identification method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011069153.3A CN112199451B (en) | 2020-09-30 | Commodity identification method, commodity identification device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011069153.3A CN112199451B (en) | 2020-09-30 | Commodity identification method, commodity identification device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112199451A true CN112199451A (en) | 2021-01-08 |
| CN112199451B CN112199451B (en) | 2024-07-16 |
Family
ID=
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113436021A (en) * | 2021-08-30 | 2021-09-24 | 深圳百胜扬工业电子商务平台发展有限公司 | Intelligent identification and storage method and system for commercial product data |
| CN113609279A (en) * | 2021-08-05 | 2021-11-05 | 湖南特能博世科技有限公司 | Material model extraction method and device and computer equipment |
| CN115545809A (en) * | 2022-12-02 | 2022-12-30 | 广州数说故事信息科技有限公司 | Standard product library construction method, data alignment method, device and equipment for E-commerce commodities |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102332137A (en) * | 2011-09-23 | 2012-01-25 | 纽海信息技术（上海）有限公司 | Goods matching method and system |
| CN105005917A (en) * | 2015-07-07 | 2015-10-28 | 上海晶赞科技发展有限公司 | Universal method for correlating single items of different e-commerce websites |
| CN105550369A (en) * | 2016-01-26 | 2016-05-04 | 上海晶赞科技发展有限公司 | Method and device for searching target commodity set |
| CN106919619A (en) * | 2015-12-28 | 2017-07-04 | 阿里巴巴集团控股有限公司 | A kind of commercial articles clustering method, device and electronic equipment |
| CN110189187A (en) * | 2019-04-15 | 2019-08-30 | 深圳平安综合金融服务有限公司 | Merchandise control method, apparatus, computer equipment and storage medium |
| CN110674384A (en) * | 2019-09-27 | 2020-01-10 | 厦门晶欣电子有限公司 | Component model matching method |
| CN111291555A (en) * | 2020-02-28 | 2020-06-16 | 深圳市微购科技有限公司 | Commodity specification identification method and device and computer readable storage medium |
| CN111460195A (en) * | 2020-03-26 | 2020-07-28 | Oppo广东移动通信有限公司 | Picture processing method and device, storage medium and electronic equipment |
| CN111695336A (en) * | 2020-04-26 | 2020-09-22 | 平安科技（深圳）有限公司 | Disease name code matching method and device, computer equipment and storage medium |
| CN111724221A (en) * | 2019-03-19 | 2020-09-29 | 北京京东尚科信息技术有限公司 | Method, system, electronic device and storage medium for determining commodity matching information |
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102332137A (en) * | 2011-09-23 | 2012-01-25 | 纽海信息技术（上海）有限公司 | Goods matching method and system |
| CN105005917A (en) * | 2015-07-07 | 2015-10-28 | 上海晶赞科技发展有限公司 | Universal method for correlating single items of different e-commerce websites |
| CN106919619A (en) * | 2015-12-28 | 2017-07-04 | 阿里巴巴集团控股有限公司 | A kind of commercial articles clustering method, device and electronic equipment |
| CN105550369A (en) * | 2016-01-26 | 2016-05-04 | 上海晶赞科技发展有限公司 | Method and device for searching target commodity set |
| CN111724221A (en) * | 2019-03-19 | 2020-09-29 | 北京京东尚科信息技术有限公司 | Method, system, electronic device and storage medium for determining commodity matching information |
| CN110189187A (en) * | 2019-04-15 | 2019-08-30 | 深圳平安综合金融服务有限公司 | Merchandise control method, apparatus, computer equipment and storage medium |
| CN110674384A (en) * | 2019-09-27 | 2020-01-10 | 厦门晶欣电子有限公司 | Component model matching method |
| CN111291555A (en) * | 2020-02-28 | 2020-06-16 | 深圳市微购科技有限公司 | Commodity specification identification method and device and computer readable storage medium |
| CN111460195A (en) * | 2020-03-26 | 2020-07-28 | Oppo广东移动通信有限公司 | Picture processing method and device, storage medium and electronic equipment |
| CN111695336A (en) * | 2020-04-26 | 2020-09-22 | 平安科技（深圳）有限公司 | Disease name code matching method and device, computer equipment and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113609279A (en) * | 2021-08-05 | 2021-11-05 | 湖南特能博世科技有限公司 | Material model extraction method and device and computer equipment |
| CN113609279B (en) * | 2021-08-05 | 2023-12-08 | 湖南特能博世科技有限公司 | Material model extraction method and device and computer equipment |
| CN113436021A (en) * | 2021-08-30 | 2021-09-24 | 深圳百胜扬工业电子商务平台发展有限公司 | Intelligent identification and storage method and system for commercial product data |
| CN115545809A (en) * | 2022-12-02 | 2022-12-30 | 广州数说故事信息科技有限公司 | Standard product library construction method, data alignment method, device and equipment for E-commerce commodities |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109284044B (en) | Function menu display method based on big data and related equipment | |
| Tang et al. | Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods | |
| JP6134444B2 (en) | Method and system for recommending information | |
| US10438133B2 (en) | Spend data enrichment and classification | |
| CN106557480B (en) | Method and device for realizing query rewriting | |
| US9031967B2 (en) | Natural language processing system, method and computer program product useful for automotive data mapping | |
| WO2018053966A1 (en) | Click rate estimation | |
| US10579651B1 (en) | Method, system, and program for evaluating intellectual property right | |
| CN111783039B (en) | Risk determination method, risk determination device, computer system and storage medium | |
| CN116308684B (en) | Online shopping platform store information pushing method and system | |
| CN106372956B (en) | Method and system for identifying intention entity based on user search log | |
| CN112750029A (en) | Credit risk prediction method, device, electronic equipment and storage medium | |
| US11682060B2 (en) | Methods and apparatuses for providing search results using embedding-based retrieval | |
| US20180024983A1 (en) | System and method for reporting based on electronic documents | |
| Malik et al. | EPR-ML: E-Commerce Product Recommendation Using NLP and Machine Learning Algorithm | |
| CN109033427B (en) | Stock screening method and device, computer equipment and readable storage medium | |
| CN111582932A (en) | Inter-scene information pushing method and device, computer equipment and storage medium | |
| CN104915440A (en) | Commodity de-duplication method and system | |
| WO2016086803A1 (en) | Product arrival time acquisition method and device | |
| CN106600360B (en) | Method and device for sorting recommended objects | |
| CN115564486A (en) | Data pushing method, device, equipment and medium | |
| US20170186083A1 (en) | Data mining a transaction history data structure | |
| CN112307199A (en) | Information identification method, data processing method, device and equipment, information interaction method | |
| CN112182126A (en) | Model training method and device for determining matching degree, electronic equipment and readable storage medium | |
| CN112199500A (en) | Emotional tendency identification method and device for comments and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant after: Jingdong Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant before: Jingdong Digital Technology Holding Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant |