A method and device for calculating item price

Abstract

The present invention discloses a method and device for calculating the price of an item, and relates to the field of computer technology. A specific implementation of the method includes: crawling web page data, obtaining text information and image information in the web page data; determining a target set according to the text information and the image information, the target set including multiple items; screening out at least one target item from the target set, and calculating the price of the at least one target item. This implementation can solve the technical problems of large pricing errors and lack of intelligence.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for calculating the price of an item. Background Art

[0002] The current pricing method of e-commerce platforms is: purchasing and sales personnel create prices for certain items on the internal ERP system (Enterprise Resource Planning) based on warehouse quotations, inventory information, item details, etc. After successful creation, the prices are approved by the department leaders corresponding to the purchasing and sales personnel. Different approval processes correspond to different promotion scales, and leaders of different ranks will approve them.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] The price of items needs to be determined by purchasing and marketing personnel based on their experience, such as climate factors, item production capacity, and even contract information signed with suppliers, sales targets, etc. Therefore, there is a certain degree of error and it is not smart enough. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for calculating the price of an item to solve the technical problems of large pricing errors and lack of intelligence.

[0006] To achieve the above object, according to one aspect of an embodiment of the present invention, a method for calculating an item price is provided, comprising:

[0007] Crawling web page data to obtain text information and image information in the web page data;

[0008] Determine a target set according to the text information and the image information, wherein the target set includes a plurality of objects;

[0009] At least one target item is selected from the target set, and a price of the at least one target item is calculated.

[0010] Optionally, determining a target set according to the text information and the image information includes:

[0011] Extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively;

[0012] The similarities between each first vector, each second vector and the preset item vector are calculated respectively, so as to determine the target set according to the magnitude of the similarities.

[0013] Optionally, extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively includes:

[0014] Extracting keywords from the text information to obtain a plurality of first items and a first vector consisting of the plurality of keywords corresponding to each of the first items;

[0015] The image information is recognized by using a text recognition technology to obtain image text, and keywords in the image text are extracted to obtain a plurality of second objects and a second vector composed of a plurality of keywords corresponding to each of the second objects.

[0016] Optionally, respectively calculating the similarity between each first vector, each second vector and a preset item vector, thereby determining a target set according to the magnitude of the similarity, includes:

[0017] Calculate the similarity between the first vector corresponding to each first item and the preset item vector respectively, and take the set of M first items with the highest similarity as the first set;

[0018] Calculate the similarity between the second vector corresponding to each second item and the preset item vector respectively, and take a set of N second items with the highest similarity as the second set;

[0019] Taking the intersection of the first set and the second set as the target set;

[0020] Wherein, M and N are both positive integers.

[0021] Optionally, selecting at least one target item from the target set and calculating a price of the at least one target item includes:

[0022] Filtering at least one target item from the target set according to the source and the number of times the webpage data is published;

[0023] The price of each target item is calculated according to the publishing time and the number of publishing times of the webpage data and the inventory of the target item.

[0024] In addition, according to another aspect of an embodiment of the present invention, there is provided a device for calculating a price of an item, comprising:

[0025] A crawling module, used to crawl web page data and obtain text information and image information in the web page data;

[0026] A screening module, configured to determine a target set according to the text information and the image information, wherein the target set includes a plurality of items;

[0027] The calculation module is used to select at least one target item from the target set and calculate the price of the at least one target item.

[0028] Optionally, the screening module is used to:

[0029] Extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively;

[0030] The similarities between each first vector, each second vector and the preset item vector are calculated respectively, so as to determine the target set according to the magnitude of the similarities.

[0031] Optionally, extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively includes:

[0032] Extracting keywords from the text information to obtain a plurality of first items and a first vector consisting of the plurality of keywords corresponding to each of the first items;

[0033] The image information is recognized by using a text recognition technology to obtain image text, and keywords in the image text are extracted to obtain a plurality of second objects and a second vector composed of a plurality of keywords corresponding to each of the second objects.

[0034] Optionally, respectively calculating the similarity between each first vector, each second vector and a preset item vector, thereby determining a target set according to the magnitude of the similarity, includes:

[0035] Calculate the similarity between the first vector corresponding to each first item and the preset item vector respectively, and take the set of M first items with the highest similarity as the first set;

[0036] Calculate the similarity between the second vector corresponding to each second item and the preset item vector respectively, and take a set of N second items with the highest similarity as the second set;

[0037] Taking the intersection of the first set and the second set as the target set;

[0038] Wherein, M and N are both positive integers.

[0039] Optionally, the calculation module is used to:

[0040] Filtering at least one target item from the target set according to the source and the number of times the webpage data is published;

[0041] The price of each target item is calculated according to the publishing time and the number of publishing times of the webpage data and the inventory of the target item.

[0042] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, including:

[0043] one or more processors;

[0044] a storage device for storing one or more programs,

[0045] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above embodiments.

[0046] According to another aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0047] An embodiment of the above invention has the following advantages or beneficial effects: because the technical means of obtaining text information and image information in web page data to determine the target set and thus calculate the price of the target item in the target set is adopted, the technical problems of large pricing errors and insufficient intelligence are overcome. In the context of borderless retail, the embodiment of the present invention combines and analyzes web page data from various platforms, extracts item information from web page data, integrates and analyzes item information from different sources, thereby determining the target set, and then calculating the price of the target item, so that the price of the item is accurately positioned; and the pricing process does not rely on the experience of the purchasing staff, which not only improves the accuracy of pricing, but also improves the intelligence of the pricing process.

[0048] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0050] Figure 1 is a schematic diagram of the main process of the method for calculating the price of an item according to an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of the main process of a method for calculating item prices according to a reference embodiment of the present invention;

[0052] Figure 3 is a schematic diagram of the main process of a method for calculating an item price according to another reference embodiment of the present invention;

[0053] Figure 4 is a schematic diagram of main modules of an apparatus for calculating item prices according to an embodiment of the present invention;

[0054] Figure 5 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0055] Figure 6 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0057] Figure 1 Schematic diagram of the main process of the method for calculating the price of an item according to an embodiment of the present invention. Figure 1 As shown, the method for calculating the price of an item may include:

[0058] Step 101, crawling web page data to obtain text information and image information in the web page data.

[0059] In this step, web page data is automatically crawled by a crawler (i.e., a web crawler, a program or script that automatically grabs World Wide Web information according to certain rules), thereby obtaining text information and image information (such as files in png, jpg, etc. formats) in the web page data. Furthermore, the link address, text information, image information, platform source, release time, etc. of the web page data can be stored in a database.

[0060] Step 102: determining a target set according to the text information and the image information, wherein the target set includes a plurality of objects.

[0061] In this step, the target set is determined based on the text information and the image information obtained in step 101, thereby narrowing the scope of the target items and accurately screening the target items. Optionally, step 102 may include: extracting multiple first vectors and multiple second vectors from the text information and the image information respectively; calculating the similarity between each first vector, each second vector and the preset item vector respectively, thereby determining the target set according to the magnitude of the similarity. The first vector includes feature information of multiple dimensions, such as the category, brand, color, size, model, performance, etc., and the second includes feature information of multiple dimensions, such as the category, brand, color, size, model, performance, etc. In fact, the embodiment of the present invention is to form a vector with each descriptive word describing an item in the web page data, and calculate the similarity between each vector.

[0062] It should be noted that if there is no feature information of a certain dimension in the crawled data, the feature information of the dimension can be configured as a preset customization to facilitate the calculation of similarity in subsequent steps. It should also be noted that in the embodiment of the present invention, the preset item vector refers to the vector corresponding to the existing items in the database, wherein the preset item can be an item under the secondary category or the tertiary category to which the first vector belongs, and the preset item can be an item under the secondary category or the tertiary category to which the second vector belongs, and can be pre-defined, and the difference is only in the difference in the calculation order.

[0063] Optionally, extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively includes: extracting keywords from the text information to obtain a plurality of first items and a first vector composed of a plurality of keywords corresponding to each of the first items; using a text recognition technology to recognize the image information to obtain image text, extracting keywords from the image text, obtaining a plurality of second items and a second vector composed of a plurality of keywords corresponding to each of the second items. In an embodiment of the present invention, OCR technology (Optical Character Recognition) can be used to recognize the image information to obtain image text.

[0064] Optionally, the similarity between each first vector, each second vector and the preset item vector is calculated respectively, so as to determine the target set according to the magnitude of the similarity, including: calculating the similarity between the first vector corresponding to each first item and the preset item vector respectively, and taking the set consisting of the M first items with the highest similarity as the first set; calculating the similarity between the second vector corresponding to each second item and the preset item vector respectively, and taking the set consisting of the N second items with the highest similarity as the second set; taking the intersection of the first set and the second set as the target set; wherein M and N are both positive integers. Taking the intersection of the first set and the second set as the target set can narrow the range of the target items and accurately screen the target items. If the intersection of the first set and the second set is empty, M and N can be appropriately expanded, and then the above steps are repeated until the target set is obtained.

[0065] Step 103: select at least one target item from the target set, and calculate the price of the at least one target item.

[0066] After determining the target set, further screen out at least one target item from the target set, and calculate the prices of the target items respectively. Optionally, step 103 includes: screening out at least one target item from the target set according to the source and release times of the web page data; calculating the prices of each target item according to the release time, release times of the web page data, and the inventory of the target item.

[0067] For example, different dimensions of indicators can be set: source, release times, release time, inventory status, and region, etc.

[0068] The formula F = O * K1 + P * K2 + Q * K3 can be used to calculate the release quantity of each item. Wherein, K1 is the coefficient of the web page data released by the manufacturer, and O is the release times of the web page data released by the manufacturer; K2 is the coefficient of the web page data released by the third-party platform, and P is the release times of the web page data released by the third-party platform; K3 is the coefficient of the web page data released by the competitor platform, and Q is the release times of the web page data released by the competitor platform.

[0069] K1 + K2 + K3 = 1, and K1, K2, and K3 can be adjusted according to actual needs. For example: K1 = 0.5, K2 = 0.3, K3 = 0.2, so as to calculate the release quantity of each item, and then screen out at least one target item with the top release quantity.

[0070] Considering calculations in the order of millions, the SKUs of the target items need to be stored in segments on different servers, and then the key-value pairs <SKU, release quantity> on each server are stored in Multimap (a data structure in the C++ language, which supports automatic sorting and allows duplicate keys). Reverse the node Multimap and sort automatically according to the advertisement quantity to obtain the sorted SKUs. Obtain at least one SKU with the top ranking.

[0071] After determining the target items, calculate the prices of these target items respectively. Specifically, the following formula can be used for calculation:

[0072] Y1 = release time - current time / current time,

[0073] Y2 = regional inventory / total inventory

[0074] Price parameter ¢ = Y1 * Z1 + Y2 * Z2 + F * Z3

[0075] Wherein, Z1 + Z2 + Z3 = 1, and Z1, Z2, and Z3 can be adjusted according to actual needs.

[0076] Since the intervals corresponding to various price parameters are preset, each interval corresponds to a different calculation formula. Therefore, different intervals can be matched according to the price parameter ￠, such as interval [A1, A2], interval [A3, A4], etc.

[0077] For [A1, A2], the corresponding calculation formula is: current price = original price * 0.9;

[0078] For [A3, A4], the corresponding calculation formula is current price = original price * 0.8.

[0079] It should be pointed out that if there is no interval that matches the price parameter ￠, no processing will be done, which means that the price of the item does not need to be adjusted.

[0080] According to the various embodiments described above, it can be seen that the present invention obtains text information and image information in web page data to determine the target set, thereby calculating the price of the target items in the target set, thereby solving the problem of large pricing errors and lack of intelligence. In the context of borderless retail, the embodiments of the present invention combine and analyze web page data from various platforms, extract item information from web page data, integrate and analyze item information from different sources, thereby determining the target set, and then calculating the price of the target item, so that the price of the item is accurately positioned; and the pricing process does not rely on the experience of purchasing personnel, which not only improves the accuracy of pricing, but also improves the intelligence of the pricing process.

[0081] Figure 2 1 is a schematic diagram of the main process of a method for calculating an item price according to a reference embodiment of the present invention. The method for calculating an item price may specifically include:

[0082] Step 201, crawling web page data to obtain text information and image information in the web page data;

[0083] Step 202, extracting keywords from the text information to obtain a plurality of first items and a first vector consisting of a plurality of keywords corresponding to each of the first items;

[0084] Step 203, using a text recognition technology to recognize the image information, obtain image text, extract keywords from the image text, obtain a plurality of second objects and a second vector consisting of a plurality of keywords corresponding to each of the second objects;

[0085] Step 204, respectively calculating the similarity between each first vector, each second vector and a preset item vector, thereby determining a target set according to the magnitude of the similarity; wherein the target set includes a plurality of items;

[0086] Step 205, screening out a plurality of target items from the target set according to the source and the number of times the webpage data is published;

[0087] Step 206, calculating the price of each target item according to the publishing time and number of publishing of the webpage data and the inventory of the target item.

[0088] In addition, the specific implementation content of the method for calculating the price of an item in a reference embodiment of the present invention has been described in detail in the method for calculating the price of an item described above, so the repeated content will not be described here.

[0089] Figure 3 : is a schematic diagram of the main process of a method for calculating an item price according to another reference embodiment of the present invention. The method for calculating an item price may specifically include:

[0090] Step 301, crawling web page data to obtain text information and image information in the web page data;

[0091] Step 302, extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively;

[0092] Step 303, respectively calculating the similarity between the first vector corresponding to each first item and the preset item vector, and taking a set of M first items with the highest similarity as the first set;

[0093] Step 304, respectively calculating the similarity between the second vector corresponding to each second item and the preset item vector, and taking a set of N second items ranked high in similarity as the second set;

[0094] Step 305, taking the intersection of the first set and the second set as a target set; wherein the target set includes a plurality of items;

[0095] Step 306, filtering out a plurality of target items from the target set according to the source and the number of times the webpage data is published;

[0096] Step 307, calculating the price of each target item according to the publishing time and number of publishing of the webpage data and the inventory of the target item.

[0097] In addition, the specific implementation content of the method for calculating the price of an item in another reference embodiment of the present invention has been described in detail in the method for calculating the price of an item described above, so the repeated content will not be described here.

[0098] Figure 4 is a schematic diagram of main modules of an apparatus for calculating item prices according to an embodiment of the present invention. Figure 4As shown, the device 400 for calculating the price of an item includes a crawling module 401, a screening module 402 and a calculation module 403. The crawling module 401 is used to crawl web page data and obtain text information and image information in the web page data; the screening module 402 is used to determine a target set according to the text information and the image information, and the target set includes multiple items; the calculation module 403 is used to screen out at least one target item from the target set and calculate the price of the at least one target item.

[0099] Optionally, the screening module 402 is used to:

[0100] Extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively;

[0101] The similarities between each first vector, each second vector and the preset item vector are calculated respectively, so as to determine the target set according to the magnitude of the similarities.

[0102] Optionally, extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively includes:

[0103] Extracting keywords from the text information to obtain a plurality of first items and a first vector consisting of the plurality of keywords corresponding to each of the first items;

[0104] The image information is recognized by using a text recognition technology to obtain image text, and keywords in the image text are extracted to obtain a plurality of second objects and a second vector composed of a plurality of keywords corresponding to each of the second objects.

[0105] Optionally, respectively calculating the similarity between each first vector, each second vector and a preset item vector, thereby determining a target set according to the magnitude of the similarity, includes:

[0106] Calculate the similarity between the first vector corresponding to each first item and the preset item vector respectively, and take the set of M first items with the highest similarity as the first set;

[0107] Calculate the similarity between the second vector corresponding to each second item and the preset item vector respectively, and take a set of N second items with the highest similarity as the second set;

[0108] Taking the intersection of the first set and the second set as the target set;

[0109] Wherein, M and N are both positive integers.

[0110] Optionally, the calculation module is used to:

[0111] Filtering at least one target item from the target set according to the source and the number of times the webpage data is published;

[0112] The price of each target item is calculated according to the publishing time and the number of publishing times of the webpage data and the inventory of the target item.

[0113] According to the various embodiments described above, it can be seen that the present invention obtains text information and image information in web page data to determine the target set, thereby calculating the price of the target items in the target set, thereby solving the problem of large pricing errors and lack of intelligence. In the context of borderless retail, the embodiments of the present invention combine and analyze web page data from various platforms, extract item information from web page data, integrate and analyze item information from different sources, thereby determining the target set, and then calculating the price of the target item, so that the price of the item is accurately positioned; and the pricing process does not rely on the experience of purchasing personnel, which not only improves the accuracy of pricing, but also improves the intelligence of the pricing process.

[0114] It should be noted that the specific implementation content of the device for calculating the price of an item in the present invention has been described in detail in the method for calculating the price of an item described above, so the repeated content will not be described here.

[0115] Figure 5 An exemplary system architecture 500 is shown to which the method for calculating item prices or the apparatus for calculating item prices according to the embodiments of the present invention may be applied.

[0116] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, 503, a network 504 and a server 505. Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0117] Users can use terminal devices 501, 502, 503 to interact with server 504 through network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0118] The terminal devices 501 , 502 , and 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0119] The server 505 may be a server that provides various services, such as a backend management server (only an example) that provides support for shopping websites browsed by users using the terminal devices 501, 502, and 503. The backend management server may analyze and process the received data such as product information query requests, and feed back the processing results (such as target push information, product information - only an example) to the terminal device.

[0120] It should be noted that the method for calculating the price of an item provided in the embodiment of the present invention is generally executed on the terminal devices 501, 502, 503 in a public place, and can also be executed by the server 505. Accordingly, the device for calculating the price of an item is generally set on the terminal devices 501, 502, 503 in a public place, and can also be set in the server 505.

[0121] It should be understood that Figure 5 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0122] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 of a terminal device suitable for implementing an embodiment of the present invention. Figure 6 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0123] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0124] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0125] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present invention are executed.

[0126] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0127] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes a crawling module, a screening module and a computing module, wherein the names of these modules do not constitute a limitation on the modules themselves in some cases.

[0129] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: crawling web page data, obtaining text information and image information in the web page data; determining a target set based on the text information and the image information, the target set including multiple items; screening out at least one target item from the target set, and calculating the price of the at least one target item.

[0130] According to the technical solution of the embodiment of the present invention, because the technical means of obtaining text information and image information in web page data to determine the target set and thus calculate the price of the target item in the target set is adopted, the technical problems of large pricing errors and lack of intelligence are overcome. In the context of borderless retail, the embodiment of the present invention combines and analyzes web page data from various platforms, extracts item information from web page data, integrates and analyzes item information from different sources, thereby determining the target set, and then calculating the price of the target item, so as to accurately locate the price of the item; and in the pricing process, it does not rely on the experience of purchasing personnel, which not only improves the accuracy of pricing, but also improves the intelligence of the pricing process.

[0131] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating the price of an item, It is characterized in that include: Crawling web page data to obtain text information and image information in the web page data; Extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively; calculating the similarity between each first vector, each second vector and a preset item vector respectively, thereby determining a target set according to the magnitude of the similarity, wherein the preset item vector refers to a vector corresponding to an item already in a database, and the target set includes a plurality of items; At least one target item is selected from the target set, and a price of the at least one target item is calculated.

2. The method according to claim 1, It is characterized in that Extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively includes: Extracting keywords from the text information to obtain a plurality of first items and a first vector consisting of the plurality of keywords corresponding to each of the first items; The image information is recognized by using a text recognition technology to obtain image text, and keywords in the image text are extracted to obtain a plurality of second objects and a second vector composed of a plurality of keywords corresponding to each of the second objects.

3. The method according to claim 1, It is characterized in that The similarities between each first vector, each second vector and a preset object vector are calculated respectively, thereby determining a target set according to the magnitude of the similarities, including: Calculate the similarity between the first vector corresponding to each first item and the preset item vector respectively, and take the set of M first items with the highest similarity as the first set; Calculate the similarity between the second vector corresponding to each second item and the preset item vector respectively, and take a set of N second items with the highest similarity as the second set; Taking the intersection of the first set and the second set as the target set; Wherein, M and N are both positive integers.

4. The method according to claim 1, It is characterized in that Screening out at least one target item from the target set and calculating a price of the at least one target item includes: Filtering at least one target item from the target set according to the source and the number of times the webpage data is published; The price of each target item is calculated according to the publishing time and the number of publishing times of the webpage data and the inventory of the target item.

5. A device for calculating the price of an item, It is characterized in that include: A crawling module, used to crawl web page data and obtain text information and image information in the web page data; A screening module, used to extract a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively; calculate the similarity between each first vector, each second vector and a preset item vector respectively, so as to determine a target set according to the magnitude of the similarity, wherein the preset item vector refers to a vector corresponding to an item already in the database, and the target set includes a plurality of items; The calculation module is used to select at least one target item from the target set and calculate the price of the at least one target item.

6. The device according to claim 5, It is characterized in that Extracting a plurality of first vectors and a plurality of second vectors from the text information and the image information respectively includes: Extracting keywords from the text information to obtain a plurality of first items and a first vector consisting of the plurality of keywords corresponding to each of the first items; The image information is recognized by using a text recognition technology to obtain image text, and keywords in the image text are extracted to obtain a plurality of second objects and a second vector composed of a plurality of keywords corresponding to each of the second objects.

7. The device according to claim 5, It is characterized in that The similarities between each first vector, each second vector and a preset object vector are calculated respectively, thereby determining a target set according to the magnitude of the similarities, including: Calculate the similarity between the first vector corresponding to each first item and the preset item vector respectively, and take the set of M first items with the highest similarity as the first set; Calculate the similarity between the second vector corresponding to each second item and the preset item vector respectively, and take a set of N second items with the highest similarity as the second set; Taking the intersection of the first set and the second set as the target set; Wherein, M and N are both positive integers.

8. The device according to claim 5, It is characterized in that The calculation module is used for: Filtering at least one target item from the target set according to the source and the number of times the webpage data is published; The price of each target item is calculated according to the publishing time and the number of publishing times of the webpage data and the inventory of the target item.

9. An electronic device, It is characterized in that include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

10. A computer readable medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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