CN110942035A - Method, system, device and storage medium for acquiring commodity information - Google Patents
Method, system, device and storage medium for acquiring commodity information Download PDFInfo
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- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
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- G07G1/0045—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader
- G07G1/0054—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader with control of supplementary check-parameters, e.g. weight or number of articles
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Abstract
The embodiment of the application discloses a method, a system, a device and a storage medium for acquiring commodity information. The method for acquiring commodity information comprises the following steps: carrying out image recognition on a commodity to be processed to obtain image characteristics of the commodity to be processed; determining a target commodity with the image characteristics meeting preset conditions; acquiring identification information of the target commodity, wherein the identification information is used for inquiring commodity information of the target commodity; and generating a shortcut for acquiring the commodity information based on the identification information, and/or establishing association between a trigger instruction of an entity key and the commodity information based on the identification information so that the commodity information can be acquired when the entity key is triggered. By generating the shortcut and/or establishing the association, the user can acquire the commodity information through the shortcut or the entity key, and the operation of acquiring the commodity information is simplified.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, a system, an apparatus, and a storage medium for acquiring commodity information.
Background
The process of purchasing some goods (such as vegetables and fruits) in a supermarket may include: the consumer picks the goods to be purchased, then the goods are weighed by staff, the goods category or numerical code (the numerical code of the goods is often represented by a character string with a specific number of digits, such as 'water spinach' corresponding to '1101', 'cabbage' corresponding to '1102' and the like) is manually input, and then the goods are bar-coded for subsequent settlement, or the settlement is directly performed. There are also some cases where the consumer weighs himself in a self-service device, enters the type of goods manually and settles.
Furthermore, for some commodities lacking the bar codes, image recognition can be performed on the commodities to acquire commodity information of the commodities. Unlike the standard presented by the bar code following a certain standard, the image of the commodity may cause the result of image recognition to be unsatisfactory due to the diversity of the image, and manual settlement by consumers is still required. Because the types of commodities are often more, the possible service time of only manually inputting the types of commodities or numerical codes is longer, the experience of consumers and the operating efficiency of supermarkets are influenced, and higher personnel training cost is possibly needed.
Therefore, it is desirable to provide a method, system, apparatus, and storage medium for acquiring commodity information to quickly acquire commodity information of a commodity when an image recognition result is not satisfactory.
Disclosure of Invention
One of the embodiments of the present application provides a method for acquiring commodity information. The method comprises the following steps: carrying out image recognition on a commodity to be processed to obtain image characteristics of the commodity to be processed; determining a target commodity with the image characteristics meeting preset conditions; acquiring identification information of the target commodity, wherein the identification information is used for inquiring commodity information of the target commodity; and generating a shortcut for acquiring the commodity information based on the identification information, and/or establishing association between a trigger instruction of an entity key and the commodity information based on the identification information so that the commodity information can be acquired when the entity key is triggered.
In some embodiments, the determining the target product with the image feature meeting the preset condition includes: and when the evaluation value of the to-be-processed commodity and the evaluation value of each candidate commodity are smaller than the threshold value of the evaluation value, determining that the to-be-processed commodity is the target commodity.
In some embodiments, the evaluation value threshold is related to a kind of the article to be processed, a kind of the candidate article, and a number of the candidate articles.
In some embodiments, the determining of the target product with the image feature meeting the preset condition includes: determining the times that the actual commodity represented by the image characteristics obtained by image recognition on the commodity to be processed is different from the commodity to be processed aiming at the same commodity to be processed; and determining the target commodity based on the times.
In some embodiments, the method further comprises: acquiring an image of the target commodity, wherein the image comprises a picture and/or a video; taking the image as sample data, and taking the identification information of the target commodity as label data of the sample data; training a recognition model for performing the image recognition using the sample data and the label data.
One of the embodiments of the present application provides a system for acquiring commodity information. The system comprises: the image recognition module is used for carrying out image recognition on the commodity to be processed to obtain the image characteristics of the commodity to be processed; the determining module is used for determining the target commodity of which the image characteristics meet the preset conditions; the identification information acquisition module is used for acquiring identification information of the target commodity, and the identification information is used for inquiring commodity information of the target commodity; and the generation establishing module is used for generating a shortcut for acquiring the commodity information based on the identification information and/or establishing the association between a trigger instruction of an entity key and the commodity information based on the identification information so that the commodity information can be acquired when the entity key is triggered.
In some embodiments, the image feature includes an evaluation value for characterizing a similarity of the item to be processed and a candidate item, and the determination module is configured to: and when the evaluation value of the to-be-processed commodity and the evaluation value of each candidate commodity are smaller than the threshold value of the evaluation value, determining that the to-be-processed commodity is the target commodity.
In some embodiments, the evaluation value threshold is related to a kind of the article to be processed, a kind of the candidate article, and a number of the candidate articles.
In some embodiments, the determination module is to: determining the times that the actual commodity represented by the image characteristics obtained by image recognition on the commodity to be processed is different from the commodity to be processed aiming at the same commodity to be processed; and determining the target commodity based on the times.
In some embodiments, the system further comprises: the image acquisition module is used for acquiring an image of the target commodity, wherein the image comprises a picture and/or a video; the data sorting module is used for taking the image as sample data and taking the identification information of the target commodity as label data of the sample data; and the training module is used for training a recognition model for carrying out the image recognition by utilizing the sample data and the label data.
One of the embodiments of the present application provides an apparatus for acquiring commodity information, which includes a processor configured to execute a method for acquiring commodity information.
One of the embodiments of the present application provides a computer-readable storage medium, where the storage medium stores computer instructions, and after a computer reads the computer instructions in the storage medium, the computer executes a method for acquiring commodity information.
Drawings
The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a system for obtaining merchandise information according to some embodiments of the present application;
FIG. 2 is a block diagram of a system for obtaining merchandise information according to some embodiments of the present application;
FIG. 3 is an exemplary flow chart of a method for obtaining merchandise information according to some embodiments of the present application;
FIG. 4 is another exemplary flow chart of a method for obtaining merchandise information according to some embodiments of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic view of an application scenario of a system for acquiring commodity information according to some embodiments of the present application.
As shown in fig. 1, the system 100 for acquiring information of commodities can be used for weighing and price settlement of commodities in a shopping mall or a supermarket. The system 100 for acquiring commodity information may include an information acquisition apparatus 110, a network 120, a terminal 130, a processing engine 140, and a storage device 150.
The information acquisition device 110 may include an acquisition device such as a camera and a sensor, and may also include data acquisition that supports related identification technologies such as Radio Frequency Identification (RFID) and product electronic tags (EPC). The camera means may comprise a video camera, video recorder, infrared camera or other device capable of acquiring image or video data. The sensor may include one or more of an infrared sensor, an ultrasonic sensor, a distance sensor, a light sensor, a gravity sensor, an acceleration sensor, a direction sensor, and the like, or any combination thereof.
The image information of the commodity acquired by the information acquisition device 110 and other related commodity information are transmitted to the terminal 130, and the terminal 130 can perform information identification on the commodity based on the related information. The terminal 130 may also be used to obtain manually entered information, display merchandise information, and otherwise process the merchandise information. In some embodiments, the information acquiring apparatus 110 may be integrated with the terminal 130, or may be separated from the terminal, and the present application is not limited thereto.
The terminal 130 may include a weighing apparatus 131 and a weighing aid 132, or any combination thereof. The terminal 130 may be a tool for weighing and settlement in a current market or supermarket, such as an electronic scale or an electromechanical combination scale. The weighing device 131 may comprise a scale pan, scale body or like weighing system. The weighing aid 132 may include force transfer systems (e.g., lever force transfer systems, sensors), indicating systems (e.g., dials, electronic displays), and buttons for entering merchandise information. In some embodiments, the terminal 130 may also include an RFID communication for contactless data exchange with a radio transceiver connected to the product.
In some embodiments, the weighing assisting device 132 may further include or be connected to a code printing device, and send the obtained weight of the commodity and the input commodity unit price information to the code printing device, and print an identifier such as a price label, a barcode, a two-dimensional code, or the like. In this specification, the terminal 130 may further include a function of directly performing settlement, and a salesperson may perform settlement for cash after weighing, or may perform weighing and settlement for payment by a customer.
In some embodiments, processing engine 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In some embodiments, storage device 150 may be connected to network 120 to communicate with one or more other components of system 100 (e.g., processing engine 140, terminal 130, etc.). One or more components of system 100 may access data or instructions stored in storage device 150 via network 120. In some embodiments, storage device 150 may be directly connected to or in communication with one or more other components of system 100 (e.g., processing engine 140, terminal 130, etc.). In some embodiments, storage device 150 may be part of processing engine 140.
FIG. 2 is a block diagram of a system for obtaining merchandise information according to some embodiments of the present application.
As shown in fig. 2, the system 200 for acquiring information of a commodity may include an image recognition module 202, a determination module 204, an identification information acquisition module 206, and a generation establishment module 208.
The image recognition module 202 may be configured to perform image recognition on a to-be-processed commodity to obtain an image feature of the to-be-processed commodity.
In some embodiments, the commodity to be processed refers to a commodity ready for weighing, settlement, or the like. For example, a customer places a commodity on an electronic scale to be weighed, or places the commodity on a settlement device to be weighed and settled, and the commodity at this time is referred to as a commodity to be processed in this specification. Further description of image recognition of the to-be-processed merchandise may be found elsewhere in this application (e.g., in step 302 and its related description), and will not be described herein.
The determining module 204 may be configured to determine a target product of which the image feature meets a preset condition.
In some embodiments, the image features are abstract representations of some characteristics of the commodity to be processed based on the images. The image features may be color features, texture features, shape features, spatial relationship features, and the like, may be features obtained by convolving and pooling images, or may be features obtained by other algorithms. Further details regarding the determination of the target product can be found elsewhere in this application (e.g., in step 304 and its related description), and will not be repeated herein.
The identification information obtaining module 206 may be configured to obtain identification information of the target product, where the identification information is used to query product information of the target product.
More description about obtaining the identification information of the target product can be found elsewhere in this application (e.g., in step 306 and its related description), and is not repeated herein.
The generation establishing module 208 may be configured to generate a shortcut for acquiring the commodity information based on the identification information, and/or establish an association between a trigger instruction of an entity key and the commodity information based on the identification information, so that the commodity information can be acquired when the entity key is triggered.
Further description of generating the shortcut and establishing the association may be found elsewhere in this application (e.g., in step 308 and its associated description), and will not be described herein.
In some embodiments, the image feature includes an evaluation value for characterizing similarity between the to-be-processed item and a candidate item, and the determining module 204 is configured to: and when the evaluation value of the to-be-processed commodity and the evaluation value of each candidate commodity are smaller than the threshold value of the evaluation value, determining that the to-be-processed commodity is the target commodity.
In some embodiments, the evaluation value threshold is related to a kind of the article to be processed, a kind of the candidate article, and a number of the candidate articles.
In some embodiments, the determination module 204 is configured to: determining the times that the actual commodity represented by the image characteristics obtained by image recognition on the commodity to be processed is different from the commodity to be processed aiming at the same commodity to be processed; and determining the target commodity based on the times.
In some embodiments, the system 200 further comprises: the image acquisition module is used for acquiring an image of the target commodity, wherein the image comprises a picture and/or a video; the data sorting module is used for taking the image as sample data and taking the identification information of the target commodity as label data of the sample data; and the training module is used for training a recognition model for carrying out the image recognition by utilizing the sample data and the label data.
It should be understood that the system and its modules shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It should be noted that the above descriptions of the candidate item display and determination system and the modules thereof are only for convenience of description, and are not intended to limit the present application within the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, for example, the image recognition module 202, the determination module 204, the identification information acquisition module 206, and the generation establishing module 208 disclosed in fig. 2 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, the image recognition module 202 and the identification information acquisition module 206 may be two modules, or one module may have both the image recognition function and the identification information acquisition function. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present application.
FIG. 3 is an exemplary flow chart of a method for obtaining merchandise information according to some embodiments of the present application. As shown in fig. 3, a method for acquiring commodity information includes:
In some embodiments, the information acquiring device 110 shown in fig. 1 may be used to acquire an image of the product to be processed, and then transmit the acquired image to the image recognition module 202 in the terminal 130 for image recognition. Of course, the images may be obtained through other interfaces, and the description is not limited in particular to how the images of the commodities are obtained.
In some embodiments, the image features are abstract representations of some characteristics of the commodity to be processed based on the image, which may reflect the distinction of the commodity to be processed from other commodities in a certain sense. The image features may include color features, texture features, shape features, spatial relationship features, and the like, may be features obtained by convolving and pooling images, or may be features obtained by other algorithms, such as an evaluation value for similarity between a feature to be processed and a candidate commodity, and the evaluation value may be obtained by comparing image features that more intuitively reflect characteristics of commodities, such as color features, texture features, shape features, and spatial features of the commodity to be processed and the candidate commodity, where the candidate commodity refers to one or more commodity information obtained through recognition or matching processing. These commodity information are commodity information that may represent the commodity to be processed. For example, after the image of the product to be processed is identified, the information of the candidate product is obtained as orange, orange and mandarin orange. The candidate commodity is used as an object for comparison with the to-be-processed commodity, the image feature of the candidate commodity can be stored in a terminal such as a computer in advance, and then when the to-be-processed commodity and the candidate commodity need to be compared to obtain the evaluation value, the image feature of the candidate commodity can be directly called.
In some embodiments, the image may be pre-processed prior to recognition, such as to adjust shading, de-noising, and the like.
In some embodiments, the recognition model for image recognition of the commodity to be processed may be a machine learning model, which may be a supervised learning model or an unsupervised learning model. Exemplary algorithms that may be used to train the supervised machine learning model may include Gradient Boosting Decision Tree (GBDT) algorithms, decision tree algorithms, random forest algorithms, logistic regression algorithms, Support Vector Machine (SVM) algorithms, naive bayes algorithms, adaptive boosting algorithms, K Nearest Neighbor (KNN) algorithms, markov chain algorithms, and the like, or any combination thereof. Exemplary algorithms that may be used to train the unsupervised machine learning model may include a k-means clustering algorithm, a hierarchical clustering algorithm, a density based clustering with noise (DBSCAN) algorithm, a self-organizing map algorithm, and the like, or any combination thereof.
In some embodiments, image recognition may also be based on other common image recognition algorithms. Common methods used in image recognition are statistical (or decision theory), syntactic (or structural) methods, neural network methods, template matching methods, and geometric transformation methods. Commonly used image statistical models are bayesian (Bayes) and markov (Markow) random field (MRF) models. Typical geometric transformation methods mainly include hough transformation ht (hough transform), and modified hough algorithms such as Fast Hough Transformation (FHT), Adaptive Hough Transformation (AHT), and Random Hough Transformation (RHT).
Taking an example that the product to be processed is an apple, a customer places the purchased apple on a to-be-identified area such as a scale of an electronic scale, a camera shoots a picture of the apple in the to-be-identified area, the shot picture is transmitted to a terminal, and the image recognition module 202 conducts image recognition, so that the image characteristics of the apple including color information, shape information, size information and evaluation values of similarity of the apple and candidate products such as a banana, a pear and an apple are obtained.
And step 304, determining the target commodity with the image characteristics meeting the preset conditions.
In particular, step 304 may be performed by the determination module 204.
In some embodiments, the condition that the image feature meets the preset condition may reflect that the image recognition process of the to-be-processed commodity fails, and may also reflect that the result obtained by performing the image recognition on the to-be-processed commodity is wrong, and in these cases, the to-be-processed commodity may be determined as the target commodity to participate in the subsequent steps 306 and 308.
In some embodiments, the image feature may include an evaluation value for characterizing similarity between the to-be-processed commodity and a candidate commodity, and the determining, in step 304, a target commodity whose image feature meets a preset condition may include: and when the evaluation value of the to-be-processed commodity and the evaluation value of each candidate commodity are smaller than the threshold value of the evaluation value, determining that the to-be-processed commodity is the target commodity.
Specifically, referring to the foregoing description, the candidate commodities are used as comparison objects of the commodities to be processed. The evaluation value of the similarity between the to-be-processed commodity and the candidate commodity is a value which is obtained based on a correlation algorithm and used for representing the similarity between the to-be-processed commodity and the candidate commodity, for example, the similarity can be cosine, a feature vector is obtained by vectorizing image features such as colors and sizes of the to-be-processed commodity and the candidate commodity, and then the cosine similarity between the to-be-processed commodity and the candidate commodity is obtained according to the feature vector of the to-be-processed commodity and the candidate commodity. Of course, the evaluation value of the similarity may also be the euclidean distance similarity, and as to which algorithm is used to obtain the evaluation value of which similarity, the description is not particularly limited, and the evaluation value may be determined according to the actual situation.
In some embodiments, after obtaining the evaluation value of the similarity between the to-be-processed commodity and each candidate commodity, each evaluation value corresponding to the to-be-processed commodity may be compared with the evaluation value threshold one by one, and when each evaluation value is smaller than the evaluation value threshold, it is described that the similarity between the to-be-processed commodity and each candidate commodity is relatively large, and there is no candidate commodity in the database, the similarity of which matches the expected similarity of the to-be-processed commodity, and the to-be-processed commodity is determined as the target commodity.
For example, in one possible embodiment, the commodity to be processed is an apple, the candidate commodities include bananas and pears, the similarity between the apple and the banana and the similarity between the apple and the pear are respectively 20% and 50%, and the similarity between the apple and the banana is 20% and is less than the evaluation value threshold value 80%, and the similarity between the apple and the pear is 50% and is also less than the evaluation value threshold value 80%, so that the apple is determined to be the target commodity meeting the preset condition.
In some embodiments, the evaluation value threshold is related to a kind of the article to be processed, a kind of the candidate article, and a number of the candidate articles. That is, different evaluation value thresholds may be determined according to the difference between the types of the product to be processed and the candidate product, and the number of the candidate products. For example, in the case where the kind of the article to be processed is closer to the kind of the candidate article, or the number of the candidate articles is larger, the probability that the article to be processed is determined as the target article is relatively low, and further, the evaluation value threshold value may be set higher, increasing the probability that the article to be processed is determined as the target article.
In some embodiments, determining the target product with the image feature meeting the preset condition may include: determining the times that the actual commodity represented by the image characteristics obtained by image recognition on the commodity to be processed is different from the commodity to be processed aiming at the same commodity to be processed; and determining the target commodity based on the times.
In some embodiments, if the actual commodity with the image feature representation obtained by performing image recognition on the commodity to be processed is different from the commodity to be processed, it indicates that an error occurs in the result of performing image recognition on the commodity to be processed, for example, the actual commodity with the image feature representation obtained by performing image recognition on an apple commodity to be processed is a pear, and the process can be determined manually. In some embodiments, the time or the identification times may be used as a reference to count the number of times of errors occurring in the image identification result of the to-be-processed commodity, for example, every fixed time, such as 2 hours, or every fixed identification times, such as 100 times, the obtained times may be counted, and the target commodity may be determined from all the to-be-processed commodities according to the counted times.
For example, the commodities to be processed include apples, bananas and oranges, and for the apples, bananas and oranges, respectively, whether actual commodities, which are characterized by image features obtained by image recognition, are the same as the commodities to be processed is judged, the number of times of dissimilarity of each commodity to be processed is counted every 2 hours, in one possible implementation manner, the number of times of dissimilarity corresponding to the apples is 5 times, the number of times of dissimilarity corresponding to the bananas is 3 times, and the number of times of dissimilarity corresponding to the oranges is 2 times, and then a target commodity is determined from the commodities to be processed based on the numbers of times.
In some embodiments, determining the target good based on the number of times may include: and sequencing the commodities to be processed according to the times, and determining a preset number of the commodities to be processed with the largest times as target commodities.
In some embodiments, the preset number may be a positive integer greater than or equal to 1 and less than or equal to the number of commodities, for example, when there are 20 commodities to be processed, the preset number may be any one of 1, 3, and 5.
Continuing with the above example, setting the preset number to be 2, sorting the apples, the oranges and the bananas according to the order from big to small according to the number of times, and determining the first 2 to-be-processed commodities apples and bananas with the largest number of times as target commodities, wherein the sorted results are the apples, the bananas and the oranges in turn.
In some embodiments, determining the target good based on the number of times may include: and comparing the times with a time threshold value, and determining the commodities to be processed with the times larger than the time threshold value as target commodities.
Continuing with the above example, the frequency threshold is set to 4 times, since the number of times corresponding to the apple is 5 times and is greater than the frequency threshold, the number of times corresponding to the banana is 3 times and is less than the frequency threshold, and the number of times corresponding to the orange is 2 times and is less than the frequency threshold, only the apple meets the preset condition, and then the apple is determined to be the target commodity.
Specifically, step 306 may be performed by the identification information obtaining module 206.
In some embodiments, the commodity information refers to a general term of information, data or knowledge, etc. about the commodity and its production, circulation or consumption, which can be received by the receiver and meet some special needs, such as the code, name, belonging category, unit price, measurement unit, discount, benefit, etc. of the commodity.
In some embodiments, the identification information may be an index used for querying commodity information of the target commodity in a database, and may be, for example, a number sequence corresponding to a commodity barcode, that is, a commodity barcode number, or a preset number including letters and numbers corresponding to a commodity to be processed.
In some embodiments, the manually input identification information of the target product may be obtained, for example, a corresponding key combination may be input through a monitoring keyboard, a product bar code number corresponding to the key combination is obtained, and the product bar code number is used as the identification information of the target product, and the key combination may specifically include character information corresponding to a key and an arrangement manner of the character information.
And 308, generating a shortcut for acquiring the commodity information based on the identification information, and/or establishing association between a trigger instruction of an entity key and the commodity information based on the identification information so that the commodity information can be acquired when the entity key is triggered.
In particular, step 308 may be performed by the generation setup module 208.
In some embodiments, the shortcut may include a virtual manner such as a shortcut icon, a virtual key, etc., which may be displayed and output through the display screen. The user can trigger the shortcut by clicking action of a mouse connected with the display, or trigger the shortcut on the touch display screen by touching with a finger.
In some embodiments, usually, the database stores the identification information and the product information of the target product in advance, and the identification information and the product information of the same target product are associated, so that the identification information can be used as an index to acquire the corresponding product information. Further, when the shortcut is generated, the shortcut may be linked to the identification information, and when the shortcut is triggered, the identification information may be obtained first, and then the corresponding commodity information may be obtained according to the identification information. The shortcut can also be directly linked to the commodity information corresponding to the identification information, and the commodity information can be directly obtained when the shortcut is triggered. In some embodiments, when the association is established, the triggering instruction of the entity key may be automatically set to acquire the identification information of the target product, and when the entity key is triggered, the identification information of the target product may be acquired through the triggering instruction of the entity key, and then the corresponding product information may be acquired according to the identification information. Or when the association is established, the triggering instruction of the entity key is automatically set to obtain the commodity information corresponding to the identification information directly, and the commodity information is not obtained by taking the identification information as an index. Of course, other embodiments may also be adopted to generate the shortcut or establish the association, and the description is not limited thereto.
In some embodiments, only the scheme of generating the shortcut, or only the scheme of establishing the association between the trigger instruction of the entity key and the commodity information may be adopted, or both schemes may be adopted.
Following the above example, an apple is placed on an electronic scale, the weight of the apple can be obtained through the electronic scale, after the apple is determined as a target commodity, a shortcut icon displayed on a touch screen is generated, the shortcut icon can be named as "apple" through manual input, a user can click the shortcut icon through a finger to obtain commodity information of the apple, such as unit price, and in combination with the weight of the apple obtained through the electronic scale, the total price of the apple can be directly output, and after the shortcut icon is generated, other users can also obtain commodity information of the apple through the shortcut icon.
FIG. 4 is another exemplary flow chart of a method for obtaining merchandise information according to some embodiments of the present application. As shown in fig. 4, the method includes:
and 402, carrying out image recognition on the commodity to obtain the image characteristics of the commodity.
And step 404, determining the target commodity with the image characteristics meeting the preset conditions.
And 406, acquiring identification information of the target commodity, wherein the identification information is used for inquiring commodity information of the target commodity.
And 408, generating a shortcut for acquiring the commodity information based on the identification information, and/or establishing association between a trigger instruction of an entity key and the commodity information based on the identification information, so that the commodity information can be acquired when the entity key is triggered.
Specifically, the step 402 and the step 408 may be executed with reference to the step 302 and the step 308, and the step 410 may be executed by the image acquisition module.
In some embodiments, the image of the target product may be obtained by the terminal by means of record storage, that is, after the image of the product is captured by an image obtaining device such as a camera and the product is determined as the target product, the image of the target product is stored in a storage device such as a hard disk. The image may comprise only pictures, such as JPEG pictures, TIFF pictures, etc., or only video, or both.
Step 412, using the image as sample data, and using the identification information of the target commodity as tag data of the sample data.
In particular, step 412 may be performed by a data marshalling module.
In some embodiments, since the image obtained in step 410 is of the target product and the identification information of the target product has been obtained in step 406, the obtained image and the identification information may be of the same target product, and the image may be used as sample data and the identification information of the same target product may be used as a tag of the sample data in step 414.
Step 414, training a recognition model for performing the image recognition by using the sample data and the label data.
In some embodiments, the recognition model may be implemented based on the aforementioned algorithm, and the sample data and the tag data obtained in step 412 are input into the recognition model to implement training of the recognition model, so as to improve the accuracy of image recognition performed by the recognition model. It should be noted that training can be performed based on the manner in the prior art, and the description is not limited.
It should be noted that the above description of the process 300 or the process 400 is only for illustration and explanation and does not limit the applicable scope of the present application. Various modifications and changes to flow 300 or flow 400 may occur to those skilled in the art in light of the teachings herein. However, such modifications and variations are intended to be within the scope of the present application.
The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to:
(1) for the commodities with wrong identification, such as commodities with wrong identification types judged by a user through related equipment, or unidentified commodities such as newly added commodities without corresponding identification codes added in stores, the user can quickly acquire the commodity information by generating shortcuts and/or establishing the association between trigger instructions of entity keys and the commodity information, so that the manual operation process is simplified, and the efficiency of acquiring the commodity information is improved;
(2) and judging the commodities with frequent buying and selling times according to the identification times of the commodities, and further generating shortcuts for the commodities with frequent buying and selling times and/or establishing the association between the triggering instructions of the entity keys and the commodity information, so that a user can acquire the commodity information of hot-sold commodities more quickly.
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (12)
1. A method for obtaining merchandise information, the method comprising:
carrying out image recognition on a commodity to be processed to obtain image characteristics of the commodity to be processed;
determining a target commodity with the image characteristics meeting preset conditions;
acquiring identification information of the target commodity, wherein the identification information is used for inquiring commodity information of the target commodity;
and generating a shortcut for acquiring the commodity information based on the identification information, and/or establishing association between a trigger instruction of an entity key and the commodity information based on the identification information so that the commodity information can be acquired when the entity key is triggered.
2. The method as claimed in claim 1, wherein the image feature includes an evaluation value for characterizing the similarity of the to-be-processed commodity and a candidate commodity, and the determining the target commodity with the image feature meeting a preset condition includes:
and when the evaluation value of the to-be-processed commodity and the evaluation value of each candidate commodity are smaller than the threshold value of the evaluation value, determining that the to-be-processed commodity is the target commodity.
3. The method of claim 2, wherein the evaluation value threshold is related to a kind of the article to be processed, a kind of the candidate article, and a number of the candidate articles.
4. The method as claimed in claim 1, wherein the determining the target product with the image feature meeting the preset condition comprises:
determining the times that the actual commodity represented by the image characteristics obtained by image recognition on the commodity to be processed is different from the commodity to be processed aiming at the same commodity to be processed;
and determining the target commodity based on the times.
5. The method of claim 1, wherein the method further comprises:
acquiring an image of the target commodity, wherein the image comprises a picture and/or a video;
taking the image as sample data, and taking the identification information of the target commodity as label data of the sample data;
training a recognition model for performing the image recognition using the sample data and the label data.
6. A system for obtaining merchandise information, the system comprising:
the image recognition module is used for carrying out image recognition on the commodity to be processed to obtain the image characteristics of the commodity to be processed;
the determining module is used for determining the target commodity of which the image characteristics meet the preset conditions;
the identification information acquisition module is used for acquiring identification information of the target commodity, and the identification information is used for inquiring commodity information of the target commodity;
and the generation establishing module is used for generating a shortcut for acquiring the commodity information based on the identification information and/or establishing the association between a trigger instruction of an entity key and the commodity information based on the identification information so that the commodity information can be acquired when the entity key is triggered.
7. The system of claim 6, wherein the image feature comprises an evaluation value characterizing a similarity of the item to be processed and a candidate item, the determination module being configured to:
and when the evaluation value of the to-be-processed commodity and the evaluation value of each candidate commodity are smaller than the threshold value of the evaluation value, determining that the to-be-processed commodity is the target commodity.
8. The system of claim 7, wherein the evaluation value threshold is related to a type of the item to be processed, a type of the candidate item, and a number of the candidate items.
9. The system of claim 6, wherein the determination module is to:
determining the times that the actual commodity represented by the image characteristics obtained by image recognition on the commodity to be processed is different from the commodity to be processed aiming at the same commodity to be processed;
and determining the target commodity based on the times.
10. The system of claim 6, wherein the system further comprises:
the image acquisition module is used for acquiring an image of the target commodity, wherein the image comprises a picture and/or a video;
the data sorting module is used for taking the image as sample data and taking the identification information of the target commodity as label data of the sample data;
and the training module is used for training a recognition model for carrying out the image recognition by utilizing the sample data and the label data.
11. An apparatus for obtaining merchandise information, comprising a processor, wherein the processor is configured to perform the method for obtaining merchandise information according to any one of claims 1-5.
12. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer executes the method for acquiring commodity information according to any one of claims 1 to 5.
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