CN114078299A - Commodity settlement method, commodity settlement device, electronic equipment and medium - Google Patents
Commodity settlement method, commodity settlement device, electronic equipment and medium Download PDFInfo
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- 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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- G01G19/4144—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups with provisions for indicating, recording, or computing price or other quantities dependent on the weight using electromechanical or electronic computing means using electronic computing means only for controlling weight of goods in commercial establishments, e.g. supermarket, P.O.S. systems
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Abstract
The embodiment of the application discloses a commodity settlement method, a commodity settlement device, electronic equipment and a medium. The method comprises the following steps: acquiring an image of a target commodity placed on a scale and weight information of the target commodity; identifying the image of the target commodity based on the commodity identification model to obtain an identification result; and settling the target commodity according to the weight information and the identification result of the target commodity. Above technical scheme utilizes artificial intelligence technique to combine together the weighing and settlement function of commodity, has avoided the limitation that customer queued up many times and weighed and settle accounts and artifical weighing, has reduced staff's the operation degree of difficulty of weighing, has improved the settlement efficiency of commodity simultaneously, has improved customer's shopping experience.
Description
Technical Field
The embodiment of the application relates to the technical field of artificial intelligence, in particular to a commodity settlement method, a commodity settlement device, electronic equipment and a medium.
Background
In the existing large supermarket, the cash register area is mainly identified and settled through a bar code on a commodity. However, the commodities in the fresh or bulk area in the supermarket are not provided with bar codes, and when a customer selects the commodities, a certain amount of commodities are put into the shopping bag, then the commodities in the shopping bag are weighed, printed with price labels and pasted on the shopping bag containing the commodities, and finally the settlement is carried out in a cash register area.
The above settlement method has the following problems: 1. customers need to queue to weigh the commodities and print price labels, and then need to queue for settlement commodities in a cash register for the second time, so that the shopping time of the customers is wasted, and the settlement efficiency is low; 2. the price label is not well adhered or the shopping bag is deformed, so that the price label is wrinkled and cracked, and the like, and under the condition, the bar code is difficult to identify by a scanning gun of the traditional cashier desk, so that a cashier is required to manually input the bar code number for settlement, and the working efficiency of the cashier is reduced; 3. when weighing fresh or bulk commodities, a worker needs to manually input commodity codes, but the commodities in the supermarket are various and frequently changed, and the worker in the weighing area needs to be familiar with the codes of various commodities, so that the working difficulty of the worker is improved.
Disclosure of Invention
The embodiment of the application provides a commodity settlement method, a commodity settlement device, an electronic device and a medium, which can improve commodity settlement efficiency and save commodity settlement time cost for customers.
In a first aspect, an embodiment of the present application provides a commodity settlement method, where the method includes:
acquiring an image of a target commodity placed on a scale and weight information of the target commodity;
identifying the image of the target commodity based on the commodity identification model to obtain an identification result;
and settling the target commodity according to the weight information and the identification result of the target commodity.
In a second aspect, an embodiment of the present application provides an article settlement apparatus, including:
the data acquisition module is used for acquiring an image of a target commodity placed on the scale and weight information of the target commodity;
the image recognition module is used for recognizing the image of the target commodity based on the commodity recognition model to obtain a recognition result;
and the commodity settlement module is used for settling the target commodity according to the weight information and the identification result of the target commodity.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable by the processor, where the processor executes the computer program to implement the product settlement method according to the embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements an article settlement method as described in embodiments of the present application.
The embodiment of the application completes the identification and settlement of the target commodity by the following method: acquiring an image of a target commodity placed on a scale and weight information of the target commodity; identifying the image of the target commodity based on the commodity identification model to obtain an identification result; and settling the target commodity according to the weight information and the identification result of the target commodity. Above technical scheme utilizes artificial intelligence technique to combine together the weighing and settlement function of commodity, has avoided the limitation that customer queued up many times and weighed and settle accounts and artifical weighing, has reduced staff's the operation degree of difficulty of weighing, has improved the settlement efficiency of commodity simultaneously, has improved customer's shopping experience.
Drawings
FIG. 1 is a flow chart of a merchandise settlement method provided in an embodiment of the present application;
fig. 2 is a front view of a commodity settlement apparatus according to an embodiment of the present application;
FIG. 3 is a side view of an article settlement device according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating interaction of functional modules of a commodity settlement device according to an embodiment of the present application;
FIG. 5 is a flow chart of a merchandise settlement method according to another embodiment of the present application;
FIG. 6 is a schematic diagram of image confirmation of a target product according to another embodiment of the present application;
FIG. 7 is a flow chart of an update of a product identification model according to another embodiment of the present application;
FIG. 8 is a schematic diagram of an update of a product identification model according to another embodiment of the present application;
FIG. 9 is a flowchart of a merchandise settlement method according to still another embodiment of the present application;
FIG. 10 is a schematic diagram of a commodity identification result analysis logic module according to yet another embodiment of the present application;
fig. 11 is a block diagram showing a structure of a product settlement apparatus according to an embodiment of the present application;
fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Fig. 1 is a flowchart of a commodity settlement method according to an embodiment of the present application, which may be applied to a scene of weighing and settling fresh and bulk commodities in a supermarket. The method can be executed by the commodity settlement device provided by the embodiment of the application, and the device can be realized by software and/or hardware and can be integrated in an electronic device.
As shown in fig. 1, the commodity settlement method provided in the embodiment of the present application may include the following steps:
and S110, acquiring an image of the target commodity placed on the scale and weight information of the target commodity.
Wherein, the scale is a component of the commodity settlement device, fig. 2 is a front view of the commodity settlement device provided by an embodiment of the present application, and as shown in fig. 2, the commodity settlement device includes a display screen, a camera, a scale, a base and a processor. Fig. 3 is a side view of a commodity settlement apparatus according to an embodiment of the present invention, as shown in fig. 3, the commodity settlement apparatus has a main display screen and a sub display screen, wherein the main display screen is displayed for a supermarket operator to use, the sub display screen is displayed for a customer, and the sub display screen can display detailed information of a settlement commodity, such as a commodity production place, a production date, an eating method, and the like, so that the customer can more clearly know the purchased commodity.
Fig. 2 and 3 show that a processor in the commodity settlement device includes a main control module, as shown in fig. 4, fig. 4 is an interaction schematic diagram of functional modules of the commodity settlement device according to an embodiment of the present application. When a commodity is placed on the scale plate, the camera senses and shoots the commodity image in real time and transmits the commodity image to the main control module. The main control module identifies the commodity, controls the scale to weigh, obtains a weighing result, calculates the total price of the commodity, and returns the result to the interaction module to be displayed, wherein the interaction module can be a display screen. After the commodity settlement, the payment can be directly collected for settlement, an external device can be used for printing a label or collecting a bill for scanning, and settlement means such as a payment receiving code can be generated by clicking a screen.
The target commodity is a commodity which is required to be settled and placed on a scale by a customer, and the type of the target commodity can be a commodity on which a bar code is printed on a package, or a commodity without the bar code, such as fresh and bulk commodities.
The image of the target commodity can be collected by an image collecting device, and the image collecting device can be installed on the commodity settlement device or can be an independent device and is connected with the commodity settlement device through a wired or wireless network. The image acquisition equipment in the embodiment of the application can sense the commodities on the scale in real time, and automatically acquires the images of the target commodities once the target commodities are sensed and placed on the scale.
Weight information for the target item may be obtained by a load cell in the scale pan. The weighing sensor can convert the gravity of the target commodity placed on the weighing plate into a measurable electric signal, and then the main control module in the commodity settlement device calculates the weight of the weighed target commodity according to the electric signal transmitted by the weighing sensor.
In an embodiment of the present application, the acquiring an image of a target commodity placed on a scale further includes:
acquiring an image through image acquisition equipment to obtain a commodity image;
identifying a scale area in the commodity image based on a scale identification model;
and taking the image corresponding to the weighing area in the commodity image as the image of the target commodity.
The scale recognition model can be built according to algorithms such as a neural network and the like.
Due to factors such as the angle and the shooting range of the image acquisition device, the image acquisition device often shoots objects around the scale pan when shooting the target commodity on the scale pan. If the image containing the non-target commodity is directly identified, the commodity identification accuracy is reduced, so that the scale area in the commodity image is identified through the scale distinguishing model after the commodity image is obtained, the image of the scale area is extracted to be used as the target commodity image and then is identified, and the influence of the surrounding environment on the identification accuracy is reduced.
And S120, identifying the image of the target commodity based on the commodity identification model to obtain an identification result.
The commodity identification model can be built based on deep learning algorithms such as a convolutional neural network. The recognition result may be a higher name such as apple, grape, banana, or more specific names such as aku apple, red fuji apple, and kungfeng grape.
And S130, settling the target commodity according to the weight information and the identification result of the target commodity.
In an embodiment of the present application, the settling a target product according to weight information and an identification result of the target product includes:
acquiring the unit price of the target commodity from the database according to the identification result;
and determining the total price of the target commodity according to the weight information and the unit price of the target commodity.
The database stores information on a plurality of products, such as the types and unit prices of the products. The database may be stored in the commodity settlement device, or may be stored in the cloud server.
In this embodiment, the commodity settlement device may obtain the unit price of the target commodity from the database according to the recognition result of the commodity recognition model on the target commodity image, and calculate the total price of the target commodity according to the weight information of the target commodity and the unit price, thereby completing the settlement of the target commodity.
The product settlement method described above is applicable to any type of product. For example, if the target product is a product with a bar code printed on a package, for example, 200ml of juice of a certain brand, and the number is two, the weighing scale obtains the total weight of the target product to be 400ml, the image acquisition device acquires an image of the target product, the product identification model performs identification according to the image to obtain an identification result to be juice of the certain brand, the product settlement device obtains the unit price of the juice of the brand from the database according to the identification result to be 3 yuan/200 ml, and then the total price of the target product is obtained by calculation according to the total weight and the unit price.
If the target commodity is fresh or bulk commodity, such as fresh shrimps, the total weight of the shrimps placed on the scale is 2 jin by the scale, meanwhile, the image acquisition equipment acquires the image of the fresh shrimps on the scale, the commodity identification model identifies according to the image to obtain the identification result of the freshwater shrimps, the commodity settlement device acquires the unit price of the freshwater shrimps from the database according to the identification result and calculates the total price of the target commodity according to the total weight and the unit price.
Optionally, in this embodiment of the application, the commodity settlement apparatus may display a plurality of pieces of information related to the recognition result in the database on a display screen of the commodity settlement apparatus according to the recognition result of the commodity recognition model, and a customer selects and determines a final recognition result, and then settles the target commodity according to the weight information and the recognition result.
For example, if the recognition result of the product recognition model for the target product is grape, the product settlement device displays a plurality of grape types related to the grape on the display screen from the database, such as Kyoho grape, red grape, green grape, rose grape, etc., and if the customer or the worker selects the recognition result matched with the target product based on experience, such as the customer or the worker clicks on the red grape on the display screen, the product settlement device obtains the unit price of the red grape from the database, calculates the total price based on the weight and the unit price of the target product, and completes the settlement of the target product.
The embodiment of the application completes the identification and settlement of the target commodity by the following method: acquiring an image of a target commodity placed on a scale and weight information of the target commodity; identifying the image of the target commodity based on the commodity identification model to obtain an identification result; and settling the target commodity according to the weight information and the identification result of the target commodity. Above technical scheme utilizes artificial intelligence technique to combine together the weighing and settlement function of commodity, has avoided the limitation that customer queued up many times and weighed and settle accounts and artifical weighing, has reduced staff's the operation degree of difficulty of weighing, has improved the settlement efficiency of commodity simultaneously, has improved customer's shopping experience.
Fig. 5 is a flowchart of a commodity settlement method according to another embodiment of the present application, and the present embodiment is optimized based on the above embodiment.
As shown in fig. 5, the commodity settlement method provided in the embodiment of the present application may include the following steps:
s210, acquiring an image of the target commodity placed on the scale and weight information of the target commodity.
And S220, identifying the image of the target commodity based on the commodity identification model to obtain an identification result.
S230, if the target commodity cannot be identified by the commodity identification model, acquiring commodity identification information input by a user, settling the target commodity according to the commodity identification information, and updating the commodity identification model;
if the target commodity can be identified by the commodity identification model, acquiring commodity attribute information according to the identification result, and settling the target commodity according to the weight information of the target commodity and the commodity attribute information; the commodity attribute information includes a type and a unit price of the target commodity.
Before identifying the target commodity, the commodity identification model needs to be trained according to the image of the target commodity. If the target commodity placed on the scale is a new commodity, the commodity identification model cannot identify the target commodity, and manual settlement is needed at the moment.
The product identification information is information for uniquely identifying a target product, and may be, for example, a barcode code or a name of the target product. When the target commodity is a new commodity and the commodity identification model cannot identify the new commodity, the staff inputs commodity identification information such as a bar code or a name of the target commodity into the commodity settlement device, and the commodity settlement device can acquire the type and unit price of the target commodity from the database according to the input commodity identification information and settle the target commodity according to the unit price and weight information of the target commodity.
The commodity settlement device can bind the updating process of the commodity identification model when manually settling the target commodity. In this embodiment of the application, the obtaining of the commodity identification information input by the user and the updating of the commodity identification model include:
acquiring commodity identification information input by a user, and determining at least one item of commodity attribute information associated with the commodity identification information;
performing optimization training on the commodity identification model according to the image of the target commodity and the commodity attribute information selected by the user from the at least one item of commodity attribute information so as to update the commodity identification model;
uploading the updated commodity identification model to a cloud server for calling to identify a target commodity;
the commodity identification information comprises a bar code of the target commodity and/or a name of the target commodity.
In the embodiment of the present application, the article attribute information refers to information for describing the article characteristics, such as the type and unit price of the article.
Illustratively, the commodity settlement device receives commodity identification information input by the collocation user as mango, and the commodity settlement device acquires at least one item of commodity attribute information associated with the mango, such as awn, rhynchus chinensis, awn 6, Binlin No. 1 and Imperial concubine. The commodity settlement device displays the commodity attribute information on a display screen, and a worker selects one commodity attribute information, such as the awn, which accords with the target commodity from the commodity attribute information according to experience, so that the commodity identification model knows that the image of the target commodity is the awn, and thus, one-time self-service learning is completed, and the updating of the model is realized.
The user inputs the commodity identification information which can also be the bar code of the commodity, the commodity settlement device obtains at least one item of commodity attribute information associated with the bar code, for example, the image of the target commodity is the awn, the commodity identification model learns that the image of the target commodity is the awn, and therefore one-time self-help learning is completed, and updating of the model is achieved.
Fig. 6 is a schematic diagram illustrating target product image confirmation according to another embodiment of the present application, and as shown in fig. 6, when a product identification model performs optimization training according to an image of a target product and product attribute information selected by a user from at least one item of product attribute information, if a hand occupation ratio in the image of the target product is large, a camera is called to obtain the image of the target product again until a clear and handleless image of the target product is obtained.
After the commodity identification model in one commodity settlement device is updated, the commodity settlement device uploads the updated commodity identification model to the cloud server, and the cloud server synchronizes the updated commodity identification model to other commodity settlement devices in the same supermarket, so that the other commodity settlement devices can directly call the updated commodity identification model, and the optimization training of the commodity identification model is not required again.
In the embodiment of the application, when the target commodity is a new commodity and cannot be identified by the commodity identification model, the commodity identification model can be optimally trained and updated through the manual settlement process without using a large amount of data in advance to train the model, the commodity identification model can continuously sense the appearance form of the target commodity in the using process and independently learn and adapt, and when the appearance form of the same target commodity is changed, for example, one watermelon and a half watermelon can also be accurately identified.
If the commodity identification model can identify the target commodity, the target commodity is not a new commodity, and at the moment, the commodity identification model identifies the image of the target commodity to obtain an identification result. The product settlement apparatus acquires product attribute information from the database based on the recognition result, for example, an image of the target product recognized by the product recognition model, and if the recognition result is mango, the product attribute information may be different types of mango and unit prices of different types, or the recognition result may be a more specific product, for example, tainong No. 1 mango, and the product attribute information may be the unit price of tainong No. 1 mango. And after the commodity attribute information is obtained, the target commodity is settled according to the weight information of the target commodity and the commodity attribute information.
Fig. 7 and 8 show flowcharts of model updating according to the recognition result, fig. 7 is a flowchart of commodity recognition model updating according to another embodiment of the present application, and fig. 8 is a schematic diagram of commodity recognition model updating according to another embodiment of the present application.
Optionally, in this embodiment of the application, if the target product is identifiable by the product identification model, obtaining product attribute information according to the identification result, and settling the target product according to the weight information of the target product and the product attribute information, includes:
acquiring candidate commodity attribute information according to the identification result;
calculating the similarity between the image of the target commodity and the image of the candidate commodity in the candidate commodity attribute information, and determining the target commodity attribute information from the candidate commodity attribute information according to the similarity;
and settling the target commodity according to the weight information of the target commodity and the attribute information of the target commodity.
The candidate product attribute information is a plurality of product attribute information related to the recognition result. For example, if the identification result is mango, the obtained candidate commodity attribute information may include awn, rhynchophorus floridulus, awn 6, binglin 1, and majeffe mango, and the image and unit price of the above various kinds of mango. And then respectively calculating the similarity between the image of the target commodity and the images of the various mangos, and taking a certain mango name with the highest similarity as target commodity attribute information. For example, if the similarity between the image of the red ivory mango and the target commodity image is the highest and 99%, the red ivory mango is regarded as the target commodity attribute information, and the target commodity is regarded as the red ivory mango. After the attribute information of the target commodity is red ivory, the unit price of the red ivory can be obtained from the database, and then the target commodity can be settled according to the weight information of the target commodity and the unit price of the red ivory.
In an embodiment of the present application, the determining, according to the similarity, the attribute information of the target product includes:
if the similarity between the image of the candidate commodity and the image of the target commodity is larger than the similarity threshold value, the commodity attribute information of the candidate commodity is used as optional commodity attribute information;
and taking the selectable commodity attribute information selected by the user as target commodity attribute information.
Wherein, the similarity threshold value can be set according to actual requirements. For example, if the similarity between the image of A, B and the image of the target product in the candidate product A, B, C, D is greater than the similarity threshold, the product attribute information of A, B, such as the type, image, unit price, etc. of A, B, is used as selectable product attribute information for the user to select. The user can select the selectable commodity attribute information displayed on the display screen according to the target commodity, for example, if the user determines that the image of the A is consistent with the target commodity through judgment, the commodity attribute information of the A is clicked, and the commodity settlement device takes the selectable commodity attribute information selected by the user as the target commodity attribute information and carries out subsequent settlement processes.
In the embodiment of the application, the hash distance between the image of the target commodity and the image of the candidate commodity in the candidate commodity attribute information can be calculated through the relevant discrimination model to obtain the similarity. By calculating the similarity, information closer to the target commodity is screened out and provided for the user to select, the selection range of the user is narrowed, and the selection efficiency is improved.
Fig. 9 is a flowchart of a commodity settlement method according to another embodiment of the present application, which is optimized based on the above embodiment.
As shown in fig. 9, the commodity settlement method provided in the embodiment of the present application may include the steps of:
s310, acquiring an image of the target commodity placed on the scale and weight information of the target commodity.
And S320, identifying the image of the target commodity based on the commodity identification model to obtain an identification result.
And S330, settling the target commodity according to the weight information and the identification result of the target commodity.
S340, calculating the identification accuracy of the identification result based on the first preset period, and comparing the identification accuracy with a preset accuracy threshold.
And S350, if the recognition accuracy is smaller than the preset accuracy threshold, performing optimization training on the commodity recognition model according to the target commodity until the recognition accuracy is larger than the preset accuracy threshold.
Since the update of the commodity identification model is performed based on a manual settlement process, and an erroneous operation may occur in the manual settlement process, for example, an erroneous name or barcode of the target commodity is input in the process of inputting the commodity identification information, or an erroneous commodity attribute information is selected in the process of selecting the commodity attribute information from at least one item of commodity attribute information, which may cause the commodity identification model to learn an erroneous feature, thereby greatly reducing the identification accuracy of the target commodity.
In order to avoid the above situation, in the embodiment of the present application, the recognition accuracy of the commodity recognition model is checked according to a first preset period. The first preset period can be set according to actual requirements, for example, the period can be one week, and the identification accuracy of the commodity identification model is checked every other week. And in the checking process, an accuracy threshold value can be set, the identification accuracy of the commodity identification model in each first preset period is compared with a preset accuracy threshold value, and if the identification accuracy is smaller than the preset accuracy threshold value, the commodity identification model is subjected to optimization training again until the identification accuracy is larger than the preset accuracy threshold value. Illustratively, the accuracy threshold is set to 80%, if the article recognition model recognizes the target article 10 times in a first preset period, wherein 6 times of recognition are incorrect, the recognition accuracy of the article recognition model in the first preset period is 40%, and is smaller than the accuracy threshold, at this time, the optimization training needs to be performed on the article recognition model again until the recognition accuracy is greater than the preset accuracy threshold.
Optionally, in this embodiment of the present application, the method may further include:
traversing the stored commodities in the model base based on a second preset period, and determining the times of determining the target commodity as the currently traversed stored commodity;
and if the times are less than a preset time threshold value, deleting the stored commodity from the model library.
Because some commodities in the supermarket are season-eligible commodities such as strawberries, the frequency of occurrence of strawberries is reduced after season passing, and the probability of purchase of the strawberries as target commodities is reduced correspondingly.
In the embodiment of the application, the stored commodities in the model library are traversed according to a second preset period, and the number of times that the target commodity is determined as the currently traversed stored commodity is determined, where the second preset period may be set according to an actual requirement, for example, may be one month. And setting a preset time threshold, and deleting the stored commodities with times less than the preset time threshold from the model library, wherein the preset time threshold can be set according to actual requirements, for example, 5 times.
Illustratively, the stored commodities in the model base include strawberries, grapes and apples, the three commodities are traversed every month, the times of determining the target commodity as the three commodities in the month are respectively 2 times, 15 times and 16 times through calculation, and the strawberry is deleted from the model base if the times of determining the target commodity as the three commodities are smaller than a time threshold value, so that the identification accuracy of the commodity identification model is improved.
Fig. 10 is a schematic diagram of a commodity identification result analysis logic module according to yet another embodiment of the present application, where each module in fig. 10 may implement the method for analyzing and processing a commodity identification result in each embodiment described above.
Fig. 11 is a block diagram of a product settlement apparatus according to an embodiment of the present application, which is capable of executing a product settlement method according to any embodiment of the present application, and includes functional blocks and advantageous effects corresponding to the execution method. As shown in fig. 11, the apparatus may include:
a data acquisition module 410, configured to acquire an image of a target product placed on a scale and weight information of the target product;
the image recognition module 420 is configured to recognize an image of the target product based on the product recognition model to obtain a recognition result;
and a product settlement module 430, configured to settle the target product according to the weight information of the target product and the identification result.
In this embodiment of the application, the commodity settlement module 430 is specifically configured to:
acquiring the unit price of the target commodity from the database according to the identification result;
and determining the total price of the target commodity according to the weight information and the unit price of the target commodity.
In this embodiment, the commodity settlement module 430 includes:
the model updating unit is used for acquiring the commodity identification information input by the user if the commodity identification model cannot identify the target commodity, settling the target commodity according to the commodity identification information and updating the commodity identification model;
the settlement unit is used for acquiring the commodity attribute information according to the identification result and settling the target commodity according to the weight information of the target commodity and the commodity attribute information if the target commodity can be identified by the commodity identification model; the commodity attribute information includes a type and a unit price of the target commodity.
In an embodiment of the present application, the model updating unit is specifically configured to:
acquiring commodity identification information input by a user, and determining at least one item of commodity attribute information associated with the commodity identification information;
performing optimization training on the commodity identification model according to the image of the target commodity and the commodity attribute information selected by the user from the at least one item of commodity attribute information so as to update the commodity identification model;
uploading the updated commodity identification model to a cloud server for calling to identify a target commodity;
the commodity identification information comprises a bar code of the target commodity and/or a name of the target commodity.
In an embodiment of the present application, the settlement unit includes:
the information acquisition subunit is used for acquiring the attribute information of the candidate commodity according to the identification result;
the similarity calculation operator unit is used for calculating the similarity between the image of the target commodity and the image of the candidate commodity in the candidate commodity attribute information and determining the target commodity attribute information from the candidate commodity attribute information according to the similarity;
and the target commodity settlement subunit is used for settling the target commodity according to the weight information of the target commodity and the attribute information of the target commodity.
In the embodiment of the present application, the similarity calculation subunit is specifically configured to:
if the similarity between the image of the candidate commodity and the image of the target commodity is larger than the similarity threshold value, the commodity attribute information of the candidate commodity is used as optional commodity attribute information;
and taking the selectable commodity attribute information selected by the user as target commodity attribute information.
In this embodiment, the apparatus further includes:
the threshold comparison module is used for calculating the identification accuracy of the identification result based on a first preset period and comparing the identification accuracy with a preset accuracy threshold;
and the model training module is used for carrying out optimization training on the commodity identification model according to the target commodity if the identification accuracy is smaller than a preset accuracy threshold value until the identification accuracy is larger than the preset accuracy threshold value.
In this embodiment, the apparatus further includes:
the commodity traversal module is used for traversing the stored commodities in the model base based on a second preset period and determining the times of determining the target commodity as the currently traversed stored commodity;
and the commodity deleting module is used for deleting the stored commodity from the model library if the times are less than a preset time threshold.
In this embodiment of the application, the data obtaining module 410 is specifically configured to:
acquiring an image through image acquisition equipment to obtain a commodity image;
identifying a scale area in the commodity image based on a scale identification model;
and taking the image corresponding to the weighing area in the commodity image as the image of the target commodity.
The product can execute the commodity settlement method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present application. FIG. 12 illustrates a block diagram of an exemplary electronic device 512 suitable for use in implementing embodiments of the present application. The electronic device 512 shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 12, the electronic device 512 may include: one or more processors 516; the memory 528 is configured to store one or more programs, and when the one or more programs are executed by the one or more processors 516, the one or more processors 516 may implement the method for settling the commodity provided in the embodiment of the present application, including:
acquiring an image of a target commodity placed on a scale and weight information of the target commodity;
identifying the image of the target commodity based on the commodity identification model to obtain an identification result;
and settling the target commodity according to the weight information and the identification result of the target commodity.
Components of the electronic device 512 may include, but are not limited to: one or more processors 516, a memory 528, and a bus 518 that connects the various device components, including the memory 528 and the processors 516.
The electronic device 512 typically includes a variety of computer device-readable storage media. These storage media may be any available storage media that can be accessed by electronic device 512 and includes both volatile and nonvolatile storage media, removable and non-removable storage media.
The memory 528 may include computer device readable storage media in the form of volatile memory, such as Random Access Memory (RAM)530 and/or cache memory 532. The electronic device 512 may further include other removable/non-removable, volatile/nonvolatile computer device storage media. By way of example only, storage system 534 may be used to read from and write to non-removable, nonvolatile magnetic storage media (not shown in FIG. 12, and commonly referred to as a "hard drive"). Although not shown in FIG. 12, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical storage medium) may be provided. In such cases, each drive may be connected to bus 518 through one or more data storage media interfaces. Memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 540 having a set (at least one) of program modules 542 may be stored, for example, in memory 528, such program modules 542 including, but not limited to, an operating device, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. The program modules 542 generally perform the functions and/or methods of the embodiments described herein.
The electronic device 512 may also communicate with one or more external devices 514 and/or a display 524, etc., and may also communicate with one or more devices that enable a user to interact with the electronic device 512, and/or with any devices (e.g., network cards, modems, etc.) that enable the electronic device 512 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 522. Also, the electronic device 512 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 520. As shown in FIG. 12, the network adapter 520 communicates with the other modules of the electronic device 512 via the bus 518. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with the electronic device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup storage devices, among others.
The processor 516 executes various functional applications and data processing by executing at least one of other programs of the programs stored in the memory 528, for example, to implement a product settlement method provided in the embodiment of the present application.
One embodiment of the present application provides a storage medium containing computer-executable instructions for performing a method of merchandise settlement when executed by a computer processor, comprising:
acquiring an image of a target commodity placed on a scale and weight information of the target commodity;
identifying the image of the target commodity based on the commodity identification model to obtain an identification result;
and settling the target commodity according to the weight information and the identification result of the target commodity.
The computer storage media of the embodiments of the present application may take any combination of one or more computer-readable storage media. The computer readable storage medium may be a computer readable signal storage medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present application, a computer readable storage medium may be any tangible storage medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus.
A computer readable signal storage medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal storage medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus.
Program code embodied on a computer readable storage medium may be transmitted using any appropriate storage medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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 device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including 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 using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims (12)
1. A method for settling a commodity, the method comprising:
acquiring an image of a target commodity placed on a scale and weight information of the target commodity;
identifying the image of the target commodity based on the commodity identification model to obtain an identification result;
and settling the target commodity according to the weight information and the identification result of the target commodity.
2. The method of claim 1, wherein the settling the target product according to the weight information of the target product and the identification result comprises:
acquiring the unit price of the target commodity from the database according to the identification result;
and determining the total price of the target commodity according to the weight information and the unit price of the target commodity.
3. The method according to claim 1, wherein the settling the target product based on the weight information of the target product and the recognition result further comprises:
if the commodity identification model cannot identify the target commodity, acquiring commodity identification information input by a user, settling the target commodity according to the commodity identification information, and updating the commodity identification model;
if the target commodity can be identified by the commodity identification model, acquiring commodity attribute information according to the identification result, and settling the target commodity according to the weight information of the target commodity and the commodity attribute information; the commodity attribute information includes a type and a unit price of the target commodity.
4. The method of claim 3, wherein the obtaining of the commodity identification information input by the user and the updating of the commodity identification model comprise:
acquiring commodity identification information input by a user, and determining at least one item of commodity attribute information associated with the commodity identification information;
performing optimization training on the commodity identification model according to the image of the target commodity and the commodity attribute information selected by the user from the at least one item of commodity attribute information so as to update the commodity identification model;
uploading the updated commodity identification model to a cloud server for calling to identify a target commodity;
the commodity identification information comprises a bar code of the target commodity and/or a name of the target commodity.
5. The method according to claim 3, wherein if the product identification model can identify the target product, acquiring product attribute information according to the identification result, and settling the target product according to the weight information of the target product and the product attribute information comprises:
acquiring candidate commodity attribute information according to the identification result;
calculating the similarity between the image of the target commodity and the image of the candidate commodity in the candidate commodity attribute information, and determining the target commodity attribute information from the candidate commodity attribute information according to the similarity;
and settling the target commodity according to the weight information of the target commodity and the attribute information of the target commodity.
6. The method according to claim 5, wherein the determining target commodity attribute information according to the similarity comprises:
if the similarity between the image of the candidate commodity and the image of the target commodity is larger than the similarity threshold value, the commodity attribute information of the candidate commodity is used as optional commodity attribute information;
and taking the selectable commodity attribute information selected by the user as target commodity attribute information.
7. The method of claim 1, further comprising:
calculating the recognition accuracy of the recognition result based on the first preset period, and comparing the recognition accuracy with a preset accuracy threshold;
and if the recognition accuracy is smaller than the preset accuracy threshold, performing optimization training on the commodity recognition model according to the target commodity until the recognition accuracy is larger than the preset accuracy threshold.
8. The method of claim 1, further comprising:
traversing the stored commodities in the model base based on a second preset period, and determining the times of determining the target commodity as the currently traversed stored commodity;
and if the times are less than a preset time threshold value, deleting the stored commodity from the model library.
9. The method of claim 1, wherein said acquiring an image of a target item placed on a scale further comprises:
acquiring an image through image acquisition equipment to obtain a commodity image;
identifying a scale area in the commodity image based on a scale identification model;
and taking the image corresponding to the weighing area in the commodity image as the image of the target commodity.
10. An article settlement apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring an image of a target commodity placed on the scale and weight information of the target commodity;
the image recognition module is used for recognizing the image of the target commodity based on the commodity recognition model to obtain a recognition result;
and the commodity settlement module is used for settling the target commodity according to the weight information and the identification result of the target commodity.
11. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, implement the article settlement method of any one of claims 1-9.
12. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing a method for settlement of an article according to any one of claims 1 to 9.
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