Dressing method and electronic device

Abstract

This application discloses a dressing method and electronic device, relating to the field of artificial intelligence. These devices can intelligently manage clothing and recommend daily outfits, improving the accuracy of outfit recommendations and saving users time and effort, thereby enhancing the user experience. The method includes: displaying outfit combinations in order of outfit scores on a first window; determining the outfit scores based on temperature information and activity information; the temperature information is used to indicate the applicable temperature for the outfit combination; and the activity information is used to indicate the style attributes of the outfit combination.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a dressing method and electronic equipment. Background Art

[0002] In daily life, the following situations often occur: 1) Users struggle with outfit choices before going out due to heavy clothing loads; 2) Users struggle with what to pack for a trip; 3) Users prepare their outfit and then face unexpected weather changes, making their outfit inappropriate, such as overheating or insufficient warmth. This can waste significant time and effort, negatively impacting user experience. Summary of the Invention

[0003] The embodiments of the present application provide a dressing method and electronic device that can intelligently manage clothing and recommend daily outfits, improve the accuracy of outfit recommendations, save users' time and energy, and thus improve user experience.

[0004] To achieve the purpose, the embodiments of the present application adopt the following technical solutions:

[0005] In a first aspect, a dressing method is provided, which includes: displaying dressing combinations in order of dressing scores on a first window; the dressing scores are determined based on temperature information and activity information; the temperature information is used to indicate the applicable temperature of the dressing combination; and the activity information is used to indicate the style attributes of the dressing combination.

[0006] The above-mentioned dressing method determines the dressing score based on temperature and activity information, and displays the dressing combinations in the first window in order of dressing scores. It can intelligently recommend daily outfits, improve the accuracy of dressing recommendations, save users time and energy, and thus enhance the user experience.

[0007] In an implementation manner of the first aspect, the activity information is obtained online or through user input.

[0008] In this implementation, activity information obtained online indicates that the local outfit combinations for the day are displayed, and activity information obtained through user input indicates that the outfit combinations for the future or other places are displayed. Outfits can be recommended based on actual conditions, improving the accuracy of outfit recommendations, saving users' time and energy, and thus improving user experience.

[0009] In one possible implementation of the first aspect, in response to a user clicking on a target outfit combination, the first N outfit combinations are displayed in order of outfit scores or an enlarged image of the target outfit combination is displayed on the first window; N is a positive integer; and the outfit combinations include the target outfit combination.

[0010] In this implementation, by clicking on the target outfit combination, the first N outfit combinations or an enlarged picture of the target outfit combination are displayed, which is based on the user's online or input two different choices to display the results.

[0011] In an implementation manner of the first aspect, the activity information includes at least one of the following: target time, target area, indoor and outdoor, and target activity.

[0012] In this implementation method, the user's dressing style can be clearly known through the target time, target area, indoor and outdoor, and target activities in the activity information, so as to accurately recommend dressing combinations.

[0013] In an implementable manner of the first aspect, the dressing score is confirmed based on temperature information and activity information, including: based on the temperature information and activity information, screening the clothes in the database to obtain target clothes; the target clothes include a first clothing set, a second clothing set and a third clothing set; the first clothing set includes outer upper body clothes, the second clothing set includes outer lower body clothes, and the third clothing set includes inner upper body clothes, general upper body clothes, inner jumpsuit clothes or general jumpsuit clothes; the first clothing, the second clothing and the third clothing are matched to obtain a dressing combination; the first clothing is any piece of clothing in the first clothing set, the second clothing is any piece of clothing in the second clothing set, and the third clothing is any piece of clothing in the third clothing set; the dressing combination is encoded to obtain a first dressing combination feature vector; the first dressing combination feature vector is input into the clustering neural network model to obtain the dressing score of the dressing combination.

[0014] In this implementation, based on temperature and activity information, the database is filtered to identify target clothing suitable for that temperature and style. These filtered clothing items are then fed into a clustering neural network model for automatic matching, which then outputs a matching score. This allows for precise clothing matching and accurate recommendations for outfit combinations.

[0015] In an implementation method of the first aspect, the training process of the clustering neural network model includes: obtaining the attribute vector of the first clothing, the attribute vector of the second clothing, and the attribute vector of the third clothing from the database; each dimension of the attribute vector is used to represent the value of an attribute of the clothing; the clustering neural network model is trained based on the second dressing combination feature vector and the corresponding dressing score; the second dressing combination feature vector includes the attribute vector of the first clothing, the attribute vector of the second clothing, and the attribute vector of the third clothing.

[0016] In this implementation, the clustering neural network model is trained based on the second dressing combination feature vector and the corresponding dressing score in the database, including the attribute vector of the first clothing, the attribute vector of the second clothing, and the attribute vector of the third clothing, so as to improve the accuracy of the clustering neural network model.

[0017] In one possible implementation of the first aspect, the dressing score is also determined based on historical dressing combinations.

[0018] In this implementation method, the outfit score is also determined by historical outfit combinations, which can avoid duplication of the recommended current local outfit combinations with outfit combinations from previous days, thereby improving the user experience.

[0019] In an implementation manner of the first aspect, the method further includes: displaying an add button on the first window; and displaying the fourth clothing item and attributes of the fourth clothing item on the first window in response to the user clicking the add button.

[0020] In this implementation, users can import photographed clothing images or downloaded clothing images through the add button, thereby intelligently archiving the imported clothing.

[0021] In an implementation manner of the first aspect, the method further includes: displaying a delete button on the first window; and in response to the user clicking the delete button, not displaying the fourth clothing item and the attributes of the fourth clothing item on the first window.

[0022] In this implementation, after importing an image, if the user is not satisfied with the imported image or the import is incorrect, the user can delete it by clicking the delete button, which is a simple operation.

[0023] In an implementation manner of the first aspect, the method further includes: displaying an edit button on the first window; and in response to the user clicking the edit button, displaying the attributes of the fourth clothing item in an editable state on the first window.

[0024] In this implementation, users can change the attributes of clothing through the edit button, avoiding the situation where the automatically identified attributes are incorrect and cannot be changed, making the attributes of clothing more accurate and simple to operate.

[0025] In the second aspect, an electronic device is provided, comprising a memory and one or more processors, wherein the memory stores computer program code, and the computer program code comprises computer instructions. When the computer instructions are executed by the processor, the electronic device executes the dressing method of the first aspect and any embodiment thereof.

[0026] In a third aspect, a computer-readable storage medium is provided, comprising computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the dressing method of the first aspect and any embodiment thereof.

[0027] In a fourth aspect, a computer program product is provided. When the computer program product is run on an electronic device, the electronic device executes the dressing method of the first aspect and any embodiment thereof.

[0028] Among them, the technical effects brought about by the design methods of the second, third and fourth aspects can refer to the technical effects brought about by the different design methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a possible hardware structure of an electronic device provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of a possible software structure of an electronic device provided in an embodiment of the present application;

[0031] Figure 3 A flowchart of a dressing method provided for related technologies;

[0032] Figure 4 A flowchart of a dressing method provided in an embodiment of the present application;

[0033] Figure 5 A flowchart of a method for displaying outfit suggestions on a card provided in an embodiment of the present application;

[0034] Figure 6 A flowchart of a method for displaying outfits in a outfit app provided in an embodiment of the present application;

[0035] Figure 7 A flowchart of an intelligent recommendation method for dressing provided in an embodiment of the present application;

[0036] Figure 8 A flowchart of clothing archiving provided in an embodiment of the present application;

[0037] Figure 9 A flowchart of adding clothes provided in an embodiment of the present application;

[0038] Figure 10 A flowchart of deleting clothing provided in an embodiment of the present application;

[0039] Figure 11 A flowchart of editing clothing attributes provided in an embodiment of the present application;

[0040] Figure 12 A flow chart of an attribute recognition model provided in an embodiment of the present application;

[0041] Figure 13 A flowchart for confirming a dressing score provided in an embodiment of the present application;

[0042] Figure 14 A training flowchart of a clustering neural network model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following describes the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings. In the description of this application, unless otherwise specified, " / " indicates that the objects associated with each other are in an "or" relationship. For example, A / B can mean A or B. "And / or" in this application is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise specified, "multiple" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete way for ease of understanding. The terms "coupling" and "connection" involved in the embodiments of the present application should be understood in a broad sense. For example, they can refer to a physical direct connection or an indirect connection achieved through an electronic device, such as a connection achieved through a resistor, inductor, capacitor or other electronic device.

[0044] Artificial intelligence (AI) is a branch of computer science dedicated to creating machines and software that can perform tasks that normally require human intelligence. A machine exhibits intelligent behaviors that are often associated with humans or other animals, such as learning, reasoning, problem solving, perception, and language understanding.

[0045] A clothing combination (also known as clothing matching) mainly refers to upper body clothing, outerwear, lower body clothing, hats, accessories and shoes, which should be coordinated in style and color to achieve an overall decent and generous effect.

[0046] An embodiment of the present application provides an electronic device having a display function. The electronic device can be mobile or fixed. The electronic device can be deployed on land (e.g., indoors or outdoors, handheld or vehicle-mounted), on water (e.g., on ships), or in the air (e.g., on airplanes, balloons, and satellites). The electronic device can be referred to as user equipment (UE), access terminal, terminal unit, subscriber unit, terminal station, mobile station (MS), mobile station, terminal agent, or terminal device. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, smart bracelet, smart screen, smart watch, virtual reality (VR) device, augmented reality (AR) device, terminal in industrial control, terminal in self-driving, terminal in remote medical care, terminal in smart grid, terminal in transportation safety, terminal in smart city, terminal in smart home, etc. The embodiments of the present application do not limit the specific type and structure of the electronic device. A possible structure of the electronic device is described below.

[0047] Take the mobile phone as an example, Figure 1 Figure 2 shows a possible structure of an electronic device 100. The electronic device 100 may include a processor 210, an external memory interface 220, an internal memory 221, a universal serial bus (USB) interface 230, a power management module 240, a battery 241, a wireless charging coil 242, a mobile communication module 250, a wireless communication module 260, an antenna 251, an antenna 261, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, an earphone interface 270D, a sensor module 280, a button 290, a motor 291, an indicator 292, a camera 293, a display 294, and a subscriber identification module (SIM) card interface 295. Optionally, in some embodiments, an audio digital signal processor (ADSP) 243 is also included.

[0048] It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0049] The processor 210 may include one or more processing units, such as a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processing unit (CPU), an application processor (AP), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, and a neural network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors. For example, the processor 210 may be an application processor (AP). Alternatively, the processor 210 may be integrated into a system on chip (SoC). Alternatively, the processor 210 may be integrated into an integrated circuit (IC) chip. The processor 210 may include an analog front end (AFE) and a micro-controller unit (MCU) in an IC chip.

[0050] Processor 210 may also include a memory for storing computer instructions and data. In some embodiments, the memory in processor 210 is a cache memory. This memory can store computer instructions or data that have just been used or are being recycled by processor 210. If processor 210 needs to use the computer instructions or data again, it can directly access the memory. This avoids repeated accesses, reduces processor 210 latency, and thus improves system efficiency.

[0051] In some embodiments, the processor 210 may include one or more interfaces, including an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a USB interface.

[0052] In some embodiments, the processor may be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. The aforementioned processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0053] The ADSP 243 can be coupled to the audio module 270 and the sensor module 280. The ADSP 243 can be used to process audio signals and sensor data. When the processor 210 is in a dormant state, the ADSP 243 can still keep working, thereby reducing the power consumption of the electronic device 100.

[0054] It is understood that the interface connection relationship between the modules illustrated in the embodiment of the present application is merely an illustrative illustration and does not constitute a structural limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection methods from those in the embodiment, or a combination of multiple interface connection methods.

[0055] The external memory interface 220 can be used to connect an external memory card to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 210 through the external memory interface 220 to implement data storage functions. For example, files such as music and videos can be stored in the external memory card.

[0056] The internal memory 221 can be used to store computer-executable program code, which includes computer instructions. The processor 210 executes the computer instructions stored in the internal memory 221 to perform various functional applications and data processing of the electronic device 100. In addition, the internal memory 221 can include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0057] In the embodiment of the present application, when the computer instructions are executed by the processor 210, the electronic device 100 executes the dressing method in the embodiment of the present application.

[0058] The memory involved in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0059] In the embodiment of the present application, the clothes in the database can be stored in a memory or in a processor 210 provided with a memory.

[0060] The electronic device 100 can implement audio functions such as music playback and recording through the audio module 270, the speaker 270A, the receiver 270B, the microphone 270C, the headphone jack 270D, and the application processor.

[0061] Keys 290 include a power button, volume button, and other buttons. Keys 290 can be mechanical or touch-sensitive. Electronic device 100 can receive key inputs and generate key signal inputs related to user settings and function control of electronic device 100. Motor 291 can generate vibration alerts. Motor 291 can be used for incoming call vibration alerts or for touch vibration feedback. Indicator 292 can be an indicator light that can indicate charging status, battery level changes, messages, missed calls, notifications, and the like. SIM card interface 295 is used to connect a SIM card. A SIM card can be connected to and disconnected from electronic device 100 by inserting or removing it from SIM card interface 295. Electronic device 100 can support one or N SIM card interfaces, where N is a positive integer greater than one. SIM card interface 295 can support nano SIM cards, micro SIM cards, and SIM cards. In some embodiments, the electronic device 100 uses an embedded SIM (eSIM) card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

[0062] The electronic device 100 can implement a camera function using an ISP, a camera 293, a video codec, a GPU, a display 294, and an application processor. The ISP is used to process data fed back by the camera 293. In some embodiments, the ISP can be provided within the camera 293. The camera 293 is used to capture still images or videos. In some embodiments, the electronic device 100 can include one or N cameras 293, where N is a positive integer greater than one.

[0063] In this embodiment of the present application, the camera 293 can collect the user's clothing and store it in a database.

[0064] Electronic device 100 can implement display functions through a GPU, display screen 294, and an application processor. A GPU is a microprocessor for image processing that connects display screen 294 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 210 may include one or more GPUs that execute computer instructions to generate or modify display information.

[0065] In an embodiment of the present application, the dressing function can be executed by the GPU or by the processor 210, and the dressing effect can be displayed on the display screen 294.

[0066] The power management module 240 is configured to receive charging input from a charger. The charger may be a wireless charger, such as a wireless charging dock or another electronic device 100 with reverse wireless charging functionality. The power management module 240 may receive wireless charging input via the electronic device's wireless charging coil 242. Alternatively, the charger may be a wired charger, for example, via the USB port 230. The power management module 240 is also referred to as a charging chip.

[0067] The power management module 240 is connected to the battery 241. The power management module 240 receives input from the battery 241 and provides power to the processor 210, internal memory 221, display 294, camera 293, and wireless communication module 260. The power management module 240 can also monitor parameters such as the battery 241 capacity, battery 241 cycle count, and battery 241 health status (leakage, impedance). In other embodiments, the power management module 240 can also be provided within the processor 210.

[0068] The wireless communication function of the electronic device 100 can be implemented through the antenna 251, the antenna 261, the mobile communication module 250, the wireless communication module 260, the modem processor, etc.

[0069] The mobile communication module 250 can provide solutions for wireless communications such as 2G / 3G / 4G / 5G for the electronic device 100. The wireless communication module 260 can provide solutions for wireless communications such as wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite systems (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) for the electronic device 100.

[0070] As attached Figure 2 As shown, taking the electronic device 100 running the Android operating system as an example, the software architecture run by the processor 210 includes an application layer, a framework layer, a system runtime layer, a hardware abstract layer (HAL) layer, and a kernel layer.

[0071] The kernel layer is the layer between hardware and software. For example, the kernel layer includes display drivers and camera drivers. The display driver drives the display to display images or receive user touch operations, while the camera driver drives the camera to capture image data.

[0072] In this embodiment of the present application, the camera driver can drive the camera 293 to capture images of the user's clothing. The display driver can receive user input touch operations, drive the user's input activity information, and can also receive user outfit touch operations and drive the display screen 294 to display images of outfit combinations.

[0073] The HAL layer abstracts the hardware. It hides the platform-specific hardware interface details and provides the operating system with a virtual hardware platform, making it hardware-independent. For example, the HAL layer includes the display module and the camera module. The display module is used to create a virtual display screen, while the camera module is used to create a virtual camera.

[0074] The system runtime layer includes C / C++ libraries and runtime libraries. Many core components and services of the Android operating system are built from native code and require C / C++ libraries written in C and C++. When an application is first installed, the runtime library is precompiled into machine code, a process called pre-compilation. This allows for acceleration when the application is launched and executed by running the machine code.

[0075] The framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The framework layer includes predefined implementation methods. For example, the framework layer includes the window manager, content provider, view system, and notification manager.

[0076] The application layer can include a series of application packages, such as gallery, music, browser, fashion and other applications (application, app).

[0077] In the related art, the following situations often occur in daily life: 1) Users have too many clothes and are troubled by what to wear before going out; 2) Users need to travel to a different place and are worried about what to bring; 3) Users dress well and then go out, but the weather suddenly changes, making their current outfit inappropriate, such as overheating or not warm enough. This consumes a lot of time and energy, affecting the user experience.

[0078] In addition, in the related art, although some electronic devices provide outfit recommendation functions, users are required to manually input information, which makes the operation cumbersome and time-consuming, affecting the user experience. Figure 3Some outfit apps (such as the first app, the second app, the third app, and the fourth app) require users to manually input desired clothing information or personalized needs, such as main pictures, categories, colors, labels, seasons, brands, merchants, prices, names, uniform resource locators (URLs), and notes, etc., into the electronic device 100 such as a mobile phone. After receiving the information input by the user, the electronic device 100 can obtain corresponding outfit recommendations based on this information, for example, Figure 3 In (a), the user enters the main picture (i.e. top picture), category (i.e. top / suit), season (i.e. summer) and other information in the app. Figure 3 In (b), today's outfit recommendations, such as skirt suits, high heels, accessories, and bags, are displayed on the app's window. However, manually entering clothing information or personalized needs can be tedious and time-consuming, impacting the user experience.

[0079] To this end, embodiments of the present application provide a dressing method that determines a dressing score based on temperature and activity information, or alternatively, determines a dressing score based on temperature, activity, and historical dressing combinations, and displays the dressing combinations in the first window in order of dressing scores. The dressing method provided in embodiments of the present application can intelligently manage clothing and recommend daily outfits, improving the accuracy of dressing recommendations, saving users time and energy, and thus enhancing the user experience.

[0080] The dressing method provided in the embodiment of the present application can be implemented on the electronic device 100 and can be displayed on the display screen 294 in the electronic device 100. The embodiment of the present application takes the electronic device 100 including the dressing recommendation function as an example to specifically illustrate the dressing method of the present application.

[0081] For example, as shown in the attached Figure 4 As shown, a dressing method provided in an embodiment of the present application may include steps S401-S402:

[0082] Step S401: Display the outfit combinations in the first window in order of outfit scores; the outfit scores are determined based on temperature information and activity information, or the outfit scores are determined based on temperature information, activity information and historical outfit combinations; the temperature information is used to indicate the applicable temperature of the outfit combination; the activity information is used to indicate the style attributes of the outfit combination.

[0083] In a possible implementation, the first window can be displayed in full screen or in split screen. The embodiment of the present application does not limit the display form of the first window.

[0084] In one possible implementation, the first window can be the first window of a dressing app, or it can be a window of a suggestion card. The embodiment of the present application does not limit the type of the first window.

[0085] Both temperature and activity information can be obtained by calling weather services. Weather services include online weather, global positioning system (GPS), and calendar. GPS is used to obtain information about the current location of electronic device 100. The calendar is used to obtain information about the current time of electronic device 100 and the user's current activity. Online weather is used to obtain weather information for the current time in the current location based on the current location information obtained by GPS and the current time information obtained by the calendar. Weather information includes temperature, humidity, wind speed, and weather conditions.

[0086] In this embodiment of the present application, the weather service accesses a remote server to obtain the corresponding weather information. In one possible implementation, the outfit score can be determined based on temperature information and activity information, or based on a combination of temperature information, activity information, and historical outfits. This not only takes temperature and activity information into account, but also prevents users from wearing the same style of clothing over the past few days, which can lead to aesthetic fatigue and affect the user experience.

[0087] In one possible implementation, the historical outfit combination can be an outfit combination from the previous two days, or it can be an outfit combination from the previous three days. The embodiment of the present application does not limit the time of the historical outfit combination.

[0088] Activity information includes at least one of the following: target time, target location, and target activity. Activity information indicates the style attributes of an outfit combination based on the user's target activity. It refers to the target activity the user needs to attend at the target time and target location, and the clothing style attributes required for the target activity. Therefore, activity information is used to indicate the style attributes of an outfit combination.

[0089] The attributes of clothing include multiple attributes, which form an attribute vector. Each dimension of the attribute vector represents an attribute, and each dimension includes multiple values, each value representing a value of the attribute. For example, see Table 1 below:

[0090] Table 1 Properties of clothing

[0091]

[0092] As shown in Table 1 above, attributes include clothing, category, style, purpose, applicable temperature, sleeve length, collar type, style, and color. An attribute vector includes each of these attributes. Each dimension of the attribute vector corresponds to an attribute. For example, the first dimension corresponds to the clothing attribute, the second dimension corresponds to the category attribute, the third dimension corresponds to the style attribute, and so on. Each attribute dimension has multiple values, each representing a value of that attribute. Different attributes in each dimension have different values, and the corresponding values ​​are also different. For example, the style attribute has nine values: sports, casual, denim, non-mainstream, business casual, business formal, ethnic, and hip-hop, corresponding to values ​​of 0, 1, 2, 3, 4, 5, 6, 7, and 8. The applicable temperature attribute has five values: <10°, 10°-15°, 15°-20°, 20°-25°, and >25°, corresponding to values ​​of 0, 1, 2, 3, and 4.

[0093] After obtaining the activity information and temperature information, we can not only understand the applicable temperature of the user's clothing combination, but also understand the style attributes of the user's clothing combination. Therefore, we can accurately recommend clothing combinations to users.

[0094] In a possible implementation, the target time and target region may be the current time and region, or a future time or a different region. The embodiment of the present application does not limit the method of the target time and target region.

[0095] When the target time and target region are the current time and current region, the electronic device 100 can automatically obtain the current local temperature information and the current local user activity information on the online weather service, and automatically display the clothing combinations in the first window in order of clothing scores.

[0096] In the following embodiments of the present application, the dressing method of the present application is specifically described by taking the electronic device 100 automatically obtaining the current local temperature information and the current local user activity information on the online weather service as an example.

[0097] In the case where the first window is a suggestion card, displaying the outfit combinations in the first window in order of outfit scores includes displaying the outfit combinations with the highest outfit scores in the first window. Figure 5 As shown in (a) in the figure, the first window 502 of the suggestion card displays the suggested outfits for the current local area (i.e., the location of the electronic device 100 on June 14), i.e., the outfit combination with the highest outfit score, for example, a long-sleeved dress with an outfit score of 98.

[0098] In the case where the first window is a dressing app, in response to an operation for clicking the dressing app icon, displaying the dressing combinations in the first window in order of dressing scores includes displaying the first N dressing combinations in the first window in order of dressing scores; N is a positive integer. Figure 6 As shown in (a) of FIG, when a user wants to open a dressing app, the electronic device 100 displays a window 501 of multiple apps, such as icons of a gallery app, a music app, a browser app, and a dressing app. Clicking the icon of the dressing app, as shown in FIG. Figure 6 As shown in (b), the first window 502 of the outfit app displays the top N outfit combinations in order of outfit scores. For example, the first outfit is a long-sleeved dress with a score of 98, the second outfit is a long-sleeved suit with a score of 94, and the third outfit is a long-sleeved top and jeans with a score of 92. The text "Today's Outfit Recommendation" is also displayed on the first window 502 of the outfit app.

[0099] When the target time and target area are in the future or in a different place, the electronic device 100 can obtain user activity information in the future or in a different place through the activity information input by the user and temperature information in the future or in a different place through an online weather service, and display the clothing combinations in the future or in a different place in the first window in the order of the clothing scores.

[0100] In the following embodiments of the present application, the dressing method of the present application is specifically described by taking the example of the electronic device 100 obtaining future or remote user activity information through user input activity information and obtaining future or remote temperature information through an online weather service.

[0101] An input button is also displayed on the first window 502 , and in response to the user clicking the input button, configuration options for the activity information are displayed on the first window 502 .

[0102] Among them, the configuration options of the activity information include at least one of the following: target time, target area, indoor and outdoor, and target activity. Similarly, the configuration options of the activity information are to indicate the style attributes of the outfit combination based on the user's target activity, which refers to the target activity that the user needs to participate in at the target time and target area, and what style attributes of clothing are required for the target activity. For example, as shown in the attached Figure 5 As shown in (b) in FIG. 1 , when the user wants to input activity information, an input button is displayed on the first window 502 of the suggestion card, and the user clicks the input button, as shown in FIG. Figure 5 As shown in (d) in FIG, the configuration options of the activity information are displayed on the first window 502 of the suggestion card, such as target time, target area, indoor and outdoor, and target activity, etc. Figure 6As shown in (b) in FIG, when the user wants to input activity information, an input button is displayed on the first window 502 of the wear app, and the user clicks the input button, as shown in FIG. Figure 6 As shown in (d) in the figure, configuration options of activity information are displayed on the first window 502 of the outfit app, such as target time, target area, indoor and outdoor, and target activity.

[0103] The first window displays outfit combinations in the order of outfit scores, indicating that the outfit app calculates the score of each outfit combination based on temperature information and activity information, or based on temperature information, activity information and historical outfit combinations, and then displays the outfit combinations in the first window in the order of outfit scores. Figure 5 As shown in (d) in FIG, when the user wants to view the outfit combinations that can be selected for an event in the future or in a different place, the user enters the event information in the first window 502 of the suggestion card. For example, if the user wants to go to Beijing to attend a class reunion on August 12, 2024, the target time is 2024.08.12, the target region is Beijing, and the target event is a class reunion, etc., as shown in the attached figure. Figure 5 As shown in (e) in FIG, the first window 502 of the suggestion card in the notification of the electronic device 100 displays the outfit combinations in the order of outfit scores, for example, the skirt suit with the highest score is 95, and the date August 12 is also displayed. Figure 6 As shown in (d) in the figure, when the user wants to view the outfit combinations that can be selected for the event he / she participates in, he / she enters the event information in the first window 502 of the outfit app. For example, if he / she wants to go to Beijing to attend a class reunion on August 12, 2024, the target time is 2024.08.12, the target region is Beijing, and the target event is a class reunion, etc., as shown in the attached figure. Figure 6 As shown in (e) in the figure, the first window 502 of the outfit app displays outfit combinations in the order of outfit scores. For example, the first set: skirt suit, score 95; the second set: T-shirt on the upper body, jeans on the lower body, score 93; the third set: dress, score 90, and the text "August 12 outfit recommendation" is also displayed.

[0104] In one possible implementation, the display of the outfit combination can directly display the outfit, or it can be displayed through virtual fitting. The embodiment of the present application does not limit the display of the outfit combination.

[0105] In the embodiments of the present application, for example, as shown in the attached Figure 7As shown, by obtaining temperature information (including current local temperature information, future or remote temperature information) and activity information (current local activity information) through weather services including online weather, the global positioning system (GPS), and a calendar, the applicable temperature attributes (including current local applicable temperature attributes, future or remote applicable temperature attributes) and style attributes (including current local style attributes) of the clothing can be obtained. Furthermore, by inputting activity information including a target time, target region, and target activity, the style attributes (including future or remote style attributes) of the clothing can be obtained. Based on the applicable temperature attributes and style attributes of the clothing obtained above, as well as historical outfit combinations, the clothing items in the database are input into an attribute recognition model to perform attribute screening on the clothing items, selecting target clothing items that meet the applicable temperature attributes and style attributes. The target clothing items are then input into the clothing matching model to calculate the scores and obtain the outfit scores for each outfit combination. The outfit combinations are then displayed in order of their outfit scores.

[0106] The clothes in the database are obtained by uploading the clothing images taken by the user or the downloaded clothing images to the dressing app for electronic wardrobe archiving. Figure 8 As shown, it includes steps S801-S804:

[0107] Step S801: The user imports a photographed or downloaded clothing image.

[0108] Users can import captured or downloaded clothing images into the outfit app by clicking the Add button on the first window of the outfit app. In one possible implementation, the Add button is displayed on the first window; in response to the user clicking the Add button, a fourth item of clothing and its attributes are displayed on the first window. The fourth item of clothing includes the captured or downloaded clothing image imported by the user.

[0109] For example, as shown in the attached Figure 9 As shown in (a) of FIG, when a user wants to import a photographed clothing image or a downloaded clothing image, an add button is displayed on the suggestion card or the first window 502 of the outfit app, and a text "Please import clothing images" is also displayed. Click the add button, as shown in FIG. Figure 9 As shown in (b) in FIG. 5 , an image of imported clothing and attributes of the imported clothing are displayed on the suggestion card or the first window 502 of the outfit app, for example, an image of a T-shirt and attributes of the T-shirt: upper body clothing, T-shirt, casual, outerwear, >25°, short sleeves, round neck, solid color, and blue, etc.

[0110] After a user imports a captured or downloaded clothing image into the Outfit app, they can delete the imported clothing if they make an import error or are dissatisfied with the imported image. Users can delete imported clothing by clicking a delete button on the Outfit app window. In one possible implementation, a delete button is displayed on the first window; in response to the user clicking the delete button, the fourth clothing item and its attributes are not displayed on the first window.

[0111] For example, as shown in the attached Figure 10 As shown in (a) of FIG, when the user wants to delete the imported clothing image, a delete button is displayed on the suggestion card or the first window 502 of the outfit app, and the user clicks the delete button, as shown in FIG. Figure 10 As shown in (b), the clothing and its attributes are not displayed on the suggestion card or the first window 502 of the outfit app. For example, the add button and the text "Please import clothing images" are displayed again.

[0112] After a user imports a captured or downloaded image of clothing into the Outfit app, if they find that the imported clothing attributes are incorrectly displayed, they can edit the attributes of the clothing item. The user can edit the attributes of the clothing item by clicking an edit button on the Outfit app window. In one possible implementation, an edit button is also displayed on the first window; in response to the user clicking the edit button, the attributes of the fourth clothing item are displayed on the first window as editable.

[0113] For example, as shown in the attached Figure 11 As shown in (a) of FIG, when the user wants to edit the attributes of the imported clothing, an edit button is displayed on the suggestion card or the first window 502 of the outfit app, and the edit button is clicked, as shown in the attached figure. Figure 11 As shown in (b), the attributes of the clothing displayed on the suggestion card or the first window 502 of the dressing app are in an editable state. For example, the user selects one of the attributes to edit through the attribute drop-down button, thereby changing the selected attribute.

[0114] In step S802 , the electronic device 100 inputs the clothing into an attribute recognition model.

[0115] This step is to identify the attributes of the imported clothes.

[0116] In step S803, the electronic device 100 obtains the attributes of the clothing.

[0117] After the attributes of the imported clothing are identified, the attributes of the clothing are obtained.

[0118] In step S804, the electronic device 100 archives the electronic wardrobe according to the attributes of the clothing.

[0119] This step is to electronically archive the imported clothing according to the obtained clothing attributes.

[0120] The electronic clothing archiving method described in steps S801-S804 above is to import the clothing images taken by the user or downloaded by the user into the suggestion card or dressing app, then input the clothing into the attribute recognition model for attribute recognition to obtain the attributes of the clothing, and finally, electronically archive the clothing according to the attributes of the clothing.

[0121] The attribute recognition model is used to recognize the attributes of the input clothing. Figure 12 As shown, the clothing is input into the CNN network model to obtain the attributes of the clothing, for example, attribute 1, attribute 2, attribute 3...attribute N.

[0122] The clothing matching model is used to calculate the score after matching the selected target clothing items. In one possible implementation, based on temperature information and activity information, clothing items in the database are screened to obtain target clothing items; the target clothing items include a first clothing set, a second clothing set, and a third clothing set; the first clothing set includes upper body clothing items, the second clothing set includes outerwear lower body clothing items, and the third clothing set includes innerwear or general-purpose tops or jumpsuits; the first, second, and third clothing items are matched to obtain a matching combination; the first clothing item is any item in the first clothing set, the second clothing item is any item in the second clothing set, and the third clothing item is any item in the third clothing set; the matching combination is encoded to obtain a first matching combination feature vector; the first matching combination feature vector is input into a clustering neural network model to obtain a matching score for the matching combination.

[0123] In one possible implementation, based on temperature information and activity information, or based on temperature information, activity information and historical wearing combinations, clothing in a database is screened to obtain target clothing; the target clothing includes a first clothing set, a second clothing set and a third clothing set; the first clothing set includes upper body clothing, the second clothing set includes outer lower body clothing, and the third clothing set includes inner wear or general-purpose tops or jumpsuits; the first clothing, the second clothing and the third clothing are matched to obtain a wearing combination; the first clothing is any piece of clothing in the first clothing set, the second clothing is any piece of clothing in the second clothing set, and the third clothing is any piece of clothing in the third clothing set; the wearing combination is encoded to obtain a first wearing combination feature vector; the first wearing combination feature vector is input into a clustering neural network model to obtain a wearing score of the wearing combination.

[0124] For example, as shown in the attached Figure 13As shown, first, the clothing in the database is conditionally screened, for example, based on the above-mentioned applicable temperature attributes and style attributes, to obtain target clothing. Second, the target clothing is divided into data sets based on the clothing attributes. In an embodiment of the present application, the target clothing can be divided into data sets based on attributes such as clothing style, category, purpose, and applicable temperature. In one possible implementation, the data sets can be three or four, and the embodiment of the present application does not limit the number of data sets. The data sets include three data sets, namely a first clothing set, a second clothing set, and a third clothing set. The first clothing set includes outerwear upper body clothing, the second clothing set includes outerwear lower body clothing, and the third clothing set includes innerwear / general-purpose tops / jumpsuits, i.e., innerwear upper body clothing, general-purpose upper body clothing, innerwear jumpsuits, or general-purpose jumpsuits. Then, a piece of clothing is randomly selected from each of the three data sets in a traversal manner to form a set of outfit combinations, which includes the first clothing set, the second clothing set, and the third clothing set. Next, the first clothing set, the second clothing set, and the third clothing set in the set of outfit combinations are encoded using the same rule to obtain a feature vector for each outfit combination, i.e., the first outfit combination feature vector. Finally, all the first dressing combination feature vectors are input into the clustering neural network model to calculate the scores and obtain the dressing score of each dressing combination.

[0125] In one possible implementation, the training process of the clustering neural network model includes: obtaining the attribute vector of the first clothing, the attribute vector of the second clothing, and the attribute vector of the third clothing from the database; each dimension of the attribute vector is used to represent the value of an attribute of the clothing; the clustering neural network model is trained based on the second dressing combination feature vector and the corresponding dressing score; the second dressing combination feature vector includes the attribute vector of the first clothing, the attribute vector of the second clothing, and the attribute vector of the third clothing.

[0126] For example, as shown in the attached Figure 14 As shown in the following example, first, the attribute vector of each piece of clothing in the database is obtained. Specifically, each piece of clothing in the database is input into the following example. Figure 12 In the CNN network model, the attribute vector of the corresponding clothing is obtained, for example, attribute 1, attribute 2, attribute 3, ... attribute N, where N is a positive integer and N>=1. Specifically, referring to Table 1, for example, the clothing attribute is the first dimension, the category attribute is the second dimension, the style attribute is the third dimension, the usage attribute is the fourth dimension, the applicable temperature attribute is the fifth dimension, the sleeve length attribute is the sixth dimension, the collar type attribute is the seventh dimension, the style attribute is the eighth dimension, and the color attribute is the ninth dimension.

[0127] Next, each dimension in each attribute vector is encoded to obtain encoded attribute features. The encoded attribute features are actually the values ​​of each dimension of the attribute vector, i.e., the encoded values, e.g., attribute 1 features, attribute 2 features, attribute 3 features, ... attribute N features, where N is a positive integer and N>=1. Specifically, referring to Table 1, for example, in the first dimension of clothing attributes, upper body clothing is coded as 0, lower body clothing is coded as 1, jumpsuits are coded as 2, accessories are coded as 3, shoes are coded as 4, and other is coded as 5, etc. In the second dimension of category attributes, T-shirts are coded as 0, polo shirts are coded as 1, shirts are coded as 2, tunics are coded as 3, cotton-padded coats are coded as 4, down jackets are coded as 5, trousers are coded as 6, overalls are coded as 7, cropped pants are coded as 8, short pants are coded as 9, vests are coded as 10, jackets are coded as 11, dresses are coded as 12, skirts are coded as 13, skirts are coded as 14, and other is coded as 15, etc. …In the fourth dimension of usage, innerwear is coded as 0, outerwear as 1, general use as 2, other as 3, and so on. …In the ninth dimension of color, red is coded as 0, yellow as 1, blue as 2, green as 3, gray as 4, other as 5, and so on.

[0128] Again, the encoded attribute vectors are spliced ​​according to attribute features such as clothing, category and purpose to obtain a dressing combination feature vector including the attribute feature sequence of the first clothing, the attribute feature sequence of the second clothing and the attribute feature sequence of the third clothing. Specifically, refer to Table 1 and use vector representation. For example, the attribute feature sequence of the first clothing is 0 1 21 2 0 1 1 3, which represents the attribute vector of upper body clothing, POLO shirt, jeans, outer wear, 15°-20°, long sleeves, lapel, cartoon, and green. Since the first clothing includes outer upper body clothing, the first dimension is encoded as 0 and the fifth dimension is encoded as 1. The attribute feature sequence of the second clothing is 1 6 4 1 1 3 6 2 1, which represents the attribute vector of lower body clothing, trousers, business casual, outer wear, 10°-15°, other, round neck, stripes, and yellow. Since the second clothing includes outer lower body clothing, the first dimension is encoded as 1, the second dimension is encoded as 6-8, and the fifth dimension is encoded as 1. The attribute feature sequence of the third item of clothing is 2 12 6 0 2 1 2 5 2, representing the attribute vectors of jumpsuit, dress, ethnic, inner wear, 15°-20°, short sleeves, V-neck, pattern print, and blue. Since the third item of clothing includes inner wear / general tops / jumpsuits, the first dimension is encoded as 2, and the fifth dimension is encoded as 0 or 2. Then, the attribute feature sequence of the first item of clothing, the attribute feature sequence of the second item of clothing, and the attribute feature sequence of the third item of clothing are combined to form a second outfit combination feature sequence. For example, the second outfit combination feature sequence formed by combining the attribute feature sequence of the first item of clothing, the attribute feature sequence of the second item of clothing, and the attribute feature sequence of the third item of clothing described above is: 0 1 2 1 2 0 1 1 3 1 6 4 1 1 3 6 2 1 2 12 6 0 2 1 2 5 2

[0130] Next, the second outfit combination feature sequence is input into the clustering algorithm to calculate the corresponding outfit score. The clustering algorithm parameter satisfies the number of samples S within a certain threshold from the cluster center.

[0131] In a possible implementation, the number of samples S may be 3000 or 2000. The embodiment of the present application does not limit the number of samples S.

[0132] The larger the number of clustered samples S, the finer the outfit classification, and the smaller the number of clustered samples S, the coarser the outfit classification. In the embodiment of the present application, considering the fineness of the outfit classification, the number of samples S is set to be greater than or equal to 3000.

[0133] In a possible implementation, the clustering algorithm may be a K-MEANS algorithm or a mean shift clustering algorithm. The embodiment of the present application does not limit the clustering algorithm.

[0134] In the clustering algorithm, the outfit score is related to the number of samples S and the distance value d, using the following expression (1):

[0135] Dress score = Formula (1).

[0136] Finally, the clustering neural network model is trained based on the second dressing combination feature vector and the corresponding dressing score until the preset training end conditions are met, and a trained clustering neural network model is obtained.

[0137] Step S402: In response to the user clicking on the target outfit combination, the first N outfit combinations are displayed in the first window in order of outfit scores or an enlarged image of the target outfit combination is displayed; the outfit combinations include the target outfit combination.

[0138] In the case where the first window is a suggestion card, there are two cases in response to the user clicking on the target outfit combination: 1) Clicking on the target outfit combination is clicking on the suggestion in the notification of the electronic device 100, and the first N outfit combinations are displayed in the first window in the order of outfit scores. Figure 5 As shown in (a) in FIG, when the user wants to view a specific outfit combination, he clicks on the first window 502 of the suggestion card in the notification of the electronic device 100, for example, he clicks on the long-sleeved dress that ranks first, i.e., the outfit score is 98, as shown in FIG. Figure 5As shown in (b), the first N outfit combinations are displayed in the order of outfit scores on the first window 502 of the suggestion card. For example, the first set: long-sleeved dress, score 98; the second set: long-sleeved suit, score 94; the third set: long-sleeved top, jeans bottom, score 92, and the text "Today's Outfit Recommendation" is also displayed; the second type is that after the outfit combinations are displayed in the order of outfit scores on the first window 502 of the suggestion card, clicking on the target outfit combination is to click on any outfit combination among the first N outfit combinations displayed in the order of outfit scores, and an enlarged image of the target outfit combination is displayed on the first window. For example, as shown in the attached figure, Figure 5 As shown in (b) in the figure, when the user wants to view a specific outfit combination, he clicks on any outfit combination in the first window 502 of the suggestion card, for example, he clicks on the long-sleeved dress that ranks first, i.e., the outfit score is 98, as shown in the attached figure. Figure 5 As shown in (c) , an enlarged image of a long-sleeved dress with a dressing score of 98 is displayed on the first window 502 of the suggestion card, for example, an enlarged image of the long-sleeved dress.

[0139] In the case where the first window is a dressing app, after the dressing combinations are displayed in the order of dressing scores on the first window, clicking on the target dressing combination is to click on any of the top N dressing combinations displayed in the order of dressing scores, and an enlarged image of the target dressing combination is displayed on the first window. Figure 6 As shown in (b) in the figure, when the user wants to view a specific outfit combination, he clicks on any outfit combination in the first window 502 of the outfit app, for example, he clicks on the long-sleeved dress that ranks first, i.e., the outfit score is 98, as shown in the attached figure. Figure 6 As shown in (c) in FIG, an enlarged image of a long-sleeved dress with a dressing score of 98 is displayed on the first window 502 of the suggestion card. Figure 6 As shown in (e) in the figure, when the user wants to view a specific outfit combination, he clicks on any outfit combination in the first window 502 of the outfit app, for example, clicks on the top T-shirt and bottom jeans that ranks second, i.e., the outfit score is 93, as shown in the attached figure. Figure 6 As shown in (f) in the figure, an enlarged picture of a T-shirt and jeans with a dressing score of 93 is displayed on the first window 502 of the dressing app.

[0140] When clicking on the target outfit combination to display any of the top N outfit combinations in order of outfit scores, in one possible implementation, you can click on the score or the outfit combination image when clicking on the target outfit combination. The embodiment of the present application does not limit the way of clicking on the target outfit combination.

[0141] After the outfit combinations are displayed in the first window in order of outfit scores, click on any outfit combination, i.e., the target outfit combination, to enter the detailed interface of the target outfit combination. At this time, an enlarged image of the target outfit combination is displayed on the first window.

[0142] In one possible implementation, after the user clicks on the target outfit combination, the enlarged view of the target outfit combination includes the outfit combination image and may also include the attributes of the target outfit combination. The embodiment of the present application does not limit the content of the enlarged view of the target outfit combination.

[0143] In the embodiments of the present application, for example, as shown in the attached Figure 7 As shown, the user selects the target outfit combination and today's outfit combination is recommended on the first window.

[0144] The outfit-matching method described in steps S401-S402 above uses temperature information and activity information, or a combination of temperature information, activity information, and historical outfits, to intelligently display outfit combinations in order of outfit scores and recommend daily outfit combinations to the user. The app also intelligently archives the clothing items entered by the user based on their attributes. This allows for intelligent clothing management and daily outfit recommendations, improving the accuracy of outfit recommendations, saving users time and effort, and enhancing the user experience.

[0145] The dressing method provided in the embodiments of the present application can intelligently manage clothing and recommend daily outfits, improve the accuracy of outfit recommendations, save users' time and energy, and thus improve user experience.

[0146] It is understandable that in order to implement the above functions, the electronic device includes hardware and / or software modules corresponding to the execution of each function. In combination with the algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in combination with the embodiments, but such implementation should not be considered to be beyond the scope of this application.

[0147] In this embodiment, the electronic device can be divided into functional modules according to the above-mentioned method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into a single processing module. The above-mentioned integrated modules can be implemented in the form of hardware. It should be noted that the module division in this embodiment is illustrative and is only a logical functional division. In actual implementation, other division methods may be used.

[0148] An embodiment of the present application further provides a computer-readable storage medium, in which computer program code is stored. When the processor executes the computer program code, the electronic device executes the relevant method steps in the method embodiment.

[0149] An embodiment of the present application further provides a computer program product, which, when executed on a computer, enables the computer to execute the relevant method steps in the above method embodiment.

[0150] Among them, the electronic device, computer storage medium or computer program product provided in this application is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0151] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described above as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The functions of the aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0155] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that makes the contribution, or all or part of the technical solution can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the above-mentioned method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program code.

[0156] The above content is only a specific embodiment of this application, but the scope of protection of this application is not limited to this. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A dressing method, characterized in that: The method comprises: The first window displays outfit combinations in order of outfit scores; the outfit scores are determined based on temperature information, activity information, and historical outfit combinations; the temperature information is used to indicate the applicable temperature of the outfit combination; the activity information is used to indicate the style attributes of the outfit combination; the historical outfit combinations are used to filter out clothing items in the outfit combinations that have the same style attributes as the historical outfit combinations; the first window is a window of an outfit app or a suggestion card; if the first window is an outfit app, in response to a user clicking on a target outfit combination, an enlarged image of the target outfit combination is displayed on the first window; In a case where the first window is a window for suggestion cards, in response to a user clicking on a target outfit combination, the first N outfit combinations in order of the outfit scores are displayed on the first window; N is a positive integer; in response to a user clicking on any one of the first N outfit combinations, an enlarged image of the clicked outfit combination is displayed on the first window; The outfit score is determined based on the temperature information, the activity information, and the historical outfit combination, including: Filtering clothing in a database to obtain target clothing based on the temperature information, the activity information, and the historical outfit combinations; the target clothing includes a first clothing set, a second clothing set, and a third clothing set; the first clothing set includes outerwear upper body clothing, the second clothing set includes outerwear lower body clothing, and the third clothing set includes innerwear upper body clothing, general-purpose upper body clothing, innerwear one-piece clothing, or general-purpose one-piece clothing; Matching the first clothing item, the second clothing item, and the third clothing item to obtain the dressing combination; encoding the dressing combination to obtain a first dressing combination feature vector; Inputting the first outfit combination feature vector into a clustering neural network model to obtain an outfit score for the outfit combination; The training process of the clustering neural network model includes: Obtaining the attribute vector of the first clothing item, the attribute vector of the second clothing item, and the attribute vector of the third clothing item from the database; each dimension of the attribute vector is used to represent a value of an attribute of the clothing item; The clustering neural network model is trained based on the second dressing combination feature vector and the corresponding dressing score; the second dressing combination feature vector includes the attribute vector of the first clothing, the attribute vector of the second clothing and the attribute vector of the third clothing.

2. The dressing method according to claim 1, characterized in that: The activity information is obtained online or through user input.

3. The dressing method according to claim 2, characterized in that: The activity information includes at least one of the following: target time, target area, indoor and outdoor, and target activity.

4. The dressing method according to claim 3, characterized in that: The historical dressing combination includes the dressing combination of the previous two days or the dressing combination of the previous three days.

5. The dressing method according to claim 4, characterized in that: The method further comprises: displaying an add button on the first window; In response to the user clicking the add button, a fourth item of clothing and attributes of the fourth item of clothing are displayed on the first window.

6. The dressing method according to claim 5, characterized in that: The method further comprises: displaying a delete button on the first window; In response to the user clicking the delete button, the fourth clothing item and the attributes of the fourth clothing item are not displayed on the first window.

7. The dressing method according to claim 5, characterized in that: The method further comprises: An edit button is also displayed on the first window; in response to the user clicking the edit button, the attributes of the fourth clothing item displayed on the first window are in an editable state.

8. An electronic device, characterized in that: It includes a memory and one or more processors, the memory stores computer program code, and the computer program code includes computer instructions. When the computer instructions are executed by the processor, the electronic device executes the dressing method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that It includes computer instructions, which, when executed on an electronic device, enable the electronic device to execute the dressing method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on an electronic device, the electronic device executes the dressing method according to any one of claims 1 to 7.

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