Television content recommendation method and system and medium

Abstract

The invention provides a television content recommendation method. The method comprises the following steps: acquiring an image of a current user watching a television; determining first identity information of the current user according to the collected image of the current user; and recommending the television content to the current user according to the first identity information of the current user. Through the method, differentiated television content recommendation can be carried out for different users, and the pertinence and adaptability of television content recommendation are improved. The invention further provides a smart home equipment linkage method, according to the first identity information and the environment data of the current user, the parameters of the television are adjusted, and the working state of one or more linkage equipment is controlled, so that the linkage capability of the smart television and other smart home equipment is enhanced, and a complete smart scene ecology is constructed.

Description

Technical Field

[0001] This application relates to the field of television, and more particularly to a method, system, and medium capable of enabling user identification and personalized television content recommendation. Background Technology

[0002] Currently, television user service functions are relatively limited. In terms of user identification, they largely rely on manual account login or simple password verification, failing to achieve automatic and accurate user identification. Regarding content recommendation, existing methods primarily depend on users' historical viewing behavior data, resulting in an inability to provide differentiated content recommendations based on the current television viewer (e.g., children, adults), leading to insufficient targeting and adaptability of the recommendation results.

[0003] Meanwhile, existing televisions lack seamless integration with other home appliances, failing to form a complete and collaborative smart ecosystem. This isolation makes it difficult for televisions to be integrated into a broader home intelligence system, and thus cannot meet users' demands for integrated services across devices and multiple scenarios.

[0004] Therefore, it is necessary to develop a method and system that can automatically identify and dynamically update user identity information for recommending television content, as well as a method to further realize the linkage control of television and smart devices by combining environmental data. Summary of the Invention

[0005] The purpose of this application is to provide a television content recommendation method, system, and medium, as well as a smart home device linkage method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this application provides the following technical solution: The first aspect of this application provides a television content recommendation method, comprising: acquiring an image of a current user watching television; determining the current user's first identity information based on the acquired image of the current user; and recommending television content to the current user based on the current user's first identity information.

[0007] In some embodiments, determining the first identity information of the current user based on the collected image of the current user includes: determining whether the current user is a registered user based on the image of the current user; in response to the current user being a registered user, obtaining the second identity information of the pre-stored registered user and determining it as the first identity information; and in response to the current user not being a registered user, prompting the current user to register as a registered user.

[0008] In some embodiments, determining whether the current user is a registered user based on the current user's image includes: preprocessing the acquired image of the current user; matching the preprocessed image with the image of a registered user; and determining whether the current user is a registered user based on the matching result.

[0009] In some embodiments, in response to the current user not being a registered user, prompting the current user to register as a registered user, the method further includes: determining the first identity information of the current user based on the registration information provided by the registered user during registration.

[0010] In some embodiments, the current user's primary identity information includes at least one of the following: the current user's viewing history, identity, gender, age, and interests. The current user's viewing history includes at least one of the following: the type of program the current user watched, the viewing time, pause / fast forward operations, and TV parameter settings.

[0011] In some embodiments, the television content recommendation method further includes: collecting environmental data, which includes at least one of the following: current time, light, temperature, humidity, and sound; and adjusting the parameters of the television and controlling the working status of one or more linked devices based on the acquired environmental data.

[0012] In some embodiments, the parameters of the television include at least one of the following: sound, hue, and brightness; one or more linked devices include at least one of the following: curtains, lights, audio equipment, air conditioning, and seats.

[0013] In some embodiments, recommending TV content to the current user based on the current user's primary identity information includes: recommending TV content to the current user based on the current user's primary identity information and context information, wherein the context information includes at least one of the following: watching movies, learning, or browsing news.

[0014] In some embodiments, the TV content recommendation method further includes: updating the current user's primary identity information by recording and analyzing the current user's TV viewing behavior.

[0015] A second aspect of this application provides a system for recommending television content, comprising: an acquisition module for acquiring an image of a current user watching television; a determination module for determining the first identity information of the current user based on the acquired image of the current user; and a content recommendation module for recommending television content to the current user based on the first identity information of the current user.

[0016] A third aspect of this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the television content recommendation method described above.

[0017] The fourth aspect of this application provides a method for linking smart home devices, the smart home devices including a television and one or more linked devices, the method comprising: collecting the first identity information of the current user watching the television; collecting environmental data, including current time, light, temperature, humidity, and sound; controlling the television to recommend content to the current user based on the first identity information of the current user; and adjusting the parameters of the television and controlling the working state of one or more of the linked devices based on the first identity information of the current user and the environmental data. Attached Figure Description

[0018] To make the technical solution and beneficial effects of this application more apparent and understandable, a detailed description is provided below by listing specific embodiments. The accompanying drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features; unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical field to which this application pertains.

[0019] Figure 1 These are schematic diagrams illustrating application scenarios of a television content recommendation system according to some embodiments of this application; Figure 2a This is an exemplary functional block diagram of a television content recommendation system according to some embodiments of this application; Figure 2b This is an exemplary functional block diagram of a television content recommendation system according to some embodiments of this application; Figure 3 This is an exemplary flowchart of a television content recommendation method according to some embodiments of this application; Figure 4 This is an exemplary flowchart of a method for determining the first identity information of the current user according to some embodiments of this application; Figure 5 This is an exemplary flowchart illustrating the linkage of smart home devices according to some embodiments of this application; and Figure 6 This is an exemplary flowchart illustrating the linkage of smart home devices according to some embodiments of this application. Detailed Implementation

[0020] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0021] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0022] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0023] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0024] Figure 1 This is a schematic diagram illustrating an application scenario of a television content recommendation system according to some embodiments of this application.

[0025] In some embodiments, the TV content recommendation system 100 can be applied to the fields of smart homes, smart transportation (e.g., cars, buses, trains, aircraft, etc.), and leisure and entertainment (e.g., cinemas), etc., without being specifically limited in this document.

[0026] The TV content recommendation system 100 may include a TV 110, a server 120, a network 130, one or more linked devices 140, and a storage device 150.

[0027] Television 110 refers to a device used for playing and / or displaying content such as images, audio, and / or video. The content played and / or displayed by Television 110 may include scene information, program information, and content recommendations. Scene information may include watching movies, learning, or browsing news. In some embodiments, scene information may be displayed on Television 110 in the form of a menu, allowing users to select the corresponding scene. Program information may include the video of a program, related introductions, etc. Content recommendations may include television content recommended to the user by the television content recommendation system 100.

[0028] In some embodiments, the television 110 may include multiple components, such as a camera, a speaker, a microphone, a display screen, sensors, chips, etc. These components may be integrated into the television 110 or disposed independently outside the television 110. The camera can be used to capture images of the viewing area, such as an image of a user watching the television; the display screen can be used to display and play images, videos, text, and other content; the sensors can be used to acquire environmental data and record user behavior. The microphone can be used to receive sound. The sensors may include environmental sensors, such as temperature sensors, humidity sensors, light sensors, sound sensors, infrared sensors, etc.

[0029] Server 120 can be used to analyze and process the collected information to generate analysis results. In some embodiments, server 120 can process user images obtained from television 110 to determine user identity information (e.g., the current user's primary identity information). In some embodiments, server 120 can obtain parameters of television 110 and parameters and / or operating status of one or more linked devices 140 directly or through network 130.

[0030] Server 120 can be a single server or a server cluster. A server cluster can be centralized, such as in a data center. It can also be distributed, such as in a distributed system. Server 120 can be local or remote. Server 120 may include engine 112. Engine 112 can be used to execute instructions (program code) of server 120. For example, engine 112 can execute instructions to determine user identity information; for another example, engine 112 can execute instructions to recommend television content; and for yet another example, engine 112 can execute instructions to control the operating status of linked devices.

[0031] Network 130 can provide a channel for information exchange. In some embodiments, television 110, server 120, one or more linked devices 140, and / or memory 150 can exchange information via network 130. For example, server 120 can receive images of the viewing area captured by television 110 via network 130. As another example, server 120 can receive stored identity information (e.g., second identity information) of registered users from memory 150 via network 130. Yet another example is that server 120 can obtain the operating status of one or more linked devices 140 via network 130.

[0032] One or more linked devices 140 refer to devices that work in conjunction with the television 110. "Working in conjunction" means that the television and multiple linked devices communicate and cooperate with each other to form an organic whole, thereby automatically realizing complex and personalized scenes. In some embodiments, one or more linked devices 140 may include smart devices such as curtains, lights, audio equipment, air conditioners, and chairs. In some embodiments, one or more linked devices 140 can adjust their working status based on the current user's identity information and / or environmental data.

[0033] The term "memory 150" can refer to any device with storage capabilities. Memory 150 is primarily used to store data collected from the television 110 and / or one or more linked devices 140, as well as various data generated during the operation of the server 120. For example, memory 150 can store user images, user identification information, environmental data, television parameters, and the operating status (e.g., historical operating status) of one or more linked devices 140. In some embodiments, memory 150 can serve as a content library for the television 110, storing television programs. Memory 150 can be local or remote.

[0034] It should be noted that the description of the television content recommendation system 100 is for illustrative purposes and is not intended to limit the scope of protection of this application. Those skilled in the art can make various variations and modifications based on the instructions in this application. However, these variations and modifications will not depart from the scope of protection of this application. For example, the memory 150 and the server 120 may be locally connected, rather than connected via network 130.

[0035] Figure 2a This is an exemplary functional block diagram of a television content recommendation system according to some embodiments of this application. Figure 2a As shown, the TV content recommendation system 200 may include a data acquisition module 210, a determination module 220, and a content recommendation module 230.

[0036] The acquisition module 210 can acquire images of the viewing area of ​​a television (e.g., television 110), such as an image of the current user watching the television. In some embodiments, the acquisition module 210 may include, but is not limited to, a camera, a scanner, a biometric device (e.g., an iris scanner, a vein scanner, a fingerprint scanner, etc.), radar, etc. Additionally, the acquisition module 210 can acquire environmental data. In some embodiments, the acquisition module 210 may include environmental sensors, such as a temperature sensor, a humidity sensor, a light sensor, a sound sensor, etc.

[0037] The determining module 220 can determine the primary identity information of the current user based on the image of the current user watching television acquired by the acquisition module 210. The primary identity information of the current user includes, but is not limited to, the current user's viewing history, identity, gender, age, and interests. The current user's viewing history includes, but is not limited to, the type of program watched, viewing time, pause / fast forward operations, and television parameter settings.

[0038] In some embodiments, the determining module 220 can determine whether the current user is a registered user. If the current user is a registered user, the determining module 220 can obtain the pre-stored second identity information of the registered user and determine it as the first identity information of the current user; if the current user is not a registered user, the determining module 220 prompts the current user to register as a registered user.

[0039] In some embodiments, the determining module 220 can update the current user's primary identity information by recording and analyzing the current user's television viewing behavior. Specific details regarding the method for determining the current user's primary identity information can be found in [reference needed]. Figure 4 And its related descriptions.

[0040] The content recommendation module 230 can recommend television content to the current user based on the primary identity information determined by the determination module 220. For example, the content recommendation module 230 can prioritize recommending news, movies, and TV series to adults, and prioritize recommending cartoons or educational content to children.

[0041] It should be noted that the description of the television content recommendation system 200 is for illustrative purposes and is not intended to limit the scope of protection of this application. Those skilled in the art can make various variations and modifications based on the instructions in this application. However, these variations and modifications will not depart from the scope of protection of this application. For example, the television content recommendation system 200 may also include a control module for adjusting television parameters and controlling the status of one or more linked devices.

[0042] Figure 2b This is an exemplary functional block diagram of a television content recommendation system according to some embodiments of this application.

[0043] The television content recommendation system 250 may include a central processing module 251, a user identification module 252, an environmental perception module 253, a user identity information management module 254, a device linkage module 255, a display module 256, and a content recommendation module 257.

[0044] The central processing module 251 is used to control and coordinate the work of other modules and to process various data and instructions. For example, the central processing module 251 can control the user identity information management module 254 to determine, store, and analyze the current user's identity information. As another example, the central processing module 251 can control the environmental perception module 253 to collect environmental data.

[0045] User identification module 252 is used to perform real-time image acquisition and identity recognition of the current user watching television. User identification module 252 can be used to determine whether the current user is a registered user.

[0046] In some embodiments, the user identification module 252 may include a camera and an associated image recognition processing unit.

[0047] The environmental sensing module 253 is used to collect environmental data, which may include current time, light, temperature, humidity, sound, etc. The environmental sensing module 253 may include environmental sensors, such as temperature sensors, humidity sensors, light sensors, sound sensors, etc.

[0048] The user identity information management module 254 is used to store the images and identity information of registered users, analyze the identity information of the current user, and store the analysis results. Furthermore, the user identity information management module 254 is used to update the current user's identity information based on the current user's television viewing behavior, forming dynamic user identity information.

[0049] The device linkage module 255 is used to control the communication and linkage between the television (e.g., television 110) and one or more linkage devices (e.g., one or more linkage devices 140) based on the environmental data collected by the environmental sensing module 253 and the first identity information of the current user determined by the user identity information management module 254. In some embodiments, the device linkage module 255 can adjust the parameters of the television and the working status of one or more linkage devices based on the environmental data and the first identity information of the current user.

[0050] Display module 256 is used to display images and information. For example, display module 256 can display television content recommended by content recommendation module 257. As another example, display module 256 can display scene information and / or reasons for content recommendations. Yet another example is that display module 256 can display parameters of the television and / or one or more linked devices.

[0051] The content recommendation module 257 recommends suitable content to different users based on user identification results, current user identity information, and context information.

[0052] Figure 3 This is an exemplary flowchart of a television content recommendation method according to some embodiments of this application. Flow 300 can be executed by television content recommendation system 200 or television content recommendation system 250.

[0053] In step 310, an image of the current user watching the television is acquired. Step 310 can be performed by the acquisition module 210 or the user identification module 252.

[0054] The current user is the user watching television after the television is turned on. In some embodiments, the current user can be an adult and / or a child.

[0055] In some embodiments, the image of the current user can be captured using the television's camera, or the image of the current user can be captured in real time using a camera independent of the television. In some embodiments, the image of the current user can also be acquired using a camera, scanner, biometric device (e.g., iris scanner, vein scanner, fingerprint scanner, etc.), radar, etc.

[0056] The current user's image may include 2D RGB images, 3D depth images, near-infrared images, thermal infrared images, etc. In some embodiments, the current user's image may include one or more features that can identify the current user, such as their face, eyes, and fingerprints.

[0057] In some embodiments, the captured image of the current user can be stored in a memory (e.g., Figure 1 In the memory of 150).

[0058] In step 320, the primary identity information of the current user is determined based on the acquired image of the current user. Step 320 can be executed by the determination module 220 or the user identity information management module 254.

[0059] The primary identity information of the current user can be used to describe the current user's characteristics, needs, behaviors, etc. In some embodiments, the primary identity information of the current user may include at least one of the following: the current user's viewing history, identity, gender, age, interests, etc. The current user's viewing history may include at least one of the following: the type of program the current user watched, viewing time, pause / fast forward operations, TV parameter settings, etc.

[0060] In some embodiments, the captured image of the current user can be matched with images of registered users stored in a television content recommendation system (e.g., memory 150). If the current user is a registered user, pre-stored second identity information of the registered user is retrieved and used as the first identity information of the current user; if the current user is not a registered user, the current user is prompted to register. Specific details regarding the method for determining the first identity information of the current user can be found in [reference needed]. Figure 4 And its related descriptions.

[0061] In step 330, television content is recommended to the current user based on the current user's primary identity information. Step 330 can be executed by content recommendation module 230 or content recommendation module 257.

[0062] In some embodiments, television content can be recommended to the current user based on one or more of the user's primary identity information. For example, if the user's primary identity information includes a child (e.g., a user under 16 years old), then cartoons or educational content will be recommended to them first; as another example, if the user's primary identity information includes an adult (e.g., a user over 18 years old and / or taller than 155 cm), then news, movies, TV series, etc., will be recommended to them; if the user's primary identity information includes an adult who likes to follow real-time news, then the latest news information obtained in real time will be recommended to them.

[0063] In some embodiments, recommending TV content to the current user based on the current user's primary identity information also includes recommending TV content to the current user based on the current user's primary identity information and scene information. Scene information refers to the type of program selected by the user, which may include at least one of the following: watching movies, learning, or browsing news. In some embodiments, the TV content recommendation system can determine the scene information based on the current user's primary identity information. For example, if the scene information in the user's viewing history is all news browsing, then the TV content recommendation system can determine the scene information as news browsing. In some embodiments, the scene information may be pre-set (e.g., a fixed menu on the TV), which the current user can manually select.

[0064] In some embodiments, suitable programs can be selected from the content library corresponding to the current user's primary identity information and scenario information, and recommended to the current user.

[0065] Based on the primary identity information of the current user watching TV, TV content is recommended to the current user, which improves the accuracy and relevance of TV content recommendations and enhances the user experience.

[0066] It should be noted that the description of process 300 is for illustrative purposes only and is not intended to limit the scope of protection of this application. Those skilled in the art can make various variations and modifications based on the instructions in this application. However, these variations and modifications will not depart from the scope of protection of this application. For example, process 300 may further include displaying recommended television content for the user on the television screen, a step that can be performed by the display module 256. As another example, process 300 may further include updating the current user's primary identity information by recording and analyzing the current user's television viewing behavior, a step that can be performed by the determination module 220 or the user identity information management module 254. Specifically, machine learning algorithms can be used to analyze the current user's television viewing behavior. For example, for programs that the current user has watched multiple times, the weight of that program in the current user's primary identity information can be increased. As another example, for programs that the user continuously tracks, the weight of that program in the current user's primary identity information can be increased. In some embodiments, the current user's television viewing behavior can be recorded and analyzed in real time to update the current user's primary identity information; alternatively, the current user's primary identity information can be updated based on their television viewing behavior each time they watch television; or the current user's television viewing behavior can be analyzed and updated at regular intervals. For example, process 300 may also include adjusting television parameters and controlling the operating status of one or more linked devices based on the current user's primary identity information and environmental data. See details... Figure 6 And its related descriptions.

[0067] Figure 4 This is an exemplary flowchart of a method for determining the first identity information of a current user according to some embodiments of this application. Flow 400 can be executed by the determination module 220 or the user identification module 252.

[0068] In step 410, based on the current user's image, it is determined whether the current user is a registered user.

[0069] In some embodiments, the image of the current user can be preprocessed. Preprocessing may include image filtering and noise reduction, image enhancement, image resizing, image rotation, etc. In some embodiments, deep learning algorithms (e.g., deep convolutional neural networks (CNN), graph convolutional networks (GCN), generative adversarial networks (GAN), etc.) can be used to detect the location of all faces in the preprocessed image (e.g., based on skin color, texture, contour, etc.) and mark them with bounding boxes; feature extraction is performed on the detected faces; the extracted features are matched with images of registered users with known identities (e.g., features extracted from the images of registered users) pre-stored in a TV content recommendation system (e.g., memory 150). If the match is successful (e.g., the matching degree is greater than the matching threshold), the current user is determined to be a registered user; if the match fails (e.g., the matching degree is less than the matching threshold), the current user is determined to be not a registered user.

[0070] As an example, if a short child is detected entering the viewing area by the TV camera, then that child is the current user watching TV. The TV camera captures an image of the child watching TV, and the image is preprocessed and features extracted. The extracted features are matched with the image of the pre-stored registered user "Xiaoming" in the system, confirming that the current user is a registered user.

[0071] In some embodiments, other methods can be used for face recognition, feature extraction, and identity verification, such as AI image recognition technology, etc., which are not specifically limited in this article.

[0072] In step 420, in response to the fact that the current user is a registered user, the pre-stored second identity information of the registered user is obtained and determined as the first identity information of the current user.

[0073] Secondary identity information refers to the identity information associated with a registered user stored in the television content system. In some embodiments, secondary identity information may be continuously updated identity information based on the user's television viewing behavior. Secondary identity information may include, but is not limited to, the registered user's viewing history, identity, gender, age, interests, or combinations thereof.

[0074] As an example, when matching the image of the current user with the registered user "Xiaoming," the pre-stored second identity information of "Xiaoming" is: a liking for cartoons and early math education programs, and having recently watched the "Little Math Genius" cartoon series multiple times. Therefore, the aforementioned second identity information of the registered user "Xiaoming" is determined as the current user's first identity information. Similarly, when matching the image of the current user with the registered user "Mr. Zhang," the pre-stored identity information of "Mr. Zhang" is: a strong interest in international and financial news, and having recently frequently watched reports on international politics and economics. Therefore, the aforementioned second identity information of the registered user "Mr. Zhang" is determined as the current user's first identity information.

[0075] In step 430, in response to the fact that the current user is not a registered user, the current user is prompted to register as a registered user.

[0076] In some embodiments, the TV content recommendation system may prompt the current user to register for an unregistered user, for example, through text, voice, or other means.

[0077] In some embodiments, the television content recommendation system can automatically generate registration information corresponding to the current user based on the current user's image. For example, the system can identify the user's height and age (e.g., inferred from skin texture, muscle tone, bone structure, etc.) as registration information based on the current user's image; alternatively, it can display general registration items (e.g., adult / child, preferred program type, age, gender, username, etc.) for the user to select, thus determining the user's registration information; still alternatively, the user can manually input the registration information (e.g., via voice input). In some embodiments, the user's registration information can be stored in the television content recommendation system's memory (e.g., memory 150).

[0078] Based on the registration information provided by the user during registration, the primary identity information of the current user can be determined. In some embodiments, one or more of the user's registration information can be determined as the primary identity information of the current user.

[0079] During the current user's TV viewing process, their TV viewing behavior (e.g., the type of program the user watches, viewing time, pause / fast forward operation, TV parameter settings (e.g., volume)) is recorded and analyzed in real time, and the user's primary identity information is continuously updated.

[0080] For first-time TV viewers, they are prompted to register to generate initial user identity information. For registered users, their identity information is continuously updated, thus establishing a one-to-one user identity for each TV viewer. This user identity information is updated in real time based on changes in the user's viewing habits and interests, thereby improving the personalization, accuracy, and timeliness of TV content recommendations.

[0081] Figure 5 This is an exemplary flowchart illustrating the linkage of smart home devices according to some embodiments of this application. Process 500 can be executed by the control module or the device linkage module 255. In some embodiments, process 500 can be executed before, after, or simultaneously with process 300.

[0082] In step 510, environmental data is collected.

[0083] Environmental data can be data related to the environment and / or atmosphere when the user is currently watching television. Environmental data can include current time, light intensity, temperature, humidity, sound, etc. The current time can be a time period of day, such as morning, noon, afternoon, dusk, or evening, or a specific point in time, such as 9:00, 12:00, 17:00, 22:00, etc. In some embodiments, environmental data can be collected using environmental sensors. For example, light intensity can be collected using a light sensor, temperature can be collected using a temperature sensor, humidity can be collected using a humidity sensor, and sound can be collected using a sound sensor.

[0084] In some embodiments, the collected environmental data may be stored in the memory of the TV content recommendation system (e.g., memory 150). In some embodiments, the environmental data may be collected continuously or at certain time intervals to update the environmental data in a timely manner, thereby improving the personalization and timeliness of TV control with one or more linked devices and enhancing the user experience.

[0085] In step 520, the parameters of the television are adjusted and the working status of one or more linked devices is controlled based on the collected environmental data.

[0086] The television's parameters may include at least one of the following: sound, hue, brightness, etc. One or more linked devices may include at least one of the following: curtains, lights, speakers, air conditioners, seats, or other smart devices. The status of one or more linked devices may include their on / off status and parameter settings. The parameters of one or more linked devices may include modes (e.g., comfort mode, learning mode, entertainment mode, sleep mode, etc.) and intensity, etc.

[0087] In some embodiments, the parameters of the television and the working status of one or more linked devices are adjusted based on the collected environmental data, thereby creating a more comfortable and convenient viewing environment and improving the overall user experience.

[0088] As an example, if the detected time is 7 PM, or if the light sensor detects that the indoor light level is below a threshold, the curtains can be closed halfway to reduce external light interference, while the TV backlight is adjusted to a warmer tone to improve viewing comfort. Alternatively, if the detected time is 3 PM and the indoor light is moderate, the TV parameters can be adjusted to a suitable viewing level, and the curtains can be closed to reduce indoor light intensity.

[0089] In some embodiments, the parameters of the television and the working status of one or more linked devices can be automatically adjusted by combining the current user's identity information and environmental data.

[0090] For example, when children are watching TV, the screen brightness can be automatically dimmed, eye protection mode activated (e.g., blue light filter), and other lights in the living room turned off to create a suitable viewing environment for children. Another example is when adults are watching a movie, the audio system can be activated to surround sound mode, the seating can be switched to movie mode, and the air conditioning temperature can be adjusted to a comfortable level (e.g., 26°C). Yet another example is when the system detects that a user has left the viewing area for a period of time, the TV can automatically enter standby mode to save energy.

[0091] Figure 6 This is an exemplary flowchart illustrating home appliance linkage according to some embodiments of this application. Flow 600 can be executed by TV content recommendation system 200 or TV content recommendation system 250.

[0092] In step 610, the television is turned on. The user can turn on the television using a remote control, voice recognition, motion recognition, or other methods.

[0093] In step 615, the camera captures an image. In some embodiments, the camera may be mounted on the television or installed independently of the television in a suitable location (e.g., a wall, roof, top of furniture, etc., where it is convenient to capture images of the user).

[0094] In step 617, the acquired image is preprocessed, which may include image noise reduction, lighting adjustment, etc. Step 617 may be performed by an image recognition processing unit (e.g., the image recognition processing unit in user recognition module 252).

[0095] In steps 619 and 620, deep learning algorithms are used to perform face detection, feature extraction, and identity recognition on the preprocessed image.

[0096] In some embodiments, other biometric technologies, such as fingerprint recognition and iris recognition, can be used to replace camera recognition.

[0097] By utilizing cameras and advanced image recognition technology, the system automatically identifies the current user watching television, eliminating the need for manual login and improving convenience and user experience. Simultaneously, it accurately determines the user's age, gender, and other information, laying the foundation for subsequent personalized services.

[0098] In step 625, it is determined whether the current user watching the TV is a registered user.

[0099] In response to the fact that the current user is not a registered user, in steps 627 and 629, the system will prompt the user to register and create a user profile for the first time using the TV.

[0100] In some embodiments, the TV content recommendation system initially establishes the user's identity information based on the information provided during user registration. During the user's TV viewing process, the user identity information management module (e.g., user identity information management module 254) records the user's viewing history, including the type of program watched, viewing time, pause / fast forward operations, TV parameter settings, and other behavioral data. Machine learning algorithms are used to analyze this behavioral data, extracting the user's interests and preferences, and continuously updating the user's identity information. For programs watched multiple times, the system increases the weight of that program type in the user's identity information, forming a more accurate record of interests and preferences.

[0101] In response to the fact that the current user is a registered user, in step 630, the user's identity information is read. Specifically, the updated user identity information is read from the user profile established in step 629.

[0102] By recording and analyzing users' viewing behavior in real time and continuously updating user identity information, the system can accurately grasp changes in users' interests and preferences, making recommended content more precise and improving the adaptability and intelligence of the recommendation system.

[0103] In step 635, the environment perception module (e.g., environment perception module 253) acquires environmental data. In step 637, the environmental data and user information are analyzed to determine the current scene in step 639.

[0104] The current scenario can include some or all of the user's identity information and environmental data. For example, the data processing unit analyzes the collected environmental data and, based on the time information (such as day or night) and user identification results, determines the current viewing scenario, such as a child's viewing scenario, an adult's viewing scenario, or a family viewing scenario. As an example, the camera detects an adult user entering the viewing area and confirms their identity as "Mr. Zhang," aged 40, through facial recognition. The environmental sensor detects that the current time is 7 PM, and the indoor lighting is dim, determining it to be an adult viewing scenario at night. As another example, the camera detects a short child entering the viewing area and confirms the child's identity as "Xiao Zhang," registered in the system, aged 6, through facial recognition. The environmental sensor detects that the current time is 3 PM, the indoor lighting is moderate, and there is no sound of other adults, determining it to be a child watching alone in the afternoon.

[0105] In some embodiments, automatic environmental awareness can be replaced by users manually selecting scene modes. Users can select "Kids Mode", "Adults Mode", "Movie Viewing Mode", "Eye Protection Mode", "Learning Mode", etc. in the TV's settings menu.

[0106] In step 640, content is recommended based on the user's identity information and the current scenario. Step 640 can be executed by the content recommendation module 257.

[0107] Once the TV content recommendation system identifies the current user's identity and the current context, the content recommendation module filters suitable programs from the content library based on this information. For example, for children, cartoons and educational content are prioritized, with educational programs of appropriate difficulty level recommended according to the child's age and learning needs. Similarly, for adults, news, movies, and TV series are recommended based on their interests; for adults who like to follow real-time news, the latest news information is provided and recommended. The recommendation algorithm considers factors such as content popularity, ratings, and relevance to the user's interests to generate a personalized recommendation list. Users can then select programs from the list. Conversely, based on the user's choices, the TV content recommendation system can update the user's identity information, thereby improving user satisfaction and viewing interest in the recommended content.

[0108] In some embodiments, when multiple users are watching television, different weights can be assigned to each user based on their identity information, and content recommendations can be made according to their respective weights. For example, when user A (an adult) and user B (a child) are watching television together, educational programs suitable for user B are generally selected. Therefore, user B can be assigned a higher weight than user A.

[0109] By providing personalized services across multiple scenarios, we have met the diverse needs of users and increased their frequency and engagement with television.

[0110] In step 642, recommended content is displayed. Step 642 can be performed by display module 256.

[0111] In some embodiments, recommended content may be displayed on the television screen for users to choose to play. In some embodiments, the reasons for the recommendation may also be displayed on the screen. The display method may include text, images, videos, lists, etc., or combinations thereof, and this document does not impose specific limitations.

[0112] In step 645, the device linkage module (e.g., device linkage module 255) automatically controls the linkage device based on the current scene and user identity information.

[0113] In some embodiments, the device linkage module communicates with the television and other linked devices, such as via network 130, to control the television and one or more linked devices. This enables the linkage control of the television with other smart home devices. Based on different scenarios and user identity information, the module automatically adjusts the working status of related devices, creating a more comfortable and convenient viewing environment, building a complete smart scene ecosystem, and improving the overall user experience.

[0114] In some embodiments, automatic linkage can be replaced by manually controlling other smart home devices, and users can control each linked device individually using a TV remote or other control devices.

[0115] In step 650, the process ends. The user can turn off the TV using a remote control, voice recognition, motion recognition, or other methods. In some embodiments, the TV content recommendation system can control the TV to turn off when it determines that the user has left the viewing area of ​​the TV for more than a predetermined time, in order to save resources.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] The above description is the basic concept of this application, presented only in the form of embodiments. Obviously, those skilled in the art can make corresponding changes, improvements, or modifications based on this application. These changes, improvements, and modifications have been implied or indirectly proposed in this application and are all included within the embodiments of this application.

[0118] The terms used to describe this application, such as “one embodiment,” “some embodiments,” or “certain embodiments,” indicate that at least one feature, structure, or characteristic associated with it is included in an embodiment of this application.

[0119] Furthermore, those skilled in the art will recognize that the embodiments in this application may involve novel processes, methods, machines, products, or related improvements. Therefore, the embodiments of this application can be implemented in pure hardware or pure software, where the software includes, but is not limited to, operating systems, resident software, or microcode; they can also be implemented in "systems," "modules," "submodules," "units," etc., that simultaneously include hardware and software. Additionally, the embodiments of this application can exist in the form of computer programs, and they can be carried on a computer-readable medium.

Claims

1. A television content recommendation method, comprising: capturing an image of a current user watching the television; determining first identity information of the current user according to the captured image of the current user; recommending television content to the current user according to the first identity information of the current user.

2. The television content recommendation method of claim 1, wherein, The determining first identity information of the current user according to the captured image of the current user comprises: determining whether the current user is a registered user according to the image of the current user; in response to the current user being a registered user, obtaining second identity information of the registered user stored in advance and determining the second identity information as the first identity information of the current user; in response to the current user not being a registered user, prompting the current user to register as a registered user.

3. The television content recommendation method of claim 2, wherein, The determining whether the current user is a registered user according to the image of the current user comprises: preprocessing the captured image of the current user; matching the preprocessed image with images of the registered users; determining whether the current user is a registered user according to the matching result.

4. The television content recommendation method of claim 2, wherein, The prompting the current user to register as a registered user in response to the current user not being a registered user further comprises: determining the first identity information of the current user according to registration information provided by the registered user when registering. 5.The television content recommendation method of claim 1, wherein: the first identity information of the current user comprises at least one of the following: a viewing history, an identity, a gender, an age, and an interest of the current user; the viewing history of the current user comprises at least one of the following: a program type, a viewing time, a pause / fast-forward operation, and a television parameter setting of the current user; 6. The television content recommendation method of claim 1, wherein, the method further comprises: capturing environmental data, the environmental data comprising at least one of the following: a current time, light, temperature, humidity, and sound; adjusting parameters of the television and a working state of one or more associated devices according to the obtained environmental data. 7.The television content recommendation method of claim 6, wherein: the parameters of the television comprise at least one of the following: sound, color tone, and brightness; the one or more associated devices comprise at least one of the following: a curtain, a lamp, a sound system, an air conditioner, and a seat.

8. The television content recommendation method of claim 1, wherein, The recommending television content to the current user according to the first identity information of the current user comprises: recommending the television content to the current user according to the first identity information of the current user and scene information, wherein the scene information comprises at least one of the following: viewing, learning, and news browsing.

9. The television content recommendation method of claim 1, wherein, The method further comprises: updating the first identity information of the current user by recording and analyzing behaviors of the current user watching the television. 10.A system for television content recommendation, comprising: a capturing module configured to capture an image of a current user watching the television; a determining module configured to determine first identity information of the current user according to the captured image of the current user. The content recommendation module is configured to recommend television content to the current user according to the first identity information of the current user. 11.A computer readable storage medium, the storage medium storing computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the television content recommendation method according to any one of claims 1-9. 12.A smart home device linkage method, the smart home device comprising a television and one or more linkage devices, the method comprising: The method comprises: determining first identity information of a current user watching the television; collecting environmental data, the environmental data comprising at least one of the following: current time, light, temperature, humidity, sound; controlling the television to recommend content to the current user according to the first identity information of the current user; adjusting parameters of the television and controlling the working state of the one or more linkage devices according to the first identity information of the current user and the environmental data. 13.The intelligent home device linkage method according to claim 12, wherein the determining of the first identity information of the current user watching the television comprises: collecting an image of the current user watching the television; determining the first identity information of the current user according to the collected image of the current user.

Images