Control method of interactive television, interactive television and storage medium

Abstract

The invention discloses a control method of an interactive television, the interactive television and a storage medium, and relates to the technical field of interactive network televisions, and the control method of the interactive television comprises the following steps: if an AI assistant module receives a control instruction, generating and sending an interaction instruction to a model module according to the control instruction; the model module analyzes semantic information corresponding to the interaction instruction; acquiring a real-time data stream from the data module according to the semantic information; and generating and sending response information to the AI assistant module based on the real-time data stream. The technical effect of improving the interactive television program recommendation efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of interactive network television, and in particular to a control method for an interactive television, an interactive television and a storage medium. Background Art

[0002] IPTV (Internet Protocol Television) is a new type of television that integrates Internet, multimedia, communication and other technologies.

[0003] In the related art, when operating IPTV, managers need to manually search the media library or query hot information, and then generate push messages to users. However, the above solution requires too much manual intervention, making the recommended content dependent on the preferences of operators, which leads to low recommendation efficiency.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present application is to provide a control method, device and storage medium for an interactive television, aiming to solve the technical problem that the recommended content depends on the preferences of the operator, thereby resulting in low recommendation efficiency.

[0006] To achieve the above object, the present application proposes a control method for an interactive television, the control method for an interactive television comprising:

[0007] If the AI ​​assistant module receives a control instruction, it generates and sends an interaction instruction to the model module according to the control instruction;

[0008] The model module parses the semantic information corresponding to the interaction instruction;

[0009] Acquire real-time data stream from the data module according to the semantic information;

[0010] Generate and send response information to the AI ​​assistant module based on the real-time data stream.

[0011] In one embodiment, if the AI ​​assistant module receives a control instruction, the step of generating and sending an interaction instruction to the model module according to the control instruction includes:

[0012] The AI ​​assistant module determines the control instruction according to the received user input or the triggered execution process;

[0013] generating the interaction instruction according to the control instruction;

[0014] The interaction instruction is sent to the model module.

[0015] In one embodiment, the semantic information includes an instruction type and instruction content, and the step of the model module parsing the semantic information corresponding to the interaction instruction includes:

[0016] The model module determines the instruction type corresponding to the interactive instruction;

[0017] Determine the instruction content corresponding to the interaction instruction.

[0018] In one embodiment, the step of acquiring the real-time data stream from the data module according to the semantic information includes:

[0019] According to the instruction type, obtaining a first data stream corresponding to the instruction type in a data module;

[0020] According to the instruction content, obtaining a second data stream corresponding to the instruction content in the first data;

[0021] The data module obtains the real-time data stream based on the user portrait and in combination with the second data stream.

[0022] In one embodiment, before the step of obtaining the real-time data stream based on the user portrait and in combination with the second data stream, the data module further includes:

[0023] The data module obtains a user data set through the collected user data;

[0024] The model module constructs the user portrait through the model module based on the user data set.

[0025] In one embodiment, the step of generating and sending response information to the AI ​​assistant module based on the real-time data stream includes:

[0026] The model module generates target information based on the real-time data stream;

[0027] Based on the user portrait and in combination with the target information, obtaining the response information;

[0028] The response information is sent to the AI ​​assistant module.

[0029] In one embodiment, the intelligent operation system further includes a display module, after the step of generating and sending response information to the AI ​​assistant module based on the real-time data stream, including:

[0030] The AI ​​assistant module calls the business interaction interface corresponding to the response information on the display module according to the response information;

[0031] The display module receives the response information through the business interaction interface, and the display module selects display content according to the response information.

[0032] In one embodiment, before the step of the AI ​​assistant module receiving a control instruction and generating and sending an interaction instruction to the model module according to the control instruction, the step further includes:

[0033] The data module collects basic data and preprocesses the basic data to obtain target data;

[0034] The model module is trained using the target data to obtain a pre-trained model;

[0035] The pre-trained model imports fuzzy data for optimization to obtain a target model;

[0036] Several of the target models are applied in the model module.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes an interactive television, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the interactive television control method as described above.

[0038] In addition, to achieve the above objectives, the present application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the interactive television control method described above are implemented.

[0039] The present application provides a control method for an interactive television, including: if the AI ​​assistant module receives a control instruction, generating and sending an interaction instruction to the model module according to the control instruction; the model module parses the semantic information corresponding to the interaction instruction; obtains a real-time data stream from the data module according to the semantic information; and generates and sends a response message to the AI ​​assistant module based on the real-time data stream. Based on data analysis and real-time hot public opinion, it not only trains and adjusts the recommendation ability well, but also realizes the recommendation of related content. Finally, combined with the recommended content, it generates a theme that meets the expectations of users or operation and maintenance personnel or forms a waterfall flow, so that the recommended content is no longer dependent on the preferences of the operator, thereby further enhancing the recommendation effect of the control method.

[0040] To sum up, this application combines real-time hot public opinions to train an AI model with accurate recommendations and achieve the expected recommendation effect. It uses comprehensive real-time data to recommend relevant content, thereby improving the content recommendation effect of IPTV. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A schematic diagram of a flow chart of a first embodiment of a control method for an interactive television of the present application;

[0044] Figure 2 A schematic diagram of a flow chart of a second embodiment of a control method for an interactive television according to the present application;

[0045] Figure 3 A schematic diagram of a flow chart of a third embodiment of a control method for an interactive television of the present application;

[0046] Figure 4 A flowchart of a fourth embodiment of the control method of an interactive television of the present application;

[0047] Figure 5 A flowchart of a fifth embodiment of the control method of an interactive television of the present application;

[0048] Figure 6 A flowchart of a sixth embodiment of the control method of an interactive television of the present application;

[0049] Figure 7 A flowchart of a seventh embodiment of the control method of an interactive television of the present application;

[0050] Figure 8 A flowchart of an eighth embodiment of the control method of an interactive television of the present application;

[0051] Fig. 9 An interactive schematic diagram of the control method of the interactive television of the present application;

[0052] Fig.10 This is a schematic diagram of the structure of the interactive television of this application.

[0053] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0055] In the related art, the content recommendation method of IPTV requires too much manual intervention, resulting in the recommended content being dependent on the preferences of operators, which in turn leads to low recommendation efficiency.

[0056] The present application provides a solution: the control instruction is received through the AI ​​assistant module, and the corresponding interaction instruction is generated and sent to the model module according to the above control instruction. The model module parses the semantic information corresponding to the interaction instruction, and then obtains the real-time data stream in the data module according to the semantic information, generates the corresponding response information according to the real-time data stream and sends it to the AI ​​assistant module. Based on data analysis and real-time hot public opinion, it not only trains and adjusts the recommendation ability well, but also realizes the recommendation of related content. Finally, combined with the recommended content, a theme that meets the expectations of users or operation and maintenance personnel is generated or a waterfall flow is formed, so that the recommended content is no longer dependent on the preferences of the operator, thereby further enhancing the recommendation effect of the control method.

[0057] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an interactive television, etc. The following takes an interactive television as an example to illustrate this embodiment and the following embodiments.

[0058] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The present application embodiment provides a control method for an interactive television, referring to Figure 1 , Figure 1 This is a flow chart of a first embodiment of a control method for an interactive television according to the present application.

[0060] In this embodiment, the interactive television control method includes steps S10 to S40:

[0061] Step S10, if the AI ​​assistant module receives a control instruction, it generates and sends an interaction instruction to the model module according to the control instruction;

[0062] In this embodiment, the intelligent operation system includes: an AI assistant module, a data module and a model module. The intelligent operation system uses AI and big data models to provide an intelligent operation method for the operation of IPTV. The AI ​​assistant module is responsible for receiving user instructions and interacting with the data module and the model module; the data module collects and updates relevant data and cooperates with the model module to screen data; the model module performs language understanding and generation tasks including IPTV operations. Control instructions include text instructions, gesture instructions, voice instructions, remote control instructions, system spontaneous instructions and timing instructions. The interactive instructions are generated by the AI ​​assistant module according to the corresponding control instructions, which act on the interaction between the AI ​​assistant module and the model module to achieve effective communication and collaboration between the AI ​​assistant and the module model.

[0063] As an optional implementation, after receiving the control instruction, the AI ​​assistant module processes or converts it, and finally generates and sends the interaction instruction corresponding to the above control instruction to the model module.

[0064] Step S20, the model module parses the semantic information corresponding to the interaction instruction;

[0065] In this embodiment, semantic information refers to the types and contents contained in the language. The instruction types of semantic information include question and answer, generating components, generating topics, generating waterfall flows, and generating recommended content. The instruction content of semantic information includes the main content or data themes it contains.

[0066] As an optional implementation, the model module parses the received interaction instructions to understand the instruction type and instruction content contained therein.

[0067] Step S30, obtaining a real-time data stream from the data module according to the semantic information;

[0068] In this embodiment, the real-time data stream refers to a data sequence, and the real-time data stream is a corresponding data stream obtained by the model module in the data module according to the semantic information.

[0069] As an optional implementation, the model module obtains real-time data from the data module based on semantic information, and the real-time data includes operation data, media asset data and big data.

[0070] Step S40, generating and sending response information to the AI ​​assistant module based on the real-time data stream.

[0071] In this embodiment, the response information is generated by the model module based on the real-time data stream generated by the data module, and the response information includes the operations that the AI ​​assistant module needs to perform and the required related information.

[0072] As an optional implementation, the model module uses real-time data stream to generate response information for the AI ​​assistant module, including the generated topic and Spring Festival-related information. The AI ​​assistant module receives the above response information and performs corresponding operations.

[0073] For example, users can ask questions to AI assistants through text and voice, and AI assistants receive control instructions. Then the model module obtains program ratings data and current popular content from the data module, and then calls the model module through the AI ​​assistant template to recommend content, ensuring that the recommended content is both in line with public interests and has high ratings potential, thereby improving the recommendation effect. Control instructions include voice, action, and preset instructions. IPTV can perform a series of operations in a certain time period through the user's preset instructions, and IPTV can also be remotely operated through the connection with the user.

[0074] Since the control method of the interactive television combines real-time data and a model module to perform content recommendation, the recommended content is no longer dependent on the preferences of the operator, thereby further enhancing the recommendation effect of the control method.

[0075] Based on any of the above embodiments, in Embodiment 2 of the present application, refer to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the control method of the interactive television of the present application. Step S10 includes steps A11 to A13:

[0076] Step A11, the AI ​​assistant module determines a control instruction according to the received user input or the triggered execution process;

[0077] In this embodiment, user input is the process of interaction between the user and the IPTV through input methods such as keyboard, voice or gesture. The execution process refers to the program instance running in the intelligent operation system. The triggered execution process is triggered by the IPTV self-start, and the corresponding control instructions include generating components, generating themes and generating waterfall flows.

[0078] As an optional implementation, the AI ​​assistant module generates Spring Festival-themed events by analyzing user input, and then generates corresponding control instructions to execute the process of generating Spring Festival themes.

[0079] Step A12, generating the interaction instruction according to the control instruction;

[0080] In this embodiment, the interaction instructions are generated by the AI ​​assistant module according to the corresponding control instructions, which act on the interaction between the AI ​​assistant module and the model module to achieve effective communication and collaboration between the AI ​​assistant and the module model.

[0081] As an optional implementation, the AI ​​assistant module generates instructions for interacting with the model module based on the control instructions.

[0082] Step A13, sending the interaction instruction to the model module.

[0083] In this embodiment, the model module is an independent module in the intelligent operation system that is responsible for specific functions or task processing and performs intelligent work based on preset algorithms, models or data structures.

[0084] As an optional implementation, the AI ​​assistant module sends the interaction instructions generated based on the control instructions input by the user to the model module.

[0085] Exemplarily, after receiving the user's voice input or the application-triggered signal, the AI ​​assistant module identifies the user's intention through natural language processing or preset rules. The AI ​​assistant module generates a specific interaction instruction based on the identified control instruction. This interaction instruction contains all the information required for execution, such as the operation type and operation content. The AI ​​assistant module sends the generated interaction instruction to the model module responsible for performing specific analysis and processing tasks. After receiving the interaction instruction, the model module parses the instruction content and performs the corresponding operation according to the instruction.

[0086] Due to the setting of the interactive instructions, the modules in the IPTV can interact quickly, thereby improving the operation efficiency of the IPTV control method.

[0087] Based on any of the above embodiments, in Embodiment 3 of the present application, refer to Figure 3 , Figure 3 This is a flow chart of the third embodiment of the control method of the interactive television of the present application. Step S20 includes steps B11-B12:

[0088] Step B11, the model module determines the instruction type corresponding to the interactive instruction;

[0089] In this embodiment, the instruction type is the basis for classifying and identifying instructions, which helps us distinguish instructions of different natures. The instruction types include question and answer, generate component, generate topic, generate waterfall flow and generate recommended content.

[0090] As an optional implementation, the model module identifies and determines the specific instruction type to which the instruction belongs by analyzing the characteristics or content of the interactive instruction.

[0091] Step B12: determine the instruction content corresponding to the interactive instruction.

[0092] In this embodiment, the instruction content is the core information constituting the instruction, which specifically describes the operation content to be executed by the instruction. The instruction content includes the main content or data subject it contains.

[0093] As an optional implementation, by parsing and processing the interactive instruction, the operation content and operation conditions specifically included in the instruction are clarified and extracted, that is, the instruction content is determined.

[0094] Exemplarily, the user inputs the control instruction of "recommend a Spring Festival theme" through the AI ​​assistant module. The AI ​​assistant module generates and sends an interaction instruction to the model module based on the above control instruction. The model module parses the above interaction instruction, determines that the instruction type of the above interaction instruction is "theme", and determines that the instruction content of the above interaction instruction is "Spring Festival".

[0095] Since the model module parses the semantic information of the interactive instructions, the AI ​​assistant module understands the user's intentions and needs, generates control instructions with clear meanings, and effectively communicates and interacts with the model module, thereby improving the convenience of the IPTV control method.

[0096] Based on any of the above embodiments, in Embodiment 4 of the present application, refer to Figure 4 , Figure 4 This is a flowchart of the fourth embodiment of the control method of the interactive television of the present application. Step S30 includes steps C11 to C13:

[0097] Step C11, according to the instruction type, obtaining a first data stream corresponding to the instruction type in a data module;

[0098] In this embodiment, the first data stream is a data stream screened out by the model module in the data module based on the instruction type, and the first data stream includes all data in the data module that conforms to the above instruction type.

[0099] As an optional implementation, based on a given instruction type, the system performs an acquisition operation in the data module to retrieve a first data stream that is closely related to the instruction type and meets the execution requirements.

[0100] Step C12, according to the instruction content, obtaining a second data stream corresponding to the instruction content in the first data;

[0101] In this embodiment, the second data stream is a data stream filtered out by the model module from the first data stream based on the instruction content, and the second data stream includes all data in the first data stream that conforms to the above instruction content.

[0102] As an optional implementation, according to the specific instruction content, a search operation is performed in the first data stream to find and obtain a second data stream that is closely related to the instruction content and meets specific requirements.

[0103] Step C13: the data module obtains the real-time data stream based on the user portrait and in combination with the second data stream.

[0104] In this embodiment, the user portrait, also known as the user role, is a representative and typical user image constructed by the model module based on the user's questions and answers, viewing and search data, and various user-related data. The user portrait integrates multiple dimensions such as the user's basic information, behavioral habits, and psychological characteristics.

[0105] As an optional implementation, by integrating the static features of the user portrait with the dynamic information of the second data stream, the data module can generate a real-time data stream that reflects the user's current status and behavior, providing immediate and accurate data support for executing recommendations.

[0106] Exemplarily, the user inputs the control instruction of "recommend a Spring Festival theme" through the AI ​​assistant module. The AI ​​assistant module generates and sends an interaction instruction to the model module based on the above control instruction. The model module parses the above interaction instruction, determines that the instruction type of the interaction instruction is "theme", and obtains the first data stream containing "theme" corresponding to the instruction type in the data module according to the instruction type; determines that the instruction content of the interaction instruction is "Spring Festival", and obtains the second data stream containing "Spring Festival" corresponding to the instruction content in the first data stream according to the instruction content; finally, combined with the user portrait, a real-time data stream that meets the user's expectations is obtained.

[0107] The introduction of artificial intelligence and large language models in IPTV, combined with real-time data for content recommendation, has enabled smarter and more efficient operations of IPTV.

[0108] Based on any of the above embodiments, in Embodiment 5 of the present application, refer to Figure 5 , Figure 5 This is a flowchart of the fifth embodiment of the control method of the interactive television of the present application. Before step S40, it also includes steps D11 to D12:

[0109] Step D11, the data module obtains a user data set through the collected user data;

[0110] In this embodiment, the core components of the user data set include: basic user attributes, such as age, gender, region, etc., which are the basis of user portraits; user behavior data, such as visits, browsing, adding to cart, placing orders, etc., which reflect user consumption habits; social and interactive data, such as comments, forwarding, etc., which reflect user social preferences.

[0111] As an optional implementation, the data module is responsible for collecting user data from multiple sources, then performing data processing and constructing a user data set, and finally obtaining the user data set.

[0112] Step D12: The model module constructs the user portrait through the model module based on the user data set.

[0113] In this embodiment, the construction of user portraits is a systematic process that aims to create virtual representatives that can represent specific user groups by collecting and analyzing user data.

[0114] As an optional implementation, the model module uses the user dataset to build and refine the user portrait.

[0115] For example, by collecting various data generated by users during normal use, the collected data and analysis results are integrated to build a three-dimensional portrait of each user. This portrait includes not only the user's basic information and behavioral characteristics, but also the user's psychological characteristics and lifestyle.

[0116] Because machine learning algorithms are used to build user portraits based on the collected data, the personalization of content recommendations is improved.

[0117] Based on any of the above embodiments, in Embodiment 6 of the present application, refer to Figure 6 , Figure 6 This is a flowchart of the sixth embodiment of the control method of the interactive television of the present application. Step S40 includes steps E11 to E13:

[0118] Step E11, the model module generates target information based on the real-time data stream;

[0119] In this embodiment, the target information is information that is processed by the model module based on the real-time data stream and is in line with the current scenario.

[0120] As an optional implementation method, the model module cleans, integrates and analyzes the real-time data stream, applies algorithms and models to extract and transform the information in the data stream, and finally obtains the target information.

[0121] Step E12, obtaining the response information based on the user portrait and in combination with the target information;

[0122] In this embodiment, the response information is the response information that the model module processes in the target information according to the user portrait and includes content that meets the user's expectations.

[0123] As an optional implementation method, customized information is generated based on comprehensive consideration of user portraits and target information to meet user-specific needs and achieve effective communication and response of target information.

[0124] Step E13, sending the response information to the AI ​​assistant module.

[0125] In this embodiment, the AI ​​assistant module acts as the information receiving party and prepares to pass the above response information to the next module in the next step.

[0126] As an optional implementation, response information containing the generated Spring Festival theme is sent to the AI ​​assistant module. The AI ​​assistant module receives the response information and generates the theme based on the response information.

[0127] Exemplarily, the information in the data stream is extracted and transformed through the model module to obtain the target information; if the user portrait involves basketball, the model module then filters out basketball-related information and games based on the user portrait, and uses the algorithm to deduce, and finally obtains the push content that meets the user's expectations.

[0128] Since the information to be recommended is derived based on the user portrait, the recommended information can meet the user's needs with a high probability, thereby improving the personalization of the recommended information.

[0129] Based on any of the above embodiments, in Embodiment 7 of the present application, refer to Figure 7 , Figure 7 This is a flow chart of the seventh embodiment of the control method of the interactive television of the present application. After step S40, it also includes steps F11 to F12:

[0130] Step F11, the AI ​​assistant module calls the business interaction interface corresponding to the response information on the display module according to the response information;

[0131] In this embodiment, the display module presents the functional effect corresponding to the response information.

[0132] As an optional implementation, the AI ​​assistant calls the display module interface to generate response information based on user requests or internal logic. The display module includes multiple business interaction interfaces for displaying different information. The AI ​​assistant selects and calls the corresponding business interaction interface on the display module based on the response information type.

[0133] Step F12: the display module receives the response information through the business interaction interface, and the display module selects display content according to the response information.

[0134] In this embodiment, the business interaction interface is the key to achieving collaborative work between modules. The business interaction interface is the interface for interaction between the AI ​​assistant module and the display module.

[0135] As an optional implementation, response information containing the Spring Festival theme from the AI ​​assistant is received through the business interaction interface, and corresponding display content is selected based on the received response information, and the selected content about the Spring Festival is displayed to the user in the form of a theme.

[0136] For example, according to the recommended content, select the appropriate components and select the appropriate posters to generate a theme, and the user can adjust and update the content or replace the components. For example, the AI ​​assistant recommends 4 programs and selects a row of 4 components with the same size as the recommended position. Select the latest poster of the program to generate a theme and display it to the user. If the user thinks that a program is not suitable, he can adjust the program or replace the component. The AI ​​assistant can assist operators in intelligently generating theme floors, intelligently generating waterfall flows and themes, automatically completing operational tasks, accurately recommending content, optimizing user experience, and at the same time, manual intervention to ensure operational safety and improve overall operational efficiency.

[0137] The introduction of artificial intelligence and large language models in IPTV, combined with real-time data for content recommendation, has enabled smarter and more efficient operations.

[0138] Based on any of the above embodiments, in Embodiment 8 of the present application, refer to Figure 8 , Figure 8 This is a flowchart of the eighth embodiment of the control method of the interactive television of the present application. Before step S10, it also includes steps G11 to G13:

[0139] Step G11, the data module collects basic data and pre-processes the basic data to obtain target data;

[0140] In this embodiment, the collected basic data may contain missing values ​​or inconsistencies. Therefore, preprocessing is required, such as data cleaning, format conversion, missing value processing, etc., to ensure the accuracy and consistency of the data. After preprocessing, the data quality is improved and can be used for further analysis and mining. These target data help to gain a deeper understanding of user needs, optimize program recommendations, etc.

[0141] As an optional implementation method, the data module is responsible for collecting basic data from various sources and performing a series of processing operations on the collected basic data, such as cleaning, conversion, normalization, etc., to eliminate errors, redundancy or inconsistency and meet the needs of subsequent analysis or application; the preprocessed data is called target data, which is the final result output by the data module for further analysis, processing or application by other modules or systems.

[0142] Step G12, the model module is trained using the target data to obtain a pre-trained model;

[0143] In this embodiment, the data module includes a large model, and the large model is trained using the above target data. The large model that has been trained in large quantities is called a pre-trained model.

[0144] As an optional implementation, the target data is output to a large model, which is usually based on the Transformer architecture and uses autonomous mechanisms to process language tasks. The model performs self-supervised learning through massive unlabeled data to learn the general laws and structures of language. The Transformer architecture is completely based on the attention mechanism, especially the self-attention mechanism, to capture the relationship between each element in the sequence and achieve efficient feature representation.

[0145] Step G13, the pre-trained model is imported into the fuzzy data for optimization to obtain a target model;

[0146] In this embodiment, the pre-trained model has been preliminarily trained on a large amount of data and has a certain generalization ability. The data collected for a specific task or field is imported into the pre-trained model. These data are more in line with the target application scenario and help the model to further learn and optimize. Through specific training algorithms and strategies, the model after importing fuzzy data is fine-tuned and optimized to make it better adapted to the target task or field. The optimized model is called the target model, which performs better in specific tasks or fields and can meet the needs of practical applications.

[0147] As an optional implementation method, by learning, pre-training and optimizing the large model, a large language model and business interaction API suitable for the IPTV field are finally formed.

[0148] Several of the target models are applied in the model module.

[0149] Exemplarily, by collecting various data on the platform, the data collection is not limited to the Internet platform, and the data collection includes various databases, voice command data and user operation data, and then pre-processing them to obtain target data, and import the target data into the large model. The large model is trained to obtain a pre-trained model, and then the pre-trained model is used to retrain and optimize the special instruction data to finally obtain the target model.

[0150] Due to the extensive training of the model, including optimization training, the model's understanding ability and task completion efficiency are improved.

[0151] For example, to help understand the implementation process of the interactive television control method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Fig. 9 , Fig. 9 An interactive schematic diagram of a control method for an interactive television is provided, specifically:

[0152] An AI operation assistant module can be added between the AI ​​assistant module and the model module. The AI ​​operation assistant module has multiple interactive interfaces. The AI ​​assistant module interacts with the AI ​​operation assistant module through an exclusive interface. The interactive interface of the AI ​​operation assistant module can also interact with other modules, including third-party device modules, Internet modules, and database modules.

[0153] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the control method of the interactive television of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0154] The present application provides an interactive television, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the control method of the interactive television in the above-mentioned embodiment 1.

[0155] Reference below Fig.10 , which shows a schematic diagram of the structure of an interactive television suitable for implementing the embodiment of the present application. The interactive television in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.10 The interactive television shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0156] like Fig.10As shown, the interactive television may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the interactive television are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. Communication device 1009 can allow interactive television to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an interactive television with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0157] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0158] The interactive TV provided by the present application adopts the control method of the interactive TV in the above embodiment, which can solve the technical problem that the recommended content depends on the preferences of the operator, thereby leading to low recommendation efficiency. Compared with the prior art, the beneficial effects of the interactive TV provided by the present application are the same as the beneficial effects of the control method of the interactive TV provided by the above embodiment, and other technical features in the interactive TV are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0159] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0160] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0161] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the control method of the interactive television in the above-mentioned embodiment.

[0162] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0163] The computer-readable storage medium may be included in the interactive television; or may exist independently without being incorporated into the interactive television.

[0164] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the interactive television, the interactive television:

[0165] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0166] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0167] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0168] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned interactive television control method, and can solve the technical problem that the recommended content depends on the preferences of the operator, thereby resulting in low recommendation efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the interactive television control method provided by the above-mentioned embodiment, and will not be repeated here.

[0169] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for controlling an interactive television, characterized in that: Applied to an intelligent operation system, the intelligent operation system includes: an AI assistant module, a data module and a model module, and the control method of the interactive television includes: If the AI ​​assistant module receives a control instruction, it generates and sends an interaction instruction to the model module according to the control instruction; The model module parses the semantic information corresponding to the interaction instruction; Acquire real-time data stream from the data module according to the semantic information; Generate and send response information to the AI ​​assistant module based on the real-time data stream.

2. The method for controlling an interactive television according to claim 1, wherein: If the AI ​​assistant module receives a control instruction, the step of generating and sending an interaction instruction to the model module according to the control instruction includes: The AI ​​assistant module determines the control instruction according to the received user input or the triggered execution process; generating the interaction instruction according to the control instruction; The interaction instruction is sent to the model module.

3. The control method of the interactive television according to claim 2, characterized in that: The semantic information includes instruction type and instruction content, and the step of the model module parsing the semantic information corresponding to the interactive instruction includes: The model module determines the instruction type corresponding to the interactive instruction; Determine the instruction content corresponding to the interaction instruction.

4. The control method of the interactive television according to claim 3, characterized in that: The step of acquiring the real-time data stream from the data module according to the semantic information comprises: According to the instruction type, obtaining a first data stream corresponding to the instruction type in a data module; According to the instruction content, obtaining a second data stream corresponding to the instruction content in the first data; The data module obtains the real-time data stream based on the user portrait and in combination with the second data stream.

5. The control method of the interactive television according to claim 4, characterized in that: Before the step of obtaining the real-time data stream based on the user portrait and in combination with the second data stream, the data module further includes: The data module obtains a user data set through the collected user data; The model module constructs the user portrait through the model module based on the user data set.

6. The control method of the interactive television according to claim 5, characterized in that: The step of generating and sending response information to the AI ​​assistant module based on the real-time data stream includes: The model module generates target information based on the real-time data stream; Based on the user portrait and in combination with the target information, obtaining the response information; The response information is sent to the AI ​​assistant module.

7. The control method of an interactive television according to claim 6, characterized in that: The intelligent operation system also includes a display module, which includes: The AI ​​assistant module calls the business interaction interface corresponding to the response information on the display module according to the response information; The display module receives the response information through the business interaction interface, and the display module selects display content according to the response information.

8. The method for controlling an interactive television according to claim 1, wherein: Before the AI ​​assistant module receives the control instruction and generates and sends the interaction instruction to the model module according to the control instruction, the step further includes: The data module collects basic data and preprocesses the basic data to obtain target data; The model module is trained using the target data to obtain a pre-trained model; The pre-trained model imports fuzzy data for optimization to obtain a target model; Several of the target models are applied in the model module.

9. An interactive television, characterized in that The interactive television comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for controlling the interactive television according to any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the interactive television control method according to any one of claims 1 to 8 are implemented.

Images