Program recommendation method, device and equipment of smart home appliance and storage medium

Abstract

The application belongs to the technical field of intelligent household appliances, and particularly relates to a program recommendation method and device for an intelligent household appliance, equipment and a storage medium, the method comprising: a server obtaining user information corresponding to a user access request, device usage information of an intelligent household appliance and environment information in response to the user access request sent by the intelligent household appliance; determining a plurality of recommended programs and a recommended priority corresponding to each recommended program based on the user information, the device usage information and the environment information; determining a target recommendation strategy according to the plurality of recommended programs and the corresponding recommended priority, and sending the target recommendation strategy to the intelligent household appliance, so that the intelligent household appliance displays at least one recommended program on a display screen according to the target recommendation strategy. By integrating the user information, the device usage information and the environment information, the limitation of a single data dimension is broken through, the recommended programs of the intelligent household appliance are dynamically adjusted, and the user experience is improved.

Description

Technical Field

[0001] This application belongs to the field of smart home appliance technology, specifically relating to a program recommendation method, apparatus, device, and storage medium for smart home appliances. Background Technology

[0002] Currently, the control interface of most smart home devices is displayed on the device's screen. Users can intuitively select the programs or functions they want to run through the screen, and also understand and obtain information such as the device's working status and operation prompts.

[0003] However, the screen size of existing smart home devices is usually limited, which restricts the amount of information that can be displayed, offering only a limited number of programs for users to choose from. Furthermore, traditional screen interfaces use a fixed list of programs, making it impossible to dynamically adjust recommendation priorities based on real-time environmental data.

[0004] Furthermore, existing technologies for recommending programs on smart screens can only recommend programs based on a single data source or rely solely on device type or basic time settings. They lack the fusion analysis of multi-dimensional data (user behavior habits, real-time energy consumption, seasonal characteristics), resulting in biased scenario judgments that cannot adapt to complex environmental changes in actual use scenarios. Consequently, there is a significant discrepancy between the recommended results and actual user needs. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for recommending smart home appliances, in order to solve the problem that in the prior art, the recommended content on the screen of smart home appliances is fixed, the analysis data is singular and the dynamic adaptability is insufficient, which leads to the recommendation program results being out of touch with the user's actual needs.

[0006] In a first aspect, embodiments of this application provide a program recommendation method for smart home appliances, applied to a server, the method comprising:

[0007] In response to a user access request sent by a smart home appliance, obtain the user information corresponding to the user access request, the device usage information of the smart home appliance, and the environmental information;

[0008] Based on the user information, the device usage information, and the environmental information, multiple recommendation programs and the recommendation priority of each recommendation program are determined.

[0009] Based on the multiple recommendation programs and their corresponding recommendation priorities, a target recommendation strategy is determined, and the target recommendation strategy is sent to the smart home appliance so that the smart home appliance displays at least one of the recommendation programs on the display screen in accordance with the target recommendation strategy.

[0010] In one possible design, the user information includes a target user identifier, and the device usage information includes model information and historical operation information. The step of determining multiple recommendation programs and their corresponding recommendation priorities based on the user information, the device usage information, and the environmental information includes:

[0011] At least one personalized program that meets a first preset condition is determined from the historical running information. The first preset condition includes: matching the target user identifier and executing more than a preset number of times. The historical running information is used to indicate the running information of multiple programs within a preset time period.

[0012] Based on the model information and the environmental information, at least one scenario program is determined, and at least one personalized program and at least one scenario program are used as the recommended program;

[0013] Multidimensional data analysis is performed on the user information, device usage information, and environmental information to obtain recommendation weight factors for the user information, device usage information, and environmental information, respectively.

[0014] The recommendation priority of each of the recommendation programs is determined based on the recommendation weight factor.

[0015] In one possible design, the environmental information includes location information and weather information, and the procedure for determining at least one scenario based on the model information and the environmental information includes:

[0016] Obtain the operating program information of all smart home appliances within the geographical range corresponding to the location information that match the model information, and the operating program information is used to indicate the number of times multiple programs are run;

[0017] Based on the number of times multiple programs are run, at least one high-frequency scenario program is determined. The high-frequency scenario program includes: programs that have been run more than a preset number of times, and / or the top N programs with the highest number of runs.

[0018] Determine whether the weather information corresponds to a clothing processing scenario;

[0019] If so, the program corresponding to the clothing processing scenario will be determined as the weather scenario program.

[0020] In one possible design, determining the target recommendation strategy based on multiple recommendation procedures and their corresponding recommendation priorities includes:

[0021] Based on the recommendation priority, a preset number of target recommendation programs are determined from the plurality of recommendation programs, and the recommendation frequency of each target recommendation program is determined, wherein the recommendation frequency is used to indicate the number of times the target recommendation program is recommended within the recommendation period;

[0022] A preset number of target recommendation programs and their corresponding recommendation frequencies are determined as the target recommendation strategy.

[0023] In one possible design, the device usage information further includes: the number of times the device was used and the nighttime hours. Based on the device usage information, a recommendation program and corresponding recommendation priorities are determined, including:

[0024] Determine whether the number of times the operation is performed is equal to the preset quantity;

[0025] If so, the cleaning procedure will be identified as the recommended procedure, and the recommended priority of the cleaning procedure will be set to the highest priority;

[0026] Additionally, it determines whether the current time is during nighttime, and if so, identifies the nighttime program as the recommended program.

[0027] Secondly, embodiments of this application provide a program recommendation method for smart home appliances, applied to smart home appliances, the method comprising:

[0028] Upon receiving a user access request, the user access request is sent to the server;

[0029] The server receives the target recommendation strategy sent by the server and analyzes and processes the target recommendation strategy to obtain a preset number of target recommendation programs and the recommendation frequency corresponding to each target recommendation program. The target recommendation program is determined by the server based on user information, device usage information and environmental information.

[0030] According to the recommended frequency, a preset number of the target recommended programs are displayed in rotation on the display screen.

[0031] Thirdly, embodiments of this application provide a program recommendation device for smart home appliances, applied to a washing machine, comprising:

[0032] The acquisition module is used to respond to a user access request sent by a smart home appliance and acquire user information, device usage information of the smart home appliance, and environmental information corresponding to the user access request.

[0033] The determination module is used to determine multiple recommendation programs and the recommendation priority of each recommendation program based on the user information, the device usage information, and the environmental information.

[0034] The determining module is further configured to determine a target recommendation strategy based on the multiple recommendation programs and their corresponding recommendation priorities;

[0035] A sending module is used to send the target recommendation strategy to the smart home appliance, so that the smart home appliance displays at least one of the recommendation programs on the display screen according to the target recommendation strategy.

[0036] In one possible design, the device further includes: an analysis module;

[0037] The determining module is further configured to determine at least one personalized program that meets a first preset condition from the historical running information. The first preset condition includes: matching the target user identifier and executing more than a preset number of times. The historical running information is used to indicate the running information of multiple programs within a preset time period.

[0038] The determining module is further configured to determine at least one scenario program based on the model information and the environment information, and to use at least one personalized program and at least one scenario program as the recommended program;

[0039] The analysis module is used to perform multi-dimensional data analysis on the user information, the device usage information, and the environmental information to obtain recommendation weight factors for the user information, the device usage information, and the environmental information, respectively.

[0040] The determining module is further configured to determine the recommendation priority of multiple recommendation programs based on the recommendation weight factor.

[0041] In one possible design, the device further includes: a judgment module;

[0042] The acquisition module is also used to acquire the operating program information of all smart home appliances that match the model information within the geographical range corresponding to the location information, and the operating program information is used to indicate the number of times multiple programs are run;

[0043] The determining module is further configured to determine at least one high-frequency scenario program based on the number of times multiple programs are run, wherein the high-frequency scenario program includes: programs that have been run more than a preset number of times, and / or the top N programs with the highest number of runs.

[0044] The judgment module is used to determine whether the weather information corresponds to a clothing processing scenario;

[0045] The determining module is further configured to, if so, determine the program corresponding to the clothing processing scenario as a weather scenario program.

[0046] In one possible design, the determining module is further configured to determine a preset number of target recommending programs from the plurality of recommending programs according to the recommendation priority, and determine the recommendation frequency of each target recommending program, wherein the recommendation frequency is used to indicate the number of times the target recommending program is recommended within the recommendation period;

[0047] The determining module is further configured to determine a preset number of target recommendation programs and their corresponding recommendation frequencies as target recommendation strategies.

[0048] In one possible design, the judgment module is further configured to determine whether the number of operations is equal to a preset quantity;

[0049] The determining module is further configured to, if so, determine the cleaning program as the recommended program and set the recommended priority of the cleaning program to the highest priority;

[0050] Furthermore, the judgment module is also used to determine whether the current time is during the nighttime period;

[0051] The determining module is also used to determine the nighttime program as the recommended program when the current time is nighttime.

[0052] Fourthly, embodiments of this application provide a program recommendation device for smart home appliances, applied to smart home appliances, the device comprising:

[0053] The sending module is used to send the user access request to the server upon receiving the user access request;

[0054] The receiving module is used to receive the target recommendation strategy sent by the server;

[0055] The analysis module is used to analyze and process the target recommendation strategy to obtain a preset number of target recommendation programs and the recommendation frequency corresponding to each target recommendation program. The target recommendation program is determined by the server based on user information, device usage information and environmental information.

[0056] The display module is used to rotate and display a preset number of the target recommendation programs on the display screen according to the recommendation frequency.

[0057] Fifthly, embodiments of this application provide a program recommendation device for smart home appliances, including: a memory and a processor;

[0058] The memory stores computer-executed instructions;

[0059] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect and the second aspect and / or various possible implementations of the second aspect.

[0060] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect, as well as the second aspect and / or various possible implementations of the second aspect.

[0061] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect, as well as the second aspect and / or various possible implementations of the second aspect.

[0062] The smart home appliance recommendation method, apparatus, device, and storage medium provided in this application involve a server responding to a user access request sent by the smart home appliance, obtaining user information, smart home appliance device usage information, and environmental information corresponding to the user access request; determining multiple recommendation programs and their corresponding recommendation priorities based on the user information, device usage information, and environmental information; determining a target recommendation strategy based on the multiple recommendation programs and their corresponding recommendation priorities, and sending the target recommendation strategy to the smart home appliance so that the smart home appliance displays at least one recommendation program on its display screen according to the target recommendation strategy. By integrating user information, device usage information, and environmental information, the limitations of a single data dimension are overcome, and the recommendation programs of the smart home appliance are dynamically adjusted, thereby improving the user experience. Attached Figure Description

[0063] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0064] Figure 1 A schematic diagram of the recommendation interface for the program recommendation method for smart home appliances provided in this application embodiment;

[0065] Figure 2 An interactive schematic diagram of the program recommendation method for smart home appliances provided in the embodiments of this application;

[0066] Figure 3 A flowchart illustrating the program recommendation method for smart home appliances provided in this application embodiment;

[0067] Figure 4 A schematic diagram of the structure of the program recommendation device for smart home appliances provided in the embodiments of this application. Figure 1 ;

[0068] Figure 5 A schematic diagram of the structure of the program recommendation device for smart home appliances provided in the embodiments of this application. Figure 2 ;

[0069] Figure 6 A schematic diagram of the structure of the program recommendation device for smart home appliances provided in the embodiments of this application.

[0070] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0072] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0073] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0074] Currently, the control interface of most smart home devices is displayed on the device's screen. Users can intuitively select the programs or functions they want to run through the screen, and also understand and obtain information such as the device's working status and operation prompts.

[0075] However, the screen size of existing smart home devices is usually limited, which restricts the amount of information that can be displayed, offering only a limited number of programs for users to choose from. Furthermore, traditional screen interfaces use a fixed list of programs, making it impossible to dynamically adjust recommendation priorities based on real-time environmental data.

[0076] Furthermore, existing technologies for recommending programs on smart screens can only recommend programs based on a single data source or rely solely on device type or basic time settings. They lack the fusion analysis of multi-dimensional data (user behavior habits, real-time energy consumption, seasonal characteristics), resulting in biased scenario judgments that cannot adapt to complex environmental changes in actual use scenarios. Consequently, there is a significant discrepancy between the recommended results and actual user needs.

[0077] To address the aforementioned technical issues, this application proposes a program recommendation method for smart home appliances. This method involves a server simultaneously collecting multi-dimensional data sources, including user identity characteristics, device operating parameters, and environmental variables, upon receiving a user access request. Based on this multi-source data, multiple candidate recommendation programs are generated, and their priorities are evaluated. Finally, a hierarchical display strategy is formed, and the optimized recommendation list is pushed to the home appliance terminal for visual presentation. This solves the problems of traditional smart home systems, such as single recommendation dimensions, low interaction efficiency, and insufficient adaptive capabilities.

[0078] Figure 1 This is a schematic diagram of the recommendation interface for the program recommendation method for smart home appliances provided in this application embodiment. For example... Figure 1 As shown, the display interface takes the smart washing machine display interface as an example. (a) can be the display result of the default program interface, and (b) can be the display result of the interface when the personalized program determined according to the user's personal historical usage data is the highest priority.

[0079] The technical solutions of this application and how they solve the aforementioned technical problems are described in detail below with specific embodiments. These specific embodiments may exist independently or in combination with each other. Identical or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] Figure 2 This is an interactive diagram provided for an embodiment of this application. For example... Figure 2 As shown, the method includes:

[0081] S201, Smart home appliances receive user access requests.

[0082] S202, The smart home appliance sends a user access request to the server.

[0083] Users can send access requests to smart home appliances via mobile apps, voice commands, or the appliance's on-screen control interface. Smart home appliances can include, for example, smart washing machines, smart refrigerators, and smart ovens—devices that can be controlled via a network server.

[0084] Understandably, taking a smart washing machine as an example, it can be remotely controlled through a network server. Users can set washing programs, which may include, for example, washing time, number of rinses, and drying time.

[0085] S203. The server responds to the user access request sent by the smart home appliance and obtains the user information, smart home appliance device usage information, and environmental information corresponding to the user access request.

[0086] When a smart home appliance receives a user's access request, it does not execute the request directly but forwards it to the server. This is to ensure the security and controllability of the operation, and also to facilitate the server's management of the user and the smart home appliance. After receiving the user access request forwarded by the smart home appliance, the server can perform a series of processes, such as verifying the user's identity, checking the user's permissions, and determining the legitimacy of the user's request.

[0087] Understandably, in processing user access requests, servers can obtain information related to the user. This information may include the user's identity information, historical operation records, preferences, etc. User information allows the server to better understand the user's needs and provide personalized services.

[0088] Optionally, the server can also obtain device usage information for smart home appliances, such as device operating status, usage time, and maintenance records. In addition, it will obtain information related to the environment in which the smart home appliances are located, such as season, weather conditions, and local peak and off-peak electricity consumption information. This environmental information helps the server more accurately determine the user's current needs and provide more precise services.

[0089] S204. Based on user information, device usage information, and environmental information, the server determines multiple recommendation programs and the recommendation priority of each program.

[0090] The server uses intelligent algorithms (such as machine learning or data analysis) to determine multiple recommendation programs based on user information, device usage information, and environmental information, and assigns a priority to each recommendation program.

[0091] Understandably, based on user information, device usage information, and environmental information, the server can generate user profiles, device status, and device scenario descriptions. Taking a smart washing machine as an example, based on information such as the user's device type, functional preferences, activity cycle, operating habits, device operating efficiency, and energy consumption, the server recommends washing modes, such as quick wash, deep cleaning, and energy-saving mode. Each recommendation program is prioritized based on factors such as time sensitivity (some operations, like energy-saving mode, may have higher priority during specific times), user preferences and historical usage data (if a user prefers a particular function or mode, related recommendation programs may have higher priority), or if the device is located in an area with heavy smog, environmental information may be considered, recommending programs like "deep wash," or in rainy weather, the server may prioritize washing programs with drying functions, considering that clothes are difficult to dry.

[0092] Optionally, taking a smart refrigerator as an example, the server can record its operating status, such as the temperature settings of the refrigerator and freezer compartments, and the number of times the door is opened. If the refrigerator door is frequently opened, it may mean that food is being consumed quickly, and the server will consider recommending restocking lists or reminding you to replenish food in time. Another example is obtaining the weather conditions of the user's area. In hot summers, the refrigerator may cool more frequently, and the server may recommend setting a more reasonable temperature to save energy, or recommend some cold drink preparation programs suitable for summer storage and consumption.

[0093] S205. The server determines the target recommendation strategy based on multiple recommendation programs and their corresponding recommendation priorities.

[0094] The server collects and integrates all relevant user, device, and environmental data. Each recommendation program may be based on different variables (such as the user's historical behavior, the device's current state, environmental changes, etc.). The server analyzes this data to identify the most relevant patterns and trends.

[0095] Understandably, the server can integrate multiple recommenders and their corresponding recommendation priorities. For example, it can classify recommenders according to their functions and characteristics, such as energy saving, efficiency improvement, and user experience optimization, and then sort them according to the priority of each recommender to ensure that higher priority programs have higher weight when recommending.

[0096] Optionally, the server can analyze the user's historical usage data, preference settings, and current environment and device status to understand the user's needs and expectations, and dynamically adjust the target recommendation strategy based on real-time information such as user feedback on recommendations, device usage status, and environmental changes.

[0097] Specifically, server recommendation strategies can be displayed on the smart home appliance screen in the form of display cards. For example, each recommendation strategy and its parameters can be used to generate corresponding program cards. Based on recommendation priority, the recommendation frequency and number of recommendations for each program card within the recommendation period can be determined. Programs with higher priority will have their corresponding cards displayed more frequently. The recommendation effect can be optimized by using methods such as loop playback, random display, or dynamic adjustment based on user behavior.

[0098] In addition, the server can be adapted and optimized for displays of different sizes and resolutions to ensure that program cards and recommendation strategies are compatible with smart home appliances of different models and brands.

[0099] S206. The server sends the target recommendation strategy to the smart home appliances.

[0100] S207. The smart home appliance analyzes and processes the target recommendation strategy to obtain a preset number of target recommendation programs and the recommendation frequency corresponding to each target recommendation program.

[0101] S208: Smart home appliances display a preset number of recommended programs on the display screen according to the recommended frequency.

[0102] In this system, smart home appliances receive target recommendation strategies from a server via a network connection. These strategies can be transmitted in the form of data packets, which may contain information such as the recommender's ID, parameters, and priority. The received strategies are then parsed by the smart home appliance's built-in processing unit (such as a microprocessor or embedded system) to extract detailed information about the recommender.

[0103] Understandably, smart home appliances can display corresponding program cards based on the target recommendation strategy. Within the recommendation period, program cards are prioritized and displayed on the smart home appliance's screen according to the set frequency and number of times. Scrolling playback, paginated display, or animated transitions can be used to enhance the user experience. During interaction, users are given the option to select or confirm the recommendation strategy, allowing them to choose and adjust according to their needs. User feedback on the recommendation strategy is also collected so that the server can continuously optimize the recommendation algorithm and strategy.

[0104] Specifically, taking a smart air conditioner as an example, when the indoor temperature is above 28°C and during the evening peak hours, the content of card one is: the air conditioner automatically adjusts to the energy-saving mode, and the control card generated is "Air Conditioner Energy Saving Program"; the content of card two is: the air conditioner mode is adjusted to "Rapid Cooling Program"; the content of card three is: the air conditioner mode is adjusted to "Silent Sleep Program", etc.

[0105] The smart home appliance program recommendation method provided in this embodiment involves a server responding to a user access request sent by a smart home appliance, obtaining user information, smart home appliance device usage information, and environmental information corresponding to the user access request; based on the user information, device usage information, and environmental information, determining multiple recommendation programs and their corresponding recommendation priorities; determining a target recommendation strategy based on the multiple recommendation programs and their corresponding priorities, and sending the target recommendation strategy to the smart home appliance so that the smart home appliance displays at least one recommendation program on its display screen according to the target recommendation strategy. By integrating user information, device usage information, and environmental information, the method overcomes the limitations of a single data dimension, dynamically adjusts the recommended programs of the smart home appliance, and after the target strategy is sent to the home appliance terminal, the device screen can intuitively display the recommended programs according to priority, allowing users to access the optimal solution with one click, thus greatly improving the user experience.

[0106] Figure 3 The flowchart of the smart home appliance program recommendation method provided in this application embodiment is applied to a server. For example... Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the process of determining the target recommendation strategy according to user information, device usage information, and environmental information in the program recommendation method for smart home appliances is described in detail. The user information includes: target user identifier; the device usage information includes: model information and historical operation information; the method includes:

[0107] S301. Determine at least one personalized program that meets the first preset condition from historical operation information.

[0108] The first preset condition includes: matching the target user identifier and executing the program more than a preset number of times. Historical running information is used to indicate the running information of multiple programs within a preset time period.

[0109] Understandably, historical operation information can be used to obtain the operation status of multiple programs within a preset time period. The preset time period can be the past three months, the past six months, or other periods, which can be determined according to the needs of device data analysis. This application does not impose any restrictions on this. By filtering through the first preset condition, programs that the target user has run and used in the historical operation information and whose number of runs exceeds a certain set threshold within a continuous time period can be identified. Based on this, programs that the user frequently or continuously uses can be identified.

[0110] Optionally, the extracted execution records can be matched with the target user identifier to filter out programs run by that user. For each matched program, the number of times it is executed within a continuous time period can be calculated to determine which programs have exceeded a preset threshold. Historical execution information may include, for example, the execution information of multiple programs within a preset time period (such as the past three months), including when each program was executed, by which user, and how many times it was executed.

[0111] Each user in the smart home appliance system can have a unique identifier, and the system can record which user identifier initiated each operation.

[0112] When determining personalized programs, the first step is to find historical execution information that matches the target user's identifier. After matching the user identifier, the system can determine the number of consecutive runs of the analyzed programs within a preset time period. For example, if the preset number is 5, the system can filter the historical execution information matching the target user's identifier to see the consecutive execution status of each program. If a program is executed consecutively by the target user more than 5 times, it meets the preset condition.

[0113] Taking a smart washing machine as an example, the intelligent control system of a smart washing machine can record user usage data for various programs, i.e., historical operation information. For example, user A's identifier could be "user001". When filtering historical operation information, the system will specifically select all program operation records executed by "user001". If user A has used the "gentle wash" program of the smart washing machine six times consecutively, then the "gentle wash" program meets the condition that the number of consecutive executions exceeds a preset number. At this point, "gentle wash" can be identified as a personalized program corresponding to this target user. After filtering, if the "energy-saving mode" program is executed seven times consecutively in user A's smart appliance historical operation information, then both "gentle wash" and "energy-saving mode" can be identified as personalized programs that meet the first preset condition, and these two can be used as personalized recommendations for user A.

[0114] By filtering out personalized programs that meet certain criteria from historical operational information, we can better understand users' usage habits and preferences, and provide them with more considerate services and recommendations that better meet their needs.

[0115] S302. Based on model information and environmental information, determine at least one scenario program, and recommend at least one personalized program and at least one scenario program as recommended programs.

[0116] The model information may include, for example, device type, version number, and hardware / software settings; environmental information may include, for example, weather conditions, location information, and time information. Based on the model information, the device type and the programs or other available functions that the device can run can be determined. Based on the environmental information, it can be determined which programs or functions are most helpful or relevant to the user in the current environment.

[0117] Understandably, different models of smart home appliances have different functionalities and applicable scenarios. For example, a certain model of smart refrigerator may have a large-capacity freezer compartment and a dedicated storage area for baby food. Based on its model information, the system can determine some suitable scenario programs for this refrigerator, such as a "dedicated storage mode for baby food." In this mode, the refrigerator automatically adjusts the temperature and humidity to better preserve the nutrition and freshness of baby food. Another example is a certain model of smart steam oven equipped with multiple professional baking modes. Based on its model characteristics, the system can determine scenario programs such as "professional bread baking program" and "delicate cake baking program." By analyzing the model information of smart home appliances and understanding their unique functions and design features, the system can more accurately determine the matching scenario programs.

[0118] Optionally, environmental information can include data on temperature, humidity, weather conditions, season, and other factors. Taking a smart air conditioner as an example, when the ambient temperature is high, the system can determine a "rapid cooling mode" as the appropriate scenario program, enabling the air conditioner to quickly lower the indoor temperature and provide a comfortable environment for the user. In humid weather, a "dehumidification mode" will be selected as the appropriate scenario program to help reduce indoor humidity and prevent clothes and furniture from getting damp. For smart washing machines, on rainy days, since clothes are difficult to dry, a "powerful spin-drying + drying mode" may be selected as the appropriate scenario program to ensure that clothes dry quickly. By acquiring and analyzing environmental information in real time, the system can determine the appropriate scenario programs for smart home appliances under different environmental conditions.

[0119] Specifically, for example, if User A's smart washing machine determines "Gentle Wash" as the personalized program based on historical operating information (because User A has used this program multiple times), and also determines "Deep Cleaning Program" as the scene program based on the washing machine's model information (this model of washing machine has excellent deep cleaning capabilities), and further determines "Intensive Spin-Dry + Short-Time Drying Program" as the scene program based on the current humid environment information, then the system will send "Gentle Wash," "Deep Cleaning Program," and "Intensive Spin-Dry + Short-Time Drying Program" as recommended programs to the smart washing machine. The smart washing machine will then display these recommended programs to User A on its display screen for User A to choose from.

[0120] After determining the scenario program based on model and environmental information, the personalized program previously determined based on historical operation information is combined with it. The resulting set of recommended programs takes into account the user's personalized usage habits, as well as the model characteristics of smart home appliances and the current environmental conditions. This provides users with more comprehensive program recommendations that better meet their needs, thereby improving the user experience and satisfaction with smart home appliances.

[0121] In one possible implementation, the environmental information includes location information and weather information. The system obtains the operating program information of all smart home appliances within the geographic range corresponding to the location information and matching the model information. This operating program information indicates the number of times multiple programs are run. Based on the number of times multiple programs are run, at least one high-frequency scene program is identified. The system then determines whether the weather information corresponds to a clothing handling scene. If so, the program corresponding to the clothing handling scene is identified as the weather scene program.

[0122] The system can determine the operating area of ​​smart home appliances based on location information, and also determine the weather conditions for that location. By combining location and weather information, a user's environmental profile can be created. Based on this profile, more accurate recommendations can be made for programs that match the user's environment and that they might need.

[0123] Understandably, the system can obtain the operating program information of all smart home appliances within the geographical range corresponding to the location information and matching the model information. For example, it can include the number of times multiple programs are run, reflecting the user's usage preferences for home appliance programs in different scenarios. Based on the number of times multiple programs are run, the system can identify at least one high-frequency scenario program. The high-frequency scenario program is the one that users select most frequently in daily use, reflecting the user's typical usage scenarios and needs.

[0124] Accordingly, the system can determine whether there is a corresponding clothing processing scenario for the weather information. For example, rainy weather may correspond to a scenario that requires drying clothes, while sunny and dry weather only requires a regular washing scenario. If a corresponding clothing processing scenario exists, the system identifies the program corresponding to that scenario as the weather scenario program.

[0125] Specifically, location information can be obtained through the smart home appliance's built-in positioning module (such as GPS, Beidou, etc.) or through devices such as mobile phones connected to the smart home appliance. A geographical range centered on this location is then defined based on actual needs, such as a radius of 5 kilometers or a specific neighborhood. Within this geographical range, other smart home appliances of the same model as the current smart home appliance are searched and identified via network communication. Through network interconnection, without compromising user privacy, the operating program information of the smart home appliances is obtained. This information records the number of times different programs on each smart home appliance are run. The running frequency data from all the smart home appliances is then organized and analyzed. A threshold for the number of runs can be set based on actual needs. When the number of runs of a program exceeds this threshold, it is considered a high-frequency program. By filtering out programs from a large pool of programs that exceed the threshold, these programs are identified as high-frequency scenario programs.

[0126] Optionally, the system retrieves current weather information, including temperature, humidity, and precipitation probability, from a weather forecast service or database based on location information. The system then determines whether a corresponding clothing handling scenario exists. For example, if the weather is humid and rain is possible, the system might recommend a washing program with a drying function. If a corresponding clothing handling scenario exists, the system identifies the program corresponding to that scenario as the weather scenario program.

[0127] A comprehensive table mapping weather conditions to clothing handling scenarios can be established. Taking a smart washing machine as an example, the scenario mapping information could be as follows: On rainy days, due to high humidity, clothes are difficult to dry, corresponding to a "wash + dry" or "spin + dry" scenario; snowy days are similar to rainy days, also requiring consideration of clothing drying, corresponding to a "powerful spin + dry" scenario; smoggy days, sandstorms, etc., may cause clothes to get dirty easily, corresponding to a "deep cleaning" scenario; in hot weather, clothes are prone to sweating and odor, possibly requiring a "high-temperature disinfection wash" scenario; in cold weather, the warmth of clothing may need to be considered, and for some thick clothes, a "gentle wash + low-temperature dry" scenario may be necessary to avoid damaging the clothing fibers.

[0128] For certain special weather events, such as typhoons, heavy rain, and blizzards, special considerations are needed for clothing handling. During typhoons and heavy rain, outdoor drying may be impossible, and emergency treatment may be necessary, such as rapid dehydration followed by indoor drying to prevent prolonged dampness and bacterial growth. In blizzards, in addition to drying clothes, special care may be needed for heavy garments soaked in snow, such as thawing at low temperatures before gentle washing and drying. Corresponding clothing handling strategies should be developed for these special weather conditions to ensure that users' clothing needs are met under various complex weather conditions.

[0129] Due to differences in geographical environment, users in different regions will have different operating habits. Taking smart washing machines as an example, the air in the north is dry and there is often sandstorm and smog. Users in the north pay more attention to the cleaning of clothes and may prefer the short and efficient "quick wash" program. On the other hand, the air in the south is humid and there is often rainy weather. Users in the south need to dry their clothes more and may prefer the control of the drying program after the cleaning program, such as the "fine wash + deep dry" program.

[0130] Based on the user's location and weather information, combined with the user's usage habits and needs, the system can recommend the most suitable operating program for home appliances.

[0131] S303. Perform multi-dimensional data analysis on user information, equipment usage information, and environmental information to obtain recommendation weight factors for user information, equipment usage information, and environmental information, respectively.

[0132] S304. Determine the recommendation priority of multiple recommendation programs based on the recommendation weight factor.

[0133] Specifically, by analyzing the correlation between user profiles and recommendation programs, the weight of user information in recommendations can be determined; based on device usage patterns, the degree of user dependence on and preferences for devices can be assessed, thereby determining the weight of device usage information in recommendations; and based on environmental scenario models, user needs in different environments can be predicted, and the weight of environmental information in recommendations can be determined accordingly.

[0134] Understandably, the weighting factors of user information, device usage information, and environmental information are comprehensively calculated to obtain the overall weight of each recommendation program. Based on the overall weight, the multiple recommendation programs are prioritized. The higher the weight of a program, the higher its recommendation priority.

[0135] Specifically, for example, the weights of various features in environmental information can be dynamically adjusted by combining seasonal changes and weather warnings. For instance, during a rainstorm warning, the environmental information weight corresponding to the "automatic retraction program" of a smart clothes drying rack would be significantly increased. Alternatively, fault diagnosis data can be combined to analyze the equipment's demand for specific programs under different operating conditions. For example, if a smart washing machine is prone to malfunction after high-load operation, the equipment usage information weight corresponding to the "gentle wash to protect the equipment" program would be increased.

[0136] The recommendation weighting factors are comprehensively calculated based on user information, device usage information, and environmental information. A weighted summation method can be used, assigning different coefficients to each weighting factor according to the importance of different dimensions. For example, assuming the weighting coefficient for user information is 0.4, the weighting coefficient for device usage information is 0.3, and the weighting coefficient for environmental information is 0.3, the comprehensive weight score for each recommendation program under these three dimensions is calculated. Multiple recommendation programs are then ranked according to their comprehensive weight scores, with programs scoring higher having higher recommendation priority.

[0137] By using the above multi-dimensional data analysis and weight calculation methods, we can more accurately determine reasonable recommendation priorities for smart home appliances based on the actual situation of users, devices, and environment, thereby improving user experience and the intelligence level of home appliances.

[0138] In one possible implementation, a recommended program and its corresponding recommendation priority are determined based on device usage information. The method includes: determining whether the number of times the cleaning program is equal to a preset number; if so, determining the cleaning program as the recommended program and setting its recommendation priority to the highest priority; and determining whether the current time is during the nighttime period, and if the current time is during the nighttime period, determining the nighttime program as the recommended program.

[0139] Among them, home appliances can record the actual number of times they are used, and the server can monitor the usage of the equipment. When the number of times the equipment is used reaches or exceeds a preset threshold, the system or algorithm will automatically identify the cleaning program as a recommended program and set its priority to the highest.

[0140] Understandably, a reasonable preset number (i.e., a threshold for the number of cleaning cycles) can be set based on the equipment type, purpose, manufacturer recommendations, or historical data. This preset number reflects when the equipment needs cleaning under normal use. If the number of cleaning cycles equals the preset number, it indicates that the equipment has reached a certain level of use, and cleaning and maintenance are necessary to ensure its normal operation and performance. In this case, the cleaning program is designated as the recommended program. Simultaneously, to encourage users to clean the equipment as soon as possible, the cleaning program's recommendation priority is set to the highest priority. This way, when recommending programs to users, the cleaning program will be displayed first, reminding users to clean the equipment promptly.

[0141] Specifically, for example, for a smart washing machine, the number of cycles refers to the number of times the washing machine performs the washing operation; for a smart oven, the number of cycles refers to the number of times it performs the baking operation. The number of cycles is compared to a preset quantity. The preset quantity is a pre-set value; for example, for a certain smart washing machine, the preset quantity might be set to 50 cycles.

[0142] Determining recommended procedures and their corresponding priorities by using equipment usage information, and setting cleaning procedures as the highest priority, helps maintain the equipment in good condition and improves user satisfaction. By regularly monitoring equipment usage and recommending cleaning procedures at appropriate times, the long-term stable operation of the equipment can be ensured.

[0143] Optionally, the system can determine whether the current time falls within a nighttime period and, if the conditions are met, identify the nighttime program as the recommended program. The nighttime period can be a pre-set time frame, such as from 10 PM to 6 AM, or it can be local peak and off-peak electricity usage time based on location information. The device can obtain the current time information through its built-in clock system and then compare it with the set nighttime period range to make the determination.

[0144] If it is determined that the current time is nighttime, the action of "setting the nighttime program as the recommended program" can be performed. Nighttime programs are specific programs designed for this particular time period. For example, for a smart air conditioner, a nighttime program might automatically adjust to a low fan speed and low noise mode while regulating a suitable temperature, ensuring user sleep comfort while saving energy; for a smart lighting system, a nighttime program might adjust the lights to a soft, warm tone and reduce brightness to avoid strong light stimulation affecting sleep.

[0145] S305. Based on the recommendation priority, determine a preset number of target recommendation programs from multiple recommendation programs, and determine the recommendation frequency of each target recommendation program.

[0146] S306. Determine the preset number of target recommendation programs and their corresponding recommendation frequencies as the target recommendation strategy.

[0147] Among them, multiple recommendation programs and their respective recommendation priorities are determined based on various information (such as user information, device usage information, environmental information, etc.). The recommendation priority can represent the ranking of the program's importance or suitability to the user.

[0148] Understandably, among recommended programs with different priorities, a preset number of programs can be selected in descending order of priority. The preset number is a pre-set specific number or a number of recommended programs determined based on the device display requirements, for example, set to 3. Assuming the server selects 5 recommended programs, with their priorities from highest to lowest as program A, program B, program C, program D, and program E, when the preset number is 3, programs A, B, and C will be selected as the target recommended programs.

[0149] Subsequently, for each selected target recommendation program, its recommendation frequency can be determined. Recommendation frequency refers to the number of times the program is recommended to the user within a certain time period or usage scenario. There are several ways to determine the recommendation frequency. For example, it can be set based on the program's priority; programs with higher priority can have a relatively higher recommendation frequency. Assuming program A has the highest priority, followed by program B, and then program C, and if a recommendation period is 5 minutes and the switching interval is once every 30 seconds, then the recommendation frequency for program A is set to 5 times within the period, for program B to 3 times within the period, and for program C to 2 times within the period. Specifically, the recommendation frequency can also be determined by comprehensively considering factors such as the user's historical usage habits and the device's status, which will not be elaborated upon here.

[0150] The selected target recommendation programs and their corresponding recommendation frequencies are aggregated to form a complete recommendation strategy. This strategy is then sent to smart home appliances, whose processing systems can analyze the strategy and begin executing the recommendation tasks.

[0151] The smart home appliance program recommendation method provided in this application combines environmental perception with user profiling to enable the recommendation system to possess contextual intelligence. It prioritizes programs based on scenario needs to form an optimal push strategy. This hierarchical recommendation strategy effectively reduces the user's selection burden and guides rapid decision-making through priority ranking. It solves the problem that smart screen program recommendations can only recommend programs based on a single data source or rely solely on device type or basic time settings, lacking multi-dimensional data fusion analysis. This leads to biased scenario judgments, an inability to adapt to complex environmental changes in actual use scenarios, and a significant deviation between recommendation results and actual user needs.

[0152] Figure 4 Schematic diagram of the program recommendation device for smart home appliances provided in this application Figure 1 It is used in servers. For example... Figure 4 As shown, the smart home appliance program recommendation device 400 provided in this embodiment includes:

[0153] The acquisition module 401 is used to respond to a user access request sent by a smart home appliance and acquire user information, device usage information of the smart home appliance, and environmental information corresponding to the user access request.

[0154] The determining module 402 is used to determine multiple recommendation programs and the recommendation priority of each recommendation program based on the user information, the device usage information and the environment information;

[0155] The determining module 402 is further configured to determine a target recommendation strategy based on the multiple recommendation programs and their corresponding recommendation priorities;

[0156] The sending module 403 is used to send the target recommendation strategy to the smart home appliance, so that the smart home appliance displays at least one of the recommendation programs on the display screen according to the target recommendation strategy.

[0157] In one possible design, the device further includes: an analysis module 404;

[0158] The determining module 402 is further configured to determine at least one personalized program that meets a first preset condition from the historical running information. The first preset condition includes: matching the target user identifier and having a continuous execution count greater than a preset number. The historical running information is used to indicate the running information of multiple programs within a preset time period.

[0159] The determining module 402 is further configured to determine at least one scenario program based on the model information and the environment information, and to use at least one personalized program and at least one scenario program as the recommended program;

[0160] The analysis module 404 is used to perform multi-dimensional data analysis on the user information, the device usage information, and the environmental information to obtain recommendation weight factors for the user information, the device usage information, and the environmental information, respectively.

[0161] The determining module 402 is further configured to determine the recommendation priority corresponding to multiple recommendation programs based on the recommendation weight factor.

[0162] In one possible design, the device further includes: a determination module 405;

[0163] The acquisition module 401 is further configured to acquire the running program information of all smart home appliances that match the model information within the geographical range corresponding to the location information, and the running program information is used to indicate the number of times multiple programs are run;

[0164] The determining module 402 is further configured to determine at least one high-frequency scenario program based on the number of times multiple programs are run, the high-frequency scenario program including: programs whose number of runs is greater than a preset number, and / or the top N programs with the highest number of runs;

[0165] The judgment module 405 is used to determine whether the weather information corresponds to a clothing processing scenario;

[0166] The determining module 402 is further configured to, if so, determine the program corresponding to the clothing processing scenario as a weather scenario program.

[0167] In one possible design, the determining module 402 is further configured to determine a preset number of target recommending programs from the plurality of recommending programs according to the recommendation priority, and determine the recommendation frequency of each target recommending program, wherein the recommendation frequency is used to indicate the number of times the target recommending program is recommended within the recommendation period;

[0168] The determining module 402 is further configured to determine a preset number of target recommendation programs and their corresponding recommendation frequencies as target recommendation strategies.

[0169] In one possible design, the judgment module 405 is further used to determine whether the number of operations is equal to a preset quantity;

[0170] The determining module 402 is further configured to, if so, determine the cleaning program as the recommended program and set the recommended priority of the cleaning program to the highest priority;

[0171] Furthermore, the judgment module 405 is also used to determine whether the current time is during the nighttime period;

[0172] The determining module 402 is further configured to determine the nighttime program as the recommended program when the current time is during the nighttime period.

[0173] Figure 5 Schematic diagram of the program recommendation device for smart home appliances provided in this application Figure 2 It is applied to smart home appliances. For example... Figure 5 As shown, the smart home appliance program recommendation device 500 provided in this embodiment includes:

[0174] The sending module 501 is used to send the user access request to the server upon receiving the user access request;

[0175] The receiving module 502 is used to receive the target recommendation strategy sent by the server;

[0176] The analysis module 503 is used to analyze and process the target recommendation strategy to obtain a preset number of target recommendation programs and the recommendation frequency corresponding to each target recommendation program. The target recommendation program is determined by the server based on user information, device usage information and environmental information.

[0177] Display module 504 is used to display a preset number of the target recommendation programs on the display screen in rotation according to the recommendation frequency.

[0178] The smart home appliance program recommendation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0179] Figure 6 A schematic diagram of the structure of the program recommendation device for the smart home appliance provided in this application. (Example) Figure 6 As shown, the smart home appliance program recommendation device 600 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 600 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0180] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0181] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0182] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0183] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0184] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0185] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0186] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0187] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0188] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0189] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0190] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0192] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0194] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for recommending programs for smart home appliances, characterized in that, Applied to a server, the method includes: In response to a user access request sent by a smart home appliance, obtain the user information corresponding to the user access request, the device usage information of the smart home appliance, and the environmental information; Based on the user information, the device usage information, and the environmental information, multiple recommendation programs and the recommendation priority of each recommendation program are determined. Based on the multiple recommendation programs and their corresponding recommendation priorities, a target recommendation strategy is determined, and the target recommendation strategy is sent to the smart home appliance so that the smart home appliance displays at least one of the recommendation programs on the display screen in accordance with the target recommendation strategy.

2. The method according to claim 1, characterized in that, The user information includes: a target user identifier; the device usage information includes: model information and historical operation information; and the determination of multiple recommendation programs and corresponding recommendation priorities based on the user information, the device usage information, and the environmental information includes: At least one personalized program that meets a first preset condition is determined from the historical running information. The first preset condition includes: matching the target user identifier and executing more than a preset number of times. The historical running information is used to indicate the running information of multiple programs within a preset time period. Based on the model information and the environmental information, at least one scenario program is determined, and at least one personalized program and at least one scenario program are used as the recommended program; Multidimensional data analysis is performed on the user information, device usage information, and environmental information to obtain recommendation weight factors for the user information, device usage information, and environmental information, respectively. The recommendation priority of each of the recommendation programs is determined based on the recommendation weight factor.

3. The method according to claim 2, characterized in that, The environmental information includes location information and weather information. The process of determining at least one scenario based on the model information and the environmental information includes: Obtain the operating program information of all smart home appliances within the geographical range corresponding to the location information that match the model information, and the operating program information is used to indicate the number of times multiple programs are run; Based on the number of times multiple programs are run, at least one high-frequency scenario program is determined. The high-frequency scenario program includes: programs that have been run more than a preset number of times, and / or the top N programs with the highest number of runs. Determine whether the weather information corresponds to a clothing processing scenario; If so, the program corresponding to the clothing processing scenario will be determined as the weather scenario program.

4. The method according to claim 2, characterized in that, The step of determining the target recommendation strategy based on multiple recommendation programs and their corresponding recommendation priorities includes: Based on the recommendation priority, a preset number of target recommendation programs are determined from the plurality of recommendation programs, and the recommendation frequency of each target recommendation program is determined, wherein the recommendation frequency is used to indicate the number of times the target recommendation program is recommended within the recommendation period; A preset number of target recommendation programs and their corresponding recommendation frequencies are determined as the target recommendation strategy.

5. The method according to claim 2, characterized in that, The device usage information also includes: number of times the device was used and nighttime hours. Based on the device usage information, a recommendation program and corresponding recommendation priority are determined, including: Determine whether the number of times the operation is performed is equal to the preset quantity; If so, the cleaning procedure will be identified as the recommended procedure, and the recommended priority of the cleaning procedure will be set to the highest priority; Additionally, it determines whether the current time is during nighttime, and if so, identifies the nighttime program as the recommended program.

6. A method for recommending programs for smart home appliances, characterized in that, Applied to smart home appliances, the method includes: Upon receiving a user access request, the user access request is sent to the server; The server receives the target recommendation strategy sent by the server and analyzes and processes the target recommendation strategy to obtain a preset number of target recommendation programs and the recommendation frequency corresponding to each target recommendation program. The target recommendation program is determined by the server based on user information, device usage information and environmental information. According to the recommended frequency, a preset number of the target recommended programs are displayed in rotation on the display screen.

7. A program recommendation device for smart home appliances, characterized in that, Applied to servers, including: The acquisition module is used to respond to a user access request sent by a smart home appliance and acquire user information, device usage information of the smart home appliance, and environmental information corresponding to the user access request. The determination module is used to determine multiple recommendation programs and the recommendation priority of each recommendation program based on the user information, the device usage information, and the environmental information. The determining module is further configured to determine a target recommendation strategy based on the multiple recommendation programs and their corresponding recommendation priorities; A sending module is used to send the target recommendation strategy to the smart home appliance, so that the smart home appliance displays at least one of the recommendation programs on the display screen according to the target recommendation strategy.

8. A program recommendation device for smart home appliances, characterized in that, Applied to smart home appliances, including: The sending module is used to send the user access request to the server upon receiving the user access request; The receiving module is used to receive the target recommendation strategy sent by the server; The analysis module is used to analyze and process the target recommendation strategy to obtain a preset number of target recommendation programs and the recommendation frequency corresponding to each target recommendation program. The target recommendation program is determined by the server based on user information, device usage information and environmental information. The display module is used to rotate and display a preset number of the target recommendation programs on the display screen according to the recommendation frequency.

9. A program recommendation device for smart home appliances, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

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