Program generation method, device and equipment of home appliance and medium

Abstract

The application belongs to the technical field of electrical appliances, and particularly relates to a program generation method and device of a household appliance, equipment and medium, comprising: when detecting a replacement binding operation of a user end, acquiring historical use data of a first household appliance; the replacement binding operation is used to indicate the operation of replacing and binding the first household appliance as a second household appliance; according to the historical use data, determining preference information corresponding to the user end; and according to device capability information of the second household appliance and the preference information, generating a user preference program corresponding to the second household appliance. The method can effectively solve the problem that the user needs to reset parameters after replacing a new device, improve the use experience, meet the user's demand for intelligent and convenient household appliances, and enable the user to quickly enjoy the use effect meeting the user's own habits on the new device.

Description

Technical Field

[0001] This application belongs to the field of electrical technology, specifically relating to a method, apparatus, equipment, and medium for generating programs for household appliances. Background Technology

[0002] With the increasing demand for replacing home appliances, users often personalize the settings of their old appliances based on their own habits, creating customized programs. However, when users replace their appliances with new ones, the differences in model and program logic and parameter standards between the old and new devices mean that the personalized settings from the old devices cannot be directly synchronized to the new ones.

[0003] When users replace their devices, they can only use the default, universal software. To adapt the new device to their specific needs, they have to relearn its functions and parameter adjustments. This inconsistency in user experience increases the learning curve and time investment for users, negatively impacting the user experience and failing to meet their demands for smarter and more convenient home appliances. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method, apparatus, device, and medium for generating programs for household appliances.

[0005] In a first aspect, this application provides a method for generating programs for home appliances, the method comprising:

[0006] When a user-side change-binding operation is detected, the historical usage data of the first home appliance is obtained; the change-binding operation is used to indicate the operation of changing the binding of the first home appliance to the second home appliance.

[0007] Based on the historical usage data, determine the preference information corresponding to the user terminal;

[0008] Based on the device capability information of the second home appliance and the preference information, a user preference program corresponding to the second home appliance is generated.

[0009] In one possible implementation, determining the user's preference information based on the historical usage data includes:

[0010] Based on the historical usage data, determine multiple combinations of parameters used by the user in the past;

[0011] Cluster analysis is performed on multiple sets of parameter combinations to obtain classification results; the classification results include multiple categories and at least one set of parameter combinations corresponding to each category;

[0012] Based on the classification results, the corresponding preference information for the user terminal is determined.

[0013] In one possible implementation, each parameter combination includes multiple first parameter items, and determining the user's corresponding preference information based on the classification result includes:

[0014] Based on the classification results, determine the proportion of the number of parameter combinations in each category to the total number of parameter combinations;

[0015] The classification with a proportion greater than a preset proportion is taken as the target classification. The representative value of each first parameter item in the parameter combination corresponding to the target classification is determined to obtain the target parameter combination. The representative value is determined based on the value of the same first parameter item in different parameter combinations in the target classification.

[0016] Determine at least one first intent label corresponding to the target parameter combination;

[0017] The preference information is obtained based on at least one first intent label corresponding to the target parameter combination; the preference information includes at least one target parameter combination and at least one first intent label corresponding to each target parameter combination.

[0018] In one possible implementation, generating a user preference program corresponding to the second home appliance based on the device capability information and the preference information includes:

[0019] Based on the device capability information, obtain multiple preset program templates corresponding to the second home appliance, and a second intent tag corresponding to each program template; each program template includes multiple second parameter items and a numerical range corresponding to each second parameter item;

[0020] For each combination of target parameters in the preference information, a program template corresponding to the combination of target parameters is matched based on the first intent tag and the second intent tag;

[0021] Based on the matched program template, determine whether the representative value of each first parameter item in the target parameter combination is within the numerical range of the corresponding second parameter item in the corresponding program template;

[0022] If all conditions are met, a corresponding user preference program is generated based on the target parameter combination and the corresponding program template.

[0023] In one possible implementation, if the target parameter combination contains a target first parameter whose value is not within the numerical range of the corresponding second parameter, the method further includes:

[0024] For each target first parameter item, determine the endpoint value that is closest to the numerical range of the corresponding second parameter item from the representative value of the target first parameter item;

[0025] The representative value of the first parameter item in the target parameter combination is updated to the endpoint value to obtain the updated target parameter combination;

[0026] Based on the updated target parameter combination and the corresponding program template, a corresponding user preference program is generated.

[0027] In one possible implementation, after generating the user preference program corresponding to the second home appliance, the method further includes:

[0028] Based on the user's historical usage information, determine the historical usage scenarios of the target parameter combination corresponding to the user preference program;

[0029] Based on the historical usage scenarios, determine the usage scenario information corresponding to the user preference program.

[0030] In one possible implementation, the method further includes:

[0031] In response to the user's trigger operation on the control page of the second home appliance, the user's preference program and corresponding usage scenario information are sent to the user's terminal for display on the user's interface.

[0032] In response to the user's selection of a preferred program on the user's device, the user's selected preferred program is stored in the program list of the second home appliance, so that the program list is displayed on the user's interface when the user turns on the second home appliance.

[0033] Secondly, this application provides a program generation apparatus for home appliances, the apparatus comprising:

[0034] The acquisition module is used to acquire historical usage data of the first home appliance when a user terminal changes its binding operation is detected; the change binding operation is used to indicate the operation of changing the binding of the first home appliance to the second home appliance.

[0035] The analysis module is used to determine the user's preference information based on the historical usage data.

[0036] In one possible implementation, the analysis module is specifically used for:

[0037] Based on the historical usage data, determine multiple combinations of parameters used by the user in the past;

[0038] Cluster analysis is performed on multiple sets of parameter combinations to obtain classification results; the classification results include multiple categories and at least one set of parameter combinations corresponding to each category;

[0039] Based on the classification results, the corresponding preference information for the user terminal is determined.

[0040] In one possible implementation, each parameter combination includes multiple first parameter items, and the analysis module is specifically used for:

[0041] Based on the classification results, determine the proportion of the number of parameter combinations in each category to the total number of parameter combinations;

[0042] The classification with a proportion greater than a preset proportion is taken as the target classification. The representative value of each first parameter item in the parameter combination corresponding to the target classification is determined to obtain the target parameter combination. The representative value is determined based on the value of the same first parameter item in different parameter combinations in the target classification.

[0043] Determine at least one first intent label corresponding to the target parameter combination;

[0044] The preference information is obtained based on at least one first intent label corresponding to the target parameter combination; the preference information includes at least one target parameter combination and at least one first intent label corresponding to each target parameter combination.

[0045] The generation module is used to generate a user preference program corresponding to the second home appliance based on the device capability information and the preference information of the second home appliance.

[0046] In one possible implementation, the generation module is specifically used for:

[0047] Based on the device capability information, obtain multiple preset program templates corresponding to the second home appliance, and a second intent tag corresponding to each program template; each program template includes multiple second parameter items and a numerical range corresponding to each second parameter item;

[0048] For each combination of target parameters in the preference information, a program template corresponding to the combination of target parameters is matched based on the first intent tag and the second intent tag;

[0049] Based on the matched program template, determine whether the representative value of each first parameter item in the target parameter combination is within the numerical range of the corresponding second parameter item in the corresponding program template;

[0050] If all conditions are met, a corresponding user preference program is generated based on the target parameter combination and the corresponding program template.

[0051] In one possible implementation, if the target parameter combination contains a target first parameter whose value is not within the numerical range of the corresponding second parameter, the generation module is further configured to:

[0052] For each target first parameter item, determine the endpoint value that is closest to the numerical range of the corresponding second parameter item from the representative value of the target first parameter item;

[0053] The representative value of the first parameter item in the target parameter combination is updated to the endpoint value to obtain the updated target parameter combination;

[0054] Based on the updated target parameter combination and the corresponding program template, a corresponding user preference program is generated.

[0055] In one possible implementation, after generating the user preference program corresponding to the second home appliance, the generation module is further configured to:

[0056] Based on the user's historical usage information, determine the historical usage scenarios of the target parameter combination corresponding to the user preference program;

[0057] Based on the historical usage scenarios, determine the usage scenario information corresponding to the user preference program.

[0058] In one possible implementation, the generation module is also used for:

[0059] In response to the user's trigger operation on the control page of the second home appliance, the user's preference program and corresponding usage scenario information are sent to the user's terminal for display on the user's interface.

[0060] In response to the user's selection of a preferred program on the user's device, the user's selected preferred program is stored in the program list of the second home appliance, so that the program list is displayed on the user's interface when the user turns on the second home appliance.

[0061] Fourthly, this application provides an electronic device, comprising: at least one processor and a memory; wherein,

[0062] The memory stores computer-executed instructions;

[0063] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any of the first aspects.

[0064] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps of the method as described in any of the first aspects.

[0065] The method, apparatus, device, and medium for generating programs for home appliances provided in this application, when detecting a user changing the bound home appliance, first acquires the historical usage data of the old device (the first home appliance) and analyzes this data to determine the user's washing preferences. Then, based on the device capability information of the new device (the second home appliance) (such as the supported parameter range) and the previously determined user preferences, a user preference program adapted to the new device is generated, achieving a smooth transition of personalized settings between different device models. This method effectively solves the problem of users needing to reset parameters after changing to a new device, improves the user experience, meets users' needs for intelligent and convenient home appliances, and allows users to quickly enjoy usage effects that suit their habits on the new device. Attached Figure Description

[0066] 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.

[0067] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0068] Figure 2 The flowchart of the program generation method for home appliances provided in the embodiments of this application Figure 1 ;

[0069] Figure 3 The flowchart of the program generation method for home appliances provided in the embodiments of this application Figure 2 ;

[0070] Figure 4 The flowchart of the program generation method for home appliances provided in the embodiments of this application Figure 3 ;

[0071] Figure 5 A diagram of a program generation device for a home appliance provided in an embodiment of the present invention;

[0072] Figure 6 This is a hardware schematic diagram of an electronic device provided in an embodiment of the present invention.

[0073] The accompanying drawings illustrate 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 particular embodiments. Detailed Implementation

[0074] 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.

[0075] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, 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.

[0076] 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.

[0077] To address the problems in existing technologies, this application provides a method for generating programs for home appliances. When a user changes their linked home appliance, the method first acquires historical usage data from the old appliance (the first appliance) and analyzes this data to determine the user's washing preferences. Then, based on the device capabilities of the new appliance (the second appliance) (such as supported parameter ranges) and the previously determined user preferences, a user preference program adapted to the new appliance is generated, enabling a smooth transition of personalized settings between different appliance models. This method effectively solves the problem of users needing to reset parameters after changing to a new appliance, improves the user experience, meets users' needs for intelligent and convenient home appliances, and allows users to quickly enjoy usage effects that suit their habits on the new appliance.

[0078] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. For example... Figure 1As shown, the first home appliance has several frequently used programs (such as Program 1, Program 2, Program 3, and Program 4), which reflect the user's habits and preferences when using the old appliance. By analyzing these frequently used programs, the user's core washing preferences can be extracted. For example, in a washing machine, the user might prefer combinations like "quick wash + low water level" or "powerful stain removal + high spin speed." When the system detects that the user has switched from the first home appliance to a second one, these user preferences are applied to the new appliance, generating corresponding preference programs (such as User Preference Program 1 and User Preference Program 2). In this way, after switching to the new appliance, the user doesn't need to reset the parameters; the new appliance will automatically adjust the programs according to the user's preferences.

[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] This embodiment provides a method for generating programs for home appliances. Figure 2 The flowchart of the program generation method for home appliances provided in the embodiments of this application Figure 1 The method includes:

[0081] S201. When a user terminal's change-binding operation is detected, historical usage data of the first home appliance is obtained; the change-binding operation is used to indicate the operation of changing the binding of the first home appliance to the second home appliance.

[0082] In this step, after a user replaces their old appliance with a new one, the differences in model and program logic and parameter standards between the old and new devices mean that the personalized parameters of the old device cannot be directly synchronized to the new one. By acquiring historical usage data from the old device, user habits and preferences can be analyzed, providing a foundation for generating personalized programs on the new device that match user habits. The device replacement and binding process refers to the user binding the new second appliance to their account through the corresponding application, official website, or other designated channels, while simultaneously unlinking it from the old first appliance. This can be done by the user entering relevant information about the new device, such as the model number and serial number, to confirm the device replacement and binding; or by pairing via Bluetooth, Wi-Fi, or other wireless networks. Historical usage data includes, but is not limited to, various information generated during the use of the old device, such as usage frequency, duration of each use, number of times different washing modes were selected, set washing temperature, water level, spin speed, and user feedback on the washing effect, all within the context of a washing machine.

[0083] It should be noted that the first and second home appliances refer to the same type of home appliance, that is, home appliances with the same or similar functions and uses. Home appliances of the same type are similar in function and usage scenarios, and users usually replace appliances to obtain better performance or experience while maintaining the same functionality. Home appliances of the same type have similar parameters and mode settings; for example, parameters such as washing time, spin speed, and water level of washing machines are comparable and transferable between different models. For example, home appliances can be connected to the equipment manufacturer's cloud platform. When a user performs a replacement binding operation, the cloud server receives the request and automatically retrieves the historical usage data of the user's old device stored in the cloud.

[0084] S202. Based on the historical usage data, determine the preference information corresponding to the user terminal.

[0085] In this step, historical usage data includes users' operations and selections of home appliances in different scenarios. By analyzing this data, user preferences during use can be identified. For example, in the case of washing machines, preferences regarding washing modes, times, and temperatures can be analyzed to provide users with services that better meet their personalized needs. Only by accurately determining user preference information can a matching user preference program be generated on new devices, enabling the migration and continuation of personalized settings. Preference information refers to users' likes and tendencies towards specific functions, parameters, modes, etc., when using home appliances. For example, in the case of washing machines, users may be accustomed to using the quick wash mode, prefer higher washing water temperatures, and choose gentle washes for cotton and linen fabrics.

[0086] For example, user preferences can be determined by pre-setting corresponding rules. For instance, in a washing machine scenario, if a user chooses the quick wash mode most of the time and the wash time is short, it can be determined that the user prefers quick washing; if a user repeatedly sets the water level to low when washing cotton and linen clothes, it can be determined that the user prefers low water level washing for cotton and linen clothes. Furthermore, machine learning algorithms such as classification and clustering can be used to train and learn from historical usage data to build user preference models. For example, in a washing machine scenario, decision tree algorithms can be used to classify user washing mode selections, water temperature settings, and other data to predict user preferences in different situations; clustering algorithms can be used to group users' historical usage data to identify user groups with similar usage patterns, thereby determining the individual user's preference tendencies. In addition, deep learning models, such as neural networks, can be built to learn and analyze historical usage data more deeply. Deep learning models can automatically extract features and patterns from data, discovering complex relationships hidden behind the data, and can more accurately determine user preference information for large-scale, high-dimensional historical usage data.

[0087] For example, determining the user's preference information based on the historical usage data includes:

[0088] Based on the historical usage data, determine multiple combinations of parameters used by the user in the past;

[0089] Cluster analysis is performed on multiple sets of parameter combinations to obtain classification results; the classification results include multiple categories and at least one set of parameter combinations corresponding to each category;

[0090] Based on the classification results, the corresponding preference information for the user terminal is determined.

[0091] In the example above, multiple parameter combinations set by the user during past use are extracted from the historical usage data of the first home appliance. For example, in a washing machine, the specific values ​​of parameters such as washing time, spin speed, and water level set by the user each time a wash is performed. These parameter combinations directly reflect the user's personalized operating habits in different washing scenarios. Cluster analysis algorithms are then used to process these multiple parameter combinations. Cluster analysis is an unsupervised learning method that can automatically divide data into different categories based on the similarity or difference of the data. In this example, based on the characteristics of the parameter combinations, such as the washing time and spin speed in a washing machine, the user's historical usage can be divided into several categories. Each category corresponds to a parameter combination with similar characteristics, thus more clearly showing the patterns and regularities of user usage habits. Based on the classification results, user preference information can be further determined. For example, if a certain category of parameter combinations occurs frequently, and the parameter settings tend to be longer washing times and higher spin speeds, then it can be determined that the user prefers a strong and thorough washing mode.

[0092] The above example uses cluster analysis to determine user preference information, enabling in-depth analysis of users' historical usage data and accurate acquisition of users' personalized habits when using home appliances.

[0093] S203. Generate a user preference program corresponding to the second home appliance based on the device capability information and the preference information.

[0094] In this step, because different models of home appliances differ in function, performance, and technical parameters, directly applying the parameters of the old device to the new device may not be suitable or may not achieve optimal results. By combining the device capability information of the new device with the user's preference information, a user preference program customized for the new device can be generated, ensuring that the user obtains a personalized user experience on the new device that is similar to or even better than the old device. In this way, the new device can automatically adjust and optimize its operating parameters according to the user's preferences, without requiring the user to manually reset them.

[0095] Equipment capability information refers to information describing the performance, functions, and technical parameters of the second household appliance. This information reflects the functions and operating range that the new equipment can achieve. User preference programs are a set of program instructions or setting parameters generated based on user preferences and the equipment capability information of the new equipment. Users can directly select and use these instructions to control the equipment to run the program parameters.

[0096] For example, a mapping relationship can be established between the parameters of the old and new equipment. Based on user preferences and the capabilities of the new equipment, the parameters of the old equipment can be converted to those suitable for the new equipment. For instance, in a washing machine, if the maximum spin speed of the old equipment is 1200 rpm, while the maximum spin speed of the new equipment is 1400 rpm, and the user prefers a higher spin speed, then the spin speed of the new equipment can be set to around 1300 rpm to match the user's preference and the capabilities of the new equipment. Alternatively, optimization algorithms, such as genetic algorithms or particle swarm optimization, can be used to find the optimal combination of parameters within the capabilities of the new equipment to maximize the satisfaction of user preferences. For example, in a washing machine, a genetic algorithm can be used to optimize the combination of multiple parameters such as washing time, spin speed, and water temperature to find parameter settings that satisfy user preferences while ensuring washing performance, and a corresponding user preference program can be generated.

[0097] The appliance program generation method provided in this embodiment, when detecting a user changing their bound appliance, first acquires the historical usage data of the old appliance (the first appliance) and analyzes this data to determine the user's washing preferences. Then, based on the device capability information of the new appliance (the second appliance) (such as the supported parameter range) and the previously determined user preferences, a user preference program adapted to the new appliance is generated, achieving a smooth transition of personalized settings between different appliance models. This method effectively solves the problem of users needing to reset parameters after changing to a new appliance, improves the user experience, meets users' needs for intelligent and convenient appliances, and allows users to quickly enjoy usage effects that suit their habits on the new appliance.

[0098] This embodiment provides a method for generating programs for home appliances. Figure 3 The flowchart of the program generation method for home appliances provided in the embodiments of this application Figure 2 .like Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the process of determining the preference information corresponding to the user terminal is described in detail. Each parameter combination includes multiple first parameter items, and the method includes:

[0099] S301. Perform cluster analysis on the multiple sets of parameter combinations used by the user in the past to obtain the classification results.

[0100] This step has been explained in the foregoing embodiments and will not be repeated here.

[0101] S302. Based on the classification results, determine the proportion of the number of parameter combinations for each category to the total number of parameter combinations.

[0102] S303. Take the category with a proportion greater than a preset proportion as the target category, determine the representative value of each first parameter item in the parameter combination corresponding to the target category, and obtain the target parameter combination.

[0103] The representative value is determined based on the value of the same first parameter item in different parameter combinations in the target classification.

[0104] It should be noted that after obtaining the classification results through cluster analysis, it is necessary to further understand the relative importance or frequency of each category in the entire user usage history. Calculating the proportion of parameter combinations in each category to the total number of parameter combinations can quantitatively reflect the prevalence of that category in user habits. The classification results refer to the multiple categories obtained from cluster analysis, and the specific parameter combination data points contained in each category. For each category, the ratio of the number of parameter combinations in that category to the total number of parameter combinations in all categories is calculated to obtain the proportion of that category in overall usage. For example, the parameter combination data after all categories can be traversed, the number of parameter combinations in each category can be counted, and then divided by the total number of parameter combinations to obtain the proportion of each category. When the parameter combination data is stored in a database, a query statement can be written to use aggregate functions to count the number of parameter combinations in each category, and then combined with the formula for calculating the proportion to obtain the result.

[0105] Typically, users' frequent usage patterns will constitute a large proportion of the data, while categories used less frequently may only represent occasional special needs or experiments. By setting a preset proportion threshold, categories with a proportion greater than that threshold can be filtered out as target categories, allowing us to focus on users' main and typical usage habits and filter out some unrepresentative data.

[0106] It's important to note that each parameter combination within the same target category typically shares the same first parameter, but their specific values ​​can differ. This is because different parameter combinations within the same category may correspond to user usage at different times or under different circumstances, and the differences might stem from minor adjustments users might make based on actual situations when using the same function. Determining a representative value for each first parameter in a target category helps identify the most representative target parameter combinations for that category. A preset percentage can be set based on experience or business needs to differentiate the importance and representativeness of categories. For example, a preset percentage of 10% could be used, meaning only categories with parameter combinations accounting for more than 10% of the total number of parameters would be retained as target categories.

[0107] For example, the average of all values ​​for each parameter in the target category can be calculated, and this average can be used as the representative value for that parameter. This method is suitable when the parameter values ​​are approximately normally distributed, reflecting the central trend of the data. Alternatively, the values ​​of each parameter in the target category can be sorted, and the median value can be used as the representative value. The median is not affected by extreme values ​​and is more suitable for situations where there are outliers or skewed distributions in the data. Furthermore, the most frequently occurring value for each parameter in the target category can be identified and used as the representative value.

[0108] S304. Determine at least one first intent label corresponding to the target parameter combination.

[0109] In this step, the parameter combinations themselves only reflect some specific settings when the user uses the device, but these settings represent a certain user intent or purpose. By determining at least one primary intent label for each target parameter combination, the user's behavior can be linked to their substantive needs or intentions, making user preference information more semantic, easier to understand, and easier to apply. For example, when using a washing machine, a user setting a higher water temperature, a longer washing time, and a higher water level may be intending to deep clean clothes. A primary intent label refers to a marker or tag used to describe the user's intent or purpose associated with the target parameter combination. Intent labels can be phrases or words with clear semantics, such as "quick wash," "deep clean," or "energy-saving mode."

[0110] For example, association rules can be predefined to associate specific parameter combinations with corresponding intent labels. For instance, in a washing machine, if the target parameter combination includes a short washing time and a low spin speed, it matches the intent label "quick wash"; if the water temperature is high, the water level is high, and the washing time is long, it matches the intent label "deep clean". Alternatively, historical parameter combination data already labeled with intent labels can be used as a training set to train a machine learning classification model (such as a decision tree, support vector machine, or neural network). Then, the trained model can be used to predict new target parameter combinations to obtain their corresponding first intent label.

[0111] S305. Based on at least one first intent label corresponding to the target parameter combination, the preference information is obtained.

[0112] The preference information includes at least one combination of target parameters and at least one first intent label corresponding to each combination of target parameters.

[0113] In this step, based on the determined target parameter combinations and their corresponding first intent tags, this information can be integrated to form complete user preference information. This user preference information includes both specific technical parameter settings and reflects the user's actual usage intent, providing a comprehensive, accurate, and semantic basis for generating personalized programs that match user preferences on new devices. For example, each target parameter combination can be directly associated with at least one corresponding first intent tag to form a list or set as the user's preference information. If a target parameter combination corresponds to multiple intent tags, a weight can be assigned to each intent tag. The weight can be determined based on the relevance or frequency of occurrence of the intent tag with the target parameter combination. Then, the weighted intent tags and the target parameter combinations together constitute the preference information.

[0114] The method for generating programs for home appliances provided in this embodiment processes multiple sets of parameter combinations used in the user's history to determine the proportion of parameter combinations in each category. It then filters out target categories with a proportion greater than a preset proportion, determines the representative value of each first parameter item in its parameter combinations and at least one corresponding first intent tag, and finally obtains preference information containing the target parameter combinations and intent tags. This information is used to generate user preference programs on new devices. This method can accurately grasp the user's main usage preferences, enabling new devices to quickly and accurately provide users with efficient and personalized services that match their usage habits, improving user experience and reducing the tedious operation of resetting parameters.

[0115] This embodiment provides a method for generating programs for home appliances. Figure 4 The flowchart of the program generation method for home appliances provided in the embodiments of this application Figure 3 .like Figure 4 As shown, in this embodiment... Figure 3 Based on the embodiments, the process of generating the user preference program corresponding to the second home appliance is described in detail. The method includes:

[0116] S401. Based on the device capability information, obtain multiple preset program templates corresponding to the second home appliance, and a second intent tag corresponding to each program template; each program template includes multiple second parameter items and a numerical range corresponding to each second parameter item.

[0117] In this step, generating a program for the new device that matches user preferences requires understanding the new device's functions and usage modes. By obtaining the preset program template of the second home appliance and its corresponding second intent tag, it is possible to determine what types of washing modes the device can provide (such as quick wash, standard wash, deep wash, etc.) and the approximate range of parameters for each mode. This provides a matching object and basic framework for accurately migrating the user's personalized preferences from the old device to the new device.

[0118] A program template is essentially a pre-defined program framework for a second home appliance, defining a specific usage mode. Each program template contains multiple parameter items and their corresponding numerical ranges. For example, in a washing machine's "Quick Wash" program template, parameters might include washing time (range, e.g., 15-30 minutes), spin speed (range, e.g., 800-1200 rpm), and water level (range, e.g., low-medium). Program templates provide standardized operating modes for the device. A second intent label is a brief description of the user intent corresponding to the program template. It can be presented in text form and reflects the user's intended purpose or need that the program template aims to fulfill.

[0119] S402. For each combination of target parameters in the preference information, match the program template corresponding to the combination of target parameters based on the first intent tag and the second intent tag.

[0120] In this step, to accurately map user preferences to a specific program template on the new device and achieve a smooth transition to personalized settings, matching based on intent tags is necessary. Intent tags are essentially semantic descriptions of a usage pattern. This method identifies similarities between the old and new devices in terms of functionality and purpose, providing a suitable base template for generating user preference programs. For example, the text content of the first and second intent tags can be directly compared; if they are completely identical, a match is considered successful. When tags have some differences in expression, natural language processing techniques can be used to calculate their similarity. If the similarity exceeds a certain threshold (e.g., 0.8), a match is deemed successful.

[0121] For example, the first intent tag and the second intent tag can be generated using the same method, or the target parameter combination / program template can be matched with the corresponding intent tag based on the same mapping logic, thereby ensuring that the tags are consistent in semantics, format and granularity. During matching, the first intent tag and the second intent tag can be compared and associated more easily, improving the success rate and efficiency of matching.

[0122] S403. Based on the matched program template, determine whether the representative value of each first parameter item in the target parameter combination is within the numerical range of the corresponding second parameter item in the corresponding program template.

[0123] S404. If so, then based on the target parameter combination and the corresponding program template, generate the corresponding user preference program.

[0124] It's important to note that after determining a program template that aligns with the user's preferences, it's necessary to verify whether the parameters in the preference information are suitable for direct application to that template. Because new and old devices may differ in specific parameter ranges, the program template can only function correctly according to the user's preferences if each representative value in the target parameter combination falls within the numerical range of the corresponding program template. In this case, the user preference program can be directly generated based on the original parameters. For example, we can sequentially check whether the representative value of each parameter item in the target parameter combination falls within the numerical range of the corresponding program template parameter item. If all values ​​match, these representative values ​​are directly assigned to the parameter items of the corresponding program template to generate the user preference program.

[0125] S405. If not, for each target first parameter item whose representative value is not within the numerical range of the corresponding second parameter item, determine the endpoint value of the target first parameter item whose representative value is closest to the numerical range of the corresponding second parameter item.

[0126] S406. Update the representative value of the first parameter item in the target parameter combination to the endpoint value to obtain the updated target parameter combination.

[0127] S407. Based on the updated target parameter combination and the corresponding program template, generate the corresponding user preference program.

[0128] It should be noted that if the representative value of a certain first parameter in the target parameter combination exceeds the numerical range of that parameter in the corresponding program template, it may be because the user's preference settings on the old device exceed the capability range of the corresponding program template on the new device, or there are differences in parameter ranges between the old and new devices. In this case, direct application may cause the device to malfunction or fail to achieve the user's expected usage effect. Therefore, by finding the endpoint value closest to the corresponding numerical range for the out-of-range representative value and updating the representative value to that endpoint value, the parameters can be adapted to the actual capabilities or parameter logic of the new device while closely aligning with the user's preference intentions. This allows for the generation of an executable user preference program based on the updated parameter combination, achieving approximate migration of user preferences. For example, in a washing machine, the spin speed range of the program template is 800–1200 RPM, while the representative spin speed on the user's old device is 1300 RPM. Therefore, the endpoint value closest to 1300 RPM is 1200 RPM.

[0129] After determining the nearest endpoint value for each target's first parameter item in the target parameter combination, the original representative value of each target's first parameter item is replaced to obtain the updated target parameter combination. The updated parameter combination and the corresponding program template can be directly assigned to the parameter items of the corresponding program template, thereby generating a program that both meets the operating requirements of the new device and best reflects user preferences.

[0130] It's important to note that the target parameter combination reflects the user's usage habits and preferences on older devices. The program template, on the other hand, is a pre-set program framework for new devices. Due to device upgrades, feature expansions, and other reasons, it may contain parameters not covered in the corresponding target parameter combination, to accommodate richer functionality and more complex usage scenarios. For these additional parameters, specific values ​​can be selected based on the user's actual usage scenario or preset conditions.

[0131] For example, after generating the user preference program corresponding to the second home appliance, it further includes:

[0132] Based on the user's historical usage information, determine the historical usage scenarios of the target parameter combination corresponding to the user preference program;

[0133] Based on the historical usage scenarios, determine the usage scenario information corresponding to the user preference program.

[0134] In the above example, historical usage scenarios refer to the specific environmental information or conditions when a user previously used a program or similar program with the target parameter combination. For example, in a washing machine, historical usage scenarios might include usage time, frequency of use, type of clothing, season, water quality, and water temperature. By analyzing users' historical usage information, we can determine in what scenarios the target parameter combination corresponding to the user's preferred program was used, thereby assigning more accurate usage scenario information to the user's preferred program. Based on the analysis results of historical usage scenarios, cluster analysis, classification algorithms, and other methods can be used to summarize and generalize historical usage scenarios, generating usage scenario information. Usage scenario information can be a concise and clear semantic description, such as "nighttime large item washing mode," "quick wash for daily office clothes," and "deep cleaning for seasonal clothes" in the context of a washing machine, to facilitate user understanding and intelligent matching with the device.

[0135] The examples above help users more intuitively understand and select user-preferred programs suitable for their current situation, saving them time in selection and setup and improving the user experience. At the same time, they also provide a valid reference for the intelligent operation of devices.

[0136] For example, the method further includes:

[0137] In response to the user's trigger operation on the control page of the second home appliance, the user's preference program and corresponding usage scenario information are sent to the user's terminal for display on the user's interface.

[0138] In response to the user's selection of a preferred program on the user's device, the user's selected preferred program is stored in the program list of the second home appliance, so that the program list is displayed on the user's interface when the user turns on the second home appliance.

[0139] In the example above, when the user triggers the control page of the second home appliance, the user's preferred programs and their corresponding usage scenario information are sent to the user's terminal for display. These preferred programs and scenario information can be displayed in a list, icon, or other way, allowing the user to intuitively understand and select programs that meet their current needs. Furthermore, after the user selects a preferred program, it is stored in the device's program list for easy access and use when the user turns on the device later.

[0140] The above examples enable users to quickly find and apply programs that match their habits and current scenarios without having to manually reset parameters, saving users' time and improving ease of use.

[0141] The method for generating programs for home appliances provided in this embodiment obtains a program template and a second intent tag for a second home appliance, matches them with target parameter combinations in user preference information, and verifies parameter compatibility to generate a user preference program. This method improves the ease of operation for users of new devices, eliminating the need for tedious parameter settings; the verification and adaptation of parameter ranges ensures the effectiveness and usability of the user preference program on new devices, thereby significantly enhancing the user experience.

[0142] This embodiment also provides a program generation device for home appliances. Figure 5 A diagram of a program generation device for a home appliance provided in an embodiment of the present invention is shown, such as... Figure 5 As shown, the program generation apparatus 50 includes:

[0143] The acquisition module 501 is used to acquire historical usage data of the first home appliance when a change-binding operation is detected on the user terminal; the change-binding operation is used to indicate the operation of changing the binding of the first home appliance to the second home appliance.

[0144] Analysis module 502 is used to determine the preference information corresponding to the user terminal based on the historical usage data;

[0145] In one possible implementation, the analysis module 502 is specifically used for:

[0146] Based on the historical usage data, determine multiple combinations of parameters used by the user in the past;

[0147] Cluster analysis is performed on multiple sets of parameter combinations to obtain classification results; the classification results include multiple categories and at least one set of parameter combinations corresponding to each category;

[0148] Based on the classification results, the corresponding preference information for the user terminal is determined.

[0149] In one possible implementation, each parameter combination includes multiple first parameter items, and the analysis module 502 is specifically used for:

[0150] Based on the classification results, determine the proportion of the number of parameter combinations in each category to the total number of parameter combinations;

[0151] The classification with a proportion greater than a preset proportion is taken as the target classification. The representative value of each first parameter item in the parameter combination corresponding to the target classification is determined to obtain the target parameter combination. The representative value is determined based on the value of the same first parameter item in different parameter combinations in the target classification.

[0152] Determine at least one first intent label corresponding to the target parameter combination;

[0153] The preference information is obtained based on at least one first intent label corresponding to the target parameter combination; the preference information includes at least one target parameter combination and at least one first intent label corresponding to each target parameter combination.

[0154] The generation module 503 is used to generate a user preference program corresponding to the second home appliance based on the device capability information and the preference information of the second home appliance.

[0155] In one possible implementation, the generation module 503 is specifically used for:

[0156] Based on the device capability information, obtain multiple preset program templates corresponding to the second home appliance, and a second intent tag corresponding to each program template; each program template includes multiple second parameter items and a numerical range corresponding to each second parameter item;

[0157] For each combination of target parameters in the preference information, a program template corresponding to the combination of target parameters is matched based on the first intent tag and the second intent tag;

[0158] Based on the matched program template, determine whether the representative value of each first parameter item in the target parameter combination is within the numerical range of the corresponding second parameter item in the corresponding program template;

[0159] If all conditions are met, a corresponding user preference program is generated based on the target parameter combination and the corresponding program template.

[0160] In one possible implementation, if the target parameter combination contains a target first parameter whose representative value is not within the numerical range of the corresponding second parameter, the generation module 503 is further configured to:

[0161] For each target first parameter item, determine the endpoint value that is closest to the numerical range of the corresponding second parameter item from the representative value of the target first parameter item;

[0162] The representative value of the first parameter item in the target parameter combination is updated to the endpoint value to obtain the updated target parameter combination;

[0163] Based on the updated target parameter combination and the corresponding program template, a corresponding user preference program is generated.

[0164] In one possible implementation, after generating the user preference program corresponding to the second home appliance, the generation module 503 is further configured to:

[0165] Based on the user's historical usage information, determine the historical usage scenarios of the target parameter combination corresponding to the user preference program;

[0166] Based on the historical usage scenarios, determine the usage scenario information corresponding to the user preference program.

[0167] In one possible implementation, the generation module 503 is also used for:

[0168] In response to the user's trigger operation on the control page of the second home appliance, the user's preference program and corresponding usage scenario information are sent to the user's terminal for display on the user's interface.

[0169] In response to the user's selection of a preferred program on the user's device, the user's selected preferred program is stored in the program list of the second home appliance, so that the program list is displayed on the user's interface when the user turns on the second home appliance.

[0170] The apparatus provided in this embodiment can execute the CAD model generation method based on a large model provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0171] Figure 6 This is a hardware schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 6 As shown, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. The device 60 also includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0172] In a specific implementation, at least one processor 601 executes computer execution instructions stored in the memory 602, causing at least one processor 601 to perform the above dehydration method.

[0173] 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.

[0174] In the above Figure 6 In the illustrated 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.

[0175] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0176] 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.

[0177] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method described above.

[0178] 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.

[0179] 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.

[0180] The division of units described herein 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.

[0181] 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.

[0182] 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.

[0183] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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 described in 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.

[0184] 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.

[0185] 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 generating a program for a home appliance, characterized in that, The method includes: When a user-side change-binding operation is detected, the historical usage data of the first home appliance is obtained; the change-binding operation is used to indicate the operation of changing the binding of the first home appliance to the second home appliance. Based on the historical usage data, determine the preference information corresponding to the user terminal; Based on the device capability information of the second home appliance and the preference information, a user preference program corresponding to the second home appliance is generated.

2. The method according to claim 1, characterized in that, The step of determining the user's preference information based on the historical usage data includes: Based on the historical usage data, determine multiple combinations of parameters used by the user in the past; Cluster analysis is performed on multiple sets of parameter combinations to obtain classification results; the classification results include multiple categories and at least one set of parameter combinations corresponding to each category; Based on the classification results, the corresponding preference information for the user terminal is determined.

3. The method according to claim 2, characterized in that, Each parameter combination includes multiple first parameter items, and determining the user's corresponding preference information based on the classification result includes: Based on the classification results, determine the proportion of the number of parameter combinations in each category to the total number of parameter combinations; The classification with a proportion greater than a preset proportion is taken as the target classification. The representative value of each first parameter item in the parameter combination corresponding to the target classification is determined to obtain the target parameter combination. The representative value is determined based on the value of the same first parameter item in different parameter combinations in the target classification. Determine at least one first intent label corresponding to the target parameter combination; The preference information is obtained based on at least one first intent label corresponding to the target parameter combination; the preference information includes at least one target parameter combination and at least one first intent label corresponding to each target parameter combination.

4. The method according to claim 3, characterized in that, The step of generating a user preference program corresponding to the second home appliance based on the device capability information and the preference information includes: Based on the device capability information, obtain multiple preset program templates corresponding to the second home appliance, and a second intent tag corresponding to each program template; each program template includes multiple second parameter items and a numerical range corresponding to each second parameter item; For each combination of target parameters in the preference information, a program template corresponding to the combination of target parameters is matched based on the first intent tag and the second intent tag; Based on the matched program template, determine whether the representative value of each first parameter item in the target parameter combination is within the numerical range of the corresponding second parameter item in the corresponding program template; If all conditions are met, a corresponding user preference program is generated based on the target parameter combination and the corresponding program template.

5. The method according to claim 4, characterized in that, If, in the target parameter combination, there exists a target first parameter whose representative value is not within the numerical range of the corresponding second parameter item, the method further includes: For each target first parameter item, determine the endpoint value that is closest to the numerical range of the corresponding second parameter item from the representative value of the target first parameter item; The representative value of the first parameter item in the target parameter combination is updated to the endpoint value to obtain the updated target parameter combination; Based on the updated target parameter combination and the corresponding program template, a corresponding user preference program is generated.

6. The method according to claim 5, characterized in that, After generating the user preference program corresponding to the second home appliance, it also includes: Based on the user's historical usage information, determine the historical usage scenarios of the target parameter combination corresponding to the user preference program; Based on the historical usage scenarios, determine the usage scenario information corresponding to the user preference program.

7. The method according to claim 6, characterized in that, The method further includes: In response to the user's trigger operation on the control page of the second home appliance, the user's preference program and corresponding usage scenario information are sent to the user's terminal for display on the user's interface. In response to the user's selection of a preferred program on the user's device, the user's selected preferred program is stored in the program list of the second home appliance, so that the program list is displayed on the user's interface when the user turns on the second home appliance.

8. A program generation device for household appliances, characterized in that, The device includes: The acquisition module is used to acquire historical usage data of the first home appliance when a user terminal changes its binding operation is detected; the change binding operation is used to indicate the operation of changing the binding of the first home appliance to the second home appliance. The analysis module is used to determine the user's preference information based on the historical usage data. The generation module is used to generate a user preference program corresponding to the second home appliance based on the device capability information and the preference information of the second home appliance.

9. 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 1-7.

10. An electronic device, characterized in that, include: At least one processor and memory; wherein, The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any one of claims 1-7.

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