Home appliance control method and device based on large language model, and home decision system

Abstract

The application relates to the technical field of intelligent household appliances, and discloses a household appliance control method based on a large language model, which is applied to a virtual smart home decision system. The method comprises the following steps: determining a change amount of virtual indoor environment data according to an external environment influence function for influencing the virtual indoor environment data or a household appliance device influence function for influencing the virtual indoor environment data; and adjusting the operation parameters and the operation state of an actual household appliance device according to the change amount of the virtual indoor environment data, so as to adjust actual indoor environment data. When the household appliance device is virtualized, the influence of outdoor environment data on indoor environment data is considered, the virtual indoor environment data is consistent with the actual indoor environment data, and therefore the accuracy of the adjustment of the actual household appliance device on the actual indoor environment data is improved. The application further discloses a household appliance control device based on a large language model, a virtual smart home decision system and a computer readable storage medium.

Description

Technical Field

[0001] This application relates to the field of smart home appliance technology, such as a home appliance control method and device based on a large language model, a virtual smart home decision-making system, and a computer-readable storage medium. Background Technology

[0002] Currently, with the advancement of artificial intelligence technology, people's demand for intelligent home appliances is becoming increasingly strong.

[0003] The related technology discloses a method for controlling home appliances, including: real-time acquisition of actual operating state parameters of multiple home appliances to construct a virtual digital image; using the virtual digital image as a reinforcement learning environment, and using a digital twin constructed from the mapping of home appliances as an agent, training a reinforcement learning model to obtain a personalized reinforcement learning model and the virtual operating state parameters of its agent; controlling the home appliances corresponding to the agent whose virtual operating state parameters have changed to adjust their actual operating state parameters, so that the actual operating state parameters are updated to the corresponding virtual operating state parameters; wherein, there is a correlation between the virtual operating state parameters of different agents, and the actual operating state parameters and the virtual operating state parameters are parallel data.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] When virtualizing home appliances, related technologies only consider indoor environmental data and not outdoor environmental data, resulting in a mismatch between virtual and actual indoor environmental data. Consequently, the actual home appliances do not accurately adjust to the actual indoor environmental data.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0008] This disclosure provides a home appliance control method and device based on a large language model, a virtual smart home decision system, and a computer-readable storage medium. When virtualizing home appliances, the method takes into account the impact of outdoor environmental data on indoor environmental data, making the virtual indoor environmental data consistent with the actual indoor environmental data, thereby improving the accuracy of the actual home appliances' adjustment of the actual indoor environmental data.

[0009] In some embodiments, applied to a virtual smart home decision-making system, the method includes: determining the amount of change in virtual indoor environment data based on an external environment influence function that affects virtual indoor environment data, or an appliance influence function that affects virtual indoor environment data; and adjusting the operating parameters and operating status of actual appliances according to the amount of change in virtual indoor environment data to adjust the actual indoor environment data.

[0010] It should be noted that the external environment influence function represents the impact of adjusting the virtual indoor environment data. The home appliance influence function represents the impact of adjusting the virtual home appliance attribute data on the virtual indoor environment data.

[0011] Optionally, the change in virtual indoor environmental data is determined based on the external environmental influence function used to influence the virtual indoor environmental data. This includes: using virtual outdoor environmental data, current virtual indoor environmental data, and virtual time interval as variables, and the next virtual indoor environmental data or the change in virtual indoor environmental data as the controlled variable, to obtain the external influence function; when the controlled variable of the external influence function is the next virtual indoor environmental data, the difference between the next virtual indoor environmental data and the current virtual indoor environmental data is determined as the change in virtual indoor environmental data; wherein, the virtual time interval is the time difference between the next time and the current time.

[0012] Specifically, using virtual outdoor environmental data, current virtual indoor environmental data, and virtual time interval as variables, and next virtual indoor environmental data as the controlled variable, the external influence function is obtained, including: f:(E out E,Δt)→E′. Where, E out `E` represents the set of key-value pairs for the virtual outdoor environment data at the current moment, such as temperature, humidity, and light intensity. `E` represents the set of key-value pairs for the virtual indoor environment data at the current moment, such as temperature, humidity, and light intensity. `E'` represents the set of key-value pairs for the virtual indoor environment data at the next moment, such as temperature, humidity, and light intensity. `Δt` represents the virtual time interval between the current moment and the next moment.

[0013] Optionally, the virtual outdoor environmental data includes virtual outdoor temperature, the indoor environmental data includes virtual indoor temperature, and the change in the virtual indoor environmental data includes the change in virtual indoor temperature; using the virtual outdoor environmental data, the current virtual indoor environmental data, and the virtual time interval as variables, and the change in the virtual indoor environmental data as the controlled variable, the external influence function is obtained, including: determining the external influence function as ΔT=β×(T out -T)×Δt. Where ΔT is the change in virtual indoor temperature, β is the thermal conductivity coefficient, and T outLet T be the virtual outdoor temperature, T be the virtual indoor temperature, and Δt be the virtual time interval.

[0014] Optionally, virtual outdoor environmental data can be determined as follows: virtual outdoor environmental data can be determined based on actual outdoor environmental data, the day-night variation amplitude of actual outdoor environmental data, and the seasonal variation amplitude of actual outdoor environmental data.

[0015] Optionally, the actual outdoor environmental data includes the actual daily average outdoor temperature; based on the actual outdoor environmental data, the diurnal variation amplitude of the actual outdoor environmental data, and the seasonal variation amplitude of the actual outdoor environmental data, the virtual outdoor environmental data is determined, including:

[0016] Among them, T out For virtual outdoor temperature, T avg denoted as the actual outdoor daily average temperature, denoted as a (diurnal variation amplitude), denoted as b (seasonal variation amplitude), denoted as t (current time), denoted as h (hour), and denoted as d (day).

[0017] Optionally, the change in virtual indoor environment data is determined based on the influence function of home appliances affecting virtual indoor environment data, including: using virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, and obtaining the influence function of home appliances from the virtual indoor environment data at the next moment; determining the change in virtual indoor environment data between the virtual indoor environment data at the next moment and the virtual indoor environment data at the current moment; wherein, the virtual time interval is the time difference between the next moment and the current moment.

[0018] Specifically, using virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, the virtual indoor environment data at the next moment yields the home appliance influence function, including: f:(A,P,E,Δt)→E′. Here, A represents the set of virtual home appliance attributes, such as the virtual home appliance's operating status, virtual home appliance's set parameters, and virtual home appliance's sensor data. P represents the virtual space object position mapping, such as the position and orientation of virtual objects in virtual 3D space. E represents the set of key-value pairs of the current virtual indoor environment data, such as temperature, humidity, and light intensity. E' represents the set of key-value pairs of the next virtual indoor environment data, such as temperature, humidity, and light intensity. Δt represents the virtual time interval between the current moment and the next moment.

[0019] Optionally, using virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, before obtaining the home appliance influence function from the virtual indoor environment data at the next moment, the method further includes: if the type of virtual home appliance is an air conditioner, determining the virtual home appliance attribute data as the virtual air conditioner efficiency coefficient and the virtual air conditioner current set temperature, and determining the virtual indoor environment data as the virtual indoor temperature; or, if the type of virtual home appliance is a lamp, determining the virtual home appliance attribute data as the virtual lamp brightness and the virtual lamp illumination influence coefficient, and determining the virtual indoor environment data as the virtual indoor light intensity, and determining the virtual space object position mapping as the distance between the lamp and the center point of the room.

[0020] Specifically, when the type of virtual home appliance is an air conditioner, the influence function of the home appliance is obtained by using the virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, and the virtual indoor environment data at the next moment, including: determining ΔT=α×(T target -T cur )×Δt. Where ΔT is the difference between the virtual indoor temperature at the next moment and the actual indoor temperature at the current moment, α is the virtual air conditioner efficiency coefficient, and T target Set the current temperature for the virtual air conditioner, T cur Δt represents the virtual indoor temperature, and Δt represents the virtual time interval between the current moment and the next moment.

[0021] Specifically, when the type of virtual home appliance is a lamp, the influence function of the home appliance is obtained by using the virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, and the virtual indoor environment data at the next moment, including: determining ΔI=β×L / d 2 Where ΔI is the difference between the virtual indoor light intensity at the next moment and the virtual indoor light intensity at the current moment, β is the virtual luminaire illumination influence coefficient, L is the virtual luminaire brightness, and d is the distance between the luminaire and the center point of the room.

[0022] Optionally, after adjusting the operating parameters and operating status of the actual home appliances according to the changes in the virtual indoor environment data, the system further includes: determining the comprehensive comfort score of the virtual smart home decision-making system based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data, in order to optimize the virtual smart home decision-making system.

[0023] Specifically, actual indoor environmental data includes actual indoor temperature, actual indoor humidity, actual indoor light intensity, and actual indoor noise level. Actual household appliance attribute data includes: actual air conditioner operating status and parameters, actual lighting fixture operating status and parameters, and actual humidifier operating status and parameters. User review data includes discretized numerical ratings, such as converting likes and dislikes into discretized numerical ratings.

[0024] Optionally, a comprehensive comfort score for the virtual smart home decision-making system is determined based on the evaluation model, actual indoor environment data, actual home appliance attribute data, and user evaluation data scores to optimize the virtual smart home decision-making system. This includes: determining the real-time comprehensive comfort score of the virtual smart home decision-making system based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data; determining the time-weighted average of the real-time comprehensive comfort scores over a set period as the comprehensive comfort score at the current moment; and feeding back the comprehensive comfort score at the current moment to the virtual smart home decision-making system to optimize the virtual smart home decision-making system.

[0025] Specifically, the time-weighted average of the real-time comprehensive comfort scores over a set period is determined as the comprehensive comfort score for the current moment. This includes calculating the CCS using an exponentially weighted moving average method. EWMA (t)=α·CCS(t)+(1-α)·CCS EWMA (t-1). Where α is the smoothing coefficient, CCS(t) is the overall comfort score at the current time step, and CCS(t-1) is the overall comfort score at the previous time step.

[0026] Optionally, the real-time comprehensive comfort score of the virtual smart home decision-making system is determined based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large-scale model evaluation data. This includes: training the evaluation model using actual indoor environment data, actual home appliance attribute data, user evaluation data, and large-scale model evaluation data as input, and using user evaluation data scores as output; and inputting real-time actual indoor environment data, real-time actual home appliance attribute data, real-time user evaluation data, and real-time large-scale model evaluation data into the evaluation model to output a real-time comprehensive comfort score.

[0027] Optionally, the evaluation model is generated based on a random forest model or a gradient boosting tree model, and the loss function of the evaluation model adopts the mean squared error or cross-entropy loss function.

[0028] Optionally, before optimizing the virtual smart home decision-making system, the comprehensive comfort score of the virtual smart home decision-making system is determined based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data. This also includes receiving user-provided evaluation data on a visual interface.

[0029] Optionally, virtual indoor environment data can be visualized. Virtual home appliance attribute data can also be visualized.

[0030] Optionally, virtual home appliances can be moved and operated. Mobility includes: adjustable position, rotation, and scaling. Operability includes: the ability to add, delete, and edit.

[0031] Optionally, the virtual indoor environment data can be debugged.

[0032] Optionally, the operating parameters and operating status of the actual home appliances are adjusted according to the changes in the virtual indoor environment data to adjust the actual indoor environment data. This includes: upon receiving a control command from the actual smart home decision system, sending the changes in the virtual indoor environment data to the actual smart home decision system so that the actual smart home decision system can adjust the operating parameters of the actual home appliances to adjust the actual indoor environment data.

[0033] Optionally, it also includes: sending virtual home appliance attribute data to the actual smart home decision-making system upon receiving an enumeration instruction from the actual smart home decision-making system.

[0034] Optionally, it also includes: upon receiving a status update instruction from the actual smart home decision system, sending virtual home appliance attribute data that has not been sent to the actual smart home decision system to the actual smart home decision system in order to update the actual home appliance attribute data.

[0035] Optionally, before determining the change in virtual indoor environment data based on the external environment influence function or the home appliance influence function that affects the virtual indoor environment data, the method further includes: configuring an initialization file when the virtual smart home decision-making system is started; loading initialization information of simulated home appliance attribute data using the initialization file; loading and visualizing virtual indoor environment data and virtual home appliances after the initialization information of simulated home appliance attribute data has been loaded; and establishing a network connection and communicating with the actual smart home decision-making system. Specifically, the initialization information of virtual home appliance attribute data can be modified.

[0036] In some embodiments, the device, applied to a virtual smart home decision-making system, includes: a determining module configured to determine the amount of change in virtual indoor environment data based on an external environment influence function that affects virtual indoor environment data, or an appliance influence function that affects virtual indoor environment data; and an adjusting module configured to adjust the operating parameters and operating status of actual appliances according to the amount of change in virtual indoor environment data, so as to adjust the actual indoor environment data.

[0037] In some embodiments, the apparatus includes a processor and a memory storing program instructions, the processor being configured to execute the method for controlling a home appliance when the program instructions are executed.

[0038] In some embodiments, the virtual smart home decision-making system includes: a virtual smart home decision-making system body; and the device for controlling home appliances is installed on the virtual smart home decision-making system body.

[0039] In some embodiments, the computer-readable storage medium stores program instructions that, when executed, cause a computer to perform the method for controlling home appliances.

[0040] The home appliance control method and device based on a large language model, the virtual smart home decision-making system, and the computer-readable storage medium provided in this disclosure can achieve the following technical effects:

[0041] Based on the external environmental influence function or the appliance influence function affecting the virtual indoor environment data, the change in the virtual indoor environment data is determined. When virtualizing appliances, the influence of outdoor environmental data on indoor environmental data is considered to ensure the virtual indoor environment data matches the actual indoor environment data. Then, the operating parameters and states of the actual appliances are adjusted according to the change in the virtual indoor environment data, thereby improving the accuracy of the actual appliances' adjustment to the actual indoor environment data. This improves the accuracy of the virtual smart home decision-making system's adjustment to the actual indoor environment data.

[0042] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0043] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0044] Figure 1 This is a schematic diagram of the system environment provided in the embodiments of this disclosure;

[0045] Figure 2 This is a schematic diagram of a home appliance control method based on a large language model provided in an embodiment of this disclosure;

[0046] Figure 3 This is a schematic diagram of another home appliance control method based on a large language model provided in this disclosure embodiment;

[0047] Figure 4 This is a schematic diagram of another home appliance control method based on a large language model provided in this disclosure embodiment;

[0048] Figure 5 This is a schematic diagram of a home appliance control device based on a large language model provided in an embodiment of this disclosure;

[0049] Figure 6 This is a schematic diagram of another home appliance control device based on a large language model provided in an embodiment of this disclosure. Detailed Implementation

[0050] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0051] The terms "first," "second," etc., used in the specification and accompanying drawings of this disclosure 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 for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0052] Unless otherwise stated, the term "multiple" means two or more.

[0053] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0054] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0055] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0056] In this embodiment of the disclosure, smart home appliances refer to home appliances formed by introducing microprocessors, sensor technology and network communication technology into home appliances. They have the characteristics of intelligent control, intelligent sensing and intelligent application. The operation of smart home appliances often relies on the application and processing of modern technologies such as the Internet of Things, the Internet and electronic chips. For example, smart home appliances can be connected to electronic devices to enable users to remotely control and manage smart home appliances.

[0057] Combination Figure 1 As shown, this embodiment of the disclosure provides an implementation environment for a home appliance control method based on a large language model, including: actual home appliance equipment 1, actual smart home decision system 2, virtual smart home decision system, and environmental data detection sensor 4.

[0058] The actual smart home decision system 2 is used to control the actual home appliances 1, and the virtual smart home decision system 3 includes virtual home appliances that can simulate the actual home appliances 1, virtual indoor environmental data that simulates the actual indoor environmental data, and virtual outdoor environmental data that simulates the actual outdoor environmental data.

[0059] The virtual indoor environment data can be adjusted by modifying the attribute data of the virtual home appliance to simulate the impact of the actual home appliance 1 on the actual indoor environment data. The actual indoor and outdoor environmental data detected by the environmental data detection sensor 4 can be uploaded to the virtual smart home decision system 3. The virtual smart home decision system 3, the actual smart home decision system 2, and the actual home appliance 1 can all communicate with each other.

[0060] In this way, by using virtual home appliances, virtual indoor environmental data, and virtual outdoor environmental data to simulate the actual home appliances 1, actual indoor environmental data, and actual outdoor environmental data respectively, it is beneficial to more accurately simulate the impact of the actual home appliances 1 and actual outdoor environmental data on the actual indoor environmental data, so as to better adjust the actual indoor environmental data.

[0061] Combination Figure 2 As shown, this disclosure provides a method for controlling home appliances based on a large language model, including:

[0062] S201, the virtual smart home decision system determines the change in virtual indoor environment data based on the external environment influence function used to influence virtual indoor environment data, or the home appliance influence function used to influence virtual indoor environment data.

[0063] S202, The virtual smart home decision-making system adjusts the operating parameters and operating status of actual home appliances according to the changes in virtual indoor environment data, so as to adjust the actual indoor environment data.

[0064] The method for controlling home appliances provided in this disclosure determines the change in virtual indoor environment data based on an external environmental influence function or a home appliance influence function that affects the virtual indoor environment data. When virtualizing home appliances, the influence of outdoor environmental data on indoor environmental data is considered, ensuring that the virtual indoor environment data matches the actual indoor environment data. The operating parameters and states of the actual home appliances are then adjusted according to the change in the virtual indoor environment data, thereby improving the accuracy of the actual home appliances' adjustment to the actual indoor environment data. This improves the accuracy of the virtual smart home decision-making system's adjustment to the actual indoor environment data.

[0065] It should be noted that the external environment influence function represents the impact of adjusting the virtual indoor environment data. The home appliance influence function represents the impact of adjusting the virtual home appliance attribute data on the virtual indoor environment data.

[0066] Optionally, the virtual smart home decision-making system determines the change in virtual indoor environment data based on an external environmental influence function that affects the virtual indoor environment data. This includes: the virtual smart home decision-making system uses virtual outdoor environment data, current virtual indoor environment data, and virtual time interval as variables, and the next virtual indoor environment data or the change in virtual indoor environment data as the controlled variable to obtain the external influence function. When the controlled variable of the external influence function is the next virtual indoor environment data, the virtual smart home decision-making system determines the difference between the next virtual indoor environment data and the current virtual indoor environment data as the change in virtual indoor environment data. Here, the virtual time interval is the time difference between the next moment and the current moment.

[0067] It should be noted that the virtual time interval is the time interval between changes in virtual indoor environment data.

[0068] In this way, an external influence function is determined using virtual outdoor environmental data, virtual indoor environmental data, and virtual time intervals to simulate the impact of actual outdoor environmental data on actual indoor environmental data. Considering the influence of outdoor environmental data on indoor environmental data, the virtual indoor environmental data is made to match the actual indoor environmental data, thereby improving the accuracy of the virtual smart home decision-making system in adjusting to the actual indoor environmental data.

[0069] Specifically, the virtual smart home decision-making system uses virtual outdoor environmental data, current virtual indoor environmental data, and virtual time intervals as variables, and next-time virtual indoor environmental data as the controlled variable, to obtain the external influence function, including: f:(E out E,Δt)→E′. Where, E out `E` represents the set of key-value pairs for the virtual outdoor environment data at the current moment, such as temperature, humidity, and light intensity. `E` represents the set of key-value pairs for the virtual indoor environment data at the current moment, such as temperature, humidity, and light intensity. `E'` represents the set of key-value pairs for the virtual indoor environment data at the next moment, such as temperature, humidity, and light intensity. `Δt` represents the virtual time interval between the current moment and the next moment.

[0070] In this way, using the current virtual outdoor environment data, the current virtual indoor environment data, and the virtual time interval to determine the next virtual outdoor environment data helps to better determine the external influence function, thereby simulating the impact of actual outdoor environment data on actual indoor environment data. Considering the influence of outdoor environment data on indoor environment data, ensuring that the virtual indoor environment data matches the actual indoor environment data improves the accuracy of the virtual smart home decision-making system in adjusting to the actual indoor environment data.

[0071] Optionally, the virtual outdoor environmental data includes virtual outdoor temperature, the indoor environmental data includes virtual indoor temperature, and the change in the virtual indoor environmental data includes the change in virtual indoor temperature. The virtual smart home decision-making system uses the virtual outdoor environmental data, the current virtual indoor environmental data, and the virtual time interval as variables, and the change in the virtual indoor environmental data as the controlled variable, to obtain the external influence function, including: the virtual smart home decision-making system determines the external influence function as ΔT = β × (T out -T)×Δt. Where ΔT is the change in virtual indoor temperature, β is the thermal conductivity coefficient, and T out Let T be the virtual outdoor temperature, T be the virtual indoor temperature, and Δt be the virtual time interval.

[0072] In this way, when simulating the external influence function of the actual outdoor temperature on the actual indoor temperature, the change in virtual indoor temperature is determined by using the thermal conductivity coefficient, the virtual indoor-outdoor temperature difference, and the virtual time interval. This helps to take into account the influence of the indoor-outdoor temperature difference on the indoor temperature, making the virtual indoor temperature match the actual indoor temperature and improving the accuracy of the virtual smart home decision-making system in regulating the actual indoor temperature.

[0073] Optionally, the virtual smart home decision-making system determines the virtual outdoor environment data in the following manner: the virtual smart home decision-making system determines the virtual outdoor environment data based on the actual outdoor environment data, the day-night variation amplitude of the actual outdoor environment data, and the seasonal variation amplitude of the actual outdoor environment data.

[0074] In this way, virtual outdoor environment data can be determined based on actual outdoor environment data and its diurnal and seasonal variation amplitudes. When simulating actual outdoor environment data, the influence of different times of day and different seasons of the year on actual outdoor environment data is taken into account, which helps to more accurately simulate the changes in actual outdoor environment data and make virtual outdoor environment data more consistent with actual outdoor environment data.

[0075] Optionally, the actual outdoor environmental data includes the actual daily average outdoor temperature. The virtual smart home decision-making system determines the virtual outdoor environmental data based on the actual outdoor environmental data, the diurnal variation amplitude of the actual outdoor environmental data, and the seasonal variation amplitude of the actual outdoor environmental data, including: Among them, T out For virtual outdoor temperature, T avg denoted as the actual outdoor daily average temperature, denoted as a (diurnal variation amplitude), denoted as b (seasonal variation amplitude), denoted as t (current time), denoted as h (hour), and denoted as d (day).

[0076] In this way, the virtual outdoor temperature is determined by the actual daily average outdoor temperature and its diurnal and seasonal variation amplitudes. When simulating the actual outdoor temperature, taking into account the influence of different times of day and different seasons of the year on the actual outdoor temperature helps to more accurately simulate the changes in the actual outdoor temperature, making the virtual outdoor temperature more consistent with the actual outdoor temperature.

[0077] Optionally, the virtual smart home decision-making system determines the change in virtual indoor environment data based on the influence function of home appliances affecting the virtual indoor environment data. This includes: the virtual smart home decision-making system uses virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, and obtains the home appliance influence function from the virtual indoor environment data at the next moment. The virtual smart home decision-making system determines the difference between the virtual indoor environment data at the next moment and the virtual indoor environment data at the current moment as the change in virtual indoor environment data; wherein, the virtual time interval is the time difference between the next moment and the current moment.

[0078] In this way, by using virtual home appliance attribute data, virtual space object position mapping, virtual indoor environment data, and virtual time intervals to determine the influence function of home appliances, it is possible to more accurately simulate the impact of actual home appliances on actual indoor environment data, thereby more accurately determining the amount of change in virtual indoor environment data.

[0079] Specifically, the virtual smart home decision-making system uses virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables. The virtual indoor environment data at the next moment yields the home appliance influence function: f:(A,P,E,Δt)→E′. Here, A represents the set of virtual home appliance attributes, such as the virtual home appliance's operating status, virtual home appliance's set parameters, and virtual home appliance's sensor data. P represents the virtual space object position mapping, such as the position and orientation of virtual objects in virtual three-dimensional space. E represents the set of key-value pairs of the current virtual indoor environment data, such as temperature, humidity, and light intensity. E' represents the set of key-value pairs of the next virtual indoor environment data, such as temperature, humidity, and light intensity. Δt represents the virtual time interval between the current moment and the next moment.

[0080] In this way, by using the set of virtual home appliance attributes, the mapping of virtual space object positions, the current virtual indoor environment data, and the virtual time interval to determine the virtual indoor environment data for the next moment, it is easier to determine the influence function of home appliances more accurately, thereby simulating the influence of actual home appliances on actual indoor environment data more accurately, and thus determining the amount of change in virtual indoor environment data more precisely.

[0081] Optionally, using virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, before obtaining the home appliance influence function from the virtual indoor environment data at the next moment, the method further includes: if the type of virtual home appliance is an air conditioner, determining the virtual home appliance attribute data as the virtual air conditioner efficiency coefficient and the virtual air conditioner current set temperature, and determining the virtual indoor environment data as the virtual indoor temperature; or, if the type of virtual home appliance is a lamp, determining the virtual home appliance attribute data as the virtual lamp brightness and the virtual lamp illumination influence coefficient, and determining the virtual indoor environment data as the virtual indoor light intensity, and determining the virtual space object position mapping as the distance between the lamp and the center point of the room.

[0082] Therefore, if the virtual appliance is an air conditioner, it's necessary to simulate the impact of the virtual air conditioner's efficiency coefficient and current set temperature on the virtual indoor environment data to more accurately and comprehensively simulate the actual air conditioner's influence on the actual indoor environment data. If the virtual appliance is a light fixture, it's necessary to consider the influence of the virtual light fixture's brightness, illumination influence coefficient, and distance from the center of the room on the virtual indoor light intensity to more accurately and comprehensively simulate the actual light fixture's influence on the actual indoor light intensity. This approach helps to better simulate the impact of different types of real-world appliances on the actual indoor environment data.

[0083] Specifically, when the virtual home appliance is an air conditioner, the virtual smart home decision-making system uses virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables. The virtual indoor environment data at the next moment is used to obtain the home appliance influence function, which includes: the virtual smart home decision-making system determining ΔT = α × (T... target -T cur )×Δt. Where ΔT is the difference between the virtual indoor temperature at the next moment and the actual indoor temperature at the current moment, α is the virtual air conditioner efficiency coefficient, and T target Set the current temperature for the virtual air conditioner, T cur Δt represents the virtual indoor temperature, and Δt represents the virtual time interval between the current moment and the next moment.

[0084] In this way, if the virtual home appliance is an air conditioner, the change in virtual indoor temperature at the next moment is the product of the virtual air conditioner efficiency coefficient, the temperature difference between the current set temperature of the virtual air conditioner and the virtual indoor temperature, and the virtual time interval. This helps to more accurately simulate the impact of the actual air conditioner on the virtual indoor environment data, thereby simulating the impact of the actual air conditioner on the actual indoor environment data more accurately and comprehensively.

[0085] Specifically, when the type of virtual home appliance is a lamp, the virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval are used as variables. The virtual indoor environment data at the next moment yields the home appliance influence function, including: the virtual smart home decision system determines ΔI = β × L / d. 2 Where ΔI is the difference between the virtual indoor light intensity at the next moment and the virtual indoor light intensity at the current moment, β is the virtual luminaire illumination influence coefficient, L is the virtual luminaire brightness, and d is the distance between the luminaire and the center point of the room.

[0086] In this way, if the virtual home appliance is a lamp, the change in virtual indoor light intensity at the next moment is determined by the virtual lamp's light influence coefficient, the virtual lamp's brightness, and the distance between the lamp and the center point of the room. This helps to more accurately simulate the influence of virtual lamps on virtual indoor light intensity, so as to more accurately and comprehensively simulate the influence of actual lamps on actual indoor light intensity.

[0087] Optionally, after adjusting the operating parameters and operating status of actual home appliances according to the changes in virtual indoor environment data, the virtual smart home decision-making system further includes: determining the comprehensive comfort score of the virtual smart home decision-making system based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data, so as to optimize the virtual smart home decision-making system.

[0088] In this way, by determining the comprehensive comfort score based on the evaluation model, actual indoor environmental data, actual home appliance attribute data, user evaluation data, and large model evaluation data, it is beneficial to provide a feedback loop for the virtual smart home decision-making system, thereby optimizing the impact of the virtual smart home decision-making system on actual indoor environmental data and improving the robustness of the virtual smart home decision-making system.

[0089] Specifically, the actual indoor environmental data includes actual indoor temperature, actual indoor humidity, actual indoor light intensity, and actual indoor noise level. Actual household appliance attribute data includes: the actual operating status and parameters of the air conditioner, the actual operating status and parameters of the lighting fixtures, and the actual operating status and parameters of the humidifier. User review data includes discretized numerical ratings, such as converting likes and dislikes into discretized numerical ratings. Specifically, the range of values ​​for the discretized numerical ratings is [0, 1].

[0090] In this way, utilizing different types of actual indoor environmental data, different types of actual home appliance attribute data, and discretized user evaluation data can help provide comprehensive and accurate data input for the evaluation model.

[0091] Optionally, the virtual smart home decision-making system determines a comprehensive comfort score based on an evaluation model, actual indoor environment data, actual appliance attribute data, and user evaluation data scores to optimize the virtual smart home decision-making system. This includes: the virtual smart home decision-making system determining a real-time comprehensive comfort score based on the evaluation model, actual indoor environment data, actual appliance attribute data, user evaluation data, and large-scale model evaluation data; the virtual smart home decision-making system determining a time-weighted average of the real-time comprehensive comfort scores over a set period as the comprehensive comfort score for the current moment; and the virtual smart home decision-making system feeding back the current comprehensive comfort score to optimize the virtual smart home decision-making system.

[0092] Specifically, the virtual smart home decision-making system determines the time-weighted average of the real-time comprehensive comfort scores over a set period as the comprehensive comfort score at the current moment. This includes: the virtual smart home decision-making system using an exponentially weighted moving average method to calculate the CCS. EWMA (t)=α·CCS(t)+(1-α)·CCS EWMA (t-1). Where α is the smoothing coefficient, CCS(t) is the overall comfort score at the current time step, and CCS(t-1) is the overall comfort score at the previous time step.

[0093] In this way, the real-time comprehensive comfort score within a set time period is weighted and averaged to provide the current comprehensive comfort score to the virtual smart home decision-making system. This helps to provide the virtual smart home decision-making system with an accurate negative feedback and evaluation mechanism.

[0094] Optionally, the virtual smart home decision-making system determines a real-time comprehensive comfort score based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large-scale model evaluation data. This includes: the virtual smart home decision-making system uses actual indoor environment data, actual home appliance attribute data, user evaluation data, and large-scale model evaluation data as input, and uses user evaluation data scores as output to train the evaluation model. Alternatively, the virtual smart home decision-making system inputs real-time actual indoor environment data, real-time actual home appliance attribute data, real-time user evaluation data, and real-time large-scale model evaluation data into the evaluation model, and outputs a real-time comprehensive comfort score.

[0095] In this way, by using data from the virtual smart home decision-making system as variables and user evaluation data scores as controlled variables, the evaluation model can be trained in real time. After training, the real-time comprehensive comfort score can be updated, which is conducive to updating the parameters of the rating model online in real time, enabling the rating model to adapt to environmental changes and user preferences, and improving the adaptability of the rating model.

[0096] Optionally, the evaluation model is generated based on a random forest model or a gradient boosting tree model, and the loss function of the evaluation model adopts the mean squared error or cross-entropy loss function.

[0097] In this way, both random forest models and gradient boosting tree models improve the robustness and interpretability of input data, effectively process the input data, and support online training.

[0098] Optionally, the virtual smart home decision-making system determines the overall comfort score of the virtual smart home decision-making system based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data. Before optimizing the virtual smart home decision-making system, it also includes receiving evaluation data provided by users through a visual interface.

[0099] This facilitates the timely receipt of user-provided evaluation data, enabling the generation of a comprehensive comfort score in real time, and subsequently, the training of the evaluation model.

[0100] Optionally, virtual indoor environment data can be visualized. Virtual home appliance attribute data can also be visualized.

[0101] This allows users to more intuitively view virtual indoor environment data and virtual home appliance attribute data, enhancing the visualization of the virtual intelligent decision-making home system.

[0102] Optionally, virtual home appliances can be moved and operated. Mobility includes: adjustable position, rotation, and scaling. Operability includes: the ability to add, delete, and edit.

[0103] This allows users to customize, move, delete, and edit virtual home appliances, thus fully considering users' personalized needs for the virtual smart home decision-making system.

[0104] Optionally, the virtual indoor environment data can be debugged.

[0105] This allows for the adjustment of virtual indoor environment data, which facilitates better calibration of the virtual indoor environment data, making it more consistent with actual indoor environment data.

[0106] Optionally, the virtual smart home decision-making system adjusts the operating parameters and operating status of the actual home appliances according to the changes in the virtual indoor environment data, in order to adjust the actual indoor environment data. This includes: upon receiving a control command from the actual smart home decision-making system, the virtual smart home decision-making system sends the changes in the virtual indoor environment data to the actual smart home decision-making system, so that the actual smart home decision-making system adjusts the operating parameters of the actual home appliances to adjust the actual indoor environment data.

[0107] In this way, indirectly controlling the operating parameters and status of actual home appliances through the actual smart home decision-making system is conducive to the efficient collaborative work between the virtual smart home decision-making system and the actual smart home decision-making system, and to the dynamic management and optimization of the operating parameters and status of actual home appliances.

[0108] Optionally, it also includes: upon receiving an enumeration instruction from the actual smart home decision system, the virtual smart home decision system sends virtual home appliance attribute data to the actual smart home decision system.

[0109] In this way, the actual smart home decision-making system can control the virtual smart home decision-making system to enumerate the attribute data of virtual home appliances, which is conducive to the efficient collaborative work between the virtual smart home decision-making system and the actual smart home decision-making system, and to the mutual exchange of attribute data of virtual home appliances.

[0110] Optionally, it also includes: upon receiving a status update instruction from the actual smart home decision system, the virtual smart home decision system sends virtual home appliance attribute data that has not been sent to the actual smart home decision system to the actual smart home decision system in order to update the actual home appliance attribute data.

[0111] In this way, the actual smart home decision-making system can control the virtual smart home decision-making system to send updated virtual home appliance attribute data, which is conducive to the efficient collaborative work between the virtual smart home decision-making system and the actual smart home decision-making system, and realizes the mapping between actual home appliance data and virtual home appliance attribute data.

[0112] Optionally, before determining the change in virtual indoor environment data based on the external environmental influence function or the appliance influence function that affects the virtual indoor environment data, the virtual smart home decision-making system further includes: configuring an initialization file when the virtual smart home decision-making system is started. The virtual smart home decision-making system uses the initialization file to load the initialization information of simulated appliance attribute data. After the initialization information of the simulated appliance attribute data is loaded, the virtual smart home decision-making system loads and visualizes the virtual indoor environment data, and also visualizes the virtual appliances. The virtual smart home decision-making system establishes a network connection with the actual smart home decision-making system and communicates with it. Specifically, the initialization information of the virtual appliance attribute data can be modified.

[0113] In this way, when starting the virtual smart home decision-making system, the initialization file is configured first, then the attribute data of the simulated home appliances is initialized, the virtual indoor environment data and virtual home appliances are visualized, and then a network connection is established with the actual smart home decision-making system. This helps to improve the predictability and reliability of the virtual smart home decision-making system.

[0114] Combination Figure 3 As shown, this disclosure provides another home appliance control method based on a large language model, including:

[0115] S301, the virtual smart home decision system determines the change in virtual indoor environment data based on the external environment influence function used to influence virtual indoor environment data, or the home appliance influence function used to influence virtual indoor environment data.

[0116] S302, the virtual smart home decision-making system adjusts the operating parameters and operating status of actual home appliances according to the changes in virtual indoor environment data, so as to adjust the actual indoor environment data.

[0117] S303, the virtual smart home decision-making system determines the comprehensive comfort score of the virtual smart home decision-making system based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data, in order to optimize the virtual smart home decision-making system.

[0118] The method for controlling home appliances provided in this disclosure determines the change in virtual indoor environmental data based on an external environmental influence function or a home appliance influence function that affects the virtual indoor environmental data. When virtualizing home appliances, the influence of outdoor environmental data on indoor environmental data is considered, ensuring that the virtual indoor environmental data matches the actual indoor environmental data. The operating parameters and states of the actual home appliances are then adjusted according to the change in the virtual indoor environmental data, thereby improving the accuracy of the actual home appliances' adjustment of the actual indoor environmental data. This improves the accuracy of the virtual smart home decision-making system's adjustment of the actual indoor environmental data. A comprehensive comfort score is determined based on the evaluation model, actual indoor environmental data, actual home appliance attribute data, user evaluation data, and large model evaluation data. This provides a feedback loop for the virtual smart home decision-making system, optimizing its impact on the actual indoor environmental data and improving the robustness of the virtual smart home decision-making system.

[0119] Combination Figure 4 As shown, this disclosure provides another home appliance control method based on a large language model, including:

[0120] S401, the virtual smart home decision system determines the change in virtual indoor environment data based on the external environment influence function used to influence virtual indoor environment data, or the home appliance influence function used to influence virtual indoor environment data.

[0121] S402, upon receiving the control command from the actual smart home decision-making system, the virtual smart home decision-making system sends the changes in virtual indoor environment data to the actual smart home decision-making system, so that the actual smart home decision-making system adjusts the operating parameters of the actual home appliances to adjust the actual indoor environment data.

[0122] The method for controlling home appliances provided in this disclosure determines the change in virtual indoor environmental data based on an external environmental influence function or a home appliance influence function that affects the virtual indoor environmental data. When virtualizing home appliances, the influence of outdoor environmental data on indoor environmental data is considered, ensuring that the virtual indoor environmental data matches the actual indoor environmental data. Furthermore, the operating parameters and status of the actual home appliances are indirectly controlled through an actual smart home decision-making system. This facilitates efficient system-wide collaboration between the virtual and actual smart home decision-making systems, enabling dynamic management and optimization of the operating parameters and status of the actual home appliances. Consequently, the accuracy of the virtual smart home decision-making system's adjustment of the actual indoor environmental data is improved.

[0123] Combination Figure 5As shown, this embodiment of the disclosure provides a home appliance control device 200 based on a large language model, including a determination module 501 and an adjustment module 502. The determination module 501 is configured to determine the change in virtual indoor environment data based on an external environment influence function that affects virtual indoor environment data, or a home appliance influence function that affects virtual indoor environment data. The adjustment module 502 is configured to adjust the operating parameters and operating states of actual home appliances according to the change in virtual indoor environment data, thereby adjusting the actual indoor environment data.

[0124] The apparatus 200 for controlling home appliances provided in this disclosure determines the change in virtual indoor environment data based on an external environmental influence function or a home appliance influence function that affects the virtual indoor environment data. When virtualizing the home appliances, the influence of outdoor environmental data on indoor environmental data is considered, ensuring that the virtual indoor environment data matches the actual indoor environment data. The operating parameters and operating states of the actual home appliances are then adjusted according to the change in the virtual indoor environment data, thereby improving the accuracy of the actual home appliances' adjustment to the actual indoor environment data. This improves the accuracy of the virtual smart home decision-making system's adjustment to the actual indoor environment data.

[0125] Combination Figure 6 As shown, this disclosure provides a home appliance control device 70 based on a large language model, including a processor 700 and a memory 701. Optionally, the device 70 may further include a communication interface 702 and a bus 703. The processor 700, communication interface 702, and memory 701 can communicate with each other via the bus 703. The communication interface 702 can transmit information. The processor 700 can call logical instructions in the memory 701 to execute the home appliance control method based on a large language model described above.

[0126] Furthermore, the logic instructions in the aforementioned memory 701 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0127] The memory 701, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 700 executes functional applications and data processing by running the program instructions / modules stored in the memory 701, thereby implementing the home appliance control method based on a large language model in the above embodiments.

[0128] The memory 701 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 701 may include high-speed random access memory and may also include non-volatile memory.

[0129] This disclosure provides a virtual smart home decision-making system 100, including: a virtual smart home decision-making system body, and the aforementioned home appliance control device 200 (300) based on a large language model. The device 200 (300) for controlling home appliances is installed in the virtual smart home decision-making system body. The installation relationship described herein is not limited to placement within the virtual smart home decision-making system body, but also includes installation connections with other components of the virtual smart home decision-making system 100, including but not limited to physical connections, electrical connections, or signal transmission connections. Those skilled in the art will understand that the device 200 (300) for controlling home appliances can be adapted to feasible virtual smart home decision-making system bodies, thereby realizing other feasible embodiments.

[0130] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to execute the aforementioned home appliance control method based on a large language model.

[0131] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more 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 method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.

[0132] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0133] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0134] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and 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. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. 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 may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0135] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A home appliance control method based on a large language model, characterized by, Applied to virtual smart home decision-making systems, the methods include: The amount of change in virtual indoor environment data is determined based on the external environment influence function used to affect virtual indoor environment data, or the home appliance influence function used to affect virtual indoor environment data. Adjusting the operating parameters and status of actual home appliances according to changes in virtual indoor environment data in order to regulate the actual indoor environment data.

2. The method of claim 1, wherein, Based on the external environmental impact function used to influence the virtual indoor environment data, determine the changes in the virtual indoor environment data, including: Using virtual outdoor environmental data, current virtual indoor environmental data, and virtual time interval as variables, and the next virtual indoor environmental data or the change in virtual indoor environmental data as the controlled variable, the external influence function is obtained. When the controlled variable of the external influence function is the virtual indoor environment data at the next time step, the difference between the virtual indoor environment data at the next time step and the virtual indoor environment data at the current time step is determined as the change in the virtual indoor environment data. The virtual time interval is the time difference between the next moment and the current moment.

3. The method of claim 2, wherein, The virtual outdoor environment data is determined in the following manner: Virtual outdoor environmental data is determined based on actual outdoor environmental data, the diurnal variation amplitude of actual outdoor environmental data, and the seasonal variation amplitude of actual outdoor environmental data.

4. The method of claim 1, wherein, Based on the influence function of home appliances affecting virtual indoor environment data, determine the changes in virtual indoor environment data, including: Using virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data and virtual time interval as variables, the home appliance influence function is obtained from the next virtual indoor environment data. The difference between the virtual indoor environment data at the next moment and the virtual indoor environment data at the current moment is determined as the change in the virtual indoor environment data; The virtual time interval is the time difference between the next moment and the current moment.

5. The method of claim 4, wherein, Using virtual home appliance attribute data, virtual space object position mapping, current virtual indoor environment data, and virtual time interval as variables, before obtaining the home appliance influence function from the next virtual indoor environment data, the following steps are also included: When the type of virtual home appliance is an air conditioner, the virtual home appliance attribute data is determined to be the virtual air conditioner efficiency coefficient and the virtual air conditioner's current set temperature, and the virtual indoor environment data is determined to be the virtual indoor temperature; or, When the type of virtual home appliance is a lamp, the attribute data of the virtual home appliance is determined to be the brightness of the virtual lamp light and the illumination influence coefficient of the virtual lamp. The virtual indoor environment data is determined to be the virtual indoor illumination intensity. The position of the virtual space object is determined to be mapped to the distance between the lamp and the center point of the room.

6. The method according to any one of claims 1 to 5, characterized in that, After adjusting the operating parameters and status of actual home appliances according to changes in virtual indoor environment data, it also includes: Based on the evaluation model, actual indoor environment data, actual home appliance attribute data, user evaluation data, and large model evaluation data, the comprehensive comfort score of the virtual smart home decision-making system is determined in order to optimize the virtual smart home decision-making system. 7.A large language model-based home appliance control apparatus, characterized by, The device, used in a virtual smart home decision-making system, includes: The determination module is configured to determine the amount of change in virtual indoor environment data based on an external environmental influence function used to affect virtual indoor environment data, or an appliance influence function used to affect virtual indoor environment data. The adjustment module is configured to adjust the operating parameters and operating status of the actual home appliances according to the changes in the virtual indoor environment data, so as to adjust the actual indoor environment data.

8. A home appliance control device based on a large language model, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute the home appliance control method based on a large language model as described in any one of claims 1 to 6 when running the program instructions.

9. A virtual smart home decision-making system, characterized in that, include: The virtual smart home decision-making system entity; The home appliance control device based on a large language model as described in claim 7 or 8 is installed on the virtual smart home decision-making system body.

10. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the home appliance control method based on a large language model as described in any one of claims 1 to 6.

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