An electrical appliance control system, method, device and storage medium based on the Internet of Things

Abstract

Some embodiments in this specification provide an electrical appliance control system, method, device and storage medium based on the Internet of Things, the system comprising: an environmental monitoring module, configured to collect environmental data in a deployment area, the environmental data including electrical appliance noise data; an electrical appliance gateway, communicatively connected to the environmental electrical appliances; a processor, communicatively connected to the environmental monitoring module and the electrical appliance gateway, and configured to: obtain operating data of environmental electrical appliances in the deployment area through the electrical appliance gateway; determine environmental monitoring parameters based on the operating data; control the environmental monitoring module to obtain environmental data based on the environmental monitoring parameters; determine linkage adjustment parameters based on the environmental data in response to electrical appliance noise data in the environmental data satisfying control conditions; generate linkage adjustment instructions based on the linkage adjustment parameters, and send them to the electrical appliance gateway to control the operating parameters of the environmental appliances.

Description

Technical Field

[0001] The present invention relates to the field of electrical appliance control based on the Internet of Things, and in particular to an electrical appliance control system, method, device and storage medium based on the Internet of Things. Background Art

[0002] With the development of the times, the number of electrical appliances in users' rooms has gradually increased, and some electrical appliances will produce continuous or intermittent noise during operation, which will affect users. The noise will also cause mental and psychological pressure on users, affecting the quality of life of users over time.

[0003] Taking environmental appliances as an example, if the indoor environment reaches a preset state, its power is reduced and the indoor noise is reduced accordingly. Therefore, an appliance control system is needed that can make the indoor environment reach a preset state without disturbing the user, and minimize the disturbance to the user and the noise of the appliance when the environmental appliances are running. Summary of the invention

[0004] One of the embodiments of the present specification provides an electrical appliance control system based on the Internet of Things, including: an environmental monitoring module, configured to collect environmental data in a deployment area, the environmental data including electrical appliance noise data; an electrical appliance gateway, communicatively connected to the environmental electrical appliances; a processor, communicatively connected to the environmental monitoring module and the electrical appliance gateway, and configured to: obtain operating data of the environmental electrical appliances in the deployment area through the electrical appliance gateway; determine environmental monitoring parameters based on the operating data; control the environmental monitoring module to obtain the environmental data based on the environmental monitoring parameters; determine linkage adjustment parameters based on the environmental data in response to the electrical appliance noise data in the environmental data satisfying a control condition; generate linkage adjustment instructions based on the linkage adjustment parameters, and send them to the electrical appliance gateway to control the operating parameters of the environmental electrical appliances.

[0005] One of the embodiments of the present specification also provides an electrical appliance control method based on the Internet of Things, which is executed by a processor, including: obtaining the operating data of the environmental appliances in the deployment area through the electrical appliance gateway; determining the environmental monitoring parameters based on the operating data; controlling the environmental monitoring module to obtain the environmental data based on the environmental monitoring parameters; in response to the electrical appliance noise data in the environmental data meeting the control conditions, determining the linkage adjustment parameters based on the environmental data; generating a linkage adjustment instruction based on the linkage adjustment parameters, and sending it to the electrical appliance gateway to control the operating parameters of the environmental appliances.

[0006] One of the embodiments of the present specification also provides an electrical appliance control device based on the Internet of Things, including a processor, wherein the processor is used to execute the above-mentioned electrical appliance control method based on the Internet of Things.

[0007] One of the embodiments of the present specification also provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the above-mentioned electrical appliance control method based on the Internet of Things.

[0008] Some embodiments of the present specification have at least the following beneficial effects: by executing the above-mentioned IoT-based electrical appliance control method, the actual indoor environmental conditions are monitored and the electrical appliances are controlled in linkage, so as to actively reduce the noise emitted by the environmental electrical appliances when necessary without the need for manual operation by the user, thereby achieving senseless adjustment of the electrical appliances. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0010] Figure 1 It is a structural schematic diagram of an electrical appliance control system based on the Internet of Things according to some embodiments of this specification;

[0011] Figure 2 is an exemplary flow chart of an electrical appliance control method based on the Internet of Things according to some embodiments of this specification;

[0012] Figure 3 is an exemplary flow chart of determining linkage adjustment parameters according to some embodiments of this specification;

[0013] Figure 4 This is an exemplary block diagram of determining linkage adjustment parameters according to some embodiments of this specification. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0015] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0016] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0018] In some embodiments, in order to reduce the impact of electrical appliance noise on users, users can control the appliance to run continuously at low power, or manually set timed start / timed shutdown and other operations. However, although this method reduces the noise in the environment, it does not fully utilize the functions of the appliance. Taking air conditioners as an example, continuous low-power operation will reduce the indoor temperature regulation effect of the air conditioner, and timed start / timed shutdown will cause the air conditioner compressor to start and stop frequently, wasting power resources and shortening the life of the compressor.

[0019] In view of this, some embodiments of the present specification provide an electrical appliance control system based on the Internet of Things, which performs environmental monitoring according to actual indoor conditions and performs linkage control on electrical appliances.

[0020] Figure 1 It is a structural diagram of an electrical appliance control system based on the Internet of Things shown in some embodiments of this specification.

[0021] like Figure 1 As shown, the appliance control system 100 based on the Internet of Things may include an environment monitoring module 110 , an appliance gateway 120 and a processor 130 .

[0022] The environment monitoring module 110 is used to collect environment data in the deployment area. In some embodiments, the environment data includes electrical noise data. In some embodiments, the environment data also includes at least one of environment temperature data, environment humidity data, and air quality data.

[0023] In some embodiments, in order to obtain environmental data, the environment monitoring module 110 may include, for example, a sound sensor, a human presence sensor, a temperature / humidity sensor, an air quality sensor, etc. For more description of environmental data, please refer to the relevant content below.

[0024] The deployment area is the area where the environmental monitoring module and the environmental electrical appliances 140 are deployed. For ease of description, the deployment area is described as indoors in the following text. It should be noted that the deployment area can also be a factory building or other area.

[0025] The appliance gateway 120 is used to communicate with the environment appliance 140 to control the operating parameters of the environment appliance 140 and receive the operating data sent by the environment appliance 140. For the operating parameters of the environment appliance 140 and the operating data of the environment appliance 140, please refer to the relevant description below.

[0026] The environmental appliance 140 may be an appliance for adjusting the environment in the deployment area. For example, the environmental appliance 140 may include one or more of an air conditioner, a heater, a fresh air blower, an air purifier, or a humidifier.

[0027] The processor 130 may be a CPU (Central Processing Unit), and the processor 130 is communicatively connected to the environment monitoring module 110 and the appliance gateway 120 respectively.

[0028] In some embodiments, the processor 130 may also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. In some embodiments, the processor 130 is used to process the operating data of the environmental electrical appliance 140 to determine the environmental monitoring parameters, and further determine the linkage adjustment parameters to achieve linkage control of the environmental electrical appliance 140. For more description of the processor 130, please refer to the relevant content below.

[0029] In some embodiments, the appliance control system 100 based on the Internet of Things may further include a storage device 150. In some embodiments, the storage device 150 may be respectively connected to the environment monitoring module 110, the appliance gateway 120, and the processor 130 for communication. In some embodiments, the environment monitoring module 110 may store the environment data acquired by the environment monitoring module 110 in the storage device 150. In some embodiments, the appliance gateway 120 may store the acquired work log of the environment appliance 140 in the storage device 150. In some embodiments, the processor 130 may acquire data in the storage device 150, such as historical environment data, historical operating parameters of the environment appliance 140, and the like.

[0030] It should be noted that the above description of the electrical control system and its device based on the Internet of Things is only for the convenience of description and cannot limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other devices without deviating from this principle.

[0031] Figure 2 This is an exemplary flow chart of an electrical appliance control method based on the Internet of Things as shown in some embodiments of this specification.

[0032] refer to Figure 2 In some embodiments of the present specification, a method for controlling an electrical appliance based on the Internet of Things is provided, including a process 200. One or more steps in the process 200 may be performed by a processor (such as Figure 1 In some embodiments, the processor 130 may execute process 200 multiple times or periodically to achieve long-term electrical appliance control, such as full-time control of electrical appliances.

[0033] Step 210: Acquire the operating data of the environmental appliances in the deployment area through the appliance gateway.

[0034] For the contents about the appliance gateway and environmental appliances, please refer to the relevant descriptions above.

[0035] In some embodiments, the operating data of the ambient electrical appliance may include the usage of the ambient electrical appliance and the work log of the ambient electrical appliance.

[0036] In some embodiments, the usage of environmental appliances may include the switching time of the environmental appliances, the environmental data corresponding to the environmental appliances when they are switched on and off (such as temperature, humidity, air quality, etc.), adjustment data (such as temperature adjustment by the air conditioner, adjustment of the gears of the humidifier and air purifier, etc.) and the environmental data corresponding to the adjustment.

[0037] In some embodiments, the working log of the ambient appliance may include the current, power, and continuous operation time of the ambient appliance when it is running. In some embodiments, depending on the ambient appliance, the working log may also include the wind speed of the air conditioner, the air intake and filter usage of the air purifier, the remaining water volume and atomization power of the humidifier, etc.

[0038] In some embodiments, the operating data of the environmental appliances can be obtained by the appliance gateway and stored in a storage device (such as Figure 1 The data is then stored in the storage device 150 so that the processor can obtain and further analyze and process it.

[0039] For example, the environmental appliance can package and send its operation data to the appliance gateway every time it runs for a period of time (such as 10 minutes or 30 minutes). As another example, the appliance gateway can send a report request at a preset frequency according to different environmental appliances to request the environmental appliances to report the corresponding operation data.

[0040] Step 220, determining environmental monitoring parameters based on the operating data.

[0041] The environmental monitoring parameters are working parameters of the environmental monitoring modules when performing environmental monitoring. In some embodiments, the environmental monitoring parameters may include monitoring time periods and monitoring frequencies corresponding to the various environmental monitoring modules.

[0042] In some embodiments, the processor can use the time period when the user is more likely to control the environmental appliances as a window period, and determine the environmental monitoring parameters based on the operating data during the window period. In some embodiments, the window period can be in days, and the processor can obtain the weather data of the day based on the Internet, and use the time period when the weather data in the historical time is close to the weather data of the day as the window period. For example, assuming that the weather on the day is 32°C and the humidity is 34%, the time period in the historical data when the weather is close to 32°C and the humidity is close to 34% is used as the window period. Weather data can be obtained directly from the weather website via the Internet. In some embodiments, the window period can be half a day or in hours.

[0043] In some embodiments, after dividing the window period, the step of determining the environmental monitoring parameters may include:

[0044] First, the processor can collect statistics based on the operating data within the historical window period to determine the user's environmental control habits. Taking air conditioning as an example, the user's environmental control habits may include the user's preferred time period for using the air conditioning, preferred temperature (i.e., the target temperature set by the user), and average usage time. In some embodiments, the operating data within the historical window period can be obtained by obtaining the historical work log of the environmental appliance.

[0045] Then, the processor estimates the operating load of the environmental appliances at different time periods according to the environmental control habits. For example, the processor can determine the operating load of the equipment under the temperature setting, usage period, and usage time according to the preferred temperature and average usage time in the user's environmental control habits and the parameters of the environmental appliances (such as the number of air conditioners). For example, if the user sets the preferred temperature to 24°C and the current ambient temperature is 32°C, the operating load of a 1.5-horsepower air conditioner during cooling may be 90%.

[0046] Finally, for the period when the operating load exceeds the preset load threshold, the environmental monitoring is performed as the monitoring period, and the monitoring frequency is the default value. Since the noise generated by different environmental electrical loads is different, the period of the preset load threshold can be used as the monitoring period, and since the correlation between the noise generated by different environmental electrical appliances and the power of the corresponding environmental electrical appliances is different, the monitoring frequency corresponding to the monitoring period can be set according to the actual situation of the environmental electrical appliances. Taking summer as an example, when the monitoring period is 15:00-18:00, since the outdoor temperature is usually the highest period of the day, the operating load of the air conditioner is higher during cooling, the monitoring frequency of the area where the air conditioner is located can be increased, such as increasing the acquisition of environmental data from every 20 minutes to every 5 minutes.

[0047] Step 230: Control the environment monitoring module to obtain environment data based on the environment monitoring parameters.

[0048] In some embodiments, the environment monitoring module can obtain environment data at a monitoring frequency during the monitoring period. For more information about the environment monitoring module and environment data, please refer to the relevant description above (such as Figure 1 ).

[0049] Step 240: In response to the electrical appliance noise data in the environmental data satisfying the control condition, a linkage adjustment parameter is determined based on the environmental data.

[0050] For the content of electrical noise data, please refer to the relevant description above. In some embodiments, there may be multiple noise sources in the deployment area. For example, the acquired sound data may contain environmental noise (such as birds singing, cars passing outdoors, etc.) and the sound generated by users talking and walking. Therefore, in some embodiments, the processor can pre-process the sound data, such as by using a bandpass filter or calculating the similarity after dividing the sound data into tracks, to separate the noise track of the ambient electrical appliances and use it as the electrical noise data. The method of separating the electrical noise data is not limited in this specification.

[0051] The control condition may be that when the electrical noise data meets the preset condition, the linkage adjustment parameter is determined based on the current environmental data. In some embodiments, the preset condition may be that the volume and / or duration of the electrical noise data is greater than a preset value. In some embodiments, the preset value may be determined based on the volume of other sounds in the environmental sound data or based on the current time (e.g., the preset value during the day is greater than the preset value at night), or the user may be invited to manually fill in the preset value.

[0052] The linkage adjustment parameters are parameters for jointly regulating the operating parameters of multiple environmental appliances. Usually, adjusting the operating parameters of one environmental appliance cannot effectively reduce the indoor noise. Therefore, it is necessary to adjust the parameters of other environmental appliances accordingly at the same time, so as to reduce the noise while meeting the user's environmental control needs. Exemplarily, the linkage adjustment parameters can be in the form of: [the air conditioner set temperature increases by 1 degree; the wind speed decreases by 1 gear], [the air purifier working gear remains unchanged], [the humidifier gear decreases by 2 gears].

[0053] The user can set the target value of the environmental appliance according to personal preference or environmental conditions. In some embodiments, the target value set by the user can be a target temperature value, a target humidity value, or a target air quality, etc. When the target value set by the user is reached, the environmental appliance can run at low power or suspend operation.

[0054] In some embodiments, the processor may determine the linkage adjustment parameters based on a preset strategy, which may be obtained based on expert experience. For example, when the indoor ambient temperature data does not reach the target value set by the user, the air purifier gear is lowered first, followed by the humidifier gear; if the indoor ambient temperature data reaches the target value set by the user, the air conditioner wind speed is lowered first, and the air purifier working gear is adjusted to the minimum extent.

[0055] Step 250 , generating a linkage adjustment instruction based on the linkage adjustment parameter, and sending the instruction to the appliance gateway to control the operating parameters of the environmental appliance.

[0056] In some embodiments, the appliance gateway can generate linkage adjustment instructions corresponding to each environmental appliance according to the linkage adjustment parameters. Continuing with the above example, when the linkage adjustment parameters are in the form of [air conditioner set temperature rises by 1 degree; wind speed decreases by 1 gear], [air purifier working gear remains unchanged], [humidifier gear decreases by 2 gears], the appliance gateway can generate the corresponding instructions of [set temperature rises by 1 degree; wind speed decreases by 1 gear] and send it to the air conditioner, and generate the corresponding instructions of [gear decreases by 2 gears] and send it to the humidifier, so as to control the operating parameters of the air conditioner and humidifier, thereby reducing the noise in the environment.

[0057] By executing the above-mentioned electrical appliance control method based on the Internet of Things, the actual indoor environmental conditions are monitored and the electrical appliances are controlled in a linkage manner, so that the noise emitted by the environmental electrical appliances can be actively reduced when necessary without the need for manual operation by the user, thereby achieving non-sensing adjustment of the electrical appliances.

[0058] In some embodiments, when a room includes multiple rooms, different rooms can be controlled separately according to different needs of users.

[0059] In some embodiments, the electrical appliance control system based on the Internet of Things further includes an activity monitoring module, which is configured to monitor the activity data of the user. In some embodiments, the activity monitoring module may include a human presence sensor or an infrared monitor, etc. In some embodiments, the activity monitoring module may be communicatively connected to the processor.

[0060] In some embodiments, step 220 further includes: determining the activity characteristics of the user in at least one sub-area based on the activity data; and determining the environmental monitoring parameters based on the activity characteristics of the user and the operating data of the environmental electrical appliances.

[0061] In some embodiments, the deployment area includes multiple sub-areas, each of which may correspond to a room (such as a living room, a dining room, a master bedroom, etc.); in some embodiments, for ease of division, multiple sub-areas may be determined using "doors" as separators.

[0062] The activity data may include the location information of the user at different time periods, and the location information may correspond to any sub-area. For example, the activity data may include: the user is in sub-area B (ie, restaurant) from 12:30 to 12:40, or similar forms.

[0063] In some embodiments, the processor may determine the activity characteristics of the user in at least one sub-area based on the activity data of each sub-area. In some embodiments, the activity characteristics may include the number of active users, activity tracks, and activity frequency characteristics. Among them, active users refer to users who need to be monitored, such as the remaining users after excluding infants or people who appear briefly. The number of active users can be directly determined based on the activity monitoring module.

[0064] In some embodiments, the processor may determine the user's activity trajectory based on the change of the user's position in different sub-areas in the activity data. In some embodiments, the processor may determine the user's activity frequency characteristics based on the number of changes and the change range of the corresponding user's position within a unit time.

[0065] It should be noted that, when there are multiple active users indoors, the activity trajectory and activity frequency characteristics corresponding to each user can be determined based on the activity data.

[0066] In some embodiments, the processor can determine more appropriate environmental monitoring parameters based on the activity characteristics and the current operating data of the environmental appliances. For example, assuming that the sub-area with a low activity frequency characteristic in the activity characteristics can reflect that the user is in a stable and quiet state at this time, and the user has a higher demand for a quiet environment at this time, the processor can relatively increase the frequency of environmental monitoring; while for the sub-area with more active users in the activity characteristics, it may be noisy, so the monitoring of noise can be reduced.

[0067] By dividing the room into sub-areas and combining the characteristics of user activities, environmental monitoring parameters can be determined more accurately, the monitoring frequency can be reduced when noise is not sensitive, monitoring resources can be saved, and unnecessary monitoring and adjustment can be avoided.

[0068] In some embodiments, determining the environmental monitoring parameters may include: determining the user's tolerance to electrical noise at different time periods based on the user's activity characteristics in at least one sub-area; predicting future electrical noise data through an electrical noise model based on the operation data, wherein the electrical noise model is a machine learning model; and determining the environmental monitoring parameters based on the electrical noise tolerance and future electrical noise data. The user's activity characteristics in at least one sub-area may be determined in the manner described above, which will not be repeated here.

[0069] The electrical noise tolerance refers to the maximum electrical noise level that users can accept, that is, the minimum value that the electrical appliances need to be adjusted to the corresponding environment. When the electrical noise data exceeds the electrical noise tolerance, it may cause dissatisfaction among users.

[0070] In some embodiments, the processor may query the noise tolerance table to determine the user's tolerance to electrical noise at different time periods based on the activity characteristics of the sub-areas. The noise tolerance table may be preset based on expert experience. For example, the noise tolerance table may indicate that the noise tolerance for older users is higher, and the noise tolerance for users in the period of 12:00 to 14:00 is lower. In some embodiments, the processor may allow the user to audition different noises through an APP or other means, and the user may select the electrical noise tolerance according to the actual situation.

[0071] Since different sub-areas correspond to different rooms, and the user's tolerance to noise in the room varies according to the room, in some embodiments, the processor may determine the activity sound data through the activity database based on the user activity characteristics and the area attributes of the current sub-area; and determine the electrical appliance noise tolerance of the current sub-area based on the activity sound data.

[0072] The area attribute of the sub-area indicates the purpose of the room corresponding to the current sub-area, such as whether the sub-area corresponds to the master bedroom, living room or dining room.

[0073] In some embodiments, the activity sound data represents the predicted value of the sound emitted by the user's activity in the current sub-region. The activity sound data can be constructed by constructing an activity vector by combining the user's activity features and the regional attributes of the sub-region, and matching the activity vector in the activity database to obtain a reference activity vector, and the activity sound data corresponding to the reference activity vector is used as the activity sound data of the user in the current sub-region. In some embodiments, the vector matching method can be to select the candidate activity vector with the closest vector distance (such as Euclidean distance or cosine distance, etc.) as the reference activity vector. In some embodiments, other vector matching methods can also be used.

[0074] The activity database contains historical activity sound data under multiple conditions of different user activity characteristics and regional attributes of sub-areas. The historical activity sound data can be obtained by manually screening the historical data of the activity monitoring module and the environment monitoring module, and multiple historical activity sound data are constructed into an activity database.

[0075] In some embodiments, based on the active sound data, the active sound data can be corrected in combination with the correction value selected by the user or the preset correction value to determine the electrical noise tolerance of the current sub-area. For example, assuming that the correction value is 1.2, and the active sound data of the current sub-area determined by the processor is 40 decibels, the electrical noise tolerance can be 40×1.2=48 decibels. In some embodiments, the correction value can be determined by the user's questionnaire survey results so that the electrical noise tolerance meets the user's actual feelings.

[0076] The activity database constructed through historical data can accurately determine the sound produced by the activity, and thus obtain an electrical noise tolerance that is more in line with the actual situation.

[0077] In some embodiments, the electrical appliance noise model in the foregoing prediction of future electrical appliance noise data by means of the electrical appliance noise model may be a machine learning model obtained by training with training samples.

[0078] In some embodiments, the appliance noise model may be a neural network model, such as RNN (Recurrent Neural Network). In some embodiments, the appliance noise model may be used to predict the appliance noise of an appliance in a certain environment. For example, when the model is used to predict the noise of an air conditioner, the input of the appliance noise model includes the operating data of the air conditioner, and the output is the noise data of the future air conditioner. In some embodiments, multiple appliance noise models may be trained according to different environment appliances, and the actual operating data may be input into the corresponding appliance noise model.

[0079] In some embodiments, the electrical noise model can input the operation data corresponding to different environmental electrical appliances to obtain the noise data of the environmental electrical appliances in the future. At this time, the input of the electrical noise model also includes the environmental electrical appliances corresponding to the current operation data. In some embodiments, the environmental electrical appliances in the input of the electrical noise model can be values. For example, the environmental electrical appliances can be converted into the form of values ​​by One-Hot encoding or the like. For example, air conditioner A can correspond to 10, air conditioner B can correspond to 11, humidifier can correspond to 20, fresh air blower can correspond to 30, etc.

[0080] In some embodiments, the noise data of future environmental appliances may be a time-ordered sequence, and the noise data of future environmental appliances represent the noise data that will be generated within a period of time in the future without changing the current operating data of the environmental appliances.

[0081] In some embodiments, the electrical noise model can be obtained by training multiple labeled training samples. Specifically, multiple labeled training samples can be input into the electrical noise model, and a loss function can be constructed by using the labels and the results of the initial electrical noise model. The parameters of the initial electrical noise model can be iteratively updated based on the loss function by gradient descent or other methods. When the preset conditions are met, the model training is completed and a trained electrical noise model is obtained. The preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0082] In some embodiments, for any type of environmental electrical appliances, noise data within multiple historical time periods selected randomly or manually can be obtained when constructing training samples, and the operating data corresponding to the initial moment in each historical time period can be used as training samples, and the noise data within the historical time period can be used as labels.

[0083] In some embodiments, when the noise volume in a certain time period of future appliance noise data exceeds the user's appliance noise tolerance, the processor can use this time period as an environmental monitoring period, and the monitoring frequency within this monitoring period can be higher than the normal setting value. In addition, the more the noise data within this time period exceeds the user's appliance noise tolerance, the higher the monitoring frequency can be.

[0084] By utilizing the user's activity information and the actual purpose of the room, the user's noise tolerance in the room can be determined more accurately, and then more appropriate environmental monitoring parameters can be determined.

[0085] In some embodiments, when a room corresponds to multiple sub-areas, the environmental monitoring parameters may include monitoring frequencies corresponding to different sub-areas to save monitoring resources. In some embodiments, determining environmental monitoring parameters includes determining spatiotemporal data of changes in the number of users in each sub-area based on activity data; determining flow characteristics of users between different sub-areas based on the spatiotemporal data of changes in the number of users; and determining monitoring frequencies corresponding to different sub-areas based on the flow characteristics.

[0086] In some embodiments, the spatiotemporal data of the change in the number of users indicates the movement of the corresponding users between the sub-areas when the number of users in the monitored sub-areas changes, that is, which room the users go from and which room they go to.

[0087] In some embodiments, the flow feature indicates the feature of users flowing from one sub-area to other sub-areas, and the flow feature may indicate the flow direction (such as leaving or entering), flow time, and the number of flowing users, etc. For example, the flow feature may include: living room - corridor - bedroom, 10:00:32-10:00:40, and the number of users is 1.

[0088] In some embodiments, based on the spatiotemporal data of the change in the number of users, the difference in time or distance between the users appearing successively in two subspaces is determined, and the flow characteristics are determined based on the relationship between the difference and the preset spatiotemporal difference.

[0089] The preset time-space difference is used to determine whether the flow characteristics determined by the processor in the above steps belong to the flow characteristics within the error range. When the difference in time or distance between the user's appearance in two subspaces is greater than the preset time-space difference, the flow characteristics may be inaccurate due to reasons such as monitoring blind spots, which will lead to inaccurate monitoring frequencies determined subsequently. Therefore, the processor can use the data where the difference in time or distance between the user's appearance in two subspaces is less than the preset time-space difference as the flow characteristics.

[0090] For example, the preset time-space difference may be a time difference of less than 10 seconds or a spatial distance of less than 50 cm, etc. In some embodiments, the preset time-space difference may be preset and determined according to the size of the room, and the preset time-space difference is positively correlated with the size of the room.

[0091] In some embodiments, the processor may classify multiple sub-areas according to flow characteristics. For example, the flow characteristics may include the number of users entering the sub-area and the number of users leaving the sub-area; when the flow characteristics indicate that the number of users entering the sub-area is greater than the number of users leaving the sub-area, the level of the sub-area is higher; accordingly, the sub-area with a higher level corresponds to a higher monitoring frequency, so that the noise of the sub-area with a larger number of users is monitored more intensively.

[0092] By determining the flow characteristics of users, we can avoid ineffective monitoring of a sub-area when users temporarily pass through or stay in the sub-area for a short time, and focus monitoring on sub-areas with more users, thereby improving monitoring efficiency and reducing the waste of monitoring resources.

[0093] In some embodiments, the environmental data also includes at least one of environmental temperature data, environmental humidity data, and air quality data. For ease of description, the following text takes the environmental data including environmental temperature data, environmental humidity data, and air quality data as well as the electrical appliance noise data in the previous text as an example for explanation. It should be noted that the composition of the environmental data can be determined according to the actual setting of the environmental appliances. When different rooms (sub-areas) have different environmental appliances, the composition of the environmental data can be different.

[0094] Figure 3 This is an exemplary flow chart for determining linkage adjustment parameters according to some embodiments of this specification.

[0095] like Figure 3 As shown, in some embodiments, step 240 may also include:

[0096] Step 310 , in response to the electrical appliance noise data satisfying the future control condition, based on at least one of the ambient temperature data, the ambient humidity data and the air quality data, determining the estimated control demand of the ambient electrical appliances in the future period.

[0097] The future control condition indicates a condition for controlling the noise of an electrical appliance in the future. In some embodiments, the future control condition may be satisfied when the monitored electrical appliance noise data continues to increase. For example, the environmental monitoring module acquires noise data multiple times over a period of time, and when the noise data gradually increases over time or shows an increasing trend (such as fluctuations but an increase in overall noise), it is determined that the electrical appliance noise data satisfies the future control condition.

[0098] In some embodiments, future control conditions may include: when the appliance noise data reaches a warning threshold and the appliance noise in a certain period of time in the future appliance noise data reaches a preset noise threshold, the similarity between the change data of the appliance noise data and the change data of the future appliance noise data of the corresponding period predicted by the appliance noise model exceeds the risk threshold.

[0099] In some embodiments, the warning threshold represents a threshold that does not reach the preset noise threshold but needs to be paid special attention to. Exemplarily, the warning threshold may be 70% or 80% of the preset noise threshold. In some embodiments, the size of the warning threshold may be determined according to the time required to determine the linkage adjustment parameter (such as the time required to perform step 240). The longer the time required, the lower the warning threshold may be, so as to leave more time to determine the linkage adjustment parameter.

[0100] In some embodiments, the change data of the electrical noise data refers to the change of the electrical noise data within a unit time period. In some embodiments, the change data of the electrical noise data can be a sequence in time order, and each element in the sequence corresponds to the change of the electrical noise data at a time point, such as a 10% increase in noise or a 3 decibel increase in noise.

[0101] For the contents of the electrical noise model, please refer to the relevant description above. In some embodiments, similar to the change data of the electrical noise data, the processor can obtain the change data of the future electrical noise data based on the future electrical noise data of the corresponding time period predicted by the electrical noise model, that is, the noise change between any moment in the future electrical noise data and the previous moment is used as an element in the change data of the future electrical noise data.

[0102] In some embodiments, for the change data of the electrical noise data and the future electrical noise data of the electrical noise data of the same length, the similarity between the two sequences can be directly calculated. In some embodiments, the change data of the electrical noise data and the future electrical noise data of the electrical noise data can also be converted into vectors and then the similarity is calculated.

[0103] Exemplarily, assuming that the processor obtains the change data of the electrical appliance noise data corresponding to 6:00-7:00, and the electrical appliance noise model predicts the future electrical appliance noise data from 6:00-12:00 and obtains the change data of the future electrical appliance noise data, then the similarity between the change data of the electrical appliance noise data from 6:00-7:00 and the change data of the future electrical appliance noise data can be calculated. When the similarity between the change data of the electrical appliance noise data and the change data of the future electrical appliance noise data of the corresponding time period predicted by the electrical appliance noise model exceeds the risk threshold, it means that the electrical appliance noise data actually monitored at present (such as the 6:00-7:00 time period) is consistent with the prediction, that is, if the electrical appliance noise of a certain time period (such as a certain time period within the 7:00-12:00 time period) in the predicted future electrical appliance noise data reaches the preset noise threshold, it is actually very likely to occur as scheduled. At this time, the processor can adjust the linkage adjustment parameters of the environmental electrical appliances in advance to control the operating parameters of the environmental electrical appliances.

[0104] By setting the future control condition in combination with the future electrical appliance noise data, the linkage adjustment parameters can be adjusted in advance to adjust the environmental electrical appliances, thereby preventing the electrical appliance noise from reaching the preset noise threshold.

[0105] The estimated control demand indicates that in the future period, the environmental control effect of the environmental appliance under the default operating power and operating duration is the minimum operating frequency required to meet the target value set by the user. In some embodiments, the default operating power and operating duration can be based on the target value set by the user (such as target temperature, target humidity, etc.), and the corresponding operating power and operating duration of the environmental appliance can be determined through simulation experiments or based on the instructions of the environmental appliance.

[0106] The minimum operating frequency required to meet the target value set by the user refers to how often the ambient electrical appliances are operated so that the ambient temperature meets the target value set by the user. For example, if the temperature set by the user is 26°C, when the room temperature reaches 26°C, the air conditioner can be started every half an hour to maintain the room temperature at 26°C. At this time, the minimum operating frequency of the air conditioner is every half an hour. In some embodiments, the operating time corresponding to the minimum operating frequency can adopt the system default value. Continuing with the above example, the air conditioner can be started every half an hour and run for 15 minutes each time to maintain the room temperature at 26°C.

[0107] In some embodiments, the processor may determine the estimated control requirements for future time periods based on the ambient temperature data, ambient humidity data, and air quality data as well as the default operating power and operating time of the corresponding ambient electrical appliances.

[0108] In some embodiments, the processor can construct an environmental control map based on the regional attributes of the sub-areas, environmental data, operating data of environmental appliances and user flow characteristics; and determine the estimated control needs of different subspaces through a control demand model based on the environmental control map.

[0109] The regional attributes, environmental data, operating data of environmental appliances and flow characteristics of users of the sub-region can be obtained based on the methods described above and will not be repeated here.

[0110] In some embodiments, the environmental control map can be a graph including nodes and edges, wherein the nodes represent sub-areas; the node features include temperature data, humidity data, air quality data, and operating data of the environmental control equipment within the sub-areas; the edges represent the flow of personnel between sub-areas, that is, there is an edge between two sub-areas where personnel can flow; the characteristics of the edges include the flow characteristics between the two subspaces.

[0111] The control demand model may be a GNN (Graph Neural Networks) model. The input of the control demand model is the environmental control map, and the output of the model is the estimated control demand corresponding to each sub-area (each node).

[0112] The training process of the control demand model can be similar to that of the electrical noise model, and the details can be found in the relevant description above. The training samples of the control demand model can be sample environmental maps constructed by temperature data, humidity data, air quality data, and operating data of environmental control equipment in each sub-area in the historical environmental electrical appliance operating data; the labels corresponding to the training samples can be manually screened and labeled based on the degree of compliance between the environmental data and the user settings in the historical data corresponding to the sample environmental map, wherein, if the environmental data in the period between two operations of the environmental control equipment in the historical data meets the user's settings for the environmental control equipment, the actual operating frequency of the equipment corresponding to this period is added to the candidates, and the actual operating frequency of the period with the minimum control demand is used as the training label.

[0113] By constructing an environmental control map and using the control demand model, the estimated control demand corresponding to each sub-area can be quickly obtained.

[0114] Step 320, determining linkage adjustment parameters based on the estimated control demand and the appliance noise data.

[0115] In some embodiments, based on the estimated control requirements and the electrical appliance noise data, the processor can arrange the operation time of multiple environmental control devices in a staggered manner according to the minimum operating frequency required by different environmental appliances. Taking the environmental appliances including air conditioners and humidifiers as an example, when the estimated control requirements are met, based on the minimum operating frequency of the air conditioners and humidifiers, the air conditioners and humidifiers are controlled to operate alternately to avoid multiple environmental appliances working at the same time and causing increased noise.

[0116] When future control conditions are met, the estimated control needs for future periods are first determined, and then the linkage adjustment parameters are determined so that environmental appliances can be operated in staggered periods, ensuring their working efficiency while reducing appliance noise.

[0117] Figure 4 This is an exemplary block diagram of determining linkage adjustment parameters according to some embodiments of this specification.

[0118] In some embodiments, in order to obtain more appropriate linkage adjustment parameters, step 320 may include:

[0119] According to the current operating parameters of the environmental electrical appliances, at least one adjustment period is determined; based on the estimated control requirements, the current operating parameters of at least one adjustment period are adjusted to obtain at least one set of optional adjustment parameters, and the estimated environmental data corresponding to at least one optional adjustment parameter is determined; based on the match between the estimated environmental data and the target value set by the user, the balance value of at least one set of optional adjustment parameters is determined; based on the balance value, the linkage adjustment parameter is determined.

[0120] The adjustment period refers to a period during which the current operating parameters of the environmental electrical appliances need to be adjusted. In some embodiments, the period during which multiple environmental control devices are running simultaneously can be used as the adjustment period.

[0121] Based on the estimated control demand, the starting point for adjusting the current operating parameters of at least one adjustment period may be to appropriately reduce the power of the environmental appliances, but extend the working time of the environmental appliances to meet the environmental requirements set by the user.

[0122] In some embodiments, due to the presence of multiple environmental appliances, with the estimated control demand as a constraint, the processor can obtain multiple sets of optional adjustment parameters based on the method described above (such as step 240), and the processor can determine the estimated environmental data corresponding to at least one optional adjustment parameter. In some embodiments, the processor can determine the estimated environmental data corresponding to all optional adjustment parameters respectively, so as to select a better optional adjustment parameter.

[0123] In some embodiments, the estimated environmental data corresponding to the optional adjustment parameters may include estimated electrical appliance noise data and estimated other environmental data. The estimated electrical appliance noise data may be obtained through the electrical appliance noise model mentioned above.

[0124] In some embodiments, determining the estimated electrical appliance noise data in the estimated environmental data corresponding to at least one set of optional adjustment parameters may include: determining reference operating data in a historical operating database based on the adjusted operating parameters; and determining the estimated electrical appliance noise data through an electrical appliance noise model based on the reference operating data.

[0125] In some embodiments, the processor may use a set of optional adjustment parameters as the adjusted operating parameters of the environmental electrical appliance. In some embodiments, reference operating data may be determined in a historical operating database based on the adjusted operating parameters, and the historical operating database may include operating data of the environmental electrical appliance under different operating parameters. In some embodiments, the processor may record the operating parameters and the corresponding operating data during daily use of the environmental electrical appliance, and construct a historical operating database.

[0126] In some embodiments, the processor may directly use the reference operation data determined in the historical operation database as the input of the electrical appliance noise model to obtain the estimated electrical appliance noise data corresponding to the adjusted operation parameters.

[0127] In some embodiments, the estimated other environmental data may include the estimated ambient temperature, estimated humidity and estimated air quality under the current optional adjustment parameters, and the estimated other environmental data may be determined based on historical environmental data corresponding to historical operating parameters similar to the current optional adjustment parameters.

[0128] The matching degree reflects the matching degree between the environmental control effect and the target value set by the user. In some embodiments, the higher the matching degree, the more consistent the estimated environmental data is with the environment expected by the user.

[0129] For example, the matching degree can be the product of the similarity between the ambient temperature and the target temperature set by the user, the similarity between the ambient humidity and the target humidity set by the user, and the difference between the air quality and the critical quality point. The critical quality point is the minimum value of the air quality in the historical records each time the user turns on the air purifier. The similarity between the ambient temperature and the target temperature set by the user can be: 1-(the absolute value of the difference between the ambient temperature and the target temperature) / target temperature. Similarly, the similarity between the ambient humidity and the target humidity set by the user can be obtained.

[0130] The balance value is a value used to balance the electrical appliance noise corresponding to the optional adjustment parameter and the environmental adjustment effect. In some embodiments, the smaller the estimated electrical appliance noise data is, the higher the matching degree is, and the larger the balance value is.

[0131] Exemplarily, the balance value can be calculated as follows: k1*(preset noise threshold / estimated appliance noise data)+k2*matching degree, where k1 and k2 are coefficients, and the values ​​of k1 and k2 are related to the difference between the target value set by the user and the natural environment. If the difference is small, it means that the user may not be in a hurry to adjust the indoor environment, so k1>k2 can be used to reduce noise as much as possible.

[0132] In some embodiments, the processor may obtain balance values ​​corresponding to multiple groups of optional adjustment parameters, and select a group of optional adjustment parameters with the largest balance value as the linkage adjustment parameters.

[0133] By taking into account the expected environmental control effect and the degree of match between the noise data and the target value set by the user, the most suitable set of optional adjustment parameters can be selected from multiple sets of optional adjustment parameters as the linkage adjustment parameters to improve the environmental control effect and ensure that the noise is controllable.

[0134] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0135] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0136] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0137] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0138] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0139] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An electrical appliance control system based on the Internet of Things, characterized in that: include: An environment monitoring module is configured to collect environmental data in a deployment area, the environmental data including electrical appliance noise data, and the deployment area includes a plurality of sub-areas; An activity monitoring module configured to monitor activity data of a user; Electrical appliance gateway, communicating with environmental electrical appliances; A processor is communicatively connected to the environment monitoring module and the appliance gateway, and is configured to: Acquiring operation data of the environmental appliances in the deployment area through the appliance gateway; Based on the operating data, an environmental monitoring parameter is determined; in order to determine the environmental monitoring parameter, the processor is further configured to: Determining, based on the activity data, an activity characteristic of the user in at least one of the sub-areas; Determine the environment monitoring parameter based on the activity characteristics of the user and the operation data; wherein, in order to determine the environment monitoring parameter, the processor is further configured to: Determining the user's tolerance to electrical appliance noise at different time periods based on the user's activity characteristics in at least one of the sub-areas; Based on the operation data, predicting future electrical appliance noise data by using an electrical appliance noise model, wherein the electrical appliance noise model is a machine learning model; Determining the environmental monitoring parameters based on the electrical appliance noise tolerance and the future electrical appliance noise data; Controlling the environment monitoring module to obtain the environment data based on the environment monitoring parameters; In response to the electrical appliance noise data in the environmental data satisfying the control condition, a linkage adjustment parameter is determined based on the environmental data, wherein the environmental data further includes at least one of environmental temperature data, environmental humidity data, and air quality data; wherein, in order to determine the linkage adjustment parameter, the processor is further configured to: In response to the electrical appliance noise data satisfying the future control condition, based on at least one of the ambient temperature data, the ambient humidity data and the air quality data, determining the estimated control demand of the ambient appliance in the future period; the determination of the estimated control demand includes: Building an environmental control map based on the regional attributes of the sub-region, the environmental data, the operating data of the environmental appliances and the flow characteristics of the users; Determine the estimated control demand of different subspaces through a control demand model based on the environmental control map, wherein the control demand model is a graph neural network model; Determining the linkage adjustment parameter based on the estimated control demand and the electrical appliance noise data; A linkage adjustment instruction is generated based on the linkage adjustment parameter and sent to the appliance gateway to control the operating parameters of the environmental appliance.

2. The system according to claim 1, characterized in that The processor is further configured to: Determining at least one adjustment period according to current operating parameters of the environmental electrical appliance; Based on the estimated control demand, the current operating parameters of the at least one adjustment period are adjusted to obtain at least one set of optional adjustment parameters, and estimated environmental data corresponding to the at least one set of optional adjustment parameters are determined; Determining a balance value of the at least one set of optional adjustment parameters based on a degree of match between the estimated environmental data and a target value set by a user; Based on the balance value, the linkage adjustment parameter is determined.

3. An electrical appliance control method based on the Internet of Things, executed by a processor of an electrical appliance control system based on the Internet of Things, characterized in that: The electrical appliance control system based on the Internet of Things also includes an environment monitoring module, an activity monitoring module, and an electrical appliance gateway; The environment monitoring module is configured to collect environment data in a deployment area, the environment data including electrical appliance noise data, and the deployment area includes a plurality of sub-areas; The activity monitoring module is configured to monitor the activity data of the user; The electrical appliance gateway is communicatively connected with the environmental electrical appliances; The processor is communicatively connected with the environment monitoring module and the electrical appliance gateway; The method comprises: Acquiring operation data of the environmental appliances in the deployment area through the appliance gateway; Determining environmental monitoring parameters based on the operating data; wherein determining environmental monitoring parameters based on the operating data includes: Determining, based on the activity data, an activity characteristic of the user in at least one of the sub-areas; Determining the environmental monitoring parameter based on the activity characteristics and the operating data of the user; wherein determining the environmental monitoring parameter based on the activity characteristics and the operating data of the user comprises: Determining the user's tolerance to electrical appliance noise at different time periods based on the user's activity characteristics in at least one of the sub-areas; Based on the operation data, predicting future electrical appliance noise data by using an electrical appliance noise model, wherein the electrical appliance noise model is a machine learning model; Determining the environmental monitoring parameters based on the electrical appliance noise tolerance and the future electrical appliance noise data; Controlling the environment monitoring module to obtain the environment data based on the environment monitoring parameters; In response to the electrical appliance noise data in the environmental data satisfying the control condition, determining a linkage adjustment parameter based on the environmental data, wherein the environmental data further includes at least one of environmental temperature data, environmental humidity data and air quality data; In response to the electrical appliance noise data satisfying the future control condition, based on at least one of the ambient temperature data, the ambient humidity data and the air quality data, determining the estimated control demand of the ambient appliance in the future period; the determination of the estimated control demand includes: Building an environmental control map based on the regional attributes of the sub-region, the environmental data, the operating data of the environmental appliances and the flow characteristics of the users; Determine the estimated control demand of different subspaces through a control demand model based on the environmental control map, wherein the control demand model is a graph neural network model; Determining the linkage adjustment parameter based on the estimated control demand and the electrical appliance noise data; A linkage adjustment instruction is generated based on the linkage adjustment parameter and sent to the appliance gateway to control the operating parameters of the environmental appliance.

4. The method according to claim 3, characterized in that The step of determining the linkage adjustment parameter based on the estimated control demand and the electrical appliance noise data includes: Determining at least one adjustment period according to current operating parameters of the environmental electrical appliance; Based on the estimated control demand, the current operating parameters of the at least one adjustment period are adjusted to obtain at least one set of optional adjustment parameters, and estimated environmental data corresponding to the at least one set of optional adjustment parameters are determined; Determining a balance value of the at least one set of optional adjustment parameters based on a degree of match between the estimated environmental data and a target value set by a user; Based on the balance value, the linkage adjustment parameter is determined.

5. An electrical appliance control device based on the Internet of Things, comprising a processing device, characterized in that: The processing device is used to execute the electrical appliance control method based on the Internet of Things as described in any one of claims 3 to 4.

6. A computer-readable storage medium storing computer instructions, characterized in that: After the computer reads the computer instructions in the storage medium, the computer executes the electrical appliance control method based on the Internet of Things as described in any one of claims 3 to 4.

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