Method and apparatus for controlling a sleep system, sleep system, storage medium

Abstract

The application relates to the technical field of intelligent household appliances, and discloses a method for controlling a sleep system, characterized in that the method comprises the following steps: obtaining new sleep environment data of the sleep system and optimal sleep environment data of an optimal sleep environment system; and calculating the similarity between the sleep system and the optimal sleep environment system according to the equipment information in the new sleep environment data and the optimal sleep environment data. According to the method, the similarity between the sleep system and the optimal sleep environment system is calculated according to the equipment information in the new sleep environment data and the optimal sleep environment data, so that when the optimal sleep environment data suitable for user sleep is introduced into the sleep system, it can be accurately judged whether the sleep system has hardware support or not, so as to create an optimal sleep environment suitable for the user's sleep habit. The application further discloses a device for controlling a sleep system, a sleep system and a storage medium.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent household appliances, for example to a method and device for controlling a sleep system, a sleep system, and a storage medium. BACKGROUND

[0002] At present, many intelligent household appliances on the market have a sleep mode. Before sleeping, a user can start the sleep mode of the intelligent household appliance, so that the intelligent household appliance can work according to the sleep mode, thereby adjusting the sleep environment of the user. However, not all intelligent household appliances have a sleep mode, and the sleep system is difficult to meet the sleep needs of different users.

[0003] The related technology discloses a sleep environment adjusting method, which comprises the following steps: obtaining attribute information of a target user, the target user being a user who needs to adjust the sleep environment; determining a candidate user with the same attribute information as the target user based on the attribute information of the target user, the candidate user being a user who has adjusted the sleep environment in a historical time period; determining a current environment parameter of the target user according to a popularity algorithm based on a historical environment parameter of the candidate user, the historical environment parameter and the current environment parameter being the same kind of environment parameter, and the environment parameter comprising at least one of temperature, humidity, air quality and light intensity; and controlling a target household device according to the current environment parameter of the target user, the target household device being used to adjust the environment parameter of the sleep environment of the target user.

[0004] In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related technology:

[0005] When the sleep system imports the best sleep environment data suitable for the user to sleep, it is difficult to determine whether there is hardware support in the sleep system to create the best sleep environment suitable for the user's sleep habits.

[0006] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present application, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] To have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not a general review, nor is it intended to determine key / important components or delineate the scope of protection of these embodiments, but as a prelude to the detailed description below.

[0008] The embodiments of the present disclosure provide a method and device for controlling a sleep system, a sleep system, and a storage medium, to accurately determine whether there is hardware support in the sleep system when the sleep system imports the best sleep environment data suitable for the user to sleep, so as to create the best sleep environment suitable for the user's sleep habits.

[0009] In some embodiments, the method comprises: obtaining new sleep environment data of the sleep system and optimal sleep environment data of the optimal sleep environment system; and calculating the similarity between the sleep system and the optimal sleep environment system according to the device information in the new sleep environment data and the optimal sleep environment data.

[0010] Optionally, the device information comprises device levels and total device functions; and the similarity between the sleep environment system and the optimal sleep environment system is calculated according to the device levels and the total device functions in the new sleep environment data and the optimal sleep environment data. Optionally, the device information further comprises a MAC address of the device.

[0011] Optionally, the device levels comprise a first type of device and a second type of device; and the similarity between the sleep environment system and the optimal sleep environment system is calculated according to the device levels and the total device functions in the new sleep environment data and the optimal sleep environment data, comprising: determining the similarity between the sleep environment system and the optimal sleep environment system as zero in the case that the first type of device in the optimal sleep environment data is not a subset of the first type of device in the new sleep environment data; and calculating the similarity between the sleep environment system and the optimal sleep environment system according to a similarity factor and the total device functions in the case that the first type of device in the optimal sleep environment data is a subset of the first type of device in the new sleep environment data.

[0012] Optionally, the first type of device comprises an air conditioner, a mattress, a bed frame, and / or a pillow; and the second type of device comprises a humidifier, a dehumidifier, a lamp, and / or a scent.

[0013] Optionally, the similarity between the sleep environment system and the optimal sleep environment system is calculated according to a similarity factor and the total device functions, comprising: determining a first similarity factor and a second similarity factor according to the number of set missing devices of the second type of device and the number of set missing device functions of the first type of device in the new sleep environment data relative to the optimal sleep environment data; and calculating the similarity between the sleep environment system and the optimal sleep environment system according to the first similarity factor, the second similarity factor, the total device functions in the new sleep environment data, and the total device functions in the optimal sleep environment data.

[0014] Optionally, a first similarity factor and a second similarity factor are determined based on the number of missing device settings for Category II devices and the number of missing device functions for Category I devices in the new sleep environment data relative to the optimal sleep environment data. This includes: determining the second similarity factor based on the first similarity value when the ratio of the number of missing settings for Category II devices in the new sleep environment data relative to the optimal sleep environment data to the total number of settings for Category II devices in the optimal sleep environment data is a first ratio, and the ratio of the number of missing device functions for Category I devices in the new sleep environment data relative to the optimal sleep environment data to the total number of settings for Category I devices in the optimal sleep environment data is a second ratio; and determining the second similarity factor based on the second similarity value when the ratio of the number of missing device functions for Category I devices in the new sleep environment data relative to the optimal sleep environment data to the total number of settings for Category I devices in the optimal sleep environment data is a third ratio. Specifically, when the total number of settings for Category I devices in the optimal sleep environment data is 10, and the total number of settings for Category II devices in the optimal sleep environment data is 10, the first ratio can be 80%, the second ratio can be 20%, and the third ratio can be 10%. The first similarity value can be 80%, and the second similarity value can be 90%.

[0015] Optionally, the total number of device functions includes the total number of Class I device functions and the total number of Class II device functions. Based on the first similarity factor, the second similarity factor, the total number of device functions in the new sleep environment data, and the total number of device functions in the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated, including: calculating S = α × (k × m1 + m2) / (k × n1 + n2); where S is the similarity between the sleep system and the optimal sleep environment system, α is the first similarity factor, k is the second similarity factor, m1 is the total number of Class I device functions in the optimal sleep environment data, m2 is the total number of Class II device functions in the optimal sleep environment data, n1 is the total number of Class I devices in the new sleep environment data that have the same Class I device functions as those in the optimal sleep environment data, and n2 is the total number of Class II devices in the new sleep environment data that have the same Class II device functions as those in the optimal sleep environment data.

[0016] Optionally, after calculating the similarity between the sleep system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data, the method further includes: determining the sleep scenario created by the sleep system based on the similarity.

[0017] Optionally, determining the sleep scenario created by the sleep system based on similarity includes: when the similarity is in the first similarity interval, determining that the sleep system can create a scenario very close to the optimal sleep environment; when the similarity is in the second similarity interval, determining that the sleep system can create a scenario relatively close to the optimal sleep environment; when the similarity is in the third similarity interval, determining that the sleep system can create a scenario similar to the optimal sleep environment but with a large difference; and when the similarity is in the fourth similarity interval, determining that the sleep system cannot create a scenario close to the optimal sleep environment. Specifically, the value of the first similarity interval can be [90%, 100%], the value of the second similarity interval can be [80%, 90%], the value of the third similarity interval can be [70%, 80%], and the value of the fourth similarity interval can be [0%, 70%].

[0018] Optionally, after calculating the similarity between the sleep system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data, the method further includes: controlling the sleep system to import the optimal sleep environment data based on the similarity.

[0019] Optionally, the sleep system can be controlled to import optimal sleep environment data based on similarity, including: when the similarity is greater than or equal to a similarity threshold, the sleep system can be controlled to import optimal sleep environment data in stages according to the device's functional level; when the similarity is less than the similarity threshold, a prompt message can be sent to the user indicating the similarity and asking the user to select the device from which the optimal sleep environment data needs to be imported. Specifically, the similarity threshold can be 80%.

[0020] Optionally, the device function level includes a first-level device function and a second-level device function; the sleep system is controlled to import optimal sleep environment data in stages according to the device function level, including: controlling a type of device in the sleep system to import optimal sleep environment data or controlling the sleep system to send a reminder message to the user that the hardware does not support it, according to the first-level device function; when importing optimal sleep environment data, the second type of device in the sleep system is controlled to continue importing optimal sleep environment data according to the second-level device function.

[0021] Optionally, based on the first-level device function, control one type of device in the sleep system to import optimal sleep environment data or control the sleep system to send a hardware-unsupported reminder message to the user, including: when one type of device in the sleep system fully meets the first-level device function, control one type of device in the sleep system to import optimal sleep environment data; when one type of device in the sleep system does not fully meet the first-level device function, control the sleep system to send a hardware-unsupported reminder message to the user.

[0022] Optionally, first-level equipment features include: a mattress or bed frame firmness that matches the optimal sleep environment, and / or, a mattress or bed frame providing the optimal sleeping angle, and / or, a mattress massage function, and / or a mattress position adjustment function, and / or, an air conditioner providing the optimal sleep temperature.

[0023] Optionally, before controlling a type of device in the sleep system to import optimal sleep environment data or control the sleep system to send a hardware-unsupported reminder message to the user based on the first-level device function, the method further includes: determining whether a type of device in the sleep system is online based on its MAC address. If a type of device in the sleep system is online, the method controls the import of optimal sleep environment data or sends a hardware-unsupported reminder message to the user based on the first-level device function. If a type of device in the sleep system is offline, an offline reminder is sent to the user until the type of device comes online.

[0024] Optionally, based on the second-level device function control, the second-class devices in the sleep system continue to import optimal sleep environment data, including: calculating the actual number of device functions of the second-class devices in the sleep system that meet the second-level device function; sending the actual number of items and the target number of device functions of the second-class devices in the optimal sleep environment data that meet the second-level device function; and controlling the second-class devices in the sleep system that meet the second-level device function to continue importing optimal sleep environment data according to user instructions.

[0025] Optionally, before calculating the actual number of device functions that satisfy the Level 2 device function for the Class II devices in the sleep system, the method further includes: determining whether the Class II devices in the sleep system are online based on their MAC addresses. If the Class II devices in the sleep system are online, the method calculates the actual number of device functions that satisfy the Level 2 device function for the Class II devices in the sleep system. If the Class II devices in the sleep system are offline, a device offline reminder is sent to the user.

[0026] In some embodiments, the apparatus includes: an acquisition module configured to acquire new sleep environment data of the sleep system and optimal sleep environment data of the optimal sleep environment system; and a calculation module configured to calculate the similarity between the sleep system and the optimal sleep environment system based on device information in the new sleep environment data and the optimal sleep environment data.

[0027] 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 sleep system when the program instructions are executed.

[0028] In some embodiments, the sleep system includes: a sleep system body; and the means for controlling the sleep system is installed on the sleep system body.

[0029] In some embodiments, the storage medium stores program instructions, which, when executed, constitute the method for controlling a sleep system.

[0030] The method, apparatus, sleep system, and storage medium for controlling a sleep system provided in this disclosure can achieve the following technical effects:

[0031] Based on the device information in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated. Thus, when the sleep system imports the optimal sleep environment data suitable for the user's sleep, it can accurately determine whether the sleep system has the hardware support to create the best sleep environment suitable for the user's sleep habits.

[0032] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0033] 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:

[0034] Figure 1 This is a schematic diagram of a method for controlling a sleep system provided in an embodiment of this disclosure;

[0035] Figure 2 This is a schematic diagram of another method for controlling a sleep system provided in an embodiment of this disclosure;

[0036] Figure 3 This is a schematic diagram of another method for controlling a sleep system provided in an embodiment of this disclosure;

[0037] Figure 4 This is a schematic diagram of another method for controlling a sleep system provided in an embodiment of this disclosure;

[0038] Figure 5 This is a schematic diagram of a device for controlling a sleep system provided in an embodiment of this disclosure;

[0039] Figure 6 This is a schematic diagram of another device for controlling a sleep system provided in an embodiment of this disclosure;

[0040] Figure 7 This is a schematic diagram of a sleep system provided in an embodiment of this disclosure. Detailed Implementation

[0041] 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.

[0042] The terms "first," "second," etc., used in the specification, claims, 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.

[0043] Unless otherwise stated, the term "multiple" means two or more.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] In the disclosed embodiments, the terminal device refers to an electronic device with wireless connectivity. The terminal device can communicate with the aforementioned smart home appliances via the internet, or directly via Bluetooth, Wi-Fi, or other methods. In some embodiments, the terminal device may be, for example, a mobile device, a computer, or an in-vehicle device built into a hovercraft, or any combination thereof. Mobile devices may include, for example, mobile phones, smart home devices, wearable devices, smart mobile devices, virtual reality devices, or any combination thereof. Wearable devices may include, for example, smartwatches, smart bracelets, pedometers, etc.

[0049] This disclosure provides a sleep system, including: a Class I device and a Class II device. The Class I device includes: an air conditioner, and / or, a mattress, and / or, a bed frame, and / or, a pillow. The Class II device includes: a humidifier, and / or, a dehumidifier, and / or, a lamp, and / or, an aromatherapy diffuser, and / or, a sound system.

[0050] Combination Figure 1 As shown, this disclosure provides a method for controlling a sleep system, including:

[0051] S101, the sleep system acquires new sleep environment data and optimal sleep environment data.

[0052] S102, the sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data.

[0053] The method for controlling a sleep system provided in this disclosure can calculate the similarity between the sleep system and the optimal sleep environment system based on device information in new sleep environment data and optimal sleep environment data. Thus, when the sleep system imports optimal sleep environment data suitable for the user's sleep, it can accurately determine whether there is hardware support in the sleep system to create the optimal sleep environment suitable for the user's sleep habits.

[0054] Optionally, the device information includes the device level and the total number of device functions. The sleep system calculates the similarity between the sleep environment system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data. This includes: the sleep system calculating the similarity between the sleep system and the optimal sleep environment system based on the device level and the total number of device functions in the new sleep environment data and the optimal sleep environment data. Optionally, the device information also includes the device's MAC address. Thus, by calculating the similarity between the sleep system and the optimal sleep environment system based on the device level and the total number of device functions in the new sleep environment data and the optimal sleep environment data, the sleep system can accurately determine whether it has the hardware support to create an optimal sleep environment suitable for the user's sleep habits when importing optimal sleep environment data suitable for the user's sleep.

[0055] Optionally, the device levels include Class I devices and Class II devices. The sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on the device levels and total number of device functions in the new sleep environment data and the optimal sleep environment data. This includes: if Class I devices in the optimal sleep environment data are not a subset of Class I devices in the new sleep environment data, the sleep system determines the similarity between the sleep system and the optimal sleep environment system to be zero. If Class I devices in the optimal sleep environment data are a subset of Class I devices in the new sleep environment data, the sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on a similarity factor and the total number of device functions. Thus, if the sleep system does not completely contain all Class I devices from the optimal sleep environment data, the similarity is determined to be zero. If the sleep system completely contains all Class I devices from the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated again based on the similarity factor and the total number of device functions. Therefore, when importing optimal sleep environment data suitable for a user's sleep, the sleep system can more accurately determine whether there is hardware support in the sleep system to create an optimal sleep environment suitable for the user's sleep habits.

[0056] Optionally, Category 1 devices include: air conditioners, and / or mattresses, and / or bed frames, and / or pillows. Category 2 devices include: humidifiers, and / or dehumidifiers, and / or lamps, and / or aromatherapy diffusers, and / or stereos. Thus, Category 1 devices such as air conditioners, mattresses, bed frames, and pillows directly affect sleep and determine sleep quality, and therefore have a higher priority. Category 2 devices such as humidifiers, dehumidifiers, lamps, aromatherapy diffusers, and stereos can improve sleep quality but do not determine it, and therefore have a lower priority.

[0057] Optionally, the sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on a similarity factor and the total number of device functions. This includes: the sleep system determining a first similarity factor and a second similarity factor based on the number of missing devices in the second category and the number of missing device functions in the first category of devices in the new sleep environment data relative to the optimal sleep environment data. The sleep system then calculates the similarity between the sleep system and the optimal sleep environment system based on the first similarity factor, the second similarity factor, the total number of device functions in the new sleep environment data, and the total number of device functions in the optimal sleep environment data. This facilitates a more accurate determination of the first and second similarity factors, thereby more accurately calculating the similarity between the sleep system and the optimal sleep environment system based on the similarity factor and the total number of device functions. Consequently, when the sleep system imports optimal sleep environment data suitable for a user's sleep, it can more accurately determine whether the sleep system has the hardware support to create an optimal sleep environment suitable for the user's sleep habits.

[0058] Optionally, the sleep system determines a first similarity factor and a second similarity factor based on the number of missing device settings for Category II devices and the number of missing device functions for Category I devices in the new sleep environment data relative to the optimal sleep environment data. This includes determining the second similarity factor based on the first similarity value when the ratio of the number of missing device settings for Category II devices in the new sleep environment data relative to the optimal sleep environment data to the total number of settable devices for Category II devices in the optimal sleep environment data is a first ratio, and the ratio of the number of missing device functions for Category I devices in the new sleep environment data relative to the optimal sleep environment data to the total number of settable devices for Category I devices in the optimal sleep environment data is a second ratio. Similarly, determining the second similarity factor based on the second similarity value when the ratio of the number of missing device functions for Category I devices in the new sleep environment data relative to the optimal sleep environment data to the total number of settable devices for Category I devices in the optimal sleep environment data is a third ratio. Specifically, when the total number of settable device functions for Category I devices in the optimal sleep environment data is 10, and the total number of settable device functions for Category II devices in the optimal sleep environment data is 10, the first ratio can be 80%, the second ratio can be 20%, and the third ratio can be 10%. The first similarity value can be 80%, and the second similarity value can be 90%. The values ​​of the first ratio, second ratio, third ratio, first similarity value, and second similarity value can be adjusted according to user habits and the attributes of the sleep system; these will not be listed here. This allows for a more accurate determination of the first and second similarity factors, and thus a more accurate calculation of the similarity between the sleep system and the optimal sleep environment system based on the similarity factors and the total number of device functions. Consequently, when the sleep system imports optimal sleep environment data suitable for the user's sleep, it can more accurately determine whether the sleep system has the hardware support to create the optimal sleep environment suitable for the user's sleep habits.

[0059] Optionally, the total number of device functions includes the total number of Class I device functions and the total number of Class II device functions. The sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on a first similarity factor, a second similarity factor, the total number of device functions in the new sleep environment data, and the total number of device functions in the optimal sleep environment data. This includes: the sleep system calculates S = α × (k × m1 + m2) / (k × n1 + n2). Where S is the similarity between the sleep system and the optimal sleep environment system, α is the first similarity factor, k is the second similarity factor, m1 is the total number of Class I device functions in the optimal sleep environment data, m2 is the total number of Class II device functions in the optimal sleep environment data, n1 is the total number of Class I devices in the new sleep environment data that have the same Class I device functions as those in the optimal sleep environment data, and n2 is the total number of Class II devices in the new sleep environment data that have the same Class II device functions as those in the optimal sleep environment data. This allows for a more accurate calculation of the similarity between the sleep system and the optimal sleep environment system based on the first similarity factor, the second similarity factor, the total number of device functions in the new sleep environment data, and the total number of device functions in the optimal sleep environment data. Furthermore, by using the device information in the new and optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system can be calculated more accurately. Consequently, when the sleep system imports optimal sleep environment data suitable for a user's sleep, it can accurately determine whether the sleep system has the necessary hardware support to create the optimal sleep environment that suits the user's sleep habits.

[0060] Combination Figure 2 As shown, this disclosure provides another method for controlling a sleep system, including:

[0061] S201, The sleep system acquires new sleep environment data and optimal sleep environment data.

[0062] S202, the sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data.

[0063] S203, The sleep system determines the sleep scenario created by the sleep system based on similarity.

[0064] The method for controlling a sleep system provided in this disclosure can calculate the similarity between the sleep system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data. Then, based on the similarity, it can determine the situation of the sleep system creating a sleep scene. Thus, when the sleep system imports the optimal sleep environment data suitable for the user's sleep, it can accurately determine whether the sleep system has hardware support and whether it can create the optimal sleep environment suitable for the user's sleep habits.

[0065] Optionally, the sleep system determines the sleep scenario it creates based on similarity, including: when the similarity is in the first similarity interval, the sleep system determines that it can create a scenario very close to the optimal sleep environment. When the similarity is in the second similarity interval, the sleep system determines that it can create a scenario that is relatively close to the optimal sleep environment. When the similarity is in the third similarity interval, the sleep system determines that it can create a scenario that is similar to the optimal sleep environment but with a large difference. When the similarity is in the fourth similarity interval, the sleep system determines that it cannot create a scenario close to the optimal sleep environment. Specifically, the value of the first similarity interval can be [90%, 100%], the value of the second similarity interval can be [80%, 90%], the value of the third similarity interval can be [70%, 80%], and the value of the fourth similarity interval can be [0%, 70%]. The values ​​of the first, second, third, and fourth similarity intervals can be adjusted based on user habits and the attributes of the sleep system; these will not be listed here. Thus, based on the device information in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated. The similarity is then used to determine how well the sleep system creates the sleep scenario; the higher the similarity, the better it can create a scenario that matches the optimal sleep environment data. Therefore, when the sleep system imports optimal sleep environment data suitable for a user's sleep, it accurately determines whether the sleep system has the hardware support and whether it can create the optimal sleep environment suitable for the user's sleep habits.

[0066] Combination Figure 3 As shown, this disclosure provides another method for controlling a sleep system, including:

[0067] S301, The sleep system acquires new sleep environment data and optimal sleep environment data.

[0068] S302, the sleep system calculates the similarity between the sleep system and the optimal sleep environment system based on the device information in the new sleep environment data and the optimal sleep environment data.

[0069] S303, the sleep system imports optimal sleep environment data based on similarity control.

[0070] The method for controlling a sleep system provided in this disclosure can calculate the similarity between the sleep system and the optimal sleep environment system based on device information in new sleep environment data and optimal sleep environment data. Then, based on the similarity, the sleep system is controlled to import the optimal sleep environment data. Thus, when the sleep system imports the optimal sleep environment data suitable for the user's sleep, it can accurately determine whether the sleep system has hardware support and import the optimal sleep environment data according to different situations, so as to create the best sleep environment suitable for the user's sleep habits.

[0071] Optionally, the sleep system controls the import of optimal sleep environment data based on similarity. This includes: when the similarity is greater than or equal to a similarity threshold, the sleep system controls the import of optimal sleep environment data in stages according to the device's functional level. When the similarity is less than the similarity threshold, the sleep system sends a similarity score to the user, prompting the user to manually select the device from which to import the optimal sleep environment data. Specifically, the similarity threshold can be 80%. Thus, when the similarity is high, the sleep system can create a new sleep environment that is closer to the optimal sleep environment, automatically importing optimal sleep environment data in stages. When the similarity is low, the sleep system struggles to create a new sleep environment that is closer to the optimal sleep environment, requiring the user to manually import the optimal sleep environment data. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether the system has the necessary hardware support and imports the optimal sleep environment data accordingly to create an optimal sleep environment suitable for the user's sleep habits.

[0072] Optionally, the device function levels include first-level device functions and second-level device functions. The sleep system controls the import of optimal sleep environment data according to the device function level, including: controlling a type of device in the sleep system to import optimal sleep environment data based on first-level device functions, or controlling the sleep system to send a reminder message to the user indicating hardware incompatibility. When importing optimal sleep environment data, the sleep system controls a type of device in the sleep system to continue importing optimal sleep environment data based on second-level device functions. In this way, when there is a high degree of similarity, the sleep system can create a new sleep environment that is closer to the optimal sleep environment, automatically importing optimal sleep environment data according to the device function level. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether the sleep system has hardware support and imports the optimal sleep environment data accordingly to create an optimal sleep environment suitable for the user's sleep habits.

[0073] Optionally, before the sleep system controls a type of device in the sleep system to import optimal sleep environment data or sends a hardware-unsupported reminder message to the user based on the first-level device functionality, the process further includes: the sleep system determining whether a type of device in the sleep system is online based on its MAC address. If a type of device in the sleep system is online, the sleep system controls the import of optimal sleep environment data or sends a hardware-unsupported reminder message to the user based on the first-level device functionality. If a type of device in the sleep system is offline, an offline reminder is sent to the user until the type of device comes online. This approach, when there is significant similarity, helps to better ensure that a type of device in the sleep system is online, thereby better controlling the sleep system to automatically import optimal sleep environment data according to device functionality levels. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether the sleep system has the necessary hardware support and imports the optimal sleep environment data accordingly to create an optimal sleep environment suitable for the user's sleep habits.

[0074] Optionally, the sleep system controls a type of device within the sleep system to import optimal sleep environment data or sends a hardware-unsupported warning message to the user based on the functionality of the first-level devices. This includes: if a type of device in the sleep system fully meets the first-level device functions, the sleep system controls that type of device to import optimal sleep environment data; if a type of device in the sleep system does not fully meet the first-level device functions, the sleep system controls that device to send a hardware-unsupported warning message to the user. Thus, in cases of high similarity, the sleep system automatically imports optimal sleep environment data in a tiered manner based on device functionality. If a type of device in the sleep system fully meets the first-level device functions, the optimal sleep environment data is automatically imported. If a type of device in the sleep system does not fully meet the first-level device functions, a hardware-unsupported warning message is sent to the user. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether the sleep system has the necessary hardware support and imports the optimal sleep environment data accordingly to create an optimal sleep environment suitable for the user's sleep habits.

[0075] Optionally, first-level device functions include: mattress or bed frame firmness matching the optimal sleep environment, and / or, mattress or bed frame providing the optimal sleeping angle, and / or, mattress massage function, and / or mattress position adjustment function, and / or, sleep system providing the optimal sleep temperature. Thus, first-level device functions directly impact sleep and determine sleep quality, hence their higher priority. In cases of high similarity, the sleep system is controlled to automatically import optimal sleep environment data based on device function levels. If a type of device in the sleep system fully meets the first-level device functions, the optimal sleep environment data is automatically imported. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether there is hardware support and imports the optimal sleep environment data accordingly to create an optimal sleep environment suitable for the user's sleep habits.

[0076] Optionally, the sleep system controls the second-level devices in the sleep system to continue importing optimal sleep environment data based on the second-level device functions. This includes: the sleep system calculating the actual number of device functions in the second-level devices that meet the second-level device functions; the sleep system sending the actual number of functions and the target number of device functions in the optimal sleep environment data that meet the second-level device functions to the user; and the sleep system controlling the second-level devices that meet the second-level device functions to continue importing optimal sleep environment data according to user instructions. In this way, the second-level device functions may not determine sleep quality, thus having a lower priority. In cases of high similarity, the sleep system automatically imports optimal sleep environment data in a tiered manner based on device function levels. If a first-level device in the sleep system fully meets the first-level device functions, the optimal sleep environment data is automatically imported, and then the second-level devices in the sleep system are controlled to continue importing optimal sleep environment data based on the second-level device functions. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether there is hardware support in the sleep system and imports the optimal sleep environment data according to different situations to create an optimal sleep environment suitable for the user's sleep habits.

[0077] Optionally, before calculating the actual number of device functions that meet the second-level device function requirements of the Class II devices in the sleep system, the sleep system further includes: determining whether the Class II devices in the sleep system are online based on their MAC addresses. If the Class II devices in the sleep system are online, the sleep system calculates the actual number of device functions that meet the second-level device function requirements. If the Class II devices in the sleep system are offline, the sleep system sends an offline device reminder to the user. Thus, in cases of high similarity, the system first determines whether the Class II devices in the sleep system are online, and then controls the sleep system to automatically import optimal sleep environment data according to the device function level. If a Class I device in the sleep system fully meets the first-level device function requirements, the optimal sleep environment data is automatically imported. Then, based on the second-level device function requirements, the system controls the Class II devices in the sleep system to continue importing optimal sleep environment data. When importing optimal sleep environment data suitable for the user's sleep, the sleep system accurately determines whether there is hardware support in the sleep system and imports the optimal sleep environment data according to different situations to create an optimal sleep environment suitable for the user's sleep habits.

[0078] Combination Figure 4 As shown, this disclosure provides another method for controlling a sleep system, including:

[0079] S401, The sleep system acquires new sleep environment data and optimal sleep environment data.

[0080] S402, the sleep system determines whether a type of device in the optimal sleep environment data is a subset of a type of device in the new sleep environment data.

[0081] S403, if a type of device in the optimal sleep environment data is not a subset of a type of device in the new sleep environment data, the sleep system determines that the similarity between the sleep system and the optimal sleep environment system is zero.

[0082] S404, when a class of devices in the optimal sleep environment data is a subset of a class of devices in the new sleep environment data, the sleep system calculates S = α × (k × m1 + m2) / (k × n1 + n2).

[0083] Where S is the similarity between the sleep system and the optimal sleep environment system, α is the first similarity factor, k is the second similarity factor, m1 is the total number of functions of the first type of devices in the optimal sleep environment data, m2 is the total number of functions of the second type of devices in the optimal sleep environment data, n1 is the total number of first type devices in the new sleep environment data that have the same functions as the first type of devices in the optimal sleep environment data, and n2 is the total number of second type devices in the new sleep environment data that have the same functions as the second type of devices in the optimal sleep environment data.

[0084] The method for controlling a sleep system provided in this disclosure can more accurately calculate the similarity between the sleep system and the optimal sleep environment system based on a first similarity factor, a second similarity factor, the total number of device functions in the new sleep environment data, and the total number of device functions in the optimal sleep environment data. This allows for a more accurate calculation of the similarity between the sleep system and the optimal sleep environment system based on device information in the new and optimal sleep environment data. Furthermore, when the sleep system imports optimal sleep environment data suitable for the user's sleep, it can accurately determine whether the sleep system has the necessary hardware support to create an optimal sleep environment suitable for the user's sleep habits.

[0085] Combination Figure 5 As shown, this disclosure provides an apparatus 200 for controlling a sleep system, including an acquisition module 501 and a calculation module 502. The acquisition module 501 is configured to acquire new sleep environment data and optimal sleep environment data of the optimal sleep environment system. The calculation module 502 is configured to calculate the similarity between the sleep system and the optimal sleep environment system based on device information in the new sleep environment data and the optimal sleep environment data.

[0086] The device for controlling a sleep system provided in this disclosure is advantageous in calculating the similarity between the sleep system and the optimal sleep environment system based on device information in new sleep environment data and optimal sleep environment data. This allows the sleep system to accurately determine whether it has hardware support when importing optimal sleep environment data suitable for the user's sleep, thereby creating the optimal sleep environment suitable for the user's sleep habits.

[0087] Combination Figure 6 As shown, this disclosure provides an apparatus 300 for controlling a sleep system, including a processor 600 and a memory 601. Optionally, the apparatus may further include a communication interface 602 and a bus 603. The processor 600, communication interface 602, and memory 601 can communicate with each other via the bus 603. The communication interface 602 can be used for information transmission. The processor 600 can call logical instructions in the memory 601 to execute the method for controlling the sleep system described in the above embodiment.

[0088] Furthermore, the logic instructions in the aforementioned memory 601 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0089] The memory 601, 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 600 executes functional applications and data processing by running the program instructions / modules stored in the memory 601, that is, it implements the method for controlling the sleep system in the above embodiments.

[0090] The memory 601 may include a program storage area and a data storage area. The program storage area may store the operating system and applications 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 601 may include high-speed random access memory and may also include non-volatile memory.

[0091] Combination Figure 7 As shown, this disclosure provides a sleep system 100, including a sleep system body and the aforementioned device 200 (300) for controlling the sleep system. The device 200 (300) for controlling the sleep system is installed in the sleep system body. The installation relationship described herein is not limited to placement within the sleep system, but also includes installation connections with other components of the sleep system, 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 the sleep system can be adapted to feasible sleep system bodies to achieve other feasible embodiments.

[0092] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for controlling a sleep system.

[0093] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0094] 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, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 method for controlling a sleep system, characterized in that, include: Acquire new sleep environment data and optimal sleep environment data for the sleep system; Based on the device information in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated; the device information includes the device level and the total number of device functions. Based on device information from new sleep environment data and optimal sleep environment data, the similarity between the sleep environment system and the optimal sleep environment system is calculated, including: Based on the device level and total number of device functions in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated. The equipment levels include Class I and Class II equipment. Based on the equipment levels and total number of equipment functions in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated, including: If a type of device in the optimal sleep environment data is not a subset of a type of device in the new sleep environment data, the similarity between the sleep system and the optimal sleep environment system is determined to be zero. When a type of device in the optimal sleep environment data is a subset of a type of device in the new sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated based on the similarity factor and the total number of device functions. The similarity is used to determine the sleep system's ability to create a sleep scenario.

2. The method according to claim 1, characterized in that, The similarity between the sleep system and the optimal sleep environment system is calculated based on the similarity factor and the total number of device functions, including: Based on the number of missing device settings for Category II devices and the number of missing device functions for Category I devices relative to the optimal sleep environment data, the first similarity factor and the second similarity factor are determined. The similarity between the sleep system and the optimal sleep environment system is calculated based on the first similarity factor, the second similarity factor, the total number of device functions in the new sleep environment data, and the total number of device functions in the optimal sleep environment data.

3. The method according to claim 1 or 2, characterized in that, Based on the device information in the new sleep environment data and the optimal sleep environment data, after calculating the similarity between the sleep system and the optimal sleep environment system, the following is also included: The sleep system imports optimal sleep environment data based on similarity.

4. A device for controlling a sleep system, characterized in that, include: The acquisition module is configured to acquire new sleep environment data and optimal sleep environment data of the optimal sleep environment system. The calculation module is configured to calculate the similarity between the sleep system and the optimal sleep environment system based on device information in the new sleep environment data and the optimal sleep environment data; the device information includes the device level and the total number of device functions. Based on device information from new sleep environment data and optimal sleep environment data, the similarity between the sleep environment system and the optimal sleep environment system is calculated, including: Based on the device level and total number of device functions in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated. The equipment levels include Class I and Class II equipment. Based on the equipment levels and total number of equipment functions in the new sleep environment data and the optimal sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated, including: If a type of device in the optimal sleep environment data is not a subset of a type of device in the new sleep environment data, the similarity between the sleep system and the optimal sleep environment system is determined to be zero. When a type of device in the optimal sleep environment data is a subset of a type of device in the new sleep environment data, the similarity between the sleep system and the optimal sleep environment system is calculated based on the similarity factor and the total number of device functions. The similarity is used to determine the sleep system's ability to create a sleep scenario.

5. A device for controlling a sleep system, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to, when executing the program instructions, perform the method for controlling a sleep system as described in any one of claims 1 to 3.

6. A sleep system, characterized in that, include: The sleep system itself; The device for controlling a sleep system as described in claim 4 or 5 is installed on the main body of the sleep system.

7. A storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the method for controlling a sleep system as described in any one of claims 1 to 3.

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