Sleep environment adjusting method and device, equipment and medium

Abstract

The invention relates to the technical field of smart home, in particular to a sleep environment adjusting method, device, equipment and medium, comprising: acquiring initial environment parameters of an indoor environment, and performing adjustability analysis on the initial environment parameters to obtain an adjustable parameter set; acquiring normal sign data of the user, and performing correlation analysis on the normal sign data and the adjustable parameter set according to a preset change condition to obtain a sign correlation set; constructing an original physical sign environment association curve based on the physical sign association set; acquiring real-time sign data of the user, and performing trend prediction on the original sign environment association curve through a preset trend prediction model and the real-time sign data to obtain a real-time sign environment association curve; performing trend analysis on the real-time physical sign environment correlation curve to obtain an environment prediction variable; generating an environment adjustment parameter according to the environment prediction variable; the indoor environment elements are adjusted according to the physiological index change condition of the elderly user, and the sleep quality of the elderly user is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart homes, and in particular to a method, device, equipment and medium for adjusting a sleeping environment. Background Art

[0002] Environmental factors have a great impact on the body's biological rhythms and emotional health, which are closely related to the body's sleep quality, especially for the elderly. The elderly are more sensitive to the sleeping environment and are easily affected by environmental factors. They often experience problems with decreased sleep quality due to rapid changes in the indoor environment.

[0003] It can be seen that the existing technology still needs to be improved and enhanced. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a sleeping environment adjustment method, device, equipment and medium, which adjust indoor environmental factors according to the changing state of physiological indicators of elderly users to improve the sleep quality of elderly users.

[0005] The first aspect of the present invention provides a method for adjusting a sleeping environment, comprising: obtaining initial environmental parameters of an indoor environment, and performing an adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set; obtaining normal vital sign data of a user, and performing a correlation analysis on the normal vital sign data and the adjustable parameter set according to preset dependent conditions to obtain a vital sign association set; constructing an original vital sign-environment association curve based on the vital sign association set; obtaining real-time vital sign data of a user, and performing trend prediction on the original vital sign-environment association curve through a preset trend prediction model and real-time vital sign data to obtain a real-time vital sign-environment association curve; performing a trend analysis on the real-time vital sign-environment association curve to obtain environmental prediction variables; and generating environmental adjustment parameters according to the environmental prediction variables.

[0006] Optionally, in a first implementation method of the first aspect of the present invention, the initial environmental parameters of the indoor environment are obtained, and the initial environmental parameters are analyzed for adjustability to obtain an adjustable parameter set, including: obtaining all initial environmental parameters in the indoor environment to obtain initial environmental parameters; obtaining all adjustable device types in the indoor environment to obtain an adjustable device type set; performing an adjustability analysis on the initial environmental parameters according to the adjustable device type set, and filtering out parameters that are compatible with the adjustable device type set from the initial environmental parameters to form an adjustable parameter set.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the method of obtaining the normal vital sign data of the user and performing a correlation analysis on the normal vital sign data and the adjustable parameter set according to preset dependent conditions to obtain a vital sign correlation set includes: obtaining the normal vital sign data of the user and extracting a heart rate reference, a respiratory rate reference, a blood glucose reference and a skin conductance reference from the normal vital sign data; obtaining a first associated environmental factor corresponding to the heart rate reference according to the preset dependent conditions; obtaining a second associated environmental factor corresponding to the respiratory rate reference according to the preset dependent conditions; obtaining a third associated environmental factor corresponding to the blood glucose reference according to the preset dependent conditions; obtaining a fourth associated environmental factor corresponding to the skin conductance reference according to the preset dependent conditions; extracting a key character set from the first associated environmental factor, the second associated environmental factor, the third associated environmental factor and the fourth associated environmental factor; traversing the adjustable parameter set according to the key character set to obtain a correlation adjustable parameter; and establishing a mapping relationship between the correlation adjustable parameter and the heart rate reference, the respiratory rate reference, the blood glucose reference and the skin conductance reference to obtain a vital sign correlation set.

[0008] Optionally, in a third implementation method of the first aspect of the present invention, the construction of the original vital sign-environment association curve based on the vital sign association set includes: performing feature extraction on the vital sign association set to obtain vital sign features, environmental features, vital sign change features and environmental change features; confirming the coordinate zero point according to the vital sign change features and the environmental change features; taking the environmental features as the horizontal axis and the vital sign features as the vertical axis, and constructing the original vital sign-environment association curve in combination with the coordinate zero point, the vital sign change features and the environmental change features.

[0009] Optionally, in a fourth implementation method of the first aspect of the present invention, the real-time vital sign data of the user is obtained, and trend prediction is performed on the original vital sign-environment association curve through a preset trend prediction model and real-time vital sign data to obtain a real-time vital sign-environment association curve, including: obtaining the user's historical vital sign change set, and extracting time dimension information from the historical vital sign data change set; obtaining the corresponding historical environment change set from the indoor environment according to the time dimension information; generating a model training set according to the historical vital sign change set and the historical environment change set; constructing an original prediction model based on the time series analysis model, and iteratively training the original prediction model through the model training set to obtain a trend prediction model; inputting the real-time vital sign data into the trend prediction model to obtain environmental trend change data; and mapping and processing the original vital sign-environment association curve based on the real-time vital sign data and the environmental trend change data to obtain a real-time vital sign-environment association curve.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the trend analysis of the real-time vital sign-environment association curve to obtain environmental prediction variables includes: obtaining all coordinate points on the real-time vital sign-environment association curve to obtain a real-time coordinate point set; obtaining all coordinate points on the original vital sign-environment association curve to obtain an original coordinate point set; filtering the real-time coordinate point set using all coordinate points on the original coordinate point set as filtering conditions to obtain a filtered real-time coordinate point set; generating a coordinate trajectory based on the filtered real-time coordinate point set, and removing jump points in the filtered real-time coordinate point set based on the coordinate trajectory to obtain a valid coordinate point set; obtaining a horizontal coordinate value set and a vertical coordinate value set from the valid coordinate point set, and determining the environmental prediction variables based on the horizontal coordinate value set and the vertical coordinate value set.

[0011] Optionally, in a sixth implementation method of the first aspect of the present invention, generating environmental adjustment parameters based on environmental prediction variables includes: classifying the environmental prediction variables into types to obtain multiple environmental variable types; extracting corresponding conversion algorithms from preset conversion format conditions based on the multiple environmental variable types; and performing parameter conversion on the corresponding environmental prediction variables using the conversion algorithm to obtain environmental adjustment parameters.

[0012] The second aspect of the present invention provides a sleeping environment adjustment device, including: a first analysis module, used to obtain the initial environmental parameters of the indoor environment, and perform an adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set; a second analysis module, used to obtain the normal vital sign data of the user, and perform a correlation analysis on the normal vital sign data and the adjustable parameter set according to a preset dependent condition to obtain a vital sign association set; a construction module, used to construct an original vital sign environment association curve based on the vital sign association set; a prediction module, used to obtain the real-time vital sign data of the user, and perform a trend prediction on the original vital sign environment association curve through a preset trend prediction model and real-time vital sign data to obtain a real-time vital sign environment association curve; a third analysis module, used to perform a trend analysis on the real-time vital sign environment association curve to obtain an environmental prediction variable; a conversion module, used to generate an environmental adjustment parameter according to the environmental prediction variable.

[0013] A third aspect of the present invention provides a sleeping environment adjustment device, comprising: a memory and at least one processor, wherein the memory stores instructions; at least one of the processors calls the instructions in the memory so that the sleeping environment adjustment device performs each step of any one of the above-mentioned sleeping environment adjustment methods.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, on which instructions are stored, and when the instructions are executed by a processor, the steps of any of the above-mentioned sleep environment adjustment methods are implemented.

[0015] In the technical solution of the present invention, by acquiring and analyzing the initial parameters of the indoor environment, adjustable environmental factors can be identified, laying the foundation for subsequent environmental adjustments and ensuring the pertinence and effectiveness of the adjustment measures. At the same time, the normal physiological data of the user is correlated with the adjustable parameter set to build a connection between the user's physiological state and environmental factors, which is helpful for more accurate environmental adjustments; the original physiological environment related curve constructed based on the physiological state related set can clearly reveal the changing trend between the user's physiological state and environmental factors, providing support for subsequent trend prediction. By acquiring the real-time physiological data of the user, and using the trend prediction model and the real-time physiological data to predict the trend of the original physiological environment related curve, a real-time physiological environment related curve can be obtained, which provides the possibility for real-time environmental adjustment. Trend analysis of the real-time physiological environment related curve can derive environmental prediction variables, predict the environmental state that the user may need in the future, and thus make environmental adjustments in advance to meet the needs of the user. Finally, environmental adjustment parameters are generated according to the environmental prediction variables, which are directly applied to adjust the sleeping environment to create an environment more suitable for the user's sleep. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a method for adjusting a sleeping environment provided by an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of the structure of a sleeping environment adjustment device provided by an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of the structure of a sleeping environment adjustment device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention provides a method, device, equipment and medium for adjusting a sleeping environment. The present invention can identify adjustable environmental factors by acquiring and analyzing the initial environmental parameters of an indoor environment, provide a basis for subsequent environmental adjustment, and ensure the pertinence and effectiveness of the adjustment measures. At the same time, the normal vital sign data of the user is analyzed for correlation with the adjustable parameter set, and the relationship between the user's vital signs and environmental factors is established, which is helpful for more targeted environmental adjustment. The original vital sign environment association curve is constructed based on the vital sign association set, which can intuitively show the change trend between the user's vital signs and environmental factors, and provide a basis for subsequent trend prediction. By acquiring the real-time vital sign data of the user, and using the trend prediction model and the real-time vital sign data to perform trend prediction on the original vital sign environment association curve, a real-time vital sign environment association curve can be obtained, which provides the possibility for real-time environmental adjustment. By performing trend analysis on the real-time vital sign environment association curve, an environmental prediction variable can be obtained, and the environmental state that the user may need in the future period of time can be predicted, so that the environmental adjustment can be performed in advance to meet the needs of the user. Finally, an environmental adjustment parameter is generated according to the environmental prediction variable, which is directly used to adjust the sleeping environment to create an environment that is more suitable for the user's sleep.

[0020] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the sleep environment adjustment method in the embodiment of the present invention includes:

[0022] 101. Obtaining initial environmental parameters of the indoor environment, and performing adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set;

[0023] In this embodiment, by obtaining the initial environmental parameters of the indoor environment and performing an adjustability analysis, it is possible to identify which environmental factors in the indoor environment can be adjusted, such as temperature, humidity, light, etc., which provides a basis for subsequent environmental adjustment. For example, if the initial data shows that the indoor temperature is too high, then adjusting the air-conditioning temperature becomes a possible improvement measure. This analysis ensures the pertinence and effectiveness of the adjustment measures. During implementation, the corresponding detection data can be obtained by connecting to relevant indoor equipment or relevant detectors. By analyzing all the detection data, it can be determined which environmental factors can be adjusted through existing electrical equipment.

[0024] 102. Obtain normal vital sign data of the user, and perform correlation analysis on the normal vital sign data and the adjustable parameter set according to preset dependent variable conditions to obtain a vital sign correlation set;

[0025] In this embodiment, the normal physical sign data of the user is obtained, and the data is analyzed for correlation with the adjustable parameter set, so that the relationship between the user's physical sign and environmental factors can be established. This helps to understand which environmental factors have the greatest impact on the user, so as to make more targeted environmental adjustments. For example, if it is found that the user's heart rate tends to rise slightly when the humidity is low, then in future adjustments, it can be given priority to increase the indoor humidity to help the user maintain a more stable heart rate.

[0026] 103. Construct the original physical sign environment association curve based on the physical sign association set;

[0027] 104. Acquire the user's real-time vital sign data, and perform trend prediction on the original vital sign-environment correlation curve by using a preset trend prediction model and the real-time vital sign data to obtain the real-time vital sign-environment correlation curve;

[0028] 105. Perform trend analysis on the real-time vital sign-environment correlation curve to obtain environmental prediction variables;

[0029] 106. Generate environmental adjustment parameters based on environmental prediction variables.

[0030] In this embodiment, the original physical sign environment association curve is constructed based on the physical sign association set, and this curve can intuitively show the changing trend between the user's physical signs and environmental factors. This provides a basis for subsequent trend prediction. For example, by observing the physical sign environment association curve, it can be found that the user's body temperature and heart rate usually decrease between 10 pm and 2 am, which indicates that during this period of time, the user may need a warmer and quieter sleeping environment; by obtaining the user's real-time physical sign data, and using the trend prediction model and the real-time physical sign data to predict the trend of the original physical sign environment association curve, a real-time physical sign environment association curve can be obtained. This curve can reflect the dynamic relationship between the user's current state and environmental factors, and provides the possibility for real-time environmental adjustment. For example, if the real-time data shows that the user's heart rate suddenly increases, it may mean that the user feels uncomfortable or nervous. At this time, the system can automatically adjust the environment, such as reducing the light brightness or playing soothing music to help the user relax; in addition, the real-time physical sign environment association curve is trend analyzed to obtain environmental prediction variables. These variables can predict the environmental state that the user may need in the future, so as to adjust the environment in advance to meet the needs of the user. For example, if the prediction model shows that the user may feel tired in the next few hours, the system can adjust the indoor temperature and light in advance to create a more comfortable resting environment; based on the environmental prediction variables, environmental adjustment parameters are generated, which can be directly used to adjust the sleeping environment, such as adjusting the temperature of the air conditioner, the humidity of the humidifier, etc., to create an environment more suitable for the user to sleep. For example, if the environmental adjustment parameters indicate that the humidity needs to be increased, the system can automatically start the humidifier to ensure that the indoor humidity remains at the user's preferred level.

[0031] In the embodiment of the present invention, by acquiring and analyzing the initial environmental parameters of the indoor environment, adjustable environmental factors can be identified, providing a basis for subsequent environmental adjustment and ensuring the pertinence and effectiveness of the adjustment measures. At the same time, the normal vital sign data of the user is analyzed for correlation with the adjustable parameter set, and the relationship between the user's vital signs and environmental factors is established, which helps to adjust the environment more pertinently; the original vital sign environment association curve is constructed based on the vital sign association set, which can intuitively show the change trend between the user's vital signs and environmental factors, and provide a basis for subsequent trend prediction. By acquiring the real-time vital sign data of the user, and using the trend prediction model and the real-time vital sign data to predict the trend of the original vital sign environment association curve, a real-time vital sign environment association curve can be obtained, which provides the possibility for real-time environmental adjustment. Trend analysis of the real-time vital sign environment association curve can obtain environmental prediction variables, predict the environmental state that the user may need in the future, so as to adjust the environment in advance to meet the needs of the user. Finally, the environmental adjustment parameters are generated according to the environmental prediction variables, which are directly used to adjust the sleeping environment to create an environment more suitable for the user's sleep.

[0032] A second embodiment of the sleeping environment adjustment method in the embodiment of the present invention includes:

[0033] 201. Acquire all initial environmental parameters in the indoor environment to obtain initial environmental parameters;

[0034] 202. Acquire all adjustable device types in the indoor environment to obtain an adjustable device type set;

[0035] 203. Perform an adjustability analysis on the initial environmental parameters according to the adjustable device type set, select parameters that are compatible with the adjustable device type set from the initial environmental parameters, and form an adjustable parameter set.

[0036] In this embodiment, by comprehensively collecting the initial parameters of the indoor environment, it is possible to gain an in-depth understanding of the current sleeping environment, which covers multiple dimensions such as temperature, humidity, light, noise, etc., and provides the necessary basic data for subsequent environmental adjustment. By identifying the types of all adjustable devices in the indoor environment, it is possible to clarify which devices can be used to improve the sleeping environment, such as air conditioners, humidifiers, curtains, audio, etc., which provides a key reference for formulating adjustment plans. Based on the set of adjustable device types, the initial environmental parameters are analyzed for adaptability, and parameters that match the set of adjustable device types can be screened out to form an adjustable parameter set, which helps to formulate adjustment plans in a targeted manner. By adjusting these adjustable parameters, the sleeping environment can be optimized, thereby improving sleep quality. For example, if the initial environmental parameters indicate that the indoor temperature is too high, the indoor temperature can be lowered by adjusting the temperature setting of the air conditioner, thereby creating a more comfortable sleeping environment.

[0037] A third embodiment of the sleeping environment adjustment method in the embodiment of the present invention includes:

[0038] 301. Obtain normal physical sign data of the user, and extract a heart rate reference value, a respiratory rate reference value, a blood sugar reference value, and a skin conductance reference value from the normal physical sign data;

[0039] 302. Acquire a first associated environmental factor corresponding to the heart rate reference value according to a preset dependent condition;

[0040] 303. Acquire a second associated environmental factor corresponding to the respiratory rate reference value according to a preset dependent condition;

[0041] 304. Acquire a third associated environmental factor corresponding to the blood glucose reference value according to a preset dependent variable condition;

[0042] 305. Acquire a fourth associated environmental factor corresponding to the skin conductance reference value according to a preset dependent variable condition;

[0043] In this embodiment, by obtaining the user's normal vital signs data, including reference quantities such as heart rate, respiratory rate, blood sugar and skin conductance, the user's physical condition and physiological needs can be accurately understood, providing a scientific basis and data support for subsequent environmental adjustments. According to the preset dependent conditions, environmental factors associated with each reference quantity are extracted from the user's vital signs data. These environmental factors may include indoor temperature, humidity, light, noise, etc., which have a direct impact on the user's sleep quality. By analyzing these factors, it can be further clarified which environmental conditions need to be adjusted to adapt to the user's physiological needs. It should be noted that the dependent conditions may be, for example, a positive correlation between blood pressure and decibel value, a positive correlation between blood sugar and temperature value, etc.

[0044] 306. Extract a key character set from the first associated environmental factor, the second associated environmental factor, the third associated environmental factor, and the fourth associated environmental factor;

[0045] 307. Perform traversal processing on the adjustable parameter set according to the key character set to obtain the associated adjustable parameter;

[0046] 308. A mapping relationship is established between the correlation adjustable parameter and the heart rate reference value, the respiratory rate reference value, the blood sugar reference value and the skin conductance reference value to obtain a physical sign correlation set.

[0047] In this embodiment, key character sets are extracted from the extracted environmental factors, and these character sets represent the main environmental factors that affect the user's sleep. Then, the adjustable parameter set is traversed according to these key character sets to obtain the relevance adjustable parameters. These adjustable parameters can be adjusted according to the actual needs of the user to achieve the purpose of optimizing the sleep environment. In this way, it can be ensured that every aspect of the sleep environment can meet the personalized needs of the user. Establishing a mapping relationship between the relevance adjustable parameters and the user's physical sign reference can adjust the sleep environment in real time according to the changes in the user's physical signs, ensuring that the user's sleep quality is guaranteed to the greatest extent, and realizing the key to dynamically adjusting the personalized sleep environment optimization.

[0048] A fourth embodiment of the sleeping environment adjustment method in the embodiment of the present invention includes:

[0049] 401. Extract features from the physical sign association set to obtain physical sign features, environmental features, physical sign change features, and environmental change features;

[0050] 402. Confirm the coordinate zero point according to the characteristics of physical sign changes and environmental changes;

[0051] 403. The environmental characteristics are used as the horizontal axis, the physical sign characteristics are used as the vertical axis, and the original physical sign-environment association curve is constructed by combining the coordinate zero point, physical sign change characteristics and environmental change characteristics.

[0052] In this embodiment, by capturing the physical sign characteristics, environmental characteristics, physical sign change characteristics and environmental change characteristics, a rich data basis is provided for the subsequent association analysis. By confirming the coordinate zero point, the reference point between the physical sign and the environmental change can be determined, which helps to accurately analyze the correlation and change trend between the two. By establishing the reference point, the interaction between the physical sign and the environmental factors can be observed more clearly. By constructing the original physical sign environment association curve, the dynamic relationship between the physical sign characteristics and the environmental characteristics can be intuitively displayed. The association curve not only helps to discover the impact of potential environmental factors on sleep signs, but also provides a scientific basis for sleep environment regulation. Through this visual method, it is easier to identify which environmental factors have a positive or negative impact on sleep quality. Based on the analysis results of the association curve, the sleep environment can be adjusted in a targeted manner, such as improving the ventilation, light, temperature, etc. of the bedroom to optimize the sleep quality and health of the elderly.

[0053] A fifth embodiment of the sleeping environment adjustment method in the embodiment of the present invention includes:

[0054] 501. Obtain a historical vital sign change set of the user, and extract time dimension information from the historical vital sign data change set;

[0055] 502. Obtain a corresponding historical environment change set from the indoor environment according to the time dimension information;

[0056] 503. Generate a model training set according to the historical physical sign change set and the historical environment change set;

[0057] 504. Construct an original prediction model based on the time series analysis model, and iteratively train the original prediction model through the model training set to obtain a trend prediction model;

[0058] 505. Input the real-time vital sign data into the trend prediction model to obtain environmental trend change data;

[0059] 506. Based on the real-time vital sign data and the environmental trend change data, the original vital sign-environment correlation curve is mapped and processed to obtain the real-time vital sign-environment correlation curve.

[0060] In this embodiment, by acquiring and analyzing the user's historical physical sign change set, combined with the time dimension information, the historical environment change set associated with the user's physical sign change can be extracted from the indoor environment. The historical environment change set helps to identify which environmental factors may affect the user's sleep quality, and can also reveal the patterns and trends of these factors changing over time. For example, environmental parameters such as temperature, humidity, light intensity and noise level may have different degrees of impact on the user's sleep in different time periods. The model training set is generated using the historical physical sign change set and the historical environment change set, and the trend prediction model is constructed and iteratively trained through the time series analysis model, which can improve the model's prediction accuracy of the relationship between the user's physical sign changes and environmental factors. This enables the system to more accurately predict the impact that future environmental changes may have on the user's physical signs, thereby providing users with personalized sleep environment adjustment suggestions. For example, if the model predicts that the temperature will rise in a certain time period, the system can adjust the air conditioning settings in advance to ensure the comfort of the sleeping environment. Inputting real-time physical sign data into the trend prediction model to obtain environmental trend change data can help users understand possible environmental changes in advance, so as to take corresponding measures to adjust the sleeping environment. For example, if real-time vital sign data shows that the user's heart rate begins to rise, the system can analyze whether this is related to the current ambient temperature and adjust the indoor temperature accordingly to help the user enter a deep sleep state faster. The original vital sign-environment association curve is mapped and processed based on the real-time vital sign data and environmental trend change data to obtain a real-time vital sign-environment association curve, which can intuitively display the dynamic relationship between the user's physical signs and environmental factors. This helps users to have a deeper understanding of their sleep environment needs and make personalized environmental adjustments accordingly. For example, by observing the vital sign-environment association curve, users may find that their sleep quality is best within a specific temperature and humidity range, and thus actively adjust the indoor environment in future sleep to achieve the best sleep effect.

[0061] A sixth embodiment of the sleeping environment adjustment method in the embodiment of the present invention includes:

[0062] 601. Acquire all coordinate points on the real-time vital sign environment association curve to obtain a real-time coordinate point set;

[0063] 602. Acquire all coordinate points on the original vital sign-environment association curve to obtain an original coordinate point set;

[0064] 603. Filter the real-time coordinate point set using all coordinate points on the original coordinate point set as filtering conditions to obtain a filtered real-time coordinate point set;

[0065] 604. Generate a coordinate track according to the filtered real-time coordinate point set, and remove the jump points in the filtered real-time coordinate point set according to the coordinate track to obtain a valid coordinate point set;

[0066] 605. Obtain a set of abscissa values ​​and a set of ordinate values ​​from the set of valid coordinate points, and determine environmental prediction variables according to the set of abscissa values ​​and the set of ordinate values.

[0067] In this embodiment, in order to accurately analyze the trend of environmental changes, it is first necessary to obtain all the coordinate points on the original vital sign environment association curve, and these coordinate points constitute a complete set of original coordinate points. Through this set, a detailed comparative analysis can be performed with real-time data, so as to effectively identify the trend of environmental changes. In addition, by comparing historical data and real-time data, those abnormal or unexpected data points can be effectively filtered out, which not only improves the accuracy of the data, but also ensures the reliability of the analysis results. Removing the jump point plays an important role in identifying and smoothing the mutations in the data, which makes the data more continuous and stable, thereby more accurately reflecting the real changes in the environment. By conducting in-depth analysis of these valid data, key environmental factors that have a significant impact on sleep, such as temperature, humidity or noise level, can be further identified. Once these predictive variables are identified, they can be used to adjust the sleeping environment in order to improve the quality of sleep and enable users to enjoy a more comfortable and healthy sleep.

[0068] A seventh embodiment of the sleeping environment adjustment method in the embodiment of the present invention includes:

[0069] 701. Classify the environmental prediction variables into types to obtain multiple environmental variable types;

[0070] 702. Extracting corresponding conversion algorithms from preset conversion format conditions according to multiple environment variable types;

[0071] 703. Use a conversion algorithm to convert the corresponding environmental prediction variables to obtain environmental adjustment parameters.

[0072] In this embodiment, the environmental prediction variables are classified into types, which can more clearly understand the various environmental factors that affect sleep, such as temperature, humidity, noise, etc., so as to provide targeted guidance for subsequent adjustments. According to multiple environmental variable types, the corresponding conversion algorithm is extracted from the preset conversion format conditions. For example, the corresponding output power is generated according to the brightness value and the corresponding parameters of the indoor lamps to achieve the dimming effect of the indoor lamps. For example, the corresponding infrared control instructions are generated according to the indoor temperature value and the corresponding parameters of the air-conditioning equipment to achieve the temperature adjustment effect of the air-conditioning equipment. Using the conversion algorithm to convert the corresponding environmental prediction variables to obtain the environmental adjustment parameters is the key to achieving accurate adjustment of the sleep environment. By adjusting these environmental adjustment parameters, the sleeping environment can be effectively improved and the sleep quality can be improved, such as reducing noise and adjusting the temperature to an appropriate range.

[0073] The above describes the sleep environment adjustment method in the embodiment of the present invention. The following describes the sleep environment adjustment device in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a sleeping environment adjustment device includes:

[0074] The first analysis module 801 is used to obtain initial environmental parameters of the indoor environment and perform adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set;

[0075] The second analysis module 802 is used to obtain normal vital sign data of the user, and perform correlation analysis on the normal vital sign data and the adjustable parameter set according to a preset dependent variable condition to obtain a vital sign correlation set;

[0076] A construction module 803 is used to construct an original physical sign environment association curve based on the physical sign association set;

[0077] The prediction module 804 is used to obtain the real-time vital sign data of the user, and perform trend prediction on the original vital sign-environment correlation curve through a preset trend prediction model and the real-time vital sign data to obtain the real-time vital sign-environment correlation curve;

[0078] The third analysis module 805 is used to perform trend analysis on the real-time physical sign-environment association curve to obtain environmental prediction variables;

[0079] The conversion module 806 is used to generate environmental adjustment parameters according to the environmental prediction variables.

[0080] above Figure 2 The sleeping environment adjustment device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The sleeping environment adjustment device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0081] Figure 31 is a schematic diagram of the structure of a sleep environment adjustment device provided by an embodiment of the present invention. The sleep environment adjustment device 900 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 (for example, one or more mass storage devices) storing application programs 933 or data 932. Among them, the memory 920 and the storage medium 930 may be temporary storage or permanent storage. The program stored in the storage medium 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the sleep environment adjustment device 900. Furthermore, the processor 910 may be configured to communicate with the storage medium 930, and execute a series of instruction operations in the storage medium 930 on the sleep environment adjustment device 900 to implement the steps of the sleep environment adjustment method provided by the above-mentioned method embodiments.

[0082] The sleeping environment adjustment device 900 may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input and output interfaces 960, and / or one or more operating systems 931, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the sleeping environment regulating device shown does not constitute a limitation on the sleeping environment regulating device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0083] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the sleep environment adjustment method.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0086] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for adjusting a sleeping environment, characterized in that: include: Acquire initial environmental parameters of the indoor environment, and perform adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set; Acquire normal vital sign data of the user, and perform correlation analysis on the normal vital sign data and the adjustable parameter set according to preset dependent variable conditions to obtain a vital sign correlation set; Construct the original physical sign-environment association curve based on the physical sign association set; Acquire the user's real-time vital sign data, and perform trend prediction on the original vital sign-environment correlation curve through a preset trend prediction model and the real-time vital sign data to obtain a real-time vital sign-environment correlation curve; Conduct trend analysis on real-time vital sign-environment correlation curves to obtain environmental prediction variables; Generate environmental adjustment parameters based on environmental predictor variables.

2. The sleeping environment adjustment method according to claim 1, characterized in that: The step of obtaining the initial environmental parameters of the indoor environment and performing an adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set includes: Obtain all initial environmental parameters in the indoor environment to obtain initial environmental parameters; Acquire all adjustable device types in the indoor environment to obtain an adjustable device type set; The adjustability analysis of the initial environment parameters is performed according to the adjustable device type set, and the parameters that are compatible with the adjustable device type set are screened out from the initial environment parameters to form an adjustable parameter set.

3. The sleeping environment adjustment method according to claim 1, characterized in that: The method of obtaining the normal vital sign data of the user and performing correlation analysis on the normal vital sign data and the adjustable parameter set according to the preset dependent variable conditions to obtain a vital sign correlation set includes: Acquire normal vital sign data of the user, and extract heart rate reference value, respiratory rate reference value, blood sugar reference value and skin conductance reference value from the normal vital sign data; Acquiring a first associated environmental factor corresponding to a heart rate reference value according to a preset dependent condition; Acquiring a second associated environmental factor corresponding to the respiratory rate reference value according to a preset dependent condition; Acquiring a third associated environmental factor corresponding to the blood glucose reference value according to a preset dependent variable condition; Acquiring a fourth associated environmental factor corresponding to the skin conductance reference value according to a preset dependent variable condition; Extracting a key character set from the first associated environmental factor, the second associated environmental factor, the third associated environmental factor, and the fourth associated environmental factor; The adjustable parameter set is traversed according to the key character set to obtain the associated adjustable parameters; A mapping relationship is established between the correlation adjustable parameter and the heart rate reference value, the respiratory rate reference value, the blood sugar reference value and the skin conductance reference value to obtain a physical sign correlation set.

4. The sleeping environment adjustment method according to claim 1, characterized in that: The constructing of the original physical sign-environment association curve based on the physical sign association set includes: Extract features from the physical sign association set to obtain physical sign features, environmental features, physical sign change features, and environmental change features; Confirm the coordinate zero point based on the characteristics of physical sign changes and environmental changes; The environmental characteristics are taken as the horizontal axis, the physical sign characteristics are taken as the vertical axis, and the original physical sign-environment association curve is constructed by combining the coordinate zero point, physical sign change characteristics and environmental change characteristics.

5. The sleeping environment adjustment method according to claim 1, characterized in that: The method of acquiring the user's real-time vital sign data and performing trend prediction on the original vital sign-environment correlation curve by using a preset trend prediction model and the real-time vital sign data to obtain the real-time vital sign-environment correlation curve includes: Obtain the user's historical vital sign change set, and extract time dimension information from the historical vital sign data change set; Obtain the corresponding historical environment change set from the indoor environment according to the time dimension information; Generate a model training set based on a historical physical sign change set and a historical environmental change set; An original prediction model is constructed based on the time series analysis model, and the original prediction model is iteratively trained through the model training set to obtain a trend prediction model; Input the real-time vital sign data into the trend prediction model to obtain the environmental trend change data; The original vital sign-environment correlation curve is mapped and processed based on the real-time vital sign data and the environmental trend change data to obtain the real-time vital sign-environment correlation curve.

6. The sleeping environment adjustment method according to claim 1, characterized in that: The trend analysis of the real-time physical sign-environment association curve to obtain environmental prediction variables includes: Obtain all coordinate points on the real-time vital sign environment association curve to obtain a real-time coordinate point set; Obtain all coordinate points on the original vital sign-environment association curve to obtain an original coordinate point set; Using all coordinate points on the original coordinate point set as filtering conditions to filter the real-time coordinate point set to obtain a filtered real-time coordinate point set; Generate a coordinate track according to the filtered real-time coordinate point set, and remove the jump points in the filtered real-time coordinate point set according to the coordinate track to obtain a valid coordinate point set; A set of abscissa values ​​and a set of ordinate values ​​are obtained from the set of valid coordinate points, and an environmental prediction variable is determined according to the set of abscissa values ​​and the set of ordinate values.

7. The sleeping environment adjustment method according to claim 1, characterized in that: The step of generating environmental adjustment parameters according to environmental prediction variables includes: Classify the environmental predictor variables into different types to obtain multiple environmental variable types; Extracting corresponding conversion algorithms from preset conversion format conditions according to multiple environment variable types; The corresponding environmental prediction variables are converted using the conversion algorithm to obtain environmental adjustment parameters.

8. A sleeping environment adjustment device, characterized in that: include: The first analysis module is used to obtain initial environmental parameters of the indoor environment and perform adjustability analysis on the initial environmental parameters to obtain an adjustable parameter set; The second analysis module is used to obtain normal vital sign data of the user, and perform correlation analysis on the normal vital sign data and the adjustable parameter set according to the preset dependent variable conditions to obtain a vital sign correlation set; A construction module, used for constructing an original physical sign-environment association curve based on the physical sign association set; The prediction module is used to obtain the user's real-time vital sign data, and to perform trend prediction on the original vital sign-environment correlation curve through a preset trend prediction model and the real-time vital sign data, so as to obtain the real-time vital sign-environment correlation curve; The third analysis module is used to perform trend analysis on the real-time physical sign-environment association curve to obtain environmental prediction variables; The conversion module is used to generate environmental adjustment parameters according to environmental prediction variables.

9. A sleeping environment adjustment device, characterized in that: The sleeping environment adjustment device comprises: a memory and at least one processor, wherein instructions are stored in the memory; At least one of the processors calls the instructions in the memory to enable the sleeping environment adjustment device to perform each step of the sleeping environment adjustment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the sleeping environment adjustment method according to any one of claims 1 to 7 are implemented.

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