A method and device for controlling the operation of an intelligent pressure-sensitive adjustable bedding system.

Abstract

This application provides a method and device for controlling the operation of intelligent pressure-sensitive adjustable bedding, belonging to the field of intelligent control technology. It addresses the problem that existing bedding adjustments mainly rely on manual operation, which is prone to generating noise and affecting the user's sleep quality. The method acquires initial pressure data from each fiber pressure sensor; based on the initial pressure data and a preset sleep posture coordination model, it determines the pending pressure data and coordination completion time. Based on the coordination completion time and sleep stage monitoring data, it determines the pressure adjustment period to generate air spring identification groups. Based on the air spring identification groups corresponding to sound monitoring information and real-time acquired sleep stage monitoring data, it determines whether the pressure adjustment state of each air spring is in a pending state; if so, according to each pending state and the corresponding pending pressure data, it controls an intelligent air pump to adjust the pressure of at least one air spring in the air spring identification group.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to a method and device for controlling the operation of an intelligent pressure-sensitive adjustable bedding. Background Technology

[0002] With the development of technology and the improvement of people's living standards, people's demand for home furnishings is no longer limited to basic functionality; the demand for personalized home furnishings is increasing. This is especially true for bedding, which has higher requirements compared to other home furnishings. People spend a large portion of their day in a static position on bedding, and if the bedding is too soft or too hard, it can lead to discomfort.

[0003] Currently, bedding manufacturers are starting to produce beds or mattresses that can adjust the support for different parts of the body. By adjusting the height of different sections of the bed or mattress, varying levels of support are provided to different parts of the user's body, allowing the user to maintain their natural physiological curves during sleep. However, existing bedding adjustment methods mainly rely on manual adjustment, which inevitably produces adjustment noise. If someone is sleeping in the bedding, this noise may wake them up, affecting their sleep experience. Summary of the Invention

[0004] This application provides a method and device for controlling the operation of intelligent pressure-sensitive adjustable bedding, which solves the problem that existing bedding control mainly relies on manual operation, and existing bedding is prone to control noise, affecting the user's sleep quality and user experience.

[0005] On one hand, embodiments of this application provide a method for controlling the operation of an intelligent pressure-sensitive adjustable bedding, wherein the intelligent pressure-sensitive adjustable bedding includes at least: an intelligent air pump, a plurality of air springs connected to the intelligent air pump and vertically disposed on the base of the bedding, and fiber pressure sensors disposed on the top of each of the air springs; the method includes:

[0006] Acquire the initial pressure data of each of the fiber pressure sensors; wherein, the initial pressure data is the load pressure data of the bedding on the current user;

[0007] Based on the initial pressure data and the preset sleeping posture coordination model, determine the pending pressure data corresponding to the completion of sleeping posture coordination and the coordination completion time corresponding to the completion of sleeping posture coordination.

[0008] Based on the coordination completion time and the current user's sleep stage monitoring data, the pressure adjustment period of the air spring corresponding to each of the pending pressure data is determined, and at least one air spring identification group is generated according to the pressure adjustment period, so as to adjust the pressure of each air spring based on the air spring identification group; the air spring identification group includes the identification of each air spring that is pressure adjusted during the same pressure adjustment period within the coordination completion time.

[0009] Acquire sound monitoring information corresponding to the pressure adjustment of the air spring indicator group; the sound monitoring information is obtained through the sound emitted by the intelligent air pump during inflation and / or deflation.

[0010] Based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data, determine whether the pressure adjustment status of each air spring in the air spring identification group is in a state to be scheduled.

[0011] If so, based on each of the scheduled states and the corresponding undetermined pressure data, the intelligent air pump is controlled to adjust the pressure of the air springs in the at least one air spring identification group.

[0012] In one implementation of this application, based on the initial pressure data and a preset sleep posture coordination model, the pending pressure data corresponding to the completion of sleep posture coordination and the coordination completion time corresponding to the completion of sleep posture coordination are determined, specifically including:

[0013] Several sleep posture pressure data samples from a pre-set database are input into a multiple linear regression model to train the model and obtain the sleep posture coordination model. The sleep posture pressure data samples include at least a first pressure data sequence of bedding on various parts of the body under different sleep postures and a second pressure data sequence corresponding to the first pressure data sequence. The first pressure data sequence is obtained based on bearing pressure data without pressure adjustment of the sleep posture. The second pressure data sequence is obtained based on bearing pressure data after pre-adjustment of the sleep posture.

[0014] The initial pressure data are input into the sleeping posture coordination model to determine whether each initial pressure data is pressure data to be coordinated.

[0015] If so, determine the respective pressure data to be determined corresponding to each of the aforementioned pressure data to be coordinated; and

[0016] The time required for each of the pressure data to be coordinated to be adjusted to the adjusted value of each of the pressure data to be determined is the coordination completion time.

[0017] In one implementation of this application, based on the initial pressure data and a preset sleep posture coordination model, the pending pressure data corresponding to the completion of sleep posture coordination and the coordination completion time corresponding to the completion of sleep posture coordination are determined, specifically including:

[0018] Determine the initial pressure data sequence corresponding to each of the initial pressure data;

[0019] The sleeping posture coordination model is used to match the first pressure data sequence corresponding to the initial pressure data sequence to determine the initial sleeping posture corresponding to the initial pressure data sequence.

[0020] Based on the body parts corresponding to the initial sleeping position, the initial pressure data sequence is divided into several initial pressure data subsequences;

[0021] Based on the preset pressure coordination standard for each part of the human body according to the sleeping posture coordination model, each undetermined pressure data subsequence corresponding to each initial pressure data subsequence corresponding to each part of the human body is determined to complete the generation of each undetermined pressure data; the pressure coordination standard includes a coordination comparison threshold for determining whether the initial pressure data of each part of the human body is the coordination comparison threshold for the undetermined pressure data.

[0022] In one implementation of this application, before determining the pressure adjustment period of the air spring corresponding to each of the pending pressure data based on the coordination completion time and the sleep stage monitoring data of the current user, the method further includes:

[0023] Determine the sleep stage prediction distribution curve corresponding to the sleep stage monitoring data; the sleep stages in the sleep stage prediction distribution curve include at least: sleep onset stage, light sleep stage, deep sleep stage, and REM sleep stage;

[0024] Based on the sleep stage prediction distribution curve, determine whether the sleep stage corresponding to the coordination completion time includes the sleep onset stage and / or the REM stage;

[0025] If so, determine the duration of the easily startled stage corresponding to the sleep onset stage and / or the REM stage within the coordination completion time, and extend the coordination completion time based on the easily startled stage duration and a preset pressure adjustment decibel ratio table.

[0026] In one implementation of this application, based on the duration of the easily startled phase and a preset pressure regulation decibel ratio table, the coordination completion time is extended, specifically including:

[0027] Determine a first number of air springs that perform pressure adjustment during the duration of the easily startled phase;

[0028] Based on the first quantity and the pressure regulation decibel ratio table, determine the first noise intensity value within the duration of the easily startled phase;

[0029] When the first noise intensity value matches the preset wake-up noise intensity table, according to the first noise intensity value and the pressure adjustment decibel ratio table, the first quantity is reduced to the second quantity according to the preset rules, and the second noise intensity value corresponding to the second quantity of air springs is determined.

[0030] If the second noise intensity value still matches the startle noise intensity table, update the second noise intensity value until the Nth noise intensity value corresponding to the Nth air spring meets a preset condition; wherein, N is a natural number greater than 2; the preset condition is at least that the Nth noise intensity value does not match the startle noise intensity table;

[0031] Based on the Nth quantity and the formula for adjusting the pressure of the air spring, the coordination completion time is extended.

[0032] In one implementation of this application, extending the coordination completion time based on the Nth quantity and the air spring pressure adjustment schedule specifically includes:

[0033] The difference between the first quantity and the Nth quantity is determined as the quantity to be scheduled;

[0034] Based on the position information of the air springs corresponding to the first quantity, the coordination priority of each air spring in the preset coordination priority comparison table is matched; wherein, the coordination priority comparison table is generated based on the comfort evaluation information of each air spring of the bedding when the pressure is adjusted in a predetermined order;

[0035] Based on the coordination priority and the number to be scheduled, the air springs corresponding to the number to be scheduled are determined as the air springs to be scheduled;

[0036] Based on the pressure adjustment duration formula and the pressure data to be determined, the pressure adjustment duration of the air spring to be scheduled is determined, and the coordination completion duration is extended according to the maximum value among the pressure adjustment durations.

[0037] In one implementation of this application, obtaining the sound monitoring information corresponding to the pressure adjustment of the air spring indicator group specifically includes:

[0038] The internal monitoring sound of the bedding operation is acquired by a sound acquisition module installed inside the bedding and electrically connected to the intelligent air pump; the internal monitoring sound includes at least the sound of the intelligent air pump drawing air from and / or inflating each of the air springs.

[0039] The sound type of the internal monitoring sound is determined by a pre-trained sound recognition model;

[0040] When the sound type of the internally monitored sound is a controlled noise type, the sound monitoring information is generated.

[0041] In one implementation of this application, based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data, it is determined whether the pressure adjustment state of each air spring in the air spring identification group is in a pending scheduling state, specifically including:

[0042] Determine the wakefulness noise intensity curve corresponding to the sleep stage monitoring data;

[0043] The sound monitoring information is compared with the startle noise intensity curve;

[0044] If the comparison result between the sound monitoring information and the startle noise intensity curve indicates that the sound monitoring information is a startle noise, then the pressure adjustment status of some or all of the air springs in the air spring identification group is determined to be in a state to be scheduled.

[0045] Based on the startle noise intensity curve, within the coordination completion time, each air spring icon corresponding to the pending scheduling state is assigned to the air spring icon group that has not entered the pressure regulation period, and it is determined whether the air spring icon group that has been reassigned has startle noise, so as to update the pressure regulation status of the air springs in the air spring icon group of each pressure regulation period.

[0046] In one implementation of this application, the intelligent air pump is controlled to adjust the pressure of the air springs in the at least one air spring identification group according to each of the scheduled states and the corresponding undetermined pressure data, specifically including:

[0047] Add the air spring corresponding to each of the scheduled states to the air spring identifier group corresponding to the next pressure adjustment period;

[0048] Based on the air spring identification group corresponding to the next pressure adjustment period and the sleep stage monitoring data, update the pressure adjustment status of the air springs in each air spring identification group that has not been pressure adjusted and / or add new air spring identification groups.

[0049] Based on the newly added air spring identification group, the coordination completion time is updated, and the intelligent air pump is controlled to inflate and / or de-inflate the air springs in the updated air spring identification group until the bearing pressure data of each air spring meets the pending pressure data.

[0050] On the other hand, this application embodiment also provides an operation control device for an intelligent pressure-sensitive adjustable bedding, the intelligent pressure-sensitive adjustable bedding including at least: an intelligent air pump, a plurality of air springs connected to the intelligent air pump and vertically arranged on the base of the bedding, and fiber pressure sensors disposed on the top of each of the air springs; the device includes:

[0051] At least one processor; and,

[0052] A memory communicatively connected to the at least one processor; wherein,

[0053] The memory stores instructions that can be executed by the at least one processor, enabling the at least one processor to perform the following:

[0054] Acquire the initial pressure data of each of the fiber pressure sensors; wherein, the initial pressure data is the load pressure data of the bedding on the current user;

[0055] Based on the initial pressure data and the preset sleeping posture coordination model, determine the pending pressure data corresponding to the completion of sleeping posture coordination and the coordination completion time corresponding to the completion of sleeping posture coordination.

[0056] Based on the coordination completion time and the current user's sleep stage monitoring data, the pressure adjustment period of the air spring corresponding to each of the pending pressure data is determined, and at least one air spring identification group is generated according to the pressure adjustment period, so as to adjust the pressure of each air spring based on the air spring identification group; the air spring identification group includes the identification of each air spring that is pressure adjusted during the same pressure adjustment period within the coordination completion time.

[0057] Acquire sound monitoring information corresponding to the pressure adjustment of the air spring indicator group; the sound monitoring information is obtained through the sound emitted by the intelligent air pump during inflation and / or deflation.

[0058] Based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data, determine whether the pressure adjustment status of each air spring in the air spring identification group is in a state to be scheduled.

[0059] If so, based on each of the scheduled states and the corresponding undetermined pressure data, the intelligent air pump is controlled to adjust the pressure of the air springs in the at least one air spring identification group.

[0060] This application utilizes the initial pressure data collected by a fiber optic pressure sensor to coordinate the user's sleeping posture. During this posture coordination process, the number of air springs requiring pressure adjustment is adjusted according to the user's sleep stage, preventing noise from the bedding's operation from affecting the user's sleep. This allows the intelligent pressure-sensitive adjustable bedding to operate intelligently, automatically, and with low noise, eliminating the need for manual control and preventing any noise that could disrupt sleep. Furthermore, it enhances the user experience of the intelligent pressure-sensitive adjustable bedding. Attached Figure Description

[0061] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0062] Figure 1 This is a top sectional view of an intelligent pressure-sensitive adjustable bedding according to an embodiment of this application;

[0063] Figure 2 This is a perspective view of an intelligent pressure-sensitive adjustable bedding according to an embodiment of this application;

[0064] Figure 3 This is a flowchart illustrating the operation control method of an intelligent pressure-sensitive adjustable bedding according to an embodiment of this application;

[0065] Figure 4 This is a schematic diagram of the sleep stage prediction distribution curve of the operation control method of an intelligent pressure-sensitive adjustable bedding according to an embodiment of this application;

[0066] Figure 5 This is a schematic diagram of the operation control device for an intelligent pressure-sensitive adjustable bedding according to an embodiment of this application.

[0067] List of components and reference numerals:

[0068] 100. Intelligent pressure-sensitive adjustable bedding; 110. Headboard end; 111. First strip-shaped airbag slot; 120. Bedding base; 121. Air spring slot; 130. Footboard end; 131. Second strip-shaped airbag slot; 140. Intelligent air pump; 210. First strip-shaped airbag; 220. Air spring; 230. Second strip-shaped airbag; 240. Fiber pressure sensor. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0070] Figure 1 This is a cross-sectional view of the intelligent pressure-sensitive adjustable bedding of this application, as shown below. Figure 1 As shown, the intelligent pressure-sensitive adjustable bedding 100 includes a plurality of first strip-shaped airbag slots 111 for installing a first strip-shaped airbag at the head end 110, an air spring slot 121 for installing an air spring at the base of the bedding, a plurality of second strip-shaped airbag slots 131 for installing a second strip-shaped airbag at the foot end 130, and an intelligent air pump 140.

[0071] Figure 2 This is a perspective view of the intelligent pressure-sensitive adjustable bedding of this application, as shown below. Figure 2 As shown, the intelligent pressure-sensitive adjustable bedding includes multiple first strip-shaped airbags 210 disposed at the head end 110, several air springs 220 vertically disposed at the bedding base 120, and multiple second strip-shaped airbags 230 disposed at the foot end 130. Each first and second strip-shaped airbag is connected to an intelligent air pump, which inflates or deflates it. The operation control method of this application is also applicable to the first and second strip-shaped airbags. Fiber pressure sensors 240 are respectively disposed in the user-facing areas of each first and second strip-shaped airbag and on the top of each air spring. The intelligent air pump is connected to each first strip-shaped airbag, each second strip-shaped airbag, and each air spring through gas delivery pipes.

[0072] In the embodiments of this application, the positions and number of the first strip airbag, the second strip airbag, and the air spring can be adjusted according to actual use, such as position shifting, quantity increase or decrease, etc., and this application does not make specific limitations in this regard.

[0073] This application provides a method and device for controlling the operation of intelligent pressure-sensitive adjustable bedding, which solves the problem that existing bedding control mainly relies on manual operation, and existing bedding is prone to control noise, affecting the user's sleep quality and user experience.

[0074] The various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0075] This application provides a method for controlling the operation of an intelligent pressure-sensitive adjustable bedding, such as... Figure 3 As shown, the method may include steps S301-S306:

[0076] S301, the processor acquires the initial pressure data from each fiber pressure sensor.

[0077] The initial pressure data refers to the pressure exerted by the bedding on the current user.

[0078] The processor can be an internal processor of the intelligent air pump. The processor is electrically connected to each fiber pressure sensor to acquire the pressure data collected by the fiber pressure sensors. The fiber pressure sensors are located on the surface of the bedding and are pressure sensors made of flexible textiles.

[0079] When a user lies, sits, or lies on a smart pressure-sensitive adjustable bedding set, the fiber pressure sensor collects the pressure data currently being used, using this pressure data as the initial pressure data. The processor, or a storage medium connected to the processor, can pre-store the pressure data of the bedding when it is not in use, and set pressure ranges. The minimum value of the pressure range can be the pressure data of the bedding supporting an infant or child, and the maximum value can be the maximum pressure the bedding can withstand. The processor can determine whether a user is present when the pressure data falls within the specified range.

[0080] Examples of storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0081] S302, the processor determines the pending pressure data and the coordination completion time corresponding to the completion of sleep posture coordination based on the initial pressure data and the preset sleep posture coordination model.

[0082] The sleeping posture coordination model is set in the processor and is used to adjust the internal gas pressure of the air springs and / or strip air bladders based on the user's current sleeping posture and the pressure data collected by the fiber pressure sensors on the surface of each air spring and / or strip air bladder (including the first strip air bladder and the second strip air bladder).

[0083] In one embodiment of this application, a sleep posture coordination model can be used to adjust the pressure data corresponding to a user's sleeping posture, such as lying flat on a bedding, and the processor obtains the initial pressure data corresponding to the user lying flat on the bedding. Using the sleep posture coordination model, the processor can determine whether the initial pressure data corresponding to the lying flat sleeping posture needs to be adjusted, wherein whether adjustment is needed can be obtained based on sample data of several users lying flat on the bedding.

[0084] For example, when a user lies flat on a bedding set, various parts of the body are supported by air springs and / or strip-shaped airbags. Areas experiencing less support, such as the lower back, require the user to bear the load themselves. Therefore, the support provided by the air springs and / or strip-shaped airbags in different parts of the body corresponding to the lying position can be adjusted during the manufacturing or testing phase of the bedding set. This adjustment provides greater support to areas with less support and less support to areas with greater support. The pressure data without adjusted support is used as the initial pressure data sample, and the corresponding adjusted pressure data is used as the undetermined pressure data sample. This results in sample data used to train a sleep posture coordination model, enabling the model to determine the undetermined pressure data based on the initial pressure data.

[0085] Understandably, since the initial pressure data samples mentioned above correspond to different sleeping positions, when training the sleep posture coordination model using these initial pressure data samples, a sleeping position label can be assigned to each initial pressure data sample, enabling the sleep posture coordination model to identify the sleeping position corresponding to the initial pressure data. The sleep posture coordination model can be a multiple linear regression model or a neural network model.

[0086] In another embodiment of this application, the sleeping posture coordination model can also be used to predict the next sleeping posture from the current sleeping posture. The sample data includes several initial pressure data samples and several undetermined pressure data samples. A pre-defined correspondence can be established, whereby any undetermined pressure data sample is used as a first sample, and undetermined pressure data samples in a different sleeping posture from the first sample are used as second samples. The first sample can correspond to multiple second samples. The first sample serves as the initial pressure data, and the second sample serves as the undetermined pressure data.

[0087] During the training phase of the sleeping posture coordination model, multiple first weights can be preset. These weights are the first weights corresponding to the first sample and each second sample. For example, if the sleeping posture of the first sample is lying flat, and the sleeping postures of the second samples are lying on their side, curled up, etc., the first weight corresponding to lying flat and lying on the side is, for example, a, and the first weight corresponding to curled up is, for example, b. If a>b, then the sleeping posture after lying flat is more likely to be lying on the side.

[0088] A second weight can also be set to control the probability of changing from the sleeping position of the first sample to the sleeping position of the second sample. The probability of changing from the sleeping position of the first sample to the sleeping position of the second sample is related to the duration of the first sample's sleeping position. For example, the longer the sleeping position is maintained, the higher the probability value. For another example, during the initial period of entering the sleep stage, the probability value of changing the sleeping position increases with time by a preset step size, and after entering the sleep stage, the probability value decreases with time by a preset step size. The correspondence between this probability value and the duration of the sleeping position can be set by the developer or manufacturer according to different bedding users or usage scenarios. This application does not make any specific limitations on this.

[0089] The second weight is multiplied by the probability value to obtain a weighted probability value, which is used to determine the probability of the user changing sleeping positions within a preset time period. This second weight is preset by the developer or manufacturer and can be adjusted based on the actual changes in the user's sleeping positions during use, making the bedding more accurate in determining the user's sleeping position changes. The preset time period can be set by the developer or manufacturer or the user, such as 1 minute, 2 minutes, etc., meaning the user changes sleeping positions within this preset time period.

[0090] The aforementioned sleeping posture coordination model can adjust the pressure of the air springs and / or strip-shaped airbags when the user is sleeping, whether the user is maintaining a sleeping posture or changing sleeping posture, thereby coordinating the support force for the user in the same or different sleeping postures and improving the user's sleep experience.

[0091] In this embodiment of the application, based on the initial pressure data and the preset sleep posture coordination model, the pending pressure data corresponding to the completion of sleep posture coordination and the coordination completion time corresponding to the completion of sleep posture coordination are determined, specifically including:

[0092] The processor inputs several sleeping posture pressure data samples from a pre-set database into a multiple linear regression model to train the model and obtain a sleeping posture coordination model. The sleeping posture pressure data samples include at least a first pressure data sequence of the bedding on various parts of the body under different sleeping postures, and a corresponding second pressure data sequence. The first pressure data sequence is obtained based on the bearing pressure data before pressure adjustment for sleeping posture. The second pressure data sequence is obtained based on the bearing pressure data after pre-adjustment of sleeping posture pressure.

[0093] The first pressure data sequence can be obtained from the initial pressure data sample mentioned above, and the second pressure data sequence can be obtained from the undetermined pressure data sample mentioned above. The multiple linear regression model can use the first pressure data sequence as input and the second pressure data sequence corresponding to the input first pressure data sequence as the output target, training the various weight parameters within the multiple linear regression model so that it can output an accurate second pressure data sequence based on the first pressure data sequence. The second pressure data sequence corresponding to the first pressure data sequence refers to the pressure data sequence after pressure regulation of the first pressure data sequence.

[0094] Subsequently, the initial pressure data are input into the sleeping posture coordination model to determine whether each initial pressure data is the pressure data to be coordinated.

[0095] Given that each initial pressure data point is determined to be a pressure data point to be coordinated, the corresponding pending pressure data points are determined. The adjustment time for each pressure data point to be coordinated to be adjusted to the corresponding pending pressure data point is also determined; this is the coordination completion time.

[0096] In other words, when using a multi-source linear regression model, the sleep posture coordination model is trained and its accuracy is verified to obtain a fully trained sleep posture coordination model. This model is then used to determine whether the initial pressure data is the pressure data to be coordinated, including both maintaining a single sleep posture and coordinating sleep posture changes. Coordinating sleep posture changes has a higher priority than coordinating a single sleep posture.

[0097] The processor's sleep posture coordination model can output pending pressure data. The processor can also calculate the time required to adjust the internal pressure of the air spring indicator group and / or the internal pressure of the strip airbag from the initial pressure data to reach each pending pressure data. This adjustment time is used as the coordination completion time.

[0098] In this embodiment, if the processor calculates each air spring and / or strip airbag individually during adjustment, it wastes computing resources, and different parts of the human body generally correspond to multiple air springs. Therefore, based on the initial pressure data and the preset sleep posture coordination model, the processor determines the pending pressure data corresponding to the completion of sleep posture coordination and the coordination completion time corresponding to the completion of sleep posture coordination, specifically including:

[0099] First, the processor determines the initial pressure data sequence corresponding to each initial pressure data.

[0100] The initial pressure data sequence can be obtained by arranging the initial pressure data in a predetermined order, such as from the head end to the foot end of the bed, with the fiber pressure sensors arranged in a priority order from left to right.

[0101] Secondly, the processor uses a sleeping posture coordination model to match the first pressure data sequence corresponding to the initial pressure data sequence in order to determine the initial sleeping posture corresponding to the initial pressure data sequence.

[0102] Then, the processor divides the initial pressure data sequence into several initial pressure data subsequences based on the body parts corresponding to the initial sleeping position.

[0103] After determining the initial sleeping position, the processor can analyze and determine the position of each part of the body corresponding to the initial sleeping position, and determine the elements in the initial pressure data sequence corresponding to each part, thereby obtaining the initial pressure data subsequence corresponding to each part.

[0104] Finally, based on the preset pressure coordination standards for different parts of the body according to the sleeping posture coordination model, the processor determines the corresponding initial pressure data subsequences for each part of the body, thus generating the pressure data to be coordinated. The pressure coordination standards include coordination comparison thresholds used to determine whether the initial pressure data for each part of the body is the pressure data to be coordinated.

[0105] Pressure coordination criteria can be understood as the standard for whether pressure adjustment is required for air springs and / or strip-shaped airbags corresponding to different parts of the human body. For example, the average value of the initial pressure data subsequence corresponding to the "hip" region and the average value of the initial pressure data subsequence corresponding to the "waist" region can be calculated. For example, the average value of the "hip" region is 30, the average value of the "waist" region is 5, and the coordination comparison threshold is 20. The average value of the "hip" region is greater than the coordination comparison threshold, while the average value of the "waist" region is less than the coordination comparison threshold. Therefore, the initial pressure data corresponding to the "hip" and "waist" regions are both pressure data to be coordinated. The processor can provide the error of the coordination comparison threshold, such as an error of 2, which constitutes the acceptable error comparison threshold range [18, 22]. When the average value of the initial pressure data subsequences of the "waist" and "hip" regions is within the acceptable error comparison threshold range, pressure adjustment is no longer required.

[0106] Those skilled in the art will understand that when adjusting pressure, it is not sufficient to adjust the internal pressure of only one or a few air spring indicator groups to ensure that the average value of the corresponding area is within the acceptable error comparison threshold range. The processor needs to adjust the pressure of each air spring in the corresponding area of ​​each part so that the average value is within the acceptable error comparison threshold range. In other words, when adjusting pressure, it is necessary to meet the usage requirements. Adjusting only one or a few air springs may lead to a poor user experience. Therefore, the processor can also set a pressure adjustment limit for an air spring and / or a strip-shaped airbag to avoid situations where adjusting the internal air pressure of only one or a few air spring indicator groups leads to danger and a poor user experience.

[0107] By generating undetermined pressure data from the aforementioned body parts, processor computing resources can be saved, allowing the processor to allocate computing resources to other operational control processes, enabling more intelligent, flexible, timely, and efficient operation control of the bedding.

[0108] S303, the processor determines the pressure adjustment period of the air spring corresponding to each pending pressure data based on the coordination completion time and the current user's sleep stage monitoring data, and generates at least one air spring identification group based on the pressure adjustment period, so as to adjust the pressure of each air spring based on the air spring identification group.

[0109] The air spring label group includes labels for each air spring that performs pressure adjustment during the same pressure adjustment period within the coordinated completion time.

[0110] Each air spring can be pre-assigned an identifier. The processor can then create at least one air spring identifier group containing a different number of air springs based on this identifier. During the coordination completion period, the time it takes for each air spring to reach the desired pressure data from the initial pressure data may differ. Based on this, the processor can create air spring identifier groups according to the different arrival times. Within the same air spring identifier group, all air springs are in pressure regulating mode.

[0111] Sleep stage monitoring data can be determined by the processor based on the continuous usage time during the current use. A sleep stage prediction neural network model can be preset and trained using sample data of historical sleep stages and continuous usage time, so that the processor can obtain sleep stage monitoring data based on the user's usage time. The continuous usage time can be obtained based on the duration of bearing pressure data recorded by the fiber pressure sensor. Sleep stage monitoring data includes the current sleep stage and the predicted future sleep stage. Sleep stage monitoring data can also be obtained by the processor from the user's wristband or mobile phone, or other wearable devices via a network or Bluetooth module; this application does not specifically limit this.

[0112] In this embodiment of the application, before determining the pressure adjustment period of the air spring corresponding to each pending pressure data based on the coordination completion time and the current user's sleep stage monitoring data, the method further includes:

[0113] The processor determines the predicted distribution curve of sleep stages corresponding to the sleep stage monitoring data (e.g., Figure 4 (As shown). The sleep stages predicted in the sleep stage distribution curve include at least the following sleep stages: sleep onset, light sleep, deep sleep, and REM sleep. The vertical axis of the sleep stage prediction distribution curve represents the sleep stage, and the horizontal axis represents time.

[0114] The processor determines whether the sleep stage corresponding to the coordinated completion time includes the sleep onset stage and / or REM stage based on the sleep stage prediction distribution curve.

[0115] If the sleep stages corresponding to the coordination completion time include the sleep onset stage and / or REM stage, determine the duration of the easily startled stage corresponding to the sleep onset stage and / or REM stage within the coordination completion time, and extend the coordination completion time based on the easily startled stage duration and the preset pressure regulation decibel comparison table.

[0116] During pressure regulation, the initial coordinated completion time involves simultaneously evacuating or inflating all air springs and / or strip-shaped airbags. Multiple air springs and / or strip-shaped airbags are involved in the coordinated completion time adjustment. Taking the adjustment of only air springs as an example, a single air spring will generate a predetermined decibel noise level during pressure regulation. If multiple air springs are pressure regulated simultaneously, the increased power output of the intelligent air pump results in a higher noise level than that of a single air spring. Therefore, this application can, through the above-mentioned scheme, schedule the pressure regulation periods of each air spring within the coordinated completion time. If the coordinated completion time includes a period prone to startling, the number of air springs originally scheduled for adjustment during this period needs to be adjusted. Air springs originally planned for adjustment during the startling phase can be moved to other pressure regulation periods. Furthermore, if it is impossible to add scheduled air springs during any of the pressure regulation periods within the coordinated completion time, the coordinated completion time can be extended, allowing the scheduled air springs to perform pressure regulation within the extended coordinated completion time.

[0117] The aforementioned pressure regulation decibel ratio table can be pre-stored in the processor or a storage medium connected to the processor. The pressure regulation decibel ratio table at least includes the correspondence between the number of air spring pressure adjustments and the generated noise intensity value. This pressure regulation decibel ratio table can be pre-set in the processor or storage medium by the developer when the bedding leaves the factory.

[0118] In this embodiment of the application, the above-mentioned extension of the coordination completion time based on the duration of the easily startled stage and a preset pressure adjustment decibel ratio table specifically includes:

[0119] First, the processor determines the initial number of air springs that will regulate pressure during the easily startled phase.

[0120] The first quantity refers to the number of air springs in the corresponding air spring label group during the easily startled phase.

[0121] Next, the processor determines the first noise intensity value within the easily startled phase based on the first quantity and pressure adjustment decibel ratio table.

[0122] In the pressure regulation decibel ratio table, when the pressure is regulated by matching the first number of air springs, the corresponding noise intensity value (decibels) is the first noise intensity value.

[0123] When the first noise intensity value matches the preset wake-up noise intensity table, according to the first noise intensity value and the pressure adjustment decibel comparison table, the first quantity is reduced to the second quantity according to the preset rules, and the second noise intensity value corresponding to the second quantity of air springs is determined.

[0124] The generation method of the startle noise intensity table and the pressure regulation decibel ratio table can be the same, or it can be set by the user during actual use. This application does not specifically limit the acquisition method. The startle noise intensity table can contain the correspondence between sleep stages and startle noise intensity values. Matching the first noise intensity value with the startle noise intensity table means that the first noise intensity value is at least equal to or greater than the startle noise intensity value under its corresponding sleep stage. If the matching relationship is met, the processor can reduce the first quantity to the second quantity according to the step size in the preset rules.

[0125] Furthermore, during the process of adjusting the first quantity according to the preset step size, the processor can calculate the difference between the first noise intensity value and the second noise intensity value to determine the correspondence between the preset step size and the noise intensity. Based on this correspondence, the processor can intelligently correct the step size in the future to reduce the number of times the first quantity is adjusted.

[0126] If the second noise intensity value still matches the startle noise intensity table, update the second noise intensity value until the Nth noise intensity value corresponding to the Nth air spring meets a preset condition. Here, N is a natural number greater than 2. The preset condition is that the Nth noise intensity value does not match the startle noise intensity table.

[0127] Subsequently, based on the Nth quantity and the formula for adjusting the pressure of the air spring, the coordination completion time is extended.

[0128] Specifically, the processor determines the difference between the first quantity and the Nth quantity as the number to be scheduled.

[0129] In other words, during the easily startled phase, how many air springs need to be adjusted to other periods for pressure regulation, and this number is designated as the number to be scheduled.

[0130] Next, based on the position information of the air springs corresponding to the first quantity, the coordination priority of each air spring in the preset coordination priority comparison table is matched. The coordination priority comparison table is generated based on the comfort evaluation information of each air spring of the bedding when the pressure is adjusted in a predetermined order.

[0131] Coordination priorities can be generated by developers based on comfort evaluation information when adjusting the number of air springs sequentially during bedding use. For example, the comfort evaluation information differs depending on whether the lumbar pressure is adjusted first and then the back pressure, or vice versa. Coordination priorities are established based on this comfort evaluation information. For instance, the coordination priority for the lumbar region is higher than that for the back region.

[0132] Next, based on the coordination priority and the quantity to be scheduled, the air springs corresponding to the quantity to be scheduled are determined, and these are the air springs to be scheduled.

[0133] Based on the coordination priority and the number of air springs to be scheduled for each air spring, select the number of air springs with lower priority from the air spring identifier group to be scheduled as the air springs to be scheduled.

[0134] Finally, based on the pressure adjustment time formula and the pressure data to be determined, the pressure adjustment time of the air spring to be scheduled is determined, and the coordination completion time is extended according to the maximum value among the pressure adjustment times.

[0135] The pressure regulation time formula is used to calculate the time required to adjust the pressure data of the air spring to be regulated to the desired pressure data using the output power of the intelligent air pump. The pressure regulation time formula is as follows:

[0136] T = α|D1 - D2|

[0137] Where T is the pressure adjustment time, α is the weight of the time taken to adjust the pressure data of the air spring to be scheduled to the desired pressure data using the output power of the intelligent air pump, D1 is the pressure data at the moment before scheduling, and D2 is the desired pressure data. Since the time taken for each air spring to be scheduled to reach the desired pressure data varies, the calculated pressure adjustment times of each air spring to be scheduled are bubble sorted to find the maximum pressure adjustment time, and the coordination completion time is increased by this maximum pressure adjustment time to obtain the extended coordination completion time.

[0138] S304, the processor obtains the sound monitoring information corresponding to the pressure adjustment of the air spring indicator group.

[0139] The sound monitoring information is obtained through the sounds emitted by the intelligent air pump during inflation and / or deflation.

[0140] In this embodiment of the application, obtaining the sound monitoring information corresponding to the pressure adjustment of the air spring indicator group specifically includes:

[0141] The processor acquires internal monitoring sounds of the bedding's operation via a sound acquisition module installed inside the bedding and electrically connected to the smart air pump. These internal monitoring sounds include at least the sounds of the smart air pump pumping air into and / or inflating the air springs. A pre-trained sound recognition model determines the sound type of the internal monitoring sounds. If the sound type is classified as a control noise type, sound monitoring information is generated.

[0142] The sound acquisition module can be a microphone array, capable of collecting external sounds. The sound recognition model can be a pre-trained neural network model, recognizing the sounds corresponding to the sound monitoring information. The sound acquisition module can collect the sound of a smart air pump inflating or puffing, as well as snoring, turning over sounds, etc. The processor can filter the sounds, retaining only the valid sounds, such as the sound of a smart air pump inflating or puffing, to obtain sound monitoring information.

[0143] S305, the processor determines whether the pressure adjustment status of each air spring in the air spring identification group is in a pending scheduling state based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data.

[0144] In this embodiment of the application, based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data, it is determined whether the pressure adjustment status of each air spring in the air spring identification group is in a state to be scheduled, specifically including:

[0145] The processor determines the wake-up noise intensity curve corresponding to the sleep stage monitoring data and compares the sound monitoring information with the wake-up noise intensity curve. If the comparison result shows that the sound monitoring information is wake-up noise, the processor determines that the pressure regulation status of some or all air springs in the air spring indicator group is in a pending scheduling state. Based on the wake-up noise intensity curve, within the coordination completion time, the processor allocates the air spring indicators corresponding to the pending scheduling state to the air spring indicator groups that have not entered the pressure regulation period, and determines whether there is wake-up noise in the reassigned air spring indicator groups to update the pressure regulation status of the air springs in each pressure regulation period.

[0146] In other words, the processor can match or generate an alarm noise intensity curve based on sleep stage monitoring data. The processor can compare the real-time acquired sound monitoring information with the alarm noise intensity curve. The sound monitoring information includes sound intensity and the time when the sound is emitted. The horizontal axis of the alarm noise intensity curve is time, and the vertical axis is the alarm noise intensity value. When the sound intensity of the real-time acquired sound monitoring information is greater than or equal to the current alarm noise intensity value, the sound corresponding to the sound monitoring information is identified as alarm noise. Then, according to the preset rules in the above technical solution, the number of air spring markers in the air spring marker group is reduced, and the pressure adjustment status of the air springs corresponding to the air spring markers reduced from the air spring marker group is changed from the pressure adjustment state to the waiting state.

[0147] The processor can also determine the pressure adjustment status of air springs in other air spring indicator groups within the coordination completion time.

[0148] S306, when the processor determines that the pressure adjustment status of each air spring in the air spring label group is a pending state, it controls the intelligent air pump to adjust the pressure of the air spring in at least one air spring label group according to each pending state and the corresponding pending pressure data.

[0149] In one embodiment of this application, the processor can further determine the number of air springs in other air spring identification groups that have not entered the pressure regulation period based on the wake-up noise intensity curve, which is the number to be allocated. It is then determined whether the number of air springs to be allocated exhibits wake-up noise during pressure regulation. If not, the air spring identification corresponding to the pending scheduling state is allocated to the air spring identification group without wake-up noise, according to the allocation rules.

[0150] Specifically, the allocation rule refers to the processor determining, based on a pressure regulation decibel ratio table, whether a startling noise exists during pressure regulation in the air spring indicator group to be reassigned. If so, the air spring indicator is not assigned to that group. If not, an initial adjustment sound intensity value is determined based on the pressure regulation decibel ratio table and the number of air spring indicators in the group. The difference between the startling noise intensity value and the initial adjustment sound intensity value is used to determine the number of air spring indicators that can be assigned to that group, thus reassigning the corresponding number of air spring indicators to be reassigned. The correspondence between the difference between the startling noise intensity value and the initial adjustment sound intensity value and the number of air spring indicators that can be assigned can be set by the user; this application does not impose specific limitations on this.

[0151] In another embodiment of this application, based on each scheduling state and the corresponding undetermined pressure data, the intelligent air pump is controlled to adjust the pressure of the air springs in at least one air spring identification group, specifically including:

[0152] The processor adds the air springs corresponding to each pending scheduling state to the air spring identifier group corresponding to the next pressure adjustment period. Based on the air spring identifier group corresponding to the next pressure adjustment period and the sleep stage monitoring data, it updates the pressure adjustment status of the air springs in each air spring identifier group that has not been pressure adjusted and / or adds new air spring identifier groups. According to the newly added air spring identifier groups, it updates the coordination completion time and controls the intelligent air pump to inflate and / or evacuate the air springs in the updated air spring identifier groups until the bearing pressure data of each air spring meets the pending pressure data.

[0153] In other words, the processor can first add air springs in the pending scheduling state to the next air spring label group, and then redetermine the pressure regulation state in the next air spring label group based on the sleep stage corresponding to the air spring label group to which the added air spring labels are located. For example, if 10 air spring labels are adjusted to the next air spring label group, there will be a total of 20 air spring labels in the next air spring label group. If the sleep stage corresponding to the next air spring label group is the light sleep stage, and the awakening noise intensity value is the noise intensity value when 15 air springs are adjusting their pressure, then the processor can update the pressure regulation state of the original air springs in that air spring label group to the pending scheduling state, so as to schedule the air spring labels.

[0154] In addition, the processor can create a new air spring tag group when the scheduled air spring tag cannot be assigned to the air spring tag group, and update the coordination completion time according to the pressure adjustment period of the newly created air spring tag group.

[0155] Furthermore, through the air spring label group obtained by the above scheme, the intelligent air pump inflates and / or adjusts the inflation pressure of the air springs corresponding to each air spring label group, thereby controlling the operation of the intelligent pressure-sensitive adjustable bedding under low noise conditions.

[0156] The aforementioned intelligent pressure-sensitive adjustable bedding can be used by adults, children, and patients. During sleep, it provides low-noise support to various parts of the user's body, ensuring even pressure distribution and improving the user's sleep experience.

[0157] Through the above solution, this application can intelligently regulate the operation of the bedding during the user's sleep process, and adjust it in stages according to the user's sleep phase, avoiding noise from the operation and regulation that may affect the user's normal sleep. It eliminates the need for manual adjustment of the bedding, improving the user's sleep quality and bedding experience.

[0158] Figure 5 This is a schematic diagram of the operation control device for an intelligent pressure-sensitive adjustable bedding provided in an embodiment of this application. The intelligent pressure-sensitive adjustable bedding includes at least: an intelligent air pump, several air springs connected to the intelligent air pump and vertically arranged on the base of the bedding, and fiber pressure sensors disposed on the top of each air spring. The device includes:

[0159] At least one processor; and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the following:

[0160] Acquire the initial pressure data from each fiber pressure sensor. This initial pressure data represents the pressure exerted by the bedding on the current user.

[0161] Based on the initial pressure data and the preset sleeping posture coordination model, the corresponding pending pressure data and the coordination completion time are determined when the sleeping posture coordination is completed.

[0162] Based on the coordination completion time and the current user's sleep stage monitoring data, the pressure adjustment period of the air spring corresponding to each pending pressure data is determined. At least one air spring identification group is generated based on the pressure adjustment period, and the pressure of each air spring is adjusted according to the air spring identification group. The air spring identification group contains the identification of each air spring whose pressure is adjusted within the same pressure adjustment period during the coordination completion time.

[0163] Obtain sound monitoring information corresponding to the pressure adjustment of the air spring indicator group. The sound monitoring information is obtained from the sounds emitted by the intelligent air pump during inflation and / or deflation.

[0164] Based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time sleep stage monitoring data, it is determined whether the pressure adjustment status of each air spring in the air spring identification group is in a state to be scheduled.

[0165] If so, based on the scheduling status and the corresponding pending pressure data, control the intelligent air pump to adjust the pressure of the air springs in at least one air spring identification group.

[0166] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0167] The devices and methods provided in this application are one-to-one correspondences. Therefore, the devices also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices will not be repeated here.

[0168] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0169] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for controlling the operation of an intelligent pressure-sensitive adjustable bedding system, characterized in that, The intelligent pressure-sensitive adjustable bedding includes at least: an intelligent air pump, a plurality of air springs connected to the intelligent air pump and vertically disposed on the base of the bedding, and fiber pressure sensors disposed on the top of each of the air springs; the method includes: Acquire the initial pressure data of each of the fiber pressure sensors; wherein, the initial pressure data is the load pressure data of the bedding on the current user; Based on the initial pressure data and the preset sleeping posture coordination model, determine the pending pressure data corresponding to the completion of sleeping posture coordination and the coordination completion time corresponding to the completion of sleeping posture coordination. Based on the coordination completion time and the current user's sleep stage monitoring data, the pressure adjustment period of the air spring corresponding to each of the pending pressure data is determined, and at least one air spring identification group is generated according to the pressure adjustment period, so as to adjust the pressure of each air spring based on the air spring identification group; the air spring identification group includes the identification of each air spring that is pressure adjusted during the same pressure adjustment period within the coordination completion time. Acquire sound monitoring information corresponding to the pressure adjustment of the air spring indicator group; the sound monitoring information is obtained through the sound emitted by the intelligent air pump during inflation and / or deflation. Based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data, determine whether the pressure adjustment status of each air spring in the air spring identification group is in a state to be scheduled. If so, based on each of the scheduled states and the corresponding undetermined pressure data, the intelligent air pump is controlled to adjust the pressure of the air springs in the at least one air spring identification group; The method further includes, before determining the pressure adjustment period of the air spring corresponding to each of the pending pressure data, based on the coordination completion time and the current user's sleep stage monitoring data: Determine the sleep stage prediction distribution curve corresponding to the sleep stage monitoring data; the sleep stages in the sleep stage prediction distribution curve include at least: sleep onset stage, light sleep stage, deep sleep stage, and REM sleep stage; Based on the sleep stage prediction distribution curve, determine whether the sleep stage corresponding to the coordination completion time includes the sleep onset stage and / or the REM stage; If so, determine the duration of the easily startled stage corresponding to the sleep onset stage and / or the REM stage within the coordination completion time, and extend the coordination completion time based on the easily startled stage duration and a preset pressure adjustment decibel ratio table.

2. The method according to claim 1, characterized in that, Based on the initial pressure data and the preset sleep posture coordination model, the pending pressure data corresponding to the completion of sleep posture coordination and the coordination completion time corresponding to the completion of sleep posture coordination are determined, specifically including: Several sleep posture pressure data samples from a pre-set database are input into a multiple linear regression model to train the model and obtain the sleep posture coordination model. The sleep posture pressure data samples include at least a first pressure data sequence of bedding on various parts of the body under different sleep postures and a second pressure data sequence corresponding to the first pressure data sequence. The first pressure data sequence is obtained based on bearing pressure data without pressure adjustment of the sleep posture. The second pressure data sequence is obtained based on bearing pressure data after pre-adjustment of the sleep posture. The initial pressure data are input into the sleeping posture coordination model to determine whether each initial pressure data is pressure data to be coordinated. If so, determine the respective pressure data to be determined corresponding to each of the aforementioned pressure data to be coordinated; and The time required for each of the pressure data to be coordinated to be adjusted to the adjusted value of each of the pressure data to be determined is the coordination completion time.

3. The method according to claim 2, characterized in that, Based on the initial pressure data and the preset sleep posture coordination model, the pending pressure data corresponding to the completion of sleep posture coordination and the coordination completion time corresponding to the completion of sleep posture coordination are determined, specifically including: Determine the initial pressure data sequence corresponding to each of the initial pressure data; The sleeping posture coordination model is used to match the first pressure data sequence corresponding to the initial pressure data sequence to determine the initial sleeping posture corresponding to the initial pressure data sequence. Based on the body parts corresponding to the initial sleeping position, the initial pressure data sequence is divided into several initial pressure data subsequences; Based on the preset pressure coordination standard for each part of the human body according to the sleeping posture coordination model, each undetermined pressure data subsequence corresponding to each initial pressure data subsequence corresponding to each part of the human body is determined to complete the generation of each undetermined pressure data; the pressure coordination standard includes a coordination comparison threshold for determining whether the initial pressure data of each part of the human body is the coordination comparison threshold for the undetermined pressure data.

4. The method according to claim 1, characterized in that, Based on the duration of the easily startled phase and the preset pressure adjustment decibel ratio table, the coordination completion time is extended, specifically including: Determine a first number of air springs that perform pressure adjustment during the duration of the easily startled phase; Based on the first quantity and the pressure regulation decibel ratio table, determine the first noise intensity value within the duration of the easily startled phase; When the first noise intensity value matches the preset wake-up noise intensity table, according to the first noise intensity value and the pressure adjustment decibel ratio table, the first quantity is reduced to the second quantity according to the preset rules, and the second noise intensity value corresponding to the second quantity of air springs is determined. If the second noise intensity value still matches the startle noise intensity table, update the second noise intensity value until the Nth noise intensity value corresponding to the Nth air spring meets a preset condition; wherein, N is a natural number greater than 2; the preset condition is at least that the Nth noise intensity value does not match the startle noise intensity table; Based on the Nth quantity and the formula for adjusting the pressure of the air spring, the coordination completion time is extended.

5. The method according to claim 4, characterized in that, Based on the Nth quantity and the air spring pressure adjustment schedule, the coordination completion time is extended, specifically including: The difference between the first quantity and the Nth quantity is determined as the quantity to be scheduled; Based on the position information of the air springs corresponding to the first quantity, the coordination priority of each air spring in the preset coordination priority comparison table is matched; wherein, the coordination priority comparison table is generated based on the comfort evaluation information of each air spring of the bedding when the pressure is adjusted in a predetermined order; Based on the coordination priority and the number to be scheduled, the air springs corresponding to the number to be scheduled are determined as the air springs to be scheduled; Based on the pressure adjustment duration formula and the pressure data to be determined, the pressure adjustment duration of the air spring to be scheduled is determined, and the coordination completion duration is extended according to the maximum value among the pressure adjustment durations.

6. The method according to claim 1, characterized in that, Obtaining the corresponding sound monitoring information for the pressure adjustment of the air spring indicator group, specifically including: The internal monitoring sound of the bedding operation is acquired by a sound acquisition module installed inside the bedding and electrically connected to the intelligent air pump; the internal monitoring sound includes at least the sound of the intelligent air pump drawing air from and / or inflating each of the air springs. The sound type of the internal monitoring sound is determined by a pre-trained sound recognition model; When the sound type of the internally monitored sound is a controlled noise type, the sound monitoring information is generated.

7. The method according to claim 1, characterized in that, Based on the air spring identification group corresponding to the sound monitoring information, the sound monitoring information, and the real-time acquired sleep stage monitoring data, it is determined whether the pressure adjustment status of each air spring in the air spring identification group is in a pending scheduling state, specifically including: Determine the awakening noise intensity curve corresponding to the sleep stage monitoring data; The sound monitoring information is compared with the startle noise intensity curve; If the comparison result between the sound monitoring information and the startle noise intensity curve indicates that the sound monitoring information is a startle noise, then the pressure adjustment status of some or all of the air springs in the air spring identification group is determined to be in a state to be scheduled. Based on the startle noise intensity curve, within the coordination completion time, each air spring icon corresponding to the pending scheduling state is assigned to the air spring icon group that has not entered the pressure regulation period, and it is determined whether the air spring icon group that has been reassigned has startle noise, so as to update the pressure regulation status of the air springs in the air spring icon group of each pressure regulation period.

8. The method according to claim 1, characterized in that, Based on the respective scheduled states and the corresponding undetermined pressure data, the intelligent air pump is controlled to adjust the pressure of the air springs in the at least one air spring identification group, specifically including: Add the air spring corresponding to each of the scheduled states to the air spring identifier group corresponding to the next pressure adjustment period; Based on the air spring identification group corresponding to the next pressure adjustment period and the sleep stage monitoring data, update the pressure adjustment status of the air springs in each air spring identification group that has not been pressure adjusted and / or add new air spring identification groups. Based on the newly added air spring identification group, the coordination completion time is updated, and the intelligent air pump is controlled to inflate and / or de-inflate the air springs in the updated air spring identification group until the bearing pressure data of each air spring meets the pending pressure data.

9. A control device for the operation of an intelligent pressure-sensitive adjustable bedding system, characterized in that, The intelligent pressure-sensitive adjustable bedding includes at least: an intelligent air pump, several air springs connected to the intelligent air pump and vertically arranged on the base of the bedding, and fiber pressure sensors disposed on the top of each of the air springs; the device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, enabling the at least one processor to perform an operation control method for an intelligent pressure-sensitive adjustable bedding according to any one of claims 1-8.

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