Intelligent Analysis Method for the Sleep Aid Scheme Driven by Intelligent Sensing

Abstract

The present invention discloses an intelligent analysis method for a sleep assistance scheme driven by intelligent perception, which relates to the technical field of data processing. The method includes: sensing and collecting pressure sensing data and temperature sensing data of the user's sleep through a sensor array pre-installed in the sleep device; performing integrated comfort analysis to obtain the user's pressure comfort information and temperature comfort information; screening and analyzing the body position change of the first user body position information sequence and the second user body position information sequence to obtain the body position change frequency; performing the sleep comfort analysis calculation of the sleep assistance scheme to obtain the sleep comfort, which is used as the analysis result of the sleep assistance scheme. It solves the technical problems in the prior art that it is difficult to comprehensively perceive and analyze the user's sleep state to reflect the user's comfort, and the lack of dynamic optimization based on body position changes, resulting in insufficient scientificity and personalization of the user's sleep scheme, and achieves the technical effect of improving the scientificity and personalized adaptation ability of the sleep scheme analysis.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to an intelligent analysis method for a sleep aid solution driven by intelligent perception. Background Art

[0002] With the acceleration of people's life rhythm and the increase in pressure, sleep problems have become one of the main factors affecting the health of modern people. As an important means to improve sleep quality, sleep aid devices have been widely used in recent years. However, most of the existing sleep aid solutions are based on fixed patterns or simple parameter adjustments, such as adjusting environmental brightness, humidity, temperature, or mattress softness. Sleep aid devices usually lack the ability to comprehensively perceive and dynamically analyze the user's sleep state, and are unable to integrate and analyze multi-dimensional data such as pressure distribution, temperature change, and body position adjustment, resulting in the sleep aid effect being difficult to reach the ideal level. Specifically, in the prior art, sleep data collection is incomplete. Especially during sleep, the perception of pressure distribution and temperature distribution data is missing or inaccurate; there is a lack of comprehensive evaluation of multi-dimensional comfort, and it is difficult to provide a comprehensive comfort analysis from multiple aspects such as pressure, temperature, and body position changes; furthermore, the sleep aid solution does not fully consider the dynamic characteristics of the user's body position changes, and is unable to optimize the sleep aid strategy according to the user's body position adjustment and change frequency, affecting the sleep aid effect and user experience.

[0003] In the related technologies of sleep aid solution analysis at the present stage, there are technical problems such as difficulty in comprehensively perceiving and analyzing the user's sleep state to reflect the user's comfort, and lack of dynamic optimization based on body position changes, resulting in insufficient scientificity and personalization of the user's sleep solution. Summary of the Invention

[0004] By providing an intelligent analysis method for a sleep aid solution driven by intelligent perception, this application solves the technical problems in the prior art, such as difficulty in comprehensively perceiving and analyzing the user's sleep state to reflect the user's comfort, and lack of dynamic optimization based on body position changes, resulting in insufficient scientificity and personalization of the user's sleep solution, and achieves the technical effect of improving the scientificity and personalized adaptation ability of sleep aid solution analysis.

[0005] The present application provides an intelligent analysis method for a sleep assistance solution driven by intelligent perception, including: under the current sleep assistance solution, through a sensor array pre-deployed in a sleep device, sensing and collecting pressure sensing data and temperature sensing data of a user's sleep, and obtaining a pressure distribution sequence and a temperature distribution sequence; according to the pressure distribution sequence and the temperature distribution sequence, performing integrated comfort analysis to obtain the user's pressure comfort information and temperature comfort information, wherein, performing perception integrity analysis on the pressure distribution sequence and the temperature distribution sequence to obtain a pressure data integrity coefficient sequence and a temperature data integrity coefficient sequence, and performing integrated comfort analysis; performing user body position fitting on the pressure distribution sequence and the temperature distribution sequence to obtain a first user body position information sequence and a second user body position information sequence, and according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, screening and performing body position change analysis on the first user body position information sequence and the second user body position information sequence to obtain the body position change frequency; according to the pressure comfort information, the temperature comfort information and the body position change frequency, performing sleep comfort analysis calculation of the sleep assistance solution to obtain the sleep comfort, as the analysis result of the sleep assistance solution.

[0006] In a possible implementation manner, under the current sleep assistance solution, through a sensor array pre-deployed in a sleep device, sensing and collecting pressure sensing data and temperature sensing data of a user's sleep, and obtaining a pressure distribution sequence and a temperature distribution sequence, the following processing is further performed: under the current sleep assistance solution, through a pressure sensor array pre-deployed in a sleep device, sensing and collecting pressure sensing data of a user's sleep at multiple time nodes, wherein the sleep device includes a mattress; according to the position information array of the pressure sensor array, combining the pressure sensing data at multiple time nodes, constructing multiple pressure distributions, and obtaining a pressure distribution sequence; through a temperature sensor array pre-deployed in a sleep device, sensing and collecting temperature sensing data of a user's sleep at multiple time nodes, and constructing a temperature distribution sequence.

[0007] In a possible implementation manner, based on the pressure distribution sequence and the temperature distribution sequence, integrated comfort analysis is performed to obtain the user's pressure comfort information and temperature comfort information, and the following processing is also performed: obtaining the minimum pressure threshold and the minimum temperature threshold for the user's sleep test, and obtaining the standard distribution ratio of the user, where the standard distribution ratio is the ratio of the pressure sensing data and temperature sensing data that are greater than or equal to the minimum pressure threshold and the minimum temperature threshold during the user's sleep within the pressure distribution and the temperature distribution; screening the ratio of the pressure sensing data that is greater than or equal to the minimum pressure threshold within each pressure distribution in the pressure distribution sequence to obtain a pressure distribution ratio sequence; calculating the ratio of multiple pressure distribution ratios within the pressure distribution ratio sequence and the standard distribution ratio to obtain a pressure data integrity coefficient sequence; screening the ratio of the temperature sensing data that is greater than or equal to the minimum temperature threshold within each temperature distribution in the temperature distribution sequence to obtain a temperature distribution ratio sequence; calculating the ratio of multiple temperature distribution ratios within the temperature distribution ratio sequence and the standard distribution ratio to obtain a temperature data integrity coefficient sequence; and performing integrated comfort analysis on the pressure distribution sequence and the temperature distribution sequence according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence to obtain the user's pressure comfort information and temperature comfort information.

[0008] In a possible implementation manner, performing integrated comfort analysis on the pressure distribution sequence and the temperature distribution sequence according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, and the following processing is also performed: respectively performing pressure comfort analysis on multiple pressure distributions within the pressure distribution sequence to obtain multiple independent pressure comfort information; allocating multiple pressure data weights according to the magnitudes of multiple pressure data integrity coefficients within the pressure data integrity coefficient sequence, and performing weighted calculation on the multiple independent pressure comfort information to obtain the user's pressure comfort information; respectively performing temperature comfort analysis on multiple temperature distributions within the temperature distribution sequence to obtain multiple independent temperature comfort information; allocating multiple temperature data weights according to the magnitudes of multiple temperature data integrity coefficients within the temperature data integrity coefficient sequence, and performing weighted calculation on the multiple independent temperature comfort information to obtain the user's temperature comfort information.

[0009] In a possible implementation, based on multiple pressure distributions within the pressure distribution sequence, pressure comfort analysis is performed separately to obtain multiple independent pressure comfort information, and the following processing is also performed: According to the sleep test data of the user within the historical time, a set of sample pressure distributions is collected, and the comfort level of the user under different sample pressure distributions is collected to obtain a set of sample independent pressure comfort information; The set of sample pressure distributions and the set of sample independent pressure comfort information are used to train the pressure comfort analyzer until the training converges; The multiple pressure distributions within the pressure distribution sequence are respectively input into the trained pressure comfort analyzer to identify and obtain multiple independent pressure comfort information.

[0010] In a possible implementation, user body position fitting is performed on the pressure distribution sequence and the temperature distribution sequence to obtain a first user body position information sequence and a second user body position information sequence, and the following processing is also performed: Filter the pressure sensing data greater than or equal to the minimum pressure threshold within each pressure distribution in the pressure distribution sequence, and index the corresponding sensor position information to obtain multiple user pressure position distributions; Fit the multiple user pressure position distributions to obtain multiple first user body position graphs as the first user body position information, and generate a first user body position information sequence, where the first user body position information sequence corresponds to the pressure data integrity coefficient sequence; Filter the temperature sensing data greater than or equal to the minimum temperature threshold within each temperature distribution in the temperature distribution sequence, and index the corresponding sensor position information to obtain multiple user temperature position distributions; Fit the multiple user temperature position distributions to obtain multiple second user body position graphs as multiple second user body position information, and generate a second user body position information sequence, where the second user body position information sequence corresponds to the temperature data integrity coefficient sequence.

[0011] In a possible implementation manner, based on the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, the first user body position information sequence and the second user body position information sequence are screened and body position change analysis is performed to obtain the body position change frequency. The following processing is also performed: Based on the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, the first user body position information and the second user body position information corresponding to the pressure data integrity coefficient and the temperature data integrity coefficient greater than or equal to a preset integrity coefficient threshold value are screened to obtain a first qualified user body position information sequence and a second qualified user body position information sequence; according to the first qualified user body position information sequence and the second qualified user body position information sequence, the first change times and the second change times when the qualified user body position information changes are respectively extracted; the first change times and the second change times are respectively divided by the total time of the first qualified user body position information sequence and the second qualified user body position information sequence to obtain a first body position change frequency and a second body position change frequency; the mean value of the first body position change frequency and the second body position change frequency is calculated to obtain the body position change frequency.

[0012] In a possible implementation manner, based on the pressure comfort information, the temperature comfort information, and the body position change frequency, the sleep comfort analysis calculation of the sleep aid scheme is performed to obtain the sleep comfort, which is used as the analysis result of the sleep aid scheme. The following processing is also performed: According to the body position change frequency, the body position comfort is classified. Among them, a sample body position change frequency set and a sample body position comfort set are collected, a mapping classification table is constructed, and the body position change frequency is input into the mapping classification table for classification to obtain the body position comfort; the pressure comfort information, the temperature comfort information, and the body position comfort are weighted and calculated to obtain the sleep comfort, which is used as the analysis result of the sleep aid scheme.

[0013] It is intended to propose an intelligent analysis method for a sleep aid scheme driven by intelligent perception through this application. Through a sensor array pre-arranged in a sleep device, the pressure sensing data and temperature sensing data of a user's sleep are sensed and collected; integrated comfort analysis is performed to obtain the pressure comfort information and temperature comfort information of the user; the first user body position information sequence and the second user body position information sequence are screened and body position change analysis is performed to obtain the body position change frequency; the sleep comfort analysis calculation of the sleep aid scheme is performed to obtain the sleep comfort, which is used as the analysis result of the sleep aid scheme. The technical problem existing in the prior art that it is difficult to comprehensively sense and analyze the user's sleep state to reflect the user's comfort and the lack of dynamic optimization based on body position changes, resulting in insufficient scientificity and personalization of the user's sleep scheme, is solved, and the technical effect of improving the scientificity and personalized adaptation ability of sleep scheme analysis is achieved. Brief Description of the Drawings

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. In this application, flowcharts are used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the operations before or below do not necessarily need to be precisely executed in sequence. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of the intelligent analysis method for the sleep assistance scheme driven by intelligent perception provided by the embodiments of the present application;

[0016] Figure 2 It is a schematic flowchart of performing user body position fitting in the intelligent analysis method for the sleep assistance scheme driven by intelligent perception provided by the embodiments of the present application. Detailed implementation manners

[0017] The above description is only an overview of the technical solutions of the present application. In order to be able to more clearly understand the technical means of the present application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0019] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first\second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0020] The embodiments of the present application provide an intelligent analysis method for a sleep assistance scheme driven by intelligent perception, as Figure 1As shown, the method includes:

[0021] Step S100, under the current sleep assistance scheme, through a sensor array pre - installed in the sleep device, sense and collect the pressure sensing data and temperature sensing data of the user's sleep, and obtain a pressure distribution sequence and a temperature distribution sequence.

[0022] Preferably, the sensor array includes multiple pressure sensors and temperature sensors, and is arranged into a sensor array according to a certain spacing and arrangement rule to achieve full coverage of the user's whole - body contact area. Specifically, under the current sleep assistance scheme, use the pressure sensors installed on the surface or inside of the sleep device (such as a mattress, a pillow), and the temperature sensors installed on the surface of the sleep device or in the area close to the user's body to respectively sense and collect the pressure values when the user's body contacts the device (such as the back, shoulders, hips, etc.), the temperature when the user's body contacts the mattress, and the temperature of other empty areas. And the temperature and pressure values at the position where the user is located are greater than those of other empty areas. Collect the pressure values and temperature values at a certain time interval (such as once per second) to form a continuous time series, and then generate a pressure distribution sequence and a temperature distribution sequence. Among them, the pressure distribution sequence reflects the force conditions of different parts of the user's body on the sleep device, such as the pressure distribution of the shoulders, back, and hips, and can detect high - pressure areas (such as excessive local pressure caused by improper posture) or low - pressure areas (such as the device not effectively supporting the body). For example, when the user lies on the side, the pressure on the shoulders and hips is relatively large, forming high - pressure areas, and the pressure on the back is relatively small, forming low - pressure areas; the temperature distribution sequence reflects the temperature changes during the user's sleep, including the user's local body temperature and the ambient temperature, monitors the temperature difference changes, and judges whether the device effectively adjusts the temperature (such as the cooling or heating function).

[0023] Further, step S100 further includes step S110, under the current sleep assistance scheme, through the pressure sensor array pre - installed in the sleep device, sense and collect the pressure sensing data of the user's sleep at multiple time nodes, where the sleep device includes a mattress; step S120, according to the position information array of the pressure sensor array, combined with the pressure sensing data at multiple time nodes, construct multiple pressure distributions to obtain a pressure distribution sequence; step S130, through the temperature sensor array pre - installed in the sleep device, sense and collect the temperature sensing data of the user's sleep at multiple time nodes, and construct a temperature distribution sequence.

[0024] Preferably, the pressure sensor array is pre-arranged on the surface or inside of the sleep device (mattress), covering the user contact areas such as the head, back, waist, and hips. Multiple time nodes are set during sleep to monitor the body support condition and contact pressure changes of the user during sleep. That is, the pressure sensors sense the pressure magnitudes at each position in real time and output digital signals. The position coordinates of the pressure sensors in the mattress are used to mark the positions of the sensors. Each time node corresponds to a pressure distribution matrix, which describes the pressure distribution of the user's body at that moment. The pressure distribution matrices at different time nodes are arranged in chronological order to form a pressure distribution sequence. Similarly, the temperature sensor array is arranged on the mattress surface or in the area close to the user's body to sense the local body temperature and ambient temperature. It is usually arranged in the main contact areas of the user, such as the head, back, and hip areas, to monitor the dynamic changes of body temperature and device temperature. The same time nodes are set. Each sensor records the temperature value at the current position. Each time node corresponds to a temperature distribution matrix, which describes the temperature distribution of the user's body and the mattress. The temperature distribution matrices at different time nodes are arranged in chronological order to form a temperature distribution sequence.

[0025] Step S200, according to the pressure distribution sequence and the temperature distribution sequence, perform integrated comfort analysis to obtain the pressure comfort information and temperature comfort information of the user. Among them, perform perception integrity analysis on the pressure distribution sequence and the temperature distribution sequence to obtain a pressure data integrity coefficient sequence and a temperature data integrity coefficient sequence, and then perform integrated comfort analysis.

[0026] Preferably, analyze the pressure comfort and temperature comfort of the user according to the pressure distribution sequence and the temperature distribution sequence respectively. That is, perform perception integrity analysis on the pressure distribution sequence and the temperature distribution sequence to determine the incomplete pressure data and temperature data, so as to obtain a pressure data integrity coefficient sequence and a temperature data integrity coefficient sequence, and then perform integrated comfort analysis to obtain the pressure comfort information and temperature comfort information of the user. Specifically, there may be data acquisition errors or temporary failures in the sensor array, resulting in incomplete pressure data and temperature data collected. Perception integrity analysis is to evaluate the pressure and temperature data collected by the sensors to determine whether the data is complete and reliable, and eliminate the interference of incomplete or incorrect data on comfort analysis. That is, check whether there are missing data points or abnormal data (such as pressure values beyond the reasonable range) in the pressure distribution sequence, and check whether there are sudden changes or inconsistent data (such as too large or too low temperature differences) in the temperature distribution sequence, quantify the integrity of the pressure and temperature data, and generate a pressure data integrity coefficient sequence and a temperature data integrity coefficient sequence.

[0027] Preferably, pressure comfort information analysis is performed to evaluate whether the pressure distribution on various parts of the user's body is uniform during sleep, identify potential discomfort areas, that is, detect the pressure concentration areas (such as shoulders and hips) of the user's body, determine whether they exceed the comfort range, and at the same time determine whether the pressure distribution is uniform and check whether the support is reasonable; temperature comfort information analysis is performed to evaluate whether the temperature distribution of the user is within the comfort range, and determine whether the ambient temperature and body temperature are balanced, that is, analyze the temperature difference of various parts of the user's body, check whether there is local overcooling or overheating, and evaluate the stability of the temperature distribution to identify sudden temperature changes; then integrated comfort analysis is performed, that is, comprehensive analysis of pressure comfort and temperature comfort is performed to form an overall comfort evaluation. Specifically, according to the goals of the sleep assistance plan and user needs, the weights of pressure comfort and temperature comfort are set, and multiple independent pressure comforts and independent temperature comforts from multiple distribution analyses are weighted to improve the accuracy of the data. For example, the pressure comfort weight is 0.6 and the temperature comfort weight is 0.4. Among them, if the integrity of pressure or temperature data is relatively low (such as a large amount of data missing or a large number of outliers), the corresponding weight needs to be reduced to reduce the interference of incomplete data on the results.

[0028] Furthermore, step S200 further includes step S210 of obtaining the minimum pressure threshold and minimum temperature threshold for the user's sleep test, and obtaining the standard distribution ratio of the user, where the standard distribution ratio is the ratio of the pressure sensing data and temperature sensing data that are greater than or equal to the minimum pressure threshold and minimum temperature threshold during the user's sleep in the pressure distribution and temperature distribution; step S220 of screening the ratio of the pressure sensing data that is greater than or equal to the minimum pressure threshold in each pressure distribution in the pressure distribution sequence to obtain a pressure distribution ratio sequence; step S230 of calculating the ratio of the multiple pressure distribution ratios in the pressure distribution ratio sequence and the standard distribution ratio to obtain a pressure data integrity coefficient sequence; step S240 of screening the ratio of the temperature sensing data that is greater than or equal to the minimum temperature threshold in each temperature distribution in the temperature distribution sequence to obtain a temperature distribution ratio sequence; step S250 of calculating the ratio of the multiple temperature distribution ratios in the temperature distribution ratio sequence and the standard distribution ratio to obtain a temperature data integrity coefficient sequence; step S260 of performing integrated comfort analysis on the pressure distribution sequence and temperature distribution sequence according to the pressure data integrity coefficient sequence and temperature data integrity coefficient sequence to obtain the user's pressure comfort information and temperature comfort information.

[0029] Preferably, the lowest support pressure value at which the user feels comfortable during sleep is obtained through a sleep test as the minimum pressure threshold, and the lowest temperature value at which the user feels comfortable during sleep is used as the minimum temperature threshold. Calculate the proportion of the user's standard distribution, that is, the proportion of the pressure sensing data and temperature sensing data greater than or equal to the minimum pressure threshold and the minimum temperature threshold during the user's sleep in the pressure distribution and temperature distribution. The larger the user's body size, the larger the proportion. Then, for each pressure distribution in the pressure distribution sequence, filter out the sensing data that meets the minimum pressure threshold, calculate the proportion of the data that meets the threshold in each pressure distribution to obtain the pressure distribution proportion sequence, calculate the ratio of the proportions of multiple pressure distributions in the pressure distribution proportion sequence and the standard distribution proportion to obtain the pressure data integrity coefficient sequence. If the ratio is equal to or greater than 1, it is complete; if it is less than 1, it is incomplete, which may be due to the temporary malfunction or incorrect acquisition of some sensors.

[0030] Preferably, for each temperature distribution in the temperature distribution sequence, filter out the sensing data that meets the minimum temperature threshold, calculate the proportion of the data that meets the threshold in each temperature distribution to obtain the temperature distribution proportion sequence, and then measure the matching degree between the proportion of each temperature distribution and the standard distribution proportion, that is, calculate the ratio of the proportions of multiple temperature distributions in the temperature distribution proportion sequence and the standard distribution proportion to obtain the temperature data integrity coefficient sequence. Based on the pressure and temperature data integrity coefficient sequences, perform a comprehensive analysis on the pressure distribution sequence and the temperature distribution sequence, that is, calculate the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence by weighting to quantify the user's comfort level, and obtain the user's pressure comfort information and temperature comfort information.

[0031] Further, step S260 further includes step S261, performing pressure comfort analysis on multiple pressure distributions in the pressure distribution sequence respectively to obtain multiple independent pressure comfort information; step S262, allocating multiple pressure data weights according to the magnitudes of multiple pressure data integrity coefficients in the pressure data integrity coefficient sequence, and calculating the weighted sum of the multiple independent pressure comfort information to obtain the user's pressure comfort information; step S263, performing temperature comfort analysis on multiple temperature distributions in the temperature distribution sequence respectively to obtain multiple independent temperature comfort information; step S264, allocating multiple temperature data weights according to the magnitudes of multiple temperature data integrity coefficients in the temperature data integrity coefficient sequence, and calculating the weighted sum of the multiple independent temperature comfort information to obtain the user's temperature comfort information.

[0032] Preferably, pressure comfort analysis is performed on multiple pressure distributions within the pressure distribution sequence, that is, for the pressure distribution matrix at each time node, the uniformity, high-pressure regions, and low-pressure regions of the pressure values are detected to evaluate the impact of the pressure distribution at each time point on the user's comfort. Each time point corresponds to an independent pressure comfort value, forming multiple independent pressure comfort information. Then, according to the coefficient magnitudes in the pressure data integrity coefficient sequence, weights are assigned and the user's pressure comfort information is calculated; temperature comfort analysis is performed on multiple temperature distributions within the temperature distribution sequence, that is, for the temperature distribution matrix at each time node, it is analyzed whether the temperature difference is uniform and whether it is within the ideal temperature range to evaluate the impact of the temperature distribution at each time point on the user's comfort. Each time point corresponds to an independent temperature comfort value, forming multiple independent temperature comfort information. According to the coefficient magnitudes in the temperature data integrity coefficient sequence, weights are assigned and the user's temperature comfort information is calculated.

[0033] Further, step S261 further includes step S261a, collecting a sample pressure distribution set according to the user's sleep test data within the historical time, and collecting the comfort levels of the user under different sample pressure distributions to obtain a set of sample independent pressure comfort information; step S261b, using the sample pressure distribution set and the set of sample independent pressure comfort information to train the pressure comfort analyzer until the training converges; step S261c, inputting the multiple pressure distributions within the pressure distribution sequence into the trained pressure comfort analyzer respectively to identify and obtain multiple independent pressure comfort information.

[0034] Preferably, multiple pressure distributions in the user's historical sleep test data form a sample pressure distribution set, and each group of pressure distributions reflects the pressure distribution characteristics of the user's body at different time points or sleep conditions. Among them, the pressure distribution matrix of the user under multiple test conditions (such as different mattress hardnesses, different sleep postures) is recorded by the pressure sensor array on the mattress. At the same time, the comfort levels of the user under different sample pressure distributions are collected, that is, the subjective comfort evaluation of the user for each sample pressure distribution. For example, the user scores each pressure distribution sample according to the feeling (such as from 1 to 10 points), outputs a set of sample independent pressure comfort information, and then based on machine learning (such as linear regression, decision tree, neural network, etc.), a prediction model is constructed by learning the sample pressure distribution set and the corresponding comfort levels. Using the sample pressure distribution as the input feature and the sample independent pressure comfort information as the target value, the prediction model is trained until convergence to obtain a pressure comfort analyzer. Finally, each pressure distribution in the real-time collected pressure distribution sequence is input into the pressure comfort analyzer to calculate and obtain multiple independent pressure comfort information.

[0035] Step S300: Fit the pressure distribution sequence and the temperature distribution sequence to the user's body position to obtain the first user body position information sequence and the second user body position information sequence. According to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, screen and analyze the body position changes of the first user body position information sequence and the second user body position information sequence to obtain the body position change frequency.

[0036] Preferably, according to the positions of the detected higher temperature and pressure data, the image contour of the user's body position during sleep on the mattress can be fitted and obtained, and then the frequency of the user's body position changes can be analyzed. Among them, according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, the pressure distribution and temperature distribution with integrity coefficients greater than the threshold value are screened for fitting the user's body position graphic contour to improve the accuracy. Specifically, the user's body position fitting is to infer the specific body position (such as supine, side-lying, prone) and its changes of the user during sleep through analyzing the pressure distribution sequence and the temperature distribution sequence based on a model trained with historical data (such as decision tree, support vector machine), and then generate the first user body position information sequence and the second user body position information sequence. Among them, by using the pressure value changes in different parts, the distribution of the support points of the user's body is judged. For example, if the pressure is higher at the shoulders and hips and distributed on both sides, it may be side-lying, and if the pressure is evenly distributed on the back, it may be supine. The change information of the user's body position (such as the body position change caused by the pressure change) is used as the first user body position information sequence; by the temperature changes in different parts of the body, the user's body position is assisted to be judged. For example, if the temperature is higher at the abdomen and chest, it may be prone. The temperature characteristics of the body position change (such as the local temperature change caused by side-lying) are used as the second user information sequence.

[0037] Preferably, according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, the first user body position information sequence and the second user body position information sequence are screened and the body position change analysis is carried out. The pressure data integrity coefficient and the temperature data integrity coefficient are used to screen the reliable body position information sequence and eliminate the wrong fitting caused by data loss or abnormality. Specifically, if the pressure data integrity coefficient of a certain time period is lower than the threshold value, it is considered that the pressure data of this time period is unavailable, and the corresponding body position information is eliminated. Similarly, if the temperature data integrity coefficient is lower than the threshold value, the temperature body position information of this time period is eliminated; for the same time period, if both the pressure and temperature data pass the screening, the two types of body position information are merged to further improve the accuracy of body position fitting. The body position change analysis refers to analyzing the body position changes of the user during sleep, including the frequency, amplitude and regularity of the body position changes. Specifically, by comparing the first user body position information sequence and the second user body position information sequence, the occurrence time of the body position change is identified. For example, the time point of changing from the side-lying position to the supine position can be detected through the redistribution of the pressure distribution and the temperature change. Then, it is judged whether the amplitude of the body position change is a local adjustment (such as slight movement of the shoulder) or an overall change (such as changing from the supine position to the prone position), and then the body position change frequency is obtained, that is, the number of body position changes of the user per unit time. For example, if the body position change frequency is relatively high (such as more than 10 times per hour), it may indicate that the user's sleep environment is uncomfortable or the body is uncomfortable.

[0038] Further, as Figure 2 shown, step S300 further includes step S310 of screening the pressure sensing data greater than or equal to the minimum pressure threshold value in each pressure distribution in the pressure distribution sequence and indexing the corresponding sensor position information to obtain a plurality of user pressure position distributions; step S320 of fitting the plurality of user pressure position distributions to obtain a plurality of first user body position graphs as the first user body position information and generating a first user body position information sequence, where the first user body position information sequence corresponds to the pressure data integrity coefficient sequence; step S330 of screening the temperature sensing data greater than or equal to the minimum temperature threshold value in each temperature distribution in the temperature distribution sequence and indexing the corresponding sensor position information to obtain a plurality of user temperature position distributions; step S340 of fitting the plurality of user temperature position distributions to obtain a plurality of second user body position graphs as the plurality of second user body position information and generating a second user body position information sequence, where the second user body position information sequence corresponds to the temperature data integrity coefficient sequence.

[0039] Preferably, data greater than or equal to the minimum pressure threshold is screened out from each pressure distribution, representing the support area where the user's body contacts the mattress, and at the same time, the position information of the sensors is indexed. That is, for each screened pressure data point, its corresponding position index is recorded. A corresponding user pressure position distribution is generated at each time point, represented as a set of sensor positions. Furthermore, multiple user pressure position distributions are obtained. Based on the user pressure position distributions, a fitting algorithm (such as polygon fitting or contour generation) is used to draw the user's pressure graph according to the sensor positions, fitting out the user's body contour graph, reflecting their sleep posture, as the first user posture information. The user posture information at each time point is arranged in chronological order to form a first user posture information sequence; Similarly, data greater than or equal to the minimum temperature threshold is screened out from each temperature distribution, representing the contact area or high-temperature area between the user's body and the mattress. For each screened temperature data point, its corresponding position index is recorded. A user temperature position distribution is generated at each time point, represented as a set of sensor positions. Multiple user temperature position distributions are obtained. Then, based on the user temperature position distributions, a fitting algorithm (such as Gaussian fitting or curve interpolation) is used to draw the user's temperature graph, fitting out the user's temperature posture graph, reflecting the temperature characteristics of the body contact area, as the second user posture information. Then, the user temperature posture information at each time point is arranged in chronological order to form a second user posture information sequence.

[0040] Further, step S300 further includes step S350, according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, screening the first user posture information and the second user posture information corresponding to the pressure data integrity coefficient and the temperature data integrity coefficient greater than or equal to the preset integrity coefficient threshold value, obtaining a first qualified user posture information sequence and a second qualified user posture information sequence; step S360, according to the first qualified user posture information sequence and the second qualified user posture information sequence, respectively extracting the first change times and the second change times when the qualified user posture information changes; step S370, respectively dividing the first change times and the second change times by the total time of the first qualified user posture information sequence and the second qualified user posture information sequence to obtain a first posture change frequency and a second posture change frequency; step S380, calculating the mean value of the first posture change frequency and the second posture change frequency to obtain the posture change frequency.

[0041] Preferably, according to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, the pressure and temperature body position information that meets the preset integrity coefficient threshold value is screened out, that is, the influence of low-integrity data on the analysis is eliminated, and the data greater than or equal to the preset integrity coefficient threshold value is retained, obtaining the first qualified user body position information sequence and the second qualified user body position information sequence. Analyze the number of changes in the user's body position in the qualified body position information sequence. Among them, if the body position information at two adjacent time points is different, it is counted as one body position change. For example, compare the body position graphics (such as the similarity of the contours, the proportion of the overlapping area). For example, if the changed area of the graphics exceeds 10%, it is considered that a body position change has occurred. Extract the occurred changes, or if the similarity is lower than the preset threshold, it is determined that a body position change has occurred. Then, according to the first qualified user body position information sequence and the second qualified user body position information sequence, extract the first change times and the second change times when the qualified user body position information changes, respectively representing the change times of the pressure body position and the temperature body position; then divide the first change times and the second change times by the total time of the first qualified user body position information sequence and the second qualified user body position information sequence respectively to obtain the first body position change frequency and the second body position change frequency, reflecting the dynamic body position adjustment of the user under the sleep aid plan; finally, calculate the average value of the first body position change frequency and the second body position change frequency to obtain the body position change frequency.

[0042] Step S400, according to the pressure comfort information, the temperature comfort information and the body position change frequency, perform the sleep aid comfort analysis calculation of the sleep aid plan to obtain the sleep aid comfort, as the analysis result of the sleep aid plan.

[0043] Preferably, by comprehensively analyzing multi-dimensional data to quantify the sleep comfort of users and evaluate the effectiveness of the current sleep aid solution, that is, according to the pressure comfort information, temperature comfort information, and body position change frequency, perform the sleep comfort analysis and calculation of the sleep aid solution, which refers to the weighted pressure comfort, temperature comfort, and body position comfort corresponding to the body position change frequency, to obtain the final sleep aid comfort. Among them, the sleep aid comfort is used to quantify the overall sleep comfort of users under the current sleep aid solution, reflecting the comprehensive impact of pressure, temperature, and body position changes on sleep quality. Specifically, the pressure comfort information, temperature comfort information, and body position change comfort are used as input data and standardized to unify their numerical ranges (such as 0 to 1). Among them, the body position change comfort is used to evaluate whether the body position change is within an appropriate range, that is, set the ideal body position change frequency range (such as 2-3 times per hour), calculate the deviation between the actual frequency and the ideal range, and the closer the score is to the ideal range, the higher the comfort. Then, according to the goal of the sleep aid solution, weights are assigned to the comfort of each dimension. For example, the pressure comfort weight is 0.5, the temperature comfort weight is 0.3, and the body position change frequency weight is 0.2. The sleep aid comfort is calculated comprehensively to quantify the overall experience of users under the current sleep aid solution. The sleep aid comfort is usually between 0 and 1, and the higher the score, the better the effect of the current sleep aid solution. For example, when the sleep aid comfort is between 0.8 and 1, it means the sleep aid solution is very effective and the user's sleep state is good; when the sleep aid comfort is between 0.6 and 0.8, it means the sleep aid solution is effective but there is still room for optimization; when the sleep aid comfort is below 0.6, it means the sleep aid solution needs to be adjusted and the user may have discomfort; and this is used as the analysis result of the sleep aid solution.

[0044] Further, step S400 further includes step S410, according to the body position change frequency, classify and obtain the body position comfort, where a sample body position change frequency set and a sample body position comfort set are collected, a mapping classification table is constructed, and the body position change frequency is input into the mapping classification table to classify and obtain the body position comfort; step S420, perform weighted calculation on the pressure comfort information, temperature comfort information, and body position comfort to obtain the sleep aid comfort as the analysis result of the sleep aid solution.

[0045] Preferably, the body position comfort level is classified and obtained according to the body position change frequency, that is, a set of sample body position change frequencies and a set of sample body position comfort levels are collected from historical data, and a mapping classification table is constructed based on the sample body position change frequencies and the sample body position comfort levels to infer the new body position comfort level, which can more accurately quantify the user's body position comfort. Specifically, the sample body position change frequencies are divided into several intervals, corresponding body position comfort level values are defined for each interval, and then the body position change frequency is input into the mapping classification table to find the corresponding body position comfort level value; finally, the pressure comfort information, the temperature comfort information and the body position comfort level are weighted and calculated to obtain the sleep assistance comfort level. Specifically, according to the design goal of the sleep assistance scheme, the weights of the three are assigned and the sleep assistance comfort level is weighted and calculated as the analysis result of the sleep assistance scheme.

[0046] The above specific implementation manners do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or the continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. Intelligent perception-driven sleep aid solution intelligent analysis method, characterized in that, The method includes: Under the current sleep assistance scheme, through a sensor array pre - arranged in a sleep device, sense and collect pressure sensing data and temperature sensing data of the user's sleep, and obtain a pressure distribution sequence and a temperature distribution sequence; According to the pressure distribution sequence and the temperature distribution sequence, conduct integrated comfort analysis to obtain the user's pressure comfort information and temperature comfort information. Among them, conduct perception integrity analysis on the pressure distribution sequence and the temperature distribution sequence to obtain a pressure data integrity coefficient sequence and a temperature data integrity coefficient sequence, and conduct integrated comfort analysis; Perform user body position fitting on the pressure distribution sequence and the temperature distribution sequence to obtain a first user body position information sequence and a second user body position information sequence. According to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, screen and analyze the body position changes of the first user body position information sequence and the second user body position information sequence to obtain the body position change frequency; According to the pressure comfort information, temperature comfort information and body position change frequency, conduct sleep assistance comfort analysis and calculation of the sleep assistance scheme to obtain the sleep assistance comfort, as the analysis result of the sleep assistance scheme; According to the pressure distribution sequence and the temperature distribution sequence, conduct integrated comfort analysis to obtain the user's pressure comfort information and temperature comfort information, including: Obtain the minimum pressure threshold and the minimum temperature threshold for the user's sleep test, and obtain the standard distribution ratio of the user, where the standard distribution ratio is the ratio of the pressure sensing data and temperature sensing data greater than or equal to the minimum pressure threshold and the minimum temperature threshold in the user's sleep in the pressure distribution and temperature distribution; Screen the ratio of the pressure sensing data greater than or equal to the minimum pressure threshold in each pressure distribution in the pressure distribution sequence to obtain a pressure distribution ratio sequence; Calculate the ratio of the sum of multiple pressure distribution ratios in the pressure distribution ratio sequence to the standard distribution ratio to obtain a pressure data integrity coefficient sequence; Screen the ratio of the temperature sensing data greater than or equal to the minimum temperature threshold in each temperature distribution in the temperature distribution sequence to obtain a temperature distribution ratio sequence; Calculate the ratio of the sum of multiple temperature distribution ratios in the temperature distribution ratio sequence to the standard distribution ratio to obtain a temperature data integrity coefficient sequence; According to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, conduct integrated comfort analysis on the pressure distribution sequence and the temperature distribution sequence to obtain the user's pressure comfort information and temperature comfort information; According to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, conduct integrated comfort analysis on the pressure distribution sequence and the temperature distribution sequence, including: According to multiple pressure distributions in the pressure distribution sequence, conduct pressure comfort analysis respectively to obtain multiple independent pressure comfort information; According to the magnitudes of multiple pressure data integrity coefficients in the pressure data integrity coefficient sequence, allocate multiple pressure data weights, and perform weighted calculation on the multiple independent pressure comfort information to obtain the user's pressure comfort information; Based on multiple temperature distributions within the temperature distribution sequence, temperature comfort analysis is respectively performed to obtain multiple independent temperature comfort information; According to the magnitudes of multiple temperature data integrity coefficients within the temperature data integrity coefficient sequence, multiple temperature data weights are assigned, and the multiple independent temperature comfort information is weighted and calculated to obtain the user's temperature comfort information.

2. The intelligent analysis method of the sleep aid scheme driven by intelligent perception according to claim 1, characterized in that Under the current sleep assistance scheme, through a sensor array pre-deployed in the sleep device, pressure sensing data and temperature sensing data of the user's sleep are sensed and collected to obtain a pressure distribution sequence and a temperature distribution sequence, including: Under the current sleep assistance scheme, through a pressure sensor array pre-deployed in the sleep device, pressure sensing data at multiple time nodes during the user's sleep is sensed and collected, where the sleep device includes a mattress; According to the position information array of the pressure sensor array and in combination with the pressure sensing data at multiple time nodes, multiple pressure distributions are constructed to obtain a pressure distribution sequence Through a temperature sensor array pre-deployed in the sleep device, temperature sensing data at multiple time nodes during the user's sleep is sensed and collected to construct a temperature distribution sequence.

3. The intelligent analysis method for the sleep aid solution driven by intelligent perception according to claim 1, wherein, Based on multiple pressure distributions within the pressure distribution sequence, pressure comfort analysis is respectively performed to obtain multiple independent pressure comfort information, including: According to the sleep test data of the user within the historical time, a sample pressure distribution set is collected, and the comfort level of the user under different sample pressure distributions is collected to obtain a sample independent pressure comfort information set; Using the sample pressure distribution set and the sample independent pressure comfort information set, the pressure comfort analyzer is trained until the training converges; The multiple pressure distributions within the pressure distribution sequence are respectively input into the trained pressure comfort analyzer to identify and obtain multiple independent pressure comfort information.

4. The intelligent analysis method for the sleep aid scheme driven by intelligent perception according to claim 1, characterized in that, User body position fitting is performed on the pressure distribution sequence and the temperature distribution sequence to obtain a first user body position information sequence and a second user body position information sequence, including: The pressure sensing data greater than or equal to the minimum pressure threshold within each pressure distribution in the pressure distribution sequence is screened, and the corresponding sensor position information is indexed to obtain multiple user pressure position distributions; The multiple user pressure position distributions are fitted to obtain multiple first user body position graphs as the first user body position information, generating a first user body position information sequence, where the first user body position information sequence corresponds to the temperature data integrity coefficient sequence; The temperature sensing data greater than or equal to the minimum temperature threshold within each temperature distribution in the temperature distribution sequence is screened, and the corresponding sensor position information is indexed to obtain multiple user temperature position distributions; The multiple user temperature position distributions are fitted to obtain multiple second user body position graphs as multiple second user body position information, generating a second user body position information sequence, where the second user body position information sequence corresponds to the temperature data integrity coefficient sequence.

5. The intelligent analysis method for the sleep aid scheme driven by intelligent perception according to claim 4, characterized in that, According to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, screening and body position change analysis are performed on the first user body position information sequence and the second user body position information sequence to obtain the body position change frequency, including: According to the pressure data integrity coefficient sequence and the temperature data integrity coefficient sequence, filter the first user body position information and the second user body position information corresponding to the pressure data integrity coefficient and the temperature data integrity coefficient that are greater than or equal to a preset integrity coefficient threshold value, and obtain a first qualified user body position information sequence and a second qualified user body position information sequence; According to the first qualified user body position information sequence and the second qualified user body position information sequence, respectively extract the first change count and the second change count of the qualified user body position information changes; Respectively divide the first change count and the second change count by the total time of the first qualified user body position information sequence and the second qualified user body position information sequence to obtain a first body position change frequency and a second body position change frequency; Calculate the mean value of the first body position change frequency and the second body position change frequency to obtain the body position change frequency.

6. The intelligent analysis method for the sleep aid scheme driven by intelligent perception according to claim 5, characterized in that, According to the pressure comfort information, the temperature comfort information and the body position change frequency, perform the sleep aid comfort analysis calculation of the sleep aid scheme to obtain the sleep aid comfort, which is used as the analysis result of the sleep aid scheme, including: According to the body position change frequency, classify and obtain the body position comfort. Specifically, collect a sample body position change frequency set and a sample body position comfort set, construct a mapping classification table, and input the body position change frequency into the mapping classification table to classify and obtain the body position comfort; Perform weighted calculation on the pressure comfort information, the temperature comfort information and the body position comfort to obtain the sleep aid comfort, which is used as the analysis result of the sleep aid scheme.

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