Electric bed sleep-aiding control system based on big data

Abstract

The invention discloses an electric bed sleep-aiding control system based on big data, and particularly relates to the technical field of electric bed automatic control, and the system comprises a sleep-aiding control scheme presetting module, a sleep monitoring analysis module, a sleep-aiding scheme correction module, a sleep state evaluation module and a database. According to the method, the big data analysis technology is adopted, sleep parameters and information parameters in the historical sleep monitoring period of each user are analyzed and screened to obtain sleep-aiding control schemes of various types of users, the use technical threshold of the electric bed is reduced, and the use convenience of the sleep-aiding control schemes of the electric bed is improved. The sleep parameters of the target user in the sleep monitoring period are continuously monitored, the sleep-aiding control scheme of the electric bed of the target user is corrected in time, the self-learning ability of the sleep-aiding control system of the electric bed is improved, and the reliability of the sleep-aiding control system of the electric bed is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric bed automatic control, and in particular to an electric bed sleep-aiding control system based on big data. Background Art

[0002] The electric bed automatic control system is a system that uses electric drive and intelligent control technology to enable the bed to automatically adjust the angle and inclination of the mattress to provide a more personalized and comfortable sleeping experience. As people's demand for health, comfort and convenient life continues to increase, electric beds and their automatic control systems have gradually become important technical applications in many fields such as modern home, medical care, and aging society. Therefore, it is necessary to study the electric bed automatic control system.

[0003] The research on the electric bed automatic control system in the prior art can meet certain needs, but there are also certain potential risks. Specifically: on the one hand, the prior art ignores the fact that the sleep status parameters of the historical sleep monitoring cycles of each user have a reference role in the sleep aid plan of the target user's sleep monitoring cycle, resulting in the initialization setting of the electric bed ignoring the reference standard, which increases the difficulty for users to quickly set to a suitable sleep aid plan during actual use, causing technical barriers for users to actually use the electric bed. On the other hand, the sleep aid plan for the electric bed in the prior art lacks the self-adjustment function, resulting in the need for users to adjust the sleep aid plan according to their personal usage experience during use, reducing the adjustment rate of the electric bed, and it is difficult to meet the actual needs of adjusting the electric bed sleep aid plan. Summary of the invention

[0004] The purpose of the present invention is to provide an electric bed sleep-aid control system based on big data, which solves the problems existing in the background technology.

[0005] To solve the above technical problems, the present invention adopts the following technical solutions: an electric bed sleep aid control system based on big data, the system comprising: a sleep aid control scheme pre-setting module, a sleep monitoring and analysis module, a sleep aid scheme correction module, a sleep state evaluation module and a database.

[0006] The sleep-aid control scheme pre-setting module is used to extract various parameters of information of each historical user, various parameters of sleep status and various parameters of electric bed control from the database, calculate the sleep status compliance coefficient of each historical user within the sleep monitoring cycle based on the various parameters of sleep status of each historical user, generate sleep-aid control schemes for each type of user, obtain various parameters of information of the target user, and match the initial sleep-aid control scheme for the target user from the sleep-aid control schemes for each type of user.

[0007] The sleep monitoring and analysis module is used to obtain various sleep parameters of the target user during the sleep monitoring period, and calculate the sleep state compliance coefficient of the target user during the sleep monitoring period based on the various sleep parameters during the sleep monitoring period.

[0008] The sleep aid plan correction module calculates the initial sleep aid control plan compliance coefficient based on the target user's sleep state compliance coefficient and the sleep state compliance coefficient of each historical user in the sleep monitoring cycle corresponding to the initial sleep aid control plan, and again selects the sleep control plan of the corresponding type of user of the target user from the database to generate a revised sleep aid control plan.

[0009] The sleep state evaluation module obtains the sleep parameters of the target user during the modified sleep monitoring cycle based on the modified sleep aid control scheme, calculates the sleep state compliance coefficient of the target user during the modified sleep monitoring cycle, and evaluates the sleep state compliance level of the target user during the modified sleep monitoring cycle.

[0010] Specifically, the sleep state compliance coefficient of each user's sleep monitoring cycle in history is calculated, and the specific analysis method is as follows: the sleep state parameters include breathing frequency, heart rate, sleep duration and number of body movements.

[0011] Based on the respiratory rate of each collection point in the sleep monitoring cycle of each user extracted from the database , heart rate , the sleep duration of each user in the historical sleep monitoring cycle and number of body movements ,in , Indicates the number of each historical user. Indicates the total number of historical users. , Indicates the number of each collection point in the sleep monitoring cycle. Indicates the total number of collection points in a sleep monitoring cycle.

[0012] By formula: , calculate and obtain the compliance judgment value of the respiratory frequency of each collection point in the historical sleep monitoring cycle of each user ,in Represents the standard breathing rate during the user's sleep monitoring cycle extracted from the database.

[0013] According to the calculation method of the compliance judgment value of the respiratory frequency of each collection point in the monitoring cycle, the heart rate of each collection point in the historical sleep monitoring cycle of each user is analyzed to obtain the compliance judgment value of the heart rate of each collection point in the historical sleep monitoring cycle of each user. .

[0014] By formula: , calculate and obtain the sleep state compliance coefficient of each user's sleep monitoring cycle in history ,in represents the influencing factor of unit sleep duration extracted from the database, represents the influencing factor of the number of unit body movements extracted from the database, Represents a natural constant.

[0015] Specifically, a sleep-aid control plan for each type of user is generated. The specific analysis method is as follows: based on the sleep state compliance coefficient of each user in the historical sleep monitoring cycle ,like , then determine the The sleep status of users was good during the sleep monitoring period, among which Indicates the sleep state judgment threshold extracted from the database.

[0016] like , then determine the The sleep status of the user during the sleep monitoring cycle is unqualified.

[0017] According to A method for determining the sleep state of each user within a sleep monitoring cycle is provided, and the sleep state compliance coefficients of each historical user within the sleep monitoring cycle are determined to obtain each user with a good sleep state within the historical sleep monitoring cycle.

[0018] The sleep aid control scheme and information parameters of each user with good sleep status during the historical sleep monitoring period are extracted from the database, and the information parameters include: gender and age.

[0019] First, users who slept well during the historical sleep monitoring period are divided according to their gender, and users who slept well during the historical sleep monitoring period are obtained after gender division. Then, users who slept well during the historical sleep monitoring period are divided according to the age range extracted from the database, and different types of users who slept well during the historical sleep monitoring period are obtained.

[0020] Based on the sleep state compliance coefficients of various types of users who have good sleep states within the historical sleep monitoring cycles, the sleep environment parameters and electric bed control parameters within the historical sleep monitoring cycles corresponding to the maximum sleep state compliance coefficients of various types of users are screened out, and the sleep environment parameters and electric bed control parameters within the historical sleep monitoring cycles corresponding to the maximum sleep state compliance coefficients of various types of users are combined to form a sleep aid control plan for various types of users.

[0021] The sleep environment parameters include: ambient temperature, ambient humidity and the frequency of the sleep-aiding audio.

[0022] Electric bed control parameters include: back angle, leg angle and overall tilt angle.

[0023] Specifically, the initial sleep-aid control plan for the target user is matched, and the specific analysis method is as follows: the information parameters of the target user are obtained from the database, and based on the gender and age of the target user, the appropriate sleep-aid control methods for each type of user are matched to obtain the initial sleep-aid control plan for the target user.

[0024] Specifically, the sleep state compliance coefficient of the target user in the sleep monitoring cycle is calculated. The specific analysis method is as follows: the sleep parameters of the target user in the sleep monitoring cycle under the initial sleep control scheme are obtained, including the breathing frequency of each monitoring point of the target user in the sleep monitoring cycle. , heart rate , the target user's sleep duration during the sleep monitoring cycle and number of body movements ,in , Indicates the number of each monitoring point in the sleep monitoring cycle. Indicates the total number of monitoring points in a sleep monitoring cycle.

[0025] By formula: , calculate and obtain the compliance judgment value of the breathing frequency of each monitoring point in the target user's sleep monitoring cycle .

[0026] According to the calculation method of the compliance judgment value of the breathing frequency of each monitoring point in the target user's sleep monitoring cycle, the heart rate of each monitoring point in the target user's sleep monitoring cycle is analyzed to obtain the compliance judgment value of the heart rate of each monitoring point in the target user's sleep monitoring cycle .

[0027] By formula: , calculate and obtain the sleep state compliance coefficient of the target user during the sleep monitoring period Specifically, the compliance coefficient of the initial sleep-aid control scheme is calculated, and the specific analysis method is as follows: the sleep state compliance coefficients of each historical user in the sleep monitoring cycle corresponding to the initial sleep-aid control scheme of the target user are extracted from the database, and the sleep state compliance coefficients of each historical user in the sleep monitoring cycle corresponding to the initial sleep-aid control scheme of the target user are averaged, and the average result is used as the standard sleep state compliance coefficient of the target user in the monitoring cycle ;

[0028] Based on the sleep state compliance coefficient of the target user during the sleep monitoring cycle , through the formula: , the initial sleep-aiding control scheme for the target user is calculated to obtain the coefficient of compliance .

[0029] Specifically, the revised sleep-aid control scheme is generated by: based on the initial sleep-aid control scheme of the target user, the coefficient of ,like If the target user is in the first interval of the initial sleep-aid control scheme compliance coefficient extracted from the database, it is determined that the initial sleep-aid control scheme compliance level of the target user is high.

[0030] like If the initial sleep aid control scheme extracted from the database is in the second interval of the compliance coefficient, it is determined that the initial sleep aid control scheme compliance level of the target user is low, and response measures that the initial sleep aid control scheme of the target user complies with the low compliance level are implemented.

[0031] The specific response measures for the target user's initial sleep-aid control plan that meets the low compliance level are: extracting the sleep state compliance coefficients of historical users of the corresponding types of each parameter of the target user's information from the database, and selecting the sleep environment parameters and electric bed control parameters in the historical sleep monitoring cycle corresponding to the second largest sleep state compliance coefficient to form a revised sleep-aid control plan.

[0032] Specifically, the sleep state compliance coefficient of the target user in the sleep monitoring cycle after the correction is calculated, and the specific analysis method is as follows: the sleep parameters of the target user in the sleep monitoring cycle under the corrected sleep control scheme are obtained, and the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the corrected sleep control scheme is obtained according to the calculation method of the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the initial sleep control scheme. .

[0033] Specifically, the sleep state compliance level of the target user in the modified sleep monitoring cycle is evaluated, and the specific analysis method is as follows: based on the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the modified sleep control scheme , through the formula: , calculate and obtain the corrected target user's sleep state compliance coefficient .

[0034] like If the sleep state of the target user after correction extracted from the database is in accordance with the first interval, it is determined that the conformity level of the sleep state of the target user after correction is high conformity.

[0035] like If the sleep state of the target user after correction extracted from the database is in accordance with the second interval, it is determined that the conformity level of the sleep state of the target user after correction is low conformity.

[0036] If the revised target user's sleep state compliance level is low compliance, the sleep environment parameters and electric bed control parameters in the historical sleep monitoring period corresponding to the sleep state compliance coefficient are selected in order from large to small, and the revised sleep aid control plan is reconstructed, and the revised target user's sleep state compliance level is evaluated again until the revised target user's sleep state compliance level is high compliance.

[0037] Compared with the prior art, the present invention has the following benefits, specifically: 1. The present invention adopts big data analysis technology to analyze various sleep parameters and information parameters in the historical sleep monitoring cycles of each user, and screens them to obtain sleep aid control plans for various types of users, so that the target user can quickly match the initial sleep aid control plan of the target user according to the information parameters, thereby lowering the technical threshold for the use of electric beds and improving the convenience of using the sleep aid control plan of electric beds.

[0038] 2. The present invention continuously monitors various sleep parameters of the target user during the sleep monitoring cycle, so as to timely discover the sleep status of the target user during the monitoring cycle, thereby timely correcting the sleep aid control scheme of the electric bed of the target user, thereby increasing the self-learning ability of the electric bed sleep aid control system and realizing the reliability of the electric bed sleep aid control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a system structure connection diagram of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Reference Figure 1 As shown, the present invention provides an electric bed sleep aid control system based on big data, and the system includes: a sleep aid control scheme pre-setting module, a sleep monitoring and analysis module, a sleep aid scheme correction module, a sleep state evaluation module and a database.

[0043] The sleep-aid control scheme pre-setting module is used to extract various parameters of information of each historical user, various parameters of sleep status and various parameters of electric bed control from the database, calculate the sleep status compliance coefficient of each historical user within the sleep monitoring cycle based on the various parameters of sleep status of each historical user, generate sleep-aid control schemes for each type of user, obtain various parameters of information of the target user, and match the initial sleep-aid control scheme for the target user from the sleep-aid control schemes for each type of user.

[0044] The sleep monitoring and analysis module is used to obtain various sleep parameters of the target user during the sleep monitoring period, and calculate the sleep state compliance coefficient of the target user during the sleep monitoring period based on the various sleep parameters during the sleep monitoring period.

[0045] The sleep aid plan correction module calculates the initial sleep aid control plan compliance coefficient based on the target user's sleep state compliance coefficient and the sleep state compliance coefficient of each historical user in the sleep monitoring cycle corresponding to the initial sleep aid control plan, and again selects the sleep control plan of the corresponding type of user of the target user from the database to generate a revised sleep aid control plan.

[0046] The sleep state evaluation module obtains the sleep parameters of the target user during the modified sleep monitoring cycle based on the modified sleep aid control scheme, calculates the sleep state compliance coefficient of the target user during the modified sleep monitoring cycle, and evaluates the sleep state compliance level of the target user during the modified sleep monitoring cycle.

[0047] The database is used to store the respiratory rate and heart rate of each collection point in the historical sleep monitoring cycle of each user, store the sleep duration and body movement frequency in the historical sleep monitoring cycle of each user, store the standard respiratory rate in the user's sleep monitoring cycle, store the influencing factors of unit sleep duration and unit body movement frequency, store the sleep state judgment threshold, store the initial sleep aid control plan compliance coefficient intervals, and store the corrected target user's sleep state compliance intervals.

[0048] It should be noted that, in a specific embodiment, the sleep aid control scheme pre-setting module is connected to the sleep monitoring and analysis module, the sleep monitoring and analysis module is connected to the sleep aid scheme correction module, the sleep aid scheme correction module is connected to the sleep state evaluation module, and the database is connected to the sleep aid control scheme pre-setting module, the sleep monitoring and analysis module, the sleep aid scheme correction module, and the sleep state evaluation module.

[0049] In a specific embodiment of the present invention, the sleep state compliance coefficient of each user's sleep monitoring cycle in history is calculated, and the specific analysis method is as follows: the sleep state parameters include breathing frequency, heart rate, sleep duration and body movement times.

[0050] Based on the respiratory rate of each collection point in the sleep monitoring cycle of each user extracted from the database , heart rate , the sleep duration of each user in the historical sleep monitoring cycle and number of body movements ,in , Indicates the number of each historical user. Indicates the total number of historical users. , Indicates the number of each collection point in the sleep monitoring cycle. Indicates the total number of collection points in a sleep monitoring cycle.

[0051] By formula: , calculate and obtain the compliance judgment value of the respiratory frequency of each collection point in the historical sleep monitoring cycle of each user ,in Represents the standard breathing rate during the user's sleep monitoring cycle extracted from the database.

[0052] According to the calculation method of the compliance judgment value of the respiratory frequency of each collection point in the monitoring cycle, the heart rate of each collection point in the historical sleep monitoring cycle of each user is analyzed to obtain the compliance judgment value of the heart rate of each collection point in the historical sleep monitoring cycle of each user. .

[0053] Specifically, based on the heart rate of each collection point in the historical sleep monitoring cycle of each user extracted from the database, a low-pass filter (such as Butterworth) is first used to eliminate the noise of the heart rate, and then eliminate extreme outliers; the heart rate data is divided according to the sleep stage (such as deep sleep, light sleep, REM), and the heart rate standards of different stages are adapted; secondly, the normal heart rate range can be determined by the general standard method and the personalized baseline method. Taking the personalized baseline method as an example, the mean and standard deviation of the historical sleep heart rate of each user are calculated, and the heart rate range is determined by the mean and standard deviation; finally, the compliance judgment value of the heart rate is calculated, which is consistent with the calculation formula of the compliance judgment value of the above-mentioned respiratory frequency, and a point-by-point judgment is performed.

[0054] By formula: , calculate and obtain the sleep state compliance coefficient of each user's sleep monitoring cycle in history ,in represents the influencing factor of unit sleep duration extracted from the database, represents the influencing factor of the number of unit body movements extracted from the database, Represents a natural constant.

[0055] In a specific embodiment of the present invention, a sleep aid control scheme for each type of user is generated, and the specific analysis method is as follows: based on the sleep state compliance coefficient of each user in the historical sleep monitoring cycle, ,like , then determine the The sleep status of users was good during the sleep monitoring period, among which Indicates the sleep state judgment threshold extracted from the database.

[0056] like , then determine the The sleep status of the user during the sleep monitoring cycle is unqualified.

[0057] It should be noted that, in a specific embodiment, the sleep state judgment threshold is preset manually by a technician based on a comprehensive analysis of the sleep state compliance coefficient of each user during the sleep monitoring cycle and the actual sleep experience of each user, and is stored in a database.

[0058] According to A method for determining the sleep state of each user within a sleep monitoring cycle is provided, and the sleep state compliance coefficients of each historical user within the sleep monitoring cycle are determined to obtain each user with a good sleep state within the historical sleep monitoring cycle.

[0059] The sleep aid control scheme and information parameters of each user with good sleep status during the historical sleep monitoring period are extracted from the database, and the information parameters include: gender and age.

[0060] First, users who slept well during the historical sleep monitoring period are divided according to their gender, and users who slept well during the historical sleep monitoring period are obtained after gender division. Then, users who slept well during the historical sleep monitoring period are divided according to the age range extracted from the database, and different types of users who slept well during the historical sleep monitoring period are obtained.

[0061] It should be noted that, in a specific embodiment, the age intervals are set according to a certain span and stored in the database. For example, the age intervals are set according to a span of 5 to obtain the following age intervals: .

[0062] Based on the sleep state compliance coefficients of various types of users who have good sleep status within the historical sleep monitoring period, the sleep environment parameters and electric bed control parameters within the historical sleep monitoring period corresponding to the maximum sleep state compliance coefficient of each type of user are screened out, and the sleep environment parameters and electric bed control parameters within the historical sleep monitoring period corresponding to the maximum sleep state compliance coefficient of each type of user are combined to form a sleep aid control plan for each type of user.

[0063] The sleep environment parameters include: ambient temperature, ambient humidity and the frequency of the sleep-aiding audio.

[0064] It should be noted that, in a specific embodiment, the ambient temperature is collected by a temperature sensor, the ambient humidity is collected by a humidity sensor, and the frequency of the sleep-aiding audio is collected by an audio sensor.

[0065] Electric bed control parameters include: back angle, leg angle and overall tilt angle.

[0066] In a specific embodiment of the present invention, the initial sleep-aid control scheme for the target user is matched, and the specific analysis method is as follows: the information parameters of the target user are obtained from the database, and based on the gender and age of the target user, suitable sleep-aid control methods for various types of users are matched to obtain the initial sleep-aid control scheme for the target user.

[0067] The present invention adopts big data analysis technology to analyze various sleep parameters and information parameters in the historical sleep monitoring cycles of each user, and screens them to obtain sleep-aid control plans for various types of users, so that the target user can quickly match the initial sleep-aid control plan of the target user according to the information parameters, thereby lowering the technical threshold for the use of electric beds and improving the convenience of using the sleep-aid control plan of electric beds.

[0068] In a specific embodiment of the present invention, the sleep state compliance coefficient of the target user in the sleep monitoring cycle is calculated, and the specific analysis method is as follows: obtaining the sleep parameters of the target user in the sleep monitoring cycle under the initial sleep control scheme, including the breathing frequency of each monitoring point of the target user in the sleep monitoring cycle , heart rate , the target user's sleep duration during the sleep monitoring cycle and number of body movements ,in ,express The number of each monitoring point in the sleep monitoring cycle, Indicates the total number of monitoring points in a sleep monitoring cycle.

[0069] It should be noted that, in a specific embodiment, the target user wears a smart device during sleep, and the breathing rate sensor integrated in the smart bracelet collects the target user's breathing rate during the sleep monitoring period, the heart rate sensor collects the target user's heart rate during the sleep monitoring period, and the smart bracelet collects the target user's sleep duration and body movement times during the sleep monitoring period.

[0070] By formula: , calculate and obtain the compliance judgment value of the breathing frequency of each monitoring point in the target user's sleep monitoring cycle .

[0071] Specifically, the compliance judgment value of the heart rate at each monitoring point in the target user's sleep monitoring cycle is consistent with the judgment method of the compliance judgment value of the heart rate at each collection point in the above-mentioned historical sleep monitoring cycle of each user, which will not be elaborated here.

[0072] According to the calculation method of the compliance judgment value of the breathing frequency of each monitoring point in the target user's sleep monitoring cycle, the heart rate of each monitoring point in the target user's sleep monitoring cycle is analyzed to obtain the compliance judgment value of the heart rate of each monitoring point in the target user's sleep monitoring cycle .

[0073] By formula: , calculate and obtain the sleep state compliance coefficient of the target user during the sleep monitoring period .

[0074] Furthermore, by integrating the actual sleep data of the target user (respiratory frequency, heart rate, sleep duration and number of body movements) and assigning influencing factors of the corresponding items for integrated calculation, the sleep state compliance coefficient is obtained, where the sleep state compliance coefficient is a normalized comprehensive score.

[0075] In a specific embodiment of the present invention, the initial sleep-aid control scheme compliance coefficient is calculated, and the specific analysis method is as follows: the sleep state compliance coefficients of each historical user in the sleep monitoring cycle corresponding to the initial sleep-aid control scheme of the target user are extracted from the database, and the sleep state compliance coefficients of each historical user in the sleep monitoring cycle corresponding to the initial sleep-aid control scheme of the target user are averaged, and the average result is used as the standard sleep state compliance coefficient of the target user in the monitoring cycle. ;

[0076] Based on the sleep state compliance coefficient of the target user during the sleep monitoring cycle , through the formula: , the initial sleep-aiding control scheme for the target user is calculated to obtain the coefficient of compliance .

[0077] In a specific embodiment of the present invention, a modified sleep-aiding control scheme is generated, and the specific analysis method is as follows: based on the initial sleep-aiding control scheme of the target user, the coefficient of compliance is ,like If the target user is in the first interval of the initial sleep-aid control scheme compliance coefficient extracted from the database, it is determined that the initial sleep-aid control scheme compliance level of the target user is high.

[0078] like If the initial sleep aid control scheme extracted from the database is in the second interval of the compliance coefficient, it is determined that the initial sleep aid control scheme compliance level of the target user is low, and response measures that the initial sleep aid control scheme of the target user complies with the low compliance level are implemented.

[0079] It should be noted that, in a specific embodiment, the initial sleep-aid control scheme complies with each coefficient interval, and the technicians conduct a comprehensive analysis based on each user's initial sleep-aid control scheme compliance coefficient and actual sleep experience, and perform artificial pre-setting and storage in the database.

[0080] The specific response measures for the target user's initial sleep-aid control plan that meets the low compliance level are: extracting the sleep state compliance coefficients of historical users of the corresponding types of each parameter of the target user's information from the database, and selecting the sleep environment parameters and electric bed control parameters in the historical sleep monitoring cycle corresponding to the second largest sleep state compliance coefficient to form a revised sleep-aid control plan.

[0081] In a specific embodiment of the present invention, the sleep state compliance coefficient of the target user in the sleep monitoring cycle after the correction is calculated, and the specific analysis method is as follows: the sleep parameters of the target user in the sleep monitoring cycle under the corrected sleep control scheme are obtained, and the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the corrected sleep control scheme is obtained according to the calculation method of the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the initial sleep control scheme. .

[0082] In a specific embodiment of the present invention, the sleep state compliance level of the target user in the modified sleep monitoring cycle is evaluated, and the specific analysis method is as follows:

[0083] Based on the sleep state compliance coefficient of the target user during the sleep monitoring cycle under the revised sleep control scheme , through the formula: , calculate and obtain the corrected target user's sleep state compliance coefficient .

[0084] like If the sleep state of the target user after correction extracted from the database is in accordance with the first interval, it is determined that the conformity level of the sleep state of the target user after correction is high conformity.

[0085] like If the sleep state of the target user after correction extracted from the database is in accordance with the second interval, it is determined that the conformity level of the sleep state of the target user after correction is low conformity.

[0086] It should be noted that, in a specific embodiment, the corrected sleep state of the target user complies with each interval, and the technicians conduct a comprehensive analysis based on the sleep state compliance coefficients of the target user after each historical correction and the actual sleep experience, and perform artificial pre-setting and storage in the database.

[0087] If the revised target user's sleep state compliance level is low compliance, the sleep environment parameters and electric bed control parameters in the historical sleep monitoring period corresponding to the sleep state compliance coefficient are selected in order from large to small, and the revised sleep aid control plan is reconstructed, and the revised target user's sleep state compliance level is evaluated again until the revised target user's sleep state compliance level is high compliance.

[0088] The present invention continuously monitors various sleep parameters of the target user during the sleep monitoring cycle, so as to timely discover the sleep state of the target user during the monitoring cycle, thereby timely correcting the sleep aid control scheme of the electric bed of the target user, thereby increasing the self-learning ability of the electric bed sleep aid control system and realizing the reliability of the electric bed sleep aid control system.

[0089] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An electric bed sleep aid control system based on big data, characterized in that: The system includes: Sleep aid control scheme pre-setting module, sleep monitoring and analysis module, sleep aid scheme correction module, sleep state assessment module and database; The sleep-aid control scheme presetting module is used to extract information parameters, sleep state parameters and electric bed control parameters of each historical user from the database, calculate the sleep state compliance coefficient of each historical user in the sleep monitoring cycle based on the sleep state parameters of each historical user, generate sleep-aid control schemes for each type of user, obtain information parameters of the target user, and match the initial sleep-aid control scheme of the target user from the sleep-aid control schemes of each type of user; A sleep monitoring and analysis module is used to obtain various sleep parameters of the target user during the sleep monitoring period, and calculate the sleep state compliance coefficient of the target user during the sleep monitoring period based on the various sleep parameters during the sleep monitoring period; The sleep aid program correction module calculates the initial sleep aid control program compliance coefficient based on the target user's sleep state compliance coefficient and the sleep state compliance coefficient of each historical user in the sleep monitoring cycle corresponding to the initial sleep aid control program, selects the sleep control program of the corresponding type of user of the target user from the database again, and generates a revised sleep aid control program; The sleep state evaluation module obtains the sleep parameters of the target user during the modified sleep monitoring cycle based on the modified sleep aid control scheme, calculates the sleep state compliance coefficient of the target user during the modified sleep monitoring cycle, and evaluates the sleep state compliance level of the target user during the modified sleep monitoring cycle.

2. The electric bed sleep aid control system based on big data according to claim 1, characterized in that: The sleep state compliance coefficient of each user's sleep monitoring cycle in history is calculated. The specific analysis method is as follows: Sleep status parameters include respiratory rate, heart rate, sleep duration and body movement frequency; Based on the respiratory rate of each collection point in the sleep monitoring cycle of each user extracted from the database , heart rate , the sleep duration of each user in the historical sleep monitoring cycle and number of body movements ,in , Indicates the number of each historical user. Indicates the total number of historical users. , Indicates the number of each collection point in the sleep monitoring cycle. Indicates the total number of collection points in the sleep monitoring cycle; By formula: , calculate and obtain the compliance judgment value of the respiratory frequency of each collection point in the historical sleep monitoring cycle of each user ,in represents the standard breathing frequency of the user in the sleep monitoring cycle extracted from the database; According to the calculation method of the compliance judgment value of the respiratory frequency of each collection point in the monitoring cycle, the heart rate of each collection point in the historical sleep monitoring cycle of each user is analyzed to obtain the compliance judgment value of the heart rate of each collection point in the historical sleep monitoring cycle of each user. ; By formula: , calculate and obtain the sleep state compliance coefficient of each user's sleep monitoring cycle in history ,in represents the influencing factor of unit sleep duration extracted from the database, represents the influencing factor of the number of unit body movements extracted from the database, Represents a natural constant.

3. The electric bed sleep aid control system based on big data according to claim 2, characterized in that: Generate sleep-aid control plans for various types of users. The specific analysis method is as follows: Based on the sleep status compliance coefficient of each user's sleep monitoring cycle in history ,like , then determine the The sleep status of users was good during the sleep monitoring period, among which represents the judgment threshold of the sleep state extracted from the database; like , then determine the The sleep status of the user during the sleep monitoring cycle is unqualified; According to A method for determining the sleep state of each user in a sleep monitoring cycle is used to determine the sleep state compliance coefficient of each user in the sleep monitoring cycle in history, and obtain each user with a good sleep state in the sleep monitoring cycle in history; Extracting the sleep-aid control scheme and information parameters of each user with good sleep status during the historical sleep monitoring period from the database, the information parameters including: gender and age; First, the users who slept well in the historical sleep monitoring period are divided according to their gender, and the users who slept well in the historical sleep monitoring period after gender division are obtained. Then, the users who slept well in the historical sleep monitoring period are divided according to the age range extracted from the database, and the users of different types who slept well in the historical sleep monitoring period are obtained. Based on the sleep state compliance coefficients of various types of users with good sleep states in historical sleep monitoring cycles, the sleep environment parameters and electric bed control parameters in the historical sleep monitoring cycles corresponding to the maximum sleep state compliance coefficients of various types of users are screened, and the sleep environment parameters and electric bed control parameters in the historical sleep monitoring cycles corresponding to the maximum sleep state compliance coefficients of various types of users are combined to form a sleep aid control plan for various types of users; Sleep environment parameters include: ambient temperature, ambient humidity and frequency of sleep-aiding audio; Electric bed control parameters include: back angle, leg angle and overall tilt angle.

4. The electric bed sleep aid control system based on big data according to claim 3, characterized in that: The specific analysis method for matching the initial sleep-aiding control plan for target users is as follows: The information parameters of the target user are obtained from the database, and based on the gender and age of the target user, suitable sleep-aid control methods for various types of users are matched to obtain an initial sleep-aid control plan for the target user.

5. The electric bed sleep aid control system based on big data according to claim 4, characterized in that: Calculate the sleep state compliance coefficient of the target user during the sleep monitoring period. The specific analysis method is as follows: Obtain the target user's sleep parameters during the sleep monitoring cycle under the initial sleep control scheme, including the target user's breathing frequency at each monitoring point during the sleep monitoring cycle , heart rate , target user's sleep duration during the sleep monitoring cycle and number of body movements ,in , Indicates the number of each monitoring point in the sleep monitoring cycle. Indicates the total number of monitoring points in a sleep monitoring cycle; By formula: , calculate and obtain the compliance judgment value of the breathing frequency of each monitoring point in the target user's sleep monitoring cycle ; According to the calculation method of the compliance judgment value of the breathing frequency of each monitoring point in the target user's sleep monitoring cycle, the heart rate of each monitoring point in the target user's sleep monitoring cycle is analyzed to obtain the compliance judgment value of the heart rate of each monitoring point in the target user's sleep monitoring cycle ; By formula: , calculate and obtain the sleep state compliance coefficient of the target user during the sleep monitoring period .

6. The electric bed sleep aid control system based on big data according to claim 5, characterized in that: Calculate the initial sleep-aiding control scheme compliance coefficient, and the specific analysis method is as follows: Extract the sleep state compliance coefficients of each historical user in the sleep monitoring cycle corresponding to the target user's initial sleep aid control scheme from the database, average the sleep state compliance coefficients of each historical user in the sleep monitoring cycle corresponding to the target user's initial sleep aid control scheme, and use the average result as the standard sleep state compliance coefficient of the target user in the monitoring cycle ; Based on the sleep state compliance coefficient of the target user during the sleep monitoring cycle , through the formula: , calculate the initial sleep-aiding control scheme compliance coefficient of the target user .

7. The electric bed sleep aid control system based on big data according to claim 6, characterized in that: Generate a revised sleep-aid control plan, and its specific analysis method is as follows: Initial sleep-aid control scheme based on target users meets the coefficient ,like If the target user is in the first range of the initial sleep-aid control scheme compliance coefficient extracted from the database, it is determined that the target user's initial sleep-aid control scheme compliance level is high; like If the initial sleep-aid control scheme extracted from the database is in the second interval of the compliance coefficient, it is determined that the compliance level of the target user's initial sleep-aid control scheme is low, and a response measure that the target user's initial sleep-aid control scheme complies with the low compliance level is implemented; The specific response measures for the target user's initial sleep-aid control plan that meets the low compliance level are: extracting the sleep state compliance coefficients of historical users of the corresponding types of each parameter of the target user's information from the database, and selecting the sleep environment parameters and electric bed control parameters in the historical sleep monitoring cycle corresponding to the second largest sleep state compliance coefficient to form a revised sleep-aid control plan.

8. The electric bed sleep aid control system based on big data according to claim 7, characterized in that: The sleep state compliance coefficient of the target user in the sleep monitoring cycle after calculation correction is analyzed in the following specific method: Obtain the sleep parameters of the target user in the sleep monitoring cycle under the revised sleep control scheme, and obtain the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the revised sleep control scheme according to the calculation method of the sleep state compliance coefficient of the target user in the sleep monitoring cycle under the initial sleep control scheme. .

9. The electric bed sleep aid control system based on big data according to claim 8, characterized in that: Evaluate the corrected sleep status compliance level of the target user during the sleep monitoring cycle. The specific analysis method is as follows: Based on the sleep state compliance coefficient of the target user during the sleep monitoring cycle under the revised sleep control scheme , through the formula: , calculate and obtain the corrected target user's sleep state compliance coefficient ; like If the sleep state of the target user after correction extracted from the database is in the first interval, it is determined that the conformity level of the sleep state of the target user after correction is high conformity; like If the sleep state of the target user after correction extracted from the database is in the second interval, it is determined that the conformity level of the sleep state of the target user after correction is low conformity; If the revised target user's sleep state compliance level is low compliance, the sleep environment parameters and electric bed control parameters in the historical sleep monitoring period corresponding to the sleep state compliance coefficient are selected in order from large to small, and the revised sleep aid control plan is reconstructed, and the revised target user's sleep state compliance level is evaluated again until the revised target user's sleep state compliance level is high compliance.

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