Active feedback method and system for assisting sleep

Abstract

The present invention discloses an active feedback method and system for assisting sleep. The method is applied to an assisting sleep treatment process and includes the following steps: collecting a user's daily free sleep time and basic sleep parameters during a first treatment course; uploading the free sleep time and basic sleep parameters to a cloud server; and receiving the user's bed rest time during a second treatment course from the cloud server based on the free sleep time and basic sleep parameters; providing the user with assisting sleep based on the bed rest time during the second treatment course, and monitoring the user's sleep quality parameters during the second treatment course; and actively adjusting the user's bed rest time during each subsequent treatment course based on the sleep quality parameters during the previous treatment course until the assisting sleep treatment process is completed. The present invention can prevent the therapeutic effect from being affected by excessive changes in sleep time during the assisting sleep process, thereby improving the quality of assisting sleep treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of sleep assistance, and in particular to an active feedback method and system for sleep assistance. Background Art

[0002] Insomnia is a major chronic disease plaguing modern people. A World Health Organization survey reveals that one-third of the global population suffers from sleep disorders, and sleep quality in China is even lower than the global average. The primary mechanism of insomnia is sleep rhythm disruption and other factors. Frequent late nights disrupt the circadian rhythm, leading to endocrine imbalances and, in turn, various health problems. These conditions can cause difficulties in work, study, and daily life for those suffering from insomnia. Long-term insomnia can also lead to cognitive, endocrine, and immune system dysfunction. Currently, a wide range of non-pharmacological treatments are available internationally, including sleep restriction therapy, sleep stimulation therapy, and sleep induction techniques using sound and breathing techniques.

[0003] Among the common insomnia treatment measures currently in use, doctors adjust the patient's bed rest time through sleep-aiding therapies such as sleep restriction therapy and sleep stimulation therapy. This artificial adjustment of the patient's bed rest time promotes sleep in the user. However, adjusting the bed rest time often leads to excessive changes in sleep time, which can cause patients to be unable to adapt or comply, thereby reducing the therapeutic effect of assisted sleep.

[0004] Therefore, there is a need for a method for adjusting the bed rest time, which can achieve a smooth transition to a reasonable bed rest time range during the process of assisting insomnia patients in sleep treatment, avoid excessive changes in the user's sleep time after adjusting the bed rest time, and affect the treatment effect, thereby improving the improvement effect of assisted sleep treatment on the user's sleep condition. Summary of the Invention

[0005] To solve the technical problem, the present invention provides a method and system for assisting sleep with active feedback. The specific technical solution is as follows:

[0006] The present invention provides an active feedback method for assisting sleep, which is applied to an assistive sleep treatment process. The assistive sleep treatment process includes several sequentially arranged treatment courses, including the following steps:

[0007] During the first course of treatment, the user's daily free sleep time and basic sleep parameters are collected;

[0008] uploading the free sleep time and the basic sleep parameters to a cloud server, and receiving the bed rest time of the user in the second treatment course from the cloud server according to the free sleep time and the basic sleep parameters;

[0009] providing the user with sleep assistance according to the bed rest time during the second treatment course, and monitoring the sleep quality parameters of the user during the second treatment course;

[0010] In each of the remaining treatment courses, the user's bed time in the current treatment course is actively adjusted according to the sleep quality parameters in the previous treatment course until the auxiliary sleep treatment process is completed.

[0011] The active feedback method for assisting sleep provided by the present invention divides the assisting sleep treatment into multiple courses and changes the bed rest time in each course in stages, thereby avoiding drastic adjustments to the bed rest time that may cause excessive changes in the patient's sleep time, which in turn reduces the therapeutic effect of assisting sleep. At the same time, during the adjustment of the sleep time in each course, active feedback and adjustment are performed based on the sleep quality parameters in the previous course as a reference, thereby improving the objectivity and accuracy of the adjustment of the bed rest time during the assisting sleep process. The method is suitable for personalized customization of assisting sleep treatment for a group of users, thereby improving the effect and efficiency of assisting sleep treatment.

[0012] In some embodiments, a first data model is generated according to the first free sleep time and the first basic sleep parameter of each user during the initial treatment course stored in the cloud server;

[0013] generating a second data model according to the first free sleep time, the first basic sleep parameter, and the bed rest time during the second treatment course of each user stored in the cloud server;

[0014] Before providing the user with sleep assistance based on the bed rest time in the second treatment course, the method further includes:

[0015] receiving a second free sleep time and a second basic sleep parameter input by the user, and uploading the second free sleep time and the second basic sleep parameter to the cloud server;

[0016] predicting the first free sleep time and the first basic sleep parameter of the user according to the second free sleep time and the second basic sleep parameter, and the first data model;

[0017] The bed time of the user in the second treatment course is generated according to the second data model and the predicted first free sleep time and the first basic sleep parameter of the user.

[0018] The active feedback method for assisting sleep provided by the present invention can predict the user's sleep time and basic sleep parameters in the first course of treatment based on the first data model and the second data model stored in the cloud server, as well as the sleep time and basic sleep parameters input by the user, and further calculate the user's bed time in the second course of treatment during the assisted sleep process, thereby greatly reducing the time required for the user to collect data in the first course of treatment, and can actively predict the user's bed time in the second course of treatment, thereby improving the efficiency of assisted sleep treatment.

[0019] In some embodiments, generating a second data model based on the first free sleep time, the first basic sleep parameter, and the bed rest time during the second treatment course of each user stored in the cloud server specifically includes:

[0020] Using a logistic regression method, a logistic regression model is established as the second data model based on the first free sleep time, the first basic sleep parameter, and the bed rest time during the second treatment course of each user stored in the cloud server;

[0021] Generating the user's bed time during the second treatment course based on the second data model and the predicted first free sleep time and the first basic sleep parameter of the user specifically includes:

[0022] generating, according to the logistic regression model, the insomnia probability corresponding to each of the bed resting times when the sleep time is the first free sleep time and the basic sleep parameter is the first basic sleep parameter;

[0023] The bed rest time corresponding to the minimum insomnia probability is selected as the bed rest time of the user in the second treatment course.

[0024] The active feedback method for assisting sleep provided by the present invention discloses a process of establishing a logistic regression model as the second data model through a logistic regression method, and discloses that its model training logic is to predict the bed rest time by judging the user's insomnia probability, thereby improving the accuracy of the assisting sleep treatment method.

[0025] In some embodiments, after monitoring the sleep quality parameters of the user in the second treatment course, the method further includes:

[0026] dividing each of the remaining said treatment courses into a number of sequentially arranged sub-treatment courses;

[0027] In each of the remaining sub-treatment sessions, the user's bed time in the current sub-treatment session is actively adjusted according to the sleep quality parameters in the previous sub-treatment session until the auxiliary sleep treatment process is completed.

[0028] The active feedback method for assisting sleep provided by the present invention divides each treatment course into multiple sub-courses, and gradually changes the user's bed time within each sub-course, further reducing the variation range of the user's bed time each time, and ensuring the stability of the user's sleep quality.

[0029] In some embodiments, monitoring the user's sleep quality parameters during any of the treatment sessions includes:

[0030] Monitoring the user's sleep time and basic sleep parameters during the treatment course, wherein the basic sleep parameters include deep sleep time, light sleep time, ambient noise, number of awakenings, ambient humidity, and ambient temperature;

[0031] converting the deep sleep time, the light sleep time, and the number of awakenings into a sleep depth coefficient using a naive Bayes model, converting the ambient humidity and the ambient temperature into an environmental coefficient, and converting the ambient noise into a sound interference coefficient using a Markov model;

[0032] The sleep quality parameter of the user during the treatment course is generated according to the sleep depth coefficient, the sleep time, the environment coefficient, the sound interference coefficient and a preset sleep quality parameter model.

[0033] The active feedback method for assisting sleep provided by the present invention discloses a method for calculating a user's sleep quality parameters, thereby improving the accuracy of the user's sleep quality judgment.

[0034] In some embodiments, monitoring the sleep quality parameters of the user during any of the treatment sessions further includes:

[0035] Dividing the user's bed rest time corresponding to each day of the treatment course into a plurality of preset time periods;

[0036] Calculating the sleep quality parameters of the user in each of the preset time periods;

[0037] The preset time periods that trigger the preset specific conditions are eliminated, and the sleep quality parameters in the remaining preset time periods are integrated according to the composite probability formula to generate the sleep quality parameters corresponding to each day of the user in the treatment course.

[0038] The active feedback method for assisting sleep provided by the present invention eliminates specific time periods of the user's day, such as time periods when phone calls disturb the user at night and when returning home late affect sleep, thereby avoiding the influence of specific time periods on the user's sleep quality analysis, improving the objective accuracy of the user's sleep quality analysis, and thus improving the effect of assisting the user's sleep.

[0039] In some embodiments, in each of the remaining treatment courses, actively adjusting the user's bed time in the current treatment course based on the sleep quality parameters in the previous treatment course specifically includes:

[0040] In each of the remaining treatment courses, if the sleep quality parameter in the previous treatment course is less than the preset sleep quality parameter threshold, adjusting the user's bed time in the current treatment course according to the quartile method and a preset bed time adjustment model;

[0041] If the sleep quality parameter in the previous treatment course is not less than the preset sleep quality parameter threshold, the bed time in the previous treatment course is used as the bed time of the user in the current treatment course.

[0042] In some embodiments, adjusting the bed rest time of the user during the current treatment course specifically includes:

[0043] Pre-sorting all the sleep quality parameters stored in the cloud server from largest to smallest;

[0044] Obtaining the basic sleep parameter and the bed rest time corresponding to the first quarter of the sleep parameters among all the sleep quality parameters according to the quartile algorithm, and using them as the first expected basic sleep parameter and the first expected bed rest time, respectively;

[0045] comparing the basic sleep parameter of the user with all the first expected basic sleep parameters, and obtaining, among all the first expected basic sleep parameters, the first expected basic sleep parameter having the least similarity to the basic sleep parameter of the user as a second expected basic sleep parameter;

[0046] The bed time of the user in the current treatment course is adjusted based on the bed time corresponding to the second expected basic sleep parameter, and the amplitude of the adjusted bed time of the user in the current treatment course does not exceed a preset adjustment amplitude.

[0047] The active feedback method for assisting sleep provided by the present invention discloses a specific scheme for adjusting the time a user spends in bed. By adjusting the time a user spends in bed, the user's sleep quality is gradually made the same as that of users with good sleep quality among matched users, thereby improving the effect of assisting sleep treatment.

[0048] In some embodiments, receiving from the cloud server the bed rest time of the user during the second treatment course specifically includes:

[0049] The cloud server receives the user's bed rest time during the second treatment course manually inputted by the backend.

[0050] The active feedback method for assisting sleep provided by the present invention accurately plans the treatment plan for assisting sleep treatment for the user by receiving the user's bed rest time during the second course of treatment input by the doctor from the cloud server during the second course of treatment, thereby facilitating the subsequent automatic adjustment of assisting sleep based on the active feedback of sleep quality parameters, thereby improving the effect of assisting sleep.

[0051] In some embodiments, the present invention further provides an active feedback system for assisting sleep, which is applied to an assistive sleep treatment process. The assistive sleep treatment process includes several sequentially arranged treatment sessions, including:

[0052] A collection module is used to collect the user's daily free sleep time and basic sleep parameters during the first course of treatment;

[0053] an interaction module connected to the acquisition module, configured to upload the free sleep time and the basic sleep parameters to a cloud server, and receive the bed rest time of the user during the second treatment course from the cloud server based on the free sleep time and the basic sleep parameters;

[0054] a monitoring module, connected to the interaction module, configured to assist the user in sleeping according to the time spent in bed during the second treatment course, and to monitor the sleep quality parameters of the user during the second treatment course;

[0055] The adjustment module is used to actively adjust the user's bed time in the current treatment course according to the sleep quality parameters in the previous treatment course in each of the remaining treatment courses until the auxiliary sleep treatment process is completed.

[0056] The present invention provides a method and system for assisting sleep with active feedback, which has at least one of the following technical effects:

[0057] (1) By dividing the assisted sleep treatment into multiple courses and changing the bed rest time in each course in stages, it is avoided that a large adjustment of the bed rest time will cause the patient's sleep time to change too much, which will reduce the therapeutic effect of the assisted sleep. At the same time, in the process of adjusting the sleep time of each course, active feedback and adjustment are performed based on the sleep quality parameters of the previous course as a reference, thereby improving the objectivity and accuracy of the adjustment of the bed rest time during the assisted sleep process. It is suitable for personalized customization of assisted sleep treatment for a group of users, thereby improving the effect and efficiency of assisted sleep treatment;

[0058] (2) Based on the first data model and the second data model stored in the cloud server, as well as the sleep time and basic sleep parameters input by the user, the sleep time and basic sleep parameters of the user in the first treatment course are predicted, and the bed rest time of the user in the second treatment course is further calculated during the assisted sleep process, thereby significantly reducing the time required for the user to collect data in the first treatment course, and actively predicting the bed rest time of the user in the second treatment course, thereby improving the efficiency of assisted sleep treatment;

[0059] (3) establishing a logistic regression model as the second data model using a logistic regression method, and disclosing its model training logic to predict the bed rest time by determining the probability of insomnia of the user, thereby improving the accuracy of auxiliary sleep treatment methods;

[0060] (4) By dividing each treatment course into multiple sub-treatment courses and gradually changing the user's bed time within each sub-treatment course, the variation range of the user's bed time each time is further reduced to ensure the stability of the user's sleep quality;

[0061] (5) By eliminating the user's specific time periods every day, such as nighttime phone calls and late return that affect sleep, the impact of specific time periods on the user's sleep quality analysis is avoided, the objective accuracy of the user's sleep quality analysis is improved, and the effect of assisting the user's sleep is thereby improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 any creative work.

[0063] Figure 1 This is a flow chart of an active feedback system method for assisting sleep according to the present invention;

[0064] Figure 2 This is a flow chart of an active feedback system method for assisting sleep according to the present invention;

[0065] Figure 3 This is a flow chart of adjusting the bed rest time in a sleep-assisting active feedback system method of the present invention;

[0066] Figure 4 Another flow chart of an active feedback system method for assisting sleep according to the present invention;

[0067] Figure 5 This is an example diagram of an active feedback system for assisting sleep according to the present invention.

[0068] The numbers in the figure are: acquisition module-10, interaction module-20, monitoring module-30 and adjustment module-40. DETAILED DESCRIPTION

[0069] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0070] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections.

[0071] To simplify the drawings, only the parts relevant to the present invention are schematically depicted in each figure; they do not represent the actual structure of the product. Furthermore, to simplify the drawings and facilitate understanding, in some figures, only one component with the same structure or function is schematically depicted or labeled. As used herein, "one" refers not only to "only one" but also to "more than one."

[0072] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0073] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings and other embodiments can be obtained based on these drawings without inventive work.

[0075] One embodiment of the present invention, as Figure 1 As shown, the present invention provides a method for assisting active feedback of sleep, comprising the steps of:

[0076] During the first course of treatment, S100 collects the user's daily free sleep time and basic sleep parameters.

[0077] Specifically, this method is applied to the assisted sleep therapy process, which includes several sequentially arranged treatment courses. In the first treatment course, the user's daily free sleep time and related basic sleep quality parameters, such as deep sleep time, light sleep time, environmental noise, number of wake-ups, environmental humidity and environmental temperature, etc. are collected through the collection device.

[0078] S200 uploads the free sleep time and basic sleep parameters to the cloud server, and receives the user's bed time in the second treatment course from the cloud server based on the free sleep time and basic sleep parameters.

[0079] Specifically, after collecting the free sleep time and basic sleep parameters, the free sleep time and basic sleep parameters are uploaded to the cloud server or data background for data processing. The doctor can judge the user's appropriate bed rest time in the second course of treatment based on the user's free sleep time and basic sleep parameters in the first course of treatment recorded in the cloud server, or automatically generate the user's bed rest time in the second course of treatment through data processing through the data model stored in the cloud server.

[0080] S400 assists the user in sleeping according to the time spent in bed during the second treatment course, and monitors the user's sleep quality parameters during the second treatment course.

[0081] In the remaining treatment courses, S500 will actively adjust the user's bed time in the current treatment course based on the sleep quality parameters in the previous treatment course until the assisted sleep treatment process is completed.

[0082] The active feedback method for assisting sleep provided in this embodiment divides the assisting sleep treatment into multiple courses and changes the bed rest time in each course in stages, thereby avoiding drastic adjustments to the bed rest time that may cause excessive changes in the patient's sleep time, which in turn reduces the therapeutic effect of assisting sleep. At the same time, during the adjustment of the sleep time in each course, active feedback and adjustment are performed based on the sleep quality parameters in the previous course as a reference, thereby improving the objectivity and accuracy of the adjustment of the bed rest time during the assisting sleep process. The method is suitable for personalized customization of assisting sleep treatment for a group of users, thereby improving the effect and efficiency of assisting sleep treatment.

[0083] In one embodiment, Figure 2 As shown, before step S400 assists the user in sleeping according to the time spent in bed during the second course of treatment and monitors the sleep quality parameters of the user during the second course of treatment, the method further includes:

[0084] S310 receives a second free sleep time and a second basic sleep parameter input by a user, and uploads the second free sleep time and the second basic sleep parameter to a cloud server.

[0085] Specifically, the second free sleep time and the second basic sleep parameters input by the user can be actively input by the user according to his or her own sleep conditions, or the user can use auxiliary equipment to measure his or her own sleep conditions to generate and input the second free sleep time and the second basic sleep parameters. The free sleep time and basic sleep parameters of the user can also be collected for a small amount of time during the first course of treatment as the second free sleep time and the second basic sleep parameters to predict the user's free sleep time and basic sleep parameters for the remaining time in the first course of treatment.

[0086] S320 predicts the user's first free sleep time and first basic sleep parameter according to the second free sleep time and the second basic sleep parameter, and the first data model.

[0087] Specifically, a first data model is stored in the cloud server. The first data model is generated based on the first free sleep time and first basic sleep parameters of each user during the initial treatment course stored in the cloud server. The first data model is used to predict the user's free sleep time and basic sleep parameters every day during the first treatment course based on the existing first free sleep time and first basic sleep parameters.

[0088] S330 generates the bed time of the user in the second treatment course according to the second data model and the predicted first free sleep time and first basic sleep parameter of the user.

[0089] Specifically, a second data model is stored in the cloud server. The second data model is generated based on the first free sleep time, the first basic sleep parameters, and the bed time during the second treatment course of each user stored in the cloud server. The second data model is used to predict the user's bed time in the second treatment course based on the user's free sleep time and the basic sleep parameters in the first treatment course.

[0090] Furthermore, the second data model establishment process adopts the logistic regression method to establish a logistic regression model as the second data model based on the first free sleep time, the first basic sleep parameter and the bed time in the second treatment course of each user stored in the cloud server.

[0091] When predicting the specific process of the user's bed time in the second course of treatment, the logistic regression model is used to generate the insomnia probability for each corresponding bed time when the sleep time is the first free sleep time and the basic sleep parameters are the first basic sleep parameters. The bed time corresponding to the minimum insomnia probability is selected as the user's bed time in the second course of treatment.

[0092] The active feedback method for assisting sleep provided in this embodiment can predict the user's sleep time and basic sleep parameters in the first course of treatment based on the first data model and the second data model stored in the cloud server, as well as the sleep time and basic sleep parameters input by the user, and further calculate the user's bed time in the second course of treatment during the assisted sleep process, thereby significantly reducing the time required for the user to collect data in the first course of treatment, and can actively predict the user's bed time in the second course of treatment, thereby improving the efficiency of assisted sleep treatment.

[0093] In one embodiment, Figure 3 As shown, the present invention also provides a method for assisting active feedback of sleep, comprising the steps of:

[0094] During the first course of treatment, S100 collects the user's daily free sleep time and basic sleep parameters.

[0095] S200 uploads the free sleep time and basic sleep parameters to the cloud server, and receives the user's bed time in the second treatment course from the cloud server based on the free sleep time and basic sleep parameters.

[0096] S400 assists the user in sleeping according to the time spent in bed during the second treatment course, and monitors the user's sleep quality parameters during the second treatment course.

[0097] Specifically, the user's sleep time and basic sleep parameters during the second course of treatment are monitored. The basic sleep parameters include deep sleep time, light sleep time, environmental noise, number of wake-up times, environmental humidity and environmental temperature. The deep sleep time, light sleep time and number of wake-up times are converted into sleep depth coefficients, the environmental humidity and ambient temperature are converted into environmental coefficients through the naive Bayes model, and the environmental noise is converted into sound interference coefficients through the Markov model. Subsequently, the user's sleep quality parameters during the second course of treatment are generated based on the sleep depth coefficient, sleep time, environmental coefficient, sound interference coefficient and the preset sleep quality parameter model.

[0098] Furthermore, in the process of calculating the sleep quality parameters of the user in the second course of treatment, the user's corresponding bed time each day in the course of treatment can be divided into several preset time periods, the sleep quality parameters of the user in each preset time period are calculated, and the preset time periods that trigger the preset specific conditions are eliminated. The sleep quality parameters of the remaining preset time periods are integrated according to the composite probability formula to generate the sleep quality parameters corresponding to the user for each day in the second course of treatment. For example, if it is identified that the user has an interfering phone call or returns home late at night, the sleep quality parameter data of the day when this situation occurs will be eliminated from the sampled data. By eliminating the user's specific time periods every day, such as night phone calls disturbing the user, late return affecting sleep and other time periods, the influence of specific time periods on the user's sleep quality analysis is avoided, the objective accuracy of the user's sleep quality analysis is improved, and the effect of assisting the user to sleep is thereby improved.

[0099] S510 In each of the remaining treatment courses, if the sleep quality parameter in the previous treatment course is less than the preset sleep quality parameter threshold, the user's bed time in the current treatment course is adjusted according to the quartile method and the preset bed time adjustment model.

[0100] Specifically, all sleep quality parameters stored in the cloud server are sorted in advance from large to small, and the basic sleep parameters and bed time corresponding to the first quarter of the sleep parameters among all the sleep quality parameters are obtained according to the quartile algorithm, and used as the first expected basic sleep parameter and the first expected bed time respectively. The user's basic sleep parameter is compared with all the first expected basic sleep parameters, and the first expected basic sleep parameter with the least similarity to the user's basic sleep parameter among all the first expected basic sleep parameters is obtained as the second expected basic sleep parameter. Based on the bed time corresponding to the second expected basic sleep parameter, the bed time of the user in the current treatment course is adjusted, and the amplitude of the adjusted bed time of the user in the current treatment course does not exceed the preset adjustment amplitude.

[0101] For example, the cloud server stores historical sleep data of 10,000 users. The sleep data of the top 2,500 users with higher sleep quality parameters are extracted through the quartile method. The sleep depth coefficient, sleep time, environmental coefficient, and sound interference coefficient corresponding to the current user are compared with the historical data of the top 2,500 users. The sleep parameters in the historical data with the smallest similarity are judged to be the expected sleep parameters of the current user, and based on this, the current user's bed time is gradually adjusted.

[0102] S520: If the sleep quality parameter in the previous treatment course is not less than the preset sleep quality parameter threshold, the bed rest time in the previous treatment course is used as the bed rest time of the user in the current treatment course.

[0103] The active feedback method for assisting sleep provided in this embodiment discloses a specific solution for adjusting the time a user spends in bed. By adjusting the time a user spends in bed, the user's sleep quality is gradually made equal to that of users with good sleep quality among matched users, thereby improving the effect of assisting sleep therapy.

[0104] In one embodiment, Figure 4 As shown, after step S400 assists the user in sleeping according to the time spent in bed during the second course of treatment and monitors the sleep quality parameters of the user during the second course of treatment, the method further includes:

[0105] S610 divides each remaining treatment course into a number of sub-treatment courses arranged in sequence.

[0106] S620 In each of the remaining sub-treatment courses, the user's bed rest time in the current sub-treatment course is actively adjusted according to the sleep quality parameters in the previous sub-treatment course until the assisted sleep treatment process is completed.

[0107] Specifically, during the process of adjusting the time in bed, if the sleep quality parameter is less than the preset sleep quality parameter threshold, the time period can be increased by an equal time period based on the user's time in bed in the previous sub-treatment course, for example, by 20 minutes relative to the previous sub-treatment course, or the time period can be increased proportionally based on the user's time in bed in the previous sub-treatment course, for example, by 5% relative to the previous sub-treatment course.

[0108] The active feedback method for assisting sleep provided in this embodiment divides each treatment course into multiple sub-treatment courses, and gradually changes the user's bed rest time within each sub-treatment course, further reducing the variation range of the user's bed rest time each time, thereby ensuring the stability of the user's sleep quality.

[0109] In one embodiment, during the execution of step S620, after the bed rest time is adjusted within each sub-course, at the end of each course, the user's treatment effect evaluation parameters for the current course are introduced. Based on the treatment effect evaluation parameters and the bed rest time corresponding to the last sub-course in the current course, the bed rest time for the next course is generated. For example, the evaluation may include whether the bed rest time adjustment range is too large, the bed rest time adjustment range is too small, and the bed rest time adjustment range is appropriate. Based on the user's evaluation results, the bed rest time adjustment range within the course is changed.

[0110] In one embodiment, if the user's sleep quality parameter remains at a low level after four treatment sessions, the current sleep-assisted treatment is terminated, indicating that the current treatment is less suitable for the current user.

[0111] In one embodiment, Figure 5 As shown, the present invention also provides an active feedback system for assisting sleep, including a collection module 10 , an interaction module 20 , a monitoring module 30 and an adjustment module 40 .

[0112] The collection module 10 is used to collect the user's daily free sleep time and basic sleep parameters during the first course of treatment.

[0113] Specifically, this system is used to assist in the sleep therapy process, which includes several sequentially arranged treatment courses. During the first treatment course, the user's daily free sleep time and related basic sleep quality parameters, such as deep sleep time, light sleep time, environmental noise, number of wake-ups, environmental humidity and ambient temperature, etc., are collected through the collection device.

[0114] The interaction module 20 is connected to the acquisition module 10 and is used to upload the free sleep time and basic sleep parameters to the cloud server, and receive the user's bed time in the second treatment course from the cloud server based on the free sleep time and basic sleep parameters.

[0115] Specifically, after the acquisition module 10 collects the free sleep time and basic sleep parameters, the free sleep time and basic sleep parameters are uploaded to the cloud server or data background for data processing. The doctor can judge the appropriate bed rest time for the user in the second course of treatment based on the free sleep time and basic sleep parameters of the user in the first course of treatment recorded in the cloud server, or automatically generate the bed rest time for the user in the second course of treatment through data processing through the data model stored in the cloud server.

[0116] The monitoring module 30 is connected to the interaction module 20 and is used to assist the user in sleeping according to the time spent in bed during the second treatment course, and to monitor the user's sleep quality parameters during the second treatment course.

[0117] The adjustment module 40 is used to actively adjust the user's bed time in the current treatment course according to the sleep quality parameters in the previous treatment course in the remaining treatment courses until the auxiliary sleep treatment process is completed.

[0118] The active feedback system for assisting sleep provided in this embodiment divides the assisting sleep treatment into multiple courses and changes the bed rest time in each course in stages, thereby avoiding drastic adjustments in bed rest time that may cause excessive changes in the patient's sleep time, which in turn reduces the therapeutic effect of assisting sleep. At the same time, during the adjustment of the sleep time in each course, active feedback and adjustment are performed based on the sleep quality parameters in the previous course as a reference, thereby improving the objectivity and accuracy of the adjustment of the bed rest time during the assisting sleep process. The system is suitable for personalized customization of assisting sleep treatment for a group of users, thereby improving the effect and efficiency of assisting sleep treatment.

[0119] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0120] Those skilled in the art will appreciate that the units and steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] In the embodiments provided in this application, it should be understood that the disclosed active feedback method and system for assisting sleep can be implemented in other ways. For example, the above-described active feedback method and system embodiment for assisting sleep is merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or modules may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the communication connections shown or discussed may be through some interfaces, communication connections of devices or units or integrated circuits, which may be electrical, mechanical or other forms.

[0122] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0124] It should be noted that the above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An active feedback system for assisting sleep, characterized in that: Applied to assist in the sleep treatment process, the sleep treatment process includes several sequentially arranged treatment courses, including: A collection module is used to collect the user's daily free sleep time and basic sleep parameters during the first course of treatment; an interaction module connected to the acquisition module, configured to upload the free sleep time and the basic sleep parameters to a cloud server, and receive the bed rest time of the user in the second treatment course from the cloud server based on the free sleep time and the basic sleep parameters; a monitoring module, connected to the interaction module, configured to assist the user in sleeping according to the time spent in bed during the second treatment course, and to monitor the sleep quality parameters of the user during the second treatment course; an adjustment module, configured to actively adjust the user's bed rest time in the current treatment course according to the sleep quality parameters in the previous treatment course, until the assisted sleep treatment process is completed; The interaction module includes the following submodules: a model submodule, configured to generate a first data model based on the first free sleep time and first basic sleep parameters of each user during the initial treatment course stored in the cloud server; and further configured to generate a second data model based on the first free sleep time, the first basic sleep parameters, and the bed rest time during the second treatment course stored in the cloud server for each user; a prediction submodule, configured to receive a second free sleep time and a second basic sleep parameter input by the user, and upload the second free sleep time and the second basic sleep parameter to the cloud server; further configured to predict the first free sleep time and the first basic sleep parameter of the user based on the second free sleep time and the second basic sleep parameter and the first data model; and generate the bed rest time of the user in the second treatment course based on the second data model and the predicted first free sleep time and the first basic sleep parameter of the user; The model submodule is further configured to: use a logistic regression method to establish a logistic regression model as the second data model based on the first free sleep time, the first basic sleep parameter, and the bed rest time during the second treatment course of each user stored in the cloud server; The prediction submodule is further used to: generate, based on the logistic regression model, the insomnia probability for each of the bed resting times corresponding to the free sleep time being the first free sleep time and the basic sleep parameter being the first basic sleep parameter; and select the bed resting time corresponding to the minimum insomnia probability as the bed resting time of the user in the second treatment course.

2. The active feedback system for assisting sleep according to claim 1, characterized in that: The adjustment module is further configured to divide each of the remaining treatment courses into a plurality of sequentially arranged sub-treatment courses; and within each of the remaining sub-treatment courses, actively adjust the user's bed rest time within the current sub-treatment course based on the sleep quality parameters within the previous sub-treatment course until the assisted sleep therapy process is completed.

3. The active feedback system for assisting sleep according to claim 1, characterized in that: The monitoring module specifically includes: A data monitoring submodule, configured to monitor the user's sleep time and basic sleep parameters during the treatment course, wherein the basic sleep parameters include deep sleep time, light sleep time, ambient noise, number of wake-up times, ambient humidity, and ambient temperature; a coefficient conversion submodule, configured to convert the deep sleep time, the light sleep time, and the number of awakenings into a sleep depth coefficient using a naive Bayes model, convert the ambient humidity and the ambient temperature into an environmental coefficient, and convert the ambient noise into a sound interference coefficient using a Markov model; The parameter generation submodule is used to generate the sleep quality parameters of the user during the treatment course according to the sleep depth coefficient, the sleep time, the environment coefficient, the sound interference coefficient and a preset sleep quality parameter model.

4. The active feedback system for assisting sleep according to claim 3, characterized in that: The monitoring module further includes: A time division submodule, for dividing the user's bed rest time corresponding to each day in the treatment course into a number of preset time periods; The parameter generation submodule is further configured to calculate the sleep quality parameters of the user during each of the preset time periods; excluding the preset time periods that trigger the preset specific conditions, and integrating the sleep quality parameters of the remaining preset time periods according to a composite probability formula to generate the sleep quality parameters corresponding to each day of the user during the treatment course.

5. The active feedback system for assisting sleep according to any one of claims 3 or 4, characterized in that: The adjustment module is further configured to adjust the user's bed time in the current treatment course according to the quartile method and a preset bed time adjustment model if the sleep quality parameter in the previous treatment course is less than a preset sleep quality parameter threshold in each of the remaining treatment courses; The adjustment module is further configured to use the bed rest time in the previous treatment course as the bed rest time of the user in the current treatment course if the sleep quality parameter in the previous treatment course is not less than the preset sleep quality parameter threshold.

6. The active feedback system for assisting sleep according to claim 5, characterized in that: The adjustment module is specifically used to: pre-sort all the sleep quality parameters stored in the cloud server from large to small; obtain the basic sleep parameters and the bed time corresponding to the first quarter of the sleep quality parameters among all the sleep quality parameters according to the quartile algorithm, and use them as the first expected basic sleep parameter and the first expected bed time, respectively; and is also used to: compare the basic sleep parameters of the user with all the first expected basic sleep parameters, and obtain the first expected basic sleep parameter with the least similarity to the basic sleep parameters of the user among all the first expected basic sleep parameters as the second expected basic sleep parameter; and adjust the bed time of the user in the current treatment course based on the bed time corresponding to the second expected basic sleep parameter, so that the amplitude of the adjusted bed time of the user in the current treatment course does not exceed the preset adjustment amplitude.

7. The active feedback system for assisting sleep according to claim 1, characterized in that: The interaction module is further configured to receive, through the cloud server, the user's bed rest time during the second treatment course manually inputted from the backend.

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