A method and system for monitoring light and sleep and improving sleep and circadian rhythm

Abstract

The application discloses a method and system for monitoring light and sleep and improving sleep and circadian rhythm, and aims at the problems of single application scene and function, lagging theory and lack of deep analysis in existing sleep intervention methods, adopts sleep monitoring and light improvement services based on the non-visual effect theory of light, forms the cyclic regulation and feedback of sleep and light monitoring, analysis and evaluation and sleep intervention guidance, guides users to select appropriate lighting lamps, light exposure time and total amount, utilizes light which is strong synchronization and non-contact to improve the circadian rhythm and sleep of personnel, and thus the system can be suitable for the sleep habits of each user and keep a stable circadian rhythm.

Description

Technical Field

[0001] This invention belongs to the field of monitoring technology, specifically relating to a method and system for monitoring light and sleep and improving sleep and circadian rhythms. Background Technology

[0002] With the improvement of economic living standards and the enhancement of people's self-health awareness, people's requirements and standards for health are also constantly increasing. They hope to receive early warnings before the onset of disease symptoms and have a more comprehensive understanding of their own health status. Drug-based and contact-based methods for improving sleep have already undergone initial market trials. The environment, as a significant factor affecting sleep quality, can be directly intervened in using technological means.

[0003] Judging from the current form of related products on the market, whether in the field of sleep monitoring or sleep aids, the sleep industry currently faces the following problems:

[0004] (1) Limited application scenarios and functions: There is a serious homogenization of similar products in the market; products are concentrated in sports and fitness and information and entertainment. When it comes to specific product application functions, it is just a standard combination of "heart rate + pedometer + calories" supplemented by sleep monitoring. Sleep monitoring is only an added bonus and not a main function, so the sleep monitoring function becomes a gimmick and lacks professionalism.

[0005] (2) Theoretical lag: While products on the market aim to develop more accurate sleep monitoring methods, they neglect products that improve sleep. Drug-assisted sleep has drawbacks such as side effects from excessive use, leading to headaches, circadian rhythm disruption, and drug dependence. Non-drug interventions (furniture products, technology products, mobile apps) suffer from insufficient basic research in preventive medicine and health promotion. Objectively, sleep monitoring products' analysis of collected data remains superficial, failing to deeply understand the physiological significance and health symptoms represented by various monitoring indicators and data, let alone delve into the underlying health assessment and management value.

[0006] (3) Lack of in-depth data analysis: Most sleep monitoring devices usually use short and quick programmatic analysis to process the various monitoring data they collect. They lack in-depth mining and special analysis in the analysis and application of big data. Therefore, the application services they can provide are similar, without depth and without system.

[0007] Therefore, there is an urgent need for a method that relies on cutting-edge theories to improve sleep and maintain a stable circadian rhythm, addressing the problem of merely monitoring sleep without improvement or providing ineffective sleep aids. Light, as the most powerful timing factor, plays a crucial role in influencing and regulating human sleep and circadian rhythms. With advancements in basic research, the explanations of the molecular, physiological, and influencing mechanisms of light on circadian rhythms and sleep have become quite comprehensive. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention provides a method and system for monitoring light and sleep and improving sleep and circadian rhythms. Addressing the problems of limited application scenarios and functions, outdated theories, and lack of in-depth analysis in existing sleep intervention methods, this invention employs sleep monitoring and light improvement services based on the theory of non-visual effects of light. This forms a cyclical regulation and feedback mechanism for monitoring, analyzing, evaluating, and intervening in sleep and light, guiding users to select appropriate lighting fixtures, light exposure time, and total amount. By utilizing light—a highly synchronous and non-contact form—to improve circadian rhythms and sleep, this system can be adapted to the sleep habits of every user, maintaining a stable circadian rhythm.

[0009] The technical solution adopted by this invention to solve its technical problem includes the following steps:

[0010] Step 1: Collect sleep data and light exposure data of the person;

[0011] Step 2: Construct a data analysis model to analyze sleep data, light data, circadian rhythm data, and the impact of light on sleep data and circadian rhythm data;

[0012] Step 3: Provide data visualization reports, suggestions for improving sleep and circadian rhythms, and software for data and user management;

[0013] Step 4: Adjust the lighting equipment to expose people to light based on the data analysis results.

[0014] Preferably, the sleep data is collected by a mobile phone, wearable device, or non-wearable device.

[0015] Preferably, the light exposure data is collected by a mobile phone, a mobile phone spectrometer, or a professional spectrometer.

[0016] Preferably, the light exposure data includes the time distribution of light, spectral power distribution, lighting equipment model, spatial distribution of light, and outdoor light data.

[0017] Preferably, the data analysis model is used to determine the start and end times of sleep, sleep latency, number of awakenings during the night, divide sleep cycles, including REM, NREM, and wakefulness periods, and assess sleep quality.

[0018] Preferably, the data analysis model is used to convert the acquired image RGB spectral data into spectral data or directly obtain spectral data using a spectrometer, and then convert the spectral data into illuminance, relative color temperature, equivalent daylight illuminance (EDI), and rhythmic lighting (CL). A Lighting indicators and circadian rhythm stimulation CS indicators;

[0019] Preferably, the data analysis model uses the cumulative light exposure and threshold values ​​under different work, life, and sleep scenarios as evaluation criteria to assess the impact of light on human sleep and circadian rhythms.

[0020] Preferably, the EDI represents the sensitivity of each of the five types of photoreceptors in a person to the light spectrum, measured in lux, and the calculation method is specified in CIE standard S026 / E:2018.

[0021] Preferably, the rhythmic lighting CL A The calculation method is as follows:

[0022]

[0023] in,

[0024] by=∫S Cλ E λ dλ-k∫V Cλ E λ d λ

[0025] Constant 1548: Set to enable CL A Normalization to obtain the CL of blackbody radiation at 2856 K under 1000 lux conditions. A

[0026] The value is 1000;

[0027] k = 0.2616 E λ : Spectral irradiance of the light source;

[0028] a b-λ =0.21 M Cλ Melanopsis sensitivity is corrected based on lens transmittance.

[0029] a rod1 =2.30 S λ : Basic S-pyramidal cells;

[0030] a rod2 =1.60 mp λ : Macular pigment transmittance;

[0031] g1 = 1.00 V′λ Photometric luminous efficiency function;

[0032] g2 = 0.16 V λ Dark-view luminous efficiency function;

[0033] RodSat = 6.50 W / m 2

[0034]

[0035] Preferably, the method for calculating the CS of the diurnal rhythm stimulation is as follows:

[0036]

[0037] The duration factor t is a continuous variable ranging from 0.5 to 3.0 in hours, and the distribution factor f is a discrete variable taking values ​​of 0.5, 1, or 2.

[0038] Preferably, the software in step 3 includes user customization settings for personal lifestyle habits and sleep-related preferences, as well as management of user login and registration information; operation instructions for collecting light exposure data and sleep data, information statistics of collected data and curve display; reports on the impact of light on sleep and circadian rhythms, and improvement suggestions; lighting fixture recommendations and an online lighting fixture store, which can recommend lighting fixtures to users based on data analysis results.

[0039] Preferably, the lighting device establishes a communication connection with the software, and adjusts the lighting parameters, illumination duration, and spatial distribution of the lighting device according to the results of the data analysis model to improve the user's sleep and circadian rhythm.

[0040] A service system for monitoring light and sleep and improving sleep and circadian rhythms includes a sleep data acquisition unit, a light exposure data acquisition unit, a mobile terminal APP unit, a cloud platform unit, and a lighting equipment adjustment unit.

[0041] The sleep data acquisition unit uses a high-precision pressure sensor or a 3-axis accelerometer and 3-axis gyroscope based on MEMS technology to sense changes in body movement information during sleep, and analyzes the data to obtain relevant indicators of the person's sleep state.

[0042] The light exposure data acquisition unit, based on a machine learning model and an equivalent solar illuminance EDI model, uses a mobile phone or other spectral acquisition hardware to acquire indoor light image RGB data reflected from a spectral detection standard card when a person is exposed to light.

[0043] The mobile terminal APP unit is communicatively connected to the sleep data acquisition unit and the light exposure data acquisition unit, and includes a user management module, a light exposure data display module, a sleep data display module, a light exposure report module, and a sleep report module. The user management module handles user login, registration, data query, order query, and online store purchases. The light exposure data display module displays outdoor and indoor light exposure data, manages rooms, displays light exposure timelines, and retrieves daily weather data. The sleep data display module performs sleep tests, builds a sleep community, selects sleep aids, and monitors sleep. The light exposure report module compiles room names, EDI values, and spectral illumination indices for light exposure, generates light exposure timeline reports, and produces reports on the impact on sleep and circadian rhythms. The sleep report module assesses and scores sleep structure and quality.

[0044] The cloud platform unit is communicatively connected to the terminal APP unit. It receives, stores, and processes sleep data and light exposure data collected by the sleep data acquisition unit and the light exposure data acquisition unit, as well as user data from the mobile terminal APP unit. It generates corresponding safety thresholds for the sleep data and compares the received health data with these safety thresholds to analyze the health status of the monitored individual during sleep. If the safety threshold is exceeded, an alarm signal is generated and sent to the mobile terminal APP unit. For light exposure data, image data processing using machine learning regression algorithms converts the RGB information of the acquired image into spectral data. This spectral data is then input into an EDI model to obtain five EDI indicators of light exposure, including circadian rhythm lighting CL. A It also generates CS indexes for circadian rhythm stimulation and generates safety thresholds and cumulative light exposure for each user to the mobile terminal APP unit; it generates reports and lighting intervention plans based on the impact of light data indicators on sleep and circadian rhythm indicators to the mobile terminal APP unit and lighting equipment adjustment unit;

[0045] The lighting equipment adjustment unit is connected to the terminal APP unit and adjusts the illuminance, relative color temperature, spectrum, illumination duration, and spatial distribution of lighting equipment according to the lighting intervention scheme generated by the cloud platform unit.

[0046] The beneficial effects of this invention are as follows:

[0047] 1. This invention adopts a closed-loop sleep and light monitoring and light improvement service, which can form a cycle of monitoring, analysis and evaluation of sleep and light and sleep-aid intervention, and can guide users to select appropriate lighting fixtures, light exposure time and total amount;

[0048] 2. This invention can guide users to select appropriate lighting fixtures, light exposure time and total amount, and use light, a highly synchronous and non-contact form, to improve people's circadian rhythm and sleep, so that the system can be applied to each user's sleep habits and maintain a stable circadian rhythm. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the method of the present invention.

[0050] Figure 2 This is a structural diagram of the data analysis model of the present invention.

[0051] Figure 3 This is a block diagram illustrating the working principle of the service system of the present invention.

[0052] Figure 4 This is a structural diagram of the mobile terminal APP unit for invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] To address the shortcomings of existing technologies, a method and system for monitoring light and sleep and improving sleep and circadian rhythms are proposed to solve the technical problems existing in the background technology.

[0055] To address the aforementioned technical problems, this invention employs the following technical approach: a closed-loop sleep and light monitoring and light improvement service is adopted, which can form a cyclical adjustment and feedback of sleep and light monitoring, analysis and evaluation, and sleep-aid intervention. This can guide users to select appropriate lighting fixtures, light exposure time and total amount, and utilize light, a highly synchronous and non-contact form, to improve people's circadian rhythm and sleep. As a result, this system can be adapted to each user's sleep habits and maintain a stable circadian rhythm.

[0056] To achieve the above-mentioned technical objectives, the present invention specifically adopts the following technical solution:

[0057] A method for monitoring light and sleep and improving sleep and circadian rhythms includes:

[0058] Hardware devices for collecting data on human sleep and light exposure; models for analyzing sleep data, light data, circadian rhythm data, and the impact of light on sleep and circadian rhythm data; software for providing data visualization reports, suggestions for improving sleep and circadian rhythms, and for data and user management; and lighting devices for adjusting light exposure based on data analysis results.

[0059] The aforementioned sleep data can be collected by devices such as mobile phones, wearable devices (e.g., smart bracelets / watches) and non-invasive devices (e.g., pressure sensors, physiological polysaccharide (PSG) devices).

[0060] In the above-mentioned collection of light exposure data, devices such as mobile phones, mobile phone spectrometers, and professional spectrometers can be used to collect the temporal distribution of light (e.g., start and end times), spectral power distribution, lighting equipment models, and spatial distribution of light (e.g., exposure location, indoor light source distribution, indoor window distribution). Local outdoor light data can also be obtained through meteorological station databases.

[0061] The aforementioned data analysis model, specifically the sleep data analysis model, is used to determine sleep start and end times, sleep latency, number of awakenings during the night, divide sleep cycles (REM, NREM, and wakefulness), and assess sleep quality and other sleep indicators.

[0062] The aforementioned data analysis model is used to convert RGB spectral data of images collected by devices such as mobile phones into spectral data or directly obtain spectral data using a spectrometer, and then convert the spectral data into illuminance, relative color temperature, equivalent daylight illuminance (EDI), and rhythmic lighting (CL). A Illumination indicators such as lighting indicators and circadian rhythm stimulation (CS) indicators;

[0063] The data analysis model described above uses the cumulative light exposure and threshold values ​​in different scenarios such as work, life, and sleep as evaluation criteria for the impact on sleep and circadian rhythms.

[0064] The EDI mentioned above represents the sensitivity of each of the five types of photoreceptors in humans to the light spectrum, and the unit is lux. The calculation method is specified by CIE standard S026 / E:2018.

[0065] The above-mentioned rhythmic lighting (CL) A The calculation method is as follows:

[0066]

[0067] in,

[0068] by=∫S Cλ E λ dλ-k∫V Cλ E λ d λ

[0069] Constant 1548: Set to enable CL A Normalization to obtain the CL of blackbody radiation at 2856 K under 1000 lux conditions. A The value is 1000

[0070] k = 0.2616 E λ Spectral irradiance of the light source

[0071] a b-λ =0.21 M Cλ Melanopsis sensitivity (corrected based on lens transmittance)

[0072] a rod1 =2.30 S λ : Basic S-pyramidal cells

[0073] a rod2 =1.60 mp λ Macular pigment transmittance

[0074] g1 = 1.00 V′ λ Photometric luminous efficiency function

[0075] g2 = 0.16 V λ Dark-vision luminous efficiency function

[0076] RodSat = 6.50 W / m 2

[0077]

[0078] The above-mentioned method for calculating circadian rhythm stimulation (CS) is as follows:

[0079]

[0080] The duration factor t is a continuous variable (in hours) ranging from 0.5 to 3.0, while the distribution factor f is a discrete variable taking values ​​of 0.5, 1, or 2, depending on the spatial distribution of the scene's light source.

[0081] The software includes user customization settings for personal lifestyle habits and sleep preferences, as well as management of personal information such as user login and registration. It also includes operation guidelines for collecting light exposure data and sleep data, information statistics and curve display of collected data, reports on the impact of light on sleep and circadian rhythms, and improvement suggestions. Additionally, it includes lighting fixture recommendations and an online lighting fixture store, which can recommend suitable lighting fixtures to users based on data analysis results.

[0082] The aforementioned lighting equipment is connected to the aforementioned software, which can adjust the lighting parameters (such as illuminance, relative color temperature, and spectrum) as well as the duration of illumination and the spatial distribution of the lighting equipment based on the results of the aforementioned data analysis model, thereby improving the user's long-term sleep and circadian rhythm.

[0083] A service system for monitoring light and sleep and improving sleep and circadian rhythms includes:

[0084] The sleep data acquisition unit uses high-precision pressure sensors or hardware such as 3-axis accelerometers and 3-axis gyroscopes based on MEMS technology to sense changes in body movement and other information during sleep, and analyzes the data to obtain relevant indicators of the sleep state.

[0085] The light exposure data acquisition unit, based on a machine learning model and an equivalent daylight illuminance (EDI) model, uses a mobile phone or other spectral acquisition hardware to acquire indoor light image RGB data reflected from a spectral detection standard card when a person is exposed to light.

[0086] The mobile terminal APP unit is communicatively connected to the sleep data acquisition unit and the light exposure data acquisition unit. It includes a user management module, a light exposure data display module, a sleep data display module, and a light and sleep report module. The user management module is used to complete user login, user registration, user data query, user order query, and online store purchase. The light exposure data display module is used to display outdoor and indoor light data, room management, light exposure timeline, and obtain the weather data for the day. The sleep data display module is used to conduct sleep tests, build a sleep community, select sleep aids, and monitor sleep. The light report module is used to compile lighting indicators such as room name, EDI value, and spectrum of light exposure to form a light timeline report and generate a report on the impact on sleep and circadian rhythm. The sleep report module is used to evaluate and score sleep structure and sleep quality.

[0087] The cloud platform unit communicates with the terminal APP unit. It receives, stores, and processes sleep data and light exposure data collected by the sleep data acquisition unit and the light exposure data acquisition unit, as well as user data from the mobile terminal APP unit. It generates corresponding safety thresholds for sleep data and compares recently received health data with these safety thresholds to analyze the health status of the monitored subject during sleep. If the safety threshold is exceeded, an alarm signal is generated and sent to the mobile terminal APP unit. For light exposure data, image data processing using machine learning regression algorithms converts the RGB information of the acquired image into (hyper)spectral data. This spectral data is then input into the EDI model to obtain five EDI indicators of light exposure, including circadian rhythmic illumination (CL). A The system generates circadian rhythm stimulation (CS) indicators and generates individual safety thresholds and cumulative light exposure values ​​for each user to the mobile app unit. Based on the impact of light data indicators on sleep and circadian rhythm indicators, the system generates reports and lighting intervention plans to the mobile app unit and lighting equipment adjustment unit.

[0088] The lighting equipment adjustment unit communicates with the terminal APP unit and can adjust the illuminance, relative color temperature, spectrum, illumination duration, and spatial distribution of lighting equipment according to the lighting intervention plan generated by the cloud platform unit.

[0089] Example:

[0090] like Figure 1 As shown, a method for monitoring light and sleep and improving sleep and circadian rhythms includes:

[0091] Hardware devices for collecting data on human sleep and light exposure; models for analyzing sleep data, light data, circadian rhythm data, and the impact of light on sleep and circadian rhythm data; software for providing data visualization reports, suggestions for improving sleep and circadian rhythms, and for data and user management; and lighting devices for adjusting light exposure based on data analysis results.

[0092] The aforementioned sleep data can be collected by devices such as mobile phones, wearable devices (e.g., smart bracelets / watches) and non-invasive devices (e.g., pressure sensors, physiological polysaccharide (PSG) devices).

[0093] In the above-mentioned collection of light exposure data, devices such as mobile phones, mobile phone spectrometers, and professional spectrometers can be used to collect the temporal distribution of light (e.g., start and end times), spectral power distribution, lighting equipment models, and spatial distribution of light (e.g., exposure location, indoor light source distribution, indoor window distribution). Local outdoor light data can also be obtained through meteorological station databases.

[0094] The aforementioned data analysis model, specifically the sleep data analysis model, is used to determine sleep start and end times, sleep latency, number of awakenings during the night, divide sleep cycles (REM, NREM, and wakefulness), and assess sleep quality and other sleep indicators.

[0095] The aforementioned data analysis model is used to convert RGB spectral data of images collected by devices such as mobile phones into spectral data or directly obtain spectral data using a spectrometer, and then convert the spectral data into illuminance, relative color temperature, equivalent daylight illuminance (EDI), and rhythmic lighting (CL). A Illumination indicators such as lighting indicators and circadian rhythm stimulation (CS) indicators;

[0096] The data analysis model described above uses the cumulative light exposure and threshold values ​​in different scenarios such as work, life, and sleep as evaluation criteria for the impact on sleep and circadian rhythms.

[0097] The EDI mentioned above represents the sensitivity of each of the five types of photoreceptors in humans to the light spectrum, and the unit is lux. The calculation method is specified by CIE standard S026 / E:2018.

[0098] The above-mentioned rhythmic lighting (CL) A The calculation method is as follows:

[0099]

[0100] in,

[0101] by=∫S Cλ E λ dλ-k∫V Cλ E λ d λ

[0102] Constant 1548: Set to enable CL A Normalization to obtain the CL of blackbody radiation at 2856 K under 1000 lux conditions. A The value is 1000

[0103] k = 0.2616 E λ Spectral irradiance of the light source

[0104] a b-λ =0.21 M Cλ Melanopsis sensitivity (corrected based on lens transmittance)

[0105] a rod1 =2.30 S λ : Basic S-pyramidal cells

[0106] a rod2 =1.60 mp λ Macular pigment transmittance

[0107] g1 = 1.00 V′ λ Photometric luminous efficiency function

[0108] g2 = 0.16 V λ Dark-vision luminous efficiency function

[0109] RodSat = 6.50 W / m 2

[0110]

[0111] The above-mentioned method for calculating circadian rhythm stimulation (CS) is as follows:

[0112]

[0113] The duration factor t is a continuous variable (in hours) ranging from 0.5 to 3.0, while the distribution factor f is a discrete variable taking values ​​of 0.5, 1, or 2, depending on the spatial distribution of the scene's light source.

[0114] The software includes user customization settings for personal lifestyle habits and sleep preferences, as well as management of personal information such as user login and registration. It also includes operation guidelines for collecting light exposure data and sleep data, information statistics and curve display of collected data, reports on the impact of light on sleep and circadian rhythms, and improvement suggestions. Additionally, it includes lighting fixture recommendations and an online lighting fixture store, which can recommend suitable lighting fixtures to users based on data analysis results.

[0115] The aforementioned lighting equipment is connected to the aforementioned software, which can adjust the lighting parameters (such as illuminance, relative color temperature, and spectrum) as well as the duration of illumination and the spatial distribution of the lighting equipment based on the results of the aforementioned data analysis model, thereby improving the user's long-term sleep and circadian rhythm.

[0116] like Figures 2-4 As shown, a service system for monitoring light and sleep and improving sleep and circadian rhythms includes:

[0117] The sleep data acquisition unit uses high-precision pressure sensors or hardware such as 3-axis accelerometers and 3-axis gyroscopes based on MEMS technology to sense changes in body movement and other information during sleep, and analyzes the data to obtain relevant indicators of the person's sleep state; through software installed on a smartphone, the device's built-in hardware such as 3-axis accelerometers and 3-axis gyroscopes based on MEMS technology is used to monitor changes in body movement and other information during the user's sleep.

[0118] The light exposure data acquisition unit, based on machine learning models and equivalent daylight illuminance (EDI) models, uses a mobile phone or other imaging hardware to acquire indoor light image RGB data of the spectral detection standard card reflected when a person is exposed to light.

[0119] Under outdoor lighting conditions, light exposure monitoring acquires light data via a mobile application connected to a light sensor or a sensor built into a smartphone. This data can be processed and analyzed directly within the application, or it can be collected and uploaded via cloud data transmission. The application can upload light data to a cloud server via mobile network or Wi-Fi connection for subsequent data analysis and storage. The uploaded light data can then be sent to a data analysis model on the cloud server for further analysis and processing.

[0120] The mobile terminal APP unit is communicatively connected to the sleep data acquisition unit and the light exposure data acquisition unit. It includes a user management module, a light exposure data display module, a sleep data display module, and a light and sleep report module.

[0121] The user management module is used to complete operations such as user login, user registration, user data query, user order query, and online store purchase;

[0122] The light exposure data display module is used to display outdoor and indoor light exposure data, manage rooms, display light exposure timelines, and obtain the day's weather data. The sleep data display module is used for sleep testing, building a sleep community, selecting sleep aids, and sleep monitoring. These applications use different types of sensors to collect sleep data and use analysis algorithms to transform the data into reports and charts about sleep quality. These reports and charts typically display sleep cycles, sleep quality indicators (such as sleep onset time, number of awakenings, sleep duration, etc.), sleep depth, and other information to help users understand their sleep patterns. The applications also provide personalized suggestions and tips to improve sleep quality, such as establishing a regular sleep schedule, relaxation techniques, or bedtime habits.

[0123] The light exposure report module is used to collect lighting indicators such as room name, EDI value and spectrum of light exposure to form a light exposure timeline report and generate a report on the impact on sleep and circadian rhythm. The sleep report module is used to evaluate and score sleep structure and sleep quality.

[0124] The mobile terminal APP described in this invention mainly includes four aspects: light exposure, sleep, improvement, and user and system information.

[0125] Regarding lighting, it is necessary to obtain the user's individual light exposure information. This invention divides the environment into two types: indoor and outdoor.

[0126] The outdoor environment assessment determines outdoor sunlight exposure. The specific steps involve obtaining the location's latitude and longitude and inputting it into the meteorological station's database API interface. On one hand, this retrieves local solar radiation (sunlight intensity), providing data at time intervals such as the past year, month, seven days, current day, current time, the next two hours, and the next two days, displaying it to the user as a curve or graph. Editing is then complete, and the file is saved. On the other hand, weather conditions are obtained, and sun protection recommendations are provided.

[0127] The indoor environment section determines the indoor light exposure. The specific steps are as follows: the initial step is to create a room. After creation, room information can be viewed and the room's length, width, and height data can be edited. The current room lighting conditions are recorded, and the primary light type is selected; this application categorizes it into natural light and artificial light.

[0128] In the natural light scenario, window information needs to be recorded, including orientation and size. Then, the distance between the main activity area and the window needs to be selected. Next, the local solar radiation and weather conditions need to be entered. Then, the data is converted into spectral data and black-view EDI illuminance values, which are displayed to the user as curves or numerical values. Finally, the editing is completed and the file is saved.

[0129] In the case of artificial light, on the one hand, it is necessary to select the main activity area. Then, the application will pop up a shooting guide, open the camera permission, take a photo of the standard version or white paper, save the photo, upload it to the cloud, and then use the RGB to convert the spectrum machine learning algorithm to obtain the spectral data. The spectral data is converted into black-view EDI illuminance value by the CIE EDI model and displayed to the user in the form of curve or numerical value. Then, the editing is finished and the file is saved.

[0130] On the other hand, you need to input the type of lighting product. You can choose to input directly or search for input to obtain data on that type of lighting. Input the information of the lighting, including quantity, height, and installation method. Then, select the main activity area, calculate the spectral data, calculate the black-view EDI illuminance value, and display it to the user in the form of a curve or numerical value. End editing and save the file.

[0131] Regarding lighting, it is also necessary to define the user's lighting timeline. The specific steps are: select the room number, select the activity (sleep, work, or other), enter the activity time in that room, repeat this process according to the activity room number, complete the timeline for the day, display it to the user, end the editing, and save it to the user's lighting exposure report;

[0132] Regarding sleep, this application will be divided into three functional modules: obtaining the user's personal sleep data, obtaining strong factors and events that affect sleep, and sleep aids.

[0133] In terms of obtaining a user's personal sleep information, the specific steps are: open the sleep recording module, start sleep recording, call the SLEEP API to record the user's sleep data, end sleep recording, obtain the user's sleep data for that period, display it to the user, and store it in the user's sleep report.

[0134] In terms of identifying strong factors affecting sleep, the specific steps are: select the type, enter the start time, enter the duration, and end editing;

[0135] Regarding sleep aids, the specific steps are to select a sleep aid method, which involves two methods: music and scent.

[0136] In terms of improvements, this application will be divided into three functional modules: viewing reports, getting lighting improvement suggestions, and the store;

[0137] In terms of viewing reports, this includes selecting the report type (including sleep reports and light exposure reports) or editing the report;

[0138] Regarding suggestions for improving lighting, this includes two aspects: the impact of light on sleep and the impact of light on work. For the former, there are two scenarios: daytime indoor lighting and nighttime lighting. Nighttime lighting is further divided into pre-sleep lighting, pre-wake lighting, and post-wake lighting. For the latter, suggestions for workplace lighting are provided.

[0139] In terms of stores, this includes recommendations for lighting fixtures, sleep-aiding music, sleep-aiding aromatherapy tablets, and other sleep-aiding products such as medications and bedding.

[0140] Regarding user information and system information, the former includes information such as account and membership information, while the latter includes system messages and software information.

[0141] The lighting equipment adjustment unit communicates with the terminal APP unit and can adjust the illuminance, relative color temperature, spectrum, illumination duration, and spatial distribution of lighting equipment according to the lighting intervention plan generated by the cloud platform unit.

[0142] The cloud platform unit communicates with the terminal APP unit. It receives, stores, and processes sleep data and light exposure data collected by the sleep data acquisition unit and the light exposure data acquisition unit, as well as user data from the mobile terminal APP unit. It generates corresponding safety thresholds for sleep data and compares recently received health data with these safety thresholds to analyze the health status of the monitored subject during sleep. If the safety threshold is exceeded, an alarm signal is generated and sent to the mobile terminal APP unit. For light exposure data, image data processing using machine learning regression algorithms converts the RGB information of the acquired image into (hyper)spectral data. This spectral data is then input into the EDI model to obtain five EDI indicators of light exposure, including circadian rhythmic illumination (CL). A The system generates circadian rhythm stimulation (CS) indicators and generates individual safety thresholds and cumulative light exposure values ​​for each user to the mobile app unit. Based on the impact of light data indicators on sleep and circadian rhythm indicators, the system generates reports and lighting intervention plans to the mobile app unit and lighting equipment adjustment unit.

[0143] Cloud-based data analytics leverages powerful computing capabilities and algorithms to process massive amounts of sleep and light exposure data, generating statistical information and reports on sleep and light exposure. These analyses can include trends, averages, and peak values ​​of lighting, sleep, and circadian rhythm indicators, as well as their correlation with factors such as time and location. Combining mobile applications and cloud-based data analytics, users can monitor and view their sleep and light exposure in real time. The application provides real-time updates of light data, along with charts and graphs to help users intuitively understand changes in sleep and light exposure. Furthermore, the application can set alarms and reminders, sending notifications when lighting or sleep indicators exceed or fall below set thresholds, allowing users to take appropriate actions, such as avoiding excessive exposure to strong light or increasing light exposure time. Through cloud-based data analytics, light exposure data can be stored long-term on cloud servers, forming a user's historical record. Users can access the application or view past light exposure data at any time through a web interface to understand their light exposure in different time periods and environments. This historical record helps users track light exposure trends, assess their impact on health and activity, and develop appropriate preventative measures and behavioral adjustments.

[0144] Using image data of light environment reflection on a standard card collected by a mobile device in a light-exposed environment, and employing a machine learning regression algorithm to perform spectral inversion to obtain spectral data, can be achieved through the following steps:

[0145] First, obtain reference spectral data: This requires acquiring reference spectral data corresponding to the RGB signals. This can be achieved by using specialized equipment such as a spectrometer to measure the spectrum of each color patch on the standard card under known illumination conditions. The spectral data of each patch will then be correlated with its corresponding RGB value.

[0146] Based on the above technical solution, further, a standard card is prepared: a standard card is a card with known colors and optical properties. When conducting light exposure monitoring, a suitable standard card is selected as a reference. A standard card typically contains a series of patches of known colors, each patch having a unique RGB value;

[0147] Based on the above technical solution, further, the image to be converted is captured: the image to be converted is captured using a camera device (such as a mobile phone camera or a professional camera), ensuring that the lighting conditions during the capture are the same as the lighting conditions when the reference spectral data is obtained, and the standard card should be at the same or similar distance and angle as the light being measured, and shadows and reflections should be avoided as much as possible;

[0148] Based on the above technical solution, further, image correction involves uploading the captured image data to the cloud for image processing, extracting the standard card region from the image, and then correcting and preprocessing the captured image. This includes steps such as white balance calibration, color correction, and noise reduction to reduce noise and interference in the image, ensuring image accuracy and consistency. Calibration can be performed using the known RGB values ​​of the standard card patch, mapping the pixel values ​​in the image to the actual color space.

[0149] Based on the above technical solution, further, RGB data is extracted: After image correction, the RGB data of the standard card patch area can be extracted. By sampling and analyzing the pixel values ​​of each patch, the corresponding RGB values ​​are obtained;

[0150] Based on the above technical solution, further data analysis and recording are performed: the extracted RGB data is further analyzed and recorded. The average RGB value of each patch can be calculated and compared with the known RGB values ​​of a standard card. This allows for the evaluation of the reflectivity characteristics of the standard card under the tested illumination.

[0151] Based on the above technical solution, further, the spectral inversion algorithm: uses spectral imaging technology and an inversion algorithm to convert the corrected RGB signal into spectral data. This process involves comparing and matching the RGB signal with reference spectral data to determine the spectral characteristics of each pixel and inferring the intensity or quality of light exposure;

[0152] Based on the above technical solution, further spectral data analysis is performed: the extracted spectral data is analyzed further. Peak wavelengths, intensity distributions, and color parameters can be calculated. To convert the spectral data into photometric model data, photometric models and related algorithms specified by the International Commission on Illumination (CIE) can be used for processing to obtain more detailed lighting indicators.

Claims

1. A method for monitoring light exposure and sleep and improving sleep and circadian rhythms, characterized in that, Includes the following steps: Step 1: Collect sleep data and light exposure data of the person; Step 2: Construct a data analysis model to analyze sleep data, light data, circadian rhythm data, and the impact of light on sleep data and circadian rhythm data. This data analysis model is used to determine sleep onset and end times, sleep latency, and the number of awakenings during the night; to divide sleep cycles, including REM, NREM, and wakefulness periods; and to assess sleep quality. The data analysis model is used to convert the collected RGB spectral data of images into spectral data or directly obtain spectral data using a spectrometer, and then convert the spectral data into illuminance, relative color temperature, equivalent daylight illuminance (EDI), and circadian lighting CL. A Lighting index and circadian rhythm stimulation CS index; the data analysis model uses the cumulative light exposure and threshold under different work, life and sleep scenarios as the evaluation criteria for the impact of light on human sleep and circadian rhythm. The rhythmic lighting CL A The calculation method is as follows: in, The method for calculating the CS of the diurnal rhythm stimulation is as follows: Among them, duration factor t It is a continuous variable ranging from 0.5 to 3.0, expressed in hours, with a distribution factor of... f It is a discrete variable that takes the value 0.5, 1, or 2; Step 3: Provide data visualization reports, suggestions for improving sleep and circadian rhythms, and software for data and user management; Step 4: Adjust the lighting equipment to expose people to light based on the data analysis results.

2. The method for monitoring light and sleep and improving sleep and circadian rhythm according to claim 1, characterized in that, The sleep data was collected from mobile phones, wearable devices, and non-wearable devices.

3. The method for monitoring light and sleep and improving sleep and circadian rhythms according to claim 1, characterized in that, The light exposure data was collected by mobile phones, mobile phone spectrometers, and professional spectrometers; the light exposure data includes the time distribution of collected light, spectral power distribution, lighting equipment model, spatial distribution of light, and outdoor light data.

4. The method for monitoring light and sleep and improving sleep and circadian rhythms according to claim 3, characterized in that, The equivalent solar illuminance (EDI) represents the sensitivity of each of the five photoreceptors in a person to the light spectrum, measured in lux, as specified in CIE standard S026 / E:2018.

5. The method for monitoring light and sleep and improving sleep and circadian rhythm according to claim 1, characterized in that, The software in step 3 includes user customization settings for personal lifestyle habits and sleep-related preferences, as well as management of user login and registration information; operation instructions for collecting light exposure data and sleep data, information statistics of collected data, and curve display; Report on the effects of light on sleep and circadian rhythms, along with recommendations for improvement; The lighting fixture recommendation and online lighting fixture store can recommend lighting fixtures to users based on data analysis results.

6. The method for monitoring light and sleep and improving sleep and circadian rhythms according to claim 1, characterized in that, The lighting equipment establishes a communication connection with the software, and adjusts the lighting parameters, illumination duration, and spatial distribution of the lighting equipment based on the results of the data analysis model to improve the user's sleep and circadian rhythm.

7. A service system for monitoring light and sleep and improving sleep and circadian rhythms using the method described in claim 1, characterized in that, It includes a sleep data acquisition unit, a light exposure data acquisition unit, a mobile terminal APP unit, a cloud platform unit, and a lighting equipment adjustment unit; The sleep data acquisition unit uses a high-precision pressure sensor or a 3-axis accelerometer and 3-axis gyroscope based on MEMS technology to sense changes in body movement information during sleep, and analyzes the data to obtain relevant indicators of the person's sleep state. The light exposure data acquisition unit, based on a machine learning model and an equivalent solar illuminance EDI model, uses a mobile phone or other spectral acquisition hardware to acquire indoor light image RGB data reflected from a spectral detection standard card when a person is exposed to light. The mobile terminal APP unit is communicatively connected to the sleep data acquisition unit and the light exposure data acquisition unit, and includes a user management module, a light exposure data display module, a sleep data display module, a light report module, and a sleep report module. The user management module is used to complete user login, user registration, user data query, user order query, and online store purchase operations; The light exposure data display module is used to display outdoor and indoor light data, room management, light exposure timeline, and obtain the weather data for the day; The sleep data display module is used for sleep testing, building a sleep community, selecting sleep aids, and sleep monitoring. The light exposure report module is used to collect statistics on the room name, EDI value, and spectral illumination index of the room with light exposure, generate a light exposure timeline report, and generate a report on the impact on sleep and circadian rhythm; the sleep report module is used to assess and score sleep structure and sleep quality; The cloud platform unit is communicatively connected to the terminal APP unit. It is used to receive, store and process sleep data and light exposure data collected by the sleep data acquisition unit and the light exposure data acquisition unit, as well as user data from the mobile terminal APP unit. It generates corresponding safety thresholds for sleep data, compares the received health data with the safety thresholds to analyze the health status of the monitored person during sleep, and generates an alarm signal to the mobile terminal APP unit when the safety threshold is exceeded. Image data processing using machine learning regression algorithms on illumination data converts the RGB information of the image into spectral data. This spectral data is then input into an EDI model to obtain five EDI indices for illumination, including rhythmic lighting CL. A It also generates circadian rhythm stimulation CS index and generates safety thresholds and cumulative light exposure for each user to the mobile terminal APP unit; Based on the impact of light data indicators on sleep and circadian rhythm indicators, reports and lighting intervention plans are generated and sent to the mobile terminal APP unit and the lighting equipment adjustment unit. The lighting equipment adjustment unit is connected to the terminal APP unit and adjusts the illuminance, relative color temperature, spectrum, illumination duration, and spatial distribution of lighting equipment according to the lighting intervention scheme generated by the cloud platform unit.

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