WO2020140913A1 - Appareil et procédé de traitement de données, dispositif électronique et support de stockage - Google Patents

Appareil et procédé de traitement de données, dispositif électronique et support de stockage Download PDF

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Publication number
WO2020140913A1
WO2020140913A1 PCT/CN2019/130837 CN2019130837W WO2020140913A1 WO 2020140913 A1 WO2020140913 A1 WO 2020140913A1 CN 2019130837 W CN2019130837 W CN 2019130837W WO 2020140913 A1 WO2020140913 A1 WO 2020140913A1
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data
sleep
target user
model
light
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PCT/CN2019/130837
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English (en)
Chinese (zh)
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郑智民
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中国移动通信有限公司研究院
中国移动通信集团有限公司
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Publication of WO2020140913A1 publication Critical patent/WO2020140913A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M21/02—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/10—Controlling the light source
    • H05B47/105—Controlling the light source in response to determined parameters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0044—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the sight sense
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/33—Controlling, regulating or measuring
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2230/00—Measuring parameters of the user
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
    • Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection

Definitions

  • the present disclosure relates to the field of information technology but is not limited to the field of information technology, in particular to a data processing method and device, electronic equipment, and storage medium.
  • the biological effects of light on people can be divided into visual effects and non-visual effects.
  • the visual effect is mainly composed of cone cells in the retina, which senses the luminosity and color;
  • the non-visual effect is mainly composed of the pineal cells of the pineal gland, which senses the light, generates bioelectricity, affects the sympathetic nerves, and innervates the pineal gland cells to release melatonin
  • hormone secretion decreases, metabolism slows down, and natural sleep is induced.
  • Embodiments of the present disclosure are expected to provide a data processing method and device, electronic equipment, and storage medium.
  • the technical solution of the present disclosure is implemented as follows:
  • a first aspect of an embodiment of the present disclosure provides a data processing method, including:
  • controlling the light emission of the light-emitting device includes: controlling the light-emitting device to emit light that promotes the sleep of the target user, or controlling the light-emitting device to suppress the emission The light that the target user sleeps.
  • the selection of a regular sleep model or an irregular sleep model as the target model according to the current sleep state of the target user includes at least one of the following:
  • the irregular sleep model is selected as the target model
  • the regular sleep model is selected as the target model.
  • the method further includes:
  • the target model is a non-sleep regular model, acquire irregular sleep data of the target user
  • the environmental data, the characteristic data and the irregular sleep data are input into the irregular sleep model to obtain the lighting control parameters.
  • the irregular sleep data includes at least one of the following:
  • Time zone deviation data of the target user's time zone
  • the acquiring environmental data includes at least one of the following:
  • the acquiring the dynamic feature data of the user includes at least one of the following:
  • the method further includes:
  • the third characteristic data and the environment data are input to the target model of the target user to obtain the lighting control parameter.
  • the parsing the second feature data to obtain third feature data characterizing the state of the target user includes:
  • time-domain feature data includes: a characteristic curve corresponding to the second feature data in the time domain Peak data and/or trough data;
  • Parsing the second feature data extracting the frequency domain feature data of the target user from the second feature data; wherein, the frequency domain feature data includes: a characteristic curve corresponding to the second feature data in the frequency domain Peak data and/or trough data.
  • the first noise data includes: jitter data with a jitter frequency of the acquisition device outside the preset frequency range, electromagnetic interference data with an electromagnetic frequency outside the preset frequency range, and electromagnetic frequency Magnetic field noise outside the set range;
  • the second noise data includes: jitter data whose jitter frequency of the acquisition device is within the preset frequency range.
  • the inputting of the environmental data and the characteristic data into the selected target model to obtain lighting control parameters includes:
  • the dimensionality reduction processing strategy performing dimensionality reduction processing on the third feature data and environment data to obtain input data of a preset dimension
  • performing the dimensionality reduction processing on the third feature data and the environmental data according to the dimensionality reduction processing strategy to obtain input data of a preset dimension includes:
  • If the target user is in an action state determine whether the target user is in the first type of partial static state according to the action feature data
  • the target user's action feature data is sampled according to the target user's current action state to obtain sampling feature data as the input data.
  • performing dimensionality reduction processing on the third feature data and environment data to obtain input data of a preset dimension further including:
  • the determination of whether the target user is in the first type of partial static state and the second type of partial static state is stopped;
  • the determination of whether the target user is in the second type of local stationary state is stopped.
  • the method further includes:
  • the effect data includes: at least one of the target user's sleep effect data, sleep activity data, and non-sleep activity data;
  • the regular sleep model and/or the irregular sleep model are optimized.
  • a second aspect of an embodiment of the present disclosure provides a data processing apparatus, including:
  • the first obtaining module is configured to obtain environmental data
  • the second obtaining module is configured to obtain the current state data and characteristic data of the target user
  • a selection module configured to select a regular sleep model or an irregular sleep model as the target model based on the current state data
  • a third acquisition module configured to input the environmental data and the characteristic data into the selected target model to obtain lighting control parameters
  • the control module is configured to use the light control parameters to control the light emission of the light emitting device, wherein the controlling the light emission of the light emitting device includes: controlling the light emitting device to emit light that promotes the sleep of the target user, or controlling the light emission The device emits light that suppresses the sleep of the target user.
  • a third aspect of the embodiments of the present disclosure provides an electronic device, including:
  • a processor connected to the memory, is configured to implement the data processing method provided by the foregoing one or more technical solutions by executing computer-executable instructions located on the memory.
  • a fourth aspect of an embodiment of the present disclosure provides a computer storage medium that stores computer-executable instructions; after the computer-executable instructions are executed, the data processing method provided by the foregoing one or more technical solutions can be provided.
  • the technical solution provided by the embodiments of the present disclosure will acquire environmental data, current state data and characteristic data of the target user, and first determine whether to use the regular sleep model or the irregular sleep model as the target model according to the current state data to form the light control parameters , In this way, the lights can be controlled according to whether the user is currently sleeping regularly or irregularly, thereby providing better light control that is beneficial to inhibit or promote sleep, regardless of whether the current target user's sleep is regular or irregular , Can achieve accurate light control, when the target user needs to promote sleep to emit light that promotes sleep, and needs to suppress the emission of light that suppresses sleep during sleep, in order to use light to accurately adjust the target user's sleep.
  • FIG. 1 is a schematic flowchart of a first data processing method provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of a second data processing method provided by an embodiment of the present disclosure
  • FIG. 3 is a schematic structural diagram of a data processing device according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic flowchart of a third data processing method provided by an embodiment of the present disclosure.
  • FIG. 5 is a schematic flowchart of a method for providing sleep adjustment for irregular sleep according to an embodiment of the present disclosure
  • FIG. 6 is a schematic structural diagram of an electronic device provided by implementation of the present disclosure.
  • FIG. 7 is a schematic diagram of an irregular sleep model provided by an embodiment of the present disclosure to control a light emitting device to emit light.
  • N1 light sleep stage (1200-7000 Lux, N4 before auto-awakening, 2500-10000lux at the REM stage
  • melatonin with circadian rhythm, secrete up to midnight Peak
  • melatonin helps to deepen the depth of sleep (the proportion of REM and N4), change the sleep-wake rhythm, adjust the physiological clock, and adjust the quality of sleep.
  • genetic studies have shown that non-visual effects are maximum at 480-485nm, and visible light is different
  • the sensitivity of the wavelength is also inconsistent, it has the highest sensitivity to yellow-green light, but the sensitivity to red light, blue light, and violet light is very low.
  • the three colors of red, green, and blue light respectively suppress the melatonin under the irradiation of 1000 lx
  • the red light is very small, the green light is the largest, and the blue light is slightly lower.
  • the irradiation of light will significantly increase the heart rate.
  • Humans are most sensitive to light between 480 and 485 nm. Different people are affected by the yellow pigment in the central macula area of the human eye. With age, the visual crystals turn yellow, which will cause individual differences. However, people with different ages and sleep constitutions may enter different sleep stages at different times.
  • the sensitivity of light is also different, only according to the time when the general population enters each sleep stage, setting the same sleep aid light adjustment mechanism for everyone. It may instead affect sleep, such as elderly people who sleep lightly, and do not enter light sleep at 24:00, may instead Sleep is affected.
  • the intensity required for irregular sleep and normal sleep is not the same, the individual differences between irregular sleep and normal sleep, such as how many consecutive hours of irregular sleep, the length of irregular sleep, irregular sleep The recent appearance of ratings, etc., is also an important consideration for training differential models.
  • Step S110 Acquire environmental data
  • Step S120 Obtain the current state data and characteristic data of the target user
  • Step S130 Select a regular sleep model or an irregular sleep model as the target model according to the current state data
  • Step S140 Input the environmental data and the characteristic data into the selected target model, and the regular sleep model and the irregular sleep model obtain the light control parameters;
  • Step S150 Using the light control parameters to control the light emission of the light emitting device, wherein the controlling the light emission of the light emitting device includes: controlling the light emitting device to emit light that promotes the sleep of the target user, or controlling the light emitting device to emit Suppressing the light of sleep of the target user.
  • the data processing method provided in this embodiment can be applied to various electronic devices, for example, user equipment or a home gateway.
  • the user equipment may be: a user's mobile phone, tablet computer or wearable device. After obtaining the light control parameters, these devices control the light-emitting devices to emit light, or control themselves to emit light.
  • the light-emitting device may be various devices capable of emitting light, and may be the user device itself, or a light-emitting device at a location of a target user such as a bedroom or a living room.
  • the electronic device first obtains environmental data, which represents the environmental conditions of the space where the target user is currently located. For example, collecting environmental data through environmental sensors.
  • the characteristic data of the target user is obtained, and the characteristic data may constitute an individual static portrait of the target user, an individual dynamic portrait of the target user, a sign portrait of the target user, an emotional portrait of the target user, and so on.
  • the user feature data may include: feature data that is collected, and also feature data that is dynamically collected, for example, feature data that is dynamically collected using a physiological sensor, and the sign data includes but is not limited to: the heartbeat data of the target user, the target user’s Brain wave data, pulse data of target users, etc.
  • the current state parameter may at least represent whether the target user is currently in a sleep state or a non-sleep state.
  • the current state parameter can also be used to characterize the sleep stage or sleep depth of the target user in the sleep state; the current state parameter can also be used to characterize the degree of awake of the target user in the non-sleep state, etc. parameter.
  • a target user corresponds to two models, namely a regular sleep model and an irregular sleep model.
  • the regular sleep model may be used to form the light control parameter when the target user's current sleep state is regular sleep; the irregular sleep model may be used to form the light control parameter when the target user's current sleep state is irregular sleep.
  • step S140 corresponding light control parameters can be formed according to whether the current sleep state of the target user is regular sleep, so as to achieve precise control of the light.
  • the environmental data and characteristic data are used as the input of the target model, which can be
  • the light control parameters are obtained and used to control the light emission of the light emitting device.
  • the light control parameter includes at least one of the following:
  • the color control parameter of the light is used to control the color of the light.
  • the color control parameter may include: a light emitting wavelength, and different wavelengths correspond to different colors of light;
  • the brightness control parameter of the light is used to control the brightness of the light emitting device.
  • the brightness control parameter may include: a light value.
  • the brightness of the light changes control parameters.
  • the brightness of the light changes to promote user sleep or inhibit user sleep. For example, in the morning, the light may be getting brighter and brighter; when falling asleep at night, the brightness of the light is getting darker and darker; and the color of the light may need to change from warm tones when it is necessary to wake up the target user
  • the lights gradually switch to cool-tone lights; and you may need to gradually switch from cool-tone lights to warm-tone lights before falling asleep.
  • the lighting angle control parameter of the light is used to control the lighting angle of the light; if the light is directly irradiated to the target user's field of view, it may inhibit sleep, and the side illumination may help the user to sleep, so the light control parameter also includes illumination Angle control parameters.
  • the environmental data and the characteristic data are input to the regular sleep model and the irregular sleep model set by the target user.
  • the light control parameters of the target user are obtained;
  • the lighting control is performed for each user, thereby ensuring the lighting control that meets the user's personality.
  • the step S130 may include at least one of the following:
  • the irregular sleep model is selected as the target model
  • the regular sleep model is selected as the target model.
  • the target user at the current time should be in a sleep state, but the target user of the current state data is still in a non-sleep state, indicating that it is currently suitable to use an irregular sleep model to form the light control parameter.
  • the current time is the user's sleep time but the time zone of the target user has changed. It can also be considered that it does not conform to the sleep rule corresponding to the regular sleep model. It is necessary to select the non-sleep rule as The target model.
  • the irregular sleep model is selected as the target model, otherwise the regular sleep model is selected as the target model.
  • the method further includes:
  • the target model is a non-sleep regular model, acquire irregular sleep data of the target user
  • the step S140 may include: inputting the environmental data, the characteristic data, and the irregular sleep data into the irregular sleep model to obtain the light control parameter.
  • the irregular sleep data may include various data representing the difference between the irregular sleep state and the regular sleep state.
  • the irregular sleep model can combine irregular sleep data to determine whether it is necessary to suppress sleep or promote sleep.
  • the irregular sleep model can combine the environmental data and the characteristic data to determine the specific lighting control parameters required to promote sleep or inhibit sleep.
  • the irregular sleep data includes at least one of the following:
  • Sleep time deviation data into sleep for example, the target user usually falls asleep at 10:00 pm, the current time is already 1:15 the next day, the target user has not yet entered sleep, the time difference between the two, 2:15 can be used as One of the time deviation data;
  • Time zone deviation data of the target user's time zone for example, the target user is flying from Beijing to London. Due to the difference between Beijing's time zone and London's time zone, the time zone deviation data can also be used as irregular sleep data in China;
  • Continuous state data of this irregular sleep may include at least one of the following: duration of the target user's irregular sleep, duration of the target user's irregularly maintained non-sleep state, and target user's irregularly maintained sleep state The length of time
  • the frequency of occurrence of irregular sleep may include at least one of the following: the number and/or frequency of occurrence of irregular sleep within a preset period such as the last half month or a month.
  • the irregular sleep model After these irregular sleep data are input to the irregular sleep model, it is convenient for the irregular sleep model to determine whether to promote sleep or suppress sleep.
  • the non-sleep data can also be used together with environmental data and characteristic data to generate light control parameters that promote sleep or suppress sleep.
  • irregular sleep data or irregular sleep factors, for example, sleep time deviation data when entering sleep, time zone deviation data of the time zone where the target user is located, irregular One or more of the occurrence frequency data of sleep and the continuous state data of this irregular sleep.
  • the step S110 may include at least one of the following:
  • Different seasons have different temperatures, different sunshine, and different sleep durations and/or deeper and lower sleep required by the target user.
  • the environmental data may further include: humidity data, and different humidity has different effects on the sleep of the target user.
  • the step S120 may include:
  • the static feature data may include: data corresponding to the user's static personal portrait, for example, the age, gender, and/or personal sleep characteristics of the target user.
  • HRV heart rate variability
  • the acquiring dynamic characteristic data of the user includes at least one of the following:
  • Collect the current motion feature data of the target user for example, hand motion data, foot motion data, head motion data and/or torso motion data;
  • Collect current physical characteristic data of the target user for example, heart rate data, blood oxygenation data, and/or HRV data, etc.
  • the method further includes:
  • Step S101 Perform a first denoising process on the feature data, remove the first noise data outside the preset frequency range, and obtain the first feature data;
  • Step S102 Perform correlation analysis filtering on the first feature data, remove second noise data located in the preset frequency range, and obtain second feature data;
  • Step S103 Analyze the second feature data to obtain third feature data characterizing the state of the target user
  • the step S130 may include: inputting the third characteristic data and the environment data to the target model of the target user to obtain the lighting control parameter.
  • the feature data in order to reduce the influence of the noise data input into the regular sleep model and the irregular sleep model on the accuracy of the light control parameters, in this embodiment, the feature data will be first denoised.
  • denoising in order to remove the noise data in the feature data as much as possible, denoising will be performed twice. For example, through the first denoising process, the first noise data outside the preset frequency range where the feature data is located can be removed, and then through correlation analysis, the second noise data within the preset frequency range where the feature data is located can be removed.
  • the user's movement characteristics and/or signs will show a certain regularity, but the second noise data may not show such a regularity, this regularity may be a variational law in the time domain, and/or, a variational law in the frequency domain,
  • this regularity may be a variational law in the time domain, and/or, a variational law in the frequency domain
  • the user's heartbeat data has a certain periodicity, which is a change law in the time domain.
  • the user's brain waves may switch between waves of different frequencies, but they may all be between specific frequency points. This law is the law of change in the frequency domain.
  • the correlation analysis may be: judging whether the separated signals satisfy the change rule of the target user's characteristics in the time and/or frequency domain, and filtering out changes that do not meet the target user's time and/or frequency domain Regular second noise data.
  • this is only an example, and the specific implementation is not limited to this.
  • the step S103 may include:
  • time-domain feature data includes: a characteristic curve corresponding to the second feature data in the time domain Peak data and/or trough data;
  • Parsing the second feature data extracting the frequency domain feature data of the target user from the second feature data; wherein, the frequency domain feature data includes: a characteristic curve corresponding to the second feature data in the frequency domain Peak data and/or trough data.
  • the action curve can be drawn in the time or frequency domain by collecting the action feature data.
  • the peak value and/or the trough value of the action curve may be very large data that characterize the user's action, so
  • peak data and/or trough data of the action curve in the time domain and/or the action curve in the frequency domain may be extracted as the characteristic data and input into the individual's sleep portrait. For example, from the second data, parameters such as the action amplitude, action intensity, maximum action amplitude and/or action intensity at each action frequency when extracting action peaks and troughs are extracted.
  • the user's sign data can also draw corresponding sign curves in the time domain and/or frequency domain. According to the sign curves, the peak data and/or trough data in the second characteristic parameters can be extracted for processing.
  • the first noise data includes: jitter data with a jitter frequency of the acquisition device outside the preset frequency range, electromagnetic interference data with an electromagnetic frequency outside the preset frequency range, and electromagnetic frequency The magnetic field noise outside the preset range;
  • the second noise data includes: jitter data whose jitter frequency of the acquisition device is within the preset frequency range.
  • the vibration of the device will affect the user's movement characteristic data, and the electromagnetic interference noise and/or magnetic field noise will affect the user's brain wave data.
  • the step S130 may include:
  • the dimensionality reduction processing strategy performing dimensionality reduction processing on the third feature data and environment data to obtain input data of a preset dimension
  • a high-latitude vector and/or matrix After actually combining the environmental data and the characteristic data according to a predetermined combination method, a high-latitude vector and/or matrix will be formed, but some of these data may not contribute much to the lighting control parameters. Or, some data combination will affect the lighting control parameters.
  • dimensionality reduction processing will be performed on the data using a dimensionality reduction processing strategy, for example, through dimensionality reduction processing of environmental parameters and feature data, only M pieces of data are obtained as the human sleep portrait model Input data; the M can be a value of 6, 9, or 12, and the specific value can be set dynamically according to demand.
  • performing dimensionality reduction processing on the third feature data and environment data according to a dimensionality reduction processing strategy to obtain input data of a predetermined dimension includes:
  • If the target user is in an action state determine whether the target user is in the first type of partial static state according to the action feature data
  • the target user's action feature data is sampled according to the target user's current action state to obtain sampling feature data as the input data.
  • the human body posture data in the motion feature data can be used as a judgment as to whether the entire body is in a stationary state. If the entire body is in a stationary state as a whole, it may indicate that the target user is currently in a sleep state or a sleep enter state.
  • the target user is in an overall still state as a whole, for example, the user's hand motion data and foot motion data are used to determine whether the target user is in an overall still state.
  • the user's hand movement data indicates that the target user's hand movements are slight
  • the foot movement data indicates that the foot movements are slight.
  • the target user may be considered to be in an overall static state, otherwise, the target user may be considered to be in an action state. If the target user is in an action state, it is necessary to further determine whether the target user is in a partial action state.
  • the target user may lie down or sit down but still play a mobile phone, the target user is in a hand action state, rather than a stationary hand status.
  • the first type of local static state and the second type of local static state are different static states, and the difference may be reflected in at least one of the following:
  • local stationary states such as local translational stationary states and local rotational stationary states
  • the part corresponding to the first partial static state is larger than the part corresponding to the second partial static state.
  • the part corresponding to the first partial static state may be the entire upper limb; the part corresponding to the second partial static state may be a finger.
  • performing dimensionality reduction processing on the third feature data and environment data to obtain input data of a preset dimension further including:
  • the determination of whether the target user is in the first type of partial static state and the second type of partial static state is stopped;
  • the determination of whether the target user is in the second type of local stationary state is stopped.
  • the method further includes:
  • effect data includes: the target user's sleep effect data and/or non-sleep activity data;
  • the regular sleep model or the irregular sleep model is optimized.
  • the regular sleep model may be trained using regular sleep data and regular sleep effect data of the regular sleep model.
  • the training data of the irregular sleep model may include:
  • Irregular sleep data lighting control data, and irregular sleep effect data.
  • the regular sleep data, light control data and regular sleep effect data are introduced into the training of irregular sleep models.
  • the model training can be performed according to the regular sleep data, light control data, and irregular sleep effect data, thereby making irregular sleep
  • the model has further improved the accuracy of lighting control parameters.
  • the sleep effect data of the user will also be collected, for example, the sleep effect data may represent the promotion and/or inhibition of the light after the controlled adjustment on the user's sleep, which is adopted in this embodiment. Obtain the effect data, you can know the effect after the current light adjustment.
  • the effect achieves the expected effect
  • the corresponding light control parameters can be used as regular sleep model and irregular sleep model to further optimize the training sample data, and perform deep optimization of the regular sleep model and irregular sleep model, so that the regular sleep
  • this embodiment also provides a data processing apparatus, including:
  • the first obtaining module 110 is configured to obtain environmental data
  • the second obtaining module 120 is configured to obtain the current state data and characteristic data of the target user
  • the selection module 130 is configured to select a regular sleep model or an irregular sleep model as the target model according to the current state data;
  • the third obtaining module 140 is configured to input the environmental data and the characteristic data into the selected target model to obtain lighting control parameters;
  • the control module 150 is configured to use the light control parameters to control the light emission of the light emitting device, wherein the controlling the light emission of the light emitting device includes: controlling the light emitting device to emit light that promotes sleep of the target user, or, controlling the The light emitting device emits light that suppresses the sleep of the target user.
  • the first acquisition module 110, the second acquisition module 120, the selection module 130, the third acquisition module 140, and the control module 150 may all be program modules, which can be implemented after being executed by the processor Acquisition of the aforementioned various data, and lighting control of the lighting device.
  • the first acquisition module 110, the second acquisition module 120, the selection module 130, the third acquisition module 140, and the control module 150 may all be soft-hard combination modules.
  • the soft-hard combination module may include: A programmable array, for example, a field programmable array or a complex programmable array.
  • the first acquisition module 110, the second acquisition module 120, the selection module 130, the third acquisition module 140, and the control module 150 may all be pure hardware modules, and the pure hardware modules may include: dedicated integrated circuit.
  • the selection module is configured to perform at least one of the following: if the current state data indicates that the current sleep state of the target user does not conform to the sleep rule corresponding to the regular sleep model, select the The irregular sleep model is the target model; if the current state data indicates that the current sleep state of the target user conforms to the sleep rule corresponding to the regular sleep model, the regular sleep model is selected as the target model.
  • the device further includes:
  • An irregular sleep data module configured to obtain irregular sleep data of the target user if the target model is an irregular sleep model
  • the third obtaining module is configured to input the environment data, the characteristic data and the irregular sleep data into the irregular sleep model to obtain the light control parameter.
  • the irregular sleep data includes at least one of the following:
  • Time zone deviation data of the target user's time zone
  • the first obtaining module 110 is configured to perform at least one of the following:
  • the second obtaining module 120 is configured to obtain the static characteristic data of the user; and obtain the dynamic characteristic data of the user.
  • the second acquisition module 120 is configured to perform at least one of the following:
  • the device further includes:
  • a first denoising module configured to perform a first denoising process on the feature data, remove the first noise data outside the preset frequency range and obtain the first feature data
  • a second denoising module configured to perform correlation analysis filtering on the first feature data, remove second noise data located in the preset frequency range, and obtain second feature data
  • An analysis module configured to analyze the second characteristic data to obtain third characteristic data characterizing the state of the target user
  • the third obtaining module is configured to input the third characteristic data and the environment data to the regular sleep model and irregular sleep model of the target user to obtain the light control parameter.
  • the parsing module is configured to parse the second feature data and extract the time-domain feature data of the target user from the second feature data, where the time-domain feature data includes: Peak data and/or trough data of the characteristic curve corresponding to the second feature data in the time domain; peak data and/or trough data of the target user's action curve or sign change curve;
  • the frequency domain feature data includes: the second feature data location in the frequency domain The peak data and/or trough data of the corresponding characteristic curve.
  • the first noise data includes: jitter data with a jitter frequency of the acquisition device outside the preset frequency range, electromagnetic interference data with an electromagnetic frequency outside the preset frequency range, and electromagnetic frequency Magnetic field noise outside the preset range; and/or, the second noise data includes: jitter data of a jitter frequency of the acquisition device within the preset frequency range.
  • the third acquiring module 140 is configured to perform dimension reduction processing on the third feature data and environment data according to a dimension reduction processing strategy to obtain input data of a preset dimension; Input data to the regular sleep model and the irregular sleep model to obtain the light control parameters.
  • the third acquiring module 140 is configured to determine whether the target user is in an overall still state based on the first preset condition and the action characteristic data of the target user; if the target user is in action State, determine whether the target user is in the first type of local static state according to the action feature data; if the target user is not in the first type of local static state, determine whether the target user is based on the action feature data In the second type of local static state; if the target user is in an action state, the target user's action feature data is sampled according to the target user's current action state to obtain the sampling feature as the input data data.
  • the third acquiring module 140 is configured to stop whether the target user is in the first type of partial static state and the second type of partial static state if the target user is in the overall static state Determination; and/or, if the target user is in the first type of local stationary state, stop determining whether the target user is in the second type of local stationary state.
  • the device further includes:
  • the fourth obtaining module is configured to obtain the effect data of the light control after the use of the light control parameter to control the light emission of the light emitting device, wherein the effect data includes: the sleep effect data of the target user and/or Sleep activity data;
  • the optimization module is used to optimize the regular sleep model and the irregular sleep model according to the effect data.
  • the present disclosure collects the user's movements and heart rate through a smart bracelet, performs color light adjustment projection through a smart projection mobile phone or gateway and other smart devices, and adaptively improves the learning and working environment.
  • the corresponding portrait model in the overall crowd is used to predict the most suitable color sleep adjustment model for each person, and the individual sleep effect evaluation model based on the heart rate and movement changes caused by the bracelet detection is supervised. Learning, so as to establish a regular sleep model or an irregular sleep model that is most suitable for the individual's color sleep adjustment, and it is continuously revised according to the genetic algorithm.
  • the opposite direction of promoting sleep at the same time is to suppress sleep, so that this awake state suppresses sleep.
  • a test sample of a first-level label is obtained, the test sample is imported into the demand model, and the accuracy rate of the demand model is obtained according to the output result of the demand model;
  • the demand model here may be Including the aforementioned regular sleep model and irregular sleep model.
  • the influence weight of each of the influence factors in the training sample and the modification of the training sample are modified;
  • the modified training sample is used to train the demand model until the accuracy rate of the obtained demand model meets the preset accuracy rate requirement.
  • the step of obtaining a general model matching a first-level tag from a plurality of general models in a database includes:
  • a general model consistent with the configuration of the next-level tag of the first-level tag is selected from the found general models according to the configuration of the next-level tag in the first-level tag.
  • each of the impact factors includes multiple features
  • the step of preprocessing the training sample includes:
  • the general model is constructed based on a neural network.
  • the neural network includes an input layer, an output layer, and a hidden layer.
  • the input layer, the output layer, and the hidden layer include multiple neurons, respectively.
  • the step of modifying the connection weight value of the neurons between the input layer, the output layer, and the hidden layer during the reverse return process to gradually reduce the final output error signal includes: :
  • W ij represents the connection weight value between the i-th neuron of the input layer to the j-th upgrade of the hidden layer
  • X P represents the i-th input value of the P-th training sample in the input layer
  • the step of modifying the influence weight of each influence factor in the training sample and modifying the training sample according to the accuracy rate of the demand model includes:
  • the accuracy rate of the current demand model is higher than the accuracy rate of more than half of the demand model obtained in the history, the current training sample is retained, and the impact weight of each of the impact factors is modified;
  • the method further includes:
  • the method further includes:
  • the information to be tested carries a plurality of different influence factors, and each of the influence factors carries corresponding influence weights;
  • the method further includes:
  • An embodiment of the present disclosure also provides a data processing apparatus.
  • the data processing apparatus includes:
  • the general model acquisition module is used to obtain a general model matching a first-level tag from multiple general models in the database;
  • the training sample acquisition module is used to obtain a training sample of a first-level label, the training sample carries a plurality of different influence factors, each of the influence factors carries a corresponding influence weight, and the training sample includes the Staff characteristics and medical resource deployment information;
  • a demand model obtaining module which is used to preprocess the training samples, import the preprocessed training samples into the general model, and train the general model to obtain the demand model corresponding to the first-level label;
  • An accuracy rate obtaining module used to obtain test samples of first-level tags, import the test samples into the demand model, and obtain the accuracy rate of the demand model according to the output result of the demand model;
  • a modification module configured to modify the influence weight of each influence factor in the training sample and modify the training sample according to the accuracy rate of the demand model when the accuracy rate does not meet the preset accuracy rate requirement;
  • the training module is used for training the demand model using the modified training samples until the accuracy rate of the obtained demand model meets the preset accuracy rate requirement.
  • a supervised classification algorithm is used, with environmental data as the input layer and individual sleep quality score as the output layer.
  • the quality of individual evaluation is used as a training supervision factor, preferably 1 and worse 0.
  • the forward propagation of the working signal During this period, the weights and thresholds of the neurons in the network remain unchanged. Each layer of neurons only affects the input and state of the next layer of neurons. If the desired output is not obtained at the output The output value, the network is transferred to the back propagation process of the error signal. The back propagation of the error signal, the error signal starts to be transmitted back layer by layer from the output end. In this propagation process, the weights and thresholds of the neurons of the network are adjusted by error feedback according to certain rules. The above two stages are carried out alternately and cyclically, and each time it is completed, it is corrected by genetic algorithm.
  • the individual as a new input factor of the corresponding group portrait population in the overall population, SVM genetic modification of the overall population corresponding portrait crowd environment model, the corresponding sleeping environment user portraits are constantly clear and refined.
  • SVM classifier fitness function f(x i ) min(1-g(x i )), Divide the correct rate of the samples for the SVM classifier. As the sample size increases, if the correct rate is higher than the historical best model, the model replaces the original best model, so as the sample size increases, the model is continuously optimized and perfected. .
  • the adaptive improvement of the model may include: With the increase of the sample size, the SVM classifier can continuously optimize and improve the adaptive.
  • SVM classifier fitness function f(x i ) min(1-g(x i )), The accuracy of dividing samples for SVM classifier includes:
  • the boundary condition here is the classification boundary of the SVM classifier.
  • melatonin with circadian rhythm, midnight secretion up to Peak
  • REM 2500-10000lux helps to deepen the depth of sleep (the proportion of REM and N4), change the sleep-wake rhythm, adjust the physiological clock, and adjust the quality of sleep.
  • this example provides a data processing method for sleep adjustment, especially sleep adjustment for irregular sleep, specifically including:
  • the irregular sleep model is used to combine the collected environmental parameters, characteristic data and current state data for lighting control.
  • the data processing method provided in this example may include the following steps when adjusting for irregular sleep:
  • control the light emitting device to emit light that inhibits sleep
  • the sleep time range may be the insomnia time range of regular sleep of the target user throughout the day;
  • control the light emitting device to emit lights that promote sleep
  • the light-emitting device is controlled to emit light that promotes sleep within a predetermined time, and after the light that emits sleep is equal to the predetermined time, the light-emitting device is controlled to emit light to suppress sleep;
  • the light emitting device is controlled to emit a light that suppresses sleep.
  • this embodiment provides an electronic device, including:
  • Memory used for information storage
  • the electronic device further includes: a communication interface and/or a human-machine interaction interface.
  • the communication interface may include: a transceiver antenna and/or a network interface, which may be used for information interaction with other electronic devices.
  • the human-computer interaction interface may be used to interact with humans, and the human-computer interaction interface may include: physical buttons and/or touch screens.
  • This embodiment provides a computer storage medium for storing computer-executable instructions; after the computer-executable instructions are executed by a processor, the method provided by one or more of the foregoing technical solutions can be implemented, for example, One or more of the methods shown in FIGS. 1, 2, 4, and 5.
  • the disclosed device and method may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a division of logical functions.
  • there may be other divisions for example, multiple units or components may be combined, or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the coupling or direct coupling or communication connection between the displayed or discussed components may be through some interfaces, and the indirect coupling or communication connection of the device or unit may be electrical, mechanical, or other forms of.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • the functional units in the embodiments of the present disclosure may all be integrated into one processing module, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above integration
  • the unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.

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Abstract

La présente invention concerne un appareil et un procédé de traitement de données, un dispositif électronique et un support de stockage. Le procédé comprend les étapes consistant à : acquérir des données environnementales (S110) ; acquérir des données d'état actuel et des données de caractéristiques d'un utilisateur cible (S120) ; sélectionner, selon les données d'état actuel, un modèle de sommeil régulier ou un modèle de sommeil irrégulier comme modèle cible (S130) ; entrer les données environnementales et les données de caractéristiques dans le modèle cible sélectionné, et obtenir des paramètres de commande de lumière au moyen du modèle de sommeil régulier et du modèle de sommeil irrégulier (S140) ; et utiliser les paramètres de commande de lumière pour commander l'émission de lumière d'un dispositif électroluminescent (S150), l'étape de commande de l'émission de lumière du dispositif électroluminescent consistant à commander au dispositif électroluminescent d'émettre de la lumière qui améliore le sommeil de l'utilisateur cible, ou à commander au dispositif électroluminescent d'émettre de la lumière qui empêche le sommeil de l'utilisateur cible.
PCT/CN2019/130837 2019-01-02 2019-12-31 Appareil et procédé de traitement de données, dispositif électronique et support de stockage WO2020140913A1 (fr)

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