203 results about "Sleep Stages" patented technology

The invention relates to the field of sleep monitoring, in particular to a sleep monitoring visual analysis method and system based on a smart bracelet. The method comprises the following steps: extracting a sleep physiological signal of a wrist part of a user based on a smart bracelet; sliding time window analysis and multi-mode physiological state sensing are carried out, and a dynamic sleep state sensing map is constructed; performing transient mutation rhythm recognition on the dynamic sleep state sensing map, performing sleep stage evolution trend analysis, and constructing a sleep stage evolution model; the method comprises the following steps: collecting multi-dimensional parameters of a sleep environment, carrying out environment parameter change detection and environment disturbance-sleep stage change association identification, and obtaining an environment disturbance-sleep rhythm mutation association rule; environment disturbance intensity evaluation is carried out according to the association rule, and a real-time sleep state visual view is constructed. According to the method, accurate and high-readability sleep monitoring data visualization is realized, the overall sleep quality is improved, and the long-term health risk is reduced.

The invention relates to a multi-device cooperative control method and device based on a user state and an air conditioner. The method comprises: monitoring a sleep state of a user; and determining a sleep stage of the user based on the sleep state, and performing cooperative control on each device according to a control strategy matched with the sleep stage. The method and the device can adapt to the difference of dynamic sleep stages, and control the devices to operate cooperatively according to the control strategies matched with the different sleep stages, so that the self-adaptive adjustment and parameter adjustment precision of the devices is improved, and the problems of poor sleep mode adjustment effect and low adaptability in the prior art are solved.

The invention discloses an intelligent temperature control mattress system based on multi-mode sensing and adaptive learning, and the system comprises a data collection module which is used for collecting a mattress surface vibration signal, a mattress surface temperature and a mattress surrounding environment parameter; the data analysis module is used for performing deep analysis on the time-frequency domain features according to a biological recognition model to obtain human body or non-human body classification, performing self-learning to obtain body movement changes, accurately recognizing various typical sleeping postures and filtering out error data; and the model prediction module is used for monitoring the physiological parameters of the user according to the spectral analysis, inputting the physiological parameters, the collected temperature and the parameters of the surrounding environment of the mattress into a temperature control model composed of three neural networks of LSTM, GNN and Transform for comprehensive analysis and decision making, and outputting an optimal temperature control strategy. According to heart rate changes or body heat fluctuations, the sleep stage and the sleep and waking time are intelligently recognized, and therefore sleep environment adjustment better conforming to the human body rhythm is achieved.

The invention relates to the technical field of sleep environment regulation and control, and discloses a sleep environment self-adjustment system based on multi-source heterogeneous data. An environmental parameter acquisition module and a physiological feature acquisition module of the system respectively acquire sleep space environmental parameters and user physiological feature data in real time; after the master control system receives the two types of original data, a fusion calculation unit cleans original environment parameters to generate a standardized environment data stream, and extracts features from the original physiological feature data to generate a physiological feature time sequence; the dynamic evaluation module divides the two types of data into a plurality of sleep stage data segments according to a preset rule, and calculates an environmental parameter fluctuation index and a physiological feature deviation index; the label distribution mechanism combines the two types of indexes to generate a comprehensive comfort label of each sleep stage; an adjustment decision engine generates a global sleep environment adjustment strategy according to all labels, and an actuator control module drives environment adjustment equipment to execute; and the feedback learning unit receives the adjusted data and updates the calculation benchmark of the dynamic evaluation module.

The invention discloses a sleep classification method and system based on time-frequency combination, and belongs to the technical field of biomedical signal processing and artificial intelligence. In order to solve the problems that an existing method is high in manual dependence, insufficient in time-frequency feature fusion and low in long-time-sequence modeling efficiency, sleep stage classification is carried out mainly through automatic time-frequency feature extraction, a dynamic attention mechanism with memory enhancement and a lightweight multi-branch neural network structure. According to the method, efficient modeling and accurate classification of the multi-scale electroencephalogram signals can be achieved, the deep sleep recognition capability and the overall classification accuracy are improved, meanwhile, the model parameter quantity and the reasoning delay are remarkably reduced, and good real-time performance and clinical applicability are achieved.

The invention relates to the technical field of sleep stage classification, and particularly provides a sleep quality monitoring method fusing multi-scale features. The method comprises the following steps: preprocessing input signals, and inputting the input signals into a multi-mode sleep signal analysis network after ensuring that the signal amplitudes are consistent; capturing features of a plurality of time scales through a multi-granularity feature learning module, and carrying out feature fusion; integrating spatial information by using a spatial-temporal feature enhancement module, and mapping the three-dimensional features into a two-dimensional time sequence feature sequence; on the basis of a time context module of Mama, long-distance time dependence is modeled with linear complexity, features are averagely aggregated along the time dimension through a classification module, a classification result is output through a full connection layer and an activation function Softmax, and the method improves the accuracy of sleep stage classification while ensuring the high efficiency of sleep stage classification.

The invention discloses a single-channel electroencephalogram signal sleep stage classification method, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining a single-channel electroencephalogram signal, carrying out the preprocessing of the single-channel electroencephalogram signal, and generating a signal segment with a fixed time length; performing multi-scale time-frequency feature extraction on the signal segment to generate a primary time-frequency feature, capturing a sleep stage conversion dependency relationship, and performing time sequence enhancement on the primary time-frequency feature to generate an enhanced time sequence feature; performing frequency spectrum statistical feature extraction on the signal segments to generate frequency spectrum statistical features; fusing the enhanced time sequence features and the frequency spectrum statistical features to generate a comprehensive feature vector; according to the comprehensive feature vector, probability distribution of different sleep stages is output through a main classifier, and a binary judgment result of the sleep stage with the minimum sample size is output through an auxiliary classifier. The method can solve the problems of class imbalance, signal complexity and calculation efficiency.

The invention relates to the technical field of integrated sleep management, in particular to a sleep data analysis management system based on artificial intelligence. The method comprises the following steps: segmenting a sleep cycle of a user based on brain wave data to obtain N sleep stages of the user; based on the body movement monitoring data, the pulse monitoring data and the respiration monitoring data, calculating to obtain a first sleep quality characteristic value of the sleep stage of the user, and based on the sleep posture category and a sleep quality weight parameter corresponding to the sleep posture category, determining a second sleep quality characteristic value of the sleep stage of the user; the sleep quality analysis index of the sleep cycle of the user is generated based on the sleep quality parameters of the sleep stage of the user, the sleep early warning signal is generated based on the sleep quality analysis index of the sleep cycle of the user, multi-dimensional data such as brain waves, body movement, pulse, breathing and sleeping postures can be combined, the sleep stage can be accurately divided, and the sleep early warning effect is improved. And sleep quality parameters are calculated through multi-modal data fusion, so that the accuracy and practicability of sleep monitoring are improved.

The invention provides an intelligent pillow control method and system, and belongs to the technical field of intelligent control. The method comprises the steps that an FPGA obtains a user sleeping posture data set collected by a pressure sensor array; and determining a current sleep stage based on the user sleep posture data set and a pre-trained sleep stage recognition model. According to the user sleeping posture data set, the historical sleeping posture data set and a preset cervical vertebra supporting curve, a first correction target parameter matrix corresponding to the current cervical vertebra curvature deviation value of the user is determined; wherein the first correction target parameter matrix comprises first correction target parameters corresponding to air bags in different areas of the intelligent pillow; the first correction target parameter is the airbag pressure adjustment amount dynamically constrained by the receptor dynamic frequency; and based on the current sleep stage and the first correction target parameter matrix, determining a corresponding airbag adjustment parameter group, operating an airbag driving module according to the airbag adjustment parameter group, performing airbag pressure adjustment, and updating the first correction target parameter matrix according to the real-time body movement frequency.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention discloses a sleep quality improving system based on big data analysis, and relates to the technical field of big data. According to the system, a multi-dimensional sleep state monitoring matrix is constructed, effective intervals of monitoring points are optimized in combination with dynamic adjustment factors and a deep learning algorithm, and redundant data interference is eliminated; generating a sleep intervention regulation factor based on the environmental influence function and the physiological rhythm constraint factor, and training the sleep intervention function through a genetic algorithm to dynamically adjust the intervention intensity; and generating an intervention instruction by utilizing the reinforcement learning model and combining the real-time physiological data of the user, the environmental parameters and the sleep stage prediction result, and controlling the starting sequence and parameter adjustment of the monitoring points until preset ideal sleep state parameters are met. According to the method, the problems of insufficient monitoring precision, poor environment adaptability, intervention lag and the like in a traditional method are solved, the accuracy and the real-time performance of sleep intervention are improved, and the method is suitable for the fields of smart home, medical health and the like.

The invention provides a sleep assisting system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of sleep assisting. The system comprises an acquisition unit, a processing unit, a stimulation unit and an evaluation feedback unit. Determining a sleep stage of the user, analyzing phases of multi-scale electroencephalogram rhythm components such as slow waves and spindle waves in a cross-scale manner, and extrapolating and predicting a phase value of a target electroencephalogram rhythm at a future moment in combination with a recent phase trajectory; taking the predicted phase as a target state, mapping a rising / falling slope of a slow wave form into an intensity change parameter of photoacoustic and magnetic vibration stimulation by constructing a mapping channel, and selecting an optimal stimulation combination for output through a decision model based on the intensity change parameter; calculating a phase locking effect index, and adjusting a phase prediction model parameter by using an optimization algorithm according to the index so as to optimize subsequent stimulation; and linkage regulation and control of an external environment are supported, and centralized monitoring of sleep states of multiple users can be realized. According to the invention, self-adaptive guidance of sleep brain rhythms is realized, and the sleep quality is effectively improved.

The invention provides a multi-mode adaptive wave simulation control system and method for a deep sleep physiotherapy cabin. Belongs to the technical field of fluid mechanics and health intervention. The method comprises the steps that multi-modal physiological parameter collection is conducted on a user on a deep sleep physiotherapy cabin, a real-time physiological data set is generated, the current sleep stage of the user is deduced through a time-frequency analysis and machine learning model on the basis of the data set, a sleep stage label is generated, a pre-trained wave mode generator is dynamically called according to the sleep stage label, and the deep sleep physiotherapy cabin is obtained. An initial set of wave parameters matching the current sleep stage is generated. By sensing the physiological state of the user in a non-contact mode, deducing the sleep stage and dynamically generating and adjusting the wave modes such as sleep guiding and deep sleep maintaining, water fluctuation accurately adapts to the physiological requirements of the user in different sleep stages, a traditional static wave mode is abandoned, highly-personalized sleep adjusting experience is provided for the user, and the sleep quality is effectively improved.

The invention discloses a sleep assistance composite audio generation method and system based on multi-frequency brain waves and a medium, and belongs to the technical field of digital audio processing, and the technical scheme is characterized by comprising the following steps: S1, collecting a brain wave signal of a user in real time through a brain wave monitoring module; s2, dynamically selecting a target frequency band combination according to the sleep stage and setting an amplitude change rule; s4, pink noise with the frequency spectrum range of 20-200 Hz is injected into the composite audio, the energy proportion of the pink noise is 5%-15%, and the amplitude is dynamically adjusted according to the environmental sound pressure level SPLenv. The method has the effect that the limitation of traditional sleep assisting audio is solved through the technologies of multi-frequency brain wave synchronization, personalized customization, dynamic adjustment, diversified audio combination and the like.

The invention provides a radar-based full-time in-bed state monitoring method, system and product, and the method comprises the steps: carrying out the signal preprocessing of a collected radar echo signal, and obtaining target existence information and target physiological information; performing feature extraction on the target existence information and the target physiological information, and adaptively dividing a daytime period and a night period based on a feature extraction result; performing multi-mode sleep state staging based on the target existence information and the target physiological information in the night time period; performing daytime activity state classification based on the target existence information and the target physiological information in the daytime period; and generating a full-time in-bed state monitoring report based on the sleep state staging result and the daytime activity state classification result. According to the method provided by the invention, both long-term bedridden people and non-bedridden people can be considered, the day and night time periods are automatically switched, and the function that single radar equipment synchronously supports night sleep staging and daytime activity classification is realized.

The invention discloses a sleep stage identification method and device based on a multi-mode signal, equipment and a medium. The method comprises the following steps: extracting a target lead signal comprising an electroencephalogram signal and an electro-oculogram signal from a polysleep monitoring signal of a target object; performing data preprocessing on the target lead signal to obtain a reference lead signal; inputting the reference lead signal into a sleep stage recognition model to obtain a sleep stage prediction result of the target object; wherein the sleep stage recognition model comprises a preliminary feature extraction unit, two deep feature extraction units, a dynamic gating fusion unit, a bidirectional long-short-term memory network unit, an attention unit and a classification unit, the deep feature extraction units are constructed based on a focus adjustment mechanism, and multi-scale one-dimensional deep convolution is adopted; and the two deep feature extraction units are used for performing deep feature extraction on the electroencephalogram signal and the electro-oculogram signal respectively. According to the scheme, the sleep stage recognition precision and robustness can be improved by using the sleep stage recognition model.

The invention discloses a sound-based sleep management method and device, equipment and a medium. The method is applied to an intelligent sleep management system and can be applied to the fields of medical health or financial insurance and the like. The method comprises the following steps: collecting a sleep audio signal, and carrying out signal preprocessing on the sleep audio signal to obtain a target audio signal; performing time-frequency analysis and feature extraction on the target audio signal to obtain a sleep feature, wherein the sleep feature comprises a sleep stage; performing event detection on the sleep features through a preset neural network to obtain a sleep event; acquiring sleep quality scores of the sleep events and the sleep stages through a preset evaluation model, and constructing a sleep management scheme according to the sleep quality scores. By implementing the method provided by the invention, the problems that the sleep problem cannot be comprehensively considered, a sleep management scheme is difficult to generate and the sleep quality of a user is difficult to improve in the prior art can be solved, so that the chronic disease onset risk is reduced, the customer health level is improved, the insurance claim rate is indirectly reduced, and a win-win situation is realized.

The present invention provides a sleep stage classification method based on zero-shot learning and contrastive learning, comprising the following steps: S1: acquiring and processing raw EEG, EMG, and EOG physiological signals; S2: acquiring semantic information related to sleep stage classification; S3: manually matching the physiological signals and semantic information; S4: data set partitioning: dividing the matched physiological signal and semantic information groups in S3 into a training set, a validation set, and a test set; S5: pre-training: inputting the training set obtained in S4 into a zero-shot learning and contrastive learning model for pre-training, and using contrastive learning to fine-tune the zero-shot learning and contrastive learning model; S6: predicting sleep stage classification. This invention addresses the problem of low sleep stage assessment accuracy in existing technologies.

The invention discloses a sleep state self-adaptive layered regulation and control method and platform based on an environment regulation window, and relates to the technical field of self-adaptive control, and the method comprises the steps: carrying out the sleep state analysis according to a physiological data sequence, obtaining a real-time sleep stage, carrying out the scheduling task distribution, and generating a function unit scheduling sequence; performing data acquisition priority management and control on the wearable device to obtain real-time sleep associated data; generating a sleep environment adjustment strategy by taking the real-time sleep stage as an environment adjustment constraint; after updating the sleep environment of the user, activating an environment adjusting window; and operating a hierarchical scheduling architecture by taking an environment adjustment window as a time constraint, and circularly performing adaptive control updating. The technical problem that in the prior art, due to the fact that the sleep environment is difficult to adjust accurately in real time according to the sleep state of the user, the sleep quality of the user is low is solved, and the technical effects that the sleep state of the user is accurately judged, the sleep environment is dynamically and adaptively adjusted, and the sleep quality is improved are achieved.

The invention discloses an earphone sleep aiding method and system, an earphone and a storage medium, and the method comprises the steps: obtaining the physiological data of a user at preset time intervals; determining the state of the user based on the change of the physiological data of the user in the interval; if the user is in the sleep state, outputting the corresponding sleep-aiding audio content based on the sleep state of the user, and enabling the sleep-aiding audio to always fit the sleep process of the user through real-time monitoring and dynamic adjustment, thereby avoiding the problem that the sleep is interfered by playing invalid audio when the user does not fall asleep or playing too strong audio in a deep sleep stage, and improving the user experience. According to the technical scheme, the sleep-aiding accuracy and comfort are remarkably improved, meanwhile, unnecessary power consumption is reduced, the use time of the earphone is prolonged, and more intelligent and efficient sleep-aiding experience is brought to a user.

The invention relates to the technical field of medical detection and monitoring, in particular to a neurology patient sleep disorder monitoring method, which comprises the following steps: S1, collecting and preprocessing body signals of a patient; s2, constructing a cross-modal association graph, and forming a heterogeneous graph structure with a time sequence weight and a modal label; s3, performing graph convolution and sparse attention operation on the cross-modal association graph, and executing node screening and edge weight updating; s4, time domain and frequency domain decomposition is carried out, and alignment is carried out based on time and frequency indexes; s5, performing segmentation and boundary calibration on the feature sequence, dividing sleep stages, and indexing and storing cross-modal fragments; s6, retrieving abnormal stage fragments, constructing abnormal feature sub-graphs and executing structure clustering operation; and S7, mapping the sleep disorder category labels and the cross-modal features to generate associated features and output results. According to the method, the cross-modal heterogeneous graph is constructed and the time-frequency features are fused, so that the recognition precision and the analysis depth of the sleep disorder of the patient in the neurology department are improved.

The invention provides a multi-mode sleep intervention system and method based on self-adaptive dynamic control. The system comprises a sensor module used for collecting brain wave activity signals in real time in a non-invasive mode; the AI module is used for performing sleep stage identification and stage division on the brain wave activity signal through a sleep stage prediction model, judging a current sleep state through a first AI strategy engine and automatically generating an optimal first intervention parameter sequence; the control module is used for issuing a corresponding control instruction to the multi-mode intervention module according to the optimal first intervention parameter sequence; and the multi-mode intervention module is used for executing one or more of sound, light, magnetism, temperature, touch and smelling multi-mode intervention according to the control instruction. Through multi-mode fusion perception, closed-loop monitoring control, dynamic adjustment of an intervention strategy and multi-mode interaction and intervention, the effects of accurately perceiving the sleep state in real time, improving the data anti-interference capability, self-adapting to sleep management, adapting to individual differences and meeting the requirements of different intervention scenes are achieved.

The invention discloses an ear acupoint closed-loop ultrasonic stimulation system and method based on sleep electroencephalogram signal feedback, and belongs to the technical field of ultrasonic stimulation nerve regulation and control. The ear acupoint closed-loop ultrasonic stimulation system comprises an electroencephalogram acquisition module, a regulation and control module, an ultrasonic transmitting module and a dynamic association module; the electroencephalogram acquisition module is used for acquiring electroencephalogram signals within limited time specified by a user according to specified sampling frequency, amplifying and filtering the acquired signals and sending the processed signals to the regulation and control module; the regulation and control module judges the current sleep stage of the user, drives the ultrasonic transmitting module, performs variable parameter closed-loop ultrasonic stimulation on the user, and automatically adjusts stimulation parameters and duration according to the current sleep stage; and the dynamic association module sets a multi-threshold triggering mechanism based on the real-time electroencephalogram characteristic parameters and discriminates a specific physiological status. According to the invention, accurate acupoint stimulation can be realized, a real-time feedback mechanism is provided, and the accuracy and effectiveness of stimulation can be ensured.

According to the self-adaptive closed-loop electroencephalogram regulation and control device and method based on multi-modal feedback, multi-modal sensing and fusion are adopted, and a system does not depend on single electroencephalogram signals and fuses data of three modals of electroencephalogram, temperature and aromatherapy. The fusion provides a more comprehensive and more stereoscopic user state portrait, overcomes the defects that a single signal is easy to interfere and information is one-sided, and greatly improves the accuracy of state recognition (such as relaxation, anxiety and sleep stages).

The invention relates to the technical field of sleep environment intelligent regulation and control, and provides a sleep thermal comfort self-adaptive regulation and control method based on electroencephalogram nerve feedback. The method comprises the following steps: firstly, acquiring three electroencephalogram channel signals FP1, FP2 and FPz of the forehead of a user, and identifying a sleep stage through preprocessing and an automatic staging algorithm; then quantile alignment, robust processing and channel confidence weighted fusion are carried out on the electroencephalogram signals of all the channels, and comprehensive electroencephalogram features and multiple sets of electroencephalogram feature parameters are extracted; calculating a nerve thermal comfort comprehensive index NTx based on the characteristics, and realizing prediction of an individual thermal comfort score TSV by using an LSTM model; finally, a temperature control instruction is automatically generated according to the TSV and the sleep stage, linkage adjustment is conducted on an air conditioner and bed area temperature control equipment, and individualized and continuous sleep thermal comfort self-adaptive optimization is achieved.

The invention discloses an ear electroencephalogram acquisition sleep monitoring device based on conductive leather and a personalized audio intervention method of the ear electroencephalogram acquisition sleep monitoring device, and aims to solve the problems that existing sleep assisting equipment is poor in wearing comfort and inaccurate in monitoring, and an intervention scheme is lack of personalization. The device comprises preparation of a conductive leather electrode and integration of the conductive leather electrode, an earplug, a loudspeaker and other devices. According to the conductive leather electrode, a conductive polymer (such as poly (3, 4-ethylenedioxythiophene) and a derivative thereof) is combined with natural leather through an in-situ polymerization method to form a flexible electrode material with high biocompatibility. The electrode is combined with an earplug, a loudspeaker and other devices and can be stably attached to the ear canal, and high-signal-to-noise-ratio sleep physiological electric signal (such as myoelectricity, electroencephalogram or electrocardiosignals) collection is achieved. The sleep stage is identified through machine learning, and the loudspeaker module is controlled to play different types of sleep-aiding audios. The core lies in that the device can synchronously evaluate sleep physiological signal changes of a user under different audio stimulation, so that personalized sleep-aiding audios which are most effective for the individual are screened out, and an exclusive scheme library is established. The system has the advantages of being comfortable to wear, high in signal quality, wide in applicability and the like, and can be widely applied to the fields of health monitoring, human-computer interaction, medical diagnosis and the like.

The invention discloses a sleep apnea monitoring system and method, and the method comprises the following steps: S1, collecting a multi-modal physiological signal, executing the preprocessing, and generating a standardized sequence; s2, recognizing a sleep stage label, and extracting breathing phase and thoracico-abdominal phase difference information; s3, detecting a suspected apnea event, calculating a consistency index and a confidence coefficient, and outputting a candidate event; s4, when a safety threshold is met, disturbance verification is carried out, responses before and after intervention are collected, and event types are judged; s5, constructing an upper airway digital twinborn model, simulating and predicting an intervention effect, and outputting an abnormal severity score and an early warning; and S6, fusing the event and the risk information, generating a self-evolution risk curve, and outputting a monitoring report. According to the method, through multi-modal physiological signal fusion and upper airway digital twin modeling, accurate recognition, typing judgment and individualized risk dynamic monitoring of sleep apnea are achieved.

The application belongs to the technical field of sleep monitoring. A closed-loop adaptive transcranial electrical stimulation sleep regulation system and device are provided. According to acquired electroencephalogram signal data, a slow wave activity index and a sleep stage are obtained. According to the slow wave activity index, a current amplitude of an anodal discharge signal is obtained. According to the sleep stage, a current frequency of the anodal discharge signal is obtained. According to an acquired real-time state of blood vessels, a current frequency variation rate of the anodal discharge signal and a current amplitude variation rate of the anodal discharge signal are dynamically adjusted to determine a dynamic discharge signal. Electrical stimulation is performed according to the dynamic discharge signal to perform sleep regulation. The application realizes precise control of transcranial electrical stimulation sleep regulation and has an outstanding sleep treatment effect.

The invention relates to the field of air purification control, in particular to a mobile air purifier operation control method, system and equipment and a medium. The method comprises the following steps: acquiring environmental parameters through a multi-source sensor, generating a multi-dimensional operation parameter matrix by using a fuzzy control algorithm, planning an optimal moving path by using an improved AI algorithm according to spatial topology sensing data, and cooperatively adjusting related components through a PID controller. A night low-noise operation strategy, a specific algorithm calculation parameter, sleep stage identification, corresponding control and the like are provided, and a look-ahead control instruction set can be generated; the invention further comprises a corresponding operation control system, electronic equipment and a computer readable storage medium. The technical effects that the ambient air quality and the user state are accurately perceived, global coverage purification and multi-system parameter joint debugging of the air purifier are achieved, low-noise operation at night is achieved, the function is dynamically adjusted according to the sleep stage, and the air purification effect and the user use experience are improved are achieved.

This invention discloses a sleep stage identification method, comprising: a multimodal data acquisition step; a preprocessing step, which normalizes the acquired physiological signals and segments them according to a set duration to obtain several original signal segments; a Williams RGB compression step, which maps the original signals to an RGB image to obtain a Williams RGB energy map; a feature extraction step, which inputs the Williams RGB energy map into a neural network model for feature extraction; and a comparison and classification step, which compares the feature values ​​with RGB standard thresholds to determine the corresponding sleep stage. This sleep stage identification method avoids directly processing the complex frequency domain features of physiological signals, thus reducing computational complexity. Three physiological signals within a certain time period only need to be represented by a single pixel in the image, achieving physiological signal compression and dimensionality reduction, further reducing computational load. This invention also effectively utilizes medical knowledge graphs to provide interpretable evidence for the sleep stage identification decision-making process.