Intelligent light wave adjusting system and method based on human body physiological indexes

Abstract

The invention discloses an intelligent light wave adjusting system and method based on human body physiological indexes, and relates to the related field of light wave adjustment, and the system comprises an index monitoring assembly which is used for obtaining the physiological indexes; the environment monitoring assembly is used for obtaining environment information; the data input assembly is used for inputting the physiological indexes and the environment information into the light wave adjusting module; the physiological activeness evaluation component is used for outputting a physiological activeness index; the scene recognition component is used for outputting a scene activeness index; and the index analysis component is used for outputting light wave adjustment parameters for adjusting the illumination device. The technical problem that an existing phototherapy product cannot carry out dynamic light wave adjustment according to real-time physiological indexes of a human body is solved, light wave parameters are intelligently adjusted according to the real-time physiological indexes of the human body and environment information, and personalized light wave adjustment experience is provided for a user. And the health is promoted while the user is ensured to keep the light comfort in different environments.

Description

Technical Field

[0001] The present application relates to the field of light wave regulation, and specifically to a light wave intelligent regulation system and method based on human physiological indicators. Background Art

[0002] Existing phototherapy products can improve a person's physiological state by adjusting the intensity and wavelength of light waves, but these products usually lack intelligent and personalized adjustment functions, and cannot perceive the physiological changes of the human body in real time and automatically adjust the light environment. In addition, the light adjustment systems of these phototherapy products mostly rely on simple preset modes or timing controls, and do not make full use of the physiological indicators and environmental data monitored in real time, making it difficult to achieve refined and personalized light environment adjustment. For example, some phototherapy devices can only be turned on or off automatically at specific times, and cannot adjust the light environment according to the user's physiological state (such as heart rate, blood pressure, etc.) or the environment in which they are located (such as indoor temperature and humidity, light intensity, etc.). This has resulted in limited effects of these phototherapy products during application, and they cannot be dynamically adjusted according to the physiological needs of different individuals, thus affecting their effectiveness and universal application. Summary of the invention

[0003] The present application provides a light wave intelligent adjustment system and method based on human physiological indicators to solve the technical problem that existing phototherapy products cannot dynamically adjust light waves according to real-time human physiological indicators, so as to achieve intelligent adjustment of light wave parameters according to real-time human physiological indicators and environmental information, provide users with a personalized light wave adjustment experience, and ensure that users maintain light comfort in different environments while promoting health effects.

[0004] The present application provides a light wave intelligent adjustment system based on human physiological indicators, including: an indicator monitoring component, which is used to monitor the physiological indicators of the human body in real time through a physiological monitoring device; an environmental monitoring component, which is used to sense the environmental information around the human body in real time through an environmental sensing device; a data input component, which is used to input the physiological indicators and the environmental information into a light wave adjustment module, the light wave adjustment module includes a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit, and the light wave adjustment module is communicatively connected with an illumination device; a physiological activity evaluation component, which is used to perform physiological activity evaluation on the physiological indicators according to the physiological state evaluation unit and output a physiological activity index; a scene recognition component, which is used to perform scene recognition on the environmental information according to the environmental scene recognition unit and output a scene activity index; an indicator analysis component, which is used to analyze the physiological activity index and the scene activity index according to the intelligent adjustment unit, and output a light wave adjustment parameter for adjusting the illumination device.

[0005] The present application also provides a light wave intelligent adjustment method based on human physiological indicators, including: real-time monitoring of human physiological indicators through a physiological monitoring device; real-time sensing of environmental information around the human body through an environmental sensing device; inputting the physiological indicators and the environmental information into a light wave adjustment module, the light wave adjustment module including a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit, the light wave adjustment module is communicatively connected with an illumination device; performing physiological activity evaluation on the physiological indicators according to the physiological state evaluation unit, and outputting a physiological activity index; performing scene recognition on the environmental information according to the environmental scene recognition unit, and outputting a scene activity index; analyzing the physiological activity index and the scene activity index according to the intelligent adjustment unit, and outputting light wave adjustment parameters for adjusting the illumination device.

[0006] The present application proposes a light wave intelligent adjustment system and method based on human physiological indicators, which monitors the physiological indicators of the human body in real time through a physiological monitoring device; senses the environmental information around the human body in real time through an environmental sensing device; inputs the physiological indicators and the environmental information into a light wave adjustment module, which includes a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit, and the light wave adjustment module is communicatively connected with the illumination device; performs physiological activity evaluation on the physiological indicators according to the physiological state evaluation unit, and outputs a physiological activity index; performs scene recognition on the environmental information according to the environmental scene recognition unit, and outputs a scene activity index; analyzes the physiological activity index and the scene activity index according to the intelligent adjustment unit, and outputs the light wave adjustment parameters for adjusting the illumination device, thereby achieving intelligent adjustment of the light wave parameters according to the real-time physiological indicators of the human body and environmental information, providing users with a personalized light wave adjustment experience, and ensuring that users maintain light comfort in different environments while promoting health. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0008] Figure 1 A schematic diagram of the structure of a light wave intelligent adjustment system based on human physiological indicators provided in an embodiment of the present application.

[0009] Figure 2 A flow chart of a light wave intelligent adjustment method based on human physiological indicators provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: indicator monitoring component 11, environment monitoring component 12, data input component 13, physiological activity assessment component 14, scene recognition component 15, indicator analysis component 16. DETAILED DESCRIPTION

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0013] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0014] The present application embodiment provides a light wave intelligent adjustment system based on human physiological indicators, such as Figure 1 As shown, the system comprises:

[0015] The index monitoring component 11 is used to monitor the physiological indexes of the human body in real time through a physiological monitoring device.

[0016] Specifically, the indicator monitoring component 11 first tracks and records the physiological data of the human body in a continuous and real-time manner through a physiological monitoring device. The physiological monitoring device usually includes a variety of sensor devices, such as heart rate sensors, respiratory rate sensors, blood pressure sensors, blood oxygen sensors, sleeping position sensors, snoring sensors, gait sensors, cortisol detection devices, etc. These sensor devices can monitor various health parameters of the human body in a non-invasive manner, such as heart rate, blood pressure, heart rate variability, respiratory rate, blood oxygen saturation, blood lactate content, maximum oxygen uptake, skin temperature, body temperature, sleeping position, snoring, sleep quality, gait balance, muscle strength, etc., and organize the collected data into physiological indicators and transmit them to the system terminal, providing necessary data support for light wave regulation to improve the health level of users.

[0017] The environment monitoring component 12 is used to sense the environment information around the human body in real time through an environment sensing device.

[0018] Specifically, the environmental monitoring component 12 first uses the environmental sensing device to continuously and in real time perceive the environmental data around the human body. The environmental sensing device usually includes a variety of sensing devices, such as temperature and humidity sensors, light sensors, air quality sensors, GPS modules, etc. These sensing devices can monitor factors such as date, time, longitude and latitude, temperature, humidity, light intensity, air quality, etc., and organize the collected data into environmental information and transmit it to the system terminal, providing necessary environmental support for light wave adjustment, optimizing the user's health environment, and thus achieving comprehensive and personalized adjustment and optimization.

[0019] The data input component 13 is used to input the physiological indicators and the environmental information into the light wave regulation module, the light wave regulation module includes a physiological state evaluation unit, an environmental scene recognition unit and an intelligent regulation unit, and the light wave regulation module is communicatively connected with the illumination device.

[0020] Specifically, the data input component 13 first transmits the collected physiological indicators and environmental information to the light wave adjustment module. The light wave adjustment module is composed of three main units, namely, a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit. The physiological state evaluation unit analyzes the health state of the human body according to the physiological indicators monitored in real time and in combination with the internal LSTM model. The environmental scene recognition unit identifies the characteristics of the current environment based on the surrounding environment information and in combination with the scene recognition model. The intelligent adjustment unit generates light wave adjustment instructions based on the analysis results of the two units and transmits these instructions to the lighting device, so as to adjust the parameters of the light wave through the light wave adjustment module. The light wave adjustment module communicates with the lighting device to ensure real-time and accurate adjustment of the light wave parameters. The adjusted light wave parameters include at least spectrum, light intensity, illumination, irradiation area, irradiation time, irradiation duration, color rendering index, refresh rate, color temperature, and lighting rule setting to optimize the light environment in which the human body is located, so as to achieve the purpose of improving the physiological state. It can not only improve safety and comfort, but also utilize the influence of light on the physiological state to promote the health state of the user by means of light wave adjustment.

[0021] The physiological activity evaluation component 14 is used to evaluate the physiological activity of the physiological index according to the physiological state evaluation unit and output the physiological activity index.

[0022] Specifically, the physiological activity assessment component 14 first analyzes and evaluates the collected physiological indicators according to the physiological state assessment unit. After normalizing the physiological indicators, the physiological state assessment unit will combine the processed heart rate, blood pressure, blood oxygen saturation and other physiological indicators, and use the LSTM model to determine whether the human body's physiological state is in an active state, and quantify it into a physiological activity index. This index can reflect the current health status and activity level of the human body, and provide an important reference for subsequent light wave adjustment to guide the corresponding light wave adjustment adjustment.

[0023] In a possible implementation, performing a physiological activity evaluation on the physiological indicator according to the physiological state evaluation unit and outputting the physiological activity indicator includes:

[0024] Among them, the physiological indicators include heart rate, blood pressure, heart rate variability, respiratory rate, blood oxygen saturation, blood lactate content, maximum oxygen uptake, skin temperature, body temperature, sleeping posture, snoring, sleep quality, gait balance, muscle strength, and cortisol level; the physiological indicators are normalized to the same scale and the processed physiological indicators are output; historical physiological indicators of the human body are collected, time window processing is performed on the historical physiological indicators, and multiple time series-physiological indicator samples and activity labels identifying each time series-physiological indicator sample are output; training is performed according to the multiple time series-physiological indicator samples and the activity labels identifying each time series-physiological indicator sample, an LSTM model is constructed, the processed physiological indicators are predicted by the LSTM model, and a physiological activity index is output.

[0025] Specifically, in the physiological activity assessment component 14, the collected physiological indicators include but are not limited to heart rate, blood pressure, heart rate variability, respiratory rate, blood oxygen saturation, blood lactate content, maximum oxygen uptake, skin temperature, body temperature, sleeping posture, snoring, sleep quality, gait balance, muscle strength, and cortisol level, wherein heart rate refers to the number of heart beats per minute, which is an important indicator for measuring heart function and health status; blood pressure is the pressure exerted by blood on the blood vessel wall, which is divided into systolic pressure and diastolic pressure. Systolic pressure is the pressure of blood pushing blood vessels when the heart contracts, and diastolic pressure is the pressure when the heart relaxes; heart rate variability refers to the change in the time interval between heart beats, which is usually measured by measuring adjacent heart beats. It is assessed by the interval between heartbeats (RR interval); respiratory rate refers to the number of breaths per unit time, which is different in a quiet state and in a state of exercise. In a quiet state, the respiratory rate of an adult is usually between 12 and 20 times per minute. During strenuous exercise, the respiratory rate may reach more than 50 times per minute, while the respiratory rate will slow down in sleep or relaxation; blood oxygen saturation refers to the proportion of oxygen in the blood bound to hemoglobin, which is a key indicator of lung health and body oxygen supply; blood lactate content refers to the concentration of lactic acid in the blood. Lactic acid is a metabolic product produced by muscles during anaerobic exercise, which is an indicator of exercise intensity and physical endurance; maximum Oxygen uptake refers to the maximum amount of oxygen that an individual can use per minute during extreme exercise, reflecting the body's ability to transport and utilize oxygen under high-intensity exercise; skin temperature refers to the temperature of the skin surface, reflecting the body's external heat balance; body temperature refers to the temperature inside the human body, and is one of the important vital signs for assessing human health. In physiology, body temperature can be divided into core temperature and surface temperature. The body temperature usually refers to core temperature, that is, the average temperature of the core part of the body; sleeping position refers to the limb movements maintained by a person during sleep. Everyone has their own unique sleeping position. Common sleeping positions include supine, side, prone, etc.; snoring is caused by narrowing or obstruction of the respiratory tract during sleep. The sound produced by the vibration of soft tissue caused by airflow; sleep quality is a comprehensive evaluation of sleep efficiency and comfort, including the speed of falling asleep, sleep depth, number of awakenings at night and sense of recovery after getting up in the morning. It is one of the important indicators for assessing individual health status; gait balance refers to the ability of the body to maintain stability and coordination while walking, which involves muscle strength, joint flexibility and coordinated control of the nervous system; muscle strength refers to the force that muscles can generate at maximum contraction, and is an important indicator for assessing muscle health, athletic performance and functional status; cortisol level is the content of cortisol secreted by the adrenal glands. Cortisol plays an important role in coping with stress and helps regulate blood sugar, blood pressure and the immune system;In order to better analyze these indicators, the system terminal will normalize these indicators by calculating the ratio of the difference between each indicator and the corresponding minimum value of the indicator and the difference between the corresponding maximum value and the minimum value of the indicator, so that each indicator is on the same scale to eliminate the dimensional differences of different indicators and ensure that each physiological indicator is comparable in subsequent analysis; then, the historical physiological indicators of the human body are collected, and these data are segmented according to the time sequence, that is, time window processing is performed (determined according to business needs). In this way, multiple time series-physiological indicator samples are generated, each sample is associated with a physiological indicator in a time period, and at the same time, an activity label is used for each time series-physiological indicator sample by expert decision-making. This label reflects the active state of the human body in the period. For example, when performing activity labeling, the International Commission on Illumination (CIE) can be referred to. The standard reaction light corresponding to the physiological indicators of the human body formulated by the Illumination) is used to improve the reference standard of the activity label; then, these time series-physiological indicator samples and activity labels are used for training to build a prediction model based on the long short-term memory (LSTM) network. The LSTM model is a model that can capture long-term dependencies in time series data and is suitable for time series prediction of physiological data. During the construction process, the LSTM model gradually processes the input time series data through multiple neural network layers (input layer, LSTM layer, fully connected layer, output layer) to capture the time-dependent features in the data. Each time series sample is forward propagated through the hierarchical network of LSTM. LSTM can remember important information within a long time span and automatically learn the relationship between input data and physiological activity during training. The output of the model is the predicted physiological activity index. These predicted values ​​will be used to evaluate the current state of the human body. Physiological state; during the training process, the mean square error (MSE) is used as the loss function to calculate the difference between the model output value and the actual label, and then the gradient of the loss to the model weight is calculated layer by layer through the back propagation algorithm, and the parameters of the model are updated according to these gradients using an optimization algorithm (such as the Adam optimizer), with the aim of minimizing the value of the loss function. This process is repeated until the predetermined number of training times is reached, or when the loss value converges, it stops; after the training is completed, the trained model is evaluated using a validation set that has been divided in advance to verify whether the performance of the model on different data meets expectations. The evaluation indicators usually include prediction accuracy. If the model performance meets the preset accuracy requirements, the current LSTM model can be applied to real-time physiological activity evaluation. If the verification result is not ideal, the model can be further optimized by adjusting hyperparameters such as the learning rate and batch size to ensure that it can better fit the actual data, thereby improving the accuracy of the prediction;Finally, the trained LSTM model is used to predict the processed physiological indicators and output the corresponding physiological activity indicators. This prediction result can reflect the current activity state of the human body and provide a basis for subsequent light wave adjustment. ;

[0026] The scene recognition component 15 is used to perform scene recognition on the environmental information according to the environmental scene recognition unit and output a scene activity index.

[0027] Specifically, the scene recognition component 15 first analyzes the collected environmental information according to the environmental scene recognition unit, and identifies the current scene type by extracting features of various factors in the environment (such as temperature, humidity, light intensity, air quality, etc.), such as different environmental types such as work scenes and rest scenes. The recognition process is achieved through a scene recognition model, and the purpose is to accurately judge the environmental state of the user; then, according to the recognized scene type, the system terminal will generate a scene activity index, which reflects the activity level of the environment in different scenes. For example, in a work scene, the scene activity may be high, while in a rest scene, the scene activity may be low. The scene activity index provides a reference basis for subsequent light wave adjustment, helping the system terminal to adjust the lighting parameters according to changes in the environment, thereby optimizing the user's physical and mental state. Preferably, if it is determined that the user is in a sleeping state, the light wave adjustment module is used for analysis according to the currently detected physiological activity, and the light wave adjustment parameters guiding the current environmental activity are output, thereby alleviating and improving the user's fatigue level while promoting the user's sleep health. The present application can adjust light wave data according to environmental changes to help regulate the physiological state of the human body, especially the effects on sleep, mood, attention, etc. For example, using cooler light during the day and warmer light at night can help maintain the circadian rhythm of a healthy sleep.

[0028] In a possible implementation, performing scene recognition on the environmental information according to the environmental scene recognition unit and outputting a scene activity index includes:

[0029] Among them, the environmental information includes indoor environmental information and outdoor environmental information; according to the environmental scene recognition unit, feature convolution is performed on the indoor environmental information and the outdoor environmental information, and indoor environmental features and outdoor environmental features are output; scene recognition is performed based on the indoor environmental features and outdoor environmental features, and a scene activity index is output.

[0030] Specifically, in the scene recognition component 15, first, the relevant data of the indoor and outdoor environments are received and analyzed respectively through the environmental scene recognition unit, including but not limited to temperature, humidity, light intensity, air quality, air pressure, etc.; then, through feature convolution, meaningful features are extracted from these original environmental data. The purpose of feature convolution is to compress high-dimensional environmental information and extract features that can effectively reflect the current environmental state. For example, indoor light intensity, temperature and humidity combination and other features may directly affect the user's comfort, while outdoor air quality, weather conditions, etc. may have an impact on health and activity status; then, the scene recognition model is used to classify the extracted indoor and outdoor environmental features, and a scene activity index is output according to the classification results. This index reflects the activity level of the current environment and provides a basis for subsequent light wave adjustment to optimize the user's physiological and psychological state.

[0031] During the convolution process, the system terminal will first train a CNN model based on the historical indoor environment information, historical outdoor environment information and the marked indoor environment feature labels and outdoor environment feature labels. This model includes an input layer, a pooling layer, a convolution layer, a fully connected layer and an output layer. The training method is consistent with the above, and is still carried out through forward propagation, loss calculation, back propagation, parameter optimization and other steps; after the training is completed, the CNN model will be embedded in the environmental scene recognition unit. Whenever scene recognition is required, the environmental scene recognition unit will activate the CNN model and perform feature recognition on the received indoor environment information and outdoor environment information to obtain indoor environment features and outdoor environment features.

[0032] In a possible implementation, performing scene recognition according to the indoor environment features and the outdoor environment features includes:

[0033] Define multiple scene types, and obtain multiple indoor environment feature thresholds and multiple outdoor environment feature thresholds corresponding to the multiple scene types, wherein the indoor environment feature threshold range and the outdoor environment feature threshold range corresponding to each scene type are different; construct a scene recognition model based on the multiple indoor environment feature thresholds and the multiple outdoor environment feature thresholds, analyze the indoor environment features and the outdoor environment features with the scene recognition model, and output a scene type label; output a scene activity index based on the scene type label.

[0034] Specifically, in the scene recognition component 15, the system terminal first defines multiple environmental scene types, including work scenes, rest scenes, and dining scenes. Each scene type corresponds to different indoor and outdoor environmental characteristics. These two environmental characteristics include light intensity, temperature, humidity, etc. For each scene type, a corresponding threshold range is set based on historical data, environmental standards, and expert decisions, including indoor environmental characteristic thresholds and outdoor environmental characteristic thresholds. For example, the characteristics of learning and working scenes are high light intensity and color temperature, suitable temperature, low humidity, and quiet environment, so the indoor environmental characteristic thresholds can be temperature [22°C, 24°C], humidity [40%, 60%], light intensity [300lux, 500lux], and color temperature [5000k, 7000k]. The outdoor environmental characteristic thresholds can be temperature [20℃, 25℃], humidity [40%, 60%], and light intensity [500lux, 700lux]. The characteristics of the rest scene are low light intensity and color temperature, high or low temperature, moderate humidity, and a more relaxed environment, so its indoor environment characteristic thresholds can be temperature [22℃, 28℃], humidity [45%, 65%], light intensity [100lux, 300lux], color temperature [2700k, 3000k], and the outdoor environment characteristic thresholds can be temperature [20℃, 28℃], humidity [50%, 70%], and light intensity [300lux, 500lux]. After obtaining the thresholds of multiple environmental features, the system terminal will build a scene recognition model. The goal of this model is to judge the current environmental scene based on the input indoor environmental features and outdoor environmental features. The construction method is to use the indoor and outdoor environmental feature thresholds of each scene type as input features, and construct a classification model through rules or decision trees. The model can define the feature threshold range of each environmental scene, match the input data with these threshold ranges, and determine the current scene type according to different matching situations; after building the scene recognition model, the scene recognition model matches the input feature data (such as current temperature, humidity, light intensity, etc.) with the feature thresholds of each scene type, and outputs the most suitable scene type label; once the scene type label is output, the system terminal will generate a scene activity index based on the scene type label and the mapping sample library. The index reflects the activity level of the current environment and can be used as the basis for subsequent light wave adjustment, providing accurate data support for intelligent light wave adjustment, thereby improving user experience.

[0035] In a possible implementation, outputting a scene activity index according to the scene type label includes:

[0036] A mapping sample library corresponding to scene type label samples and scene activity samples is constructed, the mapping sample library is called to map the scene type labels, and the corresponding scene activity index is output.

[0037] Specifically, in the scene recognition component 15, when the scene activity index is generated according to the scene type label, it is based on the pre-constructed mapping sample library. When constructing the mapping sample library corresponding to the scene type label sample and the scene activity sample, the system terminal first assigns a corresponding scene activity index to each scene type according to the defined scene type label (such as work scene, rest scene, dining scene, etc.). For example, the work scene may correspond to a higher activity (such as 0.9), the rest scene corresponds to a lower activity (such as 0.2), and the dining scene corresponds to a moderate activity (such as 0.6). These samples are determined by historical data and expert decisions, and the scene type label and the scene activity sample are stored together in a mapping sample library; when the system terminal needs to output the scene activity index, the mapping sample library will be called to compare the currently identified scene type label with the data in the mapping sample library, and the corresponding activity index will be found through the mapping rules stored in the library; finally, the system terminal outputs the corresponding scene activity index according to the mapping result. This scene activity index reflects the activity level of the current environment and provides a reference basis for the setting of lighting rules and healthiness for subsequent light wave adjustment.

[0038] The index analysis component 16 is used to analyze the physiological activity index and the scene activity index according to the intelligent adjustment unit, and output the light wave adjustment parameters for adjusting the lighting device.

[0039] Specifically, the indicator analysis component 16 first compares and analyzes the scene activity index of the current environment and the physiological activity index of the user, that is, determines whether the scene activity index and the physiological activity index are within the same activity threshold. If both are within the same activity threshold, it is considered a successful match, indicating that the user's physiological state is consistent with the environmental scene. At this time, the physiological activity index and the scene activity index will be transmitted to the intelligent adjustment unit for light wave analysis, and the light wave adjustment parameters will be mapped. If they are not within the same activity threshold, a primary and secondary analysis will be performed, and the most important indicators will be transmitted to the intelligent adjustment unit for light wave analysis, and the light wave adjustment parameters will be mapped. These parameters will be used to adjust the lighting device, change the light spectrum, light intensity, irradiation area, irradiation time, irradiation duration, color rendering index, color temperature, lighting rule settings, etc. of the lighting device to ensure that the environment that best meets current needs is provided to improve user experience and physiological health.

[0040] In a possible implementation, before the intelligent adjustment unit analyzes the physiological activity index and the scene activity index, the method includes:

[0041] Determine whether the physiological activity index and the scene activity index are at the same activity threshold; if the physiological activity index and the scene activity index are at the same activity threshold, return the physiological activity index and the scene activity index to the intelligent adjustment unit for light wave analysis.

[0042] Specifically, in the indicator analysis component 16, the system terminal compares the physiological activity indicator and the scene activity indicator according to a preset activity range. These preset activity ranges are usually set based on historical data, CIE standards or expert recommendations. The system terminal checks whether the two indicators fall within the same activity range. For example, if the physiological activity is 0.9 (indicating that the user is currently in a high activity state) and the scene activity is 0.8 (indicating that the environment is in an active state, such as a work scene), they are within the same threshold range and are determined to be a match; if the physiological activity and the scene activity are within the same activity threshold, it means that the user's physiological state and The characteristics of the current environment match. At this time, the system terminal will return the two indicators of physiological activity and scene activity to the intelligent adjustment unit for further light wave analysis. The purpose of this step is to adjust the light wave adjustment parameters of the lighting device according to the known physiological and environmental information, so that the user is in the best environment; once the physiological activity and scene activity are successfully matched, the intelligent adjustment unit will analyze the received physiological activity indicators and scene activity indicators, and output the parameters of the adjusted light wave through relationship mapping according to the user's current physiological needs and environmental characteristics to adjust the lighting device, ensuring that the environment is consistent with the user's physiological state and optimizing the user experience.

[0043] In a possible implementation, if the physiological activity index and the scene activity index are not at the same activity threshold, obtain a primary and secondary analysis instruction; perform a primary and secondary analysis on the physiological activity index and the scene activity index according to the primary and secondary analysis instruction, and return the physiological activity index to the intelligent adjustment unit for light wave analysis.

[0044] Specifically, in the indicator analysis component 16, the system terminal determines whether the physiological activity indicator and the scene activity indicator are within the same activity range based on a preset activity threshold range. If the two indicators are not within the same threshold range, for example, the physiological activity is high and the scene activity is low, they are considered to be mismatched. At this time, the system terminal generates a primary and secondary analysis instruction. The function of this instruction is to determine which indicator (physiological activity or scene activity) needs to be processed and adjusted first; then, the system terminal performs a primary and secondary analysis on the physiological activity indicator and the scene activity indicator based on this primary and secondary analysis instruction, that is, the physiological activity indicator is used as the primary indicator, and this primary indicator is transmitted to the intelligent adjustment unit for the aforementioned light wave analysis to ensure that the light wave adjustment matches the user's physiological needs.

[0045] In a possible implementation, the intelligent adjustment unit analyzes the physiological activity index and the scene activity index, and outputs a light wave adjustment parameter for adjusting the illumination device, including:

[0046] Obtain light wave adjustment parameter items of the lighting device, wherein the light wave adjustment parameter items at least include spectrum, light intensity, illumination, illumination area, illumination time, illumination duration, color rendering index, refresh rate, color temperature, and lighting rule settings; define a mapping relationship between the parameter value size and the activity threshold in the adjustment parameter items; the intelligent adjustment unit analyzes the physiological activity index and the scene activity index to obtain the lighting requirement; and output the light wave adjustment parameter corresponding to the lighting requirement according to the mapping relationship.

[0047] Specifically, in the index analysis component 16, the system terminal obtains the light wave adjustment parameter items of the lighting device, including spectrum, light intensity, illuminance, irradiation area, irradiation time, irradiation duration, color rendering index, refresh rate, color temperature and lighting rule settings, etc. These parameter items are the key adjustment parameters of the lighting device. By adjusting these parameters, the light wave characteristics of the environment can be controlled, thereby affecting the physiological and psychological state of the user. Among them, the spectrum refers to the distribution of light emitted by the light source in different wavelength ranges, that is, the color composition of the light source; light intensity refers to the luminous ability of the light source in a certain direction, which can control the brightness of the light source and affect the brightness of the environment; illuminance refers to the luminous flux received per unit area, which measures the intensity of the ambient light; the irradiation area refers to the area illuminated by the light source, which can be controlled by adjusting the distribution or focus of the light source; the irradiation time refers to the irradiation time within a specific time period. The way of lighting and the time of exposure usually affect the human body's biological rhythm. For example, bright light in the morning can help improve alertness and regulate circadian rhythm, while strong light should be avoided at night to avoid interfering with the body's natural sleep pattern; exposure time refers to the duration of light source exposure; color rendering index is a standard for measuring the ability of a light source to present the true color of an object. Changes in the color rendering index have a direct impact on the user's psychological state and physiological response. The higher the color rendering index, the more realistic the color restoration of the light source; refresh rate refers to the number of refreshes of the light source per unit time; color temperature is derived from thermal radiation theory. The lower the color temperature, the warmer the color of the light, and the higher the color temperature, the colder the color of the light. It can be used to create an atmosphere, relieve stress and anxiety, and regulate people's emotions, attention, work efficiency, etc.; lighting rule setting means that parameter rules can be set according to the needs of specific groups of people, or parameter settings can be simulated based on natural laws.Subsequently, the mapping relationship between the value of the light wave adjustment parameter item and the activity threshold is defined. According to the different requirements of physiological activity and scene activity, the system terminal will determine a mapping rule to map different activity values ​​to the corresponding spectrum, light intensity, illumination, irradiation area, irradiation time, irradiation duration, color rendering index, refresh rate, color temperature and lighting rule settings. For example, in low-activity scenes such as sleeping or resting, it is usually necessary to combine low color temperature (such as 2700-3000K) with soft diffuse lighting to inhibit melatonin secretion by reducing the blue light spectrum, while ensuring that the spatial illumination is evenly distributed to avoid local strong light interference; in high-activity scenes such as learning and working, it is necessary to increase the color temperature (above 5000K) and illumination level, and use wide-area uniform lighting with rich blue light spectrum characteristics to enhance the brightness of the space and visual concentration. If necessary, directional lighting can be used to enhance the lighting accuracy of the core area; as for medium-active scenes such as dining, In addition to maintaining a moderate full color temperature (3500-4500K), it is necessary to focus on controlling the color rendering index (CRI>90) and light focus of the food area, and use a combination of multiple light sources to balance the environmental atmosphere and functional requirements. At the same time, the intelligent dimming system dynamically adjusts the illumination gradient of different partitions to ensure the unity of visual comfort and spatial hierarchy. The specific parameter values ​​can be determined based on actual business needs; then, the intelligent adjustment unit performs weighted calculation on the received physiological activity index and scene activity index to obtain the lighting demand. The weight values ​​of the physiological activity index and the scene activity index are determined based on expert decisions and business needs. In addition, if only the physiological activity index is received, the physiological activity index will be directly used as the lighting demand; then, based on the lighting demand, the corresponding light wave adjustment parameters are output according to the pre-defined mapping relationship to optimize the characteristics of the ambient light wave to adapt to the current physiological health needs and scene needs.

[0048] In one possible implementation, it is determined whether the illumination device is a combined illumination device; if the illumination device is a combined illumination device, a collaborative impact analysis is performed on the combined illumination device based on the illumination demand, and collaborative light wave adjustment parameters are output; and light wave adjustment is performed on the combined illumination device according to the collaborative light wave adjustment parameters.

[0049] Specifically, in the indicator analysis component 16, the system terminal determines whether the currently used lighting device is a combined lighting device. The combined lighting device refers to a device composed of multiple light sources, which can be controlled independently or work together. For example, a lighting device may include multiple light sources of different types (such as LED light arrays, etc.), which can be adjusted individually or jointly to achieve different lighting effects; when the system terminal determines that the lighting device is a combined lighting device, it will analyze the collaborative work of multiple light sources based on the lighting demand. The lighting demand is an analysis result based on the current physiological activity and scene activity, which is used to describe the current required lighting level of the user, such as the required color temperature, lighting area, spectral characteristics, illumination uniformity, etc. By consulting the usage log of the combined lighting device, the lighting demand of each light source in the combined lighting device can be obtained. The aging degree of the light source is calculated, and the total aging degree of all light sources is calculated; then, according to the ratio of the aging degree of each light source to the total aging degree, the corresponding collaborative lighting demand is assigned to each light source, that is, the calculated ratio is multiplied by the lighting demand; then, based on the mapping relationship, each collaborative lighting demand is matched with the adjustment parameter item to obtain the light wave adjustment parameters of each light source in the lighting device, and these parameters are summarized into a set to obtain the collaborative light wave adjustment parameters; finally, the system terminal applies this collaborative light wave adjustment parameter to the combined lighting device to adjust the spectrum, light intensity, illumination, illumination area, illumination time, illumination time, color rendering index, refresh rate, color temperature, lighting law settings, etc. of each light source to achieve the desired environmental health effect and ensure that the user's physiological health needs are met.

[0050] In the above, refer to Figure 1 A light wave intelligent adjustment system based on human physiological indicators according to an embodiment of the present invention is described in detail. Figure 2 A light wave intelligent adjustment method based on human physiological indicators according to an embodiment of the present invention is described.

[0051] According to an embodiment of the present invention, a light wave intelligent adjustment method based on human physiological indicators is used to solve the technical problem that existing phototherapy products cannot dynamically adjust light waves according to real-time human physiological indicators, so as to achieve intelligent adjustment of light wave parameters according to real-time human physiological indicators and environmental information, provide users with personalized light wave adjustment experience, and ensure that users maintain light comfort in different environments while promoting health effects.

[0052] A light wave intelligent adjustment method based on human physiological indicators includes:

[0053] S100: Real-time monitoring of physiological indicators of the human body through a physiological monitoring device; S200: Real-time sensing of environmental information around the human body through an environmental sensing device; S300: Inputting the physiological indicators and the environmental information into a light wave adjustment module, the light wave adjustment module includes a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit, and the light wave adjustment module is communicatively connected with the illumination device; S400: Performing a physiological activity evaluation on the physiological indicators according to the physiological state evaluation unit, and outputting a physiological activity index; S500: Performing a scene recognition on the environmental information according to the environmental scene recognition unit, and outputting a scene activity index; S600: Analyzing the physiological activity index and the scene activity index according to the intelligent adjustment unit, and outputting a light wave adjustment parameter for adjusting the illumination device.

[0054] Among them, S400 includes:

[0055] S410: wherein the physiological indicators include heart rate, blood pressure, heart rate variability, respiratory rate, blood oxygen saturation, blood lactate content, maximum oxygen uptake, skin temperature, body temperature, sleeping posture, snoring, sleep quality, gait balance, muscle strength, and cortisol level; S420: normalize the physiological indicators to the same scale and output the processed physiological indicators; S430: collect historical physiological indicators of the human body, perform time window processing on the historical physiological indicators, output multiple time series-physiological indicator samples and activity labels identifying each time series-physiological indicator sample; S440: train according to the multiple time series-physiological indicator samples and the activity labels identifying each time series-physiological indicator sample, construct an LSTM model, predict the processed physiological indicators through the LSTM model, and output the physiological activity index.

[0056] Among them, S500 includes:

[0057] S510: wherein the environmental information includes indoor environmental information and outdoor environmental information; S520: performing feature convolution on the indoor environmental information and the outdoor environmental information according to the environmental scene recognition unit, and outputting indoor environmental features and outdoor environmental features; S530: performing scene recognition according to the indoor environmental features and the outdoor environmental features, and outputting a scene activity index.

[0058] Among them, S530 includes:

[0059] S531: Define multiple scene types, and obtain multiple indoor environment feature thresholds and multiple outdoor environment feature thresholds corresponding to the multiple scene types, wherein the indoor environment feature threshold range and the outdoor environment feature threshold range corresponding to each scene type are different; S532: Construct a scene recognition model based on the multiple indoor environment feature thresholds and the multiple outdoor environment feature thresholds, analyze the indoor environment features and the outdoor environment features with the scene recognition model, and output a scene type label; S533: Output a scene activity index based on the scene type label.

[0060] Among them, S533 includes:

[0061] S5331: Construct a mapping sample library corresponding to scene type label samples and scene activity samples, call the mapping sample library to map the scene type labels, and output the corresponding scene activity index.

[0062] Among them, S600 includes:

[0063] S610: Determine whether the physiological activity index and the scene activity index are at the same activity threshold; S620: If the physiological activity index and the scene activity index are at the same activity threshold, return the physiological activity index and the scene activity index to the intelligent adjustment unit for light wave analysis.

[0064] Among them, S620 includes:

[0065] S621: If the physiological activity index and the scene activity index are not at the same activity threshold, obtain a primary and secondary analysis instruction; S622: Perform a primary and secondary analysis on the physiological activity index and the scene activity index according to the primary and secondary analysis instruction, and return the physiological activity index to the intelligent adjustment unit for light wave analysis.

[0066] Among them, S600 includes:

[0067] S630: Obtain the light wave adjustment parameter items of the lighting device, and the light wave adjustment parameter items at least include spectrum, light intensity, illumination, illumination area, illumination time, illumination duration, color rendering index, refresh rate, color temperature, and lighting rule settings; S640: Define the mapping relationship between the parameter value size and the activity threshold in the adjustment parameter item; S650: The intelligent adjustment unit analyzes the physiological activity index and the scene activity index to obtain the lighting requirement; S660: Output the light wave adjustment parameter corresponding to the lighting requirement according to the mapping relationship.

[0068] Among them, S630 includes:

[0069] S631: Determine whether the illumination device is a combined illumination device; S632: If the illumination device is a combined illumination device, perform a collaborative impact analysis on the combined illumination device based on the illumination demand, and output collaborative light wave adjustment parameters; S633: Perform light wave adjustment on the combined illumination device according to the collaborative light wave adjustment parameters.

[0070] The light wave intelligent adjustment system based on human physiological indicators provided by an embodiment of the present invention can execute the light wave intelligent adjustment method based on human physiological indicators provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0071] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0072] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A light wave intelligent adjustment system based on human physiological indicators, characterized in that: The system comprises: An indicator monitoring component, used for real-time monitoring of physiological indicators of the human body through a physiological monitoring device; An environmental monitoring component is used to sense the environmental information around the human body in real time through an environmental sensing device; A data input component, used to input the physiological index and the environmental information into a light wave adjustment module, wherein the light wave adjustment module includes a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit, and the light wave adjustment module is communicatively connected with the illumination device; A physiological activity evaluation component, configured to evaluate the physiological activity of the physiological index according to the physiological state evaluation unit, and output a physiological activity index; A scene recognition component, configured to perform scene recognition on the environmental information according to the environmental scene recognition unit, and output a scene activity index; The index analysis component is used to analyze the physiological activity index and the scene activity index according to the intelligent adjustment unit, and output the light wave adjustment parameters for adjusting the lighting device.

2. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 1, characterized in that: According to the physiological state evaluation unit, the physiological activity degree of the physiological index is evaluated, and the physiological activity degree index is output, include: The physiological indicators include heart rate, blood pressure, heart rate variability, blood oxygen saturation, blood lactate content, maximum oxygen uptake, skin temperature, muscle strength, and cortisol level; Performing same-scale normalization processing on the physiological indicators, and outputting the processed physiological indicators; Collecting historical physiological indicators of the human body, performing time window processing on the historical physiological indicators, and outputting a plurality of time series-physiological indicator samples and activity labels identifying each time series-physiological indicator sample; Training is performed based on the multiple time series-physiological indicator samples and activity labels identifying each time series-physiological indicator sample, an LSTM model is constructed, the processed physiological indicators are predicted by the LSTM model, and a physiological activity indicator is output.

3. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 1, characterized in that: The environmental scene recognition unit performs scene recognition on the environmental information and outputs a scene activity index, include: Wherein, the environmental information includes indoor environmental information and outdoor environmental information; Performing feature convolution on the indoor environment information and the outdoor environment information according to the environment scene recognition unit, and outputting indoor environment features and outdoor environment features; Scene recognition is performed according to the indoor environment characteristics and the outdoor environment characteristics, and a scene activity index is output.

4. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 3, characterized in that: Performing scene recognition according to the indoor environment features and the outdoor environment features includes: Define multiple scene types, and obtain multiple indoor environment feature thresholds and multiple outdoor environment feature thresholds corresponding to the multiple scene types, wherein the indoor environment feature threshold range and the outdoor environment feature threshold range corresponding to each scene type are different; According to the multiple indoor environment feature thresholds and the multiple outdoor environment feature thresholds, a scene recognition model is constructed, the indoor environment features and the outdoor environment features are analyzed by the scene recognition model, and a scene type label is output; According to the scene type label, a scene activity index is output.

5. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 4, characterized in that: According to the scene type label, the scene activity index is output, including: A mapping sample library corresponding to scene type label samples and scene activity samples is constructed, the mapping sample library is called to map the scene type labels, and the corresponding scene activity index is output.

6. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 1, characterized in that: Before analyzing the physiological activity index and the scene activity index according to the intelligent adjustment unit, the method includes: Determining whether the physiological activity index and the scene activity index are at the same activity threshold; If the physiological activity index and the scene activity index are at the same activity threshold, the physiological activity index and the scene activity index are returned to the intelligent adjustment unit for light wave analysis.

7. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 6, characterized in that: If the physiological activity index and the scene activity index are not at the same activity threshold, obtaining a primary and secondary analysis instruction; The physiological activity index and the scene activity index are analyzed in primary and secondary order according to the primary and secondary analysis instruction, and the physiological activity index is returned to the intelligent adjustment unit for light wave analysis.

8. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 1, characterized in that: The intelligent adjustment unit analyzes the physiological activity index and the scene activity index, and outputs light wave adjustment parameters for adjusting the illumination device, including: Obtaining light wave adjustment parameter items of the illumination device, wherein the light wave adjustment parameter items at least include spectrum, light intensity, illumination, illumination area, illumination time, illumination duration, color rendering index, refresh rate, color temperature and illumination rule setting; Defining a mapping relationship between a parameter value in the adjustment parameter item and an activity threshold; The intelligent adjustment unit analyzes the physiological activity index and the scene activity index to obtain a lighting demand; The light wave adjustment parameter corresponding to the light demand is output according to the mapping relationship.

9. The light wave intelligent adjustment system based on human physiological indicators as claimed in claim 8, characterized in that: Determining whether the illumination device is a combined illumination device; If the lighting device is a combined lighting device, a coordinated impact analysis is performed on the combined lighting device based on the lighting demand, and a coordinated light wave adjustment parameter is output; The light wave of the combined illumination device is adjusted according to the coordinated light wave adjustment parameter.

10. A light wave intelligent adjustment method based on human physiological indicators, characterized in that: The method is implemented by a light wave intelligent adjustment system based on human physiological indicators according to any one of claims 1 to 9, and the method comprises: Real-time monitoring of human physiological indicators through physiological monitoring devices; Real-time sensing of environmental information around the human body through environmental sensing devices; Inputting the physiological index and the environmental information into a light wave adjustment module, wherein the light wave adjustment module includes a physiological state evaluation unit, an environmental scene recognition unit and an intelligent adjustment unit, and the light wave adjustment module is communicatively connected with the illumination device; Performing a physiological activity evaluation on the physiological index according to the physiological state evaluation unit, and outputting a physiological activity index; Performing scene recognition on the environmental information according to the environmental scene recognition unit, and outputting a scene activity index; The intelligent adjustment unit analyzes the physiological activity index and the scene activity index, and outputs light wave adjustment parameters for adjusting the lighting device.

Images