Light adjustment method and system for learning cabin, electronic equipment and storage medium

Abstract

The invention discloses a light adjustment method and system of a learning cabin, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the steps that learning content information and physiological signal information of a user in a learning cabin are acquired, the learning content information comprises an application program type and a document type, and the physiological signal information comprises heart rate data and eye movement data; calculating a fatigue index of the user based on the heart rate data, and calculating a concentration index of the user based on the eye movement data; determining a learning scene type corresponding to the application program type and the document type, and obtaining a corresponding target illumination parameter group; and when the fatigue index is greater than a first threshold value or the concentration index is smaller than a second threshold value, dynamically adjusting the illumination intensity and the color temperature, and controlling the illumination equipment of the learning cabin to illuminate according to the dynamically adjusted illumination intensity and color temperature. By implementing the technical scheme provided by the invention, the effects of dynamically adjusting the light of the learning cabin and providing a comfortable learning environment for a user are achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a light adjustment method, system, electronic device and storage medium for a learning cabin. Background Art

[0002] In the current digital age, people increasingly rely on various electronic devices for study and work. These environments usually require long periods of concentration and efficient information processing, and the lighting conditions in the learning or working environment directly affect people's physiological and psychological state, thereby affecting learning and work efficiency. The learning cabin is a facility that provides users with a closed or semi-enclosed personal learning space, designed to optimize learning efficiency by controlling environmental factors such as sound, light, temperature, etc.

[0003] At present, existing learning cabins often use a fixed light environment to ensure the basic visual comfort of users. However, in actual applications, since the learning state of users often changes during a long period of learning, it is often difficult to provide users with a comfortable learning environment by only using the existing fixed light environment to provide lighting for users. Summary of the invention

[0004] The present application provides a light adjustment method, system, electronic device and storage medium for a learning cabin, which can dynamically adjust the light in the learning cabin to provide a comfortable learning environment for users.

[0005] In a first aspect, the present application provides a light adjustment method for a learning cabin, comprising: Acquire learning content information and physiological signal information of the user in the learning cabin, wherein the learning content information includes application type and document type, and the physiological signal information includes heart rate data and eye movement data; Calculating the user's fatigue index based on the heart rate data, and calculating the user's concentration index based on the eye movement data; Determine a learning scene type corresponding to the application type and the document type, and obtain a target illumination parameter group corresponding to the learning scene type; When the fatigue index is greater than a first threshold, or the concentration index is less than a second threshold, the light intensity and color temperature in the target lighting parameter group are dynamically adjusted, and the lighting equipment of the learning cabin is controlled to illuminate according to the dynamically adjusted light intensity and color temperature.

[0006] In a second aspect of the present application, a light adjustment system for a learning cabin is provided, the system comprising: An information acquisition module, used to acquire learning content information and physiological signal information of the user in the learning cabin, wherein the learning content information includes application type and document type, and the physiological signal information includes heart rate data and eye movement data; an index determination module, configured to calculate a fatigue index of the user based on the heart rate data, and to calculate a concentration index of the user based on the eye movement data; A parameter determination module, used to determine the learning scene type corresponding to the application type and the document type, and obtain a target illumination parameter group corresponding to the learning scene type; The light adjustment module is used to dynamically adjust the light intensity and color temperature in the target light parameter group when the fatigue index is greater than a first threshold or the concentration index is less than a second threshold, and control the lighting equipment of the learning cabin to illuminate according to the dynamically adjusted light intensity and color temperature.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a light adjustment method for a learning cabin when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a light adjustment method for a learning cabin.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, the user's learning content information, including application type and document type, and physiological signal information, including heart rate data and eye movement data, are obtained in the learning cabin, and the user's fatigue index is calculated based on the heart rate data and the user's concentration index is calculated based on the eye movement data. Then, the target lighting parameter group is obtained in combination with the learning scene type corresponding to the application type and the document type. When the fatigue index is greater than the first threshold or the concentration index is less than the second threshold, the light intensity and color temperature in the target lighting parameter group are dynamically adjusted, so that the lighting equipment of the learning cabin can autonomously change the light level and color temperature according to the user's immediate learning status, so that the user can obtain a more suitable lighting environment when learning fatigue or concentration decreases, so as to avoid the aggravation of visual fatigue and maintain relative stability of attention, and finally effectively provide users with a comfortable learning environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flowchart of a method for adjusting light in a learning cabin provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a light adjustment system of a learning cabin provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a method for adjusting light in a learning cabin. In one embodiment, please refer to Figure 1 , Figure 1 This is a flow chart of the light adjustment method of the learning cabin provided in the embodiment of the present application. The method can be implemented by a computer program, which can be integrated in an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run in a light adjustment system of a learning cabin based on a von Neumann system. Specifically, the method can include the following steps: Step 101: Acquire the learning content information and physiological signal information of the user in the learning cabin, where the learning content information includes application type and document type, and the physiological signal information includes heart rate data and eye movement data.

[0016] Among them, the learning cabin in this embodiment refers to a closed or semi-closed personal learning space facility, which integrates an intelligent lighting system, an information collection system, and an environmental control system. The learning cabin is equipped with adjustable LED lighting equipment, which can adjust the light intensity and color temperature according to needs; it is also equipped with non-contact heart rate detection devices, eye tracking devices and other physiological signal collection equipment, as well as computer terminal equipment for displaying and operating learning content. The design purpose of the learning cabin is to provide users with an independent learning environment that can be intelligently adjusted, and to improve learning efficiency through dynamic regulation of environmental factors such as light.

[0017] In this embodiment, learning content information refers to two types of core information when users conduct learning activities in the learning cabin: application type and document type. The application type reflects the nature of the software tool currently used by the user, such as word processing software, programming development environment, presentation tool, etc.; the document type indicates the specific content form that the user is processing, such as text documents, program codes, image files, etc. This information is collected in real time through the operating system interface of the learning cabin and is used to identify and determine the user's specific learning scenario.

[0018] In this embodiment, physiological signal information refers specifically to two types of key physiological data that reflect the user's physical and mental state: heart rate data and eye movement data. Heart rate data is collected through a non-contact heart rate detection device to assess the user's fatigue level; eye movement data is obtained through an eye tracking device, including information such as pupil size, gaze point coordinates, and blink intervals, which are used to assess the user's concentration. These physiological signal information reflects the changes in the user's physical and mental state during the learning process in real time, and is an important basis for the system to dynamically adjust the light environment.

[0019] Specifically, the learning content information and physiological signal information of the user are first obtained in real time through the built-in data acquisition system of the learning cabin. The acquisition of learning content information is to monitor the type of application currently being used by the user, such as word processing software, programming tools or video players, etc., in real time through the operating system interface of the learning cabin, and identify the type of document being browsed by the user, such as text documents, picture documents or code files, etc. This acquisition of learning content information helps the system to accurately understand the user's current learning scene and provide a scene basis for the subsequent adjustment of light parameters. In terms of obtaining physiological signal information, the learning cabin is equipped with a non-contact heart rate detection device and an eye tracking device. Among them, the heart rate detection device adopts image photoelectric volume pulse wave technology to obtain heart rate data by analyzing the slight color changes of the user's facial skin. This method avoids the discomfort caused to the user by traditional contact heart rate detection. The eye tracking device adopts a combination of infrared light source and high-speed camera to obtain eye movement data including pupil size, fixation point coordinates and blinking frequency by capturing the user's eye movement trajectory. The acquisition process of these physiological signals is imperceptible to the user and will not interfere with normal learning activities.

[0020] Step 102: Calculate the user's fatigue index based on the heart rate data, and calculate the user's concentration index based on the eye movement data.

[0021] Among them, the fatigue index is a comprehensive evaluation indicator that reflects the user's physical fatigue level. The larger the index value, the more serious the user's fatigue level.

[0022] In this embodiment, the concentration index is a composite index obtained by weighted calculation of three dispersion indices, and is used to quantify the user's attention level. The smaller the index value, the less focused the user is.

[0023] Specifically, in order to accurately evaluate the user's state changes during the learning process, the system needs to conduct in-depth analysis of the collected heart rate data and eye movement data, and calculate the user's fatigue index and concentration index respectively. First, for the processing of heart rate data, the system calculates the heart rate change amplitude between adjacent moments to generate a heart rate fluctuation sequence. When a data segment with continuous rise or continuous fall is detected in the heart rate fluctuation sequence, the system determines it as an abnormal fluctuation segment and records the duration of these abnormal fluctuation segments. At the same time, the system calculates the user's average heart rate value and compares it with the preset baseline heart rate value to obtain the difference as the first sub-fatigue value, and the duration of the abnormal fluctuation segment is used as the second sub-fatigue value. By normalizing these two sub-fatigue values, the user's fatigue index is finally obtained. In terms of the calculation of the concentration index, the system first extracts three key features from the eye movement data: pupil size, fixation point coordinates, and blink interval. Within the preset detection time, the system calculates the change rate of pupil size and the offset distance of fixation point coordinates respectively. When the number of pupil changes exceeds the first threshold, the system records it as the first dispersion; when the number of gaze point deviations exceeds the second threshold, the system records it as the second dispersion. At the same time, the system calculates the number of blinks in a preset unit time based on the blink interval, and determines the deviation value from the standard blink frequency as the third dispersion. Finally, the system performs weighted calculation on these three dispersions to obtain the user's concentration index. The design of this dual-index evaluation mechanism is based on the scientific laws of human physiological characteristics: when the human body is fatigued, the heart rate changes will show a specific fluctuation pattern; when the attention is distracted, the eye movement characteristics will show obvious change characteristics. By monitoring the indicators of these two dimensions at the same time, the system can more comprehensively evaluate the user's learning status. For example, when the system detects that the user's heart rate has continued abnormal fluctuations, and the eye movement data shows frequent gaze point shifts, this often indicates that the user is already in a state of fatigue and difficulty concentrating. The effect of this evaluation method is mainly reflected in its accuracy and real-time performance. Through the comprehensive analysis of multiple sub-indicators, the system can effectively reduce the misjudgment that may be caused by a single indicator and improve the accuracy of state evaluation.

[0024] Based on the above embodiment, as an optional embodiment, in step 102: calculating the user's fatigue index based on the heart rate data, this step may also include the following steps: Step 201: Calculate the heart rate variation between adjacent moments in the heart rate data to obtain a heart rate fluctuation sequence.

[0025] Specifically, to accurately evaluate the user's fatigue state, the system needs to systematically process and analyze the heart rate data. First, the system preprocesses the acquired heart rate data and calculates the difference in heart rate between two adjacent moments at each sampling moment through the sliding window method. Specifically, if the heart rate value at time t is set as H(t), then the heart rate change amplitude ΔH(t) at the adjacent moment = H(t) - H(t - 1). By continuously calculating the heart rate change amplitude at each sampling moment, the system generates a heart rate fluctuation sequence {ΔH(t)} that reflects the dynamic change characteristics of the heart rate. This processing method can effectively capture the trend characteristics of heart rate changes and provide basic data for subsequent identification of abnormal fluctuations.

[0026] Step 202: When there are consecutive rising or consecutive falling data segments in the heart rate fluctuation sequence, determine the data segment as an abnormal fluctuation segment and determine the duration of the abnormal fluctuation segment.

[0027] Specifically, the system performs pattern recognition on the generated heart rate fluctuation sequence, focusing on identifying consecutive rising or consecutive falling data segments. Specifically, the system sets a sliding detection window with a length of N sampling points. Within each window, if it is detected that the heart rate change amplitudes of consecutive M (M < N) are all positive or all negative, then mark this data segment as an abnormal fluctuation segment. At the same time, the system records the start time ts and end time te of each abnormal fluctuation segment and calculates its duration T = te - ts. This method of identifying abnormal fluctuation segments can effectively detect abnormal patterns of heart rate changes, which are usually closely related to the user's fatigue state.

[0028] Step 203: Calculate the average heart rate value of the user based on the heart rate data.

[0029] Specifically, the system uses the weighted average method to calculate the average heart rate value of the user. First, the system collects all valid heart rate data points within a preset calculation time window (such as the last 10 minutes). Then, to reduce the influence of outliers, the system assigns different weights to these data points, with newer data points having higher weights and older data points having lower weights. Let the heart rate value of the i-th data point be Hi and the corresponding weight be wi, then the calculation formula for the average heart rate value is: average heart rate = Σ(wi × Hi) / Σwi. This weighted average calculation method can more accurately reflect the user's current heart rate level and has strong anti-interference ability.

[0030] Step 204: Determine the difference between the average heart rate value and the reference heart rate value as the first sub-fatigue value, and determine the duration of the abnormal fluctuation segment as the second sub-fatigue value; perform normalization processing on the first sub-fatigue value and the second sub-fatigue value to obtain the user's fatigue index.

[0031] Specifically, in order to convert the user's heart rate change characteristics into a quantifiable fatigue index, the system needs to standardize and comprehensively calculate the average heart rate value and the duration of the abnormal fluctuation segment obtained in the previous step. Specifically, the system first obtains the user's baseline heart rate value, which can be a standard value obtained by statistically analyzing the user's heart rate data in a normal state. The system calculates the absolute difference between the average heart rate value and the baseline heart rate value, and determines the difference as the first sub-fatigue value F1. For example, if the user's average heart rate value is 85 times / minute and the baseline heart rate value is 72 times / minute, the first sub-fatigue value F1 is |85-72|=13 times / minute. The larger this difference is, the more the user's current heart rate level deviates from the normal state, and the higher the fatigue level may be. At the same time, the system directly uses the duration of the detected abnormal fluctuation segment as the second sub-fatigue value F2. For example, if the duration of the abnormal fluctuation segment detected by the system in the most recent detection cycle is 45 seconds, the second sub-fatigue value F2 is 45 seconds. The longer this duration is, the more unstable the user's heart rate is, which often means a higher level of fatigue.

[0032] In order to make the two sub-fatigue values ​​of different dimensions comparable, the system needs to normalize them. The system adopts the maximum and minimum normalization method, sets the normalization range of the first sub-fatigue value to [0, 1], and the normalization formula is: F1'=(F1-F1min) / (F1max-F1min), where F1max and F1min are the preset upper and lower limits of the heart rate difference, respectively. Similarly, the second sub-fatigue value is also normalized, F2'=(F2-F2min) / (F2max-F2min), where F2max and F2min are the preset upper and lower limits of the duration of abnormal fluctuations, respectively. Finally, the system performs weighted summation on the two normalized sub-fatigue values ​​to obtain the final fatigue index FI. The calculation formula is: FI = w1×F1'+w2×F2', where w1 and w2 are weight coefficients, and satisfy w1+w2=1. The weight coefficient here can be dynamically adjusted according to the actual application scenario and individual differences of users to obtain more accurate fatigue assessment results. For example, when the user is in a learning task that requires high concentration, the weight of the heart rate difference (F1') can be appropriately increased; when the user is in a more relaxed learning environment, the weight of the abnormal fluctuation duration (F2') can be appropriately increased.

[0033] This fatigue calculation method based on dual indicators has significant advantages: first, by simultaneously considering the two dimensions of the degree of deviation and the stability of changes in the heart rate level, it can more comprehensively evaluate the user's fatigue state; second, through normalization processing, it solves the problem that indicators of different dimensions are difficult to directly compare; finally, through the flexible adjustment of the weight coefficient, the system has strong adaptability and can provide more accurate fatigue assessment results for different users and different scenarios.

[0034] Based on the above embodiment, as an optional embodiment, in step 102: calculating the user's concentration index based on the eye movement data, this step may also include the following steps: Step 205: extract pupil size, gaze point coordinates and blink interval from the eye movement data; calculate the change rate of pupil size and the offset distance of gaze point coordinates within a preset detection time, and determine the number of pupil changes greater than a first threshold and the number of gaze point offsets greater than a second threshold as the first dispersion and the second dispersion, respectively.

[0035] Specifically, the system needs to extract and analyze three key features of the user's eye activity from the eye movement data. First, the pupil size data is extracted. The system records the size of the pupil at each sampling moment and calculates the percentage change in pupil size between adjacent moments. At the same time, the gaze point coordinate data is extracted. The system records the position coordinates of the user's eye gaze point on the screen and calculates the movement distance of the gaze point position between adjacent moments. Within a preset detection time (for example, one minute), the system counts the number of times the pupil size change exceeds the first threshold and records it as the first dispersion; counts the number of times the gaze point movement distance exceeds the second threshold and records it as the second dispersion. The design of this processing method is based on the principle that when the user's attention is distracted, frequent pupil size changes and wandering of the eyes are often observed.

[0036] Step 206: Calculate the number of blinks within a preset unit time based on the blink interval, and determine the deviation value between the number of blinks and the standard blink frequency as a third dispersion.

[0037] Specifically, the system analyzes the user's blinking behavior. The system first records the time point of each blink action to obtain the time interval between two adjacent blinks. Within a preset unit time (for example, one minute), the system calculates the actual number of blinks of the user. This actual number of blinks is compared with the standard blinking frequency of the human body in a normal state (for example, fifteen times per minute), and the difference between them is calculated. This difference is determined as the third dispersion. The basis of this analysis method is that when the user is not paying attention, the blinking frequency will usually deviate from the normal level, which may manifest as frequent blinking or not blinking for a long time.

[0038] Step 207: Perform weighted calculation on the first dispersion, the second dispersion, and the third dispersion to obtain a user concentration index.

[0039] Specifically, the system comprehensively calculates the three dispersion indicators to obtain the final concentration index. The system first assigns different weight coefficients to the three dispersion indicators, and the sum of these weight coefficients is equal to one. Then, the system divides each dispersion indicator by its respective preset maximum reference value to obtain a standardized value. Finally, the system multiplies these standardized values ​​with the corresponding weight coefficients and sums them to obtain the final concentration index. The advantage of this calculation method is that by adjusting the weight coefficients, the system can adjust the importance of each indicator according to different learning scenarios. For example, in a scenario where the user needs to continue to pay attention to the screen content, the weight of the second dispersion related to the movement of the gaze point can be increased; in a scenario where the user needs to carefully observe the details, the weight of the first dispersion related to the pupil change can be increased. This flexible evaluation mechanism enables the system to more accurately evaluate the user's concentration state and provide a reliable basis for subsequent light environment adjustment.

[0040] Step 103: Determine the learning scene type corresponding to the application type and the document type, and obtain the target lighting parameter group corresponding to the learning scene type.

[0041] Among them, the learning scene type in this embodiment refers to the classification definition of the user's current learning activity characteristics, for example, including three types: video watching scenes, text reading scenes and online examination scenes. Among them, video watching scenes mainly refer to the learning state of users when watching teaching videos, online courses and other dynamic content. The requirements of this type of scene for the light environment are mainly reflected in preventing screen reflection and protecting visual comfort; reading text scenes refer to the learning state of users when reading static text content such as e-books and documents. This type of scene has high requirements for the uniformity and light-dark contrast of light; online examination scenes refer to the learning state of users when participating in online tests, answering questions and other activities that require high concentration. This type of scene has more stringent requirements on the stability and moderation of light. The system determines the specific scene type by identifying information such as the user's current screen content type, operation behavior characteristics, etc.

[0042] In this embodiment, the target lighting parameter group refers to the optimal lighting environment parameter set comprehensively determined by the system based on the user's fatigue index, concentration index, and learning scene type. This parameter group mainly includes parameters such as lighting brightness, color temperature value, and illumination angle. Among them, the lighting brightness parameter is used to control the brightness of the ambient light, and will be dynamically adjusted according to the user's fatigue state and scene requirements; the color temperature value parameter is used to adjust the warm and cold tones of the light. Higher color temperature values ​​(cold tones) help improve attention, and lower color temperature values ​​(warm tones) help relieve visual fatigue; the illumination angle parameter determines the way light is irradiated to the user's field of view, and is mainly used to optimize light distribution and reduce glare. The specific values ​​of these parameters will be dynamically optimized according to the user status indicators monitored by the system in real time to create a lighting environment that best suits current learning needs.

[0043] Specifically, in order to achieve precise adjustment of the lighting environment, the system needs to identify the specific learning scene type based on the application type and document type currently being used by the user. The system first obtains the information of the currently running application through the operating system interface, and divides the application type into video playback, document processing, and online assessment. For example, when it is detected that the user is using video playback software to watch teaching videos, the system classifies it as a video viewing scene; when the user uses a PDF reader or document editing software, the system classifies it as a text reading scene; when the user opens an online examination system or assessment platform, the system classifies it as an online examination scene. At the same time, the system also analyzes the format characteristics of the currently open document. For video file formats, the system identifies parameters such as resolution and frame rate; for text documents, the system identifies typesetting features such as font size and line spacing; for online assessment content, the system identifies question layout and interactive features. By comprehensively analyzing the characteristics of application types and document types, the system can more accurately determine the user's current learning scene type. After determining the type of learning scene, the system retrieves the target lighting parameter group that matches the scene type from the preset lighting parameter database. For scenarios such as watching videos, the system will select relatively low lighting brightness (e.g. 300-400 lux) and medium-warm color temperature (e.g. 4000-5000K) to reduce screen reflections and provide a comfortable viewing experience; for scenarios such as reading texts, the system will select higher lighting brightness (e.g. 500-600 lux) and medium-cool color temperature (e.g. 5000-6000K) to provide clear text display; for scenarios such as online exams, the system will select moderate lighting brightness (e.g. 400-500 lux) and higher color temperature (e.g. 5500-6500K) to maintain sufficient attention levels.

[0044] Based on the above embodiment, as an optional embodiment, in step 103: determining the learning scenario type corresponding to the application type and the document type, this step may also include the following steps: Step 301: Extract interaction features of application type and content features of document type.

[0045] Specifically, the system extracts the interactive features of the application and the content features of the document through the data acquisition module. For the interactive feature extraction of the application, the system monitors the interaction between the user and the application, including the interaction data such as the mouse click frequency, keyboard input mode, and page switching method. For example, in video playback applications, the system will record features such as playback control operations and progress bar adjustment; in document processing applications, the system will record features such as text selection and page scrolling; in online assessment applications, the system will record features such as answering operations and option switching. At the same time, the system performs content analysis on the open document and extracts the content features of the document, including information such as text density, image-text ratio, and multimedia element distribution. This dual feature extraction method can fully capture the characteristics of the user's current learning activities.

[0046] Step 302: Match the interaction feature with a preset first feature library to obtain a corresponding application scenario classification, and match the content feature with a preset second feature library to obtain a corresponding document scenario classification.

[0047] Specifically, the system matches and analyzes the extracted features with the preset feature library. First, the system compares the extracted interaction features with the typical interaction patterns pre-stored in the first feature library. The first feature library contains standard interaction patterns for various types of learning applications. For example, video playback applications usually perform periodic playback control operations, document processing applications perform continuous text operations and page browsing, and online assessment applications perform regular selection and confirmation operations. Through pattern matching, the system obtains application scenario classification results. At the same time, the system matches the extracted content features with the document feature templates pre-stored in the second feature library. The second feature library contains typical features of various types of learning documents. For example, teaching videos have specific encoding formats and picture features, electronic documents have standardized typesetting formats and text structures, and assessment documents have fixed question layouts and answer areas. Through feature matching, the system obtains document scenario classification results.

[0048] Step 303: Based on a preset scenario type library, determine the learning scenario type corresponding to the application scenario classification and the document scenario classification.

[0049] Specifically, the system maps the combination of application scenario classification and document scenario classification to specific learning scenario types based on a preset scenario type library. The scenario type library predefines valid combinations of application scenario classification and document scenario classification and their corresponding learning scenario types. For example, when the application scenario is classified as a video playback category and the document scenario is classified as multimedia content, the system determines it as a video viewing category scene; when the application scenario is classified as a document processing category and the document scenario is classified as text content, the system determines it as a text reading category scene; when the application scenario is classified as an online assessment category and the document scenario is classified as test content, the system determines it as an online examination category scene. This scene determination method based on dual classification has high accuracy, can effectively avoid misjudgments that may be caused by a single feature, and provide a more reliable scene basis for subsequent lighting environment adjustment.

[0050] Step 104: When the fatigue index is greater than the first threshold, or the concentration index is less than the second threshold, the light intensity and color temperature in the target lighting parameter group are dynamically adjusted, and the lighting equipment of the learning cabin is controlled to illuminate according to the dynamically adjusted light intensity and color temperature.

[0051] Among them, the first threshold in this embodiment refers to the critical reference value for judging the user's fatigue level. The threshold is set based on the calculation standard of the fatigue index, and the value range is 0 to 1, where 0 represents a completely non-fatigued state and 1 represents an extremely fatigued state. In this embodiment, the first threshold can be set to 0.7, and the determination of this value is based on the following considerations: When the fatigue index exceeds 0.7, the user usually has obvious fatigue characteristics, such as frequent blinking, slower pupil contraction reaction, etc. At this time, it is necessary to adjust the lighting environment in time to relieve fatigue. This threshold setting will neither be too sensitive to cause frequent adjustments to the system, nor will it be too lagging to affect the user's visual health. The system can also fine-tune the threshold based on the usage feedback and actual effects of different users.

[0052] The second threshold in this embodiment refers to the critical reference value used to judge the user's concentration level. The threshold also uses a standardized range of 0 to 1, where 0 represents a completely unfocused state and 1 represents a highly focused state. In this embodiment, the second threshold can be set to 0.5, and the determination of this value is based on the following considerations: When the concentration index is lower than 0.5, the user usually exhibits characteristics of distraction, such as frequent wandering of vision, drastic changes in pupil size, etc. At this time, it is necessary to adjust the lighting environment to help the user refocus. This threshold setting can intervene in time when the user's attention drops significantly, while avoiding excessive intervention in slight fluctuations in attention. The system can dynamically adjust the threshold according to different learning scenario types and user feedback.

[0053] Specifically, the system monitors the user's fatigue index and concentration index in real time and compares them with preset thresholds. When the fatigue index exceeds the first threshold (for example, set to 0.7, indicating a more fatigued state), or the concentration index is lower than the second threshold (for example, set to 0.5, indicating a more distracted state), the system will start the dynamic adjustment mechanism of the lighting parameters. The specific adjustment strategy is as follows: When the fatigue index is detected to be too high, the system first reduces the light intensity by about 10-20% to make the light environment softer, and adjusts the color temperature to a warmer tone (reduced by 300-500K). This adjustment helps to relieve visual fatigue. For example, if the light intensity in the original target lighting parameter group is 500 lux and the color temperature is 5000K, the adjusted light intensity may drop to 400-450 lux and the color temperature to 4500-4700K. When the concentration index is detected to be too low, the system will appropriately increase the light intensity by about 15-25% and adjust the color temperature to a cooler tone (increased by 400-600K). This adjustment helps to improve alertness and attention levels. The system uses a gradual adjustment method to gradually adjust the lighting parameters to the target value within 30 seconds to 1 minute to avoid sudden adjustments that cause visual discomfort to the user. At the same time, the system will continue to monitor changes in the user's status. If the user's fatigue index and concentration index have not improved within 5-10 minutes after the adjustment, the system will make a second fine-tuning until the ideal effect is achieved. In order to avoid frequent adjustments, the system sets a minimum adjustment interval (for example, 3 minutes) to ensure the relative stability of the lighting environment. The advantages of this dynamic adjustment mechanism are: first, it can respond to changes in user status in a timely manner and provide personalized lighting adjustment solutions; second, through the coordinated adjustment of light intensity and color temperature, it can both relieve visual fatigue and improve attention levels.

[0054] Based on the above embodiment, as an optional embodiment, in step 104: dynamically adjusting the illumination intensity and color temperature in the target illumination parameter group, this step may also include the following steps: Step 401: Obtain the ambient light intensity and ambient color temperature in the learning cabin to obtain an environmental parameter group; calculate the light intensity adjustment step and the color temperature adjustment step based on the parameter difference between the environmental parameter group and the target light parameter group.

[0055] Specifically, the system first obtains the actual lighting parameters of the current environment through the built-in light sensor of the learning cabin. The ambient light intensity is measured by the illuminance meter module; the ambient color temperature is measured by the color temperature sensor. These two parameters constitute the environmental parameter group. Subsequently, the system compares the environmental parameter group with the target light parameter group and calculates the difference between the two sets of parameters. Based on these differences, the system uses an adaptive algorithm to calculate the adjustment step size: for light intensity, when the difference is greater than 100 lux, the adjustment step size is set to 15% of the difference; when the difference is between 50-100 lux, the adjustment step size is set to 10% of the difference; when the difference is less than 50 lux, the adjustment step size is set to 5% of the difference. For color temperature, when the difference is greater than 500K, the adjustment step size is set to 20% of the difference; when the difference is between 200-500K, the adjustment step size is set to 15% of the difference; when the difference is less than 200K, the adjustment step size is set to 10% of the difference. This progressive adjustment step size calculation method can ensure a smooth transition of the lighting environment.

[0056] Based on the above embodiment, as an optional embodiment, in step 401: based on the parameter difference between the environment parameter group and the target illumination parameter group, the illumination intensity adjustment step and the color temperature adjustment step are calculated. This step may also include the following steps: Step 411: Calculate the light intensity difference between the ambient light intensity in the ambient parameter group and the target light intensity in the target light parameter group, and the color temperature difference between the ambient color temperature in the ambient parameter group and the target color temperature in the target light parameter group.

[0057] Specifically, the system first calculates the difference between the parameters in the environmental parameter group and the target illumination parameter group through the difference calculation module. Specifically, the system extracts the current ambient light intensity (e.g., 300 lux) and ambient color temperature (e.g., 3000K) from the environmental parameter group, and extracts the target light intensity (e.g., 500 lux) and target color temperature (e.g., 5000K) from the target illumination parameter group, and then calculates the light intensity difference (i.e., 500-300=200 lux) and color temperature difference (i.e., 5000-3000=2000K) respectively. This difference calculation provides basic data for the subsequent adjustment step determination, and also reflects the degree of difference between the current environment and the target environment.

[0058] Step 421: Divide the light intensity difference and the color temperature difference by a preset number of sampling times to obtain an initial light intensity adjustment step and an initial color temperature adjustment step.

[0059] Specifically, the system preliminarily divides the calculated difference according to the preset number of samplings to obtain the initial adjustment step. The setting of the sampling number is based on the adaptability of the human eye to changes in lighting. In this embodiment, it is set to 20 times, which means that the system will complete the transition from the current environment to the target environment within 20 sampling cycles. By dividing the light intensity difference of 200 lux by the sampling number of 20, the initial light intensity adjustment step is 10 lux; similarly, dividing the color temperature difference of 2000K by the sampling number of 20, the initial color temperature adjustment step is 100K. This step division method based on the sampling number can ensure that changes in the lighting environment have an appropriate transition time to avoid sudden changes causing discomfort to the user.

[0060] Step 431: Smoothing the initial illumination intensity adjustment step and the initial color temperature adjustment step based on a preset smoothing coefficient to obtain the illumination intensity adjustment step and the color temperature adjustment step.

[0061] Specifically, a smoothing mechanism is introduced to optimize the initial adjustment step length through the smoothing coefficient, so that the changes in the lighting environment are smoother and more natural. The smoothing coefficient is set to 0.85, and the system uses an exponential smoothing algorithm to process the initial adjustment step length. Specifically, for the light intensity adjustment step length, the initial step length of 10 lux is multiplied by the smoothing coefficient of 0.85, and the actual light intensity adjustment step length is about 8.5 lux; for the color temperature adjustment step length, the initial step length of 100K is multiplied by the smoothing coefficient of 0.85, and the actual color temperature adjustment step length is about 85K. This smoothing process can not only reduce fluctuations in the adjustment process, but also provide a transition effect that is closer to natural light changes, making users feel more comfortable during changes in the lighting environment. Through this three-step adjustment step length calculation method, the system can achieve precise control and smooth transition of the lighting environment, which not only ensures the adjustment effect, but also ensures the user experience.

[0062] Step 402: Compensate for the illumination intensity and the color temperature in the target illumination parameter group according to the illumination intensity adjustment step and the color temperature adjustment step.

[0063] Specifically, the system makes compensation adjustments to the lighting parameters according to the calculated adjustment step. The system adopts an iterative adjustment method, and the time interval for each adjustment is set to 2 seconds. For compensation of light intensity, the system controls the lighting equipment to gradually adjust the output power according to the calculated light intensity adjustment step until the target light intensity is reached. For example, if the current ambient light intensity is 300 lux, the target light intensity is 500 lux, and the difference is 200 lux, then the adjustment step is 30 lux (15% of 200 lux) each time. For compensation of color temperature, the system adjusts the mixing ratio of cold and warm light sources according to the calculated color temperature adjustment step until the target color temperature is reached. This compensation mechanism ensures that changes in the lighting environment are both smooth and precise.

[0064] Based on the above embodiment, as an optional embodiment, in step 402: according to the light intensity adjustment step size and the color temperature adjustment step size, the light intensity and the color temperature in the target light parameter group are compensated respectively. This step may also include the following steps: Step 412: When the light intensity is greater than the light intensity threshold, the light intensity is increased by the light intensity adjustment step; when the light intensity is less than or equal to the light intensity threshold, the light intensity is decreased by the light intensity adjustment step.

[0065] Specifically, the system uses a dynamic adjustment strategy to gradually adjust the light intensity based on the comparison result between the light intensity and the light intensity threshold. The light intensity threshold is determined based on the research data of human visual comfort and learning efficiency, and is set to 450 lux in this embodiment. When it is detected that the current light intensity is greater than 450 lux, the system determines that the current ambient light is too strong, and at this time, the light intensity is gradually increased according to the calculated light intensity adjustment step (for example, 8.5 lux). For example, if the current light intensity is 500 lux, the system will increase the light intensity by 8.5 lux in each adjustment cycle until the target value is reached. On the contrary, when it is detected that the current light intensity is less than or equal to 450 lux, the system determines that the current ambient light is too weak, and at this time, the light intensity is gradually reduced according to the same adjustment step. This threshold-based two-way adjustment mechanism can ensure that the light intensity is always maintained within an appropriate range, while avoiding sudden changes in light intensity that cause visual discomfort to the user through progressive adjustment.

[0066] Step 422: When the color temperature is greater than the color temperature threshold, the color temperature is increased by the color temperature adjustment step; when the color temperature is less than or equal to the color temperature threshold, the color temperature is decreased by the color temperature adjustment step.

[0067] Specifically, the system uses a similar threshold comparison strategy to dynamically adjust the color temperature. The color temperature threshold is set to 4500K based on the physiological rhythm and attention characteristics of the human body. When the current color temperature is detected to be greater than 4500K, the system determines that the current light tone is cold, and the color temperature is gradually increased according to the calculated color temperature adjustment step (e.g., 85K). For example, if the current color temperature is 5000K, the system will increase the color temperature by 85K in each adjustment cycle, so that the light gradually transitions to a warmer tone. On the contrary, when the current color temperature is detected to be less than or equal to 4500K, the system determines that the current light tone is warm, and the color temperature is gradually reduced according to the same adjustment step. This color temperature adjustment mechanism can not only create a lighting environment suitable for learning, but also maintain the visual stability of the environment through gradual adjustment, avoiding interference of color temperature changes on the user's attention. Through the coordinated adjustment of light intensity and color temperature, the system can provide users with a more comfortable and efficient learning environment.

[0068] Step 403: During the adjustment process, the user's pupil contraction rate is monitored in real time. When the pupil contraction rate exceeds a preset contraction rate range, the adjustment step length is reduced by a preset proportion.

[0069] Specifically, the system uses an eye tracking camera to monitor the user's pupil status in real time and calculate the pupil contraction rate. Under normal circumstances, the pupil contraction rate should be kept within the preset contraction rate range (usually 15%-25%). When it is detected that the user's pupil contraction rate exceeds this range, it indicates that the current lighting adjustment may cause discomfort to the user, and the system will immediately reduce the adjustment step. When the pupil contraction rate exceeds 25%, the system reduces the current adjustment step by 50%; when the pupil contraction rate is lower than 15%, the system reduces the current adjustment step by 30%. For example, if the original light intensity adjustment step is 30 lux, after detecting an abnormal pupil contraction rate, the adjustment step will be reduced to 15 lux or 21 lux. This dynamic adjustment mechanism based on physiological feedback can better protect the user's visual comfort and avoid irritation to the user caused by changes in lighting.

[0070] Reference Figure 2 , is a light adjustment system for a learning cabin provided in an embodiment of the present application, the system comprising: an information acquisition module, an index determination module, a parameter determination module, and a light adjustment module, wherein: An information acquisition module is used to acquire learning content information and physiological signal information of users in the learning cabin, where the learning content information includes application type and document type, and the physiological signal information includes heart rate data and eye movement data; An index determination module, used to calculate a user's fatigue index based on heart rate data, and to calculate a user's concentration index based on eye movement data; A parameter determination module, used to determine the learning scene type corresponding to the application type and the document type, and obtain a target illumination parameter group corresponding to the learning scene type; The light adjustment module is used to dynamically adjust the light intensity and color temperature in the target light parameter group when the fatigue index is greater than the first threshold or the concentration index is less than the second threshold, and control the lighting equipment of the learning cabin to illuminate according to the dynamically adjusted light intensity and color temperature.

[0071] On the basis of the above embodiment, the index determination module is also used to calculate the heart rate change amplitude between adjacent moments in the heart rate data to obtain a heart rate fluctuation sequence; when there is a continuously rising or continuously falling data segment in the heart rate fluctuation sequence, the data segment is judged to be an abnormal fluctuation segment, and the duration of the abnormal fluctuation segment is determined; based on the heart rate data, the user's average heart rate value is calculated; the difference between the average heart rate value and the baseline heart rate value is determined as the first sub-fatigue value, and the duration of the abnormal fluctuation segment is determined as the second sub-fatigue value; the first sub-fatigue value and the second sub-fatigue value are normalized to obtain the user's fatigue index.

[0072] Based on the above embodiments, the index determination module is also used to extract pupil size, gaze point coordinates and blink interval from eye movement data; calculate the change rate of pupil size and the offset distance of gaze point coordinates within a preset detection time, and determine the number of pupil changes greater than a first threshold and the number of gaze point offsets greater than a second threshold as a first dispersion and a second dispersion respectively; calculate the number of blinks within a preset unit time based on the blink interval, and determine the deviation value of the number of blinks and the standard blink frequency as a third dispersion; perform weighted calculation on the first dispersion, the second dispersion and the third dispersion to obtain the user's concentration index.

[0073] Based on the above embodiment, the parameter determination module is also used to extract the interaction features of the application type and the content features of the document type; match the interaction features with the preset first feature library to obtain the corresponding application scenario classification, and match the content features with the preset second feature library to obtain the corresponding document scene classification; based on the preset scene type library, determine the learning scenario type corresponding to the application scenario classification and the document scene classification.

[0074] Based on the above embodiments, the light adjustment module is also used to obtain the ambient light intensity and ambient color temperature in the learning cabin to obtain an environmental parameter group; based on the parameter difference between the environmental parameter group and the target light parameter group, the light intensity adjustment step and the color temperature adjustment step are calculated; according to the light intensity adjustment step and the color temperature adjustment step, the light intensity and color temperature in the target light parameter group are compensated respectively; during the adjustment process, the user's pupil contraction rate is monitored in real time, and when the pupil contraction rate exceeds the preset contraction rate range, the adjustment step is reduced by a preset proportion.

[0075] On the basis of the above embodiments, the light adjustment module is further used to calculate the light intensity difference between the ambient light intensity in the environmental parameter group and the target light intensity in the target light parameter group, and the color temperature difference between the ambient color temperature in the environmental parameter group and the target color temperature in the target light parameter group; the light intensity difference and the color temperature difference are respectively divided by a preset number of sampling times to obtain an initial light intensity adjustment step and an initial color temperature adjustment step; the initial light intensity adjustment step and the initial color temperature adjustment step are smoothed based on a preset smoothing coefficient to obtain a light intensity adjustment step and a color temperature adjustment step.

[0076] Based on the above embodiment, the light adjustment module is also used to increase the light intensity by the light intensity adjustment step when the light intensity is greater than the light intensity threshold, and to reduce the light intensity by the light intensity adjustment step when the light intensity is less than or equal to the light intensity threshold; when the color temperature is greater than the color temperature threshold, the color temperature is increased by the color temperature adjustment step, and when the color temperature is less than or equal to the color temperature threshold, the color temperature is reduced by the color temperature adjustment step.

[0077] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0078] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0079] The communication bus 302 is used to realize the connection and communication between these components.

[0080] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0081] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0082] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0083] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a method for adjusting light in a learning cabin.

[0084] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing a light adjustment method of a learning cabin in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0085] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0089] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0090] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0091] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A method for adjusting light in a learning cabin, characterized in that: include: Acquire learning content information and physiological signal information of the user in the learning cabin, wherein the learning content information includes application type and document type, and the physiological signal information includes heart rate data and eye movement data; Calculating the user's fatigue index based on the heart rate data, and calculating the user's concentration index based on the eye movement data; Determine a learning scene type corresponding to the application type and the document type, and obtain a target illumination parameter group corresponding to the learning scene type; When the fatigue index is greater than a first threshold, or the concentration index is less than a second threshold, the light intensity and color temperature in the target lighting parameter group are dynamically adjusted, and the lighting equipment of the learning cabin is controlled to illuminate according to the dynamically adjusted light intensity and color temperature.

2. The light adjustment method of the learning cabin according to claim 1, characterized in that: The calculating the fatigue index of the user based on the heart rate data comprises: Calculating the heart rate variation amplitude between adjacent moments in the heart rate data to obtain a heart rate fluctuation sequence; When there is a continuously rising or continuously falling data segment in the heart rate fluctuation sequence, determining the data segment as an abnormal fluctuation segment, and determining the duration of the abnormal fluctuation segment; Based on the heart rate data, calculating an average heart rate value of the user; Determine the difference between the average heart rate value and the reference heart rate value as the first sub-fatigue value, and determine the duration of the abnormal fluctuation segment as the second sub-fatigue value; The first sub-fatigue value and the second sub-fatigue value are normalized to obtain a fatigue index of the user.

3. The light adjustment method of the learning cabin according to claim 1, characterized in that: The calculating the user's concentration index based on the eye movement data includes: Extracting pupil size, fixation point coordinates and blink interval from the eye movement data; Calculating the change rate of the pupil size and the offset distance of the gaze point coordinates within a preset detection time, and determining the number of pupil changes greater than a first threshold and the number of gaze point offsets greater than a second threshold as a first dispersion and a second dispersion, respectively; Calculating the number of blinks within a preset unit time based on the blink interval, and determining the deviation value between the number of blinks and the standard blink frequency as a third dispersion; A weighted calculation is performed on the first dispersion, the second dispersion, and the third dispersion to obtain a concentration index of the user.

4. The light adjustment method of the learning cabin according to claim 1, characterized in that: The determining the learning scenario type corresponding to the application type and the document type includes: Extracting interaction features of the application type and content features of the document type; Matching the interaction feature with a preset first feature library to obtain a corresponding application scenario classification, and matching the content feature with a preset second feature library to obtain a corresponding document scenario classification; Based on a preset scenario type library, the learning scenario types corresponding to the application scenario classification and the document scenario classification are determined.

5. The light adjustment method of the learning cabin according to claim 1, characterized in that: The dynamically adjusting the illumination intensity and color temperature in the target illumination parameter group includes: Obtaining the ambient light intensity and ambient color temperature in the learning cabin to obtain an environmental parameter group; Calculating a light intensity adjustment step and a color temperature adjustment step based on a parameter difference between the environmental parameter group and the target light parameter group; According to the light intensity adjustment step and the color temperature adjustment step, respectively compensating for the light intensity and the color temperature in the target light parameter group; During the adjustment process, the pupil contraction rate of the user is monitored in real time, and when the pupil contraction rate exceeds a preset contraction rate range, the adjustment step length is reduced by a preset proportion.

6. The light adjustment method of the learning cabin according to claim 5, characterized in that: The calculating the illumination intensity adjustment step and the color temperature adjustment step based on the parameter difference between the environmental parameter group and the target illumination parameter group comprises: Calculating a light intensity difference between the ambient light intensity in the ambient parameter group and the target light intensity in the target light parameter group, and a color temperature difference between the ambient color temperature in the ambient parameter group and the target color temperature in the target light parameter group; Dividing the light intensity difference and the color temperature difference by a preset number of sampling times respectively to obtain an initial light intensity adjustment step and an initial color temperature adjustment step; The initial illumination intensity adjustment step length and the initial color temperature adjustment step length are smoothed based on a preset smoothing coefficient to obtain the illumination intensity adjustment step length and the color temperature adjustment step length.

7. The light adjustment method of the learning cabin according to claim 5, characterized in that: The compensating the illumination intensity and the color temperature in the target illumination parameter group respectively according to the illumination intensity adjustment step and the color temperature adjustment step, comprises: When the illumination intensity is greater than the illumination intensity threshold, the illumination intensity is increased by the illumination intensity adjustment step; when the illumination intensity is less than or equal to the illumination intensity threshold, the illumination intensity is reduced by the illumination intensity adjustment step; When the color temperature is greater than the color temperature threshold, the color temperature is increased by the color temperature adjustment step; when the color temperature is less than or equal to the color temperature threshold, the color temperature is reduced by the color temperature adjustment step.

8. A light adjustment system for a learning cabin, characterized in that: The system comprises: An information acquisition module, used to acquire learning content information and physiological signal information of the user in the learning cabin, wherein the learning content information includes application type and document type, and the physiological signal information includes heart rate data and eye movement data; an index determination module, configured to calculate a fatigue index of the user based on the heart rate data, and to calculate a concentration index of the user based on the eye movement data; A parameter determination module, used to determine the learning scene type corresponding to the application type and the document type, and obtain a target illumination parameter group corresponding to the learning scene type; The light adjustment module is used to dynamically adjust the light intensity and color temperature in the target light parameter group when the fatigue index is greater than a first threshold or the concentration index is less than a second threshold, and control the lighting equipment of the learning cabin to illuminate according to the dynamically adjusted light intensity and color temperature.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the light adjustment method of the learning cabin as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the light adjustment method for the learning cabin as described in any one of claims 1-7 is executed.

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