Method and device for adjusting environmental data of learning cabin

Abstract

The invention discloses a method and device for adjusting environmental data of a learning cabin, and relates to the technical field of data processing. Acquiring a first image corresponding to the target learning cabin; acquiring first monitoring data of a first user through the target device; acquiring second monitoring data of the target learning cabin; inputting the first monitoring data and the second monitoring data into a preset environment model for processing to obtain initial environment data; obtaining a first learning duration of the first user in the target learning cabin, determining a first adjustment strategy, and processing the initial environment data through the first adjustment strategy to obtain target environment data; acquiring third temperature data and third humidity data corresponding to the target learning cabin; and when the second temperature data is inconsistent with the third temperature data, and the second humidity data is inconsistent with the third humidity data, generating an environment adjustment instruction according to the target environment data, and sending the environment adjustment instruction to the target learning cabin. By implementing the technical scheme provided by the invention, the learning effect and the comfort experience are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for adjusting environmental data of a learning cabin. Background Art

[0002] With the rapid advancement of artificial intelligence (AI) and the Internet of Things (IoT) technologies, the design concept of intelligent learning environments is gradually incorporating more high-tech elements, with the core goal of optimizing learning efficiency and improving personal comfort experience. Given that environmental parameters have a significant impact on individual comfort, health and learning efficiency, precise control of humidity and temperature inside the learning cabin is particularly important, aiming to create the most suitable learning environment.

[0003] At present, most existing learning environment control systems still rely on preset fixed modes or simple human sensor feedback to adjust indoor environmental conditions, such as temperature and humidity. However, these methods fail to fully consider the dynamic changes in individual physiological needs during the learning process, resulting in environmental adjustments often lagging behind the actual needs of individuals, which in turn has an adverse impact on learning outcomes and comfort experience.

[0004] Therefore, there is an urgent need for a method and device for adjusting the environmental data of a learning cabin that can solve the above-mentioned technical problems. Summary of the invention

[0005] The present application provides a method and device for adjusting the environmental data of a learning cabin. The method can solve the problems caused by the current learning environment control system relying on a preset fixed mode for adjustment, and fully considers the dynamic changes in the individual's physiological needs during the learning process, thereby improving the learning effect and comfort experience.

[0006] In the first aspect, the present application provides a method for adjusting environmental data of a learning cabin, the method comprising: obtaining a first image corresponding to a target learning cabin, and determining that a first user exists in the first image; obtaining first monitoring data of the first user through a target device, the first monitoring data including body temperature data, heart rate data, concentration data, and brain age data; obtaining second monitoring data of the target learning cabin, the second monitoring data including lighting data, noise data, and month data; inputting the first monitoring data and the second monitoring data into a preset environmental model for processing to obtain initial environmental data, the initial environmental data including first temperature data and first humidity data; obtaining a first learning time of the first user in the target learning cabin, and adjusting the first learning time according to the first learning time. The first adjustment strategy is determined according to the duration, and the initial environmental data is processed by the first adjustment strategy to obtain the target environmental data, which includes second temperature data and second humidity data; the third temperature data and third humidity data corresponding to the target learning cabin are obtained, the third temperature data is the actual temperature data in the target learning cabin, and the third humidity data is the actual humidity data in the target learning cabin; it is determined whether the second temperature data is consistent with the third temperature data, and whether the second humidity data is consistent with the third humidity data; when the second temperature data is inconsistent with the third temperature data, and the second humidity data is inconsistent with the third humidity data, an environmental adjustment instruction is generated according to the target environmental data, and the environmental adjustment instruction is sent to the target learning cabin.

[0007] By adopting the above technical solution, the target device monitors the target user's body temperature, heart rate, concentration, brain age and other physiological data in real time, and these data can reflect the real-time physiological state and needs of the first user in the learning process. In addition to the first user's physiological data, environmental factors such as light, noise and month in the learning cabin are also considered. The first monitoring data and the second monitoring data are input into the preset environmental model to obtain initial environmental data that is closer to the actual needs of the first user, and then the first learning time of the first user in the target learning cabin is obtained. The adjustment strategy is determined according to the first learning time. The learning time is an important indicator reflecting the learning state of the first user. The initial environmental data is processed based on the adjustment strategy to obtain the target environmental data, and then the actual third temperature data and third humidity data in the target learning cabin are obtained, and compared with the target environmental data. When the actual third temperature data and third humidity data are inconsistent with the target environmental data, an environmental adjustment instruction is immediately generated and sent to the target learning cabin. This instant feedback and adjustment mechanism ensures that the environmental adjustment can keep up with the actual needs of the user, avoids the adverse effects caused by lag, and also realizes more accurate and personalized control of the learning environment, thereby improving the learning effect and comfort experience.

[0008] Optionally, before determining the first adjustment strategy based on the first learning duration, the method also includes: obtaining a second image corresponding to the target learning cabin; identifying the second image to obtain a target number, the target number being the number of users in the target learning cabin; judging whether the target number is greater than a preset number, the preset number being the number of people that can normally be accommodated in the target learning cabin; when the target number is less than or equal to the preset number, obtaining a second user from the target number, the second user being the user who enters the target learning cabin earliest; obtaining a target time point corresponding to the second user; obtaining the current time point, calculating the target time point and the current time point, and obtaining the first learning duration.

[0009] By adopting the above technical solution, the second image of the target learning cabin is obtained and identified, and the number of users in the learning cabin (the target number) can be determined in real time, and it can be judged whether the target number exceeds the preset number (that is, the number of people that the learning cabin can normally accommodate). By judging the number of people, overcrowding in the learning cabin can be avoided, thereby improving the learning experience and comfort of each user. When the target number is less than or equal to the preset number, the user who enters the learning cabin first (the second user) can be identified, and the corresponding target time point can be obtained. By calculating with the current time point, the learning time of the user (the first learning time) can be accurately tracked, thereby providing a more comfortable learning environment for the second user.

[0010] Optionally, a first adjustment strategy is determined based on the first learning duration, specifically including: obtaining the target position corresponding to the target learning cabin; inputting the target position, the number of targets and the first learning duration into a preset adjustment database for matching to obtain a suitable environment threshold; and outputting the suitable environment threshold as the first adjustment strategy.

[0011] By adopting the above technical solution, the location of the target learning cabin, the current number of users (target number) and the user's learning time (first learning time) are input into the preset adjustment database for matching, which can accurately provide the user with a suitable learning environment threshold, realize accurate matching and personalized adjustment of the learning environment, and improve the user's experience of the learning cabin.

[0012] Optionally, after determining whether the target number is greater than the preset number, the method also includes: when the target number is greater than the preset number, obtaining voice data and a first facial image, the first facial image being a facial image corresponding to each first user in the target learning cabin; analyzing the voice data and the first facial image to obtain learning topics, the learning topics including discussion topics, creative topics, and autonomous learning topics; determining a second adjustment strategy based on the learning topics and the target number.

[0013] By adopting the above technical solution, when the number of users in the target learning cabin exceeds the preset value, the learning topic of the user can be intelligently identified by acquiring and analyzing voice data and facial images. This recognition capability enables the server to understand the type of learning activity the user is currently conducting (such as discussion, creation, or self-learning), thereby more accurately grasping the user's learning needs and status, and then according to the identified learning topic and the number of users, it can determine the second adjustment strategy to dynamically adjust the learning environment. By intelligently identifying the learning topic and adjusting the environment according to the number of users, it can provide users with a more suitable learning environment, allowing users to focus more on the learning content and reduce distractions caused by external interference.

[0014] Optionally, generating an environmental adjustment instruction based on the target environmental data specifically includes: obtaining a target difference, the target difference includes a first difference and a second difference, the first difference is the difference between the second temperature data and the third temperature data, and the second difference is the difference between the second humidity data and the third humidity data; generating an environmental adjustment instruction based on the first difference and the second difference, the environmental adjustment instruction includes an increase instruction and a decrease instruction.

[0015] By adopting the above technical solution, the difference between the second temperature data and the third temperature data (first difference) and the difference between the second humidity data and the third humidity data (second difference) are calculated, and the gap between the current environmental parameters and the target environmental parameters can be accurately determined. This precision calculation can generate more accurate environmental adjustment instructions, thereby achieving fine control of the temperature and humidity in the learning cabin. Once the target difference is determined, the corresponding environmental adjustment instructions can be quickly generated, including rising instructions and descending instructions. These instructions can be sent to the environmental control system of the learning cabin in real time to achieve rapid adjustment of temperature and humidity.

[0016] Optionally, before obtaining a first learning time of the first user in the target learning cabin, determining a first adjustment strategy based on the first learning time, and processing the initial environmental data through the first adjustment strategy to obtain the target environmental data, the method also includes: obtaining a second facial image corresponding to the first user; determining whether the second facial image exists in a preset facial database; when the second facial image does not exist in the preset facial database, determining the first adjustment strategy based on the first learning time; when the second facial image exists in the preset facial database, retrieving the environmental preference information corresponding to the second facial image, and processing the initial environmental data according to the environmental preference information.

[0017] By adopting the above technical solution, the second facial image corresponding to the first user is obtained and matched in the preset facial database, so that the identity of the first user can be identified. When the first user has a record in the preset facial database, the environmental preference information of the user can be retrieved. This personalized configuration enables the server to automatically adjust the learning environment according to the user's preferences, such as temperature, humidity, light, etc., so as to provide a more comfortable and user-expected learning space. When the first user has no record in the preset facial database, the environmental conditions can be based on the current actual situation of the first user. This flexible conditional method can adapt to the needs of different user groups.

[0018] Optionally, the first monitoring data of the first user is obtained through the target device, specifically including: obtaining an eye image corresponding to the first user, the eye image being an eye image of the first user within a preset time period; analyzing the eye image to obtain a first concentration level; obtaining behavioral data corresponding to the first user, the behavioral data including a second learning time period and learning progress data; processing the behavioral data to obtain a second concentration level; evaluating the first concentration level and the second concentration level to obtain concentration data; obtaining the target age and body monitoring data corresponding to the first user, and obtaining the target test result of the first user, the target test result being the feedback result of the first user taking a cognitive function test paper; inputting the target age, body monitoring data and target test result into a preset database for evaluation to obtain brain age data.

[0019] By adopting the above technical solution, the eye image of the first user within a preset time period is obtained and analyzed to obtain the first concentration level. This eye image-based analysis can capture the user's concentration in a specific time period, because eye activities (such as blinking frequency, pupil changes, etc.) are closely related to the level of attention. At the same time, by obtaining the user's behavioral data (including the second learning time and learning progress data) and processing it, the second concentration level can be obtained. This evaluation method based on behavioral data can reflect the actual input and effect of the user in the learning process. Comprehensively evaluating the first concentration level and the second concentration level can obtain more comprehensive and accurate concentration data. The target age, body monitoring data, and target test results are input into the preset database for evaluation to obtain the user's brain age data.

[0020] In a second aspect of the present application, a device for adjusting environmental data of a learning cabin is provided, the device comprising an acquisition unit, a processing unit and a sending unit, the acquisition unit acquiring a first image corresponding to a target learning cabin, and determining that a first user exists in the first image; acquiring first monitoring data of the first user through a target device, the first monitoring data comprising body temperature data, heart rate data, concentration data and brain age data; acquiring second monitoring data of the target learning cabin, the second monitoring data comprising lighting data, noise data and month data; the processing unit inputting the first monitoring data and the second monitoring data into a preset environmental model for processing, and obtaining initial environmental data, the initial environmental data comprising first temperature data and first humidity data; obtaining a first learning state of the first user in the target learning cabin. learning time, determining a first adjustment strategy according to the first learning time, processing the initial environmental data through the first adjustment strategy to obtain target environmental data, the target environmental data including second temperature data and second humidity data; obtaining third temperature data and third humidity data corresponding to the target learning cabin, the third temperature data is the actual temperature data in the target learning cabin, and the third humidity data is the actual humidity data in the target learning cabin; judging whether the second temperature data is consistent with the third temperature data, and whether the second humidity data is consistent with the third humidity data; a sending unit, when the second temperature data is inconsistent with the third temperature data, and the second humidity data is inconsistent with the third humidity data, generates an environmental adjustment instruction according to the target environmental data, and sends the environmental adjustment instruction to the target learning cabin.

[0021] Optionally, the acquisition unit is used to acquire a second image corresponding to the target learning cabin; the processing unit is used to identify the second image and obtain a target number, which is the number of users in the target learning cabin; it is determined whether the target number is greater than a preset number, which is the number of people that can be normally accommodated in the target learning cabin; when the target number is less than or equal to the preset number, the second user is acquired from the target number, and the second user is the user who enters the target learning cabin earliest; the acquisition unit is used to acquire a target time point corresponding to the second user; the current time point is acquired, and the target time point and the current time point are calculated to obtain the first learning duration.

[0022] Optionally, the acquisition unit is used to obtain the target position corresponding to the target learning cabin; the processing unit is used to input the target position, the number of targets and the first learning duration into a preset adjustment database for matching to obtain a suitable environment threshold; and the suitable environment threshold is output as a first adjustment strategy.

[0023] Optionally, the acquisition unit is used to acquire voice data and a first facial image when the number of targets is greater than a preset number, where the first facial image is a facial image corresponding to each first user in the target learning cabin; the processing unit is used to analyze the voice data and the first facial image to obtain a learning topic, which includes a discussion topic, a creative topic, and an autonomous learning topic; and determine a second adjustment strategy based on the learning topic and the number of targets.

[0024] Optionally, the acquisition unit is used to obtain a target difference, the target difference includes a first difference and a second difference, the first difference is the difference between the second temperature data and the third temperature data, and the second difference is the difference between the second humidity data and the third humidity data; the processing unit is used to generate an environmental adjustment instruction based on the first difference and the second difference, the environmental adjustment instruction includes an increase instruction and a decrease instruction.

[0025] Optionally, the acquisition unit is used to acquire a second facial image corresponding to the first user; the processing unit is used to determine whether the second facial image exists in a preset facial database; when the second facial image does not exist in the preset facial database, determining a first adjustment strategy based on a first learning duration; when the second facial image exists in the preset facial database, retrieving environmental preference information corresponding to the second facial image, and processing the initial environmental data based on the environmental preference information.

[0026] Optionally, the acquisition unit is used to acquire an eye image corresponding to the first user, and the eye image is an eye image of the first user within a preset time period; the processing unit is used to analyze the eye image to obtain a first concentration level; the acquisition unit is used to acquire behavioral data corresponding to the first user, and the behavioral data includes a second learning time period and learning progress data; the processing unit is used to process the behavioral data to obtain a second concentration level; the first concentration level and the second concentration level are evaluated to obtain concentration data; the acquisition unit is used to acquire the target age and body monitoring data corresponding to the first user, and acquire the target test result of the first user, and the target test result is the feedback result of the first user taking a cognitive function test paper; the processing unit is used to input the target age, body monitoring data and target test result into a preset database for evaluation to obtain brain age data.

[0027] In a third aspect of the present application, an electronic device is provided, which 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 an electronic device executes any one of the methods described above in the present application.

[0028] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any one of the above methods of the present application is executed.

[0029] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. According to the target device, the target user's body temperature, heart rate, concentration, brain age and other physiological data are monitored in real time. These data can reflect the real-time physiological state and needs of the first user during the learning process. In addition to the physiological data of the first user, environmental factors such as light, noise and month in the learning cabin are also considered. The first monitoring data and the second monitoring data are input into the preset environmental model to obtain initial environmental data that is closer to the actual needs of the first user. Then, the first learning time of the first user in the target learning cabin is obtained, and the adjustment strategy is determined according to the first learning time. The learning time is an important indicator reflecting the learning state of the first user. The initial environmental data is processed based on the adjustment strategy to obtain the target environmental data, and then the actual third temperature data and third humidity data in the target learning cabin are obtained and compared with the target environmental data. When the actual third temperature data and third humidity data are inconsistent with the target environmental data, an environmental adjustment instruction is immediately generated and sent to the target learning cabin. This instant feedback and adjustment mechanism ensures that the environmental adjustment can keep up with the actual needs of the user and avoid the adverse effects caused by lag. At the same time, it also realizes more accurate and personalized control of the learning environment, thereby improving the learning effect and comfort experience.

[0030] 2. Obtain the second facial image corresponding to the first user and match it in the preset facial database to identify the identity of the first user. When the first user has a record in the preset facial database, the user's environmental preference information can be retrieved. This personalized configuration enables the server to automatically adjust the learning environment according to the user's preferences, such as temperature, humidity, light, etc., to provide a more comfortable and user-friendly learning space. When the first user has no record in the preset facial database, the environmental conditions can be based on the first user's current actual situation. This flexible conditional method can adapt to the needs of different user groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a method for adjusting environmental data of a learning cabin provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of an environmental data adjustment device for a learning cabin provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0032] Explanation of reference numerals: 201, acquisition unit; 202, processing unit; 203, sending unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] 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.

[0036] With the rapid advancement of artificial intelligence (AI) and the Internet of Things (IoT) technologies, the design concept of intelligent learning environments is gradually incorporating more high-tech elements, with the core goal of optimizing learning efficiency and improving personal comfort experience. Given that environmental parameters have a significant impact on individual comfort, health and learning efficiency, precise control of humidity and temperature inside the learning cabin is particularly important, aiming to create the most suitable learning environment.

[0037] At present, most existing learning environment control systems still rely on preset fixed modes or simple human sensor feedback to adjust indoor environmental conditions, such as temperature and humidity. However, these methods fail to fully consider the dynamic changes in individual physiological needs during the learning process, resulting in environmental adjustments often lagging behind the actual needs of individuals, which in turn has an adverse impact on learning outcomes and comfort experience.

[0038] Therefore, how to solve the problem of the current learning environment control system relying on a preset fixed mode for adjustment. The embodiment of the present application provides a method for adjusting the environmental data of a learning cabin, which is applied to a server. The server of the present application can be a platform that provides environmental data adjustment services for the learning cabin. Figure 1 This is a flow chart of a method for adjusting the environmental data of a learning cabin provided in an embodiment of the present application, with reference to Figure 1 The method includes the following steps S101-S107.

[0039] S101: Acquire a first image corresponding to a target learning cabin, and determine whether a first user exists in the first image.

[0040] In the above S101, the target learning cabin of the present application refers to an independent learning space with almost no external noise interference created by using sound insulation materials and design. Learning cabins are widely used in learning places such as colleges and universities, libraries, and study rooms to meet the needs of different learners. However, since the environmental data in the learning cabin will affect the user's learning effect, the present application provides a method for automatically adjusting and controlling the environmental data, which can adjust the environmental data according to the user's actual physiological condition and learning situation, so that the environmental data can meet the individual needs of the user, thereby improving the user's learning effect.

[0041] First, before adjusting the environmental data in the target learning cabin, it is necessary to ensure that there are learners in the target learning cabin. Therefore, before triggering the adjustment process of the environmental data, it is necessary to monitor in real time whether there are people in the target learning cabin. A camera or other image acquisition device installed in the target learning cabin can be used to capture a real-time image of the target learning cabin, that is, the first image. Then, the first image is analyzed using image processing technology (such as face recognition algorithm) to identify whether there is a user in the first image, that is, to determine whether the target learning cabin target is in a state of use by a person, and to determine whether the adjustment method of the environmental data is executed based on the state of use. If it is determined that there is a person in the first image, the person is marked as the first user. At this time, the first user refers to any person who enters the target learning cabin.

[0042] S102: Acquire the first monitoring data of the first user through the target device, and acquire the second monitoring data of the target learning cabin.

[0043] In the above S102, the first monitoring data includes body temperature data, heart rate data, concentration data and brain age data; the second monitoring data includes light data, noise data and month data. Then, the first monitoring data of the first user is obtained through the target device, and the target device includes wearable devices (such as smart bracelets, smart watches), temperature guns, heart rate monitors, etc., which can monitor the user's body temperature data, heart rate data and other physiological indicators in real time. Before obtaining the user's corresponding monitoring data from the target device, it is necessary to ensure that the target device is connected to the server and obtain the authorization of the target device, so as to obtain the current physiological indicator data of the first user. At the same time, the concentration data of the first user is obtained by using a concentration monitoring device (such as an eye tracker, an electroencephalogram monitor, etc.), and the user's brain age data is evaluated through a specific algorithm or test paper. These monitoring data are integrated into the first monitoring data.

[0044] Since the body temperature data and heart rate data in the first monitoring data can be obtained through the monitor and infrared sensor, the concentration data and brain age data in the first monitoring data need to be obtained. Next, how to obtain the first monitoring data of the first user through the target device is explained in detail, including: obtaining the eye image corresponding to the first user, the eye image is the eye image of the first user within the preset time; analyzing the eye image to obtain the first concentration level; obtaining the behavior data corresponding to the first user, the behavior data includes the second learning time and learning progress data; processing the behavior data to obtain the second concentration level; evaluating the first concentration level and the second concentration level to obtain concentration data; obtaining the target age and body monitoring data corresponding to the first user, and obtaining the target test result of the first user, the target test result is the feedback result of the first user taking the cognitive function test paper; inputting the target age, body monitoring data and target test result into the preset database for evaluation to obtain brain age data. Specifically, an eye tracking device is used, which usually includes a high-precision camera and special software. The eye image of the first user within the preset time (such as 5 minutes, 10 minutes, etc.) is captured by the camera. During the capture process, ensure the clarity and accuracy of the eye image for subsequent analysis. Use eye tracking software to analyze the captured eye image. Analysis indicators may include eye movement path, fixation point, fixation time and fixation path. Based on these indicators, evaluate the user's concentration within the preset time and give a first concentration level. The concentration level can be divided according to actual conditions, such as high, medium and low. Then obtain the behavior data of the first user after entering the target learning cabin. The behavior data includes the second learning time (i.e., the time spent by the first user on a specific learning task) and learning progress data (such as the amount of completed tasks, accuracy, etc.). The learning progress data can be obtained based on the learning plan and completion status uploaded by the user. The second learning time can record the time point when the first user starts learning, and then determine whether the first user is currently in a learning state. If in a learning state, obtain the current time point, calculate the start learning time point and the current time point, and obtain the second learning time. If not in a learning state, obtain the first time point when the first user stops learning, and compare the start learning time point with the first time point to obtain the second learning time. Clean and integrate the acquired behavioral data to remove noise and outliers. By analyzing the second learning time and learning progress data, the first user's concentration on the learning task can be evaluated and a second concentration level can be given. Similarly, the division of concentration levels can be carried out according to actual conditions. Since learning time and learning progress can indirectly reflect the user's concentration. For example, long-term continuous learning with stable progress indicates a higher user concentration level, and short-term learning with slow progress indicates a lower user concentration level. The first concentration level and the second concentration level are considered comprehensively.The evaluation can be performed using weighted average method, fuzzy comprehensive evaluation method and other methods. Based on the comprehensive evaluation results, the concentration data of the first user is given. The concentration data can be a specific value, or a level or interval. Then the target age of the first user is obtained. The target age is determined based on the first user's self-filling. After obtaining the target age, the first user's body is preliminarily monitored to obtain body monitoring data. The body monitoring data may include physiological indicators such as heart rate, blood pressure, and blood sugar, which are obtained through professional medical equipment or wearable devices. Then a cognitive function test paper is issued to the first user. The test paper may include memory test, attention test, reaction speed test, etc. Then the cognitive function test paper submitted by the first user is analyzed to obtain the target test result. The target age, body monitoring data and target test result are input into a preset database for evaluation. The preset database may contain a large amount of data and information about the relationship between age, physiological indicators and cognitive function. Through comparison and analysis, the user's brain age is evaluated, that is, the level of cognitive function shown by the user in the current physiological state is equivalent to that of a healthy person of how old.

[0045] Furthermore, in addition to obtaining the first monitoring data of the first user, it is also necessary to obtain the environmental data in the target learning cabin. Light sensors, noise sensors and other equipment can be installed in the target learning cabin to monitor the environmental factors such as the light intensity and noise level in the target learning cabin in real time. At the same time, the current month data is obtained through the date and time sensor, because the climate conditions in different months may affect the learning environment. These monitoring data are integrated into the second monitoring data.

[0046] S103: Input the first monitoring data and the second monitoring data into a preset environmental model for processing to obtain initial environmental data, where the initial environmental data includes first temperature data and first humidity data.

[0047] In the above S103, after obtaining the first monitoring data and the second monitoring data, before inputting the first monitoring data and the second monitoring data into the preset environment model for processing, it is necessary to construct a preset environment model, which is a model based on machine learning or deep learning algorithms, and it can predict the most suitable learning environment parameters for the user based on the user monitoring data and the environment monitoring data. Since the present application is to predict the environment data, a commonly used model is selected, and the commonly used modeling methods include statistical models, machine learning models, neural network models, etc. It can be selected according to the actual situation. By obtaining the user's historical physiological monitoring data, and then obtaining the historical environment monitoring data in the target learning cabin, the historical physiological monitoring data and the historical environment monitoring data are input into the initial model for training. When the environmental data of the output result can meet the actual physiological needs of the user, the training is terminated, and the model that has been trained is used as the preset environment model. The first monitoring data and the second monitoring data are input into the preset environment model. Inside the model, the input monitoring data is further preprocessed. This may include data standardization, normalization and other processing to improve the readability and analyzability of the data. The algorithm and parameters of the preset environment model are then used to calculate and analyze the input monitoring data. According to the results of model calculation, the initial environment data is output. These data include the first temperature data and the first humidity data, etc. They are predicted by analyzing the user's physiological data and environmental data to best meet the user's current physiological needs. The first monitoring data and the second monitoring data are input into the model, and the initial environment data is obtained through calculation. The initial environment data includes the first temperature data and the first humidity data.

[0048] S104: Obtain a first learning time of a first user in a target learning cabin, determine a first adjustment strategy according to the first learning time, process the initial environmental data using the first adjustment strategy, and obtain target environmental data, wherein the target environmental data includes second temperature data and second humidity data.

[0049] In the above S104, after obtaining the initial environmental data, it is also necessary to consider factors such as the length of time the first user has been in the target learning cabin, the number of people present, and the learning topic being engaged in, and then determine the adjustment strategy based on these factors, so as to adjust the initial environmental data according to the corresponding adjustment strategy, so that the predicted environmental data is more in line with the needs of the first user. The first learning time of the first user in the target learning cabin can be obtained through a timing device. At this time, the first learning time refers to the total time the first user has been in the target learning cabin. According to the first learning time and the preset adjustment strategy (such as the longer the learning time, the temperature should be appropriately lowered to stay awake; a more comfortable environment may be required in the early stages of learning to promote concentration, etc.).

[0050] In addition, when there are multiple users in the target learning cabin, how to obtain the learning time of the people in the target learning cabin is explained in detail, including: obtaining the second image corresponding to the target learning cabin; identifying the second image to obtain the target number, which is the number of users in the target learning cabin; judging whether the target number is greater than the preset number, which is the number of people that can be normally accommodated in the target learning cabin; when the target number is less than or equal to the preset number, obtaining the second user from the target number, the second user is the user who enters the target learning cabin earliest; obtaining the target time point corresponding to the second user; obtaining the current time point, calculating the target time point and the current time point, and obtaining the first learning time. Specifically, use a camera or other image acquisition device installed in the target learning cabin to capture the current image of the target learning cabin, that is, the second image. Ensure that the camera is in a suitable position to cover the entire learning cabin area so that all users can be accurately captured. Use image processing technology and target detection algorithms (such as YOLO, SSD, etc.) to analyze the second image. These algorithms can identify independent objects (users in this case) in the image. The algorithm outputs the number of detected targets, i.e., the number of users present in the target learning cabin. This may involve annotating bounding boxes for each user in the image and counting the number of these bounding boxes. The number of targets is compared with a preset number, which is the number of people that the target learning cabin can normally accommodate according to the design or regulations. This value may be determined based on factors such as the area of ​​the learning cabin, the number of seats, and safety regulations. The number of identified targets is compared with the preset number. If the number of targets is greater than the preset number, it means that there are too many people in the learning cabin, and measures may need to be taken (such as prompting users to leave, increasing the capacity of the learning cabin, etc.). If the number of targets is within the preset range, a specific user needs to be identified as the object of interest. Here, the user who entered the learning cabin the earliest is selected as the second user. To achieve this, a record table of users entering the learning cabin can be obtained by querying the time record table. This can be achieved by using RFID cards, facial recognition technology, or other tracking methods. Based on the record, the user who entered the learning cabin the earliest is found and identified as the second user. The specific time point when the second user entered the learning cabin is retrieved from the user record, i.e., the target time point. This time point may be expressed in hours, minutes, or seconds. Get the current time point using a time synchronization method such as the system clock or Network Time Protocol (NTP). Ensure that the time is accurate so that the learning time can be accurately calculated. Calculate the time difference between the current time point and the target time point, that is, the length of time the second user has studied in the learning cabin. This can be achieved through a simple subtraction operation (current time - target time). The result is the first learning time, which may be expressed in hours, minutes or seconds. Through the above method, the number of users in the learning cabin can be automatically identified, the earliest user to enter can be determined, and their learning time can be calculated.This information is critical to managing learning pods, optimizing the user experience, and ensuring a safe and comfortable learning environment.

[0051] Further, the first adjustment strategy is determined according to the first learning duration, specifically including: obtaining the target position corresponding to the target learning cabin; inputting the target position, the number of targets and the first learning duration into the preset adjustment database for matching to obtain a suitable environment threshold; and outputting the suitable environment threshold as the first adjustment strategy. Specifically, GPS positioning, Bluetooth positioning, Wi-Fi positioning, RFID positioning or UWB (ultra-wideband) positioning and other technologies are used, which can provide high-precision location information according to different scenarios and needs. In the learning cabin environment, RFID positioning or UWB positioning can be considered because these technologies can provide high positioning accuracy and are suitable for indoor environments. Positioning devices (such as RFID readers, UWB base stations, etc.) are deployed inside or around the target learning cabin. Ensure that each learning cabin has a unique identifier (such as an RFID tag or a UWB tag) so that the system can accurately identify it. When a user enters the learning cabin, the positioning device captures the user's signal and sends its location information to the central processing system. The server calculates the specific location of the target learning cabin based on the received signal and the preset algorithm. The preset adjustment database is a database that stores suitable environment thresholds under different locations, different numbers of users and different learning durations. The construction of the preset database needs to be based on a large amount of experimental data and user feedback to ensure the accuracy and applicability of the threshold. The acquired target position, number of targets and first learning duration are sent as input parameters to the preset adjustment database for query. The preset database will search and match the stored data based on these parameters to find the most suitable environmental threshold that best meets the current conditions. The suitable environmental threshold includes the suitable range of two environmental parameters, temperature and humidity. The suitable environmental threshold is usually output in the form of multiple groups of parameters, including suitable ranges or specific values ​​of environmental parameters such as temperature and humidity. According to the output suitable environmental threshold, a first adjustment strategy is generated. The first adjustment strategy can also be understood as a suitable environmental threshold. Then, the initial environmental data is adjusted according to the first adjustment strategy, and the initial environmental data is modified to a suitable environmental threshold, and the adjusted environmental data is output as target environmental data, and the target environmental data includes second humidity data and second temperature data.

[0052] Further, when the number of people in the target learning cabin exceeds the number of people that can be accommodated, an adjustment strategy needs to be determined according to the current learning situation and number of users in the learning cabin. Different learning situations require different environmental adjustment strategies, including: when the target number is greater than the preset number, voice data and a first facial image are obtained, and the first facial image is a facial image corresponding to each first user in the target learning cabin; the voice data and the first facial image are analyzed to obtain a learning topic, and the learning topic includes a discussion topic, a creation topic, and an autonomous learning topic; and a second adjustment strategy is determined according to the learning topic and the target number. Specifically, this step is triggered when it is detected that the number of users in the target learning cabin exceeds the preset number. The preset number is usually determined based on factors such as the area, facility capacity, and safety regulations of the learning cabin. Use a microphone or voice recognition device installed in the target learning cabin to capture real-time voice data in the cabin. Ensure that the device can clearly record the conversation and discussion in the cabin for subsequent analysis. Use a high-definition camera or facial recognition device to capture the facial images of each user in the target learning cabin. These images will be used for subsequent user identity confirmation and learning topic analysis. Use voice recognition and natural language processing technology to convert the captured voice data into text. Keyword extraction and topic analysis are performed on the text to determine the content of user discussion or learning. Learning topics can be identified by analyzing high-frequency words, phrases or sentence structures in the text, such as discussion topics (discussions around a specific topic), creative topics (creative activities such as writing, painting or programming), and autonomous learning topics (learning activities conducted by users alone). Facial images are used to assist in confirming the identity and emotional state of users. Facial image analysis can also provide information about the user's emotional state, such as expressions and eyes, which may indirectly reflect whether the user is involved. Combining the results of voice data analysis and facial image analysis, the learning topics of users in the learning cabin can be comprehensively judged. The formulation of the second adjustment strategy should be based on the learning topic, the number of users and the actual situation of the learning cabin. The goal is to optimize the learning environment and improve the learning efficiency and comfort of users. If the learning cabin is mainly a discussion activity and the number of users is large, consider lowering the ambient temperature to improve the comfort of each user. For creative activities, it may be necessary to provide a quiet environment and sufficient creative space, and the temperature and humidity in the environment can be neutralized. When learning autonomously, users may need a more focused environment. At this time, the learning atmosphere can be optimized by adjusting environmental parameters such as lighting and temperature. The initial environmental data is then adjusted according to the second adjustment strategy to obtain target environmental data.

[0053] S105: Obtain third temperature data and third humidity data corresponding to the target learning cabin, where the third temperature data is actual temperature data in the target learning cabin, and the third humidity data is actual humidity data in the target learning cabin.

[0054] In the above S105 , the temperature sensor and humidity sensor installed in the target learning cabin may be used to obtain the actual temperature (third temperature data) and humidity (third humidity data) in the cabin in real time.

[0055] S106: Determine whether the second temperature data is consistent with the third temperature data, and whether the second humidity data is consistent with the third humidity data.

[0056] In the above S106, the second humidity data and the second temperature data in the target environment data are compared with the third temperature data and the third humidity data actually monitored.

[0057] S107: When the second temperature data is inconsistent with the third temperature data, and the second humidity data is inconsistent with the third humidity data, an environmental adjustment instruction is generated according to the target environmental data, and the environmental adjustment instruction is sent to the target learning cabin.

[0058] In the above S107, if it is found that the target environment data is inconsistent with the actual environment data, it means that the learning environment needs to be adjusted, and corresponding environment adjustment instructions (such as adjusting the air-conditioning temperature, turning on or off the humidifier, etc.) are generated according to the target environment data (second temperature data and second humidity data).

[0059] In addition, generating an environmental adjustment instruction according to the target environmental data specifically includes: obtaining a target difference, the target difference includes a first difference and a second difference, the first difference is the difference between the second temperature data and the third temperature data, and the second difference is the difference between the second humidity data and the third humidity data; generating an environmental adjustment instruction according to the first difference and the second difference, the environmental adjustment instruction includes an ascending instruction and a descending instruction. Specifically, since the third humidity data and the third temperature data are the actual humidity and temperature data in the target learning cabin. Therefore, the third temperature data and the second temperature data are calculated to obtain a first difference, and the first difference reflects the difference between the current temperature and the predicted temperature. The third humidity data and the second humidity data are calculated to obtain a second difference, and the second difference reflects the difference between the current humidity and the predicted humidity. The second humidity data is the humidity data predicted according to the actual physiological condition of the first user, and the second temperature data is the temperature data predicted according to the actual physiological condition of the first user. If the first difference is positive, it means that the current temperature is lower than the predicted temperature; if it is negative, the current temperature is higher than the predicted temperature. Similarly, the positive and negative of the second difference represent that the current humidity is lower or higher than the predicted humidity. According to the positive and negative of the difference, the corresponding environmental adjustment instruction is generated. If the current temperature or humidity is lower than the predicted value (i.e., the first difference or the second difference is positive), an increase instruction is generated to instruct the heating or humidification system to start to increase the temperature or humidity. If the current temperature or humidity is higher than the predicted value (i.e., the first difference or the second difference is negative), a decrease instruction is generated to instruct the refrigeration or dehumidification system to start to reduce the temperature or humidity. The environmental adjustment instructions may need to be further refined, including parameters such as specific adjustment amplitude, rate or duration. Once the environmental adjustment instructions are generated, the environmental adjustment instructions are sent to the learning cabin control device so that the learning cabin control device should be able to automatically execute these instructions, that is, to adjust the temperature and humidity by controlling the corresponding environmental equipment (such as heaters, air conditioners, humidifiers, dehumidifiers, etc.). At the same time, the changes in temperature and humidity should be continuously monitored to ensure that they gradually approach and stabilize within the predicted value range.

[0060] For example, when the second humidity data is 40 and the second temperature data is 21°C, the actual third temperature data and third humidity data in the target learning cabin are obtained. When the third humidity data is 45 and the third temperature data is 23°C, the first difference is -5 and the second difference is -2°C. Since both the first difference and the second difference are negative values, it can be understood that the actual temperature and humidity are higher than the predicted humidity and temperature, so a descent instruction is generated to facilitate the adjustment of the humidity and temperature in the target learning cabin according to the descent instruction.

[0061] Further, when the second humidity data is consistent with the third humidity data, and the second temperature data is consistent with the third temperature data, it is determined that the environmental data in the target learning cabin matches the user's needs, and the environmental data in the target learning cabin and the user's physiological data continue to be monitored.

[0062] In a possible implementation, personalized adjustment of the user's learning environment is achieved through facial image recognition and retrieval of environmental preference information, specifically including: obtaining a second facial image corresponding to the first user; determining whether the second facial image exists in a preset facial database; when the second facial image does not exist in the preset facial database, determining a first adjustment strategy according to the first learning duration; when the second facial image exists in the preset facial database, retrieving the environmental preference information corresponding to the second facial image, and processing the initial environmental data according to the environmental preference information. Specifically, a facial image acquisition device, such as a camera, is required to capture the facial image of the first user. This device can be installed in an area where identity authentication or environmental adjustment is required, such as the entrance of a smart home, the interior of a learning cabin, etc. When the first user appears in the field of view of the acquisition device, the device automatically starts and captures its facial image. This process may require the user to remain in a certain static state to ensure that the acquired image is clear and complete. The acquired facial image may require some preprocessing, such as denoising, contrast enhancement, grayscale normalization, etc., to improve the image quality and recognition rate. After preprocessing, the image obtained is the second facial image corresponding to the first user. This image will be used in subsequent facial recognition and environmental adjustment processes. The preset face database is a database that stores multiple user face images and related information. This information may include the user's identity, environmental preferences, historical learning time, etc. The collected second face image will be matched with the image in the preset face database. This process may involve steps such as facial feature extraction and similarity calculation. If an image that highly matches the second face image is found in the preset face database, it is judged to exist; otherwise, it is judged to not exist. When the second face image does not exist in the preset face database, the user's environmental preference information may not be directly obtained. At this time, you can consider obtaining the learning time of the first user, that is, the first learning time. According to the first learning time, a default environmental adjustment strategy can be formulated, such as adjusting the temperature and humidity, to provide a relatively comfortable learning environment. This strategy may be formulated based on general learning habits and environmental requirements. Once the first adjustment strategy is determined, these strategies can be automatically executed, such as adjusting to turn on the air conditioner or humidifier. When the second face image exists in the preset face database, the environmental preference information associated with the image can be directly retrieved. This information may include the light intensity, temperature range, humidity level, etc. that the user likes. The initial environmental data will be processed according to the retrieved environmental preference information. The initial environmental data may include the current light intensity, temperature, humidity, etc. Based on the processed environmental data and the user's environmental preference information, a customized environmental adjustment strategy, namely the third adjustment strategy, is generated. This strategy aims to provide a learning environment that meets the user's personal preferences. Once the third adjustment strategies are generated, they can be automatically executed to adjust the environmental parameters and meet the user's needs.

[0063] The embodiment of the present application also provides a device for adjusting the environmental data of the learning cabin, Figure 2 This is a schematic diagram of the structure of a device for adjusting environmental data of a learning cabin provided in an embodiment of the present application, with reference to Figure 2 The device includes an acquisition unit 201, a processing unit 202 and a sending unit 203.

[0064] An acquisition unit 201 acquires a first image corresponding to a target learning cabin, and determines that a first user exists in the first image; acquires first monitoring data of the first user through a target device, the first monitoring data including body temperature data, heart rate data, concentration data, and brain age data; acquires second monitoring data of the target learning cabin, the second monitoring data including lighting data, noise data, and month data.

[0065] The processing unit 202 inputs the first monitoring data and the second monitoring data into a preset environmental model for processing to obtain initial environmental data, which includes first temperature data and first humidity data; obtains a first learning time of the first user in the target learning cabin, determines a first adjustment strategy according to the first learning time, processes the initial environmental data by the first adjustment strategy to obtain target environmental data, which includes second temperature data and second humidity data; obtains third temperature data and third humidity data corresponding to the target learning cabin, the third temperature data is the actual temperature data in the target learning cabin, and the third humidity data is the actual humidity data in the target learning cabin; and determines whether the second temperature data is consistent with the third temperature data, and whether the second humidity data is consistent with the third humidity data.

[0066] The sending unit 203 generates an environment adjustment instruction according to the target environment data and sends the environment adjustment instruction to the target learning cabin when the second temperature data is inconsistent with the third temperature data and the second humidity data is inconsistent with the third humidity data.

[0067] In a possible implementation, the acquisition unit 201 is used to acquire a second image corresponding to the target learning cabin; the processing unit 202 is used to identify the second image and obtain a target number, which is the number of users in the target learning cabin; it is determined whether the target number is greater than a preset number, which is the number of people that can be normally accommodated in the target learning cabin; when the target number is less than or equal to the preset number, the second user is acquired from the target number, and the second user is the user who enters the target learning cabin earliest; the acquisition unit 201 is used to acquire the target time point corresponding to the second user; the current time point is acquired, and the target time point and the current time point are calculated to obtain the first learning duration.

[0068] In a possible implementation, the acquisition unit 201 is used to obtain the target position corresponding to the target learning cabin; the processing unit 202 is used to input the target position, the number of targets and the first learning duration into a preset adjustment database for matching to obtain a suitable environment threshold; and the suitable environment threshold is output as a first adjustment strategy.

[0069] In a possible implementation, the acquisition unit 201 is used to acquire voice data and a first facial image when the target number is greater than a preset number, where the first facial image is a facial image corresponding to each first user in the target learning cabin; the processing unit 202 is used to analyze the voice data and the first facial image to obtain a learning topic, which includes a discussion topic, a creative topic, and an autonomous learning topic; and determine a second adjustment strategy based on the learning topic and the target number.

[0070] In one possible implementation, the acquisition unit 201 is used to acquire a target difference, the target difference includes a first difference and a second difference, the first difference is the difference between the second temperature data and the third temperature data, and the second difference is the difference between the second humidity data and the third humidity data; the processing unit 202 is used to generate an environmental adjustment instruction based on the first difference and the second difference, the environmental adjustment instruction includes an increase instruction and a decrease instruction.

[0071] In one possible implementation, the acquisition unit 201 is used to acquire a second facial image corresponding to the first user; the processing unit 202 is used to determine whether the second facial image exists in a preset facial database; when the second facial image does not exist in the preset facial database, determining a first adjustment strategy based on a first learning duration; when the second facial image exists in the preset facial database, retrieving environmental preference information corresponding to the second facial image, and processing the initial environmental data based on the environmental preference information.

[0072] In a possible implementation, the acquisition unit 201 is used to acquire an eye image corresponding to the first user, and the eye image is an eye image of the first user within a preset time period; the processing unit 202 is used to analyze the eye image to obtain a first concentration level; the acquisition unit 201 is used to acquire behavioral data corresponding to the first user, and the behavioral data includes a second learning time period and learning progress data; the processing unit 202 is used to process the behavioral data to obtain a second concentration level; the first concentration level and the second concentration level are evaluated to obtain concentration data; the acquisition unit 201 is used to acquire the target age and body monitoring data corresponding to the first user, and acquire the target test result of the first user, and the target test result is the feedback result of the first user taking a cognitive function test paper; the processing unit 202 is used to input the target age, body monitoring data and target test result into a preset database for evaluation to obtain brain age data.

[0073] 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.

[0074] 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 302 , and at least one communication bus 305 .

[0075] The communication bus 305 is used to realize the connection and communication between these components.

[0076] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0077] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0078] 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 302, and calling data stored in the memory 302. 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 and application requests; 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.

[0079] Among them, the memory 302 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 302 includes a non-transitory computer-readable storage medium. The memory 302 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 302 may include a program storage area and a data storage area, wherein the program storage area. 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. can be stored; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 302 can also be optionally at least one storage device located away from the aforementioned processor 301.

[0080] like Figure 3 As shown, the memory 302 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for adjusting the environmental data of the learning cabin.

[0081] 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 data input by the user; and the processor 301 can be used to call the application program for adjusting the environmental data of the learning cabin stored in the memory 302. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0082] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware 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.

[0083] 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.

[0084] In the several embodiments provided in the present 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 the 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.

[0085] 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.

[0086] 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.

[0087] 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 method described in each embodiment 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.

[0088] The above is only an exemplary embodiment 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 the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure.

Claims

1. A method for adjusting environmental data of a learning cabin, characterized in that: The method comprises: Acquire a first image corresponding to the target learning cabin, and determine that a first user exists in the first image; acquiring first monitoring data of the first user through a target device, wherein the first monitoring data includes body temperature data, heart rate data, concentration data, and brain age data; Acquire second monitoring data of the target learning cabin, where the second monitoring data includes light data, noise data, and month data; Inputting the first monitoring data and the second monitoring data into a preset environmental model for processing to obtain initial environmental data, wherein the initial environmental data includes first temperature data and first humidity data; Obtaining a first learning time of the first user in the target learning cabin, determining a first adjustment strategy according to the first learning time, and processing the initial environment data by the first adjustment strategy to obtain target environment data, wherein the target environment data includes second temperature data and second humidity data; Acquire third temperature data and third humidity data corresponding to the target learning cabin, wherein the third temperature data is actual temperature data in the target learning cabin, and the third humidity data is actual humidity data in the target learning cabin; Determining whether the second temperature data is consistent with the third temperature data, and whether the second humidity data is consistent with the third humidity data; When the second temperature data is inconsistent with the third temperature data, and the second humidity data is inconsistent with the third humidity data, an environmental adjustment instruction is generated according to the target environmental data, and the environmental adjustment instruction is sent to the target learning cabin.

2. The method according to claim 1, characterized in that Before determining the first adjustment strategy according to the first learning duration, the method further includes: Acquire a second image corresponding to the target learning cabin; Recognize the second image to obtain a target number, where the target number is the number of users in the target learning cabin; Determine whether the target number is greater than a preset number, where the preset number is the number of people that can normally be accommodated in the target learning cabin; When the target number is less than or equal to the preset number, a second user is obtained from the target number, where the second user is the user who enters the target learning cabin earliest; Obtaining a target time point corresponding to the second user; The current time point is obtained, and the target time point and the current time point are calculated to obtain the first learning duration.

3. The method according to claim 2, characterized in that The determining of the first adjustment strategy according to the first learning duration specifically includes: Obtaining a target position corresponding to the target learning cabin; Inputting the target position, the number of targets and the first learning duration into a preset adjustment database for matching to obtain a suitable environment threshold; The suitable environment threshold is output as the first adjustment strategy.

4. The method according to claim 2, characterized in that: After determining whether the target number is greater than a preset number, the method further includes: When the target number is greater than the preset number, voice data and a first facial image are acquired, where the first facial image is a facial image corresponding to each of the first users in the target learning cabin; Analyze the voice data and the first facial image to obtain learning topics, wherein the learning topics include discussion topics, creation topics, and autonomous learning topics; A second adjustment strategy is determined according to the learning topic and the target number.

5. The method according to claim 1, characterized in that The generating of the environment adjustment instruction according to the target environment data specifically includes: Acquire a target difference, where the target difference includes a first difference and a second difference, where the first difference is a difference between the second temperature data and the third temperature data, and the second difference is a difference between the second humidity data and the third humidity data; The environment adjustment instruction is generated according to the first difference and the second difference, and the environment adjustment instruction includes an ascending instruction and a descending instruction.

6. The method according to claim 1, characterized in that Before obtaining the first learning time of the first user in the target learning cabin, determining a first adjustment strategy according to the first learning time, and processing the initial environment data by the first adjustment strategy to obtain the target environment data, the method further includes: Acquire a second facial image corresponding to the first user; Determine whether the second facial image exists in a preset facial database; When the second facial image does not exist in the preset facial database, determining the first adjustment strategy according to the first learning duration; When the second facial image exists in the preset facial database, the environmental preference information corresponding to the second facial image is retrieved, and the initial environmental data is processed according to the environmental preference information.

7. The method according to claim 1, characterized in that The acquiring the first monitoring data of the first user through the target device specifically includes: Acquire an eye image corresponding to the first user, where the eye image is an eye image of the first user within a preset time period; Analyzing the eye image to obtain a first concentration level; Acquire behavior data corresponding to the first user, the behavior data including second learning duration and learning progress data; Processing the behavior data to obtain a second focus level; Evaluate the first concentration level and the second concentration level to obtain the concentration data; Obtaining the target age and physical monitoring data corresponding to the first user, and obtaining the target test result of the first user, wherein the target test result is the feedback result of the first user taking a cognitive function test paper; The target age, the body monitoring data and the target test results are input into a preset database for evaluation to obtain the brain age data.

8. A device for adjusting environmental data of a learning cabin, characterized in that: The device comprises an acquisition unit (201), a processing unit (202) and a sending unit (203). The acquisition unit (201) acquires a first image corresponding to a target learning cabin, and determines that a first user exists in the first image; acquires first monitoring data of the first user through a target device, wherein the first monitoring data includes body temperature data, heart rate data, concentration data, and brain age data; Acquire second monitoring data of the target learning cabin, where the second monitoring data includes light data, noise data, and month data; The processing unit (202) inputs the first monitoring data and the second monitoring data into a preset environment model for processing to obtain initial environment data, wherein the initial environment data includes first temperature data and first humidity data; obtains a first learning time of the first user in the target learning cabin, determines a first adjustment strategy according to the first learning time, processes the initial environment data according to the first adjustment strategy to obtain target environment data, wherein the target environment data includes second temperature data and second humidity data; obtains third temperature data and third humidity data corresponding to the target learning cabin, wherein the third temperature data is the actual temperature data in the target learning cabin, and the third humidity data is the actual humidity data in the target learning cabin; and determines whether the second temperature data is consistent with the third temperature data, and whether the second humidity data is consistent with the third humidity data; The sending unit (203) generates an environment adjustment instruction according to the target environment data and sends the environment adjustment instruction to the target learning cabin when the second temperature data is inconsistent with the third temperature data and the second humidity data is inconsistent with the third humidity data.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (302), a user interface (303) and a network interface (304), wherein the memory (302) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (302) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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