Immersive education experience system and method based on digital media
By detecting the user's activity status and physiological information, the learning content is divided into sections and presented in a personalized manner, which solves the problem that AR technology fails to utilize teaching time during user activities, improves learning efficiency and reduces the risk of user fatigue and motion sickness.
Patent Information
- Application Number
- CN202510737367.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-04
- Publication Date
- 2025-09-12
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing AR technology fails to effectively utilize teaching time during user activities and may cause user fatigue or motion sickness, especially during exercise, where the risk increases significantly.
The user's activity status and physiological information are obtained through the information detection module, fatigue is analyzed using the user analysis module, the optimization module divides the learning content into sections, and the appropriate AR module presentation form is determined through the genetic algorithm to provide personalized learning section content.
It provides a vivid and intuitive learning experience during user activities, improves learning efficiency, reduces the possibility of user fatigue and motion sickness, and uses AR devices to teach at any time.
Smart Images
Figure CN120634796A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of education and training technology, and in particular to an immersive education experience system and method based on digital media. Background Art
[0002] Augmented reality is a technology that integrates virtual information with the real world. In education, AR technology can provide users with a more vivid and intuitive learning experience. Through AR teaching materials, it provides users with a richer and more immersive interactive method, breaking the limitations of traditional screen display information, allowing users to obtain virtual information in the real environment, making the presentation of information more intuitive and vivid. This method helps to improve users' learning enthusiasm and knowledge absorption efficiency.
[0003] However, most current AR technologies require users to be in fixed locations, such as classrooms or homes, for teaching, primarily for long periods of concentrated instruction. This does not make good use of the user's time during activities, such as commuting and exercise. Furthermore, the use of AR devices may cause student fatigue or motion sickness, a risk that increases significantly during exercise. Summary of the Invention
[0004] The purpose of the present invention is to provide an immersive education experience system and method based on digital media to solve the problems raised in the prior art.
[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an immersive educational experience system based on digital media, comprising: an information detection module, a user analysis module, an optimization module and an AR module; the output end of the information detection module is connected to the input end of the user analysis module, for detecting the user's activity status and obtaining the user's activity status data; the output end of the user analysis module is connected to the input end of the optimization module, for analyzing the user's fatigue; the output end of the optimization module is interconnected with the input end of the AR module, for determining the learning section content that needs to be provided to the user; the AR module is used to provide the learning section content to the user.
[0006] Specifically, the information detection module also includes a motion detection unit, a position detection unit and a physiological detection unit; the motion detection unit is used to detect whether the user is in a stationary or moving state; the position detection unit is used to obtain the user's position information; and the physiological detection unit is used to obtain the user's physiological information.
[0007] Specifically, the user analysis module also includes an activity status analysis unit, a fatigue analysis unit and a progress management unit; the progress management unit is used to record the user's historical learning data, determine the user's learning progress, and obtain the learning content that needs to be provided to the user; the activity status analysis unit determines the user's activity status based on the user's detection data; the fatigue analysis unit determines the user's fatigue changes based on the user's activity status, and obtains the user's fatigue according to the learning time of the learning section content provided to the user by the AR module.
[0008] Specifically, the optimization module also includes a segmentation unit and a genetic algorithm unit. The segmentation unit is used to divide the learning content that needs to be provided to the user into several segments; the genetic algorithm unit is used to determine the learning segment content provided to the user by the AR module.
[0009] To achieve the above objectives, the present invention provides the following technical solution: a digital media-based immersive educational experience method, comprising the following steps:
[0010] Obtain the user's detection data, and determine the user's activity status based on the user's detection data; determine the user's fatigue changes based on the user's activity status, and determine the user's fatigue level; obtain the user's historical learning data, and obtain the user's teaching content based on the user's historical learning data; divide the user's teaching content into several learning modules, and determine the learning modules provided to the user in the current activity state through genetic algorithms based on the teaching form that the user can tolerate, and use AR devices to provide the learning module content to the user.
[0011] Specifically, determining the user's activity status based on the user's detection data further includes the following steps:
[0012] Obtain the user's historical detection data and the activity status corresponding to the detection data, and assign a label to the user's activity status; use the user's historical detection data as input and the assigned label as output to train a neural network multi-classification model; input the user's current detection data into the neural network multi-classification model to obtain the user's current activity status.
[0013] Specifically, determining the change in the user's fatigue level based on the user's activity state further includes the following steps:
[0014] Step 1: Obtain the detection data when the user enters the current activity state, and obtain the physiological information of the user when entering the current activity state from the detection data, which is recorded as the target physiological information; obtain the user's historical target physiological information from the user's historical activity data, determine the historical target physiological information with fatigue based on the user's feedback results, and cluster the historical target physiological information with fatigue to obtain clusters; for the historical target physiological information in the same cluster, according to the user activity state corresponding to the historical target physiological information, select the historical target physiological information with fatigue in the same activity state to construct an n-variable linear equation system for solution:
[0015] Step 2: Obtain the learning duration of each form of learning section content provided to the user by the AR device in the historical target physiological information with fatigue in the same activity state. Subtract the learning duration of the corresponding form of learning section content of two historical target physiological information with fatigue in the same activity state to obtain n groups of difference values (d11, d12, ..., d1n), ..., (dn1, dn2, ..., dnn); establish the equation system based on the difference in learning duration of each form of learning section content: Solve the equations to get W1, W2, ..., W n Where W1, W2, ..., W n Indicates the influence coefficient of the first, second, ..., nth types of learning content provided by the AR device to the user on fatigue. d11, ..., d1n are the differences obtained by subtracting the learning time of the first type of learning content. dn1, ..., dnn are the differences obtained by subtracting the learning time of the nth type of learning content.
[0016] Step 3: Construct a set of n-variable linear equations for the historical physiological information of fatigued targets in all activity states and solve them to obtain the influence coefficient of each form of learning section content provided by the AR device to the user on fatigue in all activity states; use the influence coefficient of each form of learning section content on fatigue as a weight, and perform weighted summation of the learning time of each form of learning section content provided by the AR device to the user in the historical physiological information of fatigued targets in the cluster to obtain the user's fatigue threshold.
[0017] Specifically, determining the user's fatigue level further includes the following steps:
[0018] Get the detection data when the user enters the current activity state and the AR device provides the user with the learning section content, so that T i Represents the learning time of the i-th form of learning content provided by the AR device to the user, and calculates the user's fatigue level PL, PL = ΣW i ×T i , where W iIt represents the influence coefficient of the i-th form of learning content provided by the AR device to the user on fatigue. The user's fatigue threshold is obtained based on the detection data when the user enters the current activity state.
[0019] Specifically, determining the learning module provided to the user in the current active state based on the teaching form that the user can tolerate through a genetic algorithm also includes the following steps:
[0020] S10, binary-encode the learning section to obtain a binary string, where each bit in the binary string corresponds to a learning section, and each binary string corresponds to an individual; if the AR device provides the learning section content to the user, the value of the bit in the binary string corresponding to the learning section is 1; if the AR device does not provide the learning section content to the user, the value of the bit in the binary string corresponding to the learning section is 0;
[0021] S11, create the initial population and set the number of iterations to 0;
[0022] S12, calculate the fitness values of all individuals in the population;
[0023] S13, select individuals from the population, and the probability of an individual being selected is positively correlated with its fitness value;
[0024] S14, randomly selecting some individuals from the individuals selected in step S13 to perform a crossover operation, exchanging parts of the chromosomes to create new individuals, and determining whether the constraint conditions are met. If so, the new individuals are retained; otherwise, the new individuals are discarded; the constraint conditions are: the AR device provides learning content to the user, the fatigue generated is less than the fatigue threshold, and the total learning time of the learning content provided by the AR device to the user is less than the time threshold;
[0025] S15, performing mutation operation on individuals in the population to determine whether the constraints are met. If so, the new individual is retained; if not, the new individual is discarded.
[0026] S16, add 1 to the number of iterations and determine whether the number of iterations reaches the set value. If so, stop the algorithm and obtain the individual binary string with the highest fitness value; if not, return to step S12.
[0027] Specifically, the fitness value is determined by the following steps:
[0028] Obtain the binary code of the individual, obtain the learning time of the learning section content with the binary string value of 1, and perform weighted summation based on the learning time to obtain the individual fitness value.
[0029] Compared with the existing technology, the beneficial effects of the present invention are: AR technology provides users with a more vivid and intuitive learning experience, provides users with a richer and more immersive interactive method, breaks the limitations of traditional screen display information, allows users to obtain virtual information in a real environment, makes the presentation of information more intuitive and vivid, and helps to improve users' learning enthusiasm and knowledge absorption efficiency; analyzes the user's activity status, and provides different forms of learning section content to the user according to the user's activity status, so that users can use all the time during the activity to learn, thereby improving the user's learning efficiency; analyzes the fatigue caused to the user by the learning content improved by the AR device, determines the learning section content that is most suitable for the user, and reduces the possibility of user fatigue and motion sickness. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 This is a structural diagram of an immersive educational experience system based on digital media in the present invention. DETAILED DESCRIPTION
[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0032] Example: Figure 1 As shown, the present invention provides a technical solution, an immersive education experience system based on digital media, comprising: an information detection module, a user analysis module, an optimization module and an AR module; the output end of the information detection module is connected to the input end of the user analysis module, for detecting the user's activity status and obtaining the user's activity status data; the output end of the user analysis module is connected to the input end of the optimization module, for analyzing the user's fatigue; the output end of the optimization module is interconnected with the input end of the AR module, for determining the learning section content that needs to be provided to the user; the AR module is used to provide the learning section content to the user.
[0033] The information detection module also includes a motion detection unit, a position detection unit, and a physiological detection unit; the motion detection unit is used to detect whether the user is in a stationary or moving state; the position detection unit is used to obtain the user's position information; and the physiological detection unit is used to obtain the user's physiological information. The user analysis module also includes an activity state analysis unit, a fatigue analysis unit, and a progress management unit; the progress management unit is used to record the user's historical learning data, determine the user's learning progress, and obtain the learning content that needs to be provided to the user; the activity state analysis unit determines the user's activity state based on the user's detection data; the fatigue analysis unit determines the user's fatigue changes based on the user's activity state, and obtains the user's fatigue based on the learning time of the learning section content provided to the user by the AR module. The optimization module also includes a segmentation unit and a genetic algorithm unit. The segmentation unit is used to divide the learning content that needs to be provided to the user into several sections; the genetic algorithm unit is used to determine the learning section content provided to the user by the AR module.
[0034] Embodiment: The present invention provides a technical solution, an immersive educational experience method based on digital media, comprising the following steps:
[0035] Obtain the user's detection data, and determine the user's activity status based on the user's detection data; determine the user's fatigue changes based on the user's activity status, and determine the user's fatigue level; obtain the user's historical learning data, and obtain the user's teaching content based on the user's historical learning data; divide the user's teaching content into several learning modules, and determine the learning modules provided to the user in the current activity state through genetic algorithms based on the teaching form that the user can tolerate, and use AR devices to provide the learning module content to the user.
[0036] Determining the user's activity status based on the user's detection data also includes the following steps:
[0037] Obtain the user's historical detection data and the activity status corresponding to the detection data, and assign a label to the user's activity status; use the user's historical detection data as input and the assigned label as output to train a neural network multi-classification model; input the user's current detection data into the neural network multi-classification model to obtain the user's current activity status.
[0038] The user's activity status includes but is not limited to being on the bus, on the subway, walking, exercising at home, etc. The user's detection data includes location information, movement information, time information, and physiological information. The location information is obtained through GPS and quantified in the form of longitude and latitude. The movement information is detected by the gyroscope or accelerometer to determine whether the user is in a moving state. Physiological information includes pulse, heart rate, blood pressure and other data. Different labels are assigned to the user's activity status. The specific structure of the multi-classification model is as follows:
[0039] Input layer: The number of neurons in the input layer is the same as the number of the user's detection data features. Each neuron corresponds to a detection data feature and is used to receive input data.
[0040] Hidden layer: consists of multiple neurons. Each neuron performs nonlinear transformation on the input data through the activation function, thereby increasing the expressiveness and flexibility of the model. The activation functions that can be selected include relu, sigmoid function, etc.
[0041] Output layer: The number of neurons in the output layer is the same as the number of classification categories, that is, the number of types of activity states, and each neuron corresponds to an activity state.
[0042] Determining the change in the user's fatigue level based on the user's activity state further includes the following steps:
[0043] Step 1: Obtain the detection data when the user enters the current activity state, and obtain the physiological information of the user when entering the current activity state from the detection data, which is recorded as the target physiological information; obtain the user's historical target physiological information from the user's historical activity data, determine the historical target physiological information with fatigue based on the user's feedback results, and cluster the historical target physiological information with fatigue to obtain clusters; for the historical target physiological information in the same cluster, according to the user activity state corresponding to the historical target physiological information, select the historical target physiological information with fatigue in the same activity state to construct an n-variable linear equation system for solution:
[0044] Step 2: Obtain the learning duration of each form of learning section content provided to the user by the AR device in the historical target physiological information with fatigue in the same activity state. Subtract the learning duration of the corresponding form of learning section content of two historical target physiological information with fatigue in the same activity state to obtain n groups of difference values (d11, d12, ..., d1n), ..., (dn1, dn2, ..., dnn); establish the equation system based on the difference in learning duration of each form of learning section content: Solve the equations to get W1, W2, ..., W n Where W1, W2, ..., W n Indicates the influence coefficient of the first, second, ..., nth types of learning content provided by the AR device to the user on fatigue. d11, ..., d1n are the differences obtained by subtracting the learning time of the first type of learning content. dn1, ..., dnn are the differences obtained by subtracting the learning time of the nth type of learning content.
[0045] Step 3: Construct a set of n-variable linear equations for the historical physiological information of fatigued targets in all activity states and solve them to obtain the influence coefficient of each form of learning section content provided by the AR device to the user on fatigue in all activity states; use the influence coefficient of each form of learning section content on fatigue as a weight, and perform weighted summation of the learning time of each form of learning section content provided by the AR device to the user in the historical physiological information of fatigued targets in the cluster to obtain the user's fatigue threshold.
[0046] The physiological information of the user when entering the active state will affect the user's fatigue state. Taking the active state of taking the bus as an example, the results will be completely different if the user takes the bus after being busy and after having a sufficient rest. When the user has had a sufficient rest, he may feel tired or dizzy after accepting the learning section content provided by the AR device for a long time during the ride, while he may feel tired or dizzy for a short time after being busy. The historical target physiological information of the user's fatigue is judged based on the user's feedback results. If the user actively interrupts the learning section content provided by the AR device, or there is no interaction for a long time, it means that the user feels tired. In this case, the physiological information of the user when entering the active state and the learning time of each form of learning section content accepted by the user are stored.
[0047] In the same cluster, users have similar initial states, so they feel similar fatigue or dizziness. The amount of fatigue required to produce fatigue or dizziness is similar, which affects the upper limit of the learning content that users can accept. Different activity states will affect the speed of fatigue accumulation. For example, the speed of fatigue accumulation on the bus and the subway will be very different. Let T i W represents the learning time of the i-th form of learning content provided by the AR device to the user, i The influence coefficient of the i-th form of learning content provided by the AR device to the user on fatigue is used to calculate the user's fatigue level PL, PL = ΣW i ×T iIn the same cluster, fatigue has the same starting point and end point, so the influence of the starting point and end point can be eliminated by taking a difference. The influence coefficient of the same activity state is the same, so it is only necessary to establish a joint equation based on the data of the same activity state in the same cluster to solve the influence coefficient; for the same activity state, the influence coefficient is the same and is not affected by the user's starting state. Therefore, after solving the influence coefficient, the influence coefficient of the same activity state in different clusters can be averaged to eliminate the influence of chance. After obtaining all the influence coefficients, the cumulative fatigue level is obtained from the historical target physiological information with fatigue based on the influence coefficient and the learning time of the learning section content provided to the user by the AR device. This is the fatigue threshold of the cluster; for the same cluster, the fatigue threshold is the same because the starting state of the user is similar; for the same activity state, the influence coefficient of each form of learning section content on fatigue is the same.
[0048] The forms of learning content provided by AR devices to users include but are not limited to: videos, pictures, text and audio, etc. Different forms will cause different rates of fatigue accumulation for users; AR devices need to observe nearby virtual content and distant real environments at the same time, which requires the human eye to frequently switch focus, resulting in continuous tension of the ciliary muscle; the lighting of virtual content and real environment is inconsistent, and repeated zooming of the pupil increases fatigue, causing users to feel dizzy. Long-term use of AR devices may cause user fatigue or motion sickness; for example, when the user is active and watching static text on a bumpy vehicle, the acceleration perceived by the user's body is inconsistent with the static or moving direction of the AR virtual image, triggering a cognitive conflict in the user's brain and making the user feel dizzy. If the AR device provides audio information, it can relatively reduce dizziness; therefore, it is necessary to select the form of content presented by the AR device to prevent the user's accumulated fatigue from exceeding the upper limit that can be tolerated.
[0049] Determining the user's fatigue level also includes the following steps:
[0050] Get the detection data when the user enters the current activity state and the AR device provides the user with the learning section content, so that T i Represents the learning time of the i-th form of learning content provided by the AR device to the user, and calculates the user's fatigue level PL, PL = ΣW i ×T i , where W i It represents the influence coefficient of the i-th form of learning content provided by the AR device to the user on fatigue. The user's fatigue threshold is obtained based on the detection data when the user enters the current activity state.
[0051] Determining the learning modules provided to the user in the current active state based on the teaching form that the user can tolerate through a genetic algorithm also includes the following steps:
[0052] S10, binary-encode the learning section to obtain a binary string, where each bit in the binary string corresponds to a learning section, and each binary string corresponds to an individual; if the AR device provides the learning section content to the user, the value of the bit in the binary string corresponding to the learning section is 1; if the AR device does not provide the learning section content to the user, the value of the bit in the binary string corresponding to the learning section is 0;
[0053] S11, create the initial population and set the number of iterations to 0;
[0054] S12, calculate the fitness values of all individuals in the population;
[0055] S13, select individuals from the population, and the probability of an individual being selected is positively correlated with its fitness value;
[0056] S14, randomly selecting some individuals from the individuals selected in step S13 to perform a crossover operation, exchanging parts of the chromosomes to create new individuals, and determining whether the constraint conditions are met. If so, the new individuals are retained; otherwise, the new individuals are discarded; the constraint conditions are: the AR device provides learning content to the user, the fatigue generated is less than the fatigue threshold, and the total learning time of the learning content provided by the AR device to the user is less than the time threshold;
[0057] S15, performing mutation operation on individuals in the population to determine whether the constraints are met. If so, the new individual is retained; if not, the new individual is discarded.
[0058] S16, add 1 to the number of iterations and determine whether the number of iterations reaches the set value. If so, stop the algorithm and obtain the individual binary string with the highest fitness value; if not, return to step S12.
[0059] The fitness value is determined by the following steps:
[0060] Obtain the binary code of the individual, obtain the learning time of the learning section content with the binary string value of 1, and perform weighted summation based on the learning time to obtain the individual fitness value.
[0061] Different forms of learning section content may bring different learning effects to users. For example, the effect of video format may be better than that of pure text format learning section content. Maintain weights for different forms of learning section content, and obtain individual fitness values through weighted summation to ensure that users achieve the highest learning efficiency without exceeding their tolerance.
[0062] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.
Claims
1. An immersive educational experience method based on digital media, characterized in that: The following steps are involved: Obtain the user's test data and determine the user's activity status based on the user's test data; determine the user's fatigue changes based on the user's activity status and determine the user's fatigue level; obtain the user's historical learning data and obtain the user's teaching content based on the user's historical learning data; The user's teaching content is divided into several learning modules. Based on the teaching form that the user can tolerate, the genetic algorithm is used to determine the learning modules provided to the user in the current activity state, and the learning module content is provided to the user using AR devices.
2. The immersive education experience method based on digital media according to claim 1, characterized in that: Determining the user's activity status based on the user's detection data further includes the following steps: Obtain the user's historical detection data and the activity status corresponding to the detection data, and assign a label to the user's activity status; use the user's historical detection data as input and the assigned label as output to train a neural network multi-classification model; input the user's current detection data into the neural network multi-classification model to obtain the user's current activity status.
3. The immersive education experience method based on digital media according to claim 2, characterized in that: The step of determining the change in the user's fatigue level based on the user's activity state further includes the following steps: Step 1: Obtain the detection data when the user enters the current activity state, and obtain the physiological information of the user when entering the current activity state from the detection data, which is recorded as the target physiological information; obtain the user's historical target physiological information from the user's historical activity data, determine the historical target physiological information with fatigue based on the user's feedback results, and cluster the historical target physiological information with fatigue to obtain clusters; for the historical target physiological information in the same cluster, according to the user activity state corresponding to the historical target physiological information, select the historical target physiological information with fatigue in the same activity state to construct an n-variable linear equation system for solution: Step 2: Obtain the learning duration of each form of learning section content provided to the user by the AR device in the historical target physiological information with fatigue in the same activity state. Subtract the learning duration of the corresponding form of learning section content of two historical target physiological information with fatigue in the same activity state to obtain n groups of difference values (d11, d12, ..., d1n), ..., (dn1, dn2, ..., dnn); establish the equation system based on the difference in learning duration of each form of learning section content: Solve the equations to get W1, W2, ..., W n Where W1, W2, ..., W n Indicates the influence coefficient of the first, second, ..., nth types of learning content provided by the AR device to the user on fatigue. d11, ..., d1n are the differences obtained by subtracting the learning time of the first type of learning content. dn1, ..., dnn are the differences obtained by subtracting the learning time of the nth type of learning content. Step 3: Construct a set of n-variable linear equations for the historical physiological information of fatigued targets in all activity states and solve them to obtain the influence coefficient of each form of learning section content provided by the AR device to the user on fatigue in all activity states; use the influence coefficient of each form of learning section content on fatigue as a weight, and perform weighted summation of the learning time of each form of learning section content provided by the AR device to the user in the historical physiological information of fatigued targets in the cluster to obtain the user's fatigue threshold.
4. The immersive education experience method based on digital media according to claim 3, characterized in that: Determining the user's fatigue level further includes the following steps: Get the detection data when the user enters the current activity state and the AR device provides the user with the learning section content, so that T i Represents the learning time of the i-th form of learning content provided by the AR device to the user, and calculates the user's fatigue level PL, PL = ΣW i ×T i , where W i It represents the influence coefficient of the i-th form of learning content provided by the AR device to the user on fatigue. The user's fatigue threshold is obtained based on the detection data when the user enters the current activity state.
5. The immersive educational experience method based on digital media according to claim 4, characterized in that: The method of determining the learning modules provided to the user in the current active state by using a genetic algorithm based on the teaching form that the user can tolerate further comprises the following steps: S10, binary-encode the learning section to obtain a binary string, where each bit in the binary string corresponds to a learning section, and each binary string corresponds to an individual; if the AR device provides the learning section content to the user, the value of the bit in the binary string corresponding to the learning section is 1; if the AR device does not provide the learning section content to the user, the value of the bit in the binary string corresponding to the learning section is 0; S11, create the initial population and set the number of iterations to 0; S12, calculate the fitness values of all individuals in the population; S13, select individuals from the population, and the probability of an individual being selected is positively correlated with its fitness value; S14, randomly selecting some individuals from the individuals selected in step S13 to perform a crossover operation, exchanging parts of the chromosomes to create new individuals, and determining whether the constraint conditions are met. If so, the new individuals are retained; otherwise, the new individuals are discarded; the constraint conditions are: the AR device provides learning content to the user, the fatigue generated is less than the fatigue threshold, and the total learning time of the learning content provided by the AR device to the user is less than the time threshold; S15, performing mutation operation on individuals in the population to determine whether the constraints are met. If so, the new individual is retained; if not, the new individual is discarded. S16, add 1 to the number of iterations and determine whether the number of iterations reaches the set value. If so, stop the algorithm and obtain the individual binary string with the highest fitness value; if not, return to step S12.
6. The immersive educational experience method based on digital media according to claim 5, characterized in that: The fitness value is determined by the following steps: Obtain the binary code of the individual, obtain the learning time of the learning section content with the binary string value of 1, and perform weighted summation based on the learning time to obtain the individual fitness value.
7. An immersive educational experience system based on digital media, characterized in that: include: An information detection module, a user analysis module, an optimization module and an AR module; the output end of the information detection module is connected to the input end of the user analysis module, and is used to detect the user's activity status and obtain the user's activity status data; the output end of the user analysis module is connected to the input end of the optimization module, and is used to analyze the user's fatigue; the output end of the optimization module and the input end of the AR module are interconnected, and are used to determine the learning section content that needs to be provided to the user; the AR module is used to provide the learning section content to the user.
8. The immersive education experience system based on digital media according to claim 7, characterized in that: The information detection module further includes a motion detection unit, a position detection unit and a physiological detection unit; the motion detection unit is used to detect whether the user is in a stationary or moving state; the position detection unit is used to obtain the user's position information; The physiological detection unit is used to obtain physiological information of the user.
9. The immersive education experience system based on digital media according to claim 8, characterized in that: The user analysis module also includes an activity status analysis unit, a fatigue analysis unit and a progress management unit; the progress management unit is used to record the user's historical learning data, determine the user's learning progress, and obtain the learning content that needs to be provided to the user; the activity status analysis unit determines the user's activity status based on the user's detection data; the fatigue analysis unit determines the user's fatigue changes based on the user's activity status, and obtains the user's fatigue according to the learning time of the learning section content provided to the user by the AR module.
10. The digital media-based immersive education experience system according to claim 9, characterized in that: The optimization module also includes a segmentation unit and a genetic algorithm unit. The segmentation unit is used to divide the learning content that needs to be provided to the user into several segments; the genetic algorithm unit is used to determine the learning segment content provided to the user by the AR module.