A Multi-User Intelligent Study and Learning Interaction System Based on Augmented Reality

Abstract

This invention discloses a multi-user intelligent research and learning interaction system based on augmented reality, relating to the field of data encryption technology. It includes: a housing for integrating a card reader module, a motion sensing module, a projection module, a main controller, a haptic module, and an environmental sensor group, providing structural support and module interconnection; the card reader module for scanning identifiable items and performing user permission verification; the motion sensing module for detecting the hovering height of the user's handheld card using sensors and identifying changes in hovering status, converting the hovering height into a switching command; the projection module for projecting multiple independent projection areas downwards, adjusting the screen resolution, brightness, and contrast in conjunction with the main controller's commands, and defining user-specific interactive areas; the main controller for controlling projection switching, volume adjustment, and housing height adjustment; and the haptic module for receiving commands from the main controller via Wi-Fi and triggering vibration feedback during specific operations.

Description

Technical Field

[0001] This invention relates to the field of intelligent educational interaction technology, and in particular to a multi-user intelligent learning interaction system based on augmented reality. Background Technology

[0002] With the deepening of digital transformation in education, augmented reality (AR) technology, due to its ability to overlay virtual information onto the real environment and achieve a fusion of virtual and real interactive experiences, has become an important technological direction in the fields of smart education and immersive learning. Currently, the application of AR technology in educational scenarios has gradually evolved from early one-way information display to a comprehensive teaching tool supporting multimodal interaction, contextualized learning, and collaborative inquiry. Relying on high-precision spatial positioning, 3D reconstruction, SLAM algorithms, and multi-user collaborative architecture, modern AR systems can construct dynamic and operable virtual learning objects, supporting students to engage in embodied cognition and inquiry-based learning in real environments. Especially in study tours, scientific experiments, and historical and cultural teaching, AR technology enhances the immersion and participation in learning by visualizing abstract knowledge, simulating high-risk or irreversible operations, and recreating historical scenes.

[0003] However, existing AR learning systems still face technical bottlenecks in practical applications. On the one hand, most systems rely on head-mounted displays or mobile terminals for information presentation, which suffers from high equipment costs, discomfort when worn, and limited interaction methods, making it difficult to support the interactive needs of multiple users simultaneously, with low barriers to entry and high concurrency. Furthermore, the integration accuracy between virtual content and physical space is limited, and there is a lack of real-time feedback mechanisms for user actions. On the other hand, existing systems generally lack the ability to dynamically perceive and adaptively adjust to the usage environment. Parameters such as projection brightness, sound output, and interactive area layout are mostly preset fixed values, unable to intelligently adjust according to ambient light, background noise, or crowd density. This can easily lead to reduced information visibility, voice interference, or spatial congestion, affecting teaching order and the learning experience. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a multi-user intelligent research and learning interaction system based on augmented reality to solve the problems of high barriers to multi-user interaction and poor environmental adaptability.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a multi-user intelligent research and learning interaction system based on augmented reality, comprising: Housing, card reader module, motion sensing module, projection module, main controller, tactile module and environmental sensor group; The housing is used to integrate six modules, providing structural support and module interconnection; The card reader module is used to scan identifiable items and perform user authorization verification. The motion sensing module detects the hovering height of the user's hand holding the card through sensors, identifies changes in the hovering state, and converts the hovering height into a switching command; The projection module is used to project multiple independent projection areas downwards, and adjusts the screen resolution, brightness and contrast in conjunction with the instructions of the main controller, and divides the user's exclusive interactive area by the projection boundary. The main controller is used to receive user identity, hovering height and environmental data, integrate them and perform interaction priority determination, and control projection switching, volume adjustment and shell height adjustment; The tactile module is used to embed a miniature vibration motor in the identifiable component and receive commands from the main controller via Wi-Fi; The environmental sensor group is used to monitor ambient light, noise and crowd density in real time, and coordinate the adjustment of projection brightness, voice noise reduction and projection area layout, and adaptively adjust the height of the housing.

[0007] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the tactile module includes a micro vibration motor and a wireless communication unit, and the main controller controls the vibration mode and intensity according to the hovering height of the identifiable object and the type of projected content.

[0008] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the main controller is configured with an adaptive algorithm to adjust the projection brightness through the illumination data of the environmental sensor group, adjust the voice volume and noise reduction level through noise data, optimize the layout of the projection area through the flow density of people, and perform interaction priority determination in combination with user identity and hovering height.

[0009] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the handheld card of the identifiable component is provided with a flexible circuit layer, in which a micro vibration motor is embedded and isolated from the height marking layer signal.

[0010] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, wherein: the shell height adjustment specifically refers to... When the infrared thermal sensor detects that the user density exceeds the threshold, the main controller controls the height adjustment mechanism to raise the housing to expand the projection coverage area.

[0011] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the volume adjustment specifically involves the following steps: The volume adjustment function relies on the voice module. The external speaker of the voice module corresponds one-to-one with the independent projection area. The main controller adjusts the directional sound beam output synchronously according to the user's position and ambient noise.

[0012] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, wherein: the speech noise reduction specifically comprises, The external speakers of the voice module are arranged in two rows or a circular array and are integrated with the housing on the mounting bracket. The volume of each speaker is independently controlled by the main controller.

[0013] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the height adjustment mechanism includes an inner rod and an outer tube. The inner rod is provided with a guide groove, and the outer tube is slidably connected by a guide protrusion and the height is fixed by a locking bolt.

[0014] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the micro vibration motor is connected to the main controller via Wi-Fi and triggers corresponding short vibration, long vibration or pulse vibration modes according to the hovering height and hovering change state.

[0015] As a preferred embodiment of the augmented reality-based multi-user intelligent research and learning interaction system of the present invention, the light sensor, noise sensor and infrared thermal sensor of the environmental sensor group are all integrated on the top of the housing and are linked with the projection module and voice module in real time.

[0016] The beneficial effects of this invention are as follows: by detecting the height of the paper card suspension through body sensing and combining it with tactile feedback, low-cost multimodal interaction is achieved, lowering the threshold and enhancing the sense of immersion; by coordinating the adjustment of the projection and shell height through environmental sensing, the system achieves self-adaptation to the environment and the flow of people, solving the problems of congestion and poor visibility, and optimizing the multi-user experience.

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a multi-user intelligent research and learning interaction system based on augmented reality.

[0019] Figure 2 This is a schematic diagram of the tactile module structure.

[0020] Figure 3 This is a schematic diagram of the height adjustment mechanism.

[0021] Figure 4 This is a flowchart of environmental adaptive control. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Example 1, referring to Figures 1 to 4 This is the first embodiment of the present invention, which provides a multi-user intelligent research and learning interaction system based on augmented reality, including the following modules: Housing, card reader module, motion sensing module, projection module, main controller, haptic module, and environmental sensor group: The housing is used to integrate the card reader module, motion sensing module, projection module, main controller, tactile module and environmental sensor group, providing structural support and module interconnection; The shell consists of an outer layer, a middle layer, and an inner layer. The outer layer is an electromagnetic shielding layer with honeycomb ventilation holes, the middle layer is a phase change material heat conduction layer, and the inner layer is an antistatic mounting substrate, forming a synergistic optimization system for electromagnetic isolation, thermal management, and mechanical load bearing. The main controller coordinates the operation centrally, while the card reader and motion sensing module are positioned at the front to ensure interactive sensitivity. The projection driver unit is side-mounted with a serpentine flow channel to enhance heat dissipation, and the environmental sensor is positioned at the rear to expand the monitoring field of view. The modules are connected in a star topology, with the main controller serving as the central node. Dynamic impedance matching technology embeds an adaptive impedance matching circuit in the signal transmission path to achieve a dynamic adjustment range of characteristic impedance Z=40-80Ω (step ΔZ=5Ω) and a signal reflection coefficient Γ≤0.1 (frequency range f=1MHz-2.4GHz). An optimal matching parameter table is established through full-band scanning, and the matching parameters are dynamically corrected based on environmental data (correction period Δt=10s) to ensure signal integrity when multiple modules are working concurrently. Here, Z represents the characteristic impedance, ΔZ represents the impedance adjustment step size, Γ represents the voltage reflection coefficient, f represents the operating frequency, t represents the initialization scan time, and Δt represents the parameter correction period. The shell has a multi-layer structure design. The outer layer is a honeycomb ventilation hole electromagnetic shielding layer, the middle layer is a phase change material heat conduction layer, and the inner layer is an anti-static mounting substrate, forming a composite functional layer system. The module spatial layout optimization is based on the three-dimensional model of the shell. The main controller is centrally located, the card reader / motion sensing module is placed in front in the ±X axis area, the projection drive unit is placed on the side in the +Y and -Y axis areas, and the serpentine flow channel forms an orthogonal heat conduction path with the middle heat conduction layer of the shell. The star communication architecture is built with the main controller as the central node, which connects each module through 6 sets of independent differential links to reduce latency deviation. The dynamic impedance matching circuit is deployed in each transmission path and embedded with 16 digitally controlled impedance chips. It has 256 preset impedance combinations of Z=40-80Ω (ΔZ=5Ω) and initializes by performing a full-band scan to generate a reference parameter table. Intelligent matching parameter correction involves the environmental sensor uploading temperature and humidity data every Δt=10s. The main controller triggers a dynamic update of the parameter table based on the change in ΔT / ΔH, and adjusts the impedance value in real time to make Γ≤0.1. The thermal and electrical synergistic verification involves verifying the temperature change rate of the phase change layer when the heat flux density of the shell is ≥5W / cm² and multiple modules are operating concurrently, thereby achieving compliance with both heat dissipation performance and signal integrity standards.

[0026] The card reader module is used to scan identifiable items and perform user authorization verification; it adopts three-dimensional feature fusion authentication technology, which includes a physical anti-counterfeiting layer, an electronic encryption layer, and a behavior verification layer. The physical anti-counterfeiting layer refers to using a high-resolution optical sensor to scan the micro-texture features on the surface of the card and generate a three-dimensional grayscale image. The electronic encryption layer obtains the user's unique code within the encryption chip through a near-field communication unit. The behavior verification layer integrates micro-sensors to measure the contact angle and distance between the card and the reader to achieve high-security access control. Specifically, the operation involves integrating optical sensors, near-field communication units, and sensors to locate the module and simultaneously collect the physical micro-texture features of the identifiable item, the encrypted electronic identifier, and spatial pose parameters. The main controller generates a dynamic seed value based on the system timestamp: S = Hash(UTC time × device serial number), and uses the Elliptic Curve Cryptography (ECDSA) secp256r1 algorithm to generate a temporary session key, the public key. ( (Base points of an elliptic curve), encrypted using the AES-256-GCM algorithm, private key ( For modulo operation, (The curve order is used to combine the user's unique code with the public key to form an encrypted challenge frame; during the two-way authentication process, the security chip embedded in the identifiable device adopts a layered response mechanism, first parsing the dynamic seed value through a preset private key to calculate the response value.) The expression is: ; in, The key hash message authentication code is based on a secure hash algorithm. The response value is then combined with the hash value of the cardholder's fingerprint template and encrypted before being sent back. User permission verification first matches the dynamic key response value, then compares the biometric hash, and finally checks whether the paper jam spatial positioning parameters are within the acceptable operating range of the contact angle. When the contact angle is between 70 and 110 degrees, it is an acceptable operation; otherwise, it is unacceptable. User permission levels include general users, teachers, and administrators. Corresponding interaction configuration files are loaded, and time-limited access tokens are generated. ,in It uses an Advanced Encryption Standard 256-bit key algorithm and sends a region activation command to the projection module through a secure channel, synchronously updating the sensitivity parameters of the motion sensing module; The three-dimensional feature acquisition uses a high-resolution optical sensor, a near-field communication unit, and a micro-sensor to simultaneously acquire the physical micro-texture features, electronic identification, and spatial pose parameters of the card, generating an initial authentication dataset. Dynamic seed value generation is a process in which the main controller generates a dynamic seed value S based on the UTC timestamp and device serial number through SHA-256 hash operation, and outputs a 256-bit random seed. Temporary key pair calculations utilize the elliptic curve secp256r1 algorithm to generate temporary ECDSA key pairs; The user's unique code is combined with the public key K and encrypted using the AES-256-GCM algorithm to generate a challenge frame. During the two-way authentication process, the security chip uses a preset private key to parse the dynamic seed S, calculates the response value R through HMAC-SHA256, and combines it with the fingerprint template hash value. After AES-256 encryption, a 128-byte response frame is generated. The three-dimensional authentication uses the main controller to verify the R value in the response frame and the locally calculated HMAC result, compares the fingerprint hash value, and determines whether the contact angle θ satisfies 70°≤θ≤110°, and outputs the three-state verification result. The permission token issuance maps the user's permission level based on the verification result, loads the corresponding configuration file, and generates a time-sensitive token. The token structure includes a permission code and a timestamp. The system executes a coordinated action by sending an area activation command to the projection module via the SPI secure channel, while simultaneously updating the sensitivity parameters of the motion sensing module and triggering a state machine switch. The dynamic protection closed loop integrates three-dimensional feature data streams, key lifecycles, and behavior monitoring logs to achieve automatic isolation of abnormal operations and dynamic risk level assessment.

[0027] The motion sensing module uses sensors to detect the hovering height of the user's hand holding the paper and recognizes changes in the hovering state, converting the hovering height into a switching command; The motion sensing module integrates a ranging unit, a Doppler frequency shift detector, and a capacitive coupling sensor to simultaneously collect data on the vertical distance, instantaneous speed, and contact status of the paper jam. When started, it automatically scans the background signal in the state without paper jam, records the ambient light intensity distribution and establishes the electromagnetic interference baseline noise spectrum. When the user operates for the first time, it guides the execution of the calibration action, presses the paper jam tightly against the reference surface to obtain the zero height reference value, and then moves the paper jam vertically and slowly to record the full range signal characteristics. In the layered signal processing stage, the raw signal measured by the ranging unit is subjected to moving average filtering to suppress high-frequency noise. The motion component in the Puller signal is separated by wavelet transform, and a height-velocity joint probability model is established to verify the effectiveness of the action. ; in, Indicates the height value of the handheld card. and paper movement speed value The joint probability density function represents the probability density of an event occurring under a specific combination of altitude and speed. This indicates the real-time detected height of the handheld paper (unit: millimeters). Indicates the paper speed (unit: mm / s). The standard deviation of height measurement data reflects the range of fluctuation in height detection accuracy. The standard deviation of speed measurement data reflects the range of fluctuation in speed detection accuracy. This represents the arithmetic mean of altitude measurement data, characterizing the altitude reference value. This represents the arithmetic mean of speed measurement data, characterizing the speed reference value. Represents the base of the natural logarithm. Represents pi; Furthermore, based on continuously collected data on vertical distance changes, instantaneous velocity changes, duration of dwell time, and contact state fluctuations, the hovering process of the handheld paper is identified, classifying the corresponding content into rising hovering, falling hovering, stable hovering, and boundary crossing states. Content with continuously increasing vertical distance and instantaneous velocity changing in the same direction is identified as rising hovering; content with continuously decreasing vertical distance and instantaneous velocity changing in the opposite direction is identified as falling hovering; content with vertical distance fluctuations within a preset range and dwell time meeting set requirements is identified as stable hovering; and content with sudden changes in vertical distance, abnormally amplified instantaneous velocity, or continuous crossing of multiple layer boundaries is identified as boundary crossing. The hovering state and real-time hovering height are then mapped to layer boundary parameters to distinguish between basic information switching, interactive interface switching, control menu switching, and lock verification trigger content. The hovering height automatically adjusts the level height boundary parameters based on the user's historical operating habits. At low levels, basic information is displayed; at medium levels, the interactive operation interface is activated; and at high levels, the system-level control menu is entered, and the effective height is converted into standard OSD control protocol commands. When the command is executed, the main controller drives the projection module to adjust the focus light intensity distribution of the interface and works with the haptic module to generate differentiated vibration feedback; the level switching critical point triggers short pulse vibration, short pulse vibration (50ms) will be started from the low level to the middle level, and double pulse vibration (100ms interval) will be started from the middle level to the high level. If the operation exceeds the boundary, a gradually increasing vibration warning will be started. If an unexpected high-speed crossing of the level is detected, the safety lock will be activated to freeze the current operation interface and re-execute the verification action. Environmental baseline calibration involves automatically scanning the ambient light intensity distribution and electromagnetic noise baseline spectrum in a paper-free state when the motion sensing module is activated, and recording the background signal characteristics as a dynamic compensation benchmark. Zero height calibration involves guiding the paper to fit snugly against the reference surface during the user's first operation, obtaining the zero height reference value through the ranging unit, and simultaneously recording the initial contact state of the capacitive coupling sensor. Full-range signal acquisition involves the Doppler frequency shift detector capturing the instantaneous velocity v when the user moves the paper jam vertically, and the ranging unit simultaneously recording the height h to generate the raw dataset of height and velocity. Layered signal preprocessing involves performing a moving average filter on the original ranging signal to suppress high-frequency noise, while simultaneously separating the motion component of the Doppler signal through wavelet transform. Joint probability modeling is based on filtered h and v data. It calculates the standard deviation of height and the standard deviation of velocity, constructs a joint height and velocity model, and outputs the confidence score of action validity. Dynamic hierarchy boundary optimization adaptively adjusts the hierarchy height boundary parameters based on the user's historical operation data and updates them to the OSD protocol mapping table; Command conversion and feedback involves converting the effective height h into standard OSD control commands according to the hierarchical boundaries. The main controller drives the projection module to adjust the focus light intensity and triggers the tactile module to generate vibration feedback. The security lock freezes the user interface and activates a gradually increasing vibration warning when an unexpected high-speed out-of-bounds violation is detected, forcing a re-execution of permission verification. The performance verification closed loop verifies the millimeter-level detection accuracy and millisecond-level response under temperature changes and electromagnetic interference by fusing multi-source data to maintain the error rate.

[0028] The projection module is used to project multiple independent projection areas downwards, and adjusts the screen resolution, brightness and contrast in conjunction with the instructions of the main controller, and divides the user's exclusive interactive area by the projection boundary. The projection module achieves high-precision dynamic projection of a multi-user exclusive interactive area through multispectral fusion positioning and intelligent optical adjustment. When started, the multispectral sensor integrated on the top of the housing scans the projection area, analyzes the ambient light color temperature gradient and surface reflection characteristics, and generates a projection surface topology model by combining structured light 3D reconstruction technology. The main controller assigns a unique spatial identifier to each user and tracks hand coordinates in real time based on UWB positioning technology. An elliptical projection area is generated centered on the user's operation position, with the major axis aligned with the user's line of sight. When projection areas overlap, priority is given to ensuring the complete display area for users with higher privileges, while deformation compression or displacement is performed on the areas of users with lower privileges. Dynamic flowing light indicators are generated at the overlapping boundaries to guide users to adjust their operation positions. When a user's finger touches the projection boundary, the boundary grating produces a pulse flashing effect, the tactile module emits a warning vibration, and the user's gesture trajectory is recorded by the ranging unit. The trajectory data is input into the LSTM network to predict the operation intention and adjust the projection content in advance. When the infrared thermal imager detects that the user has left the state, the current operation context is automatically saved and the projection resources are recovered. Ambient light field scanning uses a multispectral sensor to scan the projection area, collect the ambient light color temperature gradient and surface reflectivity, and generate an initial light field distribution map. 3D topology modeling is based on structured light 3D reconstruction technology combined with light field data to calculate the curvature of the projection surface and material reflection parameters, and output a high-precision projection surface topology model. User space identifier allocation uses the main controller to assign a unique space identifier code to each user, and the UWB positioning system tracks the three-dimensional coordinates of the hand in real time. The dedicated projection area is generated by using the user's hand coordinates as the center of an ellipse, with the major axis aligned with the user's line of sight, and generating dynamic projection area parameters. The conflict resolution strategy is to prioritize maintaining the integrity of the high-privilege user's area when the projection areas overlap, and to perform deformation compression or displacement on the low-privilege area, generating a flowing light effect at the boundary. Boundary interaction feedback is triggered when the user touches the projection boundary, causing the grating pulse to flash and the tactile module to vibrate as an alert. At the same time, the ranging unit records the gesture trajectory, and the gesture trajectory data is input into a two-layer LSTM network to predict the operation intention in the next 500ms and adjust the projection content in advance. Dynamic resource recovery is a feature whereby the infrared thermal imager automatically saves the operation context and recovers the projection resources after detecting the disappearance of the user's body temperature characteristics. This feature verifies the positioning error, response delay, and ambient light interference tolerance of the projection area in multi-user concurrent scenarios.

[0029] The main controller receives user identity, hovering height, and environmental data, integrates them, determines interaction priority, and controls projection switching, volume adjustment, and housing height adjustment. The main controller achieves precise coordination and control of system resources through multimodal data integration and intelligent decision-making mechanisms. It employs high-precision timestamp alignment technology to synchronize user identity, hovering height, and environmental sensor data streams, attaching high-precision timestamps. By checking the match between user identity and current hovering height permissions (e.g., administrators can execute higher-level commands), it verifies the consistency between environmental sensor data and physical laws (e.g., light intensity should not abruptly change in the absence of a light source). When a user's hovering height rapidly crosses a critical value, it is marked as an emergency operation. When environmental sensors detect sudden strong light or noise, system resources are dynamically allocated using three-dimensional weights, prioritizing the computational bandwidth and response time of critical tasks. The weight model is as follows: ; in, Allocate total weights to system resources. This is a user identity weighting coefficient, representing the difference in resource priority for users with different permission levels. The user identity weight value is derived from the user permission level quantification parameter (e.g., ordinary user = 1, administrator = 2). This is the environmental urgency coefficient, reflecting the urgency level of sudden environmental events. This is the environmental urgency value, calculated based on the rate of change in environmental sensor data (e.g., when the change in light intensity per second exceeds a threshold). (incremental) The hovering height sensitivity coefficient represents the importance level of the current hovering height to the system response. The sensitivity value for the handheld paper height is calculated based on the proximity of the hovering height to the critical switching threshold. The formula uses linear weighting to fuse three factors: user identity, environmental state, and operational state. , , satisfy + + The normalization condition for =1, , , The value range of each dimension is standardized to the [0,1] interval to ensure that the impact of each dimension is balanced and comparable. It should be noted that the user identity weight value is obtained by the card reader module after verifying user permissions and writing the corresponding permission level into the permission quantification parameters; the environmental urgency value is obtained by the environmental sensor group continuously monitoring ambient light, noise, and crowd density, extracting the change range of each environmental quantity between adjacent moments, and combining it with whether there are sudden changes; the sensitivity value of the handheld card height is obtained by compiling the proximity between the real-time hovering height output by the motion sensing module and the preset level switching position. The user identity weight coefficient, environmental urgency coefficient, and hovering height sensitivity coefficient are used to limit the participation ratio of the corresponding three types of data in this judgment. Then, the user identity weight value, environmental urgency value, and handheld card height sensitivity value are calculated with the corresponding coefficients and jointly incorporated into the total weight of system resource allocation to support subsequent interaction priority determination and dynamic resource allocation.

[0030] Projection switching utilizes the main controller to determine the user's holding height of the paper and categorizes operation commands into three levels: basic, intermediate, and advanced. Low-level states trigger the basic information interface, intermediate levels activate the interactive operation panel, and advanced levels access the system control menu. An ambient light sensor monitors the ambient illuminance in real time. In strong light (1000-3000 lux) environments, it automatically enhances projection contrast and color saturation, reducing glare interference through polarized light compensation. In low light (50-200 lux) environments, it activates a dark display mode, reducing blue light to alleviate eye strain. When multiple user projection areas overlap, the system dynamically adjusts content transparency based on permission levels, ensuring that the interface of high-priority users remains in visual focus. By using ultra-wideband positioning technology to obtain the user's precise coordinates, the acoustic engine uses intelligent beamforming technology to project the voice content to the target area. In noisy environments, the system analyzes the noise spectrum characteristics and selectively enhances the sound pressure level of the human voice frequency band (300-3400Hz). At the same time, it uses a deep neural network to filter out background noise in real time. In the face of multi-user concurrent scenarios, the voice system divides the available frequency band into independent sub-channels, allocates dedicated communication bandwidth to each user, and suppresses cross-regional sound crosstalk through phase cancellation technology to ensure the privacy and clarity of individual interactions. Based on the analysis of crowd density using infrared thermal imaging data, when crowd gathering is detected, the system automatically raises the height of the device housing and expands the projection angle to increase the coverage area. The lifting process uses an intelligent control algorithm to dynamically adjust the motor drive force according to the real-time height deviation, ensuring smooth and vibration-free movement that meets ergonomic comfort. In extremely dense scenes, the linkage projection module re-divides the interactive area layout, prioritizing the availability of key functions. The system uses an auxiliary camera to evaluate projection clarity, a microphone array to calibrate the sound field distribution, and laser ranging to verify the housing position, forming a collaborative optimization of the light, sound, and machine domains. Multimodal data synchronization is achieved by the main controller using the IEEE 1588 precision clock protocol to add timestamps to authentication data, hovering height values, and environmental sensor data, generating a time-stamped 3D data stream. Interaction priority determination involves jointly comparing user permission level, the current hover height corresponding to the level, hover change state, degree of environmental change, and the occupancy of the projection area to determine whether the current interaction request belongs to high-priority response, medium-priority response, or low-priority response. Specifically, content with a high user permission level, hover height entering a high-level control area, hover change state showing stable stay or rapid and effective switching, high degree of environmental change, and interaction competition in the corresponding projection area is determined to be a high-priority response, prioritizing the corresponding projection switching, voice output, and area maintenance. Content with a normal user permission level, hover height at an intermediate level, continuous hover change state but not reaching the control trigger condition, and small environmental fluctuations is determined to be a medium-priority response, maintaining the current interaction resource allocation. Content with a low user permission level, hover height only corresponding to the basic display area, hover change state showing short-term fluctuations or interruptions, and no clear operation requirement is determined to be a low-priority response, corresponding to delayed refresh, partial waiting, or resource yielding.

[0031] Permission and environment verification verifies the rationality of environmental data mutations by matching user permission levels with hover height level thresholds and outputs an operation qualification flag. When the hover height change rate or environmental parameters change abruptly, an emergency operation flag is triggered, activating the rapid response channel. Dynamic resource allocation is based on a weighted model, which calculates the resource allocation ratio in real time and prioritizes the computing resources for high-weight tasks. The projection parameters are dynamically adjusted based on the hover height level mapping display mode, combined with the ambient light intensity to adjust the projection contrast and color gamut coverage. Acoustic directional control uses ultra-wideband positioning coordinates to drive a beamforming array, enhancing the sound pressure level in the human voice frequency band, and deep neural networks to reduce noise in real time. The intelligent shell adjustment is based on infrared thermal imaging of pedestrian density data, and uses a PID algorithm to control the stepper motor to raise the shell height. Multi-domain collaborative optimization uses an auxiliary camera to evaluate the projection, a microphone array to calibrate the sound field uniformity, and a laser rangefinder to verify the housing position, achieving closed-loop feedback of optical, acoustic, and mechanical parameters. Performance verification involved testing operation latency, projection color accuracy, and anomaly recovery time in a scenario with 5 concurrent users. The performance metrics exceeded the requirements of human-computer interaction standards.

[0032] The haptic module is used to embed a miniature vibration motor in a recognizable component, receive commands from the main controller via Wi-Fi, and trigger vibration feedback during specific operations; The tactile module achieves high-precision vibration interaction through a multi-layer composite structure and intelligent feedback algorithm. A three-layer composite structure is built inside the handheld card of the identifiable part. The flexible circuit layer uses polyimide substrate to print serpentine traces to improve bending resistance. A miniaturized micro vibration motor array is embedded at the circuit nodes of the vibration layer. Each motor is independently addressed and controlled, and a nano-alumina coating is set in the isolation layer to achieve electromagnetic shielding and eliminate mutual interference between height detection signal and vibration drive. Multi-protocol compatible communication automatically switches to Bluetooth Low Energy link when Wi-Fi signal is congested. Time-division duplex technology separates control commands from status feedback data streams, including addressing, frequency, amplitude, and check fields. Combined with signal strength, the number of retransmissions is dynamically adjusted to ensure transmission reliability. ; in, For the number of retransmissions, To take the integer function upwards, The preset minimum received signal strength threshold (in dBm) represents the minimum signal strength required for reliable communication. The current received signal strength (in dBm) is detected in real time. The environmental attenuation factor (unit: dBm) reflects the loss characteristics of electromagnetic waves per unit distance in the propagation path. The formula quantifies the difference between the current signal strength and the minimum required strength, and dynamically calculates the number of retransmissions required by combining the environmental attenuation factor. When environmental interference causes the signal strength to fall below a threshold, the number of retransmissions is automatically increased to compensate for the deterioration of signal path quality. All parameters are directly measurable physical quantities. Defined by the communication protocol, Data is collected via the radio frequency front-end of the wireless module. Obtained based on experimental calibration using a multipath propagation model; It should be noted that the round-up function means rounding up the calculated retransmission count to the smallest integer not less than the result, so as to ensure that the final retransmission count is the integer number that can be directly sent.

[0033] The minimum received signal strength threshold (example range: -85dBm to -65dBm) can be determined through pre-communication testing based on the lower limit of the receiving sensitivity of the wireless communication unit used, the stability requirements for control command transmission, and the strength of electromagnetic interference on site. A higher threshold is used when the interference is strong or the transmission reliability requirements are high, and a lower threshold is used when the interference is weak or the transmission distance is short.

[0034] When monitoring the drive current waveform to identify motor stall and no-load faults, the adjacent unit is automatically switched to compensate for vibration. When local overheating occurs, the temperature protection system shuts down the corresponding area and starts internal convection cooling of the paper jam. The composite structure design involves constructing a three-layer composite structure inside the cardboard: a flexible circuit layer, a vibration layer, and an electromagnetic shielding layer, while defining the physical layout parameters. The vibration array addressing assigns an independent IP address to each micromotor, establishes a motor position coding table, and forms an independently controllable vibration matrix.

[0035] Dual-mode communication deployment involves the main controller sending commands via Wi-Fi, which automatically switches when the signal road is congested, ensuring conflict-free transmission of command frames and status feedback. The adaptive retransmission calculation dynamically calculates the number of retransmissions according to the formula, and adjusts the maximum retransmission interval in real time when the environmental attenuation factor and multipath interference change. The waveform precision control uses PWM modulation to generate the drive waveform, and PID algorithm to stabilize the current output and match the preset vibration mode. Fault diagnosis and compensation utilizes monitoring of motor current waveforms to identify stall or no-load faults, automatically activates adjacent motor units to compensate for vibration, and writes fault markers to the log. Thermal management involves shutting down the motor in the corresponding area and starting a miniature fan to force convection cooling when the infrared sensor detects local overheating. Once the temperature drops, operation resumes, and the temperature difference data is fed back to the PID parameters for adaptive adjustment.

[0036] The environmental sensor array is used to monitor ambient light, noise and crowd density in real time, and coordinate the adjustment of projection brightness, voice noise reduction and projection area layout, and adaptively adjust the housing height to optimize projection coverage. The environmental sensor group optimizes the immersive interactive experience through multi-dimensional environmental perception and intelligent control algorithms. First, a high-precision photosensitive array is deployed to perform omnidirectional spectral scanning to analyze the intensity distribution and color temperature characteristics of ambient light. At the same time, it combines infrared thermal imaging to capture heat maps of people flow and track people density and movement trends in real time. A circular microphone array collects spatial sound field data, separates human voice from environmental noise using beamforming technology, calculates the frequency domain distribution of sound pressure level in real time, and identifies the location of sudden noise sources. In bright light (1000-3000 lux) environments, polarization compensation is activated to improve visibility, while in low light (50-200 lux) environments, the proportion of blue light is reduced to protect visual comfort. The noise reduction level is optimized by dynamically configuring the filter group based on the noise spectrum characteristics. Adaptive notch filtering is implemented for low-frequency steady-state noise in the range of 20-300 Hz, and transient suppression is initiated for high-frequency impulse noise in the range of 2000-8000 Hz. The projection layout reconstruction uses infrared thermal imaging data to generate a crowd density distribution map, which is then converted into a weighted distribution model of the projection area. A spatial segmentation algorithm is used to divide a dedicated interaction area for each user, ensuring that the distance between adjacent areas is not less than the minimum visual distance of the human eye. When crowds are detected, the system dynamically expands the projection coverage area, prioritizing the visibility and smooth operation of core functions, while using edge fading technology to smoothly transition overlapping areas. By analyzing the match between the projected coverage area and spatial requirements in real time, the device housing height is dynamically adjusted to optimize the interactive experience. The housing height adjustment mechanism calculates the target lifting height based on the projected coverage area formula. ; in, It is expressed as the projected coverage area (m²). Represented as pi (π) It is expressed as the real-time height of the shell (m). The projector's optical divergence angle (unit: radians). This represents the diffusivity of the projected light rays in the vertical direction. The formula calculates the effective projection range using the geometric relationship between the shell height and the projection angle, where ( The physical meaning of the corresponding projected radius is that the squaring operation reflects the square relationship between the area and the radius. The projection region is then characterized as approximately circular, as shown in the formula. Real-time measurement via the built-in encoder of the height adjustment mechanism. The values ​​must be determined by the optical design parameters of the projection module and must meet the following requirements. Convergence conditions within the mechanical stroke of the casing (i.e.) <arctan(maximum projection radius / minimum shell height)) to ensure effective coverage of the target area at different lifting heights; Multi-domain collaborative optimization achieves environmental adaptive adjustment through cross-modal parameter coupling. In strong light environments, it simultaneously enhances the high-frequency components of speech (2000-8000Hz) to improve clarity. In high-density environments, it compresses the projection color gamut to reduce visual fatigue. It also maintains a balanced audiovisual experience by dynamically matching spectral and acoustic parameters. Omnidirectional light field scanning uses a high-precision photosensitive array to scan the ambient light intensity and color temperature gradient, generating an omnidirectional light field distribution map. Based on the light intensity data, polarization compensation or blue light suppression is enabled, and projection brightness parameters are output. Thermal density modeling uses infrared thermal imagers to capture human radiation data, and generates a thermal map of human flow density using the DBSCAN clustering algorithm, outputting density levels. The sound field characteristics are obtained by separating the sound source using MVDR beamforming with a ring microphone array, calculating the frequency domain distribution of the sound pressure level, and marking the azimuth angle θ of the noise source; based on the noise spectrum, an IIR notch filter and an FIR transient suppression module are loaded to generate a noise reduction coefficient matrix. Cross-modal parameter coupling enhances high-frequency components of speech in strong light environments and compresses the color gamut in high-density scenes to maintain color accuracy balance.

[0037] This embodiment also provides a computer device suitable for a multi-user intelligent research and learning interaction system based on augmented reality, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the multi-user intelligent research and learning interaction system based on augmented reality as proposed in the above embodiment.

[0038] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0039] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the augmented reality-based multi-user intelligent research and learning interaction system proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0040] In summary, this invention achieves low-cost multimodal interaction by using motion sensing to detect the height of the paper card suspension and combining it with tactile feedback, thereby lowering the barrier to entry and enhancing immersion; and by using environmental sensing to collaboratively adjust the projection and shell height, the system achieves self-adaptation to the environment and pedestrian flow, solving the problems of congestion and poor visibility, and optimizing the multi-user experience.

[0041] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-user intelligent research and learning interaction system based on augmented reality, characterized by: Housing, card reader module, motion sensing module, projection module, main controller, tactile module and environmental sensor group; The housing is used to integrate six modules, providing structural support and module interconnection; The card reader module is used to scan identifiable items and perform user authorization verification. The motion sensing module detects the hovering height of the user's hand holding the card through sensors, identifies changes in the hovering state, and converts the hovering height into a switching command; The projection module is used to project multiple independent projection areas downwards, and adjusts the screen resolution, brightness and contrast in conjunction with the instructions of the main controller, and divides the user's exclusive interactive area by the projection boundary. The main controller is used to receive user identity, hovering height and environmental data, integrate them and perform interaction priority determination, and control projection switching, volume adjustment and shell height adjustment; The tactile module is used to embed a miniature vibration motor in the identifiable component and receive commands from the main controller via Wi-Fi; The environmental sensor group is used to monitor ambient light, noise and crowd density in real time, and coordinate the adjustment of projection brightness, voice noise reduction and projection area layout, and adaptively adjust the height of the housing.

2. The augmented reality-based multi-user intelligent research and learning interaction system as described in claim 1, characterized in that: The tactile module includes a miniature vibration motor and a wireless communication unit. The main controller controls the vibration mode and intensity based on the hovering height of the identifiable object and the type of projected content.

3. The augmented reality-based multi-user intelligent research and learning interaction system as described in claim 2, characterized in that: The main controller is configured with an adaptive algorithm to adjust the projection brightness based on the illumination data from the environmental sensor group, adjust the voice volume and noise reduction level based on the noise data, optimize the projection area layout based on the crowd density, and determine the interaction priority based on the user's identity and hovering height.

4. The augmented reality-based multi-user intelligent study and learning interaction system as described in claim 3, characterized in that: The identifiable card has a flexible circuit layer inside, with a micro vibration motor embedded therein, and is isolated from the height marking layer signal.

5. The augmented reality-based multi-user intelligent research and learning interaction system as described in claim 4, characterized in that: The adjustment of the shell height specifically refers to, When the infrared thermal sensor detects that the user density exceeds the threshold, the main controller controls the height adjustment mechanism to raise the housing to expand the projection coverage area.

6. The augmented reality-based multi-user intelligent study and learning interaction system as described in claim 5, characterized in that: The volume adjustment process is as follows: The volume adjustment function relies on the voice module. The external speaker of the voice module corresponds one-to-one with the independent projection area. The main controller adjusts the directional sound beam output synchronously according to the user's position and ambient noise.

7. The augmented reality-based multi-user intelligent research and learning interaction system as described in claim 6, characterized in that: The speech noise reduction specifically refers to... The external speakers of the voice module are arranged in two rows or a circular array and are integrated with the housing on the mounting bracket. The volume of each speaker is independently controlled by the main controller.

8. The augmented reality-based multi-user intelligent research and learning interaction system as described in claim 7, characterized in that: The height adjustment mechanism includes an inner rod and an outer tube. The inner rod is provided with a guide groove, and the outer tube is slidably connected through a guide protrusion and fixed in height by a locking bolt.

9. The augmented reality-based multi-user intelligent study and learning interaction system as described in claim 8, characterized in that: The miniature vibration motor is connected to the main controller via Wi-Fi and triggers corresponding short vibration, long vibration, or pulse vibration modes according to the hovering height and hovering change state.

10. The augmented reality-based multi-user intelligent research and learning interaction system as described in claim 9, characterized in that: The light sensor, noise sensor, and infrared thermal sensor of the environmental sensor group are all integrated on the top of the housing and are linked in real time with the projection module and voice module.

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