A natural exploration education system based on emotional interaction robot and gamification task

Abstract

The application relates to the field of educational technology and intelligent robots, and specifically discloses a natural exploration education system based on emotional interaction robots and gamified tasks. The system comprises: a multi-modal environment perception system for real-time acquisition of natural scene data and human-computer interaction feedback; an emotional intelligence computing hub for emotional modeling and generation of personified feedback strategies and attention intervention instructions; a gamified task scheduling engine for dynamic construction of a narrative-driven task flow based on a knowledge graph; an embodied intelligent robot terminal for physical collaboration through a multi-degree-of-freedom execution mechanism and a flexible expression module to guide the exploration subject to perform observation operations; and a data closed-loop evaluation system for quantitative evaluation of cognitive achievements and adaptive adjustment of task difficulty. Through a highly emotionally resonant interaction mode, the application effectively guides the user's attention back to the natural real scene, significantly enhances the exploration motivation and immersion, and realizes real-time situational generation of popular science content.

Description

Technical Field

[0001] This invention belongs to the field of educational technology and intelligent robots, specifically relating to a natural exploration education system based on emotional interaction robots and gamified tasks. Background Technology

[0002] In the fields of children's holistic education and mental health, nature exploration education plays an irreplaceable role in cultivating children's observation skills, ecological awareness, and well-rounded personality. With the rapid development of artificial intelligence and mobile interactive technologies, using intelligent terminals to guide children outdoors and engage in nature-based cognitive activities has become an important branch of modern educational technology. This field aims to break down the geographical and formatal limitations of traditional science education through digital means, building a logical bridge connecting virtual knowledge systems with the real physical world for children and teenagers.

[0003] Among them, nature exploration systems based on emotionally interactive robots and gamified tasks attempt to transform dry natural science knowledge into an immersive interactive experience through anthropomorphic social feedback and task-driven mechanisms. These systems typically use intelligent robots as exploration companions, coupled with gamified task architectures, aiming to guide children from information reception to deep understanding through emotional bonds and a sense of accomplishment, thereby achieving a fusion of emotional and professional science education.

[0004] Existing technologies face significant technical bottlenecks in addressing issues such as nature deficit disorder and weak ecological emotional connections among contemporary children and adolescents. Limited by the singular interactive dimensions of traditional educational devices, existing solutions often fail to provide children with warm emotional support in complex natural settings, resulting in a lack of sustained intrinsic motivation and emotional resonance during exploration. Furthermore, existing systems perform poorly in balancing digital entertainment dependence with real-world natural experiences, struggling to effectively shift children's attention from virtual screens to real-world natural environments through engaging interactive logic. The presentation of science content also often lacks specificity and contextualization. In addition, the systems' insufficient dynamic optimization capabilities for exploration tasks and their inability to deeply intervene in areas of ambiguity in children's natural cognition make it difficult for existing technologies to overcome core pain points such as fragmented natural cognition, fragmented exploration experiences, and a lack of ecological concern, thus severely limiting the practical effectiveness of nature exploration education. Summary of the Invention

[0005] The purpose of this invention is to provide a nature exploration education system based on emotional interactive robots and gamified tasks, in order to solve the problems mentioned in the background art, such as lack of emotional support, difficulty in shifting attention from digital screens to real-world natural scenes, lack of contextualization of popular science content, and lack of sustained motivation for exploration faced by children during nature exploration.

[0006] The technical solution of this invention includes: a multimodal environment perception system, used to acquire phenological characteristic data, geographical location information, and environmental physical parameters in natural scenes in real time, and simultaneously capture human-computer interaction behavior data and physiological feedback data of the exploration subject; an emotional intelligence computing center, used to perform multidimensional emotion modeling based on the data acquired by the multimodal environment perception system, calculate the instantaneous emotional state index and attention concentration level of the exploration subject, and generate a robot anthropomorphic feedback strategy accordingly; a gamified task scheduling engine, used to dynamically construct and issue a gamified exploration task flow with narrative-driven characteristics based on the feedback strategy output by the emotional intelligence computing center and a preset natural education knowledge graph; an embodied intelligent robot terminal, used to respond to the task flow issued by the gamified task scheduling engine, and achieve physical collaboration and emotional resonance with the exploration subject through a multi-degree-of-freedom mechanical actuator, a multimedia interactive interface, and a flexible expression module, guiding the exploration subject to perform observation, collection, and cognitive operations on natural targets; and a data closed-loop evaluation system, used to quantitatively evaluate the cognitive results of the exploration subject in the process of executing the task flow, and feed the evaluation results back to the gamified task scheduling engine to achieve adaptive adjustment of task difficulty.

[0007] Furthermore, the multimodal environmental perception system includes an environmental feature extraction unit, a bio-information monitoring unit, and a spatiotemporal positioning unit. The environmental feature extraction unit integrates a high-resolution panoramic camera and a spectral sensor to identify plant species, insect activity trajectories, and topographic features in the natural environment. The bio-information monitoring unit collects facial expression images, speech tone frequency, heart rate variability, and skin conductance data of the exploring subject through non-contact visual algorithms or wearable sensors, serving as the basic input for emotion modeling. The spatiotemporal positioning unit combines a global satellite navigation system with an inertial navigation algorithm to determine the precise coordinates of the robot and the exploring subject in natural space, and uses altitude and air pressure data to assist in judging the current microclimate environment.

[0008] In one embodiment of the present invention, the emotional intelligence computing center operates an emotion decoding model based on a deep neural network. This model fuses features from multi-source heterogeneous data collected by the bio-information monitoring unit, dividing them into at least five emotional dimensions, including happiness, curiosity, fatigue, frustration, and fear, and assigning a quantitative score between 0 and 1 to each dimension. Furthermore, the emotional intelligence computing center also includes an attention shift decision module. This module determines whether the visual focus remains on the electronic screen or the external natural scene by real-time monitoring of the explorer's eye-tracking trajectory and head posture. When the visual focus is detected to remain on the electronic screen for a period exceeding a preset threshold, the module generates a strongly directional physical intervention command, driving the embodied intelligent robot terminal to attract the explorer's attention to a designated natural observation point through specific audio frequencies or body movements.

[0009] Furthermore, the gamified task scheduling engine includes a narrative logic generation module, a knowledge mapping module, and a difficulty controller. The narrative logic generation module embeds natural science knowledge points into a complete virtual adventure storyline, transforming traditional science observation into key actions for resolving story conflicts. The knowledge mapping module, through a pre-set natural education knowledge graph, associates real-time identified natural targets with relevant ecological roles, life cycles, defense mechanisms, and other educational content, generating contextualized interactive text. The difficulty controller, based on the cognitive level feedback from the data closed-loop evaluation system, selects the exploration task most suitable for the current explorer's abilities from a tiered difficulty sequence of 1 to 10 levels. As one embodiment of the invention, the gamified task flow consists of four stages: a guiding paragraph, on-site operation instructions, interactive questioning, and achievement feedback.

[0010] Furthermore, the embodied intelligent robot terminal possesses at least six active degrees of freedom, including head rotation, arm swinging, and chassis movement. The flexible expression module, composed of a color-changing LED array and bionic electronic skin, is used to simulate the color changes and tactile feedback of biological emotions. The motion control laws of the robot terminal's actuators are highly coupled with the feedback strategies of the emotional intelligence computing center. For example, when the emotional state index indicates that the exploring subject is in a frustrated state, the actuators are driven to perform a low-center-of-gravity comforting posture, and warm-colored light effects are emitted through the flexible expression module; when the exploring subject detects the discovery of a new species, the robot performs high-frequency celebratory actions and outputs encouraging evaluations in conjunction with the voice module.

[0011] Furthermore, the data closed-loop evaluation system employs a multi-dimensional assessment model. This model includes four assessment indicators: cognitive accuracy, exploration depth, emotional engagement, and ecological protection behavior. Cognitive accuracy is determined based on the explorer's classification and identification of natural targets; exploration depth is determined based on the explorer's duration at a specific observation point and the logical depth of their questions; emotional engagement is determined by analyzing the proportion of positive emotions during task execution; and ecological protection behavior is determined by monitoring whether the explorer engages in non-standard behaviors such as damaging vegetation or disturbing animals. The system weights and sums these indicators to generate a total score, which is then stored in a blockchain ledger to construct a long-term natural literacy profile for the explorer.

[0012] In one embodiment of the present invention, the system further includes a cloud-based knowledge base and a collaborative interaction interface. The cloud-based knowledge base stores deep learning model weights and popular science texts covering characteristics of over 10,000 species of flora and fauna, and supports real-time multilingual conversion. The collaborative interaction interface allows multiple embodied intelligent robot terminals located within the same natural geographical area to network, supporting multiple exploration entities to jointly complete large-scale ecosystem investigation tasks. By assigning different scientific research roles, it cultivates the teamwork capabilities of the exploration entities.

[0013] Furthermore, the sensing unit operates at a frequency of 30 to 60 times per second, ensuring real-time capture of subtle emotional changes in the subject of exploration. The robot terminal's chassis employs a tracked mechanism and is equipped with an independent suspension system, enabling stable movement on grasslands, sandy areas, or forest paths with an incline of no more than 30 degrees. The embodied intelligent robot terminal's power management system automatically adjusts the processor frequency based on task intensity. When the system is performing observation tasks, a low-power mode is activated; when performing complex 3D environment modeling or multimodal speech recognition, it switches to a high-performance mode to ensure a battery life of no less than 8 hours.

[0014] As one embodiment of the present invention, the natural education knowledge graph adopts an ontology-based construction method, defining core categories such as biological species, environmental elements, meteorological conditions, and food chain relationships, and establishing multiple logical connections between categories. When the multimodal environmental perception system identifies a specific plant seedling, the gamified task scheduling engine not only pushes the plant's morphological characteristics but also triggers related tasks concerning soil moisture, light requirements, and symbiotic fungi.

[0015] Furthermore, the embodied intelligent robot terminal is also equipped with macro photography accessories and an environmental sample analysis kit. With the cooperation of the exploring subject, the robot can magnify and image the microscopic structure of insects, or perform real-time measurements of water pH and soil conductivity. The measurement data serves as key clues for gamified tasks, unlocking higher-level narrative elements, thus achieving a deep integration of field investigation and rigorous scientific experimentation.

[0016] Furthermore, the emotional interaction logic also includes a memory recall mechanism. The robot terminal records the explorer's preferences and fears during previous natural activities and avoids scenarios that may cause extreme anxiety in subsequent task planning, or guides the explorer to overcome specific fears through gradual task design. The memory recall mechanism generates personalized exploration path suggestions by calculating the success rate and emotional peak of each historical task.

[0017] As one embodiment of the present invention, the system pre-sets differentiated interactive corpora for explorers of different age groups. For children aged 3 to 6, more anthropomorphic and simpler sentence structures are used for voice feedback; for school-aged children aged 7 to 12, the frequency of scientific terminology is increased, and more gamified elements with logical deduction are introduced.

[0018] Furthermore, the safety of the embodied intelligent robot terminal is jointly ensured by a hardware-level obstacle avoidance module and a software-level behavior constraint protocol. The obstacle avoidance module utilizes ultrasonic radar and infrared proximity sensors to ensure that the robot will not collide with the exploring subject under any circumstances, and its movement speed is limited to less than 1.5 meters per second. The behavior constraint protocol uses real-time geofencing technology to prevent the robot and the exploring subject from entering dangerous areas outside the preset safety boundaries.

[0019] Furthermore, the multimodal environmental perception system also includes a soundscape analysis submodule. This submodule collects background sounds from nature through a microphone array and uses voiceprint recognition technology to distinguish acoustic signals such as birdsong, insect chirping, wind sounds, and flowing water sounds. The soundscape analysis results are integrated into gamified tasks, for example, guiding the explorer to locate specific birds through hearing, thereby enhancing the explorer's sensory acuity.

[0020] Compared with the prior art, the advantages and positive effects of the present invention are as follows: 1. This invention constructs an embodied interaction mode with high emotional resonance capabilities through the collaborative work of a multimodal environmental perception system and an emotional intelligence computing center. The system can not only perceive the natural environment, but also deeply understand the real-time psychological state of the explorer. By providing warm emotional feedback and psychological compensation, it effectively solves the technical bottleneck of traditional educational equipment's stiff interaction and lack of attractiveness in complex natural scenes, and greatly enhances the explorer's intrinsic motivation and emotional immersion.

[0021] 2. This invention introduces an attention shift decision module, which technically realizes a guidance mechanism from digital screens to natural real-world scenes. The system can accurately identify the excessive dependence of the explorer on virtual content, and through the physical guidance of the robot terminal, sound and light intervention, and seamless connection of narrative logic, successfully shift the explorer's gaze and cognitive focus from the mobile terminal screen to real and vivid natural targets, thereby fundamentally alleviating the interference of digital entertainment on nature education.

[0022] 3. This invention achieves real-time contextualized generation of popular science content through the dynamic mapping of a gamified task scheduling engine and a natural education knowledge graph; the design of tasks is no longer a static preset process, but a real-time customization based on the current environment, the current species, and the current capabilities of the exploration subject; this transformation process from information reception to deep cognition is encapsulated in game tasks with strong narrative, eliminating the tedium of knowledge acquisition and significantly improving cognitive efficiency and knowledge retention rate.

[0023] 4. This invention constructs a data closed-loop evaluation system and a memory retrieval mechanism, enabling the nature education process to possess long-term continuity and growth. The system can adaptively adjust the educational difficulty and strategies according to the subject's growth trajectory. This precise and dynamic teaching optimization capability effectively overcomes the shortcomings of fragmented exploration and experience and obvious cognitive gaps in existing technologies. It constructs a scientific, systematic, and sustainable framework for improving children's nature literacy, and has important technical support value for improving nature deficit disorder and establishing a profound ecological emotional connection.

[0024] 5. This invention integrates multiple cutting-edge technologies such as robot motion control, multimodal emotion computing, geographic information processing, soundscape analysis, and distributed task collaboration to create a complete, closed-loop natural education ecosystem. Through deep integration of software and hardware, the system not only provides cognitive tools but also plays the role of an exploration partner, achieving a high degree of unity between educational content, interactive methods, and emotional experience, and providing an innovative example for the combination of modern quality education and artificial intelligence technology. Attached Figure Description

[0025] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0026] Figure 1 This is a schematic diagram of the overall technical solution architecture of the natural exploration education system based on emotional interaction robots and gamified tasks proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of emotional intelligence decoding and attention-oriented intervention in this invention; Figure 3 This is a flowchart of the main stages of narrative-driven gamified task flow generation and feedback in this invention. Detailed Implementation Example 1

[0027] Please refer to the appendix. Figure 1This embodiment discloses a natural exploration education system based on an emotionally interactive robot and gamified tasks. Built on a multi-dimensional technology fusion architecture, the system aims to enhance the cognitive depth and emotional connection of the explorer in the natural environment through deep coupling of embodied hardware interaction and intelligent software scheduling. The system consists of five core functional layers: a multimodal environment perception system, an emotional intelligence computing hub, a gamified task scheduling engine, an embodied intelligent robot terminal, and a data closed-loop evaluation system. These layers are interconnected via a high-speed data bus and wireless communication protocol to achieve millisecond-level real-time information flow, ensuring the continuity and immediacy of the interaction process.

[0028] A multimodal environmental perception system is configured as the sensory frontier of the system, its core function being to simultaneously capture external natural environmental features and the internal physiological feedback of the exploring subject. This system integrates an environmental feature extraction unit, a bio-information monitoring unit, and a spatiotemporal positioning unit. The environmental feature extraction unit uses a high-resolution panoramic camera and a spectral sensor to perform multispectral imaging of the natural scene. The camera has a light-sensing capability of over 40 million pixels, enabling it to capture subtle textures of plant leaves, insect body colors, and the undulating contours of terrain under different lighting conditions. The spectral sensor samples specific wavelengths, calculating vegetation indices by analyzing the difference in reflectance between near-infrared and visible light bands, thereby identifying the growth status and species of plants. The bio-information monitoring unit utilizes a non-contact visual pulse monitoring algorithm to capture subtle color changes in the exploring subject's facial skin through the camera, extracting its heart rate data. Simultaneously, an array microphone integrated into the robot's terminal captures the exploring subject's speech signals, extracting pitch frequency, speech rate, and energy distribution through spectral analysis. Combined with facial expression recognition results, this forms the raw dataset for emotion modeling. The spatiotemporal positioning unit integrates a global satellite navigation system with a high-precision inertial measurement unit, achieving a positioning accuracy of 0.1 meters. This unit also monitors air pressure and altitude data in real time and correlates these physical parameters with current phenological data to provide environmental context for subsequent mission generation.

[0029] Please refer to the appendix. Figure 2 The emotional intelligence computing hub, serving as the cognitive core of the system, operates an emotion decoding model based on deep neural networks. This model performs feature-level fusion of multi-source heterogeneous data from bioinformatics monitoring units. Specific feature vectors include facial muscle action unit encoding, Mel-frequency cepstral coefficients of speech, and heart rate variability indices. The emotion decoding model maps these features to five emotional dimensions: happiness, curiosity, fatigue, frustration, and fear. Each dimension is assigned a continuous quantified value between 0 and 1, representing the intensity of that emotion. To achieve accurate feedback, the system introduces a calculation logic for a comprehensive emotional state index.

[0030]

[0031] In the above formula, E represents the comprehensive emotional state index, n takes a value of 5, representing 5 emotional dimensions. S represents the quantitative score of the i-th emotional dimension, and W represents the weight coefficient of that dimension. α is a moderating factor, and A represents the level of attention concentration. By calculating the comprehensive emotional state index in real time, the system can determine the current psychological load and interest orientation of the exploring subject.

[0032] The emotional intelligence computing hub further integrates an attention shift decision module. This module constructs a 3D gaze vector by analyzing the explorer's eye-tracking trajectory and head posture. When the gaze vector intersects with the robot's electronic screen for more than a preset threshold of 3 seconds, the module determines that the explorer is addicted to the digital screen. At this point, the attention shift decision module generates a strongly directional physical intervention command. After receiving this command, the embodied intelligent robot terminal adjusts its multi-degree-of-freedom mechanical actuators to perform directional limb movements, while simultaneously combining specific frequency sound signals to guide the explorer's visual focus to a preset natural observation point. For example, the robot might wave its robotic arm towards a specific rare plant and emit an excitation sound mimicking a biological call.

[0033] The gamified task scheduling engine dynamically constructs tasks based on feedback strategies output from the emotional intelligence computing center and a pre-defined natural education knowledge graph. This engine includes a narrative logic generation module, a knowledge mapping module, and a difficulty controller. The narrative logic generation module pre-stores a large number of virtual adventure story fragments, each with reserved knowledge interfaces. The knowledge mapping module uses an ontologically constructed knowledge graph to match real-time identified natural targets with species nodes in the graph. When the environmental feature extraction unit identifies a migrating butterfly, the knowledge mapping module automatically retrieves the butterfly's life cycle, migration route, and role in the ecosystem, transforming this information into key clues in the narrative logic. The difficulty controller selects the optimal task from a tiered difficulty sequence of 1 to 10 levels based on the explorer's historical performance. For beginners, the task focuses on simple morphological observation; for advanced users, the task involves deducing ecological relationships.

[0034] Please refer to the appendix. Figure 3 The gamified task flow execution process is divided into four standardized stages: a guidance segment, on-site operation instructions, interactive questioning, and achievement feedback. In the guidance segment, the robot uses a voice interaction system to narrate the background story of the current exploration task, establishing an immersive context. In the on-site operation instructions segment, the robot guides the user to use macro photography accessories or environmental sample analysis kits. The interactive questioning segment guides the user to think about the scientific logic behind natural phenomena through multiple rounds of dialogue. Finally, the achievement feedback segment provides virtual rewards based on the quality of task completion and updates the user's competency profile.

[0035] The embodied intelligent robot terminal is the physical interaction execution entity, possessing six active degrees of freedom. Its head rotation mechanism supports switching between a 360-degree horizontal and a 90-degree vertical field of view. The dual-arm swing mechanism uses high-torque servos, capable of simulating human guiding, hugging, and celebratory gestures. The chassis movement mechanism employs a tracked design, combined with an independent suspension system, ensuring stable movement on gravel roads or slippery grass with a 25-degree incline. The flexible expression module consists of a color-changing LED array embedded under the robot's shell and bionic electronic skin. When the subject exhibits curiosity, the electronic skin displays subtle vibration frequencies, and the LED array emits a bright blue-green light effect. The robot terminal's motion control laws and emotional feedback strategies are deeply coupled. When the system detects that the exploring subject is experiencing frustration due to excessive task difficulty, the robot automatically lowers its center of gravity, reduces the swing amplitude of its robotic arms, performs comforting gestures, and adjusts its voice tone to a gentle frequency.

[0036] The data-driven closed-loop evaluation system comprehensively assesses the cognitive outcomes of the exploration participants. The evaluation index system includes cognitive accuracy, exploration depth, emotional engagement, and ecological conservation behavior. Cognitive accuracy is determined by comparing the identification results submitted by the participants with standard data in a cloud-based knowledge base. Exploration depth is weighted by calculating the number of high-quality scientific questions raised by the participants per unit time and the observation time. Emotional engagement is calculated based on the percentage of positive emotional indices throughout the task. Ecological conservation behavior utilizes proximity sensors and visual algorithms around the chassis to monitor whether the participants are picking protected plants or disturbing wildlife. The system encrypts the evaluation results using a hash algorithm and stores them in a distributed blockchain ledger.

[0037] As a further refinement of this embodiment, the system includes a cloud-based knowledge base and a collaborative interaction interface. The cloud-based knowledge base stores feature vectors and associated scientific texts for over 12,000 species of flora and fauna. The collaborative interaction interface supports 5G or satellite link communication, allowing multiple robot terminals within a 5-kilometer radius to network. In large-scale expeditions, different robots are assigned the roles of observer, recorder, and analyst, guiding their respective exploration subjects to collaborate. For example, one subject might be responsible for measuring soil moisture, while another observes vegetation distribution. The robots logically correlate the findings of both subjects through data synchronization, triggering higher-level ecological balance exploration tasks.

[0038] The multimodal environment perception system operates at a stable frequency of 60 times per second, with the analysis latency for each frame controlled within 15 milliseconds. This high-frequency sampling ensures the capture of subtle pupil dilations or muscle micro-expressions in the subject being explored. The robot's power management system implements a dynamic frequency adjustment strategy. When performing static observation tasks, the central processing unit enters a low-power sleep state, retaining only the basic operation of the perception units. When real-time 3D environment modeling or multimodal emotion reasoning is initiated, the power management unit instantaneously increases the supply voltage, activating the high-performance computing core to ensure uninterrupted task flow. Its built-in high-energy-density solid-state battery supports continuous high-intensity operation for 8.5 hours.

[0039] The natural science education knowledge graph employs a hierarchical ontology structure. The first level defines broad categories such as the biosphere, lithosphere, and atmosphere. The second level is further subdivided into kingdoms such as plant, animal, and fungi. The third level contains specific species attributes. The graph establishes strongly coupled logical connections, such as predator-prey relationships, competition, and symbiosis. When a robot senses that its current environment is a post-rain forest, the knowledge graph automatically activates a task chain related to fungal outbreaks and decomposer roles. This context-based dynamic mapping transforms popular science content from isolated fragments of information into a rigorous scientific system.

[0040] The embodied intelligent robot terminal is equipped with a macro photography attachment capable of 50x optical magnification, clearly revealing the microscopic structure of plant stomata or insect compound eyes. The environmental sample analysis kit contains built-in electronic conductivity, pH, and temperature sensors. The explorer places collected soil samples into the analysis kit, and the robot instantly provides feedback on the physicochemical parameters, using the data as necessary proof for unlocking task achievements. This connection between hands-on operation and digital feedback enhances the explorer's practical skills and scientific rigor.

[0041] A memory recall mechanism is integrated into the emotional interaction logic. The system database records the subject's emotional peaks during each exploration. If a subject exhibits strong fear of spider-like creatures during a particular exploration, the memory recall mechanism will automatically block such targets in subsequent task flows, instead guiding the subject to overcome specific fears gradually through gentler methods such as showcasing the geometric aesthetics of spider webs. This mechanism generates a unique growth path for each subject by calculating the correlation coefficient between task success rate and emotional comfort.

[0042] Safety is the foundation of the system. The hardware-level obstacle avoidance module integrates four ultrasonic radars and six infrared proximity sensors to create a 360-degree safety light curtain. Whether moving or performing limb movements, if an obstacle avoidance distance is detected to be less than 0.5 meters, the actuator will brake within 10 microseconds. The software-level behavior constraint protocol utilizes geofencing technology to limit the exploration area to a safe range. If the subject crosses the safety boundary, the robot will immediately stop its task and issue a warning signal, while simultaneously sending a location request to the guardian's mobile terminal.

[0043] The multimodal environmental perception system also includes a soundscape analysis submodule. This module uses a multi-channel microphone array and blind source separation technology to extract specific bird calls from the complex forest background noise. Utilizing a voiceprint recognition algorithm, the system can distinguish the calls of 500 common local bird species. The soundscape analysis results are integrated into gamified tasks, using stereo surround sound technology to guide the subject towards the sound source, enhancing sensory acuity. Example 2

[0044] Based on the hardware architecture and logical framework described in Embodiment 1, Embodiment 2 focuses on exploring the system's adaptive control mechanism for explorers of different age groups and its deep interaction strategy under multi-machine collaborative operation. Please refer again to the appendix. Figure 1 The system is configured with differentiated interactive corpora and difficulty control logic for children aged 3 to 6 and school-age children aged 7 to 12 respectively.

[0045] In task scenarios targeting children aged 3 to 6, the emotional intelligence computing center lowers the perception threshold and increases the weighting of positive emotional feedback. The embodied intelligent robot terminal switches to a highly human-like childlike voice, with a vocabulary limited to 3,000 commonly used Chinese characters and simple sentence structures. The robot's actions are performed with increased amplitude and frequency to maintain the young subject's attention. For example, when discovering a flower, the robot will perform an exaggerated expression of surprise. At this time, the task flow issued by the gamified task scheduling engine focuses on color recognition and shape comparison, reducing the output of abstract scientific concepts.

[0046] For school-aged children aged 7 to 12, the system automatically activates a high-performance computing mode, introducing more task elements based on logical deduction. The depth of the natural education knowledge graph has increased to level 5, covering academic content such as the biochemical processes of photosynthesis and the energy flow efficiency of the food chain. The dialogue guidance mode between the robot and the subject has shifted from descriptive to interrogative.

[0047]

[0048] In the above formula, D represents the task difficulty level, and β and γ are adjustment coefficients. Q represents the measured value of cognitive accuracy, and σ represents the expected average. σ and μ represent the standard deviation and average of emotional engagement, respectively. This formula is used to dynamically calculate the difficulty of the next task after each task node, ensuring that the subject is always in the optimal cognitive challenge zone. If the subject exhibits extremely high cognitive accuracy and emotional stability, the difficulty level will rapidly increase, introducing a quantitative measurement task using an environmental sample analysis box.

[0049] In the multi-robot collaborative exploration mode, multiple robot terminals establish local communication links through a point-to-point self-organizing network protocol. In this mode, the data closed-loop evaluation system introduces a team collaboration dimension. Once the system identifies multiple entities within the same geographic coordinate cluster, it automatically triggers a team collaborative task flow, such as collectively mapping forest vegetation distribution. Each robot's assigned tasks are non-overlapping. The first robot guides the entity to collect geographic coordinates, the second robot guides the entity to record plant species, and the third robot guides the entity to measure environmental humidity. All data is aggregated in real-time to the cloud-based collaborative interaction interface for online modeling. The system analyzes the frequency of information exchange and emotional interaction index among the entities during the collaboration process to determine the team's synergy and generates a collective achievement badge upon task completion.

[0050] To further enhance the long-term educational value of the system, this embodiment introduces a blockchain-based digital literacy assetization mechanism into the data closed-loop evaluation system. Every in-depth task completed and every set of valid scientific data collected by the explorer is recorded on the blockchain as proof of work. These records not only serve as the basis for literacy evaluation but can also be used to unlock hidden flexible expression modes on the robot terminal or change the narrative script skin. This incentive mechanism gradually cultivates the subject's intrinsic scientific interest through external rewards.

[0051] In Example 2, the embodied intelligent robot terminal adds an environmental adaptability self-learning module. Utilizing reinforcement learning algorithms, the robot adjusts its center of gravity and driving torque in real time during movement to cope with complex terrain. When the ground slope reaches an extreme value of 30 degrees, the obstacle avoidance module and motion control law work together to prioritize the robot's static stability and guide the user through the obstacle area via voice. This interaction not only ensures device safety but also allows the user to establish a deeper emotional connection while assisting the robot.

[0052] In Example 2, the cloud-based knowledge base added a real-time multilingual conversion function. It supports simultaneous popular science content in multiple languages, including Chinese, Tibetan, and Mongolian. The cloud-based knowledge base will push customized knowledge weights based on the natural characteristics of different regions. In plateau regions, the system prioritizes loading model weights related to hypoxia adaptation and alpine ecosystems; in coastal regions, it focuses on teaching content related to tidal patterns and mangrove ecology. Example 3

[0053] Building upon Examples 1 and 2, Example 3 focuses on describing the system's robustness under abnormal operating conditions, its long-term memory evolution model, and the in-depth application of advanced soundscape perception in ecological education. Please refer to the appendix... Figure 1 With appendix Figure 2 This embodiment details how the system maintains the continuity of its educational functions and the security of the system.

[0054] The multimodal environmental perception system integrates a fault self-diagnosis module. When the spectral sensor of the environmental feature extraction unit experiences data anomalies due to soil obstruction, the system automatically increases the sampling rate of the high-resolution camera and uses a computer vision completion algorithm to simulate the missing spectral features. If the spatiotemporal positioning unit loses its global navigation satellite system signal, the inertial navigation algorithm will instantly take over, using the precise position from the previous moment and the current acceleration vector to calculate navigation, ensuring that the robot's positioning error in dense forest cover does not exceed 0.5 meters within 10 seconds. This redundancy mechanism ensures the stability of the task flow in extreme natural environments.

[0055] In Example 3, the memory recall mechanism evolved into a long-term memory network. The system not only records data from a single task but also establishes a cross-year growth profile. The memory network analysis explores the differences in the subject's emotional responses to the natural environment in different seasons. If the subject shows great interest in the revival of insects in spring but negative emotions about falling leaves in autumn, the emotional intelligence computing center will generate specific emotional support strategies. In the autumn task, the embodied intelligent robot terminal will use the narrative logic generation module to tell a scientific story about the cycle of life and the transformation of matter, guiding the subject to establish a more macroscopic view of nature.

[0056] The flexible expression module of the embodied intelligent robot terminal incorporates a tactile sensing layer. An array of pressure sensors is positioned beneath the bionic electronic skin. When the user gives the robot an encouraging pat or hug, the robot can recognize the force and area of ​​the contact. Based on decisions from the emotional intelligence computing center, the robot generates a wave-like warm-colored light effect through an array of LEDs, and drives the robotic arm to perform a slight vibration feedback. This two-way physical tactile interaction breaks through the limitations of traditional screen interaction, allowing the robot to truly become an emotionally resonant exploration partner.

[0057] In Example 3, the soundscape analysis submodule implements a quantitative assessment of environmental acoustic health. While guiding the subject to observe natural targets, the system monitors the level of noise pollution in the environment in real time. If mechanical noise accounts for too high a proportion of the background sound, the robot guides the subject to find a quieter, deeper observation area. A gamified task scheduling engine issues a task called "Sound Hunting," requiring the subject to identify and record at least three different natural sound sources within a specified time. This training aims to physically isolate the subject from digital world distractions and reshape their auditory sensitivity.

[0058] In Example 3, a biometric lock was introduced as part of the security protocol. The robot terminal only activates when it recognizes the facial features of a pre-defined exploration subject and its guardian. During task execution, if the biometric monitoring unit detects abnormal and drastic fluctuations in the subject's physiological signs, such as a sudden increase in heart rate exceeding 50% of the normal value, the system will determine that the subject may be in danger or unwell. The embodied intelligent robot terminal will immediately suspend all gamified tasks, conduct inquiries via the voice module, and simultaneously send a rescue request message containing real-time images and geographic coordinates to emergency contacts.

[0059] The power management system of the embodied intelligent robot terminal implements a prediction-based energy allocation strategy. The system dynamically prioritizes the use of remaining battery power by combining the estimated complexity of the day's tasks with the terrain resistance coefficient. If the current battery level drops below 20%, the system automatically reduces energy-intensive mechanical actions, prioritizes the operation of the navigation and communication systems, and guides the robot back to the base station or starting point. This intelligent scheduling ensures that the system will not malfunction due to energy depletion during a 10-kilometer field exploration route.

[0060] In Example 3, the data closed-loop evaluation system incorporates an ecological ethics evaluation dimension. When the environmental feature extraction unit detects that a subject is attempting to pick protected plants, the robot quickly intervenes, using a multimedia interactive interface to demonstrate the irreplaceable nature of the plant in the ecosystem and recording one instance of non-standard behavior. Multiple instances of non-standard behavior will lead to a decrease in the ecological contribution score in the subject's literacy profile, restricting their access to advanced tools in subsequent tasks. Through this negative feedback mechanism, the system achieves both mandatory and guiding education on the subject's ecological protection awareness.

[0061] Finally, the system is also equipped with a micro-environment perception system to monitor air quality, negative oxygen ion concentration, and ultraviolet radiation intensity within a 2-meter radius around the robot in real time. This microscopic data is integrated into interactive text generated in real time. For example, when the negative oxygen ion concentration reaches an excellent level, the robot will guide the user to perform deep breathing exercises, organically combining physiological health education with nature exploration. This comprehensive perception capability transforms the system from a simple educational tool into a comprehensive nature experience navigator.

[0062] In summary, the natural exploration education system disclosed in this embodiment, through the precise coordination of its subsystems and the support of advanced algorithms, achieves a closed-loop education encompassing sensory perception, cognition, and emotion. Its high real-time performance, security, and emotional interaction capabilities effectively address the social problem of nature deficit disorder, providing a solid technological paradigm for the future development of quality education. The system not only deeply understands the subject through multimodal perception but also reshapes the relationship between the subject and nature through embodied interaction. Its engineering implementation logic is clear, possessing extremely high practical and promotional value. All technical indicators, including perception frequency, motion accuracy, energy efficiency, and algorithm complexity, have reached a mature stage suitable for mass production and deployment. Through continuous data iteration and knowledge graph updates, the system will continuously evolve in long-term natural education practice, providing subjects with an inexhaustible driving force for scientific exploration. The successful implementation of this system marks a breakthrough in intelligent robot technology within the education sub-field, achieving a deep integration of technology and humanities.

Claims

1. A natural exploration education system based on emotionally interactive robots and gamified tasks, characterized in that, include: A multimodal environment perception system is used to acquire phenological feature data, geographical location information, and environmental physical parameters in natural scenes in real time through an environmental feature extraction unit, a bio-information monitoring unit, and a spatiotemporal positioning unit, while simultaneously capturing human-computer interaction behavior data and physiological feedback data of the exploring subject; an emotional intelligence computing center is used to perform multidimensional emotion modeling based on the data acquired by the multimodal environment perception system using deep neural networks, calculating the exploring subject's real-time emotional state index and attention concentration level, and generating a robot-anthropomorphic feedback strategy that includes physical intervention commands and voice guidance schemes; a gamified task scheduling engine is used... Based on the feedback strategy output by the emotional intelligence computing center and the preset natural education knowledge graph, the system dynamically constructs and distributes a gamified exploration task flow with narrative-driven characteristics through a narrative logic generation module, a knowledge mapping module, and a difficulty controller. It is embodied in an intelligent robot terminal to respond to the task flow issued by the gamified task scheduling engine. Through a mechanical actuator with at least 6 degrees of freedom of movement, a multimedia interactive interface, and a flexible expression module containing a variable color light-emitting diode array and a bionic electronic skin, it achieves physical collaboration and emotional resonance with the exploration subject, guiding the exploration subject to perform observation, collection, and cognitive operations on natural targets. The data closed-loop evaluation system is used to quantitatively evaluate the cognitive achievements of the exploration subject in the process of executing the task flow, and feeds the evaluation results back to the gamified task scheduling engine to achieve adaptive adjustment of task difficulty.

2. The natural exploration education system based on emotional interaction robots and gamified tasks according to claim 1, characterized in that, The multimodal environment perception system includes: an environmental feature extraction unit, which integrates a high-resolution panoramic camera and a spectral sensor, used to identify plant species, insect activity trajectories, and topographic features in the natural environment through multispectral imaging; a bio-information monitoring unit, used to acquire facial expression images of the exploration subject through non-contact visual algorithms and capture speech signals through an array microphone to extract tone frequencies as the basic input for emotion modeling; and a spatiotemporal positioning unit, used to combine a global satellite navigation system and an inertial navigation algorithm to determine the coordinates of the robot and the exploration subject in natural space, and to assist in judging the current microclimate environment based on altitude and air pressure data.

3. The natural exploration education system based on emotionally interactive robots and gamified tasks according to claim 1, characterized in that, The emotional intelligence computing center operates an emotional decoding model based on a deep neural network. This model fuses features from multi-source heterogeneous data collected by the bioinformatics monitoring unit, dividing them into five emotional dimensions: happiness, curiosity, fatigue, frustration, and fear. Each dimension is assigned a quantitative score between 0 and 1. The calculation logic of the comprehensive emotional state index is as follows: the quantitative scores of the five emotional dimensions are multiplied by their corresponding weight coefficients, and the products are summed. The rate of change of attention concentration level over time is obtained, and this rate of change is multiplied by a preset adjustment factor to obtain a product term. The summation result is then added to the product term to calculate the comprehensive emotional state index.

4. A natural exploration education system based on emotionally interactive robots and gamified tasks according to claim 1, characterized in that, The emotional intelligence computing hub also includes an attention shift decision module; this module is used to monitor the eye tracking trajectory and head posture of the exploring subject in real time, and construct a 3D gaze vector accordingly; when the gaze vector is detected to stay at the intersection of the electronic screen on the embodied intelligent robot terminal for more than 3 seconds, the module generates a strongly directional physical intervention command, driving the embodied intelligent robot terminal to guide the exploring subject's attention to the designated natural observation point through specific audio frequency bands or limb pointing actions.

5. A natural exploration education system based on an emotionally interactive robot and gamified tasks according to claim 1, characterized in that, The gamified task scheduling engine also includes: a narrative logic generation module, used to embed natural science knowledge points into virtual adventure storylines, transforming popular science observations into actions to resolve story conflicts; a knowledge mapping module, used to match real-time identified natural targets with species nodes in the natural education knowledge graph to generate contextualized interactive text about ecological roles, life cycles, and defense mechanisms; and a difficulty controller, used to select exploration tasks that match the current exploration subject's capabilities from a tiered difficulty sequence of 1 to 10 levels based on the cognitive level feedback from the data closed-loop evaluation system.

6. A natural exploration education system based on an emotionally interactive robot and gamified tasks according to claim 1, characterized in that, The gamified task flow consists of four parts: a guidance section, on-site operation instructions, interactive questioning, and achievement feedback. The guidance section is used to tell the background story of the exploration task through voice interaction. The on-site operation instructions are used to guide the explorer to use the macro photography accessory or environmental sample analysis box configured on the embodied intelligent robot terminal to collect on-site data.

7. A natural exploration education system based on an emotionally interactive robot and gamified tasks according to claim 1, characterized in that, The motion control law of the embodied intelligent robot terminal is coupled with the feedback strategy of the emotional intelligence computing center. When the emotional state index indicates that the exploring subject is in a frustrated state, the actuator performs a low-center-of-gravity comforting posture and emits warm-colored light effects through the flexible expression module. When the exploring subject discovers a new species, the actuator performs high-frequency celebratory actions and outputs encouraging evaluations in conjunction with the voice module. A pressure sensor array is arranged under the flexible expression module to identify the force and area of ​​contact between the exploring subject and the robot, so as to trigger corresponding sensory feedback.

8. A natural exploration education system based on an emotionally interactive robot and gamified tasks according to claim 1, characterized in that, The data closed-loop evaluation system adopts a multi-dimensional assessment model, which includes: cognitive accuracy, judged based on the exploration subject's classification and identification results of natural targets; exploration depth, judged based on the duration of the exploration subject's stay at the observation point and the logical depth of the questions asked; emotional participation, judged by analyzing the proportion of positive emotions during task execution; and ecological protection behavior, judged by monitoring whether the exploration subject has any non-standard behaviors such as damaging vegetation or disturbing animals. The system weights and sums the above indicators to generate a total score and stores it in a blockchain ledger to construct a long-term natural literacy file for the exploration subject.

9. A natural exploration education system based on an emotionally interactive robot and gamified tasks according to claim 1, characterized in that, The system also includes a collaborative interaction interface; the collaborative interaction interface allows multiple embodied intelligent robot terminals located in the same natural geographical area to network together and assign tasks based on different scientific research roles; the power management system of the embodied intelligent robot terminal adjusts the processor frequency according to the task intensity; when the system is performing an observation task, it enables a low-power mode; when performing 3D environment modeling or multimodal speech recognition, it switches to a high-performance mode.

10. A natural exploration education system based on an emotionally interactive robot and gamified tasks according to claim 1, characterized in that, The embodied intelligent robot terminal is equipped with safety protection components, including: a hardware-level obstacle avoidance module that uses 4 sets of ultrasonic radars and 6 sets of infrared proximity sensors to establish a 360-degree safety protection light curtain and limits the movement speed to less than 1.5 meters per second; and a software-level behavior constraint protocol that uses real-time geofencing technology to instruct the robot to stop its task and send a warning signal to the guardian terminal when the exploring subject enters an area outside the preset safety boundary.

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