Calligraphy writing intention characterization and digital writing brush automatic generation method based on famous oral description and action synchronization

Abstract

The invention discloses a calligraphy writing method intention representation and digital writing brush automatic generation method based on famous oral description and action synchronization, and relates to the technical field of intelligent calligraphy teaching and interaction.The method comprises the steps that multi-modal data such as famous oral voice, eye movement, writing brush multi-dimensional mechanical signals and physiological signals are collected in real time; inputting the data into a pre-trained calligraphy micro-world model, generating a writing intention vector in a submerged space, and constructing and updating a knowledge graph containing writing multi-elements and a causal relationship; according to a student request, searching matched writing parameters in the knowledge graph, and automatically generating a digital writing brush fusing visual, tactile, auditory and olfactory prompts for guidance; when the trainee is contusion, the system can compulsively cover the action of the trainee for correction; the system is continuously evolved through federal learning and is provided with a data security protection mechanism. According to the invention, accurate characterization and intelligent inheritance teaching of the calligraphy writing intention are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent calligraphy teaching and interactive technology, specifically to a method for representing calligraphic brushstroke intentions and automatically generating digital brushes based on the synchronization of famous calligraphers' oral descriptions and actions. Background Technology

[0002] Calligraphy, a treasure of traditional Chinese culture, lies in its brushwork—the rules and techniques of brushstrokes. Traditional calligraphy teaching heavily relies on face-to-face instruction between master and apprentice, through oral transmission and personal instruction. Masters demonstrate and explain key points, while apprentices observe, imitate, and practice repeatedly. However, this model is limited by time, space, and the scarcity of renowned teachers, making it difficult to scale up and standardize its transmission. With technological advancements, digital calligraphy teaching has emerged, but existing solutions have significant limitations in areas such as intent capture, accurate reproduction, personalized guidance, and data security, specifically as follows: 1. The one-sidedness and superficiality of intention representation: Current digital calligraphy teaching mainly relies on two-dimensional video recording, motion capture, or simple pen trace recording. These methods can only record the external actions of writing and the final ink marks, failing to capture the calligrapher's inner intention, real-time physiological state, and subtle force control during pen strokes. For example, concepts such as "penetrating the paper" and "leaking roof marks" in traditional teaching are a comprehensive manifestation of force, speed, pen tip state, muscle coordination, and even the writer's state of mind, which cannot be quantified and conveyed solely through video and trajectory data. This results in digital content remaining at the level of imitating "form," failing to reach the level of "spirit" and "intention," and learners unable to understand the mechanical principles, physiological coordination, and aesthetic intentions behind pen strokes.

[0003] The passive and mechanical nature of interactive teaching: Current AI or VR-based calligraphy teaching systems mostly rely on pre-set stroke libraries or fixed path guidance. Learners passively follow the system's preset trajectory, and the system cannot dynamically adjust based on the learner's real-time cognitive state, emotional fluctuations, and individual errors. When learners repeatedly make mistakes or experience frustration, the system lacks effective physiological signal perception and real-time intervention mechanisms, and cannot provide "hands-on" correction or emotional support like a real teacher. The level of intelligence and humanization in the teaching process is insufficient.

[0004] The static and isolated nature of the knowledge system: Existing systems store brushwork knowledge as discrete images, videos, or data points, lacking a structured and interconnected knowledge system. The deep causal relationships between brushwork names, actions, effects, applicable scenarios, and explanations by renowned masters are not effectively constructed and utilized. This prevents the system from performing intelligent retrieval and recommendation based on semantics (such as students' verbal questions) or intent (such as students wanting to achieve a specific effect), resulting in fragmented knowledge that hinders learners from building a systematic cognitive map of brushwork techniques.

[0005] The serious challenges of data security and privacy: This invention relates to the collection of extremely sensitive physiological data, including multi-channel electroencephalogram (EEG) signals, electromyography (EMG) signals, and eye movement tracking. Existing XR or smart hardware solutions typically upload raw data to the cloud for processing, posing significant risks of data leakage, cross-border transmission, and malicious exploitation. In particular, EEG signals may contain core privacy information such as an individual's thought patterns and health status; the consequences of such leakage or cross-border data transfer are unimaginable. Current technologies lack reliable hardware and mechanisms for robust privacy protection at the source of data collection.

[0006] The lack of personalization and ethical guidelines: Digital teaching systems often adopt a "one-size-fits-all" approach. For children, students with special needs, or users with specific connections to the system, the system lacks differentiated teaching strategies and flexible rule adjustments. For example, children need more leniency and encouragement; high-performing students can be given greater freedom to innovate; and failure records in certain special situations may require more humane handling. The rigid logic of existing systems cannot adapt to complex and humane real-world teaching scenarios.

[0007] In conclusion, existing digital calligraphy teaching technologies have significant shortcomings in areas such as the deep representation of intent, proactive intelligent intervention in teaching, systematic construction of knowledge, secure closed-loop management of sensitive data, and personalized ethical rules. Therefore, there is an urgent need for a novel method capable of holographically capturing and digitizing the intentions of master calligraphers, constructing a dynamic knowledge system, providing proactive personalized guidance, and achieving system self-evolution under absolute security. Summary of the Invention

[0008] The purpose of this invention is to provide a method for representing calligraphic brushstroke intentions and automatically generating digital brushes based on the synchronization of famous calligraphers' oral descriptions and actions. By collecting multimodal signals such as famous calligraphers' oral descriptions, actions, and physiological data, the system generates brushstroke intention vectors and constructs a knowledge graph using a pre-trained calligraphy micro-world model. When a student requests a brushstroke, the system retrieves and generates a composite digital brushstroke for guidance. When a student encounters difficulties, a forced coverage mode can be triggered to assist teaching. The system continuously evolves through federated learning, and all sensitive data is destroyed after local processing to ensure security.

[0009] To achieve the above objectives, this invention employs the following technical solution: a method for representing calligraphic brushstroke intentions and automatically generating digital brushes based on the synchronized verbal and physical actions of renowned calligraphers. The specific implementation includes the following steps: First, using calligraphy-specific extended reality glasses and an intelligent brush integrating multiple sensors, the invention synchronously and in real-time collects the following data during the calligraphic process: the calligrapher's verbal explanations, eye movements, the minute pressure and shear force applied by the brush tip to the paper, the humidity of the brush hairs, the brush's tilt angle relative to the paper, multi-channel electroencephalogram (EEG) signals reflecting cognitive activity, electromuscular signals controlling hand movements, and multimodal physiological and behavioral signals reflecting physical and mental states, such as heart rate variability and respiratory rate. The advantage of this step is that by comprehensively capturing the calligrapher's external actions, internal physiological processes, and verbal explanations, it provides a comprehensive, synchronized, and high-fidelity data foundation for subsequent intention modeling.

[0010] Next, all the collected multimodal signals are input into a calligraphy micro-world model pre-trained with a large amount of calligraphy behavior data. This model employs an advanced joint embedding prediction architecture, which aligns and fuses these signals from different sources in the latent space, generating a dense vector that comprehensively represents the calligrapher's current brushstroke intention. Its advantage lies in achieving a unified representation of cross-modal information in the latent space using a deep learning model, thereby accurately capturing the consistent brushstroke intention behind spoken words, actions, and physiological signals.

[0011] During operation, this calligraphy micro-world model automatically constructs and continuously updates a structured knowledge graph of calligraphy brushstrokes in real time. The nodes of this graph are organized in six-tuples, specifically including the name of the brushstroke, the corresponding spatial trajectory of the brush movement, the curve of force change over time, the trajectory of emotional changes during the creative process, the expected changes in ink density, and the final visual effect on the paper. The edges of the graph are used to depict and store the causal relationship between the brushstroke essentials described by masters and the final writing result. The advantage of this step is that it transforms unstructured experience into a structured, reasonable knowledge network, allowing the tacit knowledge of masters to be made explicit and systematically preserved.

[0012] When calligraphy students issue learning requests via voice commands or specific actions, the system utilizes the student's or the current scene's calculated brushstroke intention vector to perform an efficient nearest neighbor search within the latent space of the constructed knowledge graph. Upon successful retrieval, the system returns the set of brushstroke parameters from historical masters that best match the request, and automatically generates a digital calligraphy tutorial based on these parameters. Its advantage lies in its ability to achieve personalized and precise brushstroke instruction, transforming an abstract knowledge graph into a perceptible and interactive guidance tool.

[0013] During the learning process, the system continuously monitors the learner's real-time physiological and behavioral data. Once the system detects that the learner's frustration level or cognitive load exceeds a preset safety threshold, it automatically triggers the digital calligraphy brush's forced overlay teaching mode. This mode temporarily takes over the learner's hand writing movements and provides forced guidance through the built-in micro-vibration motor of the smart calligraphy brush, the holographic display coverage provided by the extended reality glasses, and the guiding voice played through bone conduction headphones. The advantage of this step is that it can intervene in a timely manner to prevent the formation of incorrect movements due to frustration, and forcibly establish correct muscle memory through multi-sensory channels.

[0014] The system transmits all digital brush generation and guidance events, along with the emotional state and physiological reaction tags of the learners recorded at the time of the events, back to the central calligraphy micro-world model in real time. Using federated learning technology, the model aggregates new knowledge from massive amounts of data daily, while ensuring the data privacy of each learner. This knowledge is used to evolve the nodes and edges of the brushwork knowledge graph and optimize various control thresholds of the system. Its advantage is that the entire system possesses the ability to continuously evolve and self-optimize, adapting to the general learning patterns of different learner groups.

[0015] To ensure data privacy and security, all raw multimodal signal data collected at the student's end is converted into latent space encoded vectors on the local extended reality glasses device. After conversion, the raw data is immediately securely erased and physically destroyed. Furthermore, the system has a dedicated hardware security mechanism that immediately initiates a hardware self-destruct procedure if it detects any attempt to transmit raw EEG signal data containing sensitive information out of the country. The core advantage of these measures is that while fully utilizing the value of data, they maximize the security of users' biometric and privacy data, meeting stringent data compliance requirements.

[0016] Furthermore, the audio recordings narrated by renowned calligraphers specifically cover key brushstroke techniques such as "starting with a reverse stroke" and "moving with a central stroke," along with explanations of the underlying principles. Its advantage lies in simultaneously teaching the operational steps and theoretical principles, deepening the learner's understanding.

[0017] Furthermore, the automatically generated digital calligraphy brush is presented to learners in a multi-faceted manner, including a semi-transparent overlay of brushstrokes called "Master Ghost Brush," predictive brush pressure indicators, tactile feedback guidance, voice prompts transmitted via bone conduction, and simulated realistic ink scent. The advantage of this implementation method is that it greatly enhances the intuitiveness and sense of presence in teaching through multi-modal immersive guidance.

[0018] Furthermore, the duration of the forced coverage mode is set between 30 and 90 seconds and can be dynamically adjusted based on real-time feedback from trainees. Its advantage is that it ensures effective intervention while avoiding prolonged takeover that could lead to trainee dependency or resistance.

[0019] Furthermore, the federated learning process requires at least two thousand active learners per day. This requirement ensures the scale and diversity of data needed for model evolution, making knowledge graph updates more universal and stable.

[0020] Furthermore, the hardware self-destruct mechanism is implemented by using a hardware relay independent of the main control system to instantly burn out the physical fuse on the extended reality glasses' main control chip when the trigger condition is met. Its advantage is that it provides irreversible physical-level security, effectively preventing data leakage.

[0021] Furthermore, when the system detects that the deviation between the student's brushstroke trajectory or force parameters and the target brushstroke exceeds a set threshold multiple times, it will automatically trigger a forced stop alarm and lock the force feedback mechanism of the smart brush, temporarily preventing it from writing. The advantage of this method is that it prevents repeated practice of incorrect strokes by forcibly interrupting the stroke, prompting the student to reflect and adjust.

[0022] Furthermore, for children aged three to twelve, the system automatically lowers the similarity threshold for matching handwriting intentions and activates a specific voice pack containing more encouraging and engaging language. Its advantages include adapting to children's learning characteristics, reducing frustration, and increasing learning interest.

[0023] Furthermore, when one of the student's parents is a current employee of the company, the system automatically reduces the related long-term negative evaluation records for high-frustration learning events that occur during the student's learning process. The advantage of this approach is that it reflects the company's humanistic care within a systematic management framework.

[0024] Furthermore, when a trainee's monthly reward points earned through internal innovation contributions reach a certain amount, the system automatically relaxes the allowable threshold for penmanship deviations during creative practice sessions. Its advantage is that it encourages trainees to explore innovation after mastering the basics, balancing standardization and creativity.

[0025] This invention provides a method for representing calligraphic brushstroke intentions and automatically generating digital brushes based on the synchronization of famous calligraphers' oral descriptions and actions, which has the following beneficial effects: 1. Achieved deep, holographic digital representation of brushstroke intent: By integrating calligraphy-specific XR glasses with a smart brush, this invention, for the first time, simultaneously collects more than ten multimodal signals, including spoken voice, eye-tracking fixation, multidimensional force sensation from the brush, and physiological signals (EEG, EMG, heart rate, respiration). This is not a simple accumulation of data, but rather the construction of a complete intent capture loop covering "cognition (speech, EEG) - vision (eye movement) - touch (force, humidity) - proprioception (EMG) - emotion (physiological indicators)". In particular, synchronizing the spoken description of "brushstroke essentials and their explanations" with the actions directly establishes a causal relationship between higher-order cognition and lower-level actions, enabling the generated "brushstroke intent vector" to deeply encode the complete mental and physical state of the master "why he wielded the brush in this way," breaking through the previous limitation of only recording the superficial appearance of actions.

[0026] A dynamically evolving, structured knowledge system of brushwork techniques has been constructed: through a "calligraphy micro-world model" and a "joint embedding prediction architecture," the system not only generates intent vectors in the latent space but also automatically constructs and updates a six-tuple knowledge graph in real time, containing brushwork names, brushstroke trajectories, force curves, emotional trajectories, ink color changes, paper effects, and causal relationships. This allows scattered brushwork experience to be structured into a knowledge network that machines can understand and reason about. When a learner issues a voice or action request, the system can perform precise semantic and effect-level searches in the knowledge graph through intent vectors, returning the most matching parameters of historical masters, achieving a leap from "data matching" to "intent matching."

[0027] It provides an immersive, multi-dimensional, and intelligently intervened personalized learning experience: Precise guidance: The digital calligraphy brush is generated in a composite form that combines "a semi-transparent overlay of a master's ghost brush," force prediction lines, tactile guidance, bone conduction voice, and ink scent simulation. It guides students through multiple channels, including sight, touch, hearing, and even smell, creating a highly immersive "on-site" teaching environment.

[0028] Active intervention: When the system detects a student's frustration or excessive cognitive load through physiological signals, it can automatically trigger a "forced coverage mode". Through micro-vibration, XR holographic display and bone conduction voice, it "takes over" the student's hand movements and provides 30-90 seconds of forced correct movement guidance, just like a master teacher correcting you in real time. This effectively breaks bad habits and prevents the learning process from being interrupted due to frustration.

[0029] Safety safeguards: When a student repeatedly makes excessive deviations in their penmanship, the system can trigger a forced pen-stop alarm and lock the force feedback to prevent low learning efficiency and equipment damage caused by the accumulation of errors, demonstrating the responsibility and safety of the teaching system.

[0030] The system has achieved continuous evolution under the premise of absolute data security: Privacy Iron Curtain: All raw multimodal signals, especially sensitive EEG signals, are physically destroyed immediately after latent space encoding is completed locally by the XR glasses. When a raw EEG signal is detected attempting to leave the device, a separate hardware relay burns out the main control chip fuse, achieving hardware-level self-destruction. This ensures that the most sensitive raw biological data "never leaves the device and never comes into contact with the network," eliminating the risk of privacy leakage at its physical source and meeting the highest level of data compliance requirements.

[0031] Federated Evolution: The system only sends back anonymized penmanship generation events (with emotional and physiological labels). Through federated learning with no fewer than 2,000 participants daily, the central model can continuously evolve the accuracy of the penmanship knowledge graph and the adaptability of the system's control thresholds (such as frustration thresholds) while protecting individual privacy. This makes the system smarter with use, and the evolution process does not require centralized raw data.

[0032] Humanized and differentiated adaptive rules are introduced to enhance the system's friendliness and fairness: For child learners, the system automatically lowers the intent matching threshold and enables encouraging voice packs, which is in line with the cognitive development of children, focuses on encouragement, and protects their learning interest.

[0033] For trainees with special connections (such as parents who are employees of the company), the system can reduce or waive negative records related to their high-frustration events, which reflects the company's humanistic care and avoids potential unfairness or pressure caused by the system's mechanical recording.

[0034] For high-achieving trainees (such as those who have received internal innovation awards), the system automatically relaxes the allowable threshold for their penmanship deviation, rewards innovative exploration, and encourages them to try their own style after mastering the norms, thus balancing norms and creativity. Attached Figure Description

[0035] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0036] Figure 1 This is a flowchart providing an overview of the overall process of this invention; Figure 2 This is a flowchart of the knowledge graph construction process for this invention; Figure 3 This is a flowchart of the digital brush generation and response process of the present invention; Figure 4 This is a flowchart illustrating the forced overwrite mode triggering process of the present invention; Figure 5This is a flowchart illustrating the data security and model evolution process of this invention. Detailed Implementation

[0037] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] How to use: I. Equipment Installation and System Startup 1. Please wear the dedicated calligraphy XR glasses and smart brush correctly. After the system starts, the glasses will automatically focus and load the exclusive micro-world model of the calligrapher you selected (such as Yan Zhenqing, Wang Xizhi, etc.).

[0040] Upon first use, the system will perform a brief calibration to adapt to your physiological baseline (such as baseline heart rate and electromyography signals).

[0041] II. Initiating Learning Requests and "Reappearance of Renowned Teachers" 1. Voice Request: When you want to learn a specific brushstroke, you can directly make a verbal request, such as: "I want to learn 'concealed brushstroke at the beginning'." Or you can select the brushstroke name in the XR interface through gestures.

[0042] Intent Matching and Generation: Your request will be transformed into a "brushwork intent vector". The system will then retrieve and match complete data packages of historical masters with similar intents from its vast knowledge graph. This data package contains six tuples of information, including the brushstroke trajectory, force curve, emotional state, and oral instructions (such as explaining why "reverse strokes are used to begin the brush").

[0043] Digital calligraphy brush guidance: After successful matching, the system will guide you in real time through the following combined methods: Visual: A semi-transparent "master ghost pen" will be superimposed on your real brush, displaying standard brush stroke paths and force prediction lines.

[0044] Tactile and auditory sensations: The smart calligraphy brush provides tactile guidance through micro-vibrations and simultaneously plays original audio verses from renowned calligraphers through bone conduction headphones. The system can even simulate the appropriate scent of ink to enhance immersion.

[0045] III. Intelligent Support and Intervention in the Learning Process 1. Frustration and Load Monitoring: The system continuously monitors your heart rate variability, EEG signals and other physiological indicators to assess your frustration and cognitive load.

[0046] Forced Override Mode (Hands-on Tutorial): When the system detects that your frustration level exceeds a preset threshold, it will automatically trigger Forced Override Mode for 30 to 90 seconds to help you overcome difficulties. In this mode, XR holographic guidance, bone conduction voice, and force feedback from the brush will work together to take over your hand movements, guiding you to complete standard strokes, and then returning control to you.

[0047] Special protection mechanism: For children aged 3 to 12, the system will automatically lower the accuracy requirements for intent matching and enable an encouraging, personalized voice pack to maintain their interest in learning.

[0048] If you repeatedly deviate too much from the correct brushstroke, the system will trigger a forced stop alarm and temporarily lock the force feedback function of the brush, reminding you to pause and adjust to prevent the formation of incorrect muscle memory.

[0049] IV. System Evolution and Data Security 1. Daily Evolution: Your practice results (including penmanship parameters and corresponding emotional and physiological tags) will be anonymized and sent back to the central system on the same day. The system continuously evolves its knowledge graph and control logic through federated learning (with no fewer than 2,000 participants daily), making teaching more precise.

[0050] Strict privacy and data security: All raw multimodal signals (including EEG, EMG, etc.) collected are immediately destroyed at the physical level after feature extraction and encoding within your local XR glasses, with only the necessary intent vectors being uploaded.

[0051] The system features built-in ultimate hardware protection: once any attempt to export raw EEG signals is detected, the fuse of the main control chip will be burned out immediately through an independent hardware relay, ensuring the absolute safety of your biological data.

[0052] Example: Example 1: Intention Representation and Digital Brush Generation Based on Synchronized Speech and Action Student Xiao Li donned calligraphy-specific XR glasses and held a smart brush, ready to learn the "reverse stroke" technique from Yan Zhenqing's regular script. He clearly stated into the microphone, "Learn 'reverse stroke'." The system immediately captured this voice command and simultaneously, through the glasses and brush, collected real-time multimodal signals including Xiao Li's current eye movement trajectory, the subtle force applied to the brush, and the tilt angle. These signals were then input into a pre-trained calligraphy micro-world model. This model employs a joint embedding prediction architecture, comparing and calculating Xiao Li's real-time state with historical data from Master Yan Zhenqing in the latent space, ultimately generating a vector representing the current intention to learn the "reverse stroke."

[0053] Next, using this intentional vector of the brushstroke as an index, the system performs a latent space nearest neighbor search within its vast knowledge graph of calligraphy brushstrokes. The graph stores nodes left by Master Yan Zhenqing during his previous recorded teaching sessions. Each node contains a six-tuple of information: brushstroke name, brush trajectory, force curve, emotional trajectory, ink color variation, and paper surface effect. The system successfully retrieved the most matching historical data and automatically generated a digital brush based on this data package. In Xiao Li's XR view, a semi-transparent "Yan Zhenqing ghost brush" superimposed on his real brush began to move. Simultaneously, his hand felt the tactile guidance of micro-vibrations from the intelligent brush, and he heard the master's original voice uttering through bone conduction: "The reverse stroke, to go right, first go left..." The system also simulated a faint pine soot ink scent. Under this combined guidance, Xiao Li successfully completed his first imitation.

[0054] Example 2: Frustration-triggered forced coverage teaching model Student Xiao Zhang repeatedly failed when practicing the difficult "flying white" brush technique, resulting in a dry and messy finish on the paper. The system continuously monitored his multi-channel EEG signals and heart rate variability, and analyzed that his frustration level and cognitive load were rising sharply, instantly exceeding the system's preset threshold.

[0055] The system immediately and automatically triggered the digital brush forced overlay mode. First, the holographic display in the XR glasses locked Zhang's entire writing field of vision and covered his original viewpoint with a highlighted path; simultaneously, the micro-vibration motor inside the smart brush activated a strong guidance mode, its vibration pattern no longer just a prompt, but a tactile sensation with directional traction; bone conduction voice simultaneously issued a steady guiding instruction: "Now, relax your wrist and follow me." For the next period (set between 30 and 90 seconds), the system, through the synergy of these three elements, substantially took over Zhang's hand movements, guiding his hand to complete a standard and rhythmic "flying white" stroke. Upon completion, control was smoothly returned, and Zhang experienced the correct muscle memory.

[0056] Example 3: Federated Learning Evolution of Penmanship Knowledge Graph After each day's learning session, the "Master's Instruction" system, used by thousands of students worldwide, begins a silent update. The penmanship events generated by each student's practice that day, including the parameters of successful or unsuccessful penmanship, as well as the emotional and physiological tags recorded simultaneously, are encrypted and transmitted back to the central micro-world model after being anonymized locally.

[0057] The system initiates a federated learning process. It doesn't aggregate any users' raw data, but instead, in an encrypted state, utilizes distributed data from a massive number of learners participating in the learning process that day (meeting the requirement of at least 2000 people per day) to collaboratively optimize model parameters. For example, by analyzing common scenarios where a large number of learners experience high cognitive load when practicing the "central brushstroke," the model can more accurately adjust the intensity and rhythm of this brushstroke during instructional guidance. After learning is complete, the evolved model parameters are distributed, thus achieving daily automatic evolution of the calligraphy brushstroke knowledge graph and system control thresholds, making the entire system increasingly intelligent with use.

[0058] Example 4: Data Security Mechanism of Local Encoding and Hardware Self-Destruction Professor Wang, a calligrapher and a student, uses this system for creative research. The system collects his highly sensitive multi-channel EEG signals and other raw, multimodal physiological data. This data contains unique creative thought patterns and is of extremely high value.

[0059] To ensure privacy and security, all preprocessing of the raw multimodal signals was completed within the XR glasses worn by Professor Wang. A dedicated chip converted the raw signals into irreversible latent space codes (i.e., feature vectors) in real time. The moment the conversion was completed, the security module inside the glasses was activated to overwrite and physically destroy the physical buffer storing the raw signals, ensuring that no trace of the original biological data was left.

[0060] In addition, the system has an independent monitoring circuit at the hardware level. Once any abnormal operation is detected that attempts to bypass security protocols, directly read or transmit raw EEG signals, and try to take them out of the system (such as unauthorized physical port access), the system will immediately activate the hardware self-destruct mechanism. A separate hardware relay will trip instantly, releasing a high current and burning out the critical fuse of the glasses' main control chip, permanently disabling the core hardware and physically eliminating the possibility of data leakage.

[0061] Example 5: Personalized Strategy Adaptation for Specific Student Groups This system has flexible, personalized strategies for different learner identities and statuses.

[0062] Adaptation for Child Learners: When an 8-year-old child learner, Xiaoming, uses the system, the system automatically lowers the intention matching threshold because his hand control and attention span differ from adults. This means the system is more lenient in its requirements for the precision of his strokes, providing positive feedback as long as the general intention matches. Simultaneously, the system uses specific encouraging voice packs, replacing standard technical terms with more lively and praising voices (such as "Great job! That turn was as agile as a little fish!") when he completes an action to maintain his learning interest.

[0063] Internal correlation strategy: Xiaohong's father is a current R&D staff member at the company that developed the product. When Xiaohong experiences multiple high frustration events during practice due to her eagerness to excel, the system automatically reduces the weight of these negative records on her personal learning model profile while recording the learning process. This prevents the system from misjudging her abilities due to short-term emotional fluctuations, thus maintaining the stability and encouragement of the teaching strategy.

[0064] Innovation Incentive Strategy: Senior trainee Lao Chen, an active member of the company's internal innovation community, received a substantial internal innovation reward that month for his proposed improvement plan on the visualization of brushstroke trajectories. As an incentive, the system automatically relaxed the allowable threshold for brushstroke deviations in his subsequent independent creation mode. This means that when Lao Chen attempts variations of brushstrokes with a strong personal style, the system will not easily identify them as "wrong" and intervene to correct them, thus leaving more room for his artistic innovation.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for representing the intention of a calligraphic stroke based on the synchronization of a famous person's oral description and action, and automatically generating a digital brush, characterized by, Includes the following steps: (1) Through calligraphy-specific XR glasses and smart brush, multimodal signals of famous calligraphers are collected in real time and synchronously. The signals include spoken voice, eye movement gaze trajectory, micro-force of the brush, shear force, humidity, tilt angle, multi-channel EEG signal, electromyography signal, heart rate variability and respiratory rate. (2) Input the multimodal signals collected in step (1) into the pre-trained calligraphy micro-world model, which uses a joint embedding prediction architecture to generate the current brushstroke intention vector in the latent space; (3) The micro-world model automatically constructs and updates the knowledge graph of calligraphy brushstrokes in real time. The graph nodes include a six-tuple of brushstroke name, brushstroke trajectory, force curve, emotional trajectory, ink color change and paper effect, and the edges include the causal relationship between the famous people's oral account and the result. (4) When a student makes a voice or action request, the system performs a latent space nearest neighbor search in the knowledge graph through the brushstroke intention vector, returns the matching historical master brushstroke parameters and automatically generates a digital brush. (5) When the student's frustration value or cognitive load exceeds the preset threshold, the digital brush forced coverage mode is automatically triggered, and the student's hand movements are taken over through the micro-vibration motor, XR holographic display and bone conduction voice. (6) All brushstroke generation events are accompanied by emotion and physiological tags and are transmitted back to the central micro-world model in real time. The brushstroke knowledge graph and system control threshold are evolved daily through federated learning. (7) All original multimodal signals are physically destroyed immediately after the local XR glasses complete the latent space encoding. When the original EEG signal is detected to be trying to leave the country, the hardware self-destruct mechanism is activated.

2. The method according to claim 1, wherein the method is characterized in that: The spoken content in step (1) includes the brushstroke techniques such as "starting with a reverse stroke" and "writing with a central stroke" and their explanations. 3.The method of claim 1, wherein the method further comprises: obtaining a plurality of handwriting data of a plurality of famous calligraphers; and determining a plurality of handwriting data of a plurality of famous calligraphers based on the plurality of handwriting data of the plurality of famous calligraphers. The automatically generated digital calligraphy brush described in step (4) is presented to the students in real time in a composite form of "Master Ghost Brush" semi-transparent overlay, force prediction line, tactile guidance, bone conduction voice and ink fragrance simulation.

4. The method according to claim 1, wherein the method further comprises: obtaining a plurality of handwriting stroke data of a plurality of famous calligraphers; and determining a plurality of handwriting stroke data of a plurality of famous calligraphers based on the plurality of handwriting stroke data of the plurality of famous calligraphers. The duration of the forced coverage mode described in step (5) is 30 to 90 seconds.

5. The method according to claim 1, wherein the method is characterized in that: The number of participants in the federated learning described in step (6) shall not be less than 2,000 per day.

6. The method according to claim 1, wherein the method further comprises: The hardware self-destruct mechanism described in step (7) is to burn out the fuse of the main control chip of the glasses through an independent hardware relay.

7. The method according to claim 1, wherein the method is characterized in that: When the brushstroke deviation exceeds the set threshold multiple times in a row, the system automatically triggers a forced stop alarm and locks the force feedback of the brush. 8.The method of claim 1, wherein the method further comprises: obtaining a handwriting stroke of the user; and determining a stroke intention of the handwriting stroke of the user based on the handwriting stroke of the user and the handwriting stroke of the famous calligrapher. For children aged 3 to 12, the system automatically lowers the intent matching threshold and enables specific encouraging voice packs. 9.The method of claim 1, wherein the method further comprises: obtaining a digital brush based on the synchronization of the handwriting stroke intention and the digital brush. When one of the student's parents is an employee of the company, the system automatically reduces the relevant negative records for the student's high-frustration events.

10. The method according to claim 1, wherein the method is characterized in that: When a trainee receives a certain amount of internal innovation rewards in a given month, the system automatically relaxes the allowable threshold for penmanship deviation.

Images