A system and platform architecture for personalized life skills data generation and management

Abstract

This invention discloses a system and platform architecture for the generation and management of personalized life skills data, relating to the fields of artificial intelligence and robotics. Specifically, it is a system and platform architecture for the generation and management of personalized life skills data, comprising multimodal behavior acquisition, intent structured modeling, and skill data solidification units. It analyzes real-world user behavior data to generate a structured model containing skill intents, constraints, and prohibitions. It supports distributed computing for real-time parsing, forming a standardized cross-device protocol; it integrates version control, RBAC three-level permission management, and security auditing to ensure full lifecycle data management. It includes device adaptation interfaces, an instruction conversion engine (rules + machine learning), and a cross-platform migration module (digital twin verification), supporting multimodal biometric authentication (including spatiotemporal feature action signatures) to establish user sovereignty.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and robotics, specifically to a system and platform architecture for the generation and management of personalized life skills data. Background Technology

[0002] With the rapid development of artificial intelligence and smart device technologies, more and more life scenarios are introducing robots or smart terminals to assist in completing daily tasks, such as home cleaning, elderly care, children's education, and cooking assistance. The core of these applications lies in enabling machines to understand and respond to users' personalized needs, that is, the automated execution of "life skills." However, in the actual promotion process, how to accurately capture users' true intentions and transform them into data-driven skill models that are cross-device, reusable, and manageable has become a key bottleneck restricting the large-scale implementation of personalized smart services.

[0003] Currently, common methods for generating smart device skills mainly rely on developers manually writing scripts or configuring based on fixed templates. This approach is not only inefficient but also highly dependent on the developer's experience, making it difficult to cover users' diverse lifestyles and personalized preferences. For example, different families may have significantly different definitions of "tidying the living room"—some users require laundry to be put away before sweeping, while others emphasize the order in which items are put away. Existing systems cannot automatically recognize these differences and generate corresponding execution logic. Furthermore, most systems only support single-modal input (such as text instructions or simple voice commands), ignoring the multi-dimensional behavioral information naturally revealed by users in real-world scenarios (such as gesture guidance, tone of voice, and operating habits). This leads to a disconnect between the skill model and the user's actual needs, resulting in frequent "understanding biases" during execution.

[0004] In terms of skill data management and reuse, existing technologies generally suffer from data silos. Different brands and types of devices (such as robot vacuums, smart speakers, and humanoid robots) use their own proprietary protocols and data formats, making it impossible to share or migrate skill data from the same user across devices. Even if some systems support data export, the lack of standardized structural definitions makes it easy for key parameters such as skill execution constraints (e.g., "avoid pet activity areas") and prohibition conditions (e.g., "disable wet mopping mode in high-temperature environments") to be lost or distorted during migration, affecting the consistency of execution results. At the same time, the modification and deletion of skill data lack strict access control and audit trails, posing a risk of accidental or malicious data alteration. Especially when multiple people in a household share devices, it is difficult to clearly define the "ownership" of skill data, further limiting the popularization of personalized skills.

[0005] Furthermore, existing systems also have significant shortcomings in device adaptation and execution optimization. Due to differences in computing power, sensor configuration, and motion accuracy among different hardware devices, directly issuing standardized skill instructions to these devices often results in execution failures or low efficiency due to incompatibility. For example, a precise grasping motion designed for a high-computing-power humanoid robot may damage objects if directly applied to a low-precision robotic arm because the range of motion exceeds physical limitations. Although some research attempts to solve the adaptation problem through parameter adjustment, these methods largely rely on human experience and lack the ability to perceive and dynamically optimize device performance in real time, making it difficult to ensure the accurate transmission of skill intentions.

[0006] It is worth noting that users' demand for "personalized" skills data is essentially a digital confirmation of ownership of their own lifestyle habits. Current technology has neither established an effective biometric authentication mechanism to confirm users' ownership of skills nor provided legally valid ownership certificates, leading to a lack of protection for users' rights when sharing or trading skills data. For example, a user-trained "special tableware cleaning process" may be stolen by others, but it is difficult to prove its originality.

[0007] In summary, existing technologies for generating and managing life skills data have significant shortcomings in areas such as multimodal intent parsing, cross-device standardized modeling, full lifecycle data control, dynamic device adaptation, and user sovereignty authentication. There is an urgent need for a systematic solution that can integrate multi-source behavioral data, support personalized modeling, achieve cross-platform compatibility, and ensure data security, so as to promote the deep evolution of smart life services from "function-driven" to "personalization-driven". Summary of the Invention

[0008] The purpose of this invention is to provide a system and platform architecture for the generation and management of personalized life skills data. Through multimodal acquisition, intent modeling and skill solidification, combined with version control, permission management and cross-device adaptation, it can achieve full lifecycle management and seamless migration of skill data, clarify user sovereignty, and improve the personalization and reliability of life skills applications.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a system and platform architecture for personalized life skills data generation and management, comprising a multimodal behavior acquisition unit, an intent structured modeling unit, and a skills data solidification unit; wherein, the multimodal behavior acquisition unit is equipped with a visual sensing interface, an audio sensing interface, and an interactive signal capture interface, respectively used to synchronously acquire user-generated body movement image sequences, natural language speech waveforms, and touch / gesture interaction signals with physical devices or virtual interfaces in real-world scenarios; the intent structured modeling unit is communicatively connected to the output of the multimodal behavior acquisition unit, and incorporates a motion decomposition module, a speech translation module, and an interaction logic summarization module, used to perform joint trajectory analysis on the acquired body movement image sequences to extract key action nodes and sequences, and to perform natural language speech translation on the acquired body movement image sequences. The speech waveform is semantically segmented to identify the core operational requests, and the touch / gesture interaction signals are temporally correlated to sort out the operation triggering conditions and response logic. Then, the above three types of analysis results are integrated to generate a structured data model containing skill intent, execution constraints, and prohibition conditions. The skill intent clarifies the target state that the skill needs to achieve, the execution constraints limit the environmental parameters, resource conditions, and step boundaries that must be met during the skill implementation, and the prohibition conditions define the operation objects or risky behaviors that must not be touched during the skill execution. The skill data solidification unit is communicatively connected to the output end of the intent structured modeling unit and is equipped with a model encapsulation module and a storage index module. It is used to encapsulate the generated structured data model into searchable data entities according to a preset format and establish a classification index based on skill type and usage scenario to complete the solidification storage.

[0010] Furthermore, the intent-structured modeling unit adopts a distributed computing architecture, which includes task scheduling nodes, parallel parsing node groups, and semantic fusion nodes. The task scheduling nodes receive raw data transmitted from the multimodal behavior acquisition unit and break it down into action parsing subtasks, speech translation subtasks, and interaction summarization subtasks, allocating them to the corresponding parallel parsing node groups according to data type. Each parallel parsing node group contains at least three parsing nodes operating in parallel, dedicated to joint trajectory parsing of limb action image sequences, semantic segmentation of natural language speech waveforms, and temporal correlation of touch / gesture interaction signals, respectively. Each node synchronously outputs local parsing results. The semantic fusion node communicatively connects to the outputs of all parallel parsing node groups, performing cross-modal semantic alignment and conflict resolution on the local parsing results. This supports real-time parsing and semantic abstraction of multi-dimensional behavioral data during data acquisition, ultimately forming a standardized skill protocol executable across devices. This standardized skill protocol defines a unified description syntax and field specifications for skill intents, execution constraints, and prohibition conditions.

[0011] Furthermore, the system integrates a skills data version control module, a permission hierarchical management system, and a security audit component. The skills data version control module includes a change capture submodule, a version snapshot submodule, and a rollback management submodule. The change capture submodule monitors modifications to data entities in the skills data solidification unit and records the changes and timestamps. The version snapshot submodule generates a complete copy of the current data entity as a historical version upon detecting a preset trigger event. The rollback management submodule restores a specified data entity to a selected historical version according to user instructions. The permission hierarchical management system communicatively connects the skills data version control module and the skills data solidification unit, defining different users' access, modification, and deletion permissions for skills data. The security audit component communicatively connects the permission hierarchical management system and the skills data solidification unit, recording all user operations on skills data and forming an unmodifiable audit log. Through the collaborative work of these modules, the system achieves full lifecycle management of life skills data from generation, storage, updating to destruction, ensuring data immutability and operational traceability.

[0012] Furthermore, the permission-based hierarchical management system adopts the RBAC model to construct a three-tiered mapping relationship between roles, permissions, and users. It establishes a three-tiered permission system: a super administrator, a family master user, and ordinary users. The super administrator role is granted complete control over all skill data within the system, including creating / deleting permission policies, viewing all audit logs, and forcing data rollback. The family master user role is granted management rights over skill data within their family group, including modifying execution constraints on data within their group, approving skill data modification requests from ordinary users, and viewing audit logs of operations within their group. The ordinary user role is only granted the right to use and limited modification rights to the skill data they personally create, including executing skill data, modifying prohibited conditions for their own data, and viewing audit logs of their own operations. By assigning users to corresponding roles, hierarchical management and differentiated authorization of skill data are achieved.

[0013] Furthermore, it includes a device adaptation interface layer, an instruction conversion engine, and an execution feedback monitoring module. The device adaptation interface layer includes a hardware model registration submodule and an interface protocol library. The hardware model registration submodule stores communication protocols, instruction formats, and performance parameters for different hardware platforms. The interface protocol library pre-stores instruction encoding rules supported by each hardware platform. The instruction conversion engine is communicatively connected to the output of the device adaptation interface layer and the output of the skill data solidification unit. It includes a protocol matching submodule and an instruction sequence generation submodule. The protocol matching submodule extracts the corresponding instruction encoding rules from the interface protocol library based on the target hardware platform model. The instruction sequence generation submodule converts the skill intent, execution constraints, and prohibition conditions in the structured skill data into an instruction sequence that conforms to the instruction encoding rules of the target hardware platform. The execution feedback monitoring module is communicatively connected to the output of the instruction conversion engine and the execution status interface of the target hardware platform. It receives the status code, progress information, and exception prompts returned by the hardware platform after executing the instruction sequence and sends the feedback information back to the skill data solidification unit to update the execution effect record of the skill data. Through the cooperation of the above units, structured skill data can be dynamically converted into execution instruction sequences adapted to different hardware platforms.

[0014] Furthermore, the instruction conversion engine adopts a hybrid architecture of rule engine and machine learning, which includes a rule base, a parameter optimization model, and a decision arbitration module. The rule base pre-stores core instruction conversion rules based on the principle of invariant skill intent. The parameter optimization model is trained through historical execution data and is used to predict the performance bottleneck parameters of different hardware platforms when executing the same skill intent. The decision arbitration module is used to call the parameter optimization model to analyze the performance parameters of the target hardware platform after the rule base outputs the basic instruction sequence, and dynamically optimize the execution parameters in the instruction sequence. The execution parameters include the load threshold of a single instruction, the interval time of consecutive instructions, or the sensor sampling frequency, so as to adapt to the differences in device performance while keeping the skill intent unchanged.

[0015] Furthermore, it includes a cross-platform data conversion middleware, a device compatibility verification module, and an execution consistency verification unit. The cross-platform data conversion middleware has a source platform parsing submodule and a target platform mapping submodule. The source platform parsing submodule reads the skill data format of the source robot system and extracts the core fields of skill intent, execution constraints, and prohibition conditions. The target platform mapping submodule converts the core fields into a format supported by the target robot system. The device compatibility verification module is communicatively connected to the output of the cross-platform data conversion middleware and verifies whether the target robot system has the hardware capability and software interface support to execute the skill data after conversion. The execution consistency verification unit is communicatively connected to the output of the device compatibility verification module and compares the difference between the actual execution path and the expected path of the source platform when the target robot system simulates the execution of the skill data. When the difference is less than a preset threshold, it is determined to be a seamless migration. Through the cooperation of the above units, seamless migration of skill data between different robot systems can be achieved.

[0016] Furthermore, the device compatibility verification module employs digital twin technology to construct a virtual simulation environment for the target device. This virtual simulation environment includes a three-dimensional geometric model, a kinematic parameter model, and a functional module logic model of the target device. The three-dimensional geometric model recreates the physical dimensions and component layout of the target device. The kinematic parameter model defines the range of motion and transmission ratio of each joint of the target device. The functional module logic model simulates the sensor perception logic and actuator response logic of the target device. By importing skill data output from the cross-platform data conversion middleware into the virtual simulation environment and executing the simulation, the module collects positional errors, time deviations, and abnormal triggering events during the simulation execution process. These data are then compared with preset standards to ensure that the migrated skill execution effect meets the preset standards.

[0017] Furthermore, a multimodal biometric authentication process is established, comprising an iris recognition module, a voiceprint verification module, and a motion signature module. The iris recognition module includes an infrared imaging submodule and a texture comparison submodule. The infrared imaging submodule acquires near-infrared images of the user's iris, and the texture comparison submodule matches the acquired image with pre-stored iris texture templates for feature point matching. The voiceprint verification module includes a voice acquisition submodule and a spectrum comparison submodule. The voice acquisition submodule records the voice signal of a user-specified statement, and the spectrum comparison submodule calculates the similarity between the spectral features of the voice signal and pre-stored voiceprint spectrum templates. The motion signature module includes a motion capture submodule and a feature encoding submodule. The motion capture submodule records the spatiotemporal trajectory of a demonstration action performed by the user according to preset specifications, and the feature encoding submodule extracts features from the spatiotemporal trajectory. Only when all three authentications—iris recognition, voiceprint verification, and motion signature—are passed can the user's ownership of the skill data be established, allowing the user to manage and operate the skill data.

[0018] Furthermore, the action signature employs spatiotemporal feature encoding technology. The implementation process of this technology is as follows: the motion capture submodule collects the spatial coordinates and corresponding timestamps of key body parts in the user's demonstration action at a fixed frequency, forming a time-labeled joint trajectory dataset; the feature encoding submodule processes the joint trajectory dataset, first extracting extreme points, inflection points, and dwell points in the trajectory as key nodes, and then numerically encoding the spatial coordinate difference, time interval, and acceleration change rate of each key node to form a unique digital fingerprint; this digital fingerprint is bound to the user's identity identifier and stored in a sovereign credential repository to form a legally valid skill ownership certificate, used to prove the user's original creative sovereignty over specific skill data.

[0019] This invention provides a system and platform architecture for the generation and management of personalized life skills data, which has the following beneficial effects: 1. This system synchronously acquires user action, voice, and interaction data in real-world scenarios through a multimodal behavior acquisition unit, avoiding the limitations of traditional skill generation that relies on preset templates or manual annotation. For example, an elderly person will first hold the window sill steady before gently pushing it to close, and a housewife will first touch the bowl to test the temperature before setting the time to heat food. These subtle and personalized habits can be accurately analyzed. Combined with the real-time semantic abstraction of the intent-based structured modeling unit, these concrete behaviors can be transformed into a structured model containing "skill intent (e.g., 'safely close the window'), execution constraints (e.g., 'hold the window sill steady'), and prohibition conditions (e.g., 'do not push the window forcefully')". This modeling approach based on real-world scenarios ensures that the generated skill data perfectly matches the user's personal habits. Subsequent calls can directly match daily behavioral logic without requiring additional user adaptation, significantly improving the practicality and user acceptance of skill execution. It is especially suitable for life scenarios requiring highly personalized adaptation (e.g., age-friendly assistance, family-specific chores).

[0020] 2. The system integrates a device adaptation interface layer, an instruction conversion engine, and a cross-platform data conversion middleware. Combined with a device compatibility verification module based on digital twin technology, it effectively solves the problem of incompatibility of skills across different hardware platforms (such as robotic vacuum cleaners, smart speakers, and service robots) due to differences in performance and interaction methods. The instruction conversion engine adopts a hybrid architecture of rule engine and machine learning. While maintaining the core intent of "cleaning the living room," it dynamically converts skill data into instruction sequences such as "path planning + obstacle avoidance parameters" for robotic vacuum cleaners and "voice command triggering + area confirmation" for smart speakers. The cross-platform middleware ensures that the cleaning range, intensity, and other effects still meet preset standards after the same skill is migrated across different robot systems through consistency checks. This feature significantly reduces the cost for users to repeatedly set skills for different devices, improving the reuse efficiency of life skills and cross-device collaboration capabilities.

[0021] 3. Through skill data version control, RBAC hierarchical permission management, and security audit components, the system achieves end-to-end control from generation to destruction: Version control traces the time, content, and operator of each modification, preventing accidental or malicious tampering; the RBAC model sets up three levels of permissions: super administrator (system-level maintenance), family master user (family data management), and ordinary user (viewing only), preventing unauthorized access or unauthorized modification; the security audit component records all operation logs to ensure traceability. Simultaneously, multimodal biometric authentication (iris, voiceprint, action signature) extracts the digital fingerprint of the user's demonstrated actions through spatiotemporal feature encoding, forming a legally valid ownership certificate and clarifying the user's sovereignty over the skill data. This dual mechanism of "control + ownership confirmation" not only protects the security of family privacy and life data but also gives users absolute control over their own skill data, addressing users' concerns about data leakage or misuse.

[0022] 4. The intent-structured modeling unit adopts a distributed computing architecture, supporting real-time parsing and semantic abstraction of multi-dimensional behavioral data. It can quickly handle complex user behaviors in intricate scenarios (such as "making coffee while tidying the desktop"). The hybrid architecture of the instruction conversion engine further enhances this: the rule engine ensures the core logic of the basic intent (such as "making coffee") remains intact, while the machine learning model dynamically adjusts execution parameters based on the device's real-time status (such as current battery level and load). For example, if the robot vacuum's battery is below 20%, it will automatically shorten the cleaning path and prioritize key areas; if the smart speaker detects ambient noise, it will increase the sensitivity of voice command recognition. This "intent-locking + parameter adaptation" mechanism ensures stable execution of skills even under uncertain conditions such as device performance fluctuations and environmental interference, preventing skill failure due to device differences or unforeseen circumstances, and significantly enhancing the system's robustness and reliability in real-life situations.

[0023] 5. Traditional life skills generation relies on professional technicians writing code or configuring parameters, making it difficult for ordinary users to participate. This system automatically generates structured skill data by analyzing users' natural behaviors in real-world scenarios (such as "straightening the collar before folding clothes" or "checking soil moisture before watering plants"), without requiring users to learn programming or technical terminology. For example, a user only needs to demonstrate the "personalized tea brewing" action once (warming the cup, adding tea leaves, pouring water, and steeping), and the system can extract key nodes to form a skill model containing constraints such as "water temperature 80℃, steeping for 30 seconds." This "demonstration-based generation" model allows non-technical users such as the elderly and children to easily create their own skills, promoting the transformation of life services from "standardized supply" to "personalized customization." In particular, it can provide assistive skills tailored to the abilities of people with disabilities and special needs groups, significantly improving the accessibility and inclusiveness of life services. Attached Figure Description

[0024] 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.

[0025] Figure 1 This is a flowchart illustrating the overall process of generating and managing personalized life skills data in this invention. Figure 2 This is a flowchart illustrating the structured modeling and standardized protocol generation process of this invention. Figure 3 This is a flowchart illustrating the full lifecycle management process for skill data in this invention. Figure 4This is a flowchart illustrating the structured skill data to device execution instruction conversion process of the present invention. Figure 5 This is a flowchart illustrating the multimodal biometric authentication and skill sovereignty establishment process of this invention. Detailed Implementation

[0026] 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.

[0027] 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.

[0028] Instructions for use: Step 1: Multimodal behavioral data acquisition Users naturally complete target skills in real-life scenarios (such as organizing items, brewing drinks, etc.). The system synchronously records action trajectories (such as body posture and operation path), voice commands (such as "handle gently" and "temperature is suitable"), and interactive behavior data (such as touch / voice interaction with home devices) through a multimodal behavior collection unit. There is no need for deliberate performance or preset scripts, ensuring that the data comes from real usage habits.

[0029] Step 2: Intent-based structural modeling The collected data is transmitted in real time to the intent-based structured modeling unit. This unit, based on a distributed computing architecture, performs real-time parsing and semantic abstraction of multi-dimensional behavioral data: extracting the core skill intent (e.g., "sorting clothes by material"), execution constraints (e.g., "placing fragile items separately on the upper shelf"), and prohibitions (e.g., "avoiding storing paper items in damp areas"), generating a standardized skill protocol. This protocol supports cross-device understanding, providing a unified semantic foundation for subsequent execution.

[0030] Step 3: Solidify Skill Data and Manage its Entire Lifecycle After the structured model is stored in the skill data solidification unit, it enters the full lifecycle management stage: the skill data version control module tracks modification records; the permission hierarchical management system (based on the RBAC model) sets three levels of permissions: super administrator (full control), family master user (family data management), and ordinary user (view / use only) to achieve differentiated authorization; the security audit component records all operation logs, and combined with encryption technology, ensures that the data is tamper-proof and the operation is traceable.

[0031] Step 4: Cross-device instruction translation and execution When a skill needs to be performed, the device adaptation interface layer receives structured data, and the instruction conversion engine (a hybrid architecture of rule engine and machine learning) dynamically converts it into a sequence of execution instructions for the target hardware platform. While keeping the skill intent unchanged, it automatically optimizes parameters (such as adjusting the force of the robotic arm to adapt to different robot models). The execution feedback monitoring module collects the execution status in real time (such as whether there is a collision or whether the temperature meets the standard), and triggers parameter re-optimization when an anomaly occurs.

[0032] Step 5: Cross-platform skills transfer and verification If skills need to be migrated to other robot systems, the cross-platform data conversion middleware parses the original protocol and adapts it to the target system format; the equipment compatibility verification module uses digital twin technology to build a virtual simulation environment for the target equipment, simulates the skill execution process, and verifies whether it meets the preset standards (such as motion accuracy and response time); the execution consistency verification unit compares the execution effects before and after migration, and completes the migration after confirming that there are no deviations.

[0033] Step Six: Confirmation of Sovereignty Before using skill data, user sovereignty must be established through multimodal biometric authentication: this involves sequentially completing iris recognition (physiological uniqueness), voiceprint verification (voice feature matching), and action signature (extracting digital fingerprints of key nodes of demonstration actions using spatiotemporal feature coding technology). The digital fingerprint of the action signature serves as a legally valid certificate of skill ownership, clearly defining the user's exclusive rights to the data.

[0034] Example 1: Generation of "Household Clothing Sorting and Storage" Skills Based on Multimodal Behavior Acquisition Units Users need to develop personalized skills for "organizing clothes by material" within their homes. When using the system, users naturally operate within real-life scenarios of organizing clothes: when holding a cotton T-shirt, they softly say "cotton is breathable," fold a wool sweater into a square and place it in the middle of the wardrobe, adding "don't press it into wrinkles," and when picking up a silk scarf, they pause and emphasize "keep it away from zippers to prevent snagging." The multimodal behavior acquisition unit simultaneously records three types of data—motion trajectories (hand grasping angle, changes in folding force, and placement coordinates), voice commands (natural expressions including emphasis words), and interactive behaviors (such as the action of pushing open the top drawer of the wardrobe). After data collection, the data is directly input into the intent structured modeling unit. This unit uses a distributed computing architecture to analyze the relationship between actions and speech: abstracting "cotton breathable" into the execution constraint "cotton clothing should be placed on a ventilated shelf", transforming "avoid wrinkles" into the constraint condition "wool sweaters should be laid flat and stacked to avoid weight accumulation", and refining "keep away from zippers to prevent snagging" into the prohibition condition "silk products should be placed separately from hard accessories". Finally, a structured data model containing the skill intent "storing clothing by material", specific constraints and prohibition conditions is generated, without requiring additional annotations or preset templates from the user.

[0035] Example 2: Cross-device protocol generation of the "Elderly Medication Reminder" skill based on intent-based structured modeling units A family needs to apply the "medication reminder for the elderly" skill demonstrated by a user to both a smart speaker and a care robot. During use, the user first demonstrates according to their daily habits: at 7:00 AM, while picking up the blood pressure medication bottle, they say, "Time to take the small white pill, with warm water," and simultaneously tap the "morning dose" label on the medicine box. The multimodal behavior acquisition unit records the action (gesture of picking up the medicine bottle, tapping location), voice ("small white pill," "with warm water"), and interaction (touch with the medicine box) data. Then, the intent structured modeling unit activates a distributed computing architecture to analyze the multi-dimensional data in real time: extracting the time trigger condition from "7:00 AM," abstracting the intent to identify the medication type from "small white pill," extracting the method of administration constraint from "with warm water," and associating the action with the time from "tapping the morning dose label." Subsequently, the unit abstracts the semantics of scattered data into standardized skill protocols, clarifying the core logic of "time-medication-method," enabling smart speakers to provide reminders via voice broadcast, and nursing robots to simultaneously deliver warm water and medicine bottles, achieving unambiguous execution of the same skill on both types of devices.

[0036] Example 3: Family Collaborative Management of "Children's Meal Preparation" Skills Based on a Hierarchical Access Control System A family needs to manage the "Children's Meal Preparation" skill (including constraints such as "washing ingredients three times" and "cutting into small pieces to prevent choking"), involving three generations. When using the skill, the super administrator (head of household) first enters the skill data into the system, then configures three levels of permissions through a hierarchical access control system (RBAC model): the super administrator has the right to modify, delete, and assign permissions; the mother is designated as the primary user, granted the right to edit skill content (e.g., add "allergy food label") and adjust family member permissions (e.g., allow children to view but not modify); the child's account is set as a regular user, only allowing viewing and execution of the skill (e.g., operating the baby food maker according to the protocol). When a child attempts to modify the "cutting into small pieces" size constraint, the system automatically intercepts the attempt because regular users lack editing permissions and sends a notification to the mother's account; only after the mother adjusts the permissions can the child participate in optimization. A security audit component synchronously records the time and subject of each permission change and operation attempt, ensuring that the skill data is tamper-proof and the entire operation is traceable during collaborative use.

[0037] Example 4: Cross-hardware adaptation of the "desktop clutter organization" skill based on the instruction translation engine Users need to apply the "desktop clutter return" skill (including intentions such as "stationery into pen holder" and "return remote control to its original position") to both a heavy-duty robotic arm and a lightweight collaborative robot. During use, after the device adaptation interface layer obtains structured skill data, the instruction conversion engine (a hybrid architecture of rule engine and machine learning) initiates dynamic conversion: For the heavy-duty robotic arm, the rule engine utilizes its "high load, low speed" characteristic parameters, converting the "handle with care" constraint into execution parameters of "gripping force ≤ set threshold, movement speed ≤ set value"; for the lightweight collaborative robot, the machine learning module analyzes its "high flexibility, limited load" performance data, optimizing the motion trajectory of "stationery into pen holder" into a "small-amplitude rotation obstacle avoidance" path instruction. Throughout the conversion process, the engine remains focused on the core skill intent (categorizing and returning clutter), only adjusting parameters to adapt to device differences. The execution feedback monitoring module collects real-time execution data for both types of devices: when the robotic arm places stationery, the pressure sensor reports "gripping force compliant"; when the collaborative robot returns the remote control to its original position, the vision module confirms "position deviation < allowable range," neither triggering further parameter optimization, thus successfully completing cross-hardware adaptation.

[0038] Example 5: Cross-robot transfer of the "bookshelf dust removal" skill based on the device compatibility verification module Users need to migrate their verified "bookshelf cleaning" skill (including constraints such as "wiping from top to bottom" and "avoiding hardcover book spines") from Brand A's robot vacuum cleaner to Brand B's robot window cleaning robot. During use, the cross-platform data conversion middleware first parses the skill protocol of Robot A and converts it to a format compatible with Robot B. Then, the device compatibility verification module uses digital twin technology to build a virtual simulation environment for Robot B—simulating its robotic arm length, wiping head material, and movement step length, etc., to reproduce the "wiping from top to bottom" action in the virtual environment. When the virtual robotic arm moves according to the protocol trajectory, the system detects that "the height of the hardcover book spine exceeds the wiping head's avoidance range" and immediately marks this parameter as conflicting. The consistency verification unit compares the virtual execution with the original skill effect (such as whether the book spine is dusty). If it confirms that the standard is not met, it feeds back to the instruction conversion engine to adjust the parameters (such as reducing the robotic arm's extension range). The re-simulation displays "the wiping trajectory covers all non-spine areas without touching the books," and the consistency verification passes. The system determines that the migrated skill execution effect meets the preset standard, completing the seamless cross-robot migration. In addition, before using the skill for the first time, users need to undergo multimodal biometric authentication: iris recognition confirms physiological identity, voiceprint verification matches the voice features during the demonstration, and action signature extracts digital fingerprints of key nodes such as "wrist rotation angle during wiping and duration of wiping head pressure" through spatiotemporal feature encoding to generate a legally valid skill ownership certificate, establishing the user's exclusive sovereignty over the migrated data.

[0039] 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 system and platform architecture for generating and managing personalized life skills data, characterized in that: It includes a multimodal behavior acquisition unit, an intent structured modeling unit, and a skill data solidification unit. By analyzing the user's action, voice, and interaction behavior data in real-world scenarios, it generates a structured data model that includes skill intent, execution constraints, and prohibition conditions.

2. The system and platform architecture for personalized life skills data generation and management according to claim 1, characterized in that: The intent structured modeling unit adopts a distributed computing architecture, which supports real-time parsing and semantic abstraction of multi-dimensional behavioral data to form standardized skill protocols that can be executed across devices.

3. The system and platform architecture for personalized life skills data generation and management according to claim 1, characterized in that: It integrates a skills data version control module, a hierarchical access control system, and a security audit component to achieve full lifecycle management of life skills data, ensuring data immutability and operational traceability.

4. The system and platform architecture for personalized life skills data generation and management according to claim 3, characterized in that: The permission-based hierarchical management system adopts the RBAC model and sets up a three-level permission system of super administrator, family master user and ordinary user to realize hierarchical management and differentiated authorization of skill data.

5. The system and platform architecture for personalized life skills data generation and management according to claim 1, characterized in that: It includes a device adaptation interface layer, an instruction conversion engine, and an execution feedback monitoring module, which can dynamically convert structured skill data into execution instruction sequences adapted to different hardware platforms.

6. The system and platform architecture for personalized life skills data generation and management according to claim 5, characterized in that: The instruction conversion engine adopts a hybrid architecture of rule engine and machine learning, which supports dynamic optimization of execution parameters to adapt to differences in device performance while keeping the skill intent unchanged.

7. The system and platform architecture for personalized life skills data generation and management according to claim 1, characterized in that: It includes cross-platform data conversion middleware, device compatibility verification module and execution consistency verification unit, which can realize seamless migration of skill data between different robot systems.

8. The system and platform architecture for personalized life skills data generation and management according to claim 7, characterized in that: The device compatibility verification module uses digital twin technology to establish a virtual simulation environment for the target device, ensuring that the skill execution effect after migration meets the preset standards.

9. The system and platform architecture for personalized life skills data generation and management according to claim 1, characterized in that: A multimodal biometric authentication process is set up, which establishes the user's ownership of skill data through a triple authentication mechanism of iris recognition, voiceprint verification, and action signature.

10. The system and platform architecture for personalized life skills data generation and management according to claim 9, characterized in that: The action signature uses spatiotemporal feature encoding technology to extract digital fingerprints from key nodes of the user's demonstrated actions, forming a legally valid skill ownership certificate.

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