A method for limiting data of inhaled chemical stress sources of children's products

Abstract

This invention discloses a method for storing data on the limits of inhaled chemical stressors in children's products, relating to the field of children's product safety. The method includes: conducting time-series release tests on samples of children's products to obtain and store characteristic release kinetic parameters; constructing a multi-scenario bioaccessibility correction factor mapping library based on the correspondence between the chemical composition and physical structure of the children's products and preset children's contact behavior patterns; identifying the current usage scenario of the children's products using a sensor module integrated on the products to obtain the usage time; and determining the release pattern characterized by the characteristic release kinetic parameters and the decay process characterized by the usage time. This invention achieves continuous and reliable assessment and storage of personalized dynamic chemical exposure risks for children's products throughout their entire life cycle and for different usage scenarios, solving the problems of static and singular risk assessment in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of children's product safety, and in particular to a method for storing data on limits of inhaled chemical stressors in children's products. Background Technology

[0002] In the field of children's product safety, limiting and controlling chemical stressors is crucial. Current technology has enabled digital evidence storage, with a typical solution being a monitoring system integrating IoT sensors and blockchain. This system collects chemical concentration data in real time through product sensors and uploads it to the blockchain along with the product's digital identity and testing reports. Leveraging the immutability of blockchain, it creates traceable digital credentials, combining static detection with dynamic monitoring and improving data transparency.

[0003] The limitations of existing technologies lie in the fact that the limited data they provide are merely snapshots of discrete states, the initial test reports only represent the factory compliance status, and the sensor data only reflect the apparent environmental concentration. Neither of these technologies has established a mechanistic relationship with the chemical release kinetics of materials or the differentiated use behaviors of children. They cannot quantitatively assess the theoretical exposure risk in specific scenarios after a period of product use, and they lack time decay parameters and scenario exposure correction parameters, making it difficult to achieve personalized and forward-looking risk assessments and lacking data support for long-term safety early warnings. Summary of the Invention

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

[0005] Therefore, this invention provides a method for storing data on the limits of inhaled chemical stressors in children's products, which solves the problem that existing technologies cannot quantitatively assess the personalized dynamic exposure risks of children's products throughout their entire life cycle and under different usage scenarios.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for storing data evidence of the limits of inhaled chemical stressors in children's products, which includes performing time-series release tests on samples of children's products, obtaining characteristic release kinetic parameters of the children's products, and storing the data evidence. Based on the correspondence between the chemical composition and physical structure of children's products and the preset contact behavior patterns of children, a multi-scenario biological accessibility modification factor mapping library is constructed. By identifying the current usage scenario of children's products through sensor modules integrated into them, the usage time of the children's products can be obtained; Based on the release law characterized by the characteristic release kinetic parameters and the decay process characterized by the time used, a time-related theoretical release assessment is obtained by evolving in the time dimension. Based on the current usage scenario identifier mapped to the corresponding biological accessibility correction factor in the multi-scenario biological accessibility correction factor mapping library, the exposure efficacy of the time-related theoretical release assessment is corrected in the exposure scenario dimension. The time decay dimension and the scenario exposure dimension are integrated to generate a personalized dynamic risk value, and the personalized dynamic risk value is stored as evidence.

[0007] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps for storing the characteristic release kinetic parameters are as follows: Samples of children's products were continuously exposed under constant environmental conditions, and gas samples were collected at multiple non-equidistant time points to obtain time-series release concentration data. A first-order kinetic decay model was applied to the time-series release concentration data for nonlinear fitting to extract characteristic release kinetic parameters; The feature release kinetic parameters, the identifier of the first-order kinetic decay model used for fitting, and the metadata of the test environment conditions are associated and bound with the unique digital identity code of the children's product to form a data package containing the feature release kinetic parameters.

[0008] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps for constructing the multi-scenario biological accessibility correction factor mapping library are as follows: Based on the pre-set children's contact behavior patterns, prepare corresponding simulated migration solutions; Samples of children's products with defined chemical composition and physical structure were immersed in a simulated migration solution corresponding to children's contact behavior patterns and extracted under specified conditions. The concentration of the target chemical stressor in the simulated migration solution after extraction was determined, and the migration rate of chemical substances in the material sample under specific children's exposure behavior patterns was extracted. A mapping relationship was established between the chemical composition of materials, physical structure, children's contact behavior patterns and chemical migration rate, and the chemical migration rate was defined as the biological accessibility modification factor. By collecting records of mapping relationships between material chemical composition, physical structure, children's contact behavior patterns, and biological accessibility modification factors, a multi-scenario biological accessibility modification factor mapping library is constructed.

[0009] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps for specifying the usage time are as follows: Signals from a triaxial accelerometer, microphone, and contact sensor are collected to generate a sensor signal stream; The sensor signal stream is input into a pre-trained miniature machine learning model for classification, and the miniature machine learning model outputs a classification result for the current state of the children's products. The classification results are mapped to preset children's contact behavior pattern codes to generate current usage scenario identifiers for children's products; Record the initial timestamp of the first time the child product is activated by the user, and associate and store the initial timestamp with the child product's unique digital identity code; The usage time of children's products is calculated by the difference between the current time and the initial timestamp.

[0010] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps of the theoretical release assessment are as follows: The feature release dynamics parameters are extracted from the evidence data packet, and the feature release dynamics parameters include the initial release strength parameter and the decay rate constant parameter; The accumulated usage time of the children's product is obtained from the initial timestamp of when it was first activated by the user to the current moment; Based on the initial release intensity parameter, decay rate constant parameter and the time used, the evolution process is performed according to the first-order kinetic decay law to obtain the evolution process result. Based on the results of the evolution process, a time-dependent theoretical release assessment for the current moment is generated.

[0011] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps of the exposure efficacy correction are as follows: Based on the current usage scenario identifier of children's products, the current usage scenario identifier corresponds to the preset children's contact behavior pattern; Based on the current usage scenario identifier, the corresponding biological accessibility correction factor is matched and extracted from the multi-scenario biological accessibility correction factor mapping library; The time-dependent theoretical release assessment reflects the decay state of a chemical substance based on characteristic release kinetic parameters over time. In terms of exposure scenarios, the efficacy of time-related theoretical release assessments was adjusted using bioaccessibility correction factors to generate scenario-based exposure levels. Bioaccessibility correction factors characterize the actual accessible proportion of chemicals under specific children’s exposure behavior patterns. The contextualized exposure level integrates the time decay process with the characteristics of the exposure scenario, characterizing the level of chemical exposure faced by children under a specific child exposure behavior pattern after the usage time has elapsed.

[0012] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps for fusing and generating personalized dynamic risk values ​​are as follows: Obtain the scenario-based exposure level after exposure efficacy correction. The scenario-based exposure level integrates the time decay process and the characteristics of the exposure scenario to obtain the preset safety limit standard value of chemical stressors for children's products. Comparative analysis was conducted between the scenario-based exposure levels and the safety limit standards to obtain the comparative analysis results; Based on the comparative analysis of scenario-based exposure levels and safety limit standards, personalized dynamic risk values ​​are generated.

[0013] As a preferred embodiment of the method for storing data on the limits of inhaled chemical stressors in children's products according to the present invention, the specific steps for storing the personalized dynamic risk value are as follows: The personalized dynamic risk value, the evidence hash of the feature release dynamic parameters, the used time and the current use scenario identifier are combined to form a dynamic security status evidence data package; Perform a hash operation on the dynamic security status evidence storage data packet to generate a unique digest value for the dynamic security status evidence storage data packet; The unique digest value of the dynamic security status storage data packet is submitted to the blockchain network to generate a blockchain transaction record; The blockchain network reaches a consensus on the blockchain transaction records of the unique digest value and writes them into a new block.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the method for storing data on limits of inhaled chemical stressors in children's products as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for storing data on limits of inhaled chemical stressors in children's products as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By conducting time-series release tests on children's product samples, characteristic release kinetic parameters reflecting the decay law of chemical release over time are obtained and verified. A multi-scenario bioaccessibility correction factor mapping library is constructed based on the correspondence between material properties and children's contact behavior patterns. The current usage scenario identifier is automatically identified and the usage time is obtained through sensor modules integrated on children's products. Then, a theoretical release assessment is obtained based on the evolution of characteristic release kinetic parameters and usage time over time. The corresponding bioaccessibility correction factor is obtained from the mapping library based on the current usage scenario identifier, and the effectiveness of the theoretical release assessment is corrected in the exposure scenario dimension. Finally, personalized dynamic risk values ​​are generated and verified by integrating the two dimensions of time decay and scenario exposure. By quantifying the release kinetic law and scenario-based exposure efficiency, continuous assessment and reliable verification of personalized dynamic chemical exposure risks for different usage scenarios throughout the entire life cycle of children's products are achieved, solving the problems of static and singular risk assessment in the prior art. Attached Figure Description

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

[0018] Figure 1 A flowchart for a method of documenting data on limits for inhaled chemical stressors in children's products. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0022] Reference Figure 1As an embodiment of the present invention, this embodiment provides a method for storing data evidence of limits for inhaled chemical stressors in children's products, including the following steps: S1. Conduct time-series release tests on samples of children's products to obtain characteristic release kinetic parameters of the children's products and preserve the evidence.

[0023] S1.1. Samples of children's products were continuously exposed under constant environmental conditions, and gas samples were collected at multiple non-equidistant time points to obtain time-series release concentration data.

[0024] Furthermore, samples of children's products were continuously exposed under constant environmental conditions, and gas samples were collected at multiple non-equidistant time points to obtain time-series release concentration data. Constant environmental conditions refer to maintaining constant temperature, humidity, and air exchange rate in a closed environmental chamber, simulating the basic condition of the product in a stable space. Samples of children's products were continuously exposed in the environmental chamber, and the internal chemical stressors were released into the chamber air over time. The release process was not uniform; the release rate was rapid in the initial stage, and then gradually slowed down due to the reduction of volatile components on the material surface or changes in diffusion resistance. To capture this non-linear decaying dynamic process, the selection of gas sample collection time points followed a non-equidistant principle. More densely packed collection points were set in the early stage of release to accurately characterize the rapid change phase, while relatively sparse collection points were set in the later stage of release to record the trend towards stabilization. This non-equidistant time point setting avoided the omission of key characteristic points of the release curve that might occur with equidistant sampling, ensuring that the collected time-series release concentration data could more realistically and completely reflect the entire dynamic trajectory of the chemical stressors released from the product.

[0025] Specifically, based on the physicochemical understanding that the release of chemicals from solid materials is not linear, intensive sampling is conducted in the early stages of rapid release changes, while the sampling frequency is reduced in the later stages of gradual change. This allows for the acquisition of key data points that best define the entire release curve with the most economical sampling frequency. For example, for a newly opened plastic toy, the release of residual volatile organic compounds in the first few hours may account for the majority of the total release in the first 24 hours. Accurately capturing the concentration changes during this stage is crucial for predicting short-term exposure risk. For a similar product that has been used for several months, its release has entered a slow, long-term decay phase, and the focus at this point is on whether its release rate can be maintained at a low and stable level.

[0026] S1.2. Apply a first-order kinetic decay model to the time series release concentration data for nonlinear fitting to extract characteristic release kinetic parameters.

[0027] Furthermore, the first-order kinetic decay model is a commonly used mathematical model based on the physicochemical principle that the release rate of chemical stressors in a material is proportional to their current residual concentration in the material. Inputting time-series release concentration data into this model for nonlinear fitting is a process of finding optimal model parameters that minimize the difference between the calculated curve and the measured data points. This process reveals two key characteristic release kinetic parameters: the initial release intensity parameter and the decay rate constant parameter. The initial release intensity parameter represents the theoretical release potential or initial release rate of the chemical substance at time zero, reflecting the reserve level of releasable chemical stressors contained in the material in its virgin state. The decay rate constant parameter quantifies how quickly the release rate decays over time; a larger value indicates faster decay and a faster rate at which the material approaches a safe and stable state.

[0028] Specifically, through model fitting, the underlying kinetic laws driving concentration changes were extracted from the data. The application of the first-order kinetic decay model is not simply a borrowing of mathematical tools, but a rational choice based on common laws of chemical diffusion and desorption in materials science. It recognizes that the essence of chemical safety risk in children's products is not a fixed value, but a function that decays over time. By extracting characteristic release kinetic parameters, the chemical safety status of a product is transformed from a black box of unknown change into a predictable system with known parameters. For example, for two children's furniture pieces with the same initial formaldehyde concentration, traditional testing alone cannot distinguish their risk differences. It might be found that the decay rate constant parameter of product A is much larger than that of product B. Although the initial release level is the same, the release of product A will decrease to a low-risk level faster, thus demonstrating superior safety in dynamic risk assessment. The purpose of extracting characteristic release kinetic parameters is to achieve a leap from state description to behavioral modeling.

[0029] S1.3. Associate and bind the feature release kinetic parameters, the identifier of the first-order kinetic decay model used for fitting, and the metadata of the test environment conditions with the unique digital identification code of the children's product to form a data package containing the feature release kinetic parameters.

[0030] Furthermore, the unique digital identity code of a children's product serves as its unique identifier in the digital world. Characteristic release kinetic parameters, as data describing the chemical release behavior of the product, along with the identifier of the first-order kinetic decay model, indicate the mathematical framework upon which these parameters are interpreted. Metadata on testing environmental conditions records the boundary conditions such as temperature and humidity encountered when acquiring time-series release concentration data. These three elements together constitute a complete description of the generation process and applicable context of the characteristic release kinetic parameters. The association and binding operation establishes an inseparable link between these data elements and the unique digital identity code of the children's product, ensuring that each set of characteristic release kinetic parameters clearly belongs to a specific individual product.

[0031] Specifically, by constructing a data package containing feature release dynamics parameters, key parameters, their dependent models, and the generation environment are encapsulated in a unified manner and strongly bound to the product's unique identification code. This ensures the reproducibility and auditability of the data. For example, when it is necessary to verify or review the dynamic risk assessment results of a batch of products, it is possible to accurately trace back to which set of feature release dynamics parameters were used, what model was employed, and under what test conditions the results were obtained. This fundamentally eliminates the problem of ambiguous data sources or interpretations, and links and binds the feature release dynamics parameters to form a data package for evidence storage.

[0032] S2. Based on the correspondence between the chemical composition and physical structure of children's products and the preset children's contact behavior patterns, construct a multi-scenario biological accessibility modification factor mapping library.

[0033] S2.1. Based on the preset children's contact behavior patterns, prepare the corresponding simulated migration solution.

[0034] Furthermore, each child's contact behavior pattern corresponds to a real biochemical environment in the human body. To simulate the extraction of chemical substances from materials by this environment in the laboratory, it is necessary to prepare simulated migration solutions with matching physicochemical properties. The composition of the simulated migration solutions is designed according to the target biological environment. For example, solutions simulating the saliva environment are adjusted to a pH and ionic strength close to that of the oral cavity, while solutions simulating the sweat environment need to contain typical organic acids and electrolytes found in sweat. Preparing corresponding simulated migration solutions establishes a liquid medium that conforms to the biochemical characteristics of specific child contact behavior patterns for subsequent extraction experiments, ensuring that the chemical migration behavior measured in the laboratory can reflect the potential ability of chemical stressors to migrate from product materials to the human body in actual contact scenarios to the greatest extent possible.

[0035] Specifically, realistic interfacial chemical conditions for risk exposure were reconstructed in the laboratory. For example, when assessing the risk of a teething toy under oral biting behavior patterns, using simulated saliva solutions is far more realistic than using strong organic solvents, because the former can more accurately reflect how much plasticizer migrates from the teething toy into saliva and is ingested by the child during actual biting. This shifts the risk assessment focus from the material to the exposure scenario. By simulating the differentiated configuration of migration solutions, an operational experimental basis is provided for quantifying the differences in the actual bioaccessibility of the same chemical substance under different usage scenarios.

[0036] S2.2. Immerse a sample of children's product material with a defined chemical composition and physical structure into a simulated migration solution corresponding to a child's contact behavior pattern, and extract the material under specified conditions.

[0037] Furthermore, the chemical composition and physical structure of materials are intrinsic factors determining the migration of chemical substances from the material's interior to its surface. In the extraction process, the material sample is completely immersed in a simulated migration solution corresponding to a child's contact behavior pattern, maintained at a specific temperature and time under specified conditions. The temperature condition simulates body temperature or typical room temperature during human contact, while the time condition is set based on a reasonable estimate of the duration of the specific child's contact behavior pattern. The extraction process simulates the dissolution, extraction, and migration of chemical stressors on the material surface and near-surface areas by the simulated migration solution in actual use. The purpose of extraction under specified conditions is to accelerate and standardize the migration process of chemical substances from the material to simulated body fluids in a controlled laboratory environment, obtaining an extraction result that can be compared and quantified.

[0038] Specifically, by coupling material properties with the biochemical environment and physical conditions of specific exposure scenarios, a standardized simulation of material-scenario interactions was achieved. Emphasis was placed on matching and specifying conditions; the extraction environment must match a pre-defined child contact behavior pattern, and the extraction conditions must simulate the typical contact state of that behavior pattern. For example, for a child's undergarment, assessing the risk under prolonged skin contact requires the extraction experiment to be conducted at near-skin temperature, using simulated sweat, and under conditions representing a possible wearing duration. This better reflects the slow, continuous migration of chemicals to the skin during daily wear than a short, vigorous extraction with organic solvents at high temperatures. The specified conditions reflect consideration of exposure duration and intensity.

[0039] S2.3 Determine the concentration of the target chemical stressor in the simulated migration solution after extraction, and extract the chemical migration rate of the material sample under specific children's exposure behavior patterns.

[0040] Furthermore, instrumental analytical methods, such as liquid chromatography-mass spectrometry (LC-MS) or gas chromatography-mass spectrometry (GC-MS), are used to quantitatively determine the concentration of the target chemical stressor in the simulated migration solution after extraction. The obtained concentration value is the mass of the target chemical stressor contained in a unit volume of simulated migration solution. Chemical migration rate is calculated by combining the measured concentration of the target chemical stressor with the volume of the simulated migration solution used in the extraction experiment and the mass or surface area of ​​the material sample. Chemical migration rate characterizes the proportion or amount of the target chemical stressor migrating from a unit mass or unit surface area of ​​a child product material sample to the simulated migration solution under extraction conditions corresponding to a specific child exposure behavior pattern. Extracting chemical migration rate completes the conversion from instrumentally measured solution concentration values ​​to a normalized parameter characterizing the material migration properties.

[0041] Specifically, the endpoint concentration data detected by the instrument is transformed into a universally comparable parameter, chemical migration rate, which characterizes the migration properties of materials in specific scenarios. By extracting the chemical migration rate, data normalization and de-dependence on specific scenarios are achieved. The chemical migration rate eliminates the interference of specific experimental settings and directly reflects the release tendency or extractability of the material itself in a given scenario (defined by simulated migration solutions and extraction conditions). For example, regarding the migration of the same plasticizer in PVC toys of different hardness, comparing the concentration of the extraction solution may be misleading due to differences in sample weight, but comparing the chemical migration rate can clearly determine which material formulation's plasticizer is more likely to migrate out in a chewing scenario.

[0042] S2.4 Establish a mapping relationship between the chemical composition of materials, physical structure, children's contact behavior patterns and chemical migration rate, and define chemical migration rate as the biological accessibility modification factor.

[0043] Furthermore, the material's chemical composition describes its basic formulation and chemical components, while its physical structure describes its macroscopic morphology, microscopic pore structure, surface treatment, and other physical characteristics. Children's contact behavior patterns serve as pre-defined classification markers. Chemical migration rate is a parameter extracted from extraction experiments, characterizing the degree of migration. The mapping relationship is established using a combination of material chemical composition, physical structure, and children's contact behavior patterns as the query key, and the corresponding chemical migration rate as the query result, forming a corresponding data record. In this mapping relationship, chemical migration rate is given a new definition and role: the bioaccessibility correction factor. The bioaccessibility correction factor thus becomes a quantified coefficient, its value directly derived from experimentally measured chemical migration rates. It is used in dynamic risk assessment to characterize, under specific children's contact behavior patterns, the proportion of chemical stressors that can actually be exposed to by a person relative to standard testing conditions or the theoretical total amount.

[0044] Specifically, by establishing a mapping relationship, the dimensions determining the intrinsic properties of materials—chemical composition and physical structure—are combined with the dimension determining exposure conditions—children's contact behavior patterns—to jointly lock onto an experimentally determined value with clear physical meaning—the bioaccessibility correction factor. This creates a prototype lookup table for material-scenario-correction factor. For example, when a toy is known to be made of ABS plastic with a smooth surface and is currently identified as being handled and played with, a pre-determined bioaccessibility correction factor specific to the ABS plastic-smooth surface-hand-hand-play combination can be directly obtained through this mapping relationship. This factor can then be used to correct the theoretical release assessment of the toy. The purpose of defining chemical migration rate as the bioaccessibility correction factor is to provide a scenario-based exposure efficiency parameter based on experimental evidence for the dynamic risk assessment model. This allows the risk assessment model to move away from relying on a single release amount and instead combine material and scenario characteristics to finely adjust the theoretical release amount to match the reality of physiological exposure, thus improving the personalization and accuracy of risk assessment.

[0045] S2.5. Collect mapping relationship records of material chemical composition, physical structure, children's contact behavior patterns and biological accessibility modification factors, and construct a multi-scenario biological accessibility modification factor mapping library.

[0046] Furthermore, data is collected and summarized through multiple mapping relationship records. Each mapping relationship record fully includes four data fields: material chemical composition, physical structure, child contact behavior patterns, and bioaccessibility modification factors. The construction process includes structured storage of these mapping relationship records and the establishment of an efficient indexing mechanism, enabling the rapid retrieval and acquisition of corresponding bioaccessibility modification factors by using combinations of material chemical composition, physical structure, and child contact behavior patterns as query conditions. The multi-scenario aspect is reflected in the mapping library, which includes records covering various common material types, physical forms, and preset child contact behavior patterns. The resulting multi-scenario bioaccessibility modification factor mapping library is a queryable and scalable database that systematically organizes bioaccessibility data for materials in different usage scenarios, providing a standardized source of scenario modification parameter queries for dynamic risk assessment.

[0047] Specifically, by constructing a mapping library, all generated mapping relationship records are integrated into an organic whole, transforming it from experimental data results into an infrastructure-like query service. This enables one-time testing, multiple reuses, knowledge accumulation, and standardized assessment. For example, once the bioaccessibility correction factor corresponding to silicone-physical-oral chewing is determined through standard experiments and entered into the library, all children's products using the same silicone material and physical form (such as teethers and pacifiers of different shapes) can retrieve the same correction factor from the mapping library during dynamic risk assessment, as long as the oral chewing scenario is identified. This eliminates the need for complex migration experiments for each product. The purpose of constructing a multi-scenario bioaccessibility correction factor mapping library is to create a common parameter foundation to support dynamic risk assessment, improve assessment efficiency and consistency, reduce assessment costs, and enable scientifically experimental risk correction to be applied on a large scale to the personalized safety assessment of a vast number of children's products.

[0048] S3. By using a sensor module integrated into the children's product, identify the current usage scenario of the product and obtain the usage time of the product.

[0049] S3.1 Acquire signals from the triaxial accelerometer, microphone, and contact sensor to generate a sensor signal stream.

[0050] Furthermore, a triaxial accelerometer measures the linear acceleration changes of the child product in three-dimensional space at a fixed frequency, generating acceleration time-series data characterizing the intensity, direction, and frequency of motion. A microphone collects ambient sound pressure waves at a fixed frequency and converts them into electrical signals, generating audio time-series data characterizing the intensity and spectral characteristics of ambient sound. A contact sensor detects whether pressure is applied to the surface of the child product or whether physical contact has occurred, generating switching or analog time-series data characterizing the occurrence and duration of contact events. These time-series data from different physical sensing elements are synchronized, aligned, and packaged according to a unified time reference, forming a multimodal, parallel sensor signal stream. The sensor signal stream is a continuous data sequence whose content reflects the motion state, sound environment, and contact status of the child product in real time.

[0051] Specifically, a low-cost, low-power multimodal sensor fusion strategy is employed to collect raw physical signals characterizing children's interactions with products from three complementary dimensions: motion, sound, and contact. Traditional monitoring solutions may only focus on single environmental parameters such as chemical concentration. However, it is recognized that children's use behavior is a comprehensive, multi-sensory physical interaction process that a single sensor cannot fully characterize. A three-axis accelerometer captures macroscopic, overall motion postures, such as stillness, shaking, and throwing; a microphone captures sound features related to use behavior, such as a quiet sleeping environment, the sound of collisions during play, or the sound of talking; and contact sensors directly detect local, microscopic physical interactions, such as the pressure of a hand gripping or the sucking contact of the mouth. For example, an accelerometer alone may not be able to distinguish between a slight shaking motion in the hand and a baby placed in a shaking stroller, but combining this with noise features collected by the microphone (the ambient noise inside the stroller differs from the noise in the play environment) can improve the differentiation.

[0052] S3.2 Input the sensor signal stream into the pre-trained micro-machine learning model for classification. The micro-machine learning model outputs the classification result of the current state of the children's products.

[0053] Furthermore, the pre-trained micro-machine learning model is a classification algorithm that has been trained on a large amount of labeled sensor signal stream data, with its model size and computational complexity optimized to fit the resource constraints of embedded devices. The sensor signal stream, as input, is fed into the micro-machine learning model for processing. Internally, the micro-machine learning model performs feature extraction and pattern recognition on the input sensor signal stream, and based on the learned patterns and classification rules, determines the most likely state category of the children's product within the current time window. The state category is a predefined, discrete, semantically meaningful set of labels, such as stationary state, handheld / moving state, biting / contacting state, thrown / dropped state, etc. The micro-machine learning model outputs a classification result for the current state of the children's product, which is a category label representing the most likely state.

[0054] Specifically, embedded artificial intelligence technology is applied to real-time behavioral understanding of children's products. By deploying pre-trained micro-machine learning models on resource-constrained terminal devices, online and automatic conversion of raw sensor signals into high-level behavioral semantics is achieved. The micro-machine learning models used are lightweight neural network or decision tree models specifically designed for embedded scenarios. They can analyze sensor signal streams locally, in real-time, and with low power consumption, outputting meaningful classification results. This shifts the computational burden of behavior recognition from the cloud to the product itself, ensuring real-time response and local privacy of data processing. Through training, the micro-machine learning models learn to identify behavioral patterns highly correlated with risk assessment from multimodal sensor signal streams. The purpose of inputting sensor signal streams into the pre-trained micro-machine learning models for classification is to achieve automated and intelligent perception of the usage status of children's products.

[0055] S3.3 Map the classification results to preset children's contact behavior pattern codes to generate the current usage scenario identifier for children's products.

[0056] Furthermore, the predefined child contact behavior pattern codes are a predefined coding system used to standardize the description of typical interactions between children and products, such as MODE_ORAL, MODE_SKIN, and MODE_HANDHELD. A clear mapping relationship is established between the classification results and the predefined child contact behavior pattern codes. This mapping relationship is based on the logical association between the semantic definition of the child contact behavior pattern and the physical state categories that the micro-machine learning model can recognize. For example, the classification result of biting contact mappings to the child contact behavior pattern code MODE_ORAL, and the classification result of hand-held movement might map to MODE_HANDHELD. The classification results output by the micro-machine learning model are converted into their corresponding standardized child contact behavior pattern codes according to the mapping table. The converted child contact behavior pattern codes are defined as the current usage scenario identifier for the child product. The current usage scenario identifier is a standardized string or enumeration value that indicates the category of usage mode related to risk assessment that the child product is experiencing at the current moment.

[0057] Specifically, a standardized conversion layer was constructed from physical state recognition to risk assessment semantics, ensuring that the scenario parameters used by the subsequent risk assessment model have clear and consistent meanings. The micro-machine learning model identifies physical states such as stationary, swaying, and contact; these state descriptions do not directly correspond to the exposure scenario parameters required by the risk assessment model. By mapping these to pre-defined child contact behavior pattern codes, semantic unification and dimensionality were achieved, separating perception from application. This allows the optimization of the behavior recognition model (pursuing higher accuracy in physical state classification) to be independent of the risk assessment model's definition of the scenario, improving the flexibility and maintainability of the entire system. The mapping relationship is based on an understanding of child safety exposure research, ensuring that each physical state is associated with the most relevant exposure scenario. For example, mapping a throwing and falling state to a child contact behavior pattern code depends on the child's primary exposure pathway in that state (perhaps brief hand contact followed by movement away, indicating low risk). This ensures that the behavior recognition results based on physical sensing can be unambiguously understood and used by the risk assessment model, guaranteeing the semantic consistency of the data flow from perception to assessment, and is a key link in integrating IoT sensing with professional risk assessment.

[0058] S3.4 Record the initial timestamp of the first time the child product is activated by the user, and associate the initial timestamp with the child product's unique digital identification code and store it.

[0059] Furthermore, the first activation of a children's product by a user is a well-defined event, such as when a user completes product registration by scanning the product's unique digital identification code, when the product's integrated sensor module is powered on for the first time and successfully connects to the network, or when a user explicitly triggers the product activation process through an application. When this activation event occurs, the precise current Coordinated Universal Time (UTC) is immediately obtained from a trusted time source, and this time is recorded as the initial timestamp. The initial timestamp needs to be stored in a non-volatile manner, such as being written to the product's local non-volatile memory, or uploaded to a cloud server database along with the product's unique digital identification code for persistent storage. This associated storage operation ensures that the initial timestamp and the product's unique digital identification code form an inseparable data record, allowing a unique retrieval of the corresponding initial timestamp by querying the product's unique digital identification code. Specifically, a unique, individualized lifecycle timeline is established for each children's product, and this crucial time benchmark is strongly linked to and reliably stored as the product's digital identity. Precisely defining the product's starting point from the vague manufacturing date to the exact moment it is put into use better aligns with the actual beginning of product aging and risk accumulation. Obtaining time from a reliable source ensures the authority and tamper-proof nature of the timestamp. Linking and storing the initial timestamp with the product's unique digital identity creates a traceable digital birth certificate. For example, for two children's garments produced in the same batch, one purchased and worn immediately in the summer, and the other stored until winter, their initial timestamps will differ by several months. This difference directly leads to different usage times for the two products when assessed at the same calendar time, thus affecting the theoretical release assessment results based on the time decay model—a more accurate approach than simply using the production date. Recording and storing the initial timestamp aims to establish a reliable time benchmark for individualized product lifecycle assessment.

[0060] S3.5 Calculate the usage time of the children's products based on the difference between the current time and the initial timestamp.

[0061] Furthermore, further still, the timestamp of the current assessment moment. This is the precise time obtained when this dynamic risk assessment calculation was triggered, the product's initial activation timestamp. It is the initial timestamp retrieved from storage records and associated with the unique digital identifier of the children's product. Evaluate the time granularity. It is a pre-defined time unit, such as a day or an hour, used to calculate the balance between demand and efficiency based on a dynamic risk assessment model. The calculation process first obtains the timestamp of the current assessment moment. product initial activation timestamp The original time difference between them is divided by the evaluation time granularity. And perform a floor operation on the quotient to obtain the time granularity for evaluation. The number of complete cycles is the unit, multiplied by the evaluation time granularity. Get the time already used Time used This indicates the evaluation time granularity from product activation to the current evaluation time, expressed in integer multiples. Cumulative effective usage time.

[0062] Specifically, by introducing evaluation time granularity The rounding down operation serves a dual purpose: discretizing the continuous time stream into time units compatible with the calculation step size of risk assessment models (such as decay models based on characteristic release kinetic parameters), ensuring consistency between the time input and model parameters in terms of dimensions and scale, and avoiding unnecessary fluctuations in assessment results due to minor time jitter. The rounding operation implicitly ignores or averages out inactive periods, focusing more on calculating the effective aging time experienced by the product. For example, assessing the time granularity... If we set it to one day, then the calculated usage time for the product after 23 hours and 25 hours after activation is... Both are 1 day, and the purpose of the time already used is to provide a stable and regular time input variable for the decay evolution of the time dimension.

[0063] The time expression used is: ; in, Time already used The timestamp of the current evaluation moment. This is the initial activation timestamp for the product. To evaluate the time granularity.

[0064] S4. Based on the release law characterized by the characteristic release kinetic parameters and the decay process characterized by the time used, a time-related theoretical release assessment is obtained by evolving in the time dimension.

[0065] S4.1 Extract the feature release dynamic parameters from the evidence storage data packet. The feature release dynamic parameters include the initial release intensity parameter and the decay rate constant parameter.

[0066] Furthermore, characteristic release kinetic parameters were extracted from the evidence data package. These parameters include an initial release intensity parameter and a decay rate constant parameter, which contain key parameters related to the chemical release behavior of the children's product. The extraction operation involved parsing the data structure of the evidence data package to locate and read the contents of the characteristic release kinetic parameter fields. The initial release intensity parameter is a numerical value characterizing the theoretical intensity or rate at which the chemical stressor is released from the product at a hypothetical time zero or initial state. The decay rate constant parameter is a rate constant numerical value characterizing how quickly the release intensity of the chemical stressor decays over time. The initial release intensity parameter and decay rate constant parameter were successfully extracted from the evidence data package, obtaining mathematical coefficients describing the intrinsic law of the time evolution of the release of the specific chemical stressor of the children's product.

[0067] Specifically, static parameters characterizing the intrinsic release patterns of a product, stored in a trusted data carrier, are dynamically extracted and activated as input variables for subsequent real-time risk assessment calculations. In existing technologies, chemical testing data for products is typically archived in report form, disconnected from the real-time assessment process. By extracting characteristic release kinetic parameters from the stored data package, a data pipeline is established from static digital archives to a dynamic calculation engine. The initial release intensity parameter and decay rate constant parameter are not simple measurements, but rather intrinsic parameters obtained through model fitting of time-series release data; they collectively define the behavioral fingerprint of the product's chemical release. The purpose of extracting these parameters is to provide physically interpretable and mathematically operable driving variables for dynamic evolution over time. For example, for a plush toy, the initial release intensity parameter in its stored data package reflects its formaldehyde release potential in its virgin state, while the decay rate constant parameter reflects the rate at which this release potential decays in a well-ventilated indoor environment.

[0068] S4.2. Obtain the usage time of the children's product from the initial timestamp when the product was first activated by the user to the current time.

[0069] Furthermore, the initial timestamp of the first activation of a children's product by a user is a pre-recorded point in time associated with and stored as a unique digital identifier for the product. The current moment is the instantaneous time at which this theoretical release assessment operation is performed. The cumulative duration is the time interval between the current moment and the initial timestamp. The retrieval operation obtains the initial timestamp from the associated storage, retrieves the reliable current moment, and calculates the difference between the two. This difference represents the used time of the children's product, signifying the actual length of time the product has been in use. The used time can be a continuous quantity in units of time or a discretized value.

[0070] Specifically, the individualized, real-time information about a product's lifespan is quantified into a key time variable—used time—that can be directly used by risk assessment models. Traditional risk assessment models either ignore time variations or use a uniform production date or an estimated duration, lacking a precise correlation with the actual usage history of individual products. Obtaining the cumulative duration from initial activation to the present moment as used time precisely anchors the assessment timeframe to the beginning of the individual product's life cycle. This reflects truly personalized assessment because even products from the same batch can have vastly different used times on the same calendar date due to differences in user purchase and usage times. For example, a child's pajamas purchased in summer and worn immediately will have a used time several months different from one purchased in winter and worn in spring. Obtaining used time provides a realistic time scale for release kinetics, combining the product's inherent release decay patterns with the individual's actual usage time, allowing theoretical assessments to reflect the product's true aging state at a specific stage of use, rather than a general, average state.

[0071] S4.3. Based on the initial release intensity parameter, decay rate constant parameter and the time already used, the evolution process is performed according to the first-order kinetic decay law to obtain the evolution process result.

[0072] Furthermore, based on the initial release intensity parameter, the decay rate constant parameter, and the elapsed time, an evolutionary process is performed according to the first-order kinetic decay law to obtain the evolutionary processing result. The first-order kinetic decay law is a general law describing that in many physicochemical processes (such as radioactive decay, chemical reactant consumption, and material diffusion release), the rate of change of a physical quantity is proportional to the current value of that physical quantity. In the context of chemical release in children's products, this law states that the release rate of a chemical stressor is proportional to the current releaseable concentration or release potential of that chemical stressor in the material. The evolutionary processing is a mathematical calculation process that uses the initial release intensity parameter as the starting value, the decay rate constant parameter as the decay rate, and the elapsed time as the evolution duration, and substitutes them into the mathematical relationship established based on the first-order kinetic decay law for solving. This mathematical relationship describes the process of the release intensity decaying exponentially over time from the initial value. The result of the evolutionary processing is a numerical value, which represents the current theoretical release intensity or theoretical concentration level of the chemical stressor after a specific elapsed time, according to the release kinetic law.

[0073] Specifically, by dynamically fusing the intrinsic physicochemical laws (first-order kinetic decay laws) of materials science with the real-time state information of an individual product (used time) through mathematical calculations, a predictive back-calculation of the theoretical release level of the product at a future moment is achieved. Existing technologies either remain at the level of monitoring current environmental concentrations (results) or are unable to dynamically combine the inherent release characteristics of the product with the time variable. Evolution processing is essentially solving an initial value problem: given the product's release characteristics (initial release intensity parameters, decay rate constant parameters) and lifespan (used time), calculate the theoretical state at the current moment. Utilizing the deterministic nature of release kinetic laws, as long as the parameters and time are accurate, accurate theoretical values ​​can be calculated. For example, for a children's playmat with a known high initial release intensity but rapid decay, and a similar product with a moderate initial release intensity but slow decay, after the same used time, evolution processing may reveal that the current theoretical release level of the former is lower than that of the latter. This reveals long-term safety differences that cannot be determined by initial detection alone. The purpose of evolution processing based on the first-order kinetic decay law is to achieve the transformation from static parameters to dynamic states. This provides a method that does not rely on continuous real-time monitoring, but only on the intrinsic parameters of the product and the duration of use to scientifically predict the current theoretical risk level, thus reducing the dependence on continuous, high-density environmental monitoring. S4.4. Based on the results of the evolution processing, generate a time-dependent theoretical release assessment for the current moment.

[0074] Furthermore, based on the results of the evolutionary processing, a time-dependent theoretical release assessment for the current moment is generated. The result of the evolutionary processing is a numerical value representing the current theoretical release level, calculated according to first-order kinetic decay laws. The generation operation involves formatting, labeling, or encapsulating this evolutionary processing result as necessary, giving it explicit assessment attributes and a temporal context. The time-dependent theoretical release assessment is produced as a complete data object, containing at least the following information: the type of chemical stressor being assessed, the theoretical release level value obtained from the assessment, the assessment time corresponding to this value, and the length of time already used on which the assessment is based. The time-dependent theoretical release assessment is a standardized assessment output that clearly answers the question: based on the product's release patterns and the length of time already used, what is the theoretical release level of this chemical stressor at the current moment?

[0075] Specifically, the purely mathematical calculation results are transformed into a standardized assessment object with clear semantics, directly usable in subsequent risk assessment processes—a time-related theoretical release assessment. The result of the evolutionary processing is simply a numerical value, but generating it as a theoretical release assessment means adding key metadata such as assessment objectives, assessment time, and assessment basis, transforming it from an intermediate variable into an independent, interpretable assessment conclusion. The time-related theoretical release assessment is a crucial data interface connecting the calculation of the time decay dimension with the subsequent correction of the scenario exposure dimension. It clearly distinguishes between the theoretical level calculated based on the product's intrinsic laws and aging over time and the exposure level corrected subsequently based on specific usage scenarios. For example, for the same evolutionary processing result, when generating the time-related theoretical release assessment, it explicitly records that this is a theoretical air concentration value calculated based on a specific usage time for formaldehyde. It generates the time-related theoretical release assessment for the current moment, outputs the final conclusion of the time decay dimension assessment, and prepares for cross-dimensional fusion.

[0076] S5. Based on the current usage scenario identifier, map it to the corresponding biological accessibility correction factor in the multi-scenario biological accessibility correction factor mapping library, and correct the exposure efficacy of the time-related theoretical release assessment in the exposure scenario dimension.

[0077] S5.1 Based on the current usage scenario identifier of children's products, the current usage scenario identifier corresponds to the preset children's contact behavior pattern.

[0078] Furthermore, the current use scenario identifier for children's products is a standardized code or label generated through sensor identification and mapping. Predefined child contact behavior patterns are pre-defined classification systems used to describe typical interactions between children and products, such as oral biting behavior patterns, prolonged skin contact behavior patterns, and intermittent hand-grasping behavior patterns. The current use scenario identifier explicitly points to one of these predefined child contact behavior patterns. Confirming that the current use scenario identifier corresponds to a predefined child contact behavior pattern means clarifying the specific use category of the children's product at the time of assessment, relevant to the risk assessment.

[0079] Specifically, real-time sensed data representing the physical state of use (current usage scenario identifier) ​​is explicitly linked to a predefined classification framework of child exposure behavior patterns with toxicological and exposure science significance. This is not simply data forwarding, but rather achieving semantic alignment from sensor-identifiable states to risk assessment-understandable scenarios. The current usage scenario identifier serves as a query key, directly indexing the preset child exposure behavior pattern, which carries the scenario semantics required for risk assessment, including exposure routes, contact media, and contact intensity. For example, the current usage scenario identifier MODE_ORAL does not merely represent the detection of biting behavior. The purpose of linking the current usage scenario identifier to the preset child exposure behavior pattern is to pinpoint precise exposure scenario definitions for dynamic risk assessment, ensuring that the scenario parameters used in the risk assessment model have a consistent and professional interpretation.

[0080] S5.2 Based on the current usage scenario identifier, match and extract the corresponding biological accessibility correction factor from the multi-scenario biological accessibility correction factor mapping library.

[0081] Furthermore, the multi-scenario bioaccessibility modification factor mapping library is a dataset containing multiple mapping relationship records. Each record is associated with a specific child contact behavior pattern and a bioaccessibility modification factor. The matching operation uses the preset child contact behavior pattern corresponding to the current usage scenario identifier as the query condition to search for records matching that behavior pattern in the multi-scenario bioaccessibility modification factor mapping library. The extraction operation reads and retrieves the stored bioaccessibility modification factor value from the successfully matched mapping relationship records. The bioaccessibility modification factor is a quantified coefficient whose value comes from the migration rate of chemical substances previously measured through standardized experiments and entered into the database. The corresponding bioaccessibility modification factor was successfully extracted.

[0082] Specifically, an exposure knowledge base built upon numerous offline experiments will be dynamically applied to online, real-time personalized risk assessment processes. Traditional risk assessments either ignore scenario differences and use uniform assessment factors, or require complex online simulations to calculate scenario impacts, resulting in low efficiency. By matching and extracting from a pre-built multi-scenario bioaccessibility correction factor mapping library, scenario-based correction parameters can be readily accessed and used. This streamlines and streamlines time-consuming scenario-based transfer experiments, which previously required specialized laboratories, by pre-processing and presenting the experimental results as correction factors in the mapping library. When real-time assessment is needed, there is no need to repeat the experiment; a quick query can be performed using the current usage scenario identifier. For example, when the current scenario is identified as long-term skin contact, the assessment process immediately extracts the pre-determined bioaccessibility correction factor for that scenario from the mapping library. This factor may be significantly lower than that for oral biting scenarios because sweat has a lower extraction efficiency for certain chemicals than saliva. By matching and extracting bioaccessibility correction factors based on the current usage scenario identifier, scenario-based exposure efficiency parameters based on scientific experiments are introduced into theoretical release assessments.

[0083] S5.3. Reference time-related theoretical release assessment, which reflects the decay state of a chemical substance based on characteristic release kinetic parameters over time.

[0084] Furthermore, the time-related theoretical release assessment (TRT) reflects the decay state of a chemical substance based on characteristic release kinetic parameters over time. The TRT generates a data object characterizing the theoretical release level of the chemical stressor at the current moment. The reference operation acquires and prepares the numerical portion of this TRT. The numerical value of the TRT is essentially the theoretical concentration or theoretical release rate of the chemical substance after undergoing the decay process characterized by the time of use, based on the release pattern described by the characteristic release kinetic parameters. This value reflects the current theoretical risk level of the product, assessed solely from the perspective of material aging and decay, without considering efficiency differences in specific exposure scenarios. The TRT provides a baseline value that needs to be adjusted for further refinement in the exposure scenario dimension.

[0085] Specifically, the theoretical release level is introduced as an intermediate assessment metric, serving as a bridge variable connecting time-degradation assessment and scenario-based exposure correction. By referencing time-related theoretical release assessments, the theoretical risk changes arising from the product's intrinsic material properties and usage duration are acknowledged and calculated. This represents a pure risk state of the object, free from scenario interference, allowing subsequent scenario corrections to be made on a clear benchmark. For example, for the same time-related theoretical release assessment value (such as theoretical formaldehyde concentration), the actual exposure risk to children is clearly different in two different scenarios: static storage and close-fitting sleep. However, the theoretical release assessment value itself remains the same, separating the influence of the object's state and human-object interaction.

[0086] S5.4 In terms of exposure scenario, the efficacy of time-related theoretical release assessments is adjusted using bioaccessibility correction factors to generate scenario-based exposure levels. Bioaccessibility correction factors characterize the actual accessible proportion of chemicals under specific children's exposure behavior patterns.

[0087] Furthermore, the exposure scenario dimension focuses on assessing how children's exposure behavior patterns affect the actual human contact or absorption of chemicals. Efficacy adjustment is a mathematical process that applies a bioaccessibility correction factor to the time-related theoretical release assessment. The bioaccessibility correction factor is a proportionality coefficient between zero and one (it may be greater than one in some scenarios due to concentration effects). It quantifies the proportion of the total theoretical release or theoretical concentration of a chemical substance that, under a specific child's exposure behavior pattern, can actually reach and be absorbed by the child after release from the product. Efficacy adjustment involves multiplying the time-related theoretical release assessment value by the corresponding bioaccessibility correction factor. Scenario-based exposure level is a new value that characterizes the level of chemical exposure a child might actually face from the child's product at the current moment, after considering the exposure efficiency of a specific child's exposure behavior pattern.

[0088] Specifically, the difference between theoretical release and actual exposure is quantified and bridged using an experimentally determined proportionality factor. Existing risk assessments often equate release with exposure or use complex models requiring numerous individual behavioral parameters to estimate exposure. The proposed efficacy adjustment simplifies complex exposure scenario simulations into a multiplication operation of a key, experimentally measurable proportionality factor (bioaccessibility correction factor). The bioaccessibility correction factor is derived from migration experiments simulating real-world contact media. For example, in a hand-held play scenario, the bioaccessibility correction factor might be low because the chemical is primarily absorbed through limited skin contact with the hands, and actions such as hand sweating and wiping may further reduce actual absorption; while for the same theoretical release level in oral biting, the correction factor might be higher because saliva is a more effective migration medium, and oral mucosal absorption is highly efficient. The purpose of using the bioaccessibility correction factor for efficacy adjustment is...

[0089] S5.5, Contextualized Exposure Levels integrate the time decay process with the characteristics of the exposure scenario, characterizing the level of chemical exposure faced by children under specific child exposure behavior patterns after the usage time has elapsed.

[0090] Furthermore, the integrated scenario-based exposure level is the result of the combined effect of the time decay process and the characteristics of the exposure scenario. The impact of the time decay process is reflected in the time-related theoretical release assessment on which the scenario-based exposure level is based, which already includes information on characteristic release kinetic parameters and the time elapsed. The impact of the exposure scenario characteristics is reflected in the efficacy adjustment made in generating the scenario-based exposure level, which uses a bioaccessibility correction factor corresponding to the current usage scenario identifier. This single scenario-based exposure level encodes the aging and degradation information of the product due to usage time, as well as the exposure efficiency information due to the specific usage method.

[0091] Specifically, based on the final synthesis of a novel, scenario-based exposure level indicator with a clear risk assessment endpoint, the culmination of a multi-dimensional dynamic assessment process results in a composite risk indicator that integrates three dynamic elements: product intrinsic attributes, individual usage time, and real-time behavioral scenarios. Existing technologies cannot generate such an indicator, lacking the ability to quantify and integrate one or more of the aforementioned dimensions. Scenario-based exposure level addresses two dimensions often overlooked or simplified in traditional assessments: time and scenario. It provides assessment results that directly address children's individual risk experiences. For example, parents are not concerned with whether a toy meets formaldehyde emission standards, but rather whether it's safe for their child to play with the toy (perhaps for several months) and chew on it. Scenario-based exposure level is designed to quantitatively answer these kinds of questions. It characterizes the level of chemical exposure faced by children under specific child contact behavior patterns after a certain usage time, making the risk assessment results highly timely, individualized, and scenario-relevant. The purpose of generating scenario-based exposure level is to output quantitative results of dynamic risk assessment.

[0092] S6. Integrate time decay dimension and scene exposure dimension to generate personalized dynamic risk value.

[0093] S6.1 Obtain the scenario-based exposure level after exposure efficacy correction. The scenario-based exposure level integrates the time decay process and the characteristics of the exposure scenario to obtain the preset safety limit standard value of chemical stressors for children's products.

[0094] Furthermore, the scenario-based exposure level, after exposure efficacy correction, generates a specific numerical value representing the level of chemical exposure a child might face at a specific time and in a specific usage scenario. Obtaining this scenario-based exposure level means preparing it for subsequent risk assessment, which requires obtaining a pre-defined safety limit standard value for chemical stressors in children's products. This safety limit standard value is a pre-set threshold with regulatory or scientific basis, defining the upper limit of acceptable safe exposure levels for a specific chemical stressor within a specific assessment framework. This safety limit standard value may be derived from national mandatory standards, industry recommended standards, or health guidance values ​​derived from toxicological data.

[0095] Specifically, by juxtaposing the dynamic and personalized intermediate assessment result of scenario-based exposure level with the static and universally applicable safety limit standard value, conditions are created for conducting personalized risk assessments based on legal or scientific benchmarks. The acquired scenario-based exposure level is different; it is a dynamic value that changes in real time with usage time and scenario. Comparing it with the safety limit standard value calibrates the individualized exposure estimate with the universally applicable safety threshold. The safety limit standard value serves as an anchor, ensuring the consistency of the benchmark for risk assessment. For example, for the same safety limit standard value, a product that has been used for a long time and is currently in a static storage scenario may have a scenario-based exposure level far below the standard; while a newer similar product currently in a chewing scenario...

[0096] S6.2 Compare and analyze the scenario-based exposure level with the safety limit standard value to obtain the comparison and analysis results.

[0097] Furthermore, comparative analysis is a process of logical and numerical comparison. The analysis directly compares the numerical value of the scenario-based exposure level with the numerical value of the safety limit standard value within the same dimension and assessment framework. Comparative analysis calculates the relative relationship between the scenario-based exposure level and the safety limit standard value. This relative relationship can be quantified in various ways, such as calculating the percentage of the scenario-based exposure level relative to the safety limit standard value, calculating the margin by which the safety limit standard value exceeds the scenario-based exposure level, or making a logical judgment on the magnitude of the two. The result of comparative analysis is a quantitative or logical expression of this relative relationship, clearly revealing the position of the current scenario-based exposure level relative to the generally accepted safety limit. Obtaining the comparative analysis results provides a direct measure describing the gap between individualized exposure status and public safety standards.

[0098] Specifically, a direct benchmarking analysis method between personalized exposure levels and universal safety standards was defined and implemented. Its output is not a simple yes / no compliance judgment, but a continuous measure reflecting the safety margin or the degree of risk proximity. The focus shifts from whether a level exceeds the standard to how far it is from exceeding it or what percentage of the standard the risk level represents. This makes the risk assessment results more informative and warning-oriented. For example, for two products whose scenario-specific exposure levels are both below the safety limit standard value, a comparative analysis might show that product A's level is 10% of the standard value, while product B's is 80% of the standard value. Although both are compliant, the latter clearly requires closer attention because its safety margin is smaller and it is more likely to approach or exceed the standard with increased usage time or changes in scenarios. The purpose of comparing scenario-specific exposure levels with safety limit standard values ​​is to transform personalized exposure estimates into a measurable indicator of risk level with a clear safety reference.

[0099] S6.3. Based on the comparative analysis results of scenario-based exposure levels and safety limit standard values, generate personalized dynamic risk values.

[0100] Furthermore, the comparative analysis results are a quantitative or logical expression reflecting the relative relationship between the scenario-based exposure level and the safety limit standard value. The generation of personalized dynamic risk values ​​is based on these comparative analysis results, encoded or transformed into a final risk assessment output value through a pre-defined conversion rule or mapping relationship. This conversion rule aims to ensure that the generated personalized dynamic risk value has good interpretability and practicality. For example, the personalized dynamic risk value can directly use the percentage value obtained from the comparative analysis, in which case it directly represents what percentage of the safety standard the current exposure level corresponds to. Alternatively, the personalized dynamic risk value can be a risk level mapped from a percentage range, such as low risk, medium risk, risk of concern, high risk, etc.

[0101] Specifically, a personalized dynamic risk value is defined as the ultimate risk assessment indicator, and its generation rules are anchored to the comparative analysis results of scenario-based exposure levels and safety limit standards. This makes the personalized dynamic risk value not a score generated out of thin air, but a composite indicator with clear traceability, physical meaning, and safety benchmarks. Existing technologies lack such indicators or only provide static detection conclusions. A standardized output interface is provided, transforming multi-dimensional dynamic assessment data into a single-dimensional, user-friendly risk representation. This interface is crucial, distilling the final result of the complex scientific assessment process into a risk language that can be directly understood and used by regulators, businesses, consumers, and even machines. For example, the personalized dynamic risk value can be a simple percentage: 85% means the current exposure level is 85% of the safety limit, the risk is controllable but requires attention; 150% means the safety limit has been exceeded, requiring a warning. Alternatively, it can be a color code or an emoji, suitable for users with different knowledge backgrounds to understand. The purpose of generating personalized dynamic risk values ​​is to output the final and actionable conclusions of the dynamic risk assessment process, thereby improving the usability and dissemination efficiency of the assessment results. This enables scientific assessments based on complex models to be truly implemented and serve multiple application scenarios such as product safety early warning, informed consumer choice, precise quality control for enterprises, and efficient regulatory decision-making.

[0102] S7. Store the personalized dynamic risk value.

[0103] S7.1 Combine the personalized dynamic risk value, the evidence hash of the feature release dynamic parameters, the used time, and the current use scenario identifier to form a dynamic security status evidence data package.

[0104] Furthermore, the personalized dynamic risk value is the final risk assessment conclusion. The notarized hash of the feature release dynamics parameter is the cryptographic fingerprint of the previously notarized feature release dynamics parameter data packet used to calculate this personalized dynamic risk value, used for unique referencing and integrity verification. The used time is the duration the product has been used during calculation. The current usage scenario identifier is the usage scenario code that triggered this risk assessment. The combination operation encapsulates these four data elements together according to a preset data structure, forming a complete, structured data set, which is the dynamic security status notarized data packet.

[0105] Specifically, the results are linked to key process evidence. The personalized dynamic risk value is the conclusion; the notarized hash of the characteristic release kinetic parameters links to the intrinsic laws of the product on which the assessment is based; the used time records the assessment time point; and the current usage scenario identifier records the assessment scenario conditions. This linkage ensures the uniqueness and reproducibility of the assessment conclusion. For example, a conclusion of low risk alone cannot be considered safe; it must be confirmed that this low risk is based on the correct product release law parameters, the correct used time, and the correct usage scenario. If there is a dispute regarding the assessment results later, this data package can be retrieved, and the notarized hash within it can be used to verify the authenticity of the characteristic release kinetic parameters on the blockchain, while the used time and current usage scenario identifier can be used to verify the calculation conditions. The purpose of constructing the dynamic security status notarized data package is to create indivisible evidentiary units for a personalized dynamic risk assessment, thus solidifying and structuring the assessment evidence.

[0106] S7.2 Perform a hash operation on the dynamic security status evidence data packet to generate a unique digest value for the dynamic security status evidence data packet.

[0107] Furthermore, a cryptographic hash function is a one-way mathematical function that maps input data of arbitrary length to a fixed-length output. Dynamic security state evidence storage data packets serve as input data. The hash is fed into the cryptographic hash function Hash(·) for calculation. The result is a fixed-length, seemingly random hexadecimal string, which is the unique digest value of the dynamic security state evidence data packet. Unique digest values ​​are collision resistant, meaning it is virtually impossible to find two different dynamic security state evidence data packets that produce the same unique digest value. Even a tiny modification to any bit of data in a dynamic security state evidence data packet will cause a large and unpredictable change in the calculated unique digest value. Therefore, unique digest values ​​serve as highly reliable digital fingerprints for dynamic security state evidence data packets.

[0108] Specifically, this involves using cryptographic hash functions to generate unique and verifiable digital fingerprints for dynamic evidence packets, achieving efficient, secure, and verifiable evidence storage. The structured dynamic security state evidence storage data packets are compressed into a short, unique digest value, which is then endowed with two key attributes. The first is an integrity verification attribute; any infringement on the original data packet... Any tampering will result in a recalculated hash value that differs from the already stored unique digest value. The first is a mismatch, which is immediately detected. The second is the loss of privacy, as hashing is one-way, from the unique digest value... It is impossible to deduce the original personalized dynamic risk value, usage time, and other sensitive information.

[0109] The unique digest expression is: ; in, The unique digest value of the dynamic security state evidence data packet. For cryptographic hash functions, Data packets for storing dynamic security status.

[0110] S7.3 Submit the unique digest value of the dynamic security status storage data packet to the blockchain network to generate a blockchain transaction record.

[0111] Furthermore, the unique digest value Hdigest is used as the data payload of a transaction to be recorded, and encoded and signed according to the transaction format specified by the connected blockchain network. The encoded and signed transaction request is broadcast to the nodes of the blockchain network. Upon receiving this transaction request, the nodes verify it, including verifying the correctness of the transaction format and the validity of the signature. After successful verification, the transaction request is placed in the pending transaction pool, awaiting packaging by the verified nodes. The transaction is successfully packaged into a candidate block, generating a blockchain transaction record containing the unique digest value Hdigest. This blockchain transaction record is the blockchain network's preliminary confirmation of the fact that at a certain moment, someone submitted a unique digest value Hdigest representing a dynamic security state evidence data packet.

[0112] Specifically, the evidence fingerprint (unique digest value) of dynamic risk assessment is transformed into a publicly verifiable, timestamped transaction event on a distributed ledger, thus imprinting the evidence with an immutable time stamp. Submitting the unique digest value to the blockchain network to generate a transaction record utilizes the blockchain's public submission and joint witnessing mechanism. This initiates a public event requiring network consensus confirmation. The existence and submission time of this unique digest value (Hdigest) will be jointly witnessed and initially recorded by the distributed nodes of the entire blockchain network. This transaction record itself contains a timestamp, providing third-party (network) proof of the occurrence of the evidence preservation act. For example, when a dispute arises between a company and a consumer regarding the safety of a product on a specific date, the blockchain can be queried to see if a transaction record containing the corresponding unique digest value exists on that day. If so, it proves that an assessment evidence was indeed generated at that time, and its evidence fingerprint has been submitted to the network for witnessing.

[0113] S7.4, Blockchain Network, reaches consensus on blockchain transaction records with unique digest values ​​and writes them into new blocks.

[0114] Furthermore, validator nodes in the blockchain network select blockchain transaction records containing unique digest values ​​from the transaction pool, package them together with other transactions awaiting confirmation, and form a candidate new block. The blockchain network verifies and competes for the validity of this candidate new block according to its consensus mechanism, such as proof-of-work or proof-of-stake. Once a node successfully solves the challenge posed by the consensus mechanism or obtains enough votes, its packaged candidate new block is accepted by a majority of nodes in the network and is considered valid. Reaching consensus means that the network agrees that the blockchain transaction record containing the unique digest value, as well as other transactions within the same block, are legitimate and should be permanently recorded. This consensus-reached new block is linked to the existing blockchain, becoming part of the blockchain's immutable history. The blockchain transaction record containing the unique digest value written into the new block is permanently and immutably recorded in the distributed ledger, which anyone can query and verify, but cannot delete or modify.

[0115] Specifically, through the consensus mechanism of blockchain, transaction records containing evidentiary fingerprints are permanently solidified into an immutable, linearly growing chain data structure, thus completing the qualitative change from submitting a declaration to permanent notarization. Reaching consensus on transaction records and writing them into new blocks utilizes the game and cooperation among distributed nodes to achieve immutability of historical records—a process that requires no central authority but is extremely costly. After being written into a new block, the record containing the unique digest value is no longer isolated; it becomes a link in a continuously growing chain linked by cryptographic hashes and maintained by countless copies worldwide. Tampering with this record would require attacking a significant portion of the network's computing power or stake, which is virtually impossible in practice. For example, even the company that produces the children's product cannot later deny or modify a unique digest value submitted at a certain time to prove its product's high risk, because the record has been permanently written into the chain through consensus.

[0116] This embodiment also provides a computer device applicable to the method for storing data on limits of inhaled chemical stressors in children's products, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for storing data on limits of inhaled chemical stressors in children's products as proposed in the above embodiment.

[0117] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0118] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for storing data on limits of inhaled chemical stressors in children's products as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0119] In summary, this invention conducts time-series release tests on children's product samples to obtain and document characteristic release kinetic parameters reflecting the decay of chemical release over time. Based on the correspondence between material properties and children's contact behavior patterns, a multi-scenario bioaccessibility correction factor mapping library is constructed. Sensor modules integrated into the children's products automatically identify the current usage scenario and obtain the elapsed usage time. A theoretical release assessment is then derived based on the evolution of characteristic release kinetic parameters and elapsed usage time over time. Next, corresponding bioaccessibility correction factors are retrieved from the mapping library based on the current usage scenario, and the effectiveness of this theoretical release assessment is corrected at the exposure scenario level. Finally, personalized dynamic risk values ​​are generated and documented by integrating the time decay and scenario exposure dimensions. By quantifying release kinetic laws and scenario-based exposure efficiency, continuous assessment and reliable documentation of personalized dynamic chemical exposure risks for children's products throughout their entire lifecycle and for different usage scenarios are achieved, solving the problems of static and singular risk assessment in existing technologies.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for data authentication of the limitation of inhalation chemical stressors for children's products, characterized in that: include, Time-series release tests were conducted on samples of children's products to obtain characteristic release kinetic parameters and preserve evidence. Based on the correspondence between the chemical composition and physical structure of children's products and the preset contact behavior patterns of children, a multi-scenario biological accessibility modification factor mapping library is constructed. By identifying the current usage scenario of children's products through sensor modules integrated into them, the usage time of the children's products can be obtained; Based on the release law characterized by the characteristic release kinetic parameters and the decay process characterized by the time used, a time-related theoretical release assessment is obtained by evolving in the time dimension. Based on the current usage scenario identifier mapped to the corresponding biological accessibility correction factor in the multi-scenario biological accessibility correction factor mapping library, the exposure efficacy of the time-related theoretical release assessment is corrected in the exposure scenario dimension. The time decay dimension and the scenario exposure dimension are integrated to generate a personalized dynamic risk value, and the personalized dynamic risk value is stored as evidence.

2. The method for storing data on the limits of inhaled chemical stressors in children's products as described in claim 1, characterized in that: The specific steps for releasing the characteristic dynamic parameters and storing the evidence are as follows: Samples of children's products were continuously exposed under constant environmental conditions, and gas samples were collected at multiple non-equidistant time points to obtain time-series release concentration data. A first-order kinetic decay model was applied to the time-series release concentration data for nonlinear fitting to extract characteristic release kinetic parameters; The feature release kinetic parameters, the identifier of the first-order kinetic decay model used for fitting, and the metadata of the test environment conditions are associated and bound with the unique digital identity code of the children's product to form a data package containing the feature release kinetic parameters.

3. The method for storing data on the limits of inhaled chemical stressors in children's products as described in claim 2, characterized in that: The specific steps for constructing the multi-scenario biological accessibility correction factor mapping library are as follows: Based on the pre-set children's contact behavior patterns, prepare corresponding simulated migration solutions; Samples of children's products with defined chemical composition and physical structure were immersed in a simulated migration solution corresponding to children's contact behavior patterns and extracted under specified conditions. The concentration of the target chemical stressor in the simulated migration solution after extraction was determined, and the migration rate of chemical substances in the material samples under specific children's exposure behavior patterns was extracted. A mapping relationship was established between the chemical composition of materials, physical structure, children’s contact behavior patterns and chemical migration rate, and the chemical migration rate was defined as the biological accessibility modification factor. By collecting records of mapping relationships between material chemical composition, physical structure, children's contact behavior patterns, and biological accessibility modification factors, a multi-scenario biological accessibility modification factor mapping library is constructed.

4. The method for storing data on the limits of inhaled chemical stressors in children's products as described in claim 3, characterized in that: The specific steps for specifying the used time are as follows: Signals from a triaxial accelerometer, microphone, and contact sensor are collected to generate a sensor signal stream; The sensor signal stream is input into a pre-trained miniature machine learning model for classification, and the miniature machine learning model outputs a classification result for the current state of the children's products. The classification results are mapped to preset children's contact behavior pattern codes to generate current usage scenario identifiers for children's products; Record the initial timestamp of the first time the child product is activated by the user, and associate and store the initial timestamp with the child product's unique digital identity code; The usage time of children's products is calculated by the difference between the current time and the initial timestamp.

5. The method of claim 4, wherein the child product inhalation chemical stressor limit data attestation method is further characterized by: The specific steps for the theoretical release assessment are as follows: The feature release dynamics parameters are extracted from the evidence data packet, and the feature release dynamics parameters include the initial release strength parameter and the decay rate constant parameter; The total usage time of the children's product is calculated from the initial timestamp when it was first activated by the user to the current moment. Based on the initial release intensity parameter, decay rate constant parameter and the time used, the evolution process is performed according to the first-order kinetic decay law to obtain the evolution process result. Based on the results of the evolution process, a time-dependent theoretical release assessment for the current moment is generated.

6. The method of claim 5, wherein the child product inhalation chemical stressor limit data attestation method is further characterized by: The specific steps for correcting the exposure efficacy are as follows: Based on the current usage scenario identifier of children's products, the current usage scenario identifier corresponds to the preset children's contact behavior pattern; Based on the current usage scenario identifier, the corresponding biological accessibility correction factor is matched and extracted from the multi-scenario biological accessibility correction factor mapping library; The time-dependent theoretical release assessment reflects the decay state of a chemical substance based on characteristic release kinetic parameters over time. In terms of exposure scenarios, the efficacy of time-related theoretical release assessments was adjusted using bioaccessibility correction factors to generate scenario-based exposure levels. Bioaccessibility correction factors characterize the actual accessible proportion of chemicals under specific children’s exposure behavior patterns. The contextualized exposure level integrates the time decay process with the characteristics of the exposure scenario, characterizing the level of chemical exposure faced by children under a specific child exposure behavior pattern after the usage time has elapsed.

7. The method of claim 6, wherein the child product inhalation chemical stressor limit data attestation method is further characterized by: The specific steps for generating personalized dynamic risk values ​​through fusion are as follows: Obtain the scenario-based exposure level after exposure efficacy correction. The scenario-based exposure level integrates the time decay process and the characteristics of the exposure scenario to obtain the preset safety limit standard value of chemical stressors for children's products. Comparative analysis was conducted between the scenario-based exposure levels and the safety limit standards to obtain the comparative analysis results; Based on the comparative analysis of scenario-based exposure levels and safety limit standards, personalized dynamic risk values ​​are generated.

8. The method of claim 7, wherein the child product inhalation chemical stressor limit data attestation is further characterized by, The specific steps for storing the personalized dynamic risk value are as follows: The personalized dynamic risk value, the evidence hash of the feature release dynamic parameters, the used time and the current use scenario identifier are combined to form a dynamic security status evidence data package; Perform a hash operation on the dynamic security status evidence storage data packet to generate a unique digest value for the dynamic security status evidence storage data packet; The unique digest value of the dynamic security status storage data packet is submitted to the blockchain network to generate a blockchain transaction record; The blockchain network reaches a consensus on the blockchain transaction records of the unique digest value and writes them into a new block.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for storing data on the limits of inhaled chemical stressors in children's products as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the data storage method for limiting inhaled chemical stressors in children's products according to any one of claims 1 to 8.

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