Toy toxicity dynamic monitoring method and system

Abstract

The invention provides a toy toxicity dynamic monitoring method and system, and relates to the technical field of toy safety monitoring, and the key points of the technical scheme are as follows: obtaining preset part feature information; acquiring monitoring data; based on component feature information and use situation information representing the current use environment or use mode of the toy, risk weighted evaluation processing is carried out on the monitoring data of all the plastic material components, so that the risk levels of all the plastic material components are obtained; and generating a structured early warning report based on the risk level of each plastic material part. The toy toxicity dynamic monitoring method and system provided by the invention have the advantages that refined risk assessment can be carried out aiming at the aging state of the multi-material plastic part in the toy, and a structured early warning report is generated.

Description

Technical Field

[0001] The present application relates to the technical field of toy safety monitoring, and in particular to a method and system for dynamic monitoring of toy toxicity. Background Art

[0002] Toy manufacturers integrate IoT-connected monitoring modules into their children's toys to track the aging process of toy plastic materials throughout their lifecycle due to environmental factors (such as light, temperature, and humidity) and wear and tear. The data collected by the monitoring module is transmitted to a remote server via a wireless network. The server-side analysis system uses this data to assess the degree of material aging and predict the increased risk of the release of potentially toxic substances (such as degradation products of specific chemical additives or small molecules produced by the cracking of the material matrix itself) that may result from aging. When the assessed risk level reaches a preset threshold, the system issues an alert to the toy's manager.

[0003] Some toys on the market, particularly large modular toys or multi-functional activity centers, have complex structures, often composed of multiple different types of plastic components. For example, an outdoor children's playhouse might use high-density polyethylene (HDPE) for the main frame, polypropylene (PP) for the roof and wall panels, acrylonitrile butadiene styrene (ABS) for the window frames and doors, and polyvinyl chloride (PVC) for some soft protective edges or decorative elements. Each of these plastic materials has distinct physical and chemical properties, resulting in different degradation mechanisms, types of degradation products, and toxicity profiles during aging. To comprehensively and accurately monitor the overall safety of such complex toys, it is necessary to deploy a variety of sensors or integrated chemical indicators, such as optical color sensors, micro gas sensors, electrochemical sensors, or color-change test strips, on or within the various material components. These sensors or indicators, based on different principles, vary in their operating characteristics, including data output type, signal amplitude range, response time, sampling frequency requirements, power consumption, sensitivity to environmental factors, and cross-interference characteristics.

[0004] Because different components of large toys experience varying microenvironmental conditions during use, such as sunny and shady sides, frequently gripped and infrequently touched areas, and parts near water or susceptible to detergents, these differences can lead to asynchronous and non-uniform aging rates and toxicity release patterns among different plastic components of the same toy. The toy's built-in IoT monitoring module uniformly collects raw data from all these heterogeneous sensors or indicators. This data may undergo preliminary local processing before being uploaded to a remote cloud platform. The cloud platform's analytical algorithms parse and standardize this multi-source, heterogeneous data. Based on pre-defined material aging models and toxicology databases, they convert the sensor's physical or chemical signals into a quantitative assessment of each component's aging degree and potential toxicity risk.

[0005] When conducting comprehensive risk assessments and early warning decisions, remote analysis systems must not only independently assess the risk profile of each component but also consider the potential interactions between aging components and the synergistic toxic effects of multiple low-dose toxic substances. When one or more specific toy components age faster than others, or release potentially toxic substances at a higher risk level than other components, the early warning system must be able to accurately identify these critical components as the "weak link" in the toy's overall safety.

[0006] However, some existing remote monitoring and early warning methods, when processing complex monitoring data from toys with multiple components and materials, may employ simple risk superposition models or make overall risk assessments based on a single indicator of the worst component. This approach may not fully reflect the varying weights of different components in terms of overall functional safety, frequency and mode of child contact, and potential harmful consequences. If the early warning system simply provides a single, general conclusion that "the toy is aged and presents a risk," users will struggle to determine the severity and specific location of the problem. For example, the risk of aging a small, non-load-bearing decorative component is significantly different from the risk of aging and failure of a load-bearing structural component. A simple, generalized early warning could lead to the premature disposal of toys that are still largely structurally safe and reliable, resulting in a waste of resources. Alternatively, users may be unable to implement effective localized avoidance or remediation measures due to a lack of clarity about the specific risk points. Consequently, the generated early warning information may lack sufficient specificity and hierarchy, failing to provide users with a clear and accurate picture of the specific safety status of each component and differentiated action priority recommendations based on component importance and risk level. This makes it difficult for users to make the most appropriate and cost-effective response decisions when faced with an early warning.

[0007] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0008] In order to address the deficiencies of the prior art, the present application provides a method and system for dynamic monitoring of toy toxicity, which has the advantages of being able to perform refined risk assessment on the aging status of multi-material plastic components in toys and generate structured early warning reports.

[0009] This application provides a method for dynamic monitoring of toy toxicity, the technical points of which are:

[0010] Obtaining preset component characteristic information, wherein the component characteristic information represents the material type, functional properties, and inherent importance level of each plastic material component in the toy;

[0011] Acquiring monitoring data, where the monitoring data is collected by sensors deployed for each of the plastic material components and reflects a current aging state of each of the plastic material components;

[0012] performing a risk-weighted assessment on the monitoring data of each of the plastic material components based on the component characteristic information and usage context information representing a current usage environment or usage mode of the toy to obtain a risk level for each of the plastic material components;

[0013] Based on the risk level of each plastic material component, a structured early warning report is generated, which contains at least location information, current aging state description, risk level and disposal suggestions for the plastic material component that reaches a preset risk threshold.

[0014] Through the above solution, a comprehensive risk assessment can be performed based on component feature information, monitoring data, and usage scenario information, thereby achieving differentiated risk identification of the aging status of different plastic material components in toys, and improving the accuracy and pertinence of risk warnings.

[0015] To further solve the problem, this application also proposes:

[0016] When the monitoring data of a certain plastic material component indicates multiple aging and degradation manifestations, the steps of performing a risk-weighted assessment on the monitoring data of each plastic material component based on the component characteristic information and usage context information representing the current usage environment or usage mode of the toy to obtain a risk level for each plastic material component include:

[0017] Obtaining the core functional attributes and importance levels preset for the plastic material component in the component characteristic information;

[0018] For each of the multiple aging degradation manifestations, determining a risk impact parameter associated with the aging degradation manifestation based on the core functional attribute and the importance level, wherein the risk impact parameter represents a potential impact of the aging degradation manifestation on the core functional attribute;

[0019] In combination with the quantitative values ​​of each aging and degradation manifestation indicated by the monitoring data, the risk impact parameters corresponding to each aging and degradation manifestation, the component feature information and the usage scenario information, a risk-weighted assessment is performed on the plastic material component to calculate the risk level of the plastic material component.

[0020] Through the above solution, when there are multiple aging degradation manifestations, differentiated risk impact analysis can be performed on each aging manifestation in combination with the core functional attributes and importance level of the component, thereby improving the accuracy of risk assessment.

[0021] To improve the solution, this application also proposes:

[0022] For each of the multiple aging degradation manifestations, determining the risk impact parameter associated with the aging degradation manifestation based on the core functional attribute and the importance level, wherein the risk impact parameter represents the potential impact of the aging degradation manifestation on the core functional attribute, includes:

[0023] Obtaining association effect information, wherein the association effect information represents the synergistic or antagonistic effects between the multiple aging degradation manifestations on the core functional attributes;

[0024] For each of the multiple aging degradation manifestations, assessing the potential impact of the aging degradation manifestation on the core functional attribute based on the core functional attribute and the importance level, and obtaining a basic impact assessment result;

[0025] Determining whether the aging-related degeneration manifestation belongs to at least one group of aging-related degeneration manifestations that are indicated by the association effect information to have synergistic or antagonistic effects;

[0026] If so, adjusting the risk impact parameter of the aging degradation manifestation based on the basic impact assessment result and in combination with the associated effect information so that the risk impact parameter reflects the potential impact of the synergistic effect or the antagonistic effect on the core functional attribute;

[0027] If not, the basic impact assessment result is the risk impact parameter of the aging degradation performance.

[0028] Through the above scheme, the interaction between various aging degradation manifestations can be considered, further improving the scientificity and rationality of risk impact parameter assessment.

[0029] To improve the design, this application also proposes:

[0030] The step of obtaining association effect information, wherein the association effect information characterizes the synergistic or antagonistic effects of the multiple aging degradation manifestations on the core functional attributes, comprises:

[0031] Acquiring environmental parameters representing the actual current use environment of the toy;

[0032] Obtaining preset baseline correlation effect information, where the baseline correlation effect information represents the synergistic or antagonistic effects between the multiple aging degradation manifestations under a preset reference environment;

[0033] Based on the difference between the obtained environmental parameters and the preset reference environment, and applying the preset adjustment logic to the baseline correlation effect information, the correlation effect information is generated to characterize the dynamic impact of the actual usage environment on the correlation effects between the multiple aging and degradation manifestations. The correlation effect information characterizes the synergistic or antagonistic effects between the multiple aging and degradation manifestations on the core functional attributes.

[0034] Through the above solution, the correlation effect information can be dynamically adjusted according to the actual usage environment, making the risk assessment closer to the real usage scenario.

[0035] To improve the design, this application also proposes:

[0036] The steps of generating, based on the difference between the obtained environmental parameters and the preset reference environment and applying preset adjustment logic to the baseline correlation effect information, the correlation effect information capable of characterizing the dynamic impact of the actual usage environment on the correlation effects between the multiple aging and degradation manifestations, wherein the correlation effect information characterizes the synergistic or antagonistic effects between the multiple aging and degradation manifestations on the core functional attributes include:

[0037] Acquire a difference characteristic parameter, where the difference characteristic parameter represents a difference between the environmental parameter and the preset reference environment;

[0038] Acquiring a baseline information characteristic parameter, wherein the baseline information characteristic parameter represents a specific attribute of the baseline correlation effect information;

[0039] Based on the difference characteristic parameter and the reference information characteristic parameter, selecting a target adjustment logic corresponding to a specific range of the difference characteristic parameter and a specific category of the reference information characteristic parameter from a plurality of preset adjustment logics;

[0040] Applying the target adjustment logic to the baseline association effect information generates the association effect information that can characterize the dynamic impact of the actual usage environment on the association effects between the multiple aging and degradation manifestations, and the association effect information characterizes the synergistic or antagonistic effects between the multiple aging and degradation manifestations on the core functional attributes.

[0041] Through the above solution, it is possible to achieve intelligent selection of adjustment logic and improve the adaptability and flexibility of the generation of correlation effect information.

[0042] To improve the design, this application also proposes:

[0043] The step of selecting, based on the difference characteristic parameter and the reference information characteristic parameter, a target adjustment logic corresponding to a specific range of the difference characteristic parameter and a specific category of the reference information characteristic parameter from a plurality of preset adjustment logics comprises:

[0044] Obtaining an applicable condition for each of the preset adjustment logics, the applicable condition including a preset range of the difference characteristic parameter and a preset category of the reference information characteristic parameter corresponding to the adjustment logic;

[0045] For the currently acquired difference characteristic parameters and the reference information characteristic parameters, evaluating their compliance with the applicable conditions of each preset adjustment logic;

[0046] Based on the degree of compliance, the target adjustment logic is determined from the plurality of preset adjustment logics.

[0047] Through the above solution, the most appropriate adjustment logic can be accurately selected according to the matching between the current environmental characteristics and the preset conditions, thereby improving the accuracy of risk assessment.

[0048] To improve the design, this application also proposes:

[0049] The step of evaluating the degree of compliance of the currently acquired difference characteristic parameters and the reference information characteristic parameters with the applicable conditions of each preset adjustment logic includes:

[0050] For each of the preset adjustment logics, calculating a first matching degree between the currently acquired difference characteristic parameter and a preset range of the difference characteristic parameter in the applicable condition of the adjustment logic;

[0051] For each of the preset adjustment logics, calculating a second matching degree between the currently acquired reference information characteristic parameter and a preset category of the reference information characteristic parameter in the applicable condition of the adjustment logic;

[0052] Based on the first matching degree and the second matching degree, the degree of compliance with the applicable condition of the preset adjustment logic is determined.

[0053] Through the above solution, the applicability of the adjustment logic can be objectively evaluated by quantifying the matching degree, thereby improving the intelligence level of the system.

[0054] To improve the design, this application also proposes:

[0055] The step of determining the degree of compliance with the applicable condition of the preset adjustment logic based on the first matching degree and the second matching degree includes:

[0056] Obtaining type information, current aging stage information, and current usage context information of the toy's current components;

[0057] Based on the type information, the aging stage information, and the usage context information, selecting a target weight set corresponding to the current condition from a preset weight set, the target weight set including the weight of the first matching degree and the weight of the second matching degree;

[0058] The target weight set is applied to perform a weighted combination on the first matching degree and the second matching degree to determine the degree of compliance with the applicable condition of the preset adjustment logic.

[0059] Through the above solution, the matching weight can be dynamically adjusted according to the current status of different components, thereby improving the system's adaptability to complex usage scenarios.

[0060] To improve the design, this application also proposes:

[0061] The step of applying the target weight set to perform weighted combination on the first matching degree and the second matching degree includes:

[0062] determining whether either the first matching degree or the second matching degree reaches a preset critical threshold;

[0063] If either the first matching degree or the second matching degree reaches or exceeds the preset critical threshold, adjusting the corresponding weight in the target weight set or the logic of the weighted combination according to the reaching or exceeding of the critical threshold;

[0064] The adjusted target weight set or the adjusted weighted combination logic is applied to perform weighted combination on the first matching degree and the second matching degree to determine the degree of compliance with the applicable condition of the preset adjustment logic.

[0065] Through the above solution, adaptive adjustments can be made when key parameters are abnormal, thereby improving the robustness of the system and the reliability of early warning.

[0066] To improve the design, this application also proposes:

[0067] A dynamic toy toxicity monitoring system is used to provide detailed information on the safety status of various toy components and provide guidance on their disposal. The system includes:

[0068] A component characteristic information acquisition module, configured to acquire preset component characteristic information, wherein the component characteristic information represents the material type, functional properties, and inherent importance level of each plastic material component in the toy;

[0069] a monitoring data acquisition module, configured to acquire monitoring data, wherein the monitoring data is collected by sensors deployed for each of the plastic material components and reflects the current aging state of each of the plastic material components;

[0070] a risk weighted assessment module configured to perform a risk weighted assessment on the monitoring data of each of the plastic material components based on the component characteristic information and usage context information characterizing a current usage environment or usage mode of the toy, so as to obtain a risk level for each of the plastic material components;

[0071] An early warning report generation module is used to generate a structured early warning report based on the risk level of each of the plastic material components, wherein the structured early warning report includes at least the location information, current aging status description, risk level and disposal suggestions for the plastic material components that reach a preset risk threshold.

[0072] Through the above solution, a complete monitoring and early warning system can be built to achieve real-time dynamic monitoring of the aging status of various plastic material components in toys and present structured risks.

[0073] In summary, the dynamic monitoring method and system for toy toxicity provided in this application can achieve differentiated identification and early warning of the aging status of different plastic material components in toys by combining component feature information, monitoring data, and usage context information for multi-dimensional risk assessment, thereby improving the accuracy of risk warnings and enhancing users' decision-making capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of a dynamic monitoring method for toy toxicity provided in this application.

[0075] Figure 2 This is a schematic diagram of the structure of a toy toxicity dynamic monitoring system provided in this application.

[0076] Figure 3 This is a schematic diagram of the structure of a toy toxicity dynamic monitoring system provided in this application.

[0077] In the figure: 210, component feature information acquisition module; 220, monitoring data acquisition module; 230, risk weighted assessment module; 240, early warning report generation module. DETAILED DESCRIPTION

[0078] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0079] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0080] Traditional existing toy toxicity monitoring technologies typically employ a single, holistic risk assessment approach, which is unable to differentiate the aging status of different materials and functional components within a toy. This is particularly true for large modular toys or multi-functional activity centers, which are made of a variety of plastic materials, such as high-density polyethylene, polypropylene, acrylonitrile butadiene styrene copolymer, and polyvinyl chloride. These materials exhibit different aging behaviors during use due to factors such as light, temperature, humidity, and wear and tear, and may release potentially toxic substances of varying types and concentrations. To dynamically monitor the aging of these materials, existing technologies often employ the deployment of sensors to collect data reflecting the aging status of each component and assess the overall risk based on this data. However, due to the varying importance of various toy components in their structural functions, the frequency and manner of contact by children, and the differences in the microenvironmental conditions in which they are located, significant differences exist in the aging rates and toxicity release patterns of each component, and existing assessment methods lack a comprehensive mechanism for considering these differences.

[0081] For example, in an outdoor children's playhouse application scenario, the main frame is made of high-density polyethylene, the roof and wall panels are made of polypropylene, the window frames and doors are made of acrylonitrile butadiene styrene copolymer, and the soft protective edges are made of polyvinyl chloride. Different types of sensors, including optical color sensors, micro gas sensors, and electrochemical sensors, are deployed on each component to collect data reflecting the aging status of the materials. However, due to differences in output signal types, response times, sampling frequencies, power consumption levels, and environmental sensitivity among different sensors, the collected monitoring data exhibits multi-source heterogeneity. Furthermore, the different microenvironments of each component, such as sunny and shady sides, and frequently grasped and non-contact areas, lead to inconsistent aging rates and toxicity release patterns. When the system performs risk assessment on this data, using a unified risk assessment model will not accurately reflect the true risk level of each component.

[0082] If these issues are not addressed, risk assessment results will lack specificity and hierarchy, making it impossible to accurately identify key risk components, thus impacting the effectiveness of early warning information. For example, the aging of a non-load-bearing decorative component may be given the same weight as the aging of a load-bearing structural component, making it difficult for users to judge the severity and specific location of the risk. This situation can lead to two adverse consequences: on the one hand, users may prematurely discard toys that are still safe to use due to misjudging the overall risk, resulting in a waste of resources; on the other hand, due to a lack of clear guidance, users may be unable to take effective local repair or avoidance measures in a timely manner, thereby increasing the risk of children being exposed to toxic substances.

[0083] In this regard, refer to Figures 1 to 3 , this application proposes the following technical solutions:

[0084] A dynamic monitoring method for toy toxicity, comprising:

[0085] S110, obtaining preset component characteristic information, where the component characteristic information represents the material type, functional properties, and inherent importance level of each plastic material component in the toy;

[0086] S120: Acquire monitoring data, where the monitoring data is collected by sensors deployed for each plastic material component and reflects the current aging state of each plastic material component;

[0087] S130, performing a risk-weighted assessment on the monitoring data of each plastic material component based on the component characteristic information and the usage context information representing the current usage environment or usage mode of the toy to obtain a risk level for each plastic material component;

[0088] S140. Generate a structured warning report based on the risk level of each plastic material component. The structured warning report includes at least location information, current aging status description, risk level, and disposal suggestions for the plastic material component that reaches a preset risk threshold.

[0089] Component characteristic information refers to a data set describing the material type, functional properties, and inherent importance level of each plastic component in a toy. This information can be implemented as a structured data table or database record, for example, through pre-defined material classification codes, functional labels, and importance rating systems. Its primary purpose is to differentiate the role and risk weight of different components within the overall toy structure. Monitoring data refers to raw data collected by sensors deployed on each plastic component, reflecting its current aging state. This information can be implemented using various sensor output signals, such as color change data collected by optical sensors, material degradation product concentration data collected by electrochemical sensors, and environmental aging impact data collected by temperature and humidity sensors. It is primarily used to obtain real-time information on the aging status of each component. Risk-weighted assessment processing refers to the process of comprehensively analyzing the monitoring data of each component and calculating its risk level based on component characteristic information and usage context. This process can be implemented using a multi-factor weighted scoring model, fuzzy comprehensive evaluation method, or neural network model. For example, a baseline risk factor is set based on material type, risk weights are adjusted based on functional properties, and assessment results are dynamically modified based on usage environment parameters. This is primarily intended to achieve differentiated risk assessments for different components. A structured early warning report refers to a report document presented in a standardized format that contains the location information of risk components, a description of the aging status, the risk level, and disposal recommendations. It can be implemented in the form of a combination of tables, graphic symbols, and text descriptions. For example, the location of high-risk components can be marked on a map, risk levels can be distinguished by color, and corresponding maintenance or replacement recommendations can be provided. Its main purpose is to enable users to clearly understand the safety status of each component and take targeted measures.

[0090] The core innovation of this application lies in that by combining component feature information with usage context information and performing risk-weighted assessment processing based on monitoring data, it achieves a refined risk assessment of each plastic material component in the toy, and provides a structured early warning report to support users in making differentiated disposal decisions.

[0091] Specifically, the technical solution of the present application first establishes a basic weight system for subsequent risk assessment by obtaining information on the material type, functional properties and importance level of each plastic material component. Subsequently, the monitoring data of the current aging status of each component is collected through sensors deployed on each component as a dynamic input for risk assessment. On this basis, the monitoring data of each component is weighted in combination with the current use environment and usage method of the toy, and the risk level of each component is calculated. Finally, a structured early warning report is generated according to the risk level. The report clearly indicates the location, aging status, risk level and disposal recommendations of the components that reach the preset risk threshold, so that users can quickly identify high-risk components and take corresponding measures. This technical solution realizes a dynamic and accurate assessment of the aging status and toxicity risks of various toy components by comprehensively considering the inherent properties of the components and external usage conditions.

[0092] As a preferred embodiment, refer to Figure 3 The technical solution of this application is specifically implemented as follows: Taking the illustrated children's playhouse as a typical application scenario, optical sensors (to monitor color changes), electrochemical sensors (to detect degradation product concentrations), and temperature and humidity sensors (to obtain environmental aging parameters) are deployed on different plastic material components such as its high-density polyethylene main frame, polypropylene roof wall panels, ABS window frames and doors, and PVC protective edges to form a distributed heterogeneous monitoring network. The collected multi-source monitoring data is transmitted to the cloud platform in real time in the form of wireless signals through the Internet of Things communication module with an integrated antenna. After being received and stored by a high-performance server cluster, it is sent to the data analysis engine for preprocessing and feature extraction. The core risk assessment algorithm module is based on the pre-stored component feature information combined with the real-time sensed temperature The system takes environmental influencing factors such as temperature change, humidity change, light intensity and wear and tear into consideration, conducts correlation effect analysis on the various aging and degradation manifestations of each component and dynamically adjusts the risk impact parameters. Through intelligent weighted calculation, it obtains accurate component-level risk levels, and finally generates a structured early warning report containing location identification of high-risk components, description of aging status and differentiated disposal suggestions. The report is transmitted to user terminal devices such as mobile phones and tablets via the data flow path, providing toy managers with visual presentation of safety status and intelligent early warning push, thus building a complete technical implementation system from distributed monitoring at the perception layer, reliable transmission at the network layer, intelligent analysis at the platform layer to accurate early warning at the application layer, realizing the transition of safety monitoring of complex multi-material toys from extensive overall assessment to refined component-level intelligent early warning.

[0093] Through the above-mentioned solution, this application achieves dynamic monitoring and refined early warning of the aging status and potential toxicity risks of various plastic material components in toys. This technical solution solves the problem of the lack of targeted early warning information caused by the existing technology that relies on a single indicator or a simple superposition method for overall risk assessment. It improves the accuracy and practicality of risk assessment, allowing users to quickly identify key risk components based on structured early warning reports and take appropriate measures, thereby ensuring child safety and extending the life of toys.

[0094] In some of the aforementioned embodiments of this application, a method is proposed for performing a risk-weighted assessment of monitoring data for plastic material components based on component feature information and usage context information to determine a risk level. Specifically, this method can be performed by uniformly processing monitoring data for multiple aging characteristics, such as by weighted averaging, linear superposition, or normalization, to comprehensively assess the overall risk level of the plastic material component. This allows for a unified risk assessment of different plastic components in a toy. However, during this implementation, the same component may exhibit multiple aging degradation characteristics. Simply superimposing or averaging these aging characteristics may mask key risk factors, resulting in inaccurate assessment results.

[0095] In this regard, the present application further proposes that, when the monitoring data of a plastic material component indicates multiple signs of aging and degradation, the monitoring data of each plastic material component is subjected to a risk-weighted assessment based on the component characteristic information and usage context information characterizing the current usage environment or usage mode of the toy to obtain a risk level for each plastic material component, including the following steps:

[0096] Obtaining the core functional attributes and importance levels preset for the plastic material component in the component feature information;

[0097] For each of the multiple aging degradation manifestations, determine a risk impact parameter associated with the aging degradation manifestation based on the core functional attributes and importance level, where the risk impact parameter represents the potential impact of the aging degradation manifestation on the core functional attributes;

[0098] Based on the quantitative values ​​of each aging and degradation manifestation indicated by the monitoring data, the risk impact parameters corresponding to each aging and degradation manifestation, component feature information and usage scenario information, a risk-weighted assessment is performed on the plastic material component to calculate the risk level of the plastic material component.

[0099] The core functional attribute refers to the critical function of the plastic component within the toy, which can be achieved through physical functional attributes such as load-bearing capacity, connection stability, and motion control. The importance level refers to the importance of the plastic component within the overall toy structure, which can be categorized as high, medium, or low. The risk impact parameter refers to the degree of damage that a specific aging degradation phenomenon may cause to the core functional attributes of the component, which can be quantitatively measured using numerical weights, impact coefficients, or impact factors.

[0100] In this technical solution, the core functional attributes and importance levels of plastic material components are first obtained as the basis for risk assessment. Next, for each aging degradation manifestation, the corresponding risk impact parameter is determined based on the core functional attributes and importance level, thereby linking the aging manifestation to the component function. Finally, a risk-weighted assessment is performed by combining the aging quantitative values, risk impact parameters, component characteristics, and usage context information from the monitoring data to obtain a more accurate risk level. By introducing functional attributes and importance levels, this solution allows the contribution of different aging manifestations to the overall risk of the component to be differentiated, avoiding the errors caused by simple superposition or averaging in traditional methods.

[0101] During the specific implementation process, the solution first obtains the core functional attributes and importance levels of plastic material components from the database. For example, a plastic component used to support the structure of a toy has its core functional attribute as load-bearing capacity and its importance level as high. Subsequently, for the various aging manifestations detected on the component, such as surface cracks, color changes, material embrittlement, etc., the corresponding risk impact parameters are calculated respectively. For example, surface cracks have a greater impact on load-bearing capacity, and its risk impact parameter is higher; while color changes have a smaller impact on load-bearing capacity, and its risk impact parameter is lower. Finally, the quantitative value of each aging manifestation is multiplied by the corresponding risk impact parameter, and combined with the component feature information and usage scenario information (such as whether the component is often touched by children, whether it is in a high temperature and high humidity environment, etc.), a weighted calculation is performed to obtain the risk level of the component.

[0102] This technical solution, by introducing core functional attributes and importance levels, distinguishes the contribution of different aging behaviors to a component's overall risk, thereby improving the accuracy of risk assessment. Furthermore, by incorporating usage context information, the assessment results are more closely aligned with actual usage conditions, enhancing the relevance and practicality of early warnings. This method effectively identifies high-risk aging components in toys, avoiding risk misjudgments caused by simple aggregation or averaging, thereby ensuring child safety and extending the effective lifespan of toys.

[0103] In the dynamic monitoring method for toy toxicity, when conducting risk assessments on various aging and degradation manifestations of plastic material components, it is necessary to consider the impact of the interaction between different aging manifestations on the core functional properties. When the monitoring data of a plastic material component indicates multiple aging and degradation manifestations, the risk impact parameters need to be determined based on the core functional properties and importance levels in the component characteristic information. However, there may be synergistic or antagonistic effects between the various aging and degradation manifestations, and these correlation effects will affect the stability of the core functional properties. If these correlation effects are ignored, it will be impossible to accurately assess the potential impact of aging and degradation manifestations on the core functional properties, which will cause the risk level assessment results to deviate from the actual situation.

[0104] In this regard, the present application further proposes to obtain association effect information, which characterizes the synergistic or antagonistic effects of multiple aging and degeneration manifestations on core functional attributes. For each aging and degeneration manifestation, its potential impact on the core functional attributes is evaluated based on the core functional attributes and importance level, and a basic impact assessment result is obtained to determine whether the aging and degeneration manifestation belongs to the aging and degeneration manifestation group indicated by the association effect information to have synergistic or antagonistic effects. If it does, the risk impact parameter is adjusted based on the basic impact assessment result combined with the association effect information; if not, the basic impact assessment result is directly used as the risk impact parameter.

[0105] Among them, correlation effect information is a correlation model established through analysis of experimental data or historical data, which is used to quantify the interaction strength between different aging degradation manifestations. For example, when a plastic component simultaneously develops surface cracks and color changes, the surface cracks may accelerate the migration rate of the color change products. This synergistic effect needs to be characterized by correlation effect information. When adjusting the risk impact parameters, if a synergistic effect is detected, the correction factor is added to the basic impact assessment results; if an antagonistic effect is detected, the correction factor is reduced accordingly. The value range of the correction factor is determined by the material type and the aging environment parameters.

[0106] In specific implementation, a correlation database containing various aging and degradation manifestations is first established to store the synergistic / antagonistic effect coefficients of different material types under different environmental conditions. After obtaining the monitoring data of a certain plastic component, the type of aging and degradation manifestation contained in it is identified, and the corresponding correlation effect information is retrieved from the database. For each aging and degradation manifestation, a basic impact assessment result is independently calculated. This result is based on the importance level of the core functional attributes and the quantitative value of the monitoring data. The correlation effect information is then used to determine whether there is an interaction. If so, the risk impact parameter is adjusted using a weighted superposition method, where the weight coefficient is determined according to the intensity of the interaction in the correlation database.

[0107] Taking load-bearing components made of polypropylene as an example, when surface cracks (manifestation A) and plasticizer migration (manifestation B) are detected simultaneously, the correlation database shows that there is a synergistic effect between the two manifestations. The basic impact assessment results show that the risk impact value of manifestation A is 0.6, and the risk impact value of manifestation B is 0.4. According to the synergy coefficient of 1.3 in the correlation effect information, the adjusted risk impact parameters are: manifestation A is adjusted to 0.6×1.3=0.78, and manifestation B is adjusted to 0.4×1.3=0.52. When the comprehensive risk level of the component is finally calculated, the adjusted risk impact parameters are weighted with the quantitative value of the monitoring data to more accurately reflect the actual risk level.

[0108] By incorporating information about associated effects and dynamically adjusting risk impact parameters, this approach can more accurately quantify the combined impact of multiple aging degradation manifestations. In monitoring the PVC protective edges of outdoor children's playhouses, when both UV degradation and mechanical wear occur simultaneously, this approach can identify the antagonistic effects between the two and reduce the risk impact parameters, avoiding the waste of resources caused by excessive warnings. Compared to traditional independent assessment methods, this technical solution improves the consistency between risk level assessment results and actual material performance degradation trends, providing a more reliable basis for decision-making regarding the safe maintenance of toys.

[0109] In some of the aforementioned embodiments of this application, it is proposed to obtain correlation effect information. This correlation effect information can specifically capture information characterizing the synergistic or antagonistic effects between multiple aging and degradation manifestations on core functional attributes. This allows for a more accurate assessment of component risk levels. However, during implementation, changes in environmental parameters in the actual usage environment can dynamically impact the correlation effects between aging and degradation manifestations. Utilizing pre-set baseline correlation effect information that does not consider environmental factors can result in risk assessment results that are inconsistent with actual conditions, reducing the accuracy of early warnings.

[0110] In this regard, the present application further proposes obtaining environmental parameters that characterize the actual current usage environment of the toy;

[0111] Obtaining preset baseline correlation effect information, where the baseline correlation effect information represents the synergistic or antagonistic effects between multiple aging degradation manifestations under a preset reference environment;

[0112] Based on the differences between the obtained environmental parameters and the preset reference environment, and applying the preset adjustment logic to the baseline correlation effect information, correlation effect information is generated that can characterize the dynamic impact of the actual usage environment on the correlation effects between multiple aging and degradation manifestations. The correlation effect information represents the synergistic or antagonistic effects between multiple aging and degradation manifestations on core functional attributes.

[0113] Environmental parameters refer to physical or chemical parameters that reflect the actual state of the toy's current usage environment. These parameters can be implemented using parameters such as temperature, humidity, light intensity, and air velocity. Baseline correlation effect information refers to pre-defined information characterizing the synergistic or antagonistic effects of various aging degradation manifestations on core functional attributes under a preset reference environment. This information can be implemented using correlation matrices derived from statistical analysis of historical data, empirical formulas, or rules from an expert knowledge base. Adjustment logic refers to an algorithm or set of rules used to dynamically adjust baseline correlation effect information based on the differences between environmental parameters and a preset reference environment. This information can be implemented using linear interpolation, fuzzy inference systems, neural network models, or rule-based mapping tables.

[0114] This solution obtains the environmental parameters of the current actual use environment, combines them with the preset baseline correlation effect information, and applies the preset adjustment logic to dynamically adjust the baseline correlation effect information based on the difference between the environmental parameters and the preset reference environment, thereby generating correlation effect information that can reflect the impact of the actual use environment. This correlation effect information can more accurately characterize the synergistic or antagonistic effects of various aging and degradation manifestations on core functional attributes under the current actual use environment, thereby improving the accuracy of risk assessment. Specifically, obtaining the environmental parameters of the actual use environment provides an environmental basis for the subsequent dynamic adjustment of the correlation effect information; obtaining the preset baseline correlation effect information provides basic data for the subsequent dynamic adjustment; based on the difference between the environmental parameters and the preset reference environment, and applying the preset adjustment logic to the baseline correlation effect information, generating correlation effect information that can characterize the dynamic impact of the actual use environment on the correlation effects between various aging and degradation manifestations, realizing dynamic adjustment of the correlation effect information, so that it can reflect the impact of the actual use environment, thereby improving the accuracy of risk assessment.

[0115] For example, in one specific embodiment, current environmental parameters such as temperature, humidity, and light intensity are first acquired through sensors deployed in the toy's surrounding environment. Then, preset baseline correlation effect information is retrieved from local memory. This information stores, in matrix form, the synergistic or antagonistic coefficients between different aging degradation manifestations under a standard laboratory environment. Next, the currently acquired environmental parameters are compared with those under a standard reference environment to calculate environmental difference characteristic parameters. Subsequently, corresponding adjustment logic is selected based on these difference characteristic parameters, such as using linear interpolation to perform weighted adjustments on the coefficients in the baseline correlation effect information, ultimately generating dynamic correlation effect information applicable to the current environmental conditions.

[0116] Through the above technical solution, the correlation effect information can be dynamically adjusted to more accurately reflect the impact of the actual usage environment on the correlation effects between various aging and degradation manifestations, thereby improving the accuracy of risk assessment, avoiding misjudgments due to environmental changes, and improving the reliability and applicability of the early warning system.

[0117] In some of the aforementioned embodiments of the present application, a method is proposed for generating correlation effect information that can characterize the dynamic impact of the actual use environment on the correlation effects between various aging and degradation manifestations by applying a preset adjustment logic to the baseline correlation effect information based on the differences between the obtained environmental parameters and the preset reference environment. Specifically, this method can be performed by analyzing the degree of deviation between the current environment parameters, such as temperature, humidity, and light intensity, and the standard laboratory environment, and selecting a universal adjustment coefficient to correct the baseline correlation effect information based on the known effects of these deviations on the material aging process. This method can, to a certain extent, reflect the synergistic or antagonistic effects of environmental changes on aging and degradation manifestations. However, in its implementation, relying solely on a single adjustment logic may result in insufficient adaptability to different material combinations and different aging stages, and may fail to accurately capture the nonlinear effects of environmental changes on the correlation effects, thereby affecting the accuracy of risk assessment.

[0118] In this regard, the present application further proposes generating correlation effect information that can characterize the dynamic impact of the actual usage environment on the correlation effects between multiple aging and degradation manifestations based on the differences between the acquired environmental parameters and the preset reference environment, and applying preset adjustment logic to the baseline correlation effect information. The steps of characterizing the synergistic or antagonistic effects between multiple aging and degradation manifestations on core functional attributes in the correlation effect information include:

[0119] Obtaining difference characteristic parameters, where the difference characteristic parameters represent the difference between the environmental parameters and the preset reference environment;

[0120] Obtaining baseline information characteristic parameters, where the baseline information characteristic parameters represent specific attributes of baseline correlation effect information;

[0121] Based on the difference feature parameter and the reference information feature parameter, selecting a target adjustment logic corresponding to a specific range of the difference feature parameter and a specific category of the reference information feature parameter from a plurality of preset adjustment logics;

[0122] The target adjustment logic is applied to the baseline association effect information to generate association effect information that can characterize the dynamic impact of the actual usage environment on the association effects between multiple aging and degradation manifestations. The association effect information characterizes the synergistic or antagonistic effects between multiple aging and degradation manifestations on core functional attributes.

[0123] Among them, the difference characteristic parameters refer to technical parameters used to quantify the difference between the actual use environment and the preset reference environment, which can be implemented in the form of environmental factor vectors, environmental deviation indexes, multidimensional environmental difference matrices, etc. The baseline information characteristic parameters refer to technical parameters used to describe the intrinsic properties of baseline correlation effect information, which can be implemented in the form of correlation effect intensity values, correlation effect type identifiers, correlation effect stability indicators, etc. The target adjustment logic refers to the algorithm or rule set for dynamically adjusting the correlation effect information based on the matching selection of difference characteristic parameters and baseline information characteristic parameters, which can be implemented in the form of linear weighted adjustment models, nonlinear function mapping adjustment models, dynamic adjustment models based on machine learning, etc.

[0124] This technical solution obtains difference characteristic parameters and baseline information characteristic parameters as the basis for selecting adjustment logic, thus avoiding the problem of blindly using a single adjustment logic in traditional methods. The difference characteristic parameters reflect the degree of deviation between the current environment and the standard environment, while the baseline information characteristic parameters reflect the characteristics of the current correlation effect information itself. Based on the combination of these two parameters, the system can select the most suitable one from multiple preset adjustment logics to ensure that the adjustment logic is adapted to the current environment and material status. Applying the selected target adjustment logic to the baseline correlation effect information can generate more accurate dynamic correlation effect information that can reflect the impact of the actual use environment, thereby improving the accuracy of risk assessment.

[0125] For example, in a specific embodiment, the system collects the current ambient temperature of 40°C, the humidity of 85%, and the ultraviolet intensity of 8W / m 2 , while the preset reference environment is 25°C, 60% humidity, and no UV exposure. The system calculates the differential characteristic parameter as a combination of high temperature, high humidity, and strong UV rays. At the same time, the system reads baseline correlation effect information indicating a moderate synergistic effect between two aging degradation manifestations. Based on these two parameters, the system selects a nonlinear adjustment model from multiple adjustment logics that enhances synergy in high temperature and high humidity environments. After applying this model, the system-generated correlation effect information shows an increase in synergy between the two aging degradation manifestations, thereby improving the corresponding risk assessment level.

[0126] This technical solution uses differential and baseline information characteristic parameters as the basis for selecting adjustment logic, making the selection of adjustment logic more targeted and adaptable, avoiding the assessment errors caused by environmental changes in traditional methods. By dynamically selecting the most suitable adjustment logic, the system can more accurately reflect the impact of the actual use environment on the correlation effect of aging and degradation, thereby improving the accuracy of risk assessment, enabling the dynamic toy toxicity monitoring system to more effectively identify key risk points, and enhancing the reliability and practicality of early warnings.

[0127] In some of the aforementioned embodiments of the present application, a method is proposed for selecting a target adjustment logic from a plurality of preset adjustment logics based on difference characteristic parameters and baseline information characteristic parameters. Specifically, the selection of the target adjustment logic based on the difference characteristic parameters and baseline information characteristic parameters can be performed by presetting applicable conditions for each adjustment logic, including a preset range for the difference characteristic parameters and a preset category for the baseline information characteristic parameters. The currently acquired parameters are then compared with these applicable conditions to select the most suitable adjustment logic. This can achieve automatic selection of the adjustment logic to a certain extent. However, during its implementation, ensuring that the selected target adjustment logic accurately reflects the dynamic impact of the correlation effect between the actual use environment and the aging degradation performance is a problem that needs to be solved when selecting the adjustment logic.

[0128] In this regard, the present application further proposes that, based on the difference feature parameter and the baseline information feature parameter, the step of selecting a target adjustment logic corresponding to a specific range of the difference feature parameter and a specific category of the baseline information feature parameter from a plurality of preset adjustment logics includes:

[0129] Obtaining applicable conditions for each preset adjustment logic, where the applicable conditions include a preset range of the difference characteristic parameter and a preset category of the reference information characteristic parameter corresponding to the adjustment logic;

[0130] For the currently acquired difference characteristic parameters and baseline information characteristic parameters, evaluate their compliance with the applicable conditions of each preset adjustment logic;

[0131] Based on the degree of compliance, a target adjustment logic is determined from a plurality of preset adjustment logics.

[0132] Applicable conditions refer to the parameter ranges and categories to which the preset adjustment logic applies. They are used to define the application boundaries of the adjustment logic and can be implemented using methods such as numerical intervals, classification labels, and rule sets. Conformance refers to the degree of match between the currently acquired parameters and the applicable conditions of a particular adjustment logic and can be implemented using methods such as distance measurement, similarity calculation, and fuzzy matching.

[0133] The overall operating mechanism of this solution is as follows: First, the system obtains the applicable conditions for each preset adjustment logic. These applicable conditions are specifically manifested as preset ranges for differential characteristic parameters and preset categories for baseline information characteristic parameters. By pre-defining the applicable range of each adjustment logic, a clear standard is provided for the subsequent selection process. Then, the system evaluates the degree of compliance of the currently obtained differential characteristic parameters and baseline information characteristic parameters with the applicable conditions of each preset adjustment logic. This step is key to selecting the target adjustment logic. By comparing the actual parameter characteristics with the applicable conditions of the adjustment logic, it can determine whether the adjustment logic is suitable for the current usage environment. Finally, based on the degree of compliance, the system determines the target adjustment logic from multiple preset adjustment logics. The higher the degree of compliance, the more suitable the adjustment logic is for the current usage environment. Therefore, by selecting the adjustment logic with the highest degree of compliance as the target adjustment logic, it ensures that the selected adjustment logic accurately reflects the dynamic impact of the actual usage environment on the correlation effect of aging degradation.

[0134] For example, in one specific embodiment, after obtaining the difference characteristic parameters between the current environmental parameters and the preset reference environment, as well as the baseline information characteristic parameters of the baseline association effect information, the system first reads the applicable conditions of all preset adjustment logics. The applicable conditions of each adjustment logic include its applicable difference characteristic parameter range (for example, the temperature difference is within ±5°C, the humidity difference is within ±10%) and the baseline information characteristic parameter category (for example, the synergistic effect intensity is high, medium or low). The system then compares the current difference characteristic parameters with the preset range of each adjustment logic to calculate a first degree of match; at the same time, it compares the current baseline information characteristic parameters with the preset category of each adjustment logic to calculate a second degree of match. Based on the comprehensive results of the two matching degrees, the system evaluates the applicability of the adjustment logic, and ultimately selects the adjustment logic with the highest degree of compliance to generate the association effect information.

[0135] Through this technical solution, the system can evaluate the degree of match between the currently acquired differential and baseline information characteristic parameters and various preset adjustment logics, and select the most appropriate adjustment logic from among them, thereby improving the accuracy of the selected adjustment logic. This solution solves the problem of ensuring that the selected target adjustment logic accurately reflects the dynamic impact of the correlation effect between the actual usage environment and aging degradation performance. This makes the generation of correlation effect information more closely aligned with real-world usage conditions, thereby improving the accuracy and reliability of the overall risk assessment.

[0136] In some of the aforementioned embodiments of the present application, a method is proposed for obtaining the applicable conditions for each preset adjustment logic, the applicable conditions including the preset range of the differential characteristic parameters and the preset category of the baseline information characteristic parameters corresponding to the adjustment logic; evaluating the degree of compliance of the currently acquired differential characteristic parameters and baseline information characteristic parameters with the applicable conditions for each preset adjustment logic; and determining a target adjustment logic scheme from multiple preset adjustment logics based on the degree of compliance. However, accurately evaluating the degree of compliance of the currently acquired differential characteristic parameters and baseline information characteristic parameters with the applicable conditions for each preset adjustment logic to ensure the selection of the most appropriate target adjustment logic is a key issue affecting the accuracy of the correlation effect information.

[0137] In this regard, the present application further proposes that the steps of evaluating the degree of compliance of the currently acquired difference feature parameters and baseline information feature parameters with the applicable conditions of each preset adjustment logic include:

[0138] For each preset adjustment logic, calculating a first matching degree between the currently acquired difference feature parameter and a preset range of the difference feature parameter in the applicable condition of the adjustment logic;

[0139] For each preset adjustment logic, calculating a second matching degree between the currently acquired reference information characteristic parameter and a preset category of the reference information characteristic parameter in the applicable condition of the adjustment logic;

[0140] Based on the first matching degree and the second matching degree, the compliance degree of the applicable condition of the preset adjustment logic is determined.

[0141] Among them, the difference characteristic parameter refers to the specific quantitative expression of the difference between the environmental parameter and the preset reference environment, which can be implemented by numerical parameters, classification parameters or a combination thereof. The baseline information characteristic parameter refers to the description of the specific attributes of the baseline correlation effect information, which can be implemented by feature vectors, category labels or a combination thereof. The first matching degree refers to the degree of matching between the currently acquired difference characteristic parameter and the preset range of the difference characteristic parameter in the applicable conditions of a certain adjustment logic, which can be implemented by distance calculation, similarity calculation or membership calculation. The second matching degree refers to the degree of matching between the currently acquired baseline information characteristic parameter and the preset category of the baseline information characteristic parameter in the applicable conditions of a certain adjustment logic, which can be implemented by classification matching, feature similarity or a combination thereof.

[0142] The technical solution of the present application calculates the first matching degree between the difference characteristic parameters and the preset range, and the second matching degree between the baseline information characteristic parameters and the preset category, and determines the degree of compliance with the applicable conditions of the adjustment logic based on the combination of the two, thereby more accurately evaluating the matching situation between the current conditions and the adjustment logic and improving the accuracy of the correlation effect information.

[0143] Specifically, this technical solution first calculates, for each preset adjustment logic, a first degree of match between the currently acquired differential characteristic parameter and the preset range of the differential characteristic parameter in the adjustment logic's applicable conditions. For example, if the differential characteristic parameter is a temperature difference, and the applicable range of an adjustment logic is ±5°C, and the currently acquired temperature difference is 3°C, the first degree of match can be calculated by calculating its membership or similarity within this range. Secondly, for each preset adjustment logic, a second degree of match is calculated between the currently acquired baseline information characteristic parameter and the preset category of the baseline information characteristic parameter in the adjustment logic's applicable conditions. For example, if the baseline information characteristic parameter is a type of aging degradation, if an adjustment logic is applicable to the "oxidative degradation" category and the currently acquired baseline information characteristic parameter is also "oxidative degradation," the second degree of match is higher. Finally, based on the first and second degrees of match, the degree of compliance with the preset adjustment logic's applicable conditions is determined. For example, the two degrees of match can be combined into a single compliance indicator using methods such as weighted averaging, logical judgment, or fuzzy comprehensive evaluation to select the most appropriate adjustment logic.

[0144] In some of the above-mentioned embodiments of the present application, it is proposed to evaluate the degree of compliance of the currently acquired difference characteristic parameters and the baseline information characteristic parameters with the applicable conditions of each preset adjustment logic by calculating the first matching degree between the difference characteristic parameters and the preset range of the difference characteristic parameters in the applicable conditions of the adjustment logic, and calculating the second matching degree between the currently acquired baseline information characteristic parameters and the preset category of the baseline information characteristic parameters in the applicable conditions of the adjustment logic. In this way, the applicable adjustment logic can be selected based on the matching degree, thereby dynamically generating the associated effect information. However, in its implementation process, factors such as the type of toy, aging stage, and usage scenario will affect the importance of the first matching degree and the second matching degree. If the first matching degree and the second matching degree are simply combined and the influence of these factors is ignored, it may lead to inaccurate evaluation results, thereby affecting the selection of the target adjustment logic and ultimately affecting the accuracy of toxicity monitoring.

[0145] In this regard, the present application further proposes that the step of determining the degree of compliance with the applicable condition of the preset adjustment logic based on the first matching degree and the second matching degree includes:

[0146] Obtain information about the type of the toy's current components, its current aging stage, and its current usage context;

[0147] Based on the type information, the aging stage information, and the usage context information, a target weight set corresponding to the current condition is selected from a preset weight set, where the target weight set includes a weight of the first matching degree and a weight of the second matching degree;

[0148] The target weight set is applied to perform a weighted combination on the first matching degree and the second matching degree to determine the degree of compliance with the applicable condition of the preset adjustment logic.

[0149] The type information refers to the material type or functional attributes of the current component of the toy, which can be realized by using the type of plastic material, structural function classification or manufacturing process parameters.

[0150] The aging stage information refers to the aging status level of the current components of the toy, which can be achieved by using aging index values ​​collected by sensors, aging time interval division, or an aging trend prediction model based on historical data.

[0151] Among them, usage context information refers to the current usage environment or usage method of the toy, which can be achieved using environmental parameters such as temperature, humidity, and light intensity, or behavioral parameters such as usage frequency and contact method.

[0152] Among them, the weight set refers to a plurality of pre-set weight combinations, each weight combination corresponds to a different type, aging stage and usage scenario condition, which can be implemented using a table, database record or function mapping relationship.

[0153] Among them, the target weight set refers to a specific weight combination selected from the weight set based on the current type, aging stage and usage context information, which can be implemented using a lookup table, logical judgment rules or machine learning model output.

[0154] Among them, weighted combination refers to multiplying the first matching degree and the second matching degree by the corresponding weights and then summing them or performing other mathematical operations to obtain a comprehensive evaluation value, which can be achieved by linear weighting, nonlinear weighting or fuzzy comprehensive evaluation methods.

[0155] After obtaining the type information, aging stage information, and usage context information of the toy's current components, the system will select a target weight set from a preset weight set based on this information. This weight set contains the weight values ​​of the first and second matching degrees under the current conditions. Subsequently, the system will apply this target weight set to perform a weighted combination of the first and second matching degrees to obtain a comprehensive compliance assessment result. In this way, the system can dynamically adjust the relative importance of the two matching degrees in the evaluation process based on different types of toys, different aging stages, and different usage contexts, thereby improving the accuracy of the assessment and, in turn, the rationality of the adjustment logic selection.

[0156] In some of the aforementioned embodiments of the present application, a target weight set is proposed for weighted combination of the first and second matching degrees to determine the degree of compliance with the pre-set applicable conditions of the adjustment logic. Specifically, this application can be based on the type information, aging stage information, and usage context information of the current toy component, selecting a target weight set corresponding to the current conditions from multiple pre-set weight sets, and applying this target weight set to weighted combination of the first and second matching degrees to determine the degree of compliance with the applicable conditions of the adjustment logic. This allows for dynamic selection of weights based on the specific conditions of different components, improving the accuracy of the adjustment logic selection. However, in actual applications, the first and second matching degrees may have extreme conditions, such as one with a very high matching degree, approaching or reaching a pre-set critical threshold, while the other with a relatively low matching degree. If the weighted combination is still performed according to the original weight set, the factors with high matching degrees may be overly diluted, failing to fully play their decisive role in selecting the adjustment logic, or the factors with low matching degrees may be overly amplified, thereby affecting the accuracy of the adjustment logic selection.

[0157] In this regard, the present application proposes that the steps of applying a target weight set to perform a weighted combination on the first matching degree and the second matching degree include:

[0158] determining whether either the first matching degree or the second matching degree reaches a predetermined critical threshold;

[0159] If either the first matching degree or the second matching degree reaches or exceeds a preset critical threshold, then the logic of the corresponding weight or weighted combination in the target weight set is adjusted according to the situation of reaching or exceeding the critical threshold;

[0160] The adjusted target weight set or the adjusted weighted combination logic is applied to perform weighted combination on the first matching degree and the second matching degree to determine the degree of compliance with the applicable condition of the preset adjustment logic.

[0161] Among them, the critical threshold refers to the numerical limit used to determine whether the matching degree has a decisive influence. It can be implemented by a set fixed value, a floating threshold that is dynamically adjusted according to historical data, or an adaptive threshold that changes according to the application scenario. The target weight set refers to a set of weighted parameters selected based on the type information, aging stage information, and usage context information of the current component. It can be implemented by a combination of multiple preset weights, a weight allocation dynamically generated based on a machine learning model, or a weight configuration obtained through expert system rule reasoning. The logic of the weighted combination refers to the method of comprehensively calculating multiple matching degrees, which can be implemented by linear weighted summation, nonlinear function transformation, piecewise weighting strategy, or combination logic based on a decision tree.

[0162] The technical solution proposed in this application can be implemented by relying on a complete risk assessment system, which can specifically include a data acquisition module, a matching degree calculation module, a weight selection module, a dynamic adjustment module, and a logic judgment module. The data acquisition module is used to obtain environmental parameters and baseline correlation effect information; the matching degree calculation module is used to calculate the first matching degree and the second matching degree; the weight selection module is used to select a target weight set; the dynamic adjustment module is used to adjust the weight or weighted combination logic based on whether the matching degree reaches a critical threshold; and the logic judgment module is used to apply the adjusted weight or logic for weighted combination to determine the degree of compliance with the applicable conditions of the adjusted logic.

[0163] Specifically, in some specific embodiments, the dynamic adjustment module first determines whether either the first matching degree or the second matching degree reaches a preset critical threshold. If the judgment result is yes, the corresponding weight in the target weight set or the logic of the weighted combination is adjusted according to the situation of reaching or exceeding the critical threshold. For example, when the first matching degree reaches or exceeds the critical threshold, the weight of the first matching degree can be increased while the weight of the second matching degree can be reduced; or, the calculation method of the weighted combination can be changed, for example, from linear weighting to priority judgment mode. The adjusted weight or logic is then applied to the weighted combination process to more accurately evaluate the applicability of the adjustment logic.

[0164] For example, in one embodiment, it is assumed that the current component is a load-bearing structural member of an outdoor children's playhouse, its material type is high-density polyethylene (HDPE), it is in a moderate aging stage, and the usage scenario is high-intensity outdoor use. At this time, the weight selection module selects a target weight set from a preset weight set based on the type information, aging stage information, and usage scenario information, where the weight of the first matching degree is 0.6 and the weight of the second matching degree is 0.4. The matching degree calculation module calculates the first matching degree to be 0.92, the second matching degree to be 0.35, and the critical threshold is set to 0.9. Since the first matching degree reaches the critical threshold, the dynamic adjustment module adjusts the weight of the first matching degree to 0.85 and the weight of the second matching degree to 0.15, and applies the adjusted weights for weighted combination, and finally obtains a degree of compliance value that is more in line with the actual situation, thereby improving the accuracy of the adjustment logic selection.

[0165] The above technical solution dynamically adjusts weights or weighted combination logic when the first or second matching degree reaches a critical threshold, thereby preventing dilution of highly matched factors or amplification of less matched factors, and improving the accuracy and reliability of the adjustment logic. This solution addresses the existing problem of irrational weight distribution caused by extreme matching degrees, making the weighted combination results more reflective of the actual matching situation, thereby enhancing the intelligence and adaptability of the overall risk assessment system.

[0166] Secondly, refer to Figure 2 , this application proposes a toy toxicity dynamic monitoring system, the system includes:

[0167] The component characteristic information acquisition module 210 is used to obtain preset component characteristic information, where the component characteristic information represents the material type, functional properties, and inherent importance level of each plastic material component in the toy;

[0168] A monitoring data acquisition module 220 is used to acquire monitoring data. The monitoring data is collected by sensors deployed for each plastic material component and reflects the current aging status of each plastic material component.

[0169] A risk weighted assessment module 230 is configured to perform a risk weighted assessment on the monitoring data of each plastic material component based on the component characteristic information and the usage context information representing the current usage environment or usage mode of the toy, so as to obtain a risk level for each plastic material component;

[0170] The warning report generation module 240 is used to generate a structured warning report based on the risk level of each plastic material component. The structured warning report includes at least the location information, current aging status description, risk level and disposal suggestions for the plastic material components that reach the preset risk threshold.

[0171] Through the above solution, by combining component feature information, monitoring data and usage context information to conduct multi-dimensional risk assessment, it is possible to achieve differentiated identification and early warning of the aging status of different plastic material components in toys, thereby improving the accuracy of risk warnings and enhancing users' decision-making capabilities.

[0172] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for dynamic monitoring of toy toxicity, characterized in that: include: Obtaining preset component characteristic information, wherein the component characteristic information represents the material type, functional properties, and inherent importance level of each plastic material component in the toy; Acquiring monitoring data, where the monitoring data is collected by sensors deployed for each of the plastic material components and reflects a current aging state of each of the plastic material components; performing a risk-weighted assessment on the monitoring data of each of the plastic material components based on the component characteristic information and usage context information representing a current usage environment or usage mode of the toy to obtain a risk level for each of the plastic material components; Based on the risk level of each plastic material component, a structured early warning report is generated, which contains at least location information, current aging state description, risk level and disposal suggestions for the plastic material component that reaches a preset risk threshold.

2. A method for dynamic monitoring of toy toxicity according to claim 1, characterized in that: When the monitoring data of a certain plastic material component indicates multiple aging and degradation manifestations, the step of performing a risk-weighted assessment on the monitoring data of each plastic material component based on the component characteristic information and usage context information characterizing the current usage environment or usage mode of the toy to obtain a risk level for each plastic material component includes: Obtaining the core functional attributes and importance levels preset for the plastic material component in the component characteristic information; For each of the multiple aging degradation manifestations, determining a risk impact parameter associated with the aging degradation manifestation based on the core functional attribute and the importance level, wherein the risk impact parameter represents a potential impact of the aging degradation manifestation on the core functional attribute; In combination with the quantitative values ​​of each aging and degradation manifestation indicated by the monitoring data, the risk impact parameters corresponding to each aging and degradation manifestation, the component feature information and the usage scenario information, a risk-weighted assessment is performed on the plastic material component to calculate the risk level of the plastic material component.

3. A method for dynamic monitoring of toy toxicity according to claim 2, characterized in that: The step of determining, for each of the multiple aging degradation manifestations, the risk impact parameter associated with the aging degradation manifestation based on the core functional attribute and the importance level, wherein the risk impact parameter represents the potential impact of the aging degradation manifestation on the core functional attribute, comprises: Obtaining association effect information, wherein the association effect information represents the synergistic or antagonistic effects between the multiple aging degradation manifestations on the core functional attributes; For each of the multiple aging and degradation manifestations, assessing the potential impact of the aging and degradation manifestation on the core functional attribute based on the core functional attribute and the importance level to obtain a basic impact assessment result; and determining whether the aging and degradation manifestation belongs to at least one group of aging and degradation manifestations for which the association effect information indicates synergistic or antagonistic effects; If so, adjusting the risk impact parameter of the aging degradation manifestation based on the basic impact assessment result and in combination with the associated effect information so that the risk impact parameter reflects the potential impact of the synergistic effect or the antagonistic effect on the core functional attribute; If not, the basic impact assessment result is the risk impact parameter of the aging degradation performance.

4. A method for dynamic monitoring of toy toxicity according to claim 3, characterized in that: The step of obtaining the correlation effect information, wherein the correlation effect information represents the synergistic or antagonistic effects between the multiple aging degradation manifestations on the core functional attributes, includes: Acquiring environmental parameters representing the actual current use environment of the toy; Obtaining preset baseline correlation effect information, where the baseline correlation effect information represents the synergistic or antagonistic effects between the multiple aging degradation manifestations under a preset reference environment; Based on the difference between the obtained environmental parameters and the preset reference environment, and applying the preset adjustment logic to the baseline correlation effect information, the correlation effect information is generated to characterize the dynamic impact of the actual usage environment on the correlation effects between the multiple aging and degradation manifestations. The correlation effect information characterizes the synergistic or antagonistic effects between the multiple aging and degradation manifestations on the core functional attributes.

5. A method for dynamic monitoring of toy toxicity according to claim 4, characterized in that: The step of applying a preset adjustment logic to the baseline correlation effect information based on the difference between the obtained environmental parameters and the preset reference environment to generate the correlation effect information capable of characterizing the dynamic influence of the actual use environment on the correlation effects between the multiple aging and degradation manifestations, wherein the correlation effect information characterizes the synergistic effect or antagonistic effect between the multiple aging and degradation manifestations on the core functional attribute includes: Acquire a difference characteristic parameter, wherein the difference characteristic parameter represents a difference between the environmental parameter and the preset reference environment; acquire a baseline information characteristic parameter, wherein the baseline information characteristic parameter represents a specific attribute of the baseline correlation effect information; Based on the difference characteristic parameter and the reference information characteristic parameter, selecting a target adjustment logic corresponding to a specific range of the difference characteristic parameter and a specific category of the reference information characteristic parameter from a plurality of preset adjustment logics; Applying the target adjustment logic to the baseline association effect information generates the association effect information that can characterize the dynamic impact of the actual usage environment on the association effects between the multiple aging and degradation manifestations, and the association effect information characterizes the synergistic or antagonistic effects between the multiple aging and degradation manifestations on the core functional attributes.

6. A method for dynamic monitoring of toy toxicity according to claim 5, characterized in that: The step of selecting, based on the difference characteristic parameter and the reference information characteristic parameter, a target adjustment logic corresponding to a specific range of the difference characteristic parameter and a specific category of the reference information characteristic parameter from a plurality of preset adjustment logics includes: Obtaining an applicable condition for each of the preset adjustment logics, the applicable condition including a preset range of the difference characteristic parameter and a preset category of the reference information characteristic parameter corresponding to the adjustment logic; For the currently acquired difference characteristic parameters and the reference information characteristic parameters, evaluating their compliance with the applicable conditions of each preset adjustment logic; Based on the degree of compliance, the target adjustment logic is determined from the plurality of preset adjustment logics.

7. A method for dynamic monitoring of toy toxicity according to claim 6, characterized in that: The step of evaluating the degree of compliance of the currently acquired difference characteristic parameters and the reference information characteristic parameters with the applicable conditions of each preset adjustment logic includes: For each of the preset adjustment logics, calculating a first matching degree between the currently acquired difference characteristic parameter and a preset range of the difference characteristic parameter in the applicable condition of the adjustment logic; For each of the preset adjustment logics, calculating a second matching degree between the currently acquired reference information characteristic parameter and a preset category of the reference information characteristic parameter in the applicable condition of the adjustment logic; Based on the first matching degree and the second matching degree, the degree of compliance with the applicable condition of the preset adjustment logic is determined.

8. A method for dynamic monitoring of toy toxicity according to claim 7, characterized in that: The step of determining the compliance degree of the applicable condition of the preset adjustment logic based on the first matching degree and the second matching degree includes: Obtaining type information, current aging stage information, and current usage context information of the toy's current components; Based on the type information, the aging stage information, and the usage context information, selecting a target weight set corresponding to the current condition from a preset weight set, the target weight set including the weight of the first matching degree and the weight of the second matching degree; The target weight set is applied to perform a weighted combination on the first matching degree and the second matching degree to determine the degree of compliance with the applicable condition of the preset adjustment logic.

9. A method for dynamic monitoring of toy toxicity according to claim 8, characterized in that: The step of applying the target weight set to perform weighted combination on the first matching degree and the second matching degree includes: determining whether either the first matching degree or the second matching degree reaches a preset critical threshold; If either the first matching degree or the second matching degree reaches or exceeds the preset critical threshold, the corresponding weight in the target weight set or the logic of the weighted combination is adjusted according to the situation of reaching or exceeding the critical threshold; the first matching degree and the second matching degree are weightedly combined using the adjusted target weight set or the adjusted weighted combination logic to determine the degree of compliance with the applicable conditions of the preset adjustment logic.

10. A dynamic monitoring system for toy toxicity, used to present the safety status of each toy component in a refined manner and provide treatment guidance, characterized by: The system includes: A component characteristic information acquisition module is used to acquire preset component characteristic information, wherein the component characteristic information represents the material type, functional properties and inherent importance level of each plastic material component in the toy; a monitoring data acquisition module, configured to acquire monitoring data, wherein the monitoring data is collected by sensors deployed for each of the plastic material components and reflects the current aging state of each of the plastic material components; a risk weighted assessment module configured to perform a risk weighted assessment on the monitoring data of each of the plastic material components based on the component characteristic information and usage context information representing a current usage environment or usage mode of the toy, so as to obtain a risk level for each of the plastic material components; An early warning report generation module is used to generate a structured early warning report based on the risk level of each of the plastic material components, wherein the structured early warning report includes at least the location information, current aging status description, risk level and disposal suggestions for the plastic material components that reach a preset risk threshold.

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