A hierarchical processing method for cross-border clothing quality inspection data

Abstract

This invention discloses a hierarchical processing method for cross-border apparel quality inspection data, belonging to the field of quality inspection data processing technology. Based on the multi-dimensional quality inspection data received by the cross-border e-commerce platform and the corresponding target compliance requirements, the method dynamically updates the local knowledge graph to form a dynamic knowledge network reflecting the real-time hierarchical situation. On this basis, a timeliness identifier is added to the preliminary hierarchical results, and based on predefined hierarchical interaction relationships, combined with abnormal / coupled quality inspection indicators of the current quality inspection data, cross-dimensional quantitative cross-analysis is performed to identify potential superposition / amplification effects. The identified coupled abnormal relationships are strength-assessed and prioritized to dynamically adjust the task execution order and monitor changes in timeliness identifiers. Simultaneously, combined with predictive dynamic queue scheduling, it is determined whether to output the final comprehensive hierarchical processing result. This achieves intelligent hierarchical processing of cross-border apparel quality inspection data, supporting efficient risk management and business decision-making.

Description

Technical Field

[0001] This invention relates to the field of quality inspection data processing technology, and in particular to a method for hierarchical processing of quality inspection data for cross-border apparel. Background Technology

[0002] As one of the core categories in cross-border trade, the quality control of apparel is a crucial link in ensuring smooth trade and protecting consumer rights. Cross-border apparel must simultaneously meet the dual quality inspection standards of both the exporting and importing countries, involving multiple dimensions of quality inspection indicators such as fabric defects, harmful substance content, and sewing techniques. The resulting quality inspection data is massive in volume, complex in type, and varies significantly in standards, placing higher demands on the efficiency and accuracy of data classification and processing.

[0003] Existing technologies typically employ a combination of automated machine testing and manual review: First, based on target market regulations and customer requirements, a static rule base is pre-defined for key items (such as formaldehyde and pH value) and secondary items (such as loose threads) for different product categories (e.g., children's clothing, adult outerwear), allowing for preliminary data classification. Basic physicochemical indicators (such as pH value and formaldehyde content) are automatically collected and digitized by instruments, while appearance defects (such as stains and loose threads) are identified and classified using machine vision algorithms. Complex or borderline cases rely on manual final judgment. The classification results are ultimately fed back to the quality auditors or supply chain management system of the cross-border e-commerce platform in the form of structured reports or alert lists.

[0004] However, when faced with the complex and ever-changing quality inspection scenarios of cross-border apparel, existing quality inspection data processing systems still fall short in terms of integration and intelligence, struggling to handle the fusion analysis and comprehensive classification of multi-source heterogeneous data. Taking the quality inspection data processing workflow of a cross-border apparel trader as an example, the problems are specifically reflected in two interrelated levels: On the one hand, when processing the same batch of products, data from different testing stages are fragmented: chemical substance test results from the physicochemical laboratory, appearance defect image analysis reports output by machine vision equipment, and manual review records are written to separate database tables or asynchronous message queues. The lack of a unified identification and association mechanism among these data prevents the aggregation of multi-source evidence for the same garment / batch at the computational layer, resulting in a discrete list of outputs.

[0005] On the other hand, even if some data is initially integrated, the system's data classification for each test item (such as flame retardancy and cord length) remains isolated. It often relies solely on preset thresholds for individual judgments, ignoring the potential interrelationships and synergistic effects between different test items. For example, there is a coupling relationship between excessive cord length (physical risk) and near-critical flame retardancy (chemical safety risk) in specific usage scenarios, which may lead to a significant improvement in the overall safety level. However, the current system lacks a mechanism to define, calculate, and infer such cross-index coupling relationships, and cannot identify such complex correlation patterns, causing the evaluation results to remain at a mechanical superposition rather than an organic integration.

[0006] The two issues mentioned above together result in the existing system being limited to providing fragmented anomaly alerts, failing to support accurate and interpretable overall quality grading of cross-border apparel, and ultimately affecting the efficiency and reliability of data-driven business decisions. Summary of the Invention

[0007] In view of the above problems, embodiments of the present invention provide a method for hierarchical processing of cross-border apparel quality inspection data. The technical solution is as follows: Based on the multi-dimensional quality inspection data received by the cross-border e-commerce platform and the corresponding target compliance requirements, the status of quality nodes in the local knowledge graph is dynamically updated to form a dynamic knowledge network that reflects the real-time hierarchical situation.

[0008] For the current preliminary quality inspection results, a timeliness label is added, and based on the predefined hierarchical interaction relationship in the dynamic knowledge network, combined with the abnormal / coupled quality inspection indicators of the current quality inspection data, cross-dimensional quantitative cross-analysis is carried out to identify the potential superposition / amplification effects between different quality node states.

[0009] The strength of the identified abnormal coupling relationships is assessed and prioritized, thereby dynamically adjusting the execution order of hierarchical processing.

[0010] During the dynamic adjustment of the execution order, the timeliness indicators of each hierarchical processing task are monitored. Through predictive dynamic queue scheduling, the composite hierarchical decision-making process converges within the time constraints, thereby determining whether to output the final comprehensive hierarchical processing result.

[0011] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: 1. This invention first dynamically updates the node status in a local knowledge graph based on multi-dimensional quality inspection data, forming a real-time risk situation network. This step overcomes the computational layer fragmentation caused by data isolation and inconsistent labeling in different inspection stages, enabling multi-source evidence such as physicochemical, visual, and manual evidence to be aggregated around the same quality inspection object, providing a unified data view for overall analysis. Secondly, timeliness labels are added to the preliminary grading results, and combined with the grading interaction relationships in the dynamic network, cross-dimensional cross-analysis is performed on abnormal or coupled quality inspection indicators to identify potential superposition or amplification effects between quality nodes. This allows for the quantitative identification and evaluation of cross-dimensional risk coupling relationships, thereby upgrading risk judgment from mechanical superposition to organic integration. Subsequently, the identified coupled abnormal relationships are evaluated for strength and prioritized, dynamically adjusting the execution order of grading processing tasks, and using predictive dynamic queue scheduling by monitoring timeliness labels. This mechanism ensures that high-timeliness, high-risk quality inspection tasks are prioritized, while enabling the composite grading decision-making process to converge effectively within time constraints, balancing processing efficiency and the completeness of risk coverage. Ultimately, through the synergistic effect of the aforementioned integration, analysis, and scheduling, the previously fragmented anomaly alerts were transformed into accurate and interpretable overall quality grading results. This not only improved the accuracy of cross-border apparel quality assessment but also significantly enhanced the reliability of data-driven business decisions, thus systematically addressing the current shortcomings in the integration and intelligence of the quality inspection process.

[0012] 2. First, by constructing a quantized phase space and mapping quality inspection results to dynamic nodes, a unified mathematical expression framework is established for multi-source heterogeneous quality inspection data. This allows inspection results from different dimensions, such as physicochemical and visual, to be aligned and compared within the same computational space, fundamentally solving the problem of data fragmentation caused by different sources. Second, effective indicator sequences are extracted using normalization, differencing, and threshold filtering. Cumulative offset and collaborative bias are quantified using Euclidean distance and correlation coefficient complements, respectively, enabling an objective and quantitative characterization of the compliance deviation and temporal correlation strength between indicators. Furthermore, node risk states are determined and classified based on dynamic coupling coefficients. This not only identifies obvious single indicator anomalies but also captures potential transmission risks hidden in indicator correlations. This mechanism breaks through the limitations of traditional quality inspection data processing systems that only perform single-item threshold determinations, allowing for explicit modeling and discovery of risk couplings across physical and chemical dimensions, such as rope length and flame retardancy. Finally, the knowledge network topology is dynamically reconstructed based on the updated node states and connection relationships, forming a risk situation map that evolves in real time with the quality inspection data. This not only provides an aggregated view of the risk status of quality inspection objects, but also forms the intelligent foundation for subsequent priority sorting and dynamic scheduling, thereby transforming the fragmented list of anomalies into a visualized overall risk classification.

[0013] 3. When processing abnormal quality inspection indicators, the system calculates their absolute deviation and performs gradient propagation calculations along the transmission paths in the interaction network, enabling a quantitative assessment of the impact of a single risk source on its associated indicators. The cumulative transmission deviation of downstream nodes is obtained by summing the impact values ​​of different transmission paths, and a direct or potential transmission superposition effect is determined based on whether this deviation exceeds a threshold or historical fluctuation range. This process upgrades the traditionally isolated anomaly assessment to the simulation and prediction of risk diffusion along a preset path, making the chain reaction of quality impacts that may be triggered by a certain chemical substance exceeding the standard explicit and quantitatively assessed, effectively overcoming the fragmented problem of risk assessment. When processing coupled quality inspection indicator groups, the system comprehensively quantifies the synergistic effects of multiple associated risk sources by identifying downstream nodes with common influences and calculating joint impact factors. In particular, by detecting and calculating feedback loop effects, the system can capture the self-reinforcing or cyclical amplification phenomena that may occur in the interaction network, which cannot be identified in the previous mechanical superposition assessment system. Finally, by combining the joint impact factors and comparing them with preset thresholds and historical baselines, the system can objectively determine whether significant or local cross-dimensional risk amplification has occurred. These two analytical processes together constitute a dynamic and quantitative risk propagation analysis engine, which can not only reveal the direct transmission path of risk, but also characterize the nonlinear amplification effect under the coupling of multiple risk sources, providing a solid analytical foundation for the final output of accurate and interpretable overall risk classification.

[0014] 4. First, prioritizing the completion of high-time-sensitive tasks while simultaneously requiring intermediate results of regular-time-sensitive tasks to reach convergence and stability. This mechanism prioritizes the processing time of urgent orders and other critical business processes, avoiding decision-making delays caused by waiting for all regular tasks to complete, and enabling timely risk assessments for critical business operations. Simultaneously, monitoring the stability of intermediate results from regular tasks ensures the overall reliability and consistency of the final output conclusions, preventing repetitive or inaccurate conclusions due to fluctuations in some long-tail tasks. Second, efficient processing rules for two boundary cases are clearly defined: when there are no non-high-time-sensitive tasks, output is based solely on the convergence and stability of regular tasks; when there are no non-regular tasks, output is generated immediately after the completion of high-time-sensitive tasks. This differentiated strategy achieves precise allocation of processing resources, avoids unnecessary waiting, and significantly improves overall throughput efficiency and response agility. Finally, a maximum allowable processing time limit is set. When a task cannot be fully completed, the system will provide a confidence level risk indicator for the unconverged portion and output the current optimal result. This ensures that a usable decision-making basis with a clear explanation of uncertainty is provided under any circumstances, preventing the process from falling into an indefinite waiting or no-output deadlock and guaranteeing the continuity of business decisions. This output determination logic ensures the quality of results through priority and convergence, while ensuring processing efficiency through differentiated strategies and final deadlines. As a result, the entire hierarchical processing flow can stably and reliably support high-paced cross-border trade quality inspection decisions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a method for grading and processing cross-border apparel quality inspection data provided in an embodiment of the present invention; Figure 2 This is a flowchart of cross-dimensional quantitative cross-analysis provided in an embodiment of the present invention; Figure 3 A schematic diagram illustrating the calculation and meaning of the dynamic coupling coefficient provided in this embodiment of the invention; Figure 4 A time-series line graph for comparing task priority is provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] This invention provides a method for hierarchical processing of cross-border apparel quality inspection data, such as... Figure 1 The flowchart shown illustrates a tiered processing method for quality inspection data of cross-border apparel. The multi-dimensional quality inspection data refers to a collection of structured and unstructured data received from cross-border e-commerce platforms and their integrated testing processes. This data includes, but is not limited to: chemical content test values, azo dye content, formaldehyde content, etc., from physicochemical laboratories (chemical dimension); appearance defect types, location coordinates, and confidence scores from machine vision (visual dimension); tensile strength, color fastness, dimensional deviation, etc., from physical tests (physical dimension); and textual comments, defect classification labels, and compliance judgment results from manual review (semantic dimension). These multi-source heterogeneous data collectively constitute a comprehensive quality description of the same batch of apparel products.

[0021] The implementation steps of the technical solution of the present invention are as follows: Step 1 Within a pre-defined compliance knowledge modeling framework—that is, a pre-constructed system of rules and relationships based on industry standards, regulations of target trading countries, and internal quality standards of enterprises—indicator sequences corresponding to multi-dimensional quality inspection data are obtained. Where i = 1, 2, ..., n, n represents the total number of quality inspection indicators in the multi-dimensional quality inspection data involved in cross-border apparel, i represents the number of the quality inspection indicator, and s i Represents the set of observations of the i-th quality inspection indicator over time; and represents the reference sequence corresponding to the target compliance limit. , where r iThis represents the regulatory or standard threshold for the i-th quality inspection indicator. The target compliance limit refers to the mandatory or agreed-upon pass threshold set for each quality inspection indicator (such as formaldehyde content, color fastness, and rope length) in cross-border trade, based on mandatory regulations (such as EU REACH regulations and US CPSC standards), industry safety standards (such as ISO and ASTM standards), and the quality requirements stipulated in the buyer's contract. A quantitative phase space is constructed to characterize the mapping relationship between the indicator sequence and the reference sequence. This phase space is an n-dimensional metric space, where each dimension corresponds to a quality inspection indicator, and the coordinate values ​​are defined by the normalized ratio of the measured value of the indicator to its reference limit.

[0022] The quality inspection results of the current batch are mapped to one or more dynamic nodes in the quantization phase space. Specifically, for each quality inspection object in the batch (such as a piece of clothing or a production batch), the current detection values ​​v of all its indicators are mapped. i According to the formula The normalized offset is calculated, and the vector is... As the coordinate point of the object in the quantization phase space, this coordinate point is defined as a dynamic node N of the quality inspection object in the current network.

[0023] Within a pre-defined quality inspection sequence interval T (e.g., a production cycle or a testing window), each indicator sequence s is processed. i Normalization is performed to generate a normalized index sequence. Specifically, the min-max normalization method is used: ,in, This represents the original detection value of the i-th quality inspection indicator at time point t. This represents the maximum value of the i-th quality inspection indicator among all test values ​​within T. It represents the minimum value of the i-th quality inspection indicator among all test values ​​within T, and t represents a specific time point or test sequence number within the preset quality inspection sequence interval T.

[0024] Subsequently, the normalized index sequence was processed using a first-order difference operator to obtain dynamic change characteristics. ,in, This represents the value of the normalized index sequence at time point t. This represents the value of the normalized index sequence at the previous time point t-1 (t 2) Based on the summation and averaging of the historical quality inspection indicator sequence change characteristics during the historical cross-border apparel quality inspection data grading process, a preset change threshold is set. It will be lower than Corresponding dynamic change characteristics The data points corresponding to the indicator sequence are reset to zero, that is, if Then let .

[0025] After statistical zeroing, the number of data points c corresponding to the index sequence with a value greater than zero in the entire index sequence. i and will satisfy The sequence of indicators is denoted as the effective indicator sequence S. effective ; Calculate the effective index sequence S effective With reference sequence r i The Euclidean distance between them is used as the cumulative offset magnitude D. i : Calculate s i Compared with all other effective indicator sequences s j The Pearson correlation coefficient p between (j≠i) ij and through its complement 1- Quantitative Coordination Deviation Distance C ij This reflects the degree of difference and correlation deviation between indicators i and j in the current batch of processing.

[0026] The cumulative offset magnitude D i Distance C from the coordinated deviation ij average (m is the total number of valid indicator sequences) are combined to obtain the dynamic coupling coefficient K, which characterizes the strength of the temporal correlation between indicators and the degree of compliance deviation. i : ,in, and K is a preset weighting coefficient, whose value is pre-set based on the relative importance of compliance deviation and correlation strength in the business scenario. i This is used to characterize the temporal correlation strength and compliance deviation of indicator i.

[0027] like Figure 3The diagram illustrating the calculation and meaning of the dynamic coupling coefficient shows that the horizontal axis is typically represented by the working condition slice / time window number, indicating the sequential position of the graded processing as the time window progresses; the vertical axis represents the normalized values ​​(0-1) of the cumulative offset amplitude and the coordination deviation distance. Normalization achieves dimensional consistency, facilitating comparison of their changes and convergence relationships within the same coordinate system. The two curves in the diagram represent: the purple solid line, representing the normalized change of the cumulative offset amplitude, reflecting the cumulative offset trend of a single indicator relative to the compliance benchmark within a continuous time window; and the orange dashed line, representing the normalized change of the coordination deviation distance, reflecting the overall trend of the coordination deviation between this indicator and other effective indicators. An overall upward trend in the curves indicates that the cumulative offset and coordination deviation are increasing overall, while local fluctuations correspond to stage anomalies or the reconstruction of interactive relationships. Taking segments 6 to 10 as an example, the two curves are close to each other and change synchronously, indicating that the two are highly similar and have a strong coupling, which means that the risks are more likely to overlap or be amplified. Corresponding to segments 14 to 18, the two curves deviate more and are not synchronous, indicating that the similarity decreases and the coupling strength is relatively weakened, which means that the temporal correlation of this stage is reduced or the risk transmission link changes.

[0028] When the coupling strength is detected to be increased or persistently high, the pre-set personnel can be prompted to promptly suppress the superposition / amplification of risks and avoid the spillover of non-compliance by increasing the priority of graded processing and triggering dynamic queue scheduling and rearrangement, performing stricter spot checks or full inspections on related batches / nodes, tracing and locating the risk transmission link, and issuing early warning and rectification instructions to suppliers / responsible links. When the pre-set personnel observe that the coupling strength is low and stable, routine spot checks can be maintained and continuous monitoring can be carried out to optimize the processing time while ensuring compliance.

[0029] Based on the dynamic coupling coefficient K obtained above i The node state corresponding to dynamic node N is dynamically determined and corrected. Based on the summation and averaging of the dynamic coupling coefficients of historical quality inspection indicators during the historical cross-border apparel quality inspection data grading process, a dynamic coupling threshold is set. ,like If the state of the corresponding indicator i in the dynamic node N is determined to have a significant abnormal risk, then the indicator sequence s is... i Anomalies are marked, anomaly tags are generated, and this status is synchronized to the local knowledge graph to update the risk status attribute of dynamic node N. Specifically, the risk status field of node N is updated to explicit anomaly.

[0030] like If the state of the corresponding indicator i in the dynamic node N is determined to have a potential correlation risk, then the indicator sequence s is... iPerform association path enhancement processing: Traverse other indicator nodes j in the knowledge graph that have predefined associations with indicator i. For each association pair (i, j), calculate its current collaborative deviation distance C. ij Then calculate the enhanced correlation strength. ,in, For K i With C ij The fusion function, through the product interaction method, combines the risk level K of indicator i. i The method combines the enhancement with its covariance relationship with other indicator nodes j to jointly determine the driving force of the enhancement. Specifically, the method normalizes the two and multiplies them, so that the fusion result has a high driving value only when the risk itself and the covariance relationship are both significant. This avoids the single factor dominating the enhancement and is more in line with the identification logic that implicit transmission relationships need to be triggered twice. The initial association weights are predefined in the graph. To enhance the association strength, a hyperparameter that needs to be preset or adaptively adjusted, it is typically set to a fixed value, such as 0.3, based on business history or simulation tests. However, it can be fine-tuned based on the uncertainty of the current data or the confidence level of the node states in the dynamic knowledge network. This balances the contribution of prior knowledge and current observations to the association strength update, preventing excessive fluctuations in weight updates due to data noise or network instability. This process aims to identify implicit transmission relationships. It also aims to dynamically identify and strengthen implicit risk transmission relationships based on current data. The updated association strength E... ij This will be synchronized to the local knowledge graph, meaning the weight of the edge connecting node i and node j will be updated to E. ij .

[0031] Based on the updated state changes (e.g., from normal to explicit anomaly) or changes in connectivity (e.g., enhanced edge weights) of dynamic nodes in the corresponding local knowledge graph, a knowledge network topology reconstruction algorithm is triggered. This algorithm recalculates the centrality, community division, and other topological features of all nodes in the network, forming a new dynamic knowledge network that reflects real-time hierarchical situational awareness. This process realizes the transformation from static knowledge storage to dynamic situational awareness. Through the above steps, a complete process is completed, from multi-source heterogeneous data access, quantized spatial mapping, feature extraction and coupling analysis, to dynamic knowledge graph updates and network reconstruction, providing a real-time, aggregated, and intelligent data foundation for subsequent risk classification and decision-making.

[0032] The core purpose of the above calculation process is to transform the scattered and heterogeneous cross-border clothing quality inspection data into a quantifiable, associative, and dynamically evolving knowledge network to support accurate comprehensive risk classification.

[0033] First, by constructing a quantized phase space and performing data normalization mapping, a unified mathematical expression framework was established for quality inspection indicators from different detection stages (such as chemical, physical, and visual). This solved the problem that multi-source data could not be directly integrated and compared at the computational level due to differences in dimensions and scales, and provided a standardized data foundation for subsequent analysis.

[0034] Secondly, by employing first-order difference, change threshold screening, and correlation analysis, effective information with significant change characteristics and correlation patterns is extracted from time-series data. This effectively filters out random fluctuations and noise interference, focuses on characteristics that reflect the true quality trend and the synergistic relationship between indicators, and enhances the robustness and representativeness of subsequent risk feature extraction.

[0035] Finally, the compliance deviation and temporal correlation strength of the indicators are comprehensively characterized by the dynamic coupling coefficient. Based on this, the node status and connection relationship in the knowledge graph are updated and the topology is reconstructed in real time, transforming the static compliance knowledge base into a dynamic cognitive network that can reflect the risk transmission and coupling situation in real time.

[0036] The entire process achieves a progressive transformation from data to features and then to a knowledge network, which aligns with the technical requirements for data fusion, intelligent analysis, and dynamic response in cross-border apparel quality inspection scenarios.

[0037] Step Two For the current preliminary quality inspection results—that is, intermediate datasets that have completed single-indicator compliance judgments but have not yet undergone cross-dimensional risk coupling analysis—a timeliness identifier is added. This includes: based on a preset timeliness assessment logic, a dynamic judgment mechanism based on business rules and status, used to intelligently classify the timeliness level of task processing according to the external urgency of the quality inspection task and the real-time availability of internal processing resources. The classification is mainly based on two dimensions: the urgency of the order to which the quality inspection result belongs and the current processing queue status. Specifically: Order urgency: Orders are categorized as expedited or non-expedited based on their attributes (e.g., customer requirements, delivery deadline, trade terms). For example, cross-border e-commerce promotional orders and orders requiring 48-hour delivery are typically marked as expedited. Processing queue status: The number of pending tasks and resource utilization are monitored in real time. When the backlog of tasks exceeds a preset threshold for normal processing capacity (e.g., 80%), the queue is considered full capacity; otherwise, it is considered non-full capacity.

[0038] The type determination is based on the acquired intensity indicators. Specifically, the indicator sequence corresponding to the current quality inspection data and its calculated dynamic coupling coefficient are input into the association determination matrix generated by the predefined hierarchical interaction relationship to form a binary association matrix, where each element is a binary value: if the dynamic coupling coefficient is not less than the preset dynamic coupling coefficient, it is recorded as 1; otherwise, it is recorded as 0. The association determination matrix generated by the predefined hierarchical interaction relationship is a pre-constructed table of association relationships between indicators based on historical quality inspection data and compliance rules.

[0039] The generation of this matrix integrates three methods: rule definition, historical data mining, and real-time computation. First, based on business expert knowledge and compliance standards, strong logical relationships between certain indicators are clarified (for example, if a certain type of dye is banned, its corresponding azo compounds must also be restricted), and these relationships are directly encoded into fixed correlation bits in the matrix in the form of rules.

[0040] Secondly, historical quality inspection data is used for association rule mining or graph neural network training to automatically discover high-frequency co-occurrence or statistically significant indicator association patterns. For example, by analyzing historical batch data, it was found that in samples with excessive formaldehyde content, the co-occurrence probability of abnormal pH value was as high as 85%, that is, an initial association label is automatically preset at the intersection of the formaldehyde row and the pH column in the matrix.

[0041] Finally, in the real-time classification process, the preset association is activated based on the dynamic coupling coefficient calculated for the current batch: if the current dynamic coupling coefficient is not lower than the preset threshold, the corresponding element in the matrix is ​​set to 1; otherwise, it is set to 0. The resulting binary association matrix can dynamically characterize whether there is a valid association between the various quality inspection indicators of the current batch that is statistically supported and verified by real-time data, providing a basis for subsequent type determination.

[0042] If all elements in the binary correlation matrix are 0, it indicates that the indicator is not significantly correlated with other indicators, and its risk manifests as independent deviation. In this case, if the quality inspection indicator of the corresponding quality inspection data exceeds the corresponding compliance threshold, that is, the single compliance limit set in advance according to the target market regulations, industry standards or procurement contracts, such as the limit of 30mg / kg for azo dyes under the EU REACH regulation, it is judged as an isolated anomaly and is therefore marked as an abnormal quality inspection indicator. If there is at least one 1 in the binary correlation matrix, it indicates that the indicator is correlated with at least one other indicator, and its risk may be transmitted or amplified through the correlation path. Therefore, even if it does not exceed the limit independently, it needs to be evaluated as part of the coupling effect and is therefore marked as a coupled quality inspection indicator.

[0043] If the risk manifests as an independent deviation, and the corresponding quality inspection indicators in the quality inspection data do not exceed the corresponding compliance thresholds, then the indicator is deemed a qualified item, and its risk is within an acceptable safety range. Therefore, it is marked as a normal quality inspection indicator, does not trigger anomalies or coupled risk assessment processes, and its value is only recorded for subsequent statistics or trend tracking. It is not included in the comprehensive risk decision calculation for this time.

[0044] Based on this, the present invention provides, as follows Figure 2 The flowchart shown below illustrates the cross-dimensional quantitative analysis process, which includes the following steps: Scenario 1: If it is an abnormal quality inspection indicator: The absolute deviation is calculated by taking the absolute value of the difference between the detected value of the abnormal quality inspection indicator and the compliance threshold. Within a predefined hierarchical interaction relationship, multiple transmission paths may exist from the node corresponding to the abnormal quality inspection indicator, leading to different downstream indicator nodes. For each path, the absolute deviation is multiplied by the transmission coefficient along that path to obtain the deviation impact value received by the downstream node from that path. This process simulates the attenuating propagation of abnormal influences along network paths. The transmission coefficient represents the proportion of the abnormality of the quality inspection indicator transmitted from the current node to the downstream node. For example, if historical data shows that the probability of insufficient buckle pull strength due to excessive cord length is 70%, the transmission coefficient can be set to 0.7. Since a downstream node may be affected by the same abnormal quality inspection indicator through multiple different transmission paths, the deviation impact values ​​from all paths need to be accumulated to obtain the cumulative transmission deviation of that downstream node.

[0045] If the cumulative transmission deviation exceeds the corresponding compliance threshold, it indicates that the impact of the upstream abnormal indicator has caused the downstream indicator to deviate from the safe range, constituting a clear and immediate risk. This means that the risk has been successfully transmitted from the source and has triggered new compliance issues. Therefore, it is determined that the corresponding downstream node has a direct transmission superposition effect. If the cumulative transmission deviation does not exceed the corresponding compliance threshold, but exceeds the historical cumulative transmission deviation fluctuation range (defined by the mean and standard deviation of historical cumulative transmission deviations obtained from historical cross-border apparel batches), it indicates that although the transmission impact of the upstream abnormal indicator has not broken through the compliance red line, it has caused its state to deviate from the normal statistical pattern. This deviation means that risk transmission has occurred and is accumulating, which may indicate an increase in the risk of exceeding the standard, or reflect a hidden, gradual damage process. Therefore, it is determined that the corresponding downstream node has a potential transmission superposition effect. Conversely, it is determined that the current abnormal quality inspection indicator has not had a transmission impact on the downstream node, and its state can be regarded as normal fluctuation, without the need to upgrade the risk level.

[0046] Scenario 2: If the quality inspection indicators are coupled: By traversing the directed edges originating from each index node in the predefined hierarchical interaction relationship (i.e., there are at least one interconnected quality inspection indexes in the association judgment matrix), and taking the intersection of the nodes pointed to by these edges, all downstream influencing nodes commonly pointed to by the coupled quality inspection index group are identified. Based on the temporal covariance relationship between coupled quality inspection indicator groups, i.e., the coordinated change pattern of each indicator in the time series (such as simultaneous rise / fall or the existence of a fixed phase difference), the joint impact factor of the coupled quality inspection indicator group on each downstream node is calculated. The specific calculation method is as follows: First, the independent impact value of each indicator in the group on the downstream node is calculated based on its dynamic coupling coefficient and the current deviation, i.e., the product of the dynamic coupling coefficient and the current deviation. Then, these independent impact values ​​are weighted and fused with the Pearson correlation coefficient (absolute value) among the indicators in the group. During weighting, the higher the correlation coefficient of the indicator pair, the greater the fusion weight of its impact value, thus quantifying the synergistic enhancement effect. The final weighted sum is the joint impact factor of the downstream node, characterizing the comprehensive influence strength of the coupled indicator group through synergistic effect.

[0047] If a feedback path exists in the predefined hierarchical interaction relationship, pointing from downstream nodes back to the coupled quality inspection index group (i.e., forming a closed loop), the impact may be amplified cyclically within the loop, leading to non-convergence or distorted results. Therefore, iterative calculations of the feedback loop are required, and an attenuation coefficient must be introduced to simulate the convergence of the loop effect. Specifically, in each iteration calculating the impact propagation along the loop, the propagated value is multiplied by an attenuation coefficient less than 1 (e.g., 0.8), set based on the attenuation characteristics of the loop effect in historical data. Iteration continues until the increment of the loop impact value is less than a preset tolerance. At this point, the impact values ​​from each iteration are summed to obtain the net impact contribution after convergence, which is then added to the initial joint impact factor. If no feedback path exists, the calculated joint impact factor is directly used as the impact intensity of the corresponding downstream impact node, without iterative correction.

[0048] The sum of the joint impact factors of all downstream affected nodes is calculated to obtain the overall impact value of this coupling. If the overall impact value exceeds the preset risk amplification threshold, it is determined that a direct risk amplification effect has occurred. The preset risk amplification threshold is usually set by the preset personnel based on the maximum acceptable coupling risk level in historical business. If the overall impact value does not exceed the preset risk amplification threshold, but exceeds the fluctuation range of historical impact values, it is determined that a local risk amplification effect has occurred. The fluctuation range of historical impact values ​​is calculated by statistically analyzing the overall impact values ​​obtained in historical batches without abnormal coupling or with only normal fluctuations, and taking the mean plus or minus a certain number of standard deviations (the specific multiple is set by the preset personnel based on the actual strictness of risk control and combined with the distribution characteristics of historical data) as the normal fluctuation range. Otherwise, it is determined that the current coupled quality inspection indicator group has not triggered a cross-dimensional risk amplification effect.

[0049] Step 3 Obtain abnormal quality inspection indicators with a transmission superposition effect, or coupled quality inspection indicator groups with a risk amplification effect, and perform initial priority ranking: the abnormal quality inspection indicators with a direct transmission superposition effect or coupled quality inspection indicator groups with a direct risk amplification effect are recorded as the first initial priority; the abnormal quality inspection indicators with an indirect transmission superposition effect or coupled quality inspection indicator groups with a local risk amplification effect are recorded as the second initial priority. Based on either the first or second initial priority, a dynamic task dispatcher allocates tiered processing tasks and monitors the priority adjustment coefficient during task execution to prioritize processing items, ensuring that high-timeliness and high-risk quality inspection results are processed first. The priority adjustment coefficient quantifies the relative urgency of a tiered processing task in the current processing queue; the higher the coefficient, the stronger the urgency for the task to be scheduled and executed. Its position in the processing queue is dynamically adjusted based on this value. The calculation method can be as follows: This coefficient is equal to the preset value corresponding to its initial priority (e.g., the first initial priority is 10, and the second initial priority is 5). During monitoring, two key indicators are acquired in real time: the task remaining processing time ratio (remaining time / total estimated time) and the average queue waiting time growth rate. In calculation, the reciprocal of the task remaining processing time ratio is multiplied by the queue waiting time growth rate, and then multiplied by a load factor (current queue length / maximum queue capacity). Finally, this product is added to the task's base coefficient to obtain the priority adjustment coefficient. The formula can be expressed as: Priority adjustment coefficient = base coefficient + remaining processing time ratio × waiting time growth rate × current level processing load factor. Through this calculation, tasks with shorter remaining time and more severe queue congestion have a faster coefficient increase, thus gaining higher scheduling priority in dynamic sorting.

[0050] Specifically, the priority processing items are sorted as follows: if the number of tiered processing tasks in the current processing queue is not higher than the first preset threshold, the execution order of the current tiered processing tasks is maintained, and the priority adjustment coefficient is decremented by one, and the priority adjustment coefficient is not less than zero. If the priority adjustment coefficient is already zero at this time, the decrement operation of the coefficient is paused; if the number of tiered processing tasks in the current processing queue is higher than the first preset threshold but not higher than the second preset threshold, the priority adjustment coefficient is incremented by one; if the number of tiered processing tasks in the current processing queue is higher than the second preset threshold, the current tiered processing tasks are marked as urgent tasks.

[0051] The first and second preset thresholds are dynamically set based on the historical load data and processing capacity of the existing quality inspection data processing system: the first preset threshold is typically set to 70% of the normal load to warn of queue backlog; the second preset threshold is set to 90% of the maximum stable load to indicate that the queue is about to become overloaded. The specific threshold values ​​can also be periodically fine-tuned according to actual business fluctuations and seasonal peaks to ensure that the thresholds always align with real-time processing status and business needs.

[0052] The aforementioned priority adjustment mechanism embodies the principle of load-sensitive adaptive scheduling. When the queue has few tasks (below the first preset threshold), the priority adjustment coefficient is decreased, gradually reducing the intensity of scheduling intervention. When the queue has many tasks (between the first and second preset thresholds), the priority adjustment coefficient is increased, enhancing the scheduler's authority to adjust the task order to accelerate processing. When the queue is close to overload (above the second preset threshold), urgent tasks are directly marked and an emergency channel is activated to avoid congestion. This design ensures operational efficiency under low load while effectively preventing processing delays under high load through dynamic priority adjustment, ensuring that high-priority tasks always receive priority processing, thereby optimizing the overall balance between throughput and timeliness.

[0053] Step Four If all high-time-sensitivity tasks in the current hierarchical processing task have been completed, and the intermediate results of the remaining regular-time-sensitivity tasks meet the preset convergence stability conditions, then the composite hierarchical decision-making process is determined to be ready for output, and the final comprehensive hierarchical processing result is output. If all high-time-sensitivity tasks in the current hierarchical processing task have not been completed, or the intermediate results of the remaining regular-time-sensitivity tasks do not meet the preset convergence stability conditions, then the hierarchical processing process of each hierarchical processing task continues to be monitored, and a re-prediction instruction for dynamic queue scheduling is sent. If the maximum allowable processing time limit is reached and the current hierarchical processing task is still not completed, then the uncompleted hierarchical processing task is marked as an unconverged part, and a confidence risk warning is given. For example, the output report may indicate that some indicators have not achieved final convergence due to processing timeout, the conclusion confidence level is 85%, and manual review is recommended.

[0054] The intermediate results represent the convergence status markers of the dynamic nodes corresponding to the current state evolution trend of each quality inspection indicator in the dynamic knowledge network during the hierarchical processing. Only tasks corresponding to dynamic nodes marked as stable or with fixed risks can be judged as having met the convergence conditions. The preset convergence stability conditions are set by the preset personnel based on the simulation verification of the existing quality inspection data processing system to ensure that the dynamic knowledge network reaches a state of overall cognitive stability and information completeness, thereby supporting the reliable output of the final hierarchical processing results.

[0055] It is important to understand that if there are no high-time-sensitivity tasks in the current tiered processing tasks, the final comprehensive tiered processing result will be output when all tasks of the regular time-sensitivity level are completed and the corresponding intermediate results meet the preset convergence stability conditions. If there are no tasks of the regular time-sensitivity level in the current tiered processing tasks, the final comprehensive tiered processing result will be output directly after all high-time-sensitivity tasks are completed, without waiting for the convergence judgment of the regular time-sensitivity level tasks. This further ensures that processing efficiency and result reliability can be adapted to different task combinations, avoiding unnecessary waiting or premature output due to missing task types. The comprehensive tiered processing result mentioned in this embodiment refers to the result generated after coupling analysis of various quality inspection indicators based on a dynamic knowledge network, which includes time-sensitivity level (regular time-sensitivity level and high time-sensitivity level), risk transmission path, risk amplification effect or superposition effect, and the completion status of tiered processing tasks.

[0056] In a specific embodiment, for example, a batch of children's knitted pajamas exported to the EU is subjected to quality inspection, receiving test data on formaldehyde content (chemical indicator), cord length (physical indicator), and saliva fastness (chemical indicator). First, a quantitative phase space is constructed, mapping each indicator to a dynamic node, and the dynamic coupling coefficient is calculated. If the cord length exceeds the standard and the formaldehyde content is critical, and the dynamic coupling coefficient is high, then this group of indicators is marked as a coupled quality inspection indicator group, and its association strength is updated in the knowledge graph. During cross-dimensional analysis, it is identified that both jointly affect the comprehensive suffocation risk and skin irritation risk as two downstream nodes. After calculating the joint impact factor, it is found that the overall impact value exceeds the corresponding preset threshold, indicating a direct risk amplification effect, suggesting that the overall safety risk level of this batch of pajamas needs to be upgraded. Subsequently, this task is given the first initial priority due to its high timeliness and high risk level, and is processed first through dynamic queue scheduling. After all high-timeliness tasks are completed, the evaluation of the regular tasks has converged, and finally, a comprehensive classification conclusion that this batch is high-risk is output, along with a detailed transmission path and confidence level explanation.

[0057] In summary, the beneficial effects of this invention lie in its systematic solution to the problems of fragmented multi-source data and isolated risk assessment in cross-border apparel quality inspection scenarios. By constructing a dynamic knowledge network, it achieves unified fusion and real-time situational awareness of multi-dimensional data, including chemical, physical, and visual data. By introducing dynamic coupling coefficients and transmission path analysis, cross-dimensional risk couplings, such as cord length and flame retardancy, can be quantified, identified, and modeled. Combined with a time-driven priority scheduling and convergence determination mechanism, it ensures timely response to critical tasks while guaranteeing the integrity and reliability of output results. Ultimately, it transforms the traditional fragmented anomaly list into an interpretable and actionable overall risk classification, improving the accuracy and efficiency of quality inspection decisions and providing intelligent and systematic technical support for quality compliance and risk management in cross-border trade.

[0058] It should be added that, such as Figure 4 The time-series line graph comparing task priorities is used to compare the changes in the overall priority of multiple tiered processing tasks under different time-sensitive indicators, thus supporting a visual explanation of the process from intensity assessment and priority ranking to dynamic adjustment of execution order. The vertical axis represents the progression of tiered decisions according to time windows; the horizontal axis represents the overall priority score (the higher the value, the higher the priority should be, which can be understood as a combined result of higher risk / closer timeliness / stronger coupling impact). In the graph, the blue solid line (Task A), the orange dashed line (Task B), and the green dotted line (Task C) represent the overall priorities calculated for the three tasks in each time-sensitive window. The overall priority score is obtained by averaging the priority adjustment coefficients during the dynamic adjustment of execution order, and therefore can be directly used to classify tasks into high-time-sensitive and regular-time-sensitive levels and dynamically sort them: high-time-sensitive tasks usually exhibit a consistently high overall priority or a sudden increase. An upward curve indicates that the task's priority has been raised due to abnormal exacerbation or a shortened remaining time; a downward curve indicates that the risk has eased or the time-sensitive pressure has decreased. When the three curves intersect or lead each other, it means that the queue scheduling should be reordered. For example, if task B exceeds task A, the execution order will switch from A priority to B priority.

[0059] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0060] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0061] In various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0062] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0063] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for hierarchical processing of quality inspection data for cross-border apparel, characterized in that, The method includes: Based on the multi-dimensional quality inspection data received by the cross-border e-commerce platform and the corresponding target compliance requirements, the status of quality nodes in the local knowledge graph is dynamically updated to form a dynamic knowledge network that reflects the real-time hierarchical situation. For the current preliminary quality inspection results, a timeliness label is added, and based on the predefined hierarchical interaction relationship in the dynamic knowledge network, combined with the abnormal / coupled quality inspection indicators of the current quality inspection data, cross-dimensional quantitative cross-analysis is performed to identify the potential superposition / amplification effects between different quality node states. The strength of the identified abnormal coupling relationships is assessed and their priorities are ranked, thereby dynamically adjusting the execution order of hierarchical processing. During the dynamic adjustment of the execution order, the timeliness indicators of each hierarchical processing task are monitored. Through predictive dynamic queue scheduling, the composite hierarchical decision-making process converges within the time constraints, thereby determining whether to output the final comprehensive hierarchical processing result.

2. The method for graded processing of cross-border apparel quality inspection data as described in claim 1, characterized in that, The formation of a dynamic knowledge network reflecting real-time hierarchical situation specifically includes: Under the pre-defined compliance knowledge modeling framework, based on the indicator sequence corresponding to multi-dimensional quality inspection data and the reference sequence corresponding to the target compliance limit, a quantitative phase space is constructed to represent the mapping relationship between the indicator sequence and the reference sequence, and the quality inspection results of the current batch are mapped to one or more dynamic nodes in the quantitative phase space. The dynamic coupling coefficient is used to dynamically determine and correct the node state corresponding to the dynamic node, specifically as follows: If the dynamic coupling coefficient is not less than the preset dynamic coupling coefficient, it is determined that the corresponding node state has an explicit abnormal risk, the corresponding indicator sequence is marked as abnormal, and synchronized to the local knowledge graph to update the risk state of the dynamic node. Otherwise, the corresponding node status is determined to have potential association risks, and the corresponding indicator sequence is subjected to association path enhancement processing to identify implicit transmission relationships, and synchronized to the local knowledge graph to update the connection relationships between dynamic nodes; Based on the state changes or connection relationships of dynamic nodes in the updated local knowledge graph, the knowledge network topology of the local knowledge graph is updated, thereby completing the formation and updating of the dynamic knowledge network.

3. The method for graded processing of cross-border apparel quality inspection data as described in claim 2, characterized in that, The dynamic coupling coefficient is obtained as follows: Within a preset quality inspection sequence range, each indicator sequence is normalized to generate a normalized indicator sequence, and the normalized indicator sequence is processed using a first-order difference operator to obtain dynamic change characteristics. The indicator sequences with dynamic change characteristics that are below the preset change threshold are zeroed out, the number of indicator sequences with values ​​greater than zero is counted, and the corresponding indicator sequences are recorded as valid indicator sequences. The Euclidean distance between the effective index sequence and the reference sequence is calculated as the cumulative offset, and the co-variance distance is quantified by the complement of the Pearson correlation coefficient, thereby reflecting the degree of difference and correlation deviation in the current processing batch. The cumulative offset amplitude and the cooperative deviation distance are coupled to obtain a dynamic coupling coefficient that characterizes the temporal correlation strength and compliance deviation degree between indicators.

4. The method for graded processing of cross-border apparel quality inspection data as described in claim 1, characterized in that, The quality inspection results for the current preliminary classification are given an additional timeliness indicator, including: Based on the preset timeliness assessment logic, and combined with the urgency of the order to which the quality inspection result belongs and the current processing queue status, the corresponding quality inspection result is divided into high timeliness level and regular timeliness level. The high timeliness level indicates that the order is in an expedited state and the current processing queue is not at full capacity. The standard timeliness level indicates that the order is in a non-urgent state and the current processing queue is in a full state. The cross-dimensional quantitative cross-analysis previously included type determination based on the acquired intensity indicators, specifically: The index sequence corresponding to the current quality inspection data and its calculated dynamic coupling coefficient are input into the association judgment matrix generated by the predefined hierarchical interaction relationship to form a binary association matrix, where each element is a binary value: if the dynamic coupling coefficient is not less than the preset dynamic coupling coefficient, it is recorded as 1, otherwise it is recorded as 0. The type determination includes: If all elements in the binary association matrix are 0, and the quality inspection indicators of the corresponding quality inspection data exceed the corresponding compliance threshold, then the quality inspection indicators are marked as abnormal quality inspection indicators. If an element in the binary correlation matrix contains at least one 1, then its quality inspection index is marked as a coupled quality inspection index.

5. The method for hierarchical processing of cross-border apparel quality inspection data as described in claim 1, characterized in that, If the quality inspection indicator corresponding to the current quality inspection data is an abnormal quality inspection indicator, the cross-dimensional quantitative cross-analysis is performed as follows: Calculate the absolute deviation between the detection value corresponding to the abnormal quality inspection indicator and the compliance threshold; Based on the preset transmission coefficient, the absolute deviation is calculated by gradient transmission along each transmission path in the predefined hierarchical interaction relationship to obtain the deviation influence value received by each downstream node. The deviation impact values ​​of the same abnormal quality inspection index received through different transmission paths are accumulated to calculate the cumulative transmission deviation of each downstream node; If the cumulative transmission deviation exceeds the corresponding compliance threshold, it is determined that the corresponding downstream node has a direct transmission superposition effect. If the cumulative transmission deviation does not exceed the corresponding compliance threshold, but exceeds the historical cumulative transmission deviation fluctuation range, it is determined that the corresponding downstream node has a potential transmission superposition effect. Conversely, if the abnormal quality inspection indicators do not have a transmission effect on downstream nodes, it is determined that the current abnormal quality inspection indicators have not had a transmission effect.

6. The method for hierarchical processing of cross-border apparel quality inspection data as described in claim 1, characterized in that, If the quality inspection indicators corresponding to the current quality inspection data are coupled quality inspection indicators, the cross-dimensional quantitative cross-analysis is performed as follows: In the predefined hierarchical interaction relationship, identify all downstream influencing nodes that the coupled quality inspection index group points to together; Based on the temporal covariance relationship between the coupled quality inspection index groups, the joint impact factor of the coupled quality inspection index groups on each downstream affected node is calculated. If there is a feedback path in the predefined hierarchical interaction relationship where the downstream node points back to the coupled quality inspection index group, then the feedback loop is iteratively calculated and an attenuation coefficient is introduced to simulate the convergence of the loop effect. If it does not exist, the calculated joint impact factor is used directly as the impact intensity of the corresponding downstream impact node. Calculate the sum of the joint impact factors of all downstream affected nodes to obtain the overall impact value of this coupling. If the overall impact value exceeds the preset risk amplification threshold, it is determined that a direct risk amplification effect has occurred. If the overall impact value does not exceed the preset risk amplification threshold, but exceeds the historical impact value fluctuation range, it is determined that a local risk amplification effect has occurred. Conversely, it is determined that the current coupled quality inspection indicator group has not triggered a cross-dimensional risk amplification effect.

7. A method for grading and processing quality inspection data of cross-border apparel as described in claim 5 or 6, characterized in that, The intensity assessment and priority ranking include: Identify abnormal quality inspection indicators with a cumulative effect or a group of coupled quality inspection indicators with a risk amplification effect, and perform initial priority ranking: The urgency of handling abnormal quality inspection indicators that have a direct transmission and superposition effect or coupled quality inspection indicator groups that have a direct risk amplification effect is recorded as the first initial priority. The urgency of handling abnormal quality inspection indicators with indirect transmission superposition effects or coupled quality inspection indicator groups with local risk amplification effects is recorded as the second initial priority. Based on the first initial priority or the second initial priority, graded processing tasks are allocated, and the priority adjustment coefficient during the execution of graded processing tasks is monitored to sort the priorities, so as to ensure that high-timeliness and high-risk quality inspection results are processed first. The priority adjustment coefficient is used to quantify the relative urgency of the graded processing tasks in the current processing queue.

8. The method for hierarchical processing of cross-border apparel quality inspection data as described in claim 1, characterized in that, The priority sorting is specifically performed as follows: If the number of hierarchical processing tasks in the current processing queue is not higher than the first preset threshold, the execution order of the current hierarchical processing tasks is maintained, and the priority adjustment coefficient is reduced by one, and the priority adjustment coefficient is not less than zero. If the priority adjustment coefficient is already zero at this time, the decrement operation of the coefficient is paused. If the number of hierarchical processing tasks in the current processing queue is higher than the first preset threshold but not higher than the second preset threshold, then the priority adjustment coefficient is incremented by one. If the number of tiered processing tasks in the current processing queue exceeds the second preset threshold, then all current tiered processing tasks will be marked as urgent tasks. The constraint condition of the first preset threshold is lower than the constraint condition of the second preset threshold.

9. The method for hierarchical processing of cross-border apparel quality inspection data as described in claim 1, characterized in that, The determination of whether to output the final comprehensive classification result includes: If all high-time-sensitivity tasks in the current hierarchical processing task have been completed, and the intermediate results of the remaining regular-time-sensitivity tasks meet the preset convergence stability conditions, then the composite hierarchical decision-making process is determined to have reached the output state, and the final comprehensive hierarchical processing result is output. If all high-time-sensitivity tasks in the current hierarchical processing task have not been completed, or if the intermediate results of the remaining regular-time-sensitivity tasks do not meet the preset convergence stability conditions, then the hierarchical processing process of each hierarchical processing task will continue to be monitored, and a re-prediction instruction for dynamic queue scheduling will be sent. If the maximum allowed processing time limit is reached and the current hierarchical processing tasks are still not fully processed, the unprocessed hierarchical processing tasks will be marked as non-converged parts, and a confidence risk warning will be issued. The intermediate results represent the convergence state markers of the corresponding dynamic nodes during the hierarchical processing, based on the current state evolution trend of each quality inspection indicator in the dynamic knowledge network.

10. The method for hierarchical processing of cross-border apparel quality inspection data as described in claim 9, characterized in that, The determination of whether to output the final comprehensive classification result also includes: If there are no high-time-sensitivity tasks in the current hierarchical processing tasks, the final comprehensive hierarchical processing result will be output when all regular time-sensitivity tasks are completed and the corresponding intermediate results meet the preset convergence stability conditions. If there are no tasks of the regular time-sensitive level in the current hierarchical processing task, the final comprehensive hierarchical processing result will be output directly after all high time-sensitive level tasks are completed, without waiting for the convergence judgment of the regular time-sensitive level tasks.

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