3C product export compliance automatic detection method based on AI

Abstract

The invention relates to an AI-based 3C product export compliance automatic detection method, which comprises the following steps: monitoring laws and regulations issuing platforms of 37 national supervision organizations at a frequency of not less than 30 minutes per time through a distributed crawler cluster deployed at a plurality of geographic nodes, and obtaining technical laws and regulations updating data of a target export country; a dynamic compliance mapping knowledge domain with a version tracing function is constructed, nodes comprise a standard term entity, a test method entity and an exemption condition entity, and edges comprise a logic dependence edge, a conflict edge and a reference edge. According to the AI-based 3C product export compliance automatic detection method, through collaborative optimization of a distributed crawler cluster and a hybrid neural network, single product detection is shortened to 3.4 minutes, vision-text-signal three-mode coupling analysis is constructed, the bottleneck of single-mode processing is broken through, the technical document analysis speed is increased by 18 times, real-time frame processing is achieved through video stream analysis, and the detection efficiency is improved. The 60fps video stream delay is less than 0.1 s, and the detection efficiency is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of 3C product export testing technology, specifically to an AI-based automated testing method for the compliance of 3C product exports. Background Technology

[0002] In recent years, 3C products have emerged along with the development of the electronics industry. They mainly refer to computer, communication, and consumer electronics products. In this industry, technological development is advancing rapidly, with frequent upgrades and replacements. Almost every month, a large number of new products are launched, which has become a hot area for product design. Various design companies have set up 3C departments to specialize in the design and development of such products.

[0003] Compliance testing is essential in the export process of 3C products. Currently, compliance testing mainly relies on the cooperation of hardware and software. After obtaining product information, it is matched with the corresponding international export regulations to determine compliance. For example, Chinese Patent 202311773802.1, "Compliance Testing Method, Device, Equipment, and Storage Medium," uses multiple target detection modules to perform multiple target detection items on engineering documents in parallel, obtaining test results. The test results include the detection sub-results output by each target detection module, and are used to indicate non-compliant content in the engineering documents. This method improves testing efficiency while ensuring accuracy.

[0004] However, current automated testing methods for compliance of 3C products exported generally have the following shortcomings:

[0005] 1. Manual review is inefficient in some cases: a single product needs to be compared with 5,000+ international regulations and clauses;

[0006] 2. Lagging dynamic updates: The average monthly update frequency of technical standards in various countries reaches 2.3 times (WTO data);

[0007] 3. Lack of multimodal data processing: Traditional systems cannot simultaneously parse heterogeneous data such as technical documents, product images, and test videos;

[0008] 4. Insufficient ability to predict compliance risks: The existing solution lacks a mechanism for predicting emerging technologies such as quantum encryption and flexible batteries.

[0009] Therefore, an AI-based automated detection method for the export compliance of 3C products is proposed to solve the above problems. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides an AI-based automated inspection method for the export compliance of 3C products. This method boasts advantages such as improved processing speed and parallel processing of multimodal data. It solves problems associated with traditional automated inspection methods for 3C product export compliance, which suffer from low efficiency due to reliance on manual review in some cases, the need to compare over 5000 international regulations for a single product, lagging dynamic updates (with national technical standards updating an average of 2.3 times per month), lack of multimodal data processing capabilities, inability to simultaneously analyze heterogeneous data such as technical documents, product images, and test videos, insufficient compliance risk prediction capabilities, and a lack of predictive mechanisms for emerging technologies such as quantum encryption and flexible batteries.

[0011] To achieve the above objectives, the present invention provides the following technical solution: an AI-based automated detection method for the export compliance of 3C products, comprising the following steps:

[0012] 1) By deploying a distributed crawler cluster across multiple geographical nodes, monitor the regulatory publishing platforms of 37 national regulatory agencies at a frequency of no less than 30 minutes per session to obtain updated technical regulations data of the target exporting countries;

[0013] 2) Construct a dynamic compliance knowledge graph with version traceability functionality, where nodes include standard clause entities, test method entities, and exemption condition entities, and edges include three types: logical dependency edges, conflict edges, and reference edges;

[0014] 3) Employ a hybrid neural network that incorporates visual-text-signal three-modal coupled analysis to perform joint feature extraction and compliance matching on the target product's technical documents, test images, and hardware parameters;

[0015] 4) Generate an executable report based on a deep reinforcement learning model, which includes risk quantification scoring, compliance defect location and automated remediation suggestions, and output an immutable audit trail through a blockchain evidence storage interface.

[0016] Furthermore, the multimodal data obtained in step 1) includes:

[0017] Technical document collection: structured and unstructured texts of product specifications, test reports, and declarations of materials;

[0018] Test image data: Electromagnetic compatibility test video stream, multi-angle images of product markings;

[0019] Hardware parameter set: Component model, RF parameters, and battery specification data obtained through API interface;

[0020] In step 1), the distributed crawler cluster adopts an adaptive scheduling strategy. When the update of key regulatory source data from the US BIS and the EU OJ is detected, the crawling frequency is automatically increased to 5 minutes / time.

[0021] The data acquisition process implements a multi-level verification mechanism, including:

[0022] Verify the authenticity of the regulatory text through digital signature comparison;

[0023] Use hash value verification to ensure file integrity;

[0024] The table data is extracted using a dual verification method combining OCR and a rules engine.

[0025] Furthermore, the method for constructing the dynamic knowledge graph in step 2) includes:

[0026] 201) Use a bidirectional LSTM-CRF model to perform semantic segmentation on the regulatory text and identify the obligor, technical indicators, and test method entities in the clauses;

[0027] 202) Infer the relationships between entities through graph neural networks and establish a three-layer logical edge structure of "requirement-exception-reference";

[0028] 203) When a regulatory version update is detected, the subgraph reconstruction algorithm is automatically triggered to maintain the timeliness of the knowledge graph.

[0029] Furthermore, the subgraph reconstruction algorithm performs the following operations:

[0030] 2031) Calculate the cosine similarity matrix between the old and new legal texts:

[0031] Sim_Matrix=[s(f_i,f_j)]_{m×n}

[0032] Where f_i∈Old regulation feature set, f_j∈New regulation feature set;

[0033] 2032) When the similarity value is lower than the threshold θ, a map node change notification is generated and the manual review process is initiated.

[0034] Furthermore, the hybrid neural network architecture in step 3) includes:

[0035] Visual analysis branch: Extracting multi-scale features from product images using an improved ResNet-50 network;

[0036] Text analysis branch: Generating semantic embedding vectors for technical documents based on the BERT-wwm model;

[0037] Signal processing branch: Use the time-frequency analysis module to analyze the spectral characteristics in the test video;

[0038] Cross-modal attention layer: Calculates the correlation weight matrix between features of different modalities.

[0039] Furthermore, the calculation process of the cross-modal attention layer is expressed as follows:

[0040]

[0041] Where Q is the text feature matrix, K and V are the visual feature matrices, W_q and W_k are the trainable parameter matrices, and d is the feature dimension.

[0042] The visual analysis branch performs geometric feature detection on the CE / FCC certification mark and verifies the clarity of the mark pattern based on sub-pixel segmentation.

[0043] Furthermore, the compliance index calculation model in step 4) is as follows:

[0044] C_Index = 0.4 * Sigmoid (Technical Compliance) + 0.3 * Ln (Document Completeness) + 0.3 * (1 - Historical Violation Rate)

[0045] Technical compliance is calculated by comparing the deviation of actual parameters from regulatory thresholds.

[0046] Furthermore, the executable report in step 4) includes:

[0047] 3D Risk Heat Map: Displays the risk distribution across different countries, product categories, and time dimensions;

[0048] Automated correction suggestions: Provide test plan adjustments, supplementary documentation lists, and recommendations for alternative components;

[0049] Trace the audit path: Record the evidence chain and confidence score for each test result.

[0050] Furthermore, the deep reinforcement learning model in step 4) includes:

[0051] State space S is defined as a triple of <product feature vector, target country's legal constraints, and historical violation records>.

[0052] Action Space A includes 15 types of compliance optimization actions such as parameter adjustment, test plan optimization, and document supplementation;

[0053] Reward function R:

[0054] R=w_1·ΔC_Index+w_2·Cost_Reduction+w_3·Time_Saving

[0055] Where w_1 = 0.6, w_2 = 0.3, w_3 = 0.1;

[0056] The policy network parameters θ are updated using the Proximal Policy Optimization (PPO) algorithm, which is as follows:

[0057] L^{CLIP}(θ)=E_t[min(r_t(θ)A_t,clip(r_t(θ),1-ε,1+ε)A_t)]

[0058] Furthermore, it also includes a privacy computing module:

[0059] Use a federated learning framework to process sensitive enterprise data locally;

[0060] Only homomorphically encrypted feature vectors are uploaded to the central server;

[0061] Differential privacy protection is implemented during the knowledge graph update process.

[0062] Furthermore, the method is deployed on an edge computing architecture, which includes:

[0063] Local lightweight testing terminal: performs real-time data acquisition and preprocessing;

[0064] Cloud analytics server: Runs core AI models and returns decision results;

[0065] Blockchain evidence storage node: Hash and upload key detection processes to the blockchain.

[0066] Compared with the prior art, the technical solution of this application has the following beneficial effects:

[0067] 1. This AI-based automated inspection method for 3C product export compliance, through the collaborative optimization of distributed crawler clusters and hybrid neural networks, reduces the inspection time for a single product from 8 hours of traditional manual review to 3.4 minutes, improving efficiency by 142 times. It constructs a three-modal coupled analysis of vision, text, and signal, breaking through the bottleneck of single-modal processing, and improves the technical document parsing speed by 18 times, from 12 pages / hour to 216 pages / hour. Video stream analysis achieves real-time frame processing, with a latency of <0.1s for 60fps video streams, significantly improving inspection efficiency.

[0068] 2. This AI-based automated inspection method for the export compliance of 3C products improves the accuracy of clause matching to 98.7% through dynamic association reasoning of knowledge graphs, solves the problem of inconsistency between images and text by adopting a cross-modal attention mechanism, achieves a compliance verification accuracy of 99.3% between product identification images and text descriptions, and achieves a deviation detection sensitivity of 0.1dB between test video spectral features and report data. It also achieves a prediction accuracy of 89.5% for the impact of regulations in the next 6 months by adopting a deep reinforcement learning model, significantly improving the detection accuracy.

[0069] 3. This AI-based automated method for detecting compliance in 3C product exports achieves a regulatory update capture latency of less than 15 minutes through a distributed web crawler system. The knowledge graph subgraph reconstruction algorithm can complete an impact assessment within 23 minutes after detecting new regulations, enabling real-time response to regulatory changes. Attached Figure Description

[0070] Figure 1 This is a flowchart of the AI-based automated testing method for the export compliance of 3C products according to the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] Please see Figure 1 The AI-based automated inspection method for export compliance of 3C products in this embodiment includes the following steps:

[0073] 1) By deploying a distributed crawler cluster across multiple geographical nodes, monitor the regulatory publishing platforms of 37 national regulatory agencies at a frequency of no less than 30 minutes per session to obtain updated technical regulations data of the target exporting countries;

[0074] 2) Construct a dynamic compliance knowledge graph with version traceability functionality, where nodes include standard clause entities, test method entities, and exemption condition entities, and edges include three types: logical dependency edges, conflict edges, and reference edges;

[0075] 3) Employ a hybrid neural network that incorporates visual-text-signal three-modal coupled analysis to perform joint feature extraction and compliance matching on the target product's technical documents, test images, and hardware parameters;

[0076] 4) Generate an executable report based on a deep reinforcement learning model, which includes risk quantification scoring, compliance defect location and automated remediation suggestions, and output an immutable audit trail through a blockchain evidence storage interface.

[0077] In this embodiment, the multimodal data includes:

[0078] Technical document collection: structured and unstructured texts of product specifications, test reports, and declarations of materials;

[0079] Test image data: Electromagnetic compatibility test video stream, multi-angle images of product markings;

[0080] Hardware parameter set: Component model, RF parameters, and battery specification data obtained through API interface;

[0081] Specifically, the distributed crawler cluster adopts an adaptive scheduling strategy. When it detects updates to key regulatory source data from the US BIS and the EU OJ, it automatically increases the crawling frequency to once every 5 minutes.

[0082] In this embodiment, the data acquisition process implements a multi-level verification mechanism, including:

[0083] Verify the authenticity of the regulatory text through digital signature comparison;

[0084] Use hash value verification to ensure file integrity;

[0085] The table data is extracted using a dual verification method combining OCR and a rules engine.

[0086] In this embodiment, the method for constructing a dynamic knowledge graph includes:

[0087] 201) Use a bidirectional LSTM-CRF model to perform semantic segmentation on the regulatory text, identifying the obligor, technical indicators, and testing method entities in the clauses, where:

[0088] The entity recognition module additionally labels the units of measurement and the effective range of technical indicators;

[0089] The relation extraction module distinguishes between mandatory requirements and recommended guidelines;

[0090] 202) Inferring relationships between entities using graph neural networks, establishing a three-layer logical edge structure of "requirement-exception-reference", and generating node embedding vectors based on graph attention networks (GAT). The calculation method is as follows:

[0091] h_i^{(l+1)}=σ(∑_{j∈N_i}α_{ij}W^{(l)}h_j^{(l)})

[0092] Where α_{ij} are the attention coefficients of nodes i and j, and W is the trainable weight matrix;

[0093] 203) When a regulatory version update is detected, the subgraph reconstruction algorithm is automatically triggered to maintain the timeliness of the knowledge graph; when a regulatory revision is detected, an incremental graph update operation is performed.

[0094] Retain the validity timestamps of historical version nodes;

[0095] A retrospective review process has been initiated for the testing tasks of the affected products.

[0096] Specifically, in the incremental map update operation:

[0097] Establish a mapping matrix between the old and new clauses. Where m is the number of old clauses and n is the number of new clauses;

[0098] Define the clause similarity metric function:

[0099] S(c_old,c_new)=λ_1·cos(v_old,v_new)+λ_2·Jaccard(KW_old,KW_new)

[0100] Where λ_1+λ_2=1, and KW represents the keyword set;

[0101] When S(c_old,c_new) < 0.7, it is marked as a major change and an early warning notification is triggered.

[0102] Specifically, the subgraph reconstruction algorithm performs the following operations:

[0103] 2031) Calculate the cosine similarity matrix between the old and new legal texts:

[0104] Sim_Matrix=[s(f_i,f_j)]_{m×n}

[0105] Where f_i∈Old regulation feature set, f_j∈New regulation feature set;

[0106] 2032) When the similarity value is lower than the threshold θ, a map node change notification is generated and the manual review process is initiated.

[0107] In this embodiment, the hybrid neural network architecture includes:

[0108] Visual analysis branch: Extracting multi-scale features from product images using an improved ResNet-50 network;

[0109] Text analysis branch: Generating semantic embedding vectors for technical documents based on the BERT-wwm model;

[0110] Signal processing branch: Use the time-frequency analysis module to analyze the spectral characteristics in the test video;

[0111] Cross-modal attention layer: Calculates the correlation weight matrix between features of different modalities.

[0112] Specifically, in the visual analysis branch: a Cascaded Feature Pyramid Network (Cascade-FPN) is used to extract identifier region features from product images, with the following loss function:

[0113] L_det=α·L_cls+β·L_reg+γ·L_mask

[0114] Among them, α=0.4, β=0.4, and γ=0.2.

[0115] Specifically, the text analysis branch: uses a domain-pre-trained Legal-BERT model to generate context-aware embeddings of technical documents and constructs a clause matching matrix.

[0116] M_ij=sim(E_doc[i],E_rule[j])

[0117] Where sim() is used to calculate cosine similarity.

[0118] Specifically, the cross-modal alignment module optimizes the consistency of the multimodal feature space through contrastive learning, employing the NT-Xent loss function.

[0119] L_cont=-log[exp(sim(z_i,z_j) / τ) / ∑_{k≠i}exp(sim(z_i,z_k) / τ)].

[0120] Specifically, the computation process of the cross-modal attention layer is as follows:

[0121]

[0122] Where Q is the text feature matrix, K and V are the visual feature matrices, W_q and W_k are the trainable parameter matrices, and d is the feature dimension.

[0123] In this embodiment, the visual analysis branch performs geometric feature detection of the CE / FCC certification mark and verifies the clarity of the mark pattern based on sub-pixel segmentation.

[0124] Specifically, the geometric feature inspection of certification marks such as CE / FCC includes:

[0125] Printing position deviation (distance from product edge);

[0126] Character height to line width ratio;

[0127] Differences in CIE-Lab values ​​across color spaces.

[0128] Based on the sub-pixel level segmentation to verify the sharpness of the marker pattern, the modulation transfer function (MTF) value is calculated:

[0129] MTF = (Max - Min) / (Max + Min)

[0130] When MTF < 0.8, the printing quality is considered unqualified.

[0131] In this embodiment, the compliance index calculation model is as follows:

[0132] C_Index = 0.4 * Sigmoid (Technical Compliance) + 0.3 * Ln (Document Completeness) + 0.3 * (1 - Historical Violation Rate)

[0133] Technical compliance is calculated by comparing the deviation of actual parameters from regulatory thresholds.

[0134] In this embodiment, the executable report includes:

[0135] 3D Risk Heat Map: Displays the risk distribution across different countries, product categories, and time dimensions;

[0136] Automated correction suggestions: Provide test plan adjustments, supplementary documentation lists, and recommendations for alternative components;

[0137] Trace the audit path: Record the evidence chain and confidence score for each test result.

[0138] In this embodiment, the deep reinforcement learning model includes:

[0139] State space S is defined as a triple of <product feature vector, target country's legal constraints, and historical violation records>.

[0140] Action Space A includes 15 types of compliance optimization actions such as parameter adjustment, test plan optimization, and document supplementation;

[0141] Reward function R:

[0142] R=w_1·ΔC_Index+w_2·Cost_Reduction+w_3·Time_Saving

[0143] Where w_1 = 0.6, w_2 = 0.3, w_3 = 0.1;

[0144] The policy network parameters θ are updated using the Proximal Policy Optimization (PPO) algorithm, which is as follows:

[0145] L^{CLIP}(θ)=E_t[min(r_t(θ)A_t,clip(r_t(θ),1-ε,1+ε)A_t)]

[0146] In this embodiment, the method further includes a privacy computing module:

[0147] Use a federated learning framework to process sensitive enterprise data locally;

[0148] Only homomorphically encrypted feature vectors are uploaded to the central server;

[0149] Differential privacy protection is implemented during the knowledge graph update process.

[0150] In this embodiment, the method is deployed on an edge computing architecture, which includes:

[0151] Local lightweight testing terminal: performs real-time data acquisition and preprocessing;

[0152] Cloud analytics server: Runs core AI models and returns decision results;

[0153] Blockchain evidence storage node: Hash and upload key detection processes to the blockchain.

[0154] Example 1: System Architecture Implementation

[0155] The system adopts a microservice architecture and is deployed in a hybrid cloud environment, and includes the following core modules:

[0156] Data Acquisition Layer

[0157] A 12-node web crawler cluster deployed in the AWS Global region, configured as follows:

[0158] Each node has 4 CPU cores, 16GB of memory, and 1Gbps of bandwidth.

[0159] Distributed scheduling is implemented using the Scrapy-Redis framework.

[0160] Data validation module:

[0161] The digital signature of the regulatory PDF file was extracted using PyPDF2 and compared with the EUTrustedList.

[0162] The HTML format announcements are parsed using a dual-engine approach of BeautifulSoup and XPath.

[0163] The table data was recognized using Tesseract OCR 5.0, with an accuracy rate improved to 96.2%.

[0164] Knowledge Graph Engine

[0165] Building a graph database using Neo4j 4.4 Enterprise Edition includes:

[0166] 2.3 million nodes: covering core standards such as FCC Part 15 and EN55032;

[0167] 4.7 million relationship edges: including “citation” (58%), “conflict” (12%), and “substitution” (30%).

[0168] Incremental update mechanism:

[0169] Perform version difference analysis every day at midnight;

[0170] When an update to the EU OJL series of gazettes is detected, a real-time update task is triggered.

[0171] Example 2: Inspection process for smartphones exported to the EU

[0172] Taking the export of a certain model of 5G mobile phone (containing 1200 components) to the EU as an example:

[0173] Data input stage

[0174] Upload product technical documents:

[0175] PDF specification sheet (82 pages, containing 278 technical parameters);

[0176] Excel format BOM (1200 rows, including HS code and RoHS compliance declaration).

[0177] Collect test image data:

[0178] Electromagnetic compatibility test video (1080P@60fps, 2 minutes 17 seconds);

[0179] Macro image of product logo (resolution 4096×2160, CIE-Lab color space).

[0180] API integration to obtain real-time data:

[0181] Extract historical shipping records from the company's ERP system (covering 23 countries);

[0182] Access the global certification database to query the status of CE / NCC certificates.

[0183] Multimodal analysis process

[0184] Text analysis:

[0185] The Legal-BERT model was used to extract key clauses from the instruction manual, identifying "Supports 5G NRn78 band (3300-3800MHz)";

[0186] Match with the RED instruction Article3(2) to generate a conformity matrix.

[0187] Visual inspection:

[0188] Inspect the printing quality of the CE mark:

[0189] Positional deviation 0.08mm (standard requirement <0.5mm);

[0190] Line width ratio 1:12 (compliant with EN300328V2.2.2 requirements).

[0191] MTF value calculation:

[0192] The central region is 0.91 and the peripheral region is 0.83 (threshold 0.8).

[0193] Signal Analysis:

[0194] From the test video decoding spectrum (RBW = 1MHz);

[0195] Measure conducted interference values:

[0196] The maximum peak value in the 30MHz-1GHz band is 58.2dBμV (limit 60dBμV).

[0197] Risk assessment and output

[0198] Compliance Index Calculation:

[0199] C_Index=0.4*(0.95)+0.3*ln(0.89)+0.3*(1-0.02)=0.872

[0200] in:

[0201] Technical compliance rate 0.95 (all RF parameters meet the standards);

[0202] File integrity score: 0.89 (missing scanned copy of South Korean KC certificate);

[0203] The historical violation rate is 2% (including 2 minor violations in the past three years).

[0204] Example 3: Experimental Verification Data

[0205] Data from actual measurements at 200 3C manufacturing companies (January 2023 - March 2024):

[0206]

[0207]

[0208] In summary, this AI-based automated inspection method for 3C product export compliance has achieved a paradigm shift from "manual, delayed review" to "AI-driven proactive governance" in 3C product export compliance inspection. Experimental data shows that it has achieved orders-of-magnitude improvements in core indicators such as efficiency, accuracy, and cost. Simultaneously, it has constructed a secure and reliable technical implementation system, providing key technological infrastructure for the digital transformation of global trade compliance.

[0209] Specifically, through the collaborative optimization of distributed crawler clusters and hybrid neural networks, the time required for single-product inspection has been reduced from 8 hours of traditional manual review to 3.4 minutes, an efficiency improvement of 142 times. A three-modal coupled analysis of vision, text, and signal has been constructed, overcoming the bottleneck of single-modal processing, increasing the speed of technical document parsing by 18 times, from 12 pages / hour to 216 pages / hour. Video stream analysis achieves real-time frame processing, with a latency of <0.1s for 60fps video streams, significantly improving inspection efficiency. Dynamic association reasoning through knowledge graphs has increased the accuracy of clause matching to 98.7%. A cross-modal attention mechanism has been adopted to solve the problem of inconsistency between images and text, achieving a 99.3% accuracy rate in verifying the conformity of product identification images and text descriptions. The sensitivity for detecting deviations between test video spectral features and report data reaches 0.1dB. A deep reinforcement learning model has achieved a prediction accuracy of 89.5% for the impact of regulations over the next 6 months, significantly improving detection precision. The distributed crawler system achieves a regulatory update capture latency of <15 minutes. The knowledge graph subgraph reconstruction algorithm can complete the impact assessment within 23 minutes after detecting new regulations, enabling real-time response to regulatory changes.

[0210] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0211] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI-based automated inspection method for the compliance of 3C product exports, characterized in that, Includes the following steps: 1) By deploying a distributed crawler cluster across multiple geographical nodes, monitor the regulatory publishing platforms of 37 national regulatory agencies at a frequency of no less than 30 minutes per session to obtain updated technical regulations data of the target exporting countries; 2) Construct a dynamic compliance knowledge graph with version traceability functionality, where nodes include standard clause entities, test method entities, and exemption condition entities, and edges include three types: logical dependency edges, conflict edges, and reference edges; 3) Employ a hybrid neural network that incorporates visual-text-signal three-modal coupled analysis to perform joint feature extraction and compliance matching on the target product's technical documents, test images, and hardware parameters; 4) Generate an executable report based on a deep reinforcement learning model, which includes risk quantification scoring, compliance defect location and automated remediation suggestions, and output an immutable audit trail through a blockchain evidence storage interface.

2. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The multimodal data obtained in step 1) includes: Technical document collection: structured and unstructured texts of product specifications, test reports, and declarations of materials; Test image data: Electromagnetic compatibility test video stream, multi-angle images of product markings; Hardware parameter set: Component model, RF parameters, and battery specification data obtained through API interface; In step 1), the distributed crawler cluster adopts an adaptive scheduling strategy. When the update of key regulatory source data from the US BIS and the EU OJ is detected, the crawling frequency is automatically increased to 5 minutes / time. The data acquisition process implements a multi-level verification mechanism, including: Verify the authenticity of the regulatory text through digital signature comparison; Use hash value verification to ensure file integrity; The table data is extracted using a dual verification method combining OCR and rule engine.

3. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The method for constructing the dynamic knowledge graph in step 2) includes: 201) Use a bidirectional LSTM-CRF model to perform semantic segmentation on the regulatory text and identify the obligor, technical indicators, and test method entities in the clauses; 202) Infer the relationships between entities through graph neural networks and establish a three-layer logical edge structure of "requirement-exception-reference"; 203) When a regulatory version update is detected, the subgraph reconstruction algorithm is automatically triggered to maintain the timeliness of the knowledge graph.

4. The AI-based automated inspection method for compliance of 3C product exports according to claim 3, characterized in that, The subgraph reconstruction algorithm performs the following operations: 2031) Calculate the cosine similarity matrix between the old and new legal texts: Sim_Matrix=[s(f_i,f_j)]_{m×n} Where f_i∈Old regulation feature set, f_j∈New regulation feature set; 2032) When the similarity value is lower than the threshold θ, a map node change notification is generated and the manual review process is initiated.

5. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The hybrid neural network architecture in step 3) includes: Visual analysis branch: Extracting multi-scale features from product images using an improved ResNet-50 network; Text analysis branch: Generating semantic embedding vectors for technical documents based on the BERT-wwm model; Signal processing branch: Use the time-frequency analysis module to analyze the spectral characteristics in the test video; Cross-modal attention layer: Calculates the correlation weight matrix between features of different modalities.

6. The AI-based automated inspection method for compliance of 3C product exports according to claim 5, characterized in that, The calculation process of the cross-modal attention layer is expressed as follows: Where Q is the text feature matrix, K and V are the visual feature matrices, W_q and W_k are the trainable parameter matrices, and d is the feature dimension. The visual analysis branch performs geometric feature detection on the CE / FCC certification mark and verifies the clarity of the mark pattern based on sub-pixel segmentation.

7. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The compliance index calculation model in step 4) is as follows: C_Index = 0.4 * Sigmoid (Technical Compliance) + 0.3 * Ln (Document Completeness) + 0.3 * (1 - Historical Violation Rate) Technical compliance is calculated by comparing the deviation of actual parameters from regulatory thresholds.

8. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The executable report in step 4) includes: 3D Risk Heat Map: Displays the risk distribution across different countries, product categories, and time dimensions; Automated correction suggestions: Provide test plan adjustments, supplementary documentation lists, and recommendations for alternative components; Trace the audit path: Record the evidence chain and confidence score for each test result.

9. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The deep reinforcement learning model in step 4) includes: State space S is defined as a triple of <product feature vector, target country's legal constraints, and historical violation records>. Action Space A includes 15 types of compliance optimization actions such as parameter adjustment, test plan optimization, and document supplementation; Reward function R: R=w_1·ΔC_Index+w_2·Cost_Reduction+w_3·Time_Saving Where w_1 = 0.6, w_2 = 0.3, w_3 = 0.1; The policy network parameters θ are updated using the Proximal Policy Optimization (PPO) algorithm, which is as follows: L^{CLIP}(θ)=E_t[min(r_t(θ)A_t,clip(r_t(θ),1-ε,1+ε)A_t)].

10. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, It also includes a privacy computing module: Use a federated learning framework to process sensitive enterprise data locally; Only homomorphically encrypted feature vectors are uploaded to the central server; Differential privacy protection is implemented during the knowledge graph update process.

11. The AI-based automated inspection method for compliance of 3C product exports according to claim 1, characterized in that, The method is deployed on an edge computing architecture, which includes: Local lightweight testing terminal: performs real-time data acquisition and preprocessing; Cloud analytics server: Runs core AI models and returns decision results; Blockchain evidence storage node: Hash and upload key detection processes to the blockchain.

12. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by a processor, it implements the steps of the method according to any one of claims 1-11.

Images