Garment supply chain risk analysis method and system based on artificial intelligence

Abstract

The invention discloses a garment supply chain risk analysis method and system based on artificial intelligence, belongs to the technical field of garment supply chain analysis, and effectively enhances crisis pre-judgment and response capability of a supply chain through a dual mechanism of simulation deduction and dynamic optimization. A built-in game learning framework of the system can autonomously construct a multi-factor coupled complex interruption scene, for example, an extreme condition that a port is closed in a typhoon season, cross-border tax policy mutation and alternative raw material transportation route congestion occur at the same time is simulated. The deduction not only reveals the problem of excessive dependence of a single node, which is difficult to discover by traditional auditing, but also can verify the practical feasibility of a standby scheme, and helps an enterprise to establish a multi-layer defense system. The continuous self-optimization characteristic of the system enables the system to be prominent in response to novel challenges, and when the international logistics network is suddenly adjusted, a supplier recombination scheme considering both cost and time efficiency can be quickly generated, and the potential chain breakage risk is resolved in the germination stage.

Description

Technical Field

[0001] The present invention belongs to the technical field of clothing supply chain analysis, and specifically relates to a clothing supply chain risk analysis method and system based on artificial intelligence. Background Art

[0002] The apparel supply chain refers to the network of links and enterprises involved in the entire apparel production process, from raw material sourcing, production and processing, logistics and transportation, distribution and retail, to the final consumer delivery. It encompasses upstream fiber and fabric suppliers, midstream apparel designers and manufacturers, and downstream wholesalers, retailers, and consumers. Apparel supply chain risk analysis is the process of identifying, assessing, and addressing potential risks across the supply chain. These risks include natural disasters, political instability, raw material price fluctuations, rising labor costs, quality issues, logistics delays, demand fluctuations, exchange rate fluctuations, and regulatory changes. By analyzing these risks, companies can develop appropriate risk mitigation strategies, such as diversified sourcing, strengthened inventory management, flexible production systems, optimized logistics networks, and insurance, to enhance supply chain resilience and stability, ensuring the timely supply and market competitiveness of apparel products.

[0003] However, existing supply chain risk management technologies mainly rely on historical data statistics and manual experience judgment, which makes it difficult to effectively predict new risk events coupled with multiple factors. At the same time, the emergency plan generation mechanism is limited by the lag in updating the expert knowledge base, and decision-making blind spots and response delays often occur when faced with sudden global supply chain shocks. Summary of the Invention

[0004] The purpose of the present invention is to provide a clothing supply chain risk analysis method and system based on artificial intelligence in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a clothing supply chain risk analysis method based on artificial intelligence, the method comprising the following steps:

[0006] S1: Integrate the entire supply chain information through multi-source heterogeneous data fusion technology, including external risk factors such as raw material price fluctuations, factory production capacity data, logistics time records, and weather policies;

[0007] S2: Build a dynamic knowledge graph to associate upstream and downstream entity relationships, and use graph neural networks to mine potential dependencies and vulnerable nodes;

[0008] S3: Deploy a self-evolving risk simulation engine, create virtual disruption scenarios based on a generative adversarial network, and generate black swan event models that are difficult for human experts to foresee through adversarial training.

[0009] S4: Develop a multimodal real-time monitoring system that integrates satellite imagery to analyze factory operating status, natural language processing to analyze supplier communication records, and IoT sensors to track goods in transit.

[0010] S5: Establish a quantum risk assessment matrix, transform traditional risk indicators into superimposable quantum state vectors, and reveal cross-level risk transmission paths through entanglement calculations;

[0011] S6: Apply deep reinforcement learning to dynamically optimize supplier portfolio strategies and generate the most risk-resilient procurement solutions under cost constraints.

[0012] S7: Design an immersive risk tracing visualization interface, use neural radiation field technology to 3D reconstruct the supply chain network, and support gesture operation to explore risk hotspots;

[0013] S8: Embeds a self-explanatory decision-making module to generate executable risk mitigation measures through causal reasoning and predict the impact weight of each measure on key performance indicators;

[0014] S9: Build a closed-loop feedback evolution mechanism and use federated learning to continuously update risk models while protecting data privacy, thereby achieving the self-healing capability of the supply chain system.

[0015] In a preferred embodiment, in step S1, the full-link information integration of the supply chain is achieved by building a unified data lake architecture. First, a distributed crawler system is deployed to capture real-time quotation data from the raw material exchange, and at the same time, the production work order information flow of the factory MES system is connected, and the edge computing equipment is used to perform millisecond-level analysis on the production capacity data collected by the workshop sensors. For the unstructured freight logs of logistics companies, a bidirectional LSTM network is used to extract the transportation delay pattern, and it is linked with the API interface of the Meteorological Bureau to establish a correlation matrix between weather events and transportation timeliness. After all data streams are standardized, multi-dimensional features are spliced ​​through the timestamp alignment engine, and finally a supply chain panoramic data cube containing 300+ dynamic indicators is formed.

[0016] In a preferred embodiment, in step S2, the dynamic knowledge graph is constructed using hybrid entity recognition technology. A pre-trained BERT model is first used to extract legal entities and performance terms from supplier contract texts. A relationship extraction algorithm is then used to establish the supplier-factory-distributor triple relationship. The graph neural network is designed as a two-layer attention mechanism: the bottom-layer GAT network captures direct business relationships, while the top-layer GraphSAGE aggregates indirect dependencies across layers. The knowledge graph is incrementally updated hourly. When a supplier's raw material arrival delay exceeds a threshold, a vulnerability analysis module is automatically triggered to calculate the weights of potentially affected upstream and downstream nodes along the graph path.

[0017] In a preferred embodiment, step S3 constructs a dynamic adversarial training framework using a generative adversarial network. Its core is a two-way game mechanism consisting of a risk generator and a risk discriminator. The risk generator learns common risk patterns such as raw material shortages and logistics paralysis based on historical supply chain data. It also injects random noise through latent space perturbations to synthesize physically feasible virtual disruption events. The risk discriminator integrates the experience of industry experts with real-time supply chain status to conduct a multi-dimensional assessment of the destructive intensity and probability of the generated events. The two networks continuously push the boundaries of each other's cognition during adversarial iterations, ultimately enabling the generator to output extreme event models that surpass historical databases, such as cascading collapse effects caused by multiple concurrent failures or intercontinental supply chain disruptions triggered by geopolitical upheavals.

[0018] The calculation formula of the adversarial risk generation function is:

[0019]

[0020] in:

[0021] z represents the latent space noise variable, which follows the standard normal distribution p z , serves as the input seed of the risk generator G, driving the generator to explore unknown risk forms. ΔS is the real-time supply chain state increment tensor, which continuously injects key indicators such as the current logistics delay rate and inventory level through the data stream interface. virtual Characterizes the probability distribution of generated events, p expert Corresponding to the benchmark distribution of expert experience encoding, the KL divergence term dynamically adjusts the feasibility and destructiveness balance of the generated events through λ. The value of λ changes adaptively as the adversarial training progresses, focusing on conservative learning in the early stage and gradually releasing creative generation capabilities in the later stage.

[0022] The parameter formula of the multidimensional risk discriminant function is:

[0023] D adv (x)=σ(W d ·GNN(x)+α·Entropy(x)-β·Cascading(x))

[0024] in:

[0025] W dα is a trainable weight matrix for graph neural networks, responsible for extracting supply chain topology features. α is defined as the risk entropy sensitivity coefficient, positively correlated with environmental uncertainty captured by the real-time monitoring system. When satellite imagery detects port congestion, it automatically increases awareness of unusual patterns. β is the cascading effect suppression coefficient, which correlates with the average recovery time from historical disruptions and increases the penalty for risk transmission paths when vulnerable nodes are detected. The entropy term, Entropy(x), quantifies the complexity of the event, while the Cascading term, Cascading(x), calculates the number of upstream and downstream node failures that may be triggered by the event. Together, they construct a three-dimensional risk assessment coordinate.

[0026] In a preferred embodiment, in step S4, the satellite imagery analysis module utilizes a modified U-Net architecture, incorporating synthetic aperture radar data augmentation technology during training to enable it to identify the operating status of factory rooftop equipment under varying lighting conditions. The natural language processing submodule deploys a multi-task learning framework, simultaneously performing sentiment analysis of supplier emails, anomaly detection of contract terms, and entity linking of instant messaging records. After noise reduction using an adaptive Kalman filter, the IoT data stream is spatiotemporally aligned with logistics node information stored on the blockchain to construct a digital twin trajectory of goods in transit. After the trimodal data is fused in the time series alignment layer, it is input into a convolutional-self-attention hybrid model for joint feature extraction.

[0027] In a preferred embodiment, in step S5, the quantized risk assessment matrix encodes traditional indicators such as supplier credit ratings and inventory turnover rates into quantum bit superpositions, with each risk dimension corresponding to an independent Bloch sphere coordinate. By designing quantum entanglement gates, the quantum states of raw material price increase risk and logistics delay risk are entangled. Measuring the risk value of a supplier node automatically triggers the cross-level risk wave function collapse calculation. This process utilizes a quantum annealing algorithm to determine the optimal collapse path, revealing the butterfly effect transmission chain hidden within the multi-tier supplier network, ultimately outputting a three-dimensional risk heat map.

[0028] In a preferred embodiment, in step S6, a deep reinforcement learning model constructs a Markov decision process, where the state space consists of a 200-dimensional real-time supply chain feature vector and the action space is defined as a binary selection matrix for supplier combinations. The reward function is designed as a three-fold structure: a base reward based on the satisfaction of the cost budget constraint, an exploration reward incentivizing the selection of new suppliers with a historical collaboration frequency below 15%, and a robustness reward that estimates the risk tolerance of the selected combination through Monte Carlo sampling. A double-delayed deep deterministic policy gradient algorithm is employed, with a risk-value assessment network added to the action selection network to ensure that each policy update simultaneously optimizes short-term costs and long-term resilience.

[0029] In a preferred embodiment, in step S7, neural radiation field technology generates a 3D point cloud model from multi-view photos of supply chain nodes, constructs a light propagation equation using a differentiable renderer, and converts abstract relationships such as supplier geographic location and logistics route topology into stereoscopic light field data. The gesture interaction system integrates a millimeter-wave radar and an inertial measurement unit to capture the user's finger motion trajectory and map it to a three-dimensional query vector using a spatiotemporal attention mechanism. The risk hotspot detection module trains a conditional generative adversarial network, encodes the supply chain stress test results into a color gradient texture, and projects it onto the radiation field surface of the corresponding node in real time, creating a visible dynamic effect of risk diffusion.

[0030] In a preferred embodiment, in step S8, the self-explanatory decision-making module employs a causal discovery framework, first performing a do-calculus calculation on historical disruption events to construct a Bayesian network containing over 50 potential causal factors. When a change in raw material prices is detected, counterfactual reasoning is performed to generate three intervention scenarios. For example, if the number of alternative second-tier suppliers is increased, the expected impact of this action on on-time delivery performance is calculated along the causal graph. Each decision scenario is accompanied by an impact weight matrix, and the contribution of each measure to the KPI is quantified using Shapley value decomposition. Ultimately, a decision tree with a causal explanation chain is generated for managers to choose from.

[0031] In a preferred embodiment, in step S9, the federated learning framework is designed as a star topology, with the core coordination server holding the global parameters of the risk model and each regional data center retaining local supply chain data. During each round of training, participating nodes download the latest model, calculate the gradient update using local data, and upload it to the coordinator via homomorphic encryption. The model aggregation phase uses an adaptive weighted average algorithm to dynamically adjust the fusion weights based on the freshness of node data and sample diversity. The evolutionary mechanism sets dual threshold trigger conditions: the model architecture search is initiated when the accuracy improvement is less than 1% over three consecutive training cycles, and the feature space dimension is automatically expanded upon detection of new risk events.

[0032] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0033] 1. In the present invention, the crisis prediction and response capabilities of the supply chain are effectively enhanced through the dual mechanisms of simulation and dynamic optimization. The system's built-in game learning architecture can autonomously construct complex interruption scenarios with multiple factors coupled together, such as simulating extreme conditions such as port closures during typhoon season, sudden changes in cross-border tariff policies, and congestion in alternative raw material transportation routes. This kind of deduction not only reveals the problem of over-reliance on a single node that is difficult to detect through traditional audits, but also verifies the practical feasibility of alternative plans, helping companies establish a multi-layered defense system. The system's continuous self-optimization characteristics enable it to perform outstandingly in response to new challenges. When sudden adjustments occur in the international logistics network, it can quickly generate a supplier restructuring plan that takes into account both cost and timeliness, and resolve potential chain break risks in the bud.

[0034] 2. In the present invention, a risk management system with a forward-looking vision is formed by deeply coupling industry experience and intelligent evolution laws. The system's ability to mine hidden risk associations can identify potential threats such as the excessive regional concentration of suppliers of a certain type of low-value consumables. Such problems are often overlooked by conventional assessments due to short-term cost advantages. The dynamic learning mechanism ensures that the model absorbs the latest market changes in real time. For example, when the climate pattern of a cotton-producing area undergoes long-term changes, the system can automatically adjust the risk weight distribution and plan raw material substitution plans in advance. This intelligent evolutionary feature significantly shortens the decision-making response cycle. When responding to regional industrial fluctuations, it can simultaneously complete risk warning, solution generation and execution path optimization, providing strong support for enterprises to seize the initiative in disposal. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process principle of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0037] Example:

[0038] Reference Figure 1 ,

[0039] An artificial intelligence-based clothing supply chain risk analysis method comprises the following steps:

[0040] S1: Integrate the entire supply chain information through multi-source heterogeneous data fusion technology, including external risk factors such as raw material price fluctuations, factory production capacity data, logistics time records, and weather policies;

[0041] S2: Build a dynamic knowledge graph to associate upstream and downstream entity relationships, and use graph neural networks to mine potential dependencies and vulnerable nodes;

[0042] S3: Deploy a self-evolving risk simulation engine, create virtual disruption scenarios based on a generative adversarial network, and generate black swan event models that are difficult for human experts to foresee through adversarial training.

[0043] S4: Develop a multimodal real-time monitoring system that integrates satellite imagery to analyze factory operating status, natural language processing to analyze supplier communication records, and IoT sensors to track goods in transit.

[0044] S5: Establish a quantum risk assessment matrix, transform traditional risk indicators into superimposable quantum state vectors, and reveal cross-level risk transmission paths through entanglement calculations;

[0045] S6: Apply deep reinforcement learning to dynamically optimize supplier portfolio strategies and generate the most risk-resilient procurement solutions under cost constraints.

[0046] S7: Design an immersive risk tracing visualization interface, use neural radiation field technology to 3D reconstruct the supply chain network, and support gesture operation to explore risk hotspots;

[0047] S8: Embeds a self-explanatory decision-making module to generate executable risk mitigation measures through causal reasoning and predict the impact weight of each measure on key performance indicators;

[0048] S9: Build a closed-loop feedback evolution mechanism and use federated learning to continuously update risk models while protecting data privacy, thereby achieving the self-healing capability of the supply chain system.

[0049] In step S1, a unified data lake architecture is constructed to integrate information across the entire supply chain. First, a distributed crawler system is deployed to capture real-time quotes from the raw material exchange. This system also connects to the production order information stream from the factory's MES system. Edge computing devices are used to perform millisecond-level analysis on production capacity data collected by sensors on the shop floor. A bidirectional LSTM network is used to extract transport delay patterns from unstructured logistics company freight logs. This network is then linked to the Meteorological Bureau's API to establish a correlation matrix between weather events and transport timeliness. After all data streams are standardized, multi-dimensional features are stitched together using a timestamp alignment engine, ultimately forming a comprehensive supply chain data cube containing over 300 dynamic indicators.

[0050] In step S2, the dynamic knowledge graph is constructed using hybrid entity recognition technology. A pre-trained BERT model is first used to extract legal entities and performance terms from supplier contract texts. A relationship extraction algorithm is then used to establish the supplier-factory-distributor triple relationship. The graph neural network is designed as a two-layer attention mechanism: the underlying GAT network captures direct business relationships, while the top-level GraphSAGE network aggregates indirect dependencies across multiple layers. The knowledge graph is incrementally updated hourly. When a supplier's raw material arrival delay exceeds a threshold, the vulnerability analysis module is automatically triggered to calculate the weights of potentially affected upstream and downstream nodes along the graph path.

[0051] In step S3, a dynamic adversarial training framework is constructed using a generative adversarial network. Its core is a two-way game mechanism consisting of a risk generator and a risk discriminator. The risk generator learns common risk patterns such as raw material shortages and logistics disruptions based on historical supply chain data. It also injects random noise through latent space perturbations to synthesize physically feasible virtual disruption events. The risk discriminator integrates the experience of industry experts with real-time supply chain status to conduct a multi-dimensional assessment of the destructive intensity and probability of the generated events. Through iterative adversarial processes, the two networks continuously push the boundaries of each other's cognition. Ultimately, the generator can output models of extreme events that surpass historical databases, such as cascading collapse effects caused by multiple concurrent failures or transcontinental supply chain disruptions triggered by geopolitical upheavals.

[0052] The calculation formula of the adversarial risk generation function is:

[0053]

[0054] in:

[0055] z represents the latent space noise variable, which follows the standard normal distribution p z , serves as the input seed of the risk generator G, driving the generator to explore unknown risk forms. ΔS is the real-time supply chain state increment tensor, which continuously injects key indicators such as the current logistics delay rate and inventory level through the data stream interface. virtual Characterizes the probability distribution of generated events, p expert Corresponding to the benchmark distribution of expert experience encoding, the KL divergence term dynamically adjusts the feasibility and destructiveness balance of the generated events through λ. The value of λ changes adaptively as the adversarial training progresses, focusing on conservative learning in the early stage and gradually releasing creative generation capabilities in the later stage.

[0056] The parameter formula of the multidimensional risk discriminant function is:

[0057] D adv (x)=σ(W d ·GNN(x)+α·Entropy(x)-β·Cascading(x))

[0058] in:

[0059] W d α is a trainable weight matrix for graph neural networks, responsible for extracting supply chain topology features. α is defined as the risk entropy sensitivity coefficient, positively correlated with environmental uncertainty captured by the real-time monitoring system. When satellite imagery detects port congestion, it automatically increases awareness of unusual patterns. β is the cascading effect suppression coefficient, which correlates with the average recovery time from historical disruptions and increases the penalty for risk transmission paths when vulnerable nodes are detected. The entropy term, Entropy(x), quantifies the complexity of the event, while the Cascading term, Cascading(x), calculates the number of upstream and downstream node failures that may be triggered by the event. Together, they construct a three-dimensional risk assessment coordinate.

[0060] In step S4, the satellite imagery analysis module utilizes a modified U-Net architecture, incorporating synthetic aperture radar data augmentation techniques during training to enable it to identify the operating status of factory rooftop equipment under varying lighting conditions. The natural language processing submodule deploys a multi-task learning framework, simultaneously performing sentiment analysis of supplier emails, anomaly detection of contract terms, and entity linking of instant messaging records. After noise reduction using an adaptive Kalman filter, the IoT data stream is spatially and temporally aligned with logistics node information stored on the blockchain to construct a digital twin trajectory of goods in transit. After the trimodal data is fused in the time series alignment layer, it is input into a convolutional-self-attention hybrid model for joint feature extraction.

[0061] In step S5, the quantized risk assessment matrix encodes traditional indicators such as supplier credit ratings and inventory turnover rates into quantum bit superpositions, with each risk dimension corresponding to a separate Bloch sphere coordinate. By designing quantum entanglement gates, the quantum states of raw material price increase risk and logistics delay risk are entangled. Measuring the risk value of a supplier node automatically triggers the cross-level risk wave function collapse calculation. This process utilizes a quantum annealing algorithm to determine the optimal collapse path, revealing the butterfly effect transmission chain hidden within the multi-tier supplier network, ultimately generating a three-dimensional risk heat map.

[0062] In step S6, a deep reinforcement learning model constructs a Markov decision process. The state space consists of a 200-dimensional real-time supply chain feature vector, and the action space is defined as a binary selection matrix for supplier combinations. The reward function is designed as a three-fold structure: a base reward based on the satisfaction of the cost budget constraint, an exploration reward incentivizing the selection of new suppliers with a historical cooperation frequency of less than 15%, and a robustness reward that estimates the risk tolerance of the selected combination through Monte Carlo sampling. A double-delayed deep deterministic policy gradient algorithm is employed, with a risk-value assessment network added to the action selection network to ensure that each policy update simultaneously optimizes short-term costs and long-term resilience.

[0063] In step S7, neural radiation field technology generates a 3D point cloud model from multi-view photos of supply chain nodes. A differentiable renderer is used to construct a light propagation equation, transforming abstract relationships such as supplier geolocation and logistics route topology into stereoscopic light field data. The gesture interaction system integrates a millimeter-wave radar and an inertial measurement unit to capture the user's finger motion trajectory and map it into a three-dimensional query vector using a spatiotemporal attention mechanism. The risk hotspot detection module trains a conditional generative adversarial network to encode supply chain stress test results into a color gradient texture, which is projected in real time onto the radiation field surface of the corresponding node, creating a visible dynamic effect of risk diffusion.

[0064] In step S8, the self-explanatory decision-making module employs a causal discovery framework, first performing a do-calculus calculation on historical disruption events to construct a Bayesian network containing over 50 potential causal factors. When a raw material price fluctuation is detected, counterfactual reasoning is performed to generate three intervention scenarios. For example, if the number of alternative second-tier suppliers is increased, the expected impact of this action on on-time delivery performance is calculated along the causal graph. Each decision scenario is accompanied by an impact weight matrix, which uses Shapley value decomposition to quantify the contribution of each measure to the KPI. Ultimately, a decision tree with a causal explanation chain is generated for managers to choose from.

[0065] In step S9, the federated learning framework is designed as a star topology. The core coordination server holds the global risk model parameters, while regional data centers retain local supply chain data. During each training round, participating nodes download the latest model, calculate gradient updates using local data, and upload them to the coordinator via homomorphic encryption. The model aggregation phase utilizes an adaptive weighted averaging algorithm, dynamically adjusting fusion weights based on the freshness of node data and sample diversity. The evolutionary mechanism employs dual threshold trigger conditions: a model architecture search is initiated when the accuracy improvement is less than 1% over three consecutive training cycles, and the feature space dimension is automatically expanded upon detection of new risk events.

[0066] A clothing supply chain risk analysis system based on artificial intelligence, which runs the clothing supply chain risk analysis method based on artificial intelligence of the above embodiment when in use.

[0067] From the above we can know:

[0068] In the present invention, the crisis prediction and response capabilities of the supply chain are effectively enhanced through the dual mechanisms of simulation and dynamic optimization. The system's built-in game learning architecture can autonomously construct complex interruption scenarios with multiple factors coupled together, such as simulating extreme conditions such as port closures during typhoon season, sudden changes in cross-border tariff policies, and congestion in alternative raw material transportation routes. This kind of deduction not only reveals the problem of over-reliance on a single node that is difficult to detect through traditional audits, but also verifies the practical feasibility of alternative plans and helps companies establish a multi-layer defense system. The system's continuous self-optimization feature enables it to perform outstandingly in response to new challenges. When sudden adjustments occur in the international logistics network, it can quickly generate a supplier restructuring plan that takes into account both cost and timeliness, and resolve potential chain break risks in the bud.

[0069] In the present invention, a risk management system with a forward-looking vision is formed by deeply coupling industry experience and intelligent evolution laws. The system's ability to mine hidden risk associations can identify potential threats such as the excessive regional concentration of suppliers of a certain type of low-value consumables. Such problems are often overlooked by conventional assessments due to short-term cost advantages. The dynamic learning mechanism ensures that the model absorbs the latest market changes in real time. For example, when the climate pattern of a cotton-producing area undergoes long-term changes, the system can automatically adjust the risk weight distribution and plan raw material substitution plans in advance. This intelligent evolutionary feature significantly shortens the decision-making response cycle. When responding to regional industrial fluctuations, it can simultaneously complete risk warning, solution generation and execution path optimization, providing strong support for enterprises to seize the initiative in disposal.

[0070] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0071] The above description is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A clothing supply chain risk analysis method based on artificial intelligence, characterized by: The method comprises the following steps: S1: Integrate the entire supply chain information through multi-source heterogeneous data fusion technology, including external risk factors such as raw material price fluctuations, factory production capacity data, logistics time records, and weather policies; S2: Build a dynamic knowledge graph to associate upstream and downstream entity relationships, and use graph neural networks to mine potential dependencies and vulnerable nodes; S3: Deploy a self-evolving risk simulation engine, create virtual disruption scenarios based on a generative adversarial network, and generate black swan event models that are difficult for human experts to foresee through adversarial training. S4: Develop a multimodal real-time monitoring system that integrates satellite imagery to analyze factory operating status, natural language processing to analyze supplier communication records, and IoT sensors to track goods in transit. S5: Establish a quantum risk assessment matrix, transform traditional risk indicators into superimposable quantum state vectors, and reveal cross-level risk transmission paths through entanglement calculations; S6: Apply deep reinforcement learning to dynamically optimize supplier portfolio strategies and generate the most risk-resilient procurement solutions under cost constraints. S7: Design an immersive risk tracing visualization interface, use neural radiation field technology to 3D reconstruct the supply chain network, and support gesture operation to explore risk hotspots; S8: Embeds a self-explanatory decision-making module to generate executable risk mitigation measures through causal reasoning and predict the impact weight of each measure on key performance indicators; S9: Build a closed-loop feedback evolution mechanism and use federated learning to continuously update risk models while protecting data privacy, thereby achieving the self-healing capability of the supply chain system.

2. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, characterized in that: In step S1, information integration across the entire supply chain is achieved by building a unified data lake architecture. First, a distributed crawler system is deployed to capture real-time quotation data from the raw material exchange, while simultaneously accessing the production order information flow from the factory's MES system. Edge computing devices are then used to perform millisecond-level analysis on the production capacity data collected by workshop sensors.

3. The artificial intelligence-based clothing supply chain risk analysis method according to claim 1, characterized in that: In step S2, the dynamic knowledge graph is constructed using a hybrid entity recognition technology. The pre-trained BERT model is first used to extract the legal entity and performance terms from the supplier contract text, and then the supplier-factory-distributor triple relationship is established through the relationship extraction algorithm.

4. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, wherein: In step S3, a dynamic adversarial training framework is constructed through a generative adversarial network, with a two-way game mechanism consisting of a risk generator and a risk discriminator. The risk generator learns conventional risk patterns such as raw material shortages and logistics paralysis based on historical supply chain data, and simultaneously injects random noise through potential space perturbations to synthesize virtual disruption events with physical feasibility. The calculation formula of the adversarial risk generation function is: in: z represents the latent space noise variable, which follows the standard normal distribution p z , as the input seed of the risk generator G, driving the generator to explore unknown risk forms; ΔS is the real-time supply chain state increment tensor, which continuously injects key indicators such as the current logistics delay rate and inventory level through the data stream interface; p virtual Characterizes the probability distribution of generated events, p expert Corresponding to the benchmark distribution of expert experience encoding, the KL divergence term dynamically adjusts the feasibility and destructiveness balance of the generated events through λ; the λ value changes adaptively with the progress of adversarial training, focusing on conservative learning in the early stage and gradually releasing creative generation capabilities in the later stage. The parameter formula of the multidimensional risk discriminant function is: D adv (x)=σ(W d ·GNN(x)+α·Entropy(x)-β·Cascading(x)) in: W d is a trainable weight matrix of the graph neural network, responsible for extracting the topological structure characteristics of the supply chain; α is defined as the risk entropy sensitivity coefficient, and its value is positively correlated with the environmental uncertainty captured by the real-time monitoring system. When satellite images detect port congestion, it automatically increases the alertness to abnormal patterns; β is the cascade effect suppression coefficient, which is associated with the average recovery time indicator in historical interruption events and increases the penalty for risk transmission paths when vulnerable nodes are detected; the entropy term Entropy(x) quantifies the complexity of the event, and the cascade term Cascading(x) calculates the number of upstream and downstream node failures that may be triggered by the event. The two together construct a three-dimensional risk assessment coordinate.

5. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, wherein: In step S4, the satellite image analysis module adopts an improved U-Net architecture and injects synthetic aperture radar data enhancement technology during training, so that it can identify the operating status of factory rooftop equipment under different lighting conditions; the natural language processing submodule deploys a multi-task learning framework to simultaneously perform sentiment analysis of supplier emails, anomaly detection of contract terms, and entity linking of instant messaging records.

6. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, wherein: In step S5, the quantized risk assessment matrix encodes traditional indicators such as supplier credit rating and inventory turnover rate into quantum bit superposition states, and each risk dimension corresponds to an independent Bloch spherical coordinate. By designing quantum entanglement gate operations, the quantum states of raw material price increase risk and logistics delay risk are correlated and entangled. When the risk value of a supplier node is measured, a cross-level risk wave function collapse calculation is automatically triggered.

7. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, wherein: In step S6, the deep reinforcement learning model constructs a Markov decision process, the state space contains a 200-dimensional real-time supply chain feature vector, and the action space is defined as a binary selection matrix of supplier combinations; The reward function is designed as a triple structure: the basic reward is based on the satisfaction of the cost budget constraint, the exploration reward encourages the selection of new suppliers with a historical cooperation frequency of less than 15%, and the robustness reward estimates the risk resistance of the selected combination through Monte Carlo sampling.

8. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, wherein: In step S7, the neural radiation field technology generates a 3D point cloud model through multi-perspective supply chain node photos, uses a differentiable renderer to construct a light propagation equation, and converts abstract relationships such as supplier geographic location and logistics route topology into stereoscopic light field data; the gesture interaction system integrates millimeter-wave radar and inertial measurement unit to capture the user's finger movement trajectory and map it into a three-dimensional query vector through a spatiotemporal attention mechanism.

9. The method for analyzing clothing supply chain risk based on artificial intelligence according to claim 1, wherein: In step S8, the self-explanatory decision module adopts a causal discovery framework to first perform a do-calculus operation on historical interruption events and construct a Bayesian network containing 50+ potential causal factors; In step S9, the federated learning framework is designed as a star topology, with the core coordination server holding the global parameters of the risk model, and each regional data center retaining local supply chain data. During each round of training, participating nodes download the latest model, calculate the gradient update using local data, and upload it to the coordinator via homomorphic encryption. An adaptive weighted average algorithm is used in the model aggregation stage to dynamically adjust the fusion weight according to the freshness of node data and sample diversity; the evolutionary mechanism sets a dual-threshold trigger condition: when the accuracy rate increases by less than 1% for three consecutive training cycles, the model architecture search is initiated, and the feature space dimension is automatically expanded after a new risk event is detected.

10. An artificial intelligence-based clothing supply chain risk analysis system, characterized by: When in use, the system runs the artificial intelligence-based clothing supply chain risk analysis method as described in any one of claims 1 to 9.

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