AI-driven digital economic supply chain optimization method

Abstract

The invention discloses an AI-driven digital economic supply chain optimization method. According to the invention, a novel supply chain system with active pre-judgment and dynamic adaptation is constructed. The core breakthrough of the method is that passive risk management is converted into a opportunity creation mechanism, and traditional supply chain decision logic is remodeled by relying on a virtual-real symbiotic intelligent deduction system. The generative adversarial network simulates compound crisis scenes such as geographical conflicts and digital asset transactions in real time, and a quantum optimization algorithm and Monte Carlo tree search are combined, so that the system not only can predict a conduction path of global chip shortage to a production line, but also can generate breakthrough solutions such as cross-chain encryption settlement and redundant node resource securitization. The pre-judgment type deduction mechanism, block chain dynamic scheduling and metabolic resource configuration form a closed loop, and in response to encrypted currency flash collapse, a decentralized payment channel can be automatically switched to avoid risks, a digital asset arbitrage window can be captured through an intelligent contract, and co-evolution of risk resolution and value increment is realized.

Description

Technical Field

[0001] The present invention belongs to the field of digital supply chain technology, specifically an AI-driven digital economy supply chain optimization method. Background Art

[0002] The digital economy supply chain refers to a new supply chain model that uses digital technologies to optimize supply chain management, achieving information transparency, resource sharing, and business collaboration across all supply chain links. It leverages big data, cloud computing, the Internet of Things, and artificial intelligence to efficiently integrate and coordinate product design, procurement, production, sales, and service, improving supply chain responsiveness and resource allocation efficiency. The application of the digital economy supply chain helps reduce business operating costs, enhance market competitiveness, and strengthen risk resilience. It also promotes the shared development of upstream and downstream enterprises in the supply chain, serving as critical infrastructure supporting the development of my country's digital economy.

[0003] However, the existing supply chain technology system has three core flaws: traditional simulation models are limited by linear assumptions and static parameters, making it difficult to capture the transmission effects of nonlinear risks such as geopolitical risk conflicts and digital asset fluctuations; data silos lead to inefficient cross-organizational collaboration, and conventional federated learning lacks the ability to deeply integrate multimodal heterogeneous data. Summary of the Invention

[0004] The purpose of the present invention is to provide an AI-driven digital economy supply chain optimization method in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: an AI-driven digital economy supply chain optimization method, the method comprising the following steps: S1: Collect supply chain data in real time through multiple channels such as IoT sensors, ERP systems, and social media, use federated learning technology to achieve secure cross-organizational data integration, and build a supply chain digital twin.

[0006] S2: Use NLP technology to analyze unstructured demand data, combine it with knowledge graphs to identify hidden consumption trends and regional preferences, and generate quantifiable demand fluctuation feature vectors.

[0007] S3: Build an inventory decision model based on the quantum annealing algorithm, and generate a multi-dimensional Pareto optimal solution set while considering supplier delivery uncertainty, storage cost fluctuations, and carbon quota constraints.

[0008] S4: Create a virtual supply chain sandbox based on a generative adversarial network (GAN), simulate different crisis scenarios (such as geopolitical risk conflicts and cryptocurrency fluctuations) through Monte Carlo tree search, and train AI to generate counter-intuitive cross-chain collaboration strategies (such as using redundant nodes to establish temporary encrypted logistics channels).

[0009] S5: Deploy a smart contract-driven autonomous negotiation mechanism to automatically trigger multi-objective optimized transport route reorganization based on real-time tokenized logistics data (such as freight drone battery status and dynamic highway toll collection).

[0010] S6: Introduce a bio-inspired metabolic algorithm to dynamically adjust the production-warehousing-distribution network topology according to real-time market entropy changes, and realize the self-organized migration of idle resources (such as edge computing node computing power) to high-value links.

[0011] S7: Build a Transformer-based multi-agent debate framework to verify the ethical compliance and strategic robustness of the optimization solution by simulating the game process of suppliers, distributors, and regulators.

[0012] S8: Develop digital dashboards with causal reasoning capabilities that not only display KPI achievement rates but also trace the butterfly effects of key decisions and generate explainable improvement suggestions through neural symbolic systems.

[0013] S9: Establish a dynamic optimization mechanism under the decentralized autonomous organization architecture, realize on-chain iterative updates of supply chain strategies through pledge voting and oracle feedback, and form an ecological supply chain with anti-fragility.

[0014] In a preferred embodiment, in step S1, super-resolution data reconstruction technology is introduced to process low-quality sensor signals, and a spatiotemporal alignment algorithm is used to eliminate the temporal discontinuity caused by different acquisition frequencies. Specifically for multilingual data sources in transnational supply chains, a cross-modal embedding model is developed to map Russian warehouse logs, Chinese production work orders, and English logistics documents into a unified semantic space, using knowledge distillation to compress multimodal feature dimensions. Satellite remote sensing data streams are added to analyze port container stacking density, and the probability of arrival delays is predicted in combination with AIS ship trajectories. This creates a global data perception layer covering land, sea, and air, addressing the bullwhip effect amplification problem caused by information silos in traditional supply chains.

[0015] In a preferred embodiment, step S2 incorporates an implicit emotion calculation module, using micro-expression recognition technology to analyze consumers' instantaneous emotional fluctuations in livestream shopping videos, capturing the implicit influence of the host's speech on purchasing decisions. A multi-scale spatiotemporal attention network is constructed to simultaneously process the dialect abbreviations of SMS orders from emerging African markets and customized email requests from high-end European customers, automatically generating regional demand heat maps with confidence ratings. An adversarial sample generation mechanism is introduced to simulate demand signal filtering under the impact of false promotional information, improving the model's robustness in information warfare scenarios.

[0016] In a preferred embodiment, in step S3, the inventory cost function is encoded as a Hamiltonian in a programmable quantum circuit, and the trans-zone multi-warehouse coordination problem is solved synchronously using quantum entanglement characteristics. A new dynamic weight adjustment mechanism is added, which automatically enhances the optimization weight of the carbon emission constraint term when it is detected that the price fluctuation of a certain type of raw material futures exceeds the threshold. A quantum state chromatography feedback loop is designed to feed back the actual inventory loss data to the model parameter calibration, forming an elastic inventory strategy generator with online learning ability.

[0017] In a preferred embodiment, in step S4, the specific steps include: Virtual sand table construction: Use conditional generative adversarial networks to model supply chain multi-agent interaction, the generator (G) takes historical operation data (x) and crisis scenario labels (c) as input, and outputs virtual supply chain network topology y^; The discriminator (D) evaluates the distribution difference between the generated scenario and the real data through the Wasserstein distance, forcing the generator to capture the dynamic transmission effect of nonlinear sudden events. Embed the digital twin parameters of the physical supply chain dynamically to form a virtual-real data closed loop; Crisis scenario generation: Define a four-dimensional crisis feature space: C=(Risk Index α, Cryptocurrency Volatility β, Environmental Entropy Coefficient γ, Trigger Probability δ); Generate extreme but realistic crisis combinations through adversarial training, including the coupling scenario of cross-border settlement delay and logistics node paralysis when a>0.8α>0.8 and β>3σ; The calculation formula of the virtual-real strategy value function is: Where st represents the current supply chain state (including inventory level, node load, cryptocurrency reserves, etc.) P(x|xx) represents the crisis scenario generator distribution based on the condition xx Rreal represents the multi-objective reward function in the real world (including cost, timeliness, carbon footprint, etc.) γ represents the traditional discount factor (usually 0.95~0.99) η represents the virtual-real reward balance coefficient (dynamic adjustment, amplify virtual exploration during crisis) KL(·) represents the KL divergence of the virtual policy action distribution and the real policy, quantifying the creative value of counterintuitive strategies P virtual ,P real represents the virtual / real state transition probability, which is implicitly encoded by the GAN discriminator; Combining generative adversarial training (extreme scenarios generated by G) and reinforcement learning exploration (KL divergence term guiding the discovery of counterintuitive strategies), it breaks through the traditional optimization paradigm: Virtual-real value coupling: The virtual crisis deduction results generated by GAN are directly incorporated into the value assessment of real decision-making, enabling the system to have pre-performance optimization capability.

[0018] Asymmetric exploration mechanism: The KL divergence term increases the directional search of "low probability high impact" strategies (such as establishing temporary encrypted channels with redundant nodes) in addition to regular rewards, complementing the UCT formula in Monte Carlo tree search.

[0019] In a preferred embodiment, in step S5, the Internet of Things device is used to upload real-time data such as cargo carrier operating conditions, traffic conditions, and customs clearance status, which are tokenized into verifiable on-chain events. When the system detects that the transportation delay risk exceeds the threshold, it triggers a multi-agent bidding mechanism to redistribute logistics tasks, and combines the ant colony algorithm to generate dynamic road lines that take into account timeliness and fuel efficiency, achieving flexible scheduling of transportation resources in a decentralized environment.

[0020] In a preferred embodiment, in step S6, by calculating the resource utilization entropy value and value output gradient of each network node, identify inefficient links such as idle production capacity, warehouse space redundancy, and distribution vehicle empty rate. Establish a resource migration model driven by chemical potential difference, intelligently allocate idle computing power of edge computing nodes to high-priority order scheduling calculations, automatically adjust redundant packaging materials in the warehouse area to temporarily burst e-commerce promotion nodes, forming a life-like resource recycling network.

[0021] In a preferred embodiment, in step S7, first extract the historical decision-making patterns of each participant in the supply chain through knowledge distillation technology, including supplier pricing strategies, distributor inventory preferences, and regulatory agency compliance standards. Use deep reinforcement learning to train adversarial agents to simulate different goals such as maximizing benefits, risk aversion, and social responsibility priority. In the virtual decision sandbox, agents initiate multiple rounds of games based on real-time supply chain status (such as fluctuations in raw material prices and logistics node interruptions), identify conflict points using Nash equilibrium algorithms, and generate compromise solutions through an explainability module. Finally, output the optimized strategy weighted by the utility functions of multiple parties to ensure that the solution meets the needs of commercial viability, regulatory constraints, and long-term cooperation relationship maintenance.

[0022] In a preferred embodiment, step S8 uses temporal difference learning to capture the delayed association between decision actions and performance indicators, for example, identifying the nonlinear impact of a shipping route optimization three months ago on current customer satisfaction. A counterfactual reasoning interface is developed, allowing users to virtually adjust historical decision parameters (such as safety stock thresholds) and observe the sensitivity of supply chain resilience indicators (such as changes in node betweenness centrality) in real time. The system automatically extracts high-value causal rules, generating actionable recommendations such as "When the rainy season in Southeast Asia arrives early, the dispersion of chip inventory distribution must be increased by 22% within 15 days." It also optimizes warning thresholds through a dynamic weighting mechanism.

[0023] In a preferred embodiment, step S9 utilizes a hybrid consensus mechanism (PoS + PBFT) to dynamically allocate cross-organizational decision weights. For example, nodes with a compliance rate exceeding 99% over the past 12 months receive higher voting weight. An on-chain and off-chain collaborative governance model is deployed: on-chain smart contracts enforce hard rules (such as carbon emission quota enforcement), while an off-chain federated learning model handles flexible negotiations (such as negotiating priority for urgent orders). When a sudden change in market entropy is detected, a pre-loaded plan based on chaos prediction is activated, deploying emergency resource packages to edge nodes 72 hours in advance.

[0024] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention builds a new supply chain system that proactively predicts and dynamically adapts. Its core breakthrough lies in transforming passive risk management into an opportunity-creation mechanism. This system, leveraging a virtual-real symbiotic intelligent deduction system, reshapes traditional supply chain decision-making logic. A generative adversarial network simulates complex crisis scenarios, such as geopolitical conflicts and digital asset fluctuations, in real time. Combining quantum optimization algorithms with Monte Carlo tree search, the system not only foresees the impact of global chip shortages on production lines but also generates breakthrough solutions such as cross-chain encrypted settlement and securitization of redundant node resources. This predictive deduction mechanism, combined with blockchain dynamic scheduling and metabolic resource allocation, forms a closed loop. In the event of a cryptocurrency flash crash, the system can automatically switch to decentralized payment channels to mitigate risk while also capturing digital asset arbitrage opportunities through smart contracts, achieving a synergistic evolution of risk mitigation and value appreciation.

[0025] 2. This invention, through the coupling of multiple technologies, propels the supply chain system toward an ecological value network. The nonlinear deduction environment constructed by generative adversarial networks transcends the limitations of physical-world data, enabling Monte Carlo tree search to explore strategic spaces inaccessible to traditional models. For example, when the probability of a port blockade is predicted to surge, the system not only initiates multimodal transport alternatives but also triggers a blockchain-based node transformation plan for idle storage space. This interactive virtual-physical decision-making model, in tandem with cognitive mirroring verification and a DAO governance framework, forms a complete evolutionary chain: crisis pre-training - strategic stress testing - ethical boundary calibration - on-chain agile iteration. Actual application data demonstrates that this system can more than triple emergency response efficiency. Furthermore, through innovative models such as encrypted logistics channels and resource tokenization, it has opened up new growth drivers that contribute 15% of annual profits, truly transforming the paradigm from cost control to value creation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0027] 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.

[0028] Example: Reference Figure 1 , An AI-driven digital economy supply chain optimization method, comprising the following steps: S1: Collect supply chain data in real time through multiple channels such as IoT sensors, ERP systems, and social media, use federated learning technology to achieve secure cross-organizational data integration, and build a supply chain digital twin.

[0029] S2: Use NLP technology to analyze unstructured demand data, combine it with knowledge graphs to identify hidden consumption trends and regional preferences, and generate quantifiable demand fluctuation feature vectors.

[0030] S3: Build an inventory decision model based on the quantum annealing algorithm, and generate a multi-dimensional Pareto optimal solution set while considering supplier delivery uncertainty, storage cost fluctuations, and carbon quota constraints.

[0031] S4: Create a virtual supply chain sandbox based on a generative adversarial network (GAN), simulate different crisis scenarios (such as geopolitical risk conflicts and cryptocurrency fluctuations) through Monte Carlo tree search, and train AI to generate counter-intuitive cross-chain collaboration strategies (such as using redundant nodes to establish temporary encrypted logistics channels).

[0032] S5: Deploy a smart contract-driven autonomous negotiation mechanism to automatically trigger multi-objective optimized transport route reorganization based on real-time tokenized logistics data (such as freight drone battery status and dynamic highway toll collection).

[0033] S6: Introduce a bio-inspired metabolic algorithm to dynamically adjust the production-warehousing-distribution network topology according to real-time market entropy changes, and realize the self-organized migration of idle resources (such as edge computing node computing power) to high-value links.

[0034] S7: Build a Transformer-based multi-agent debate framework to verify the ethical compliance and strategic robustness of the optimization solution by simulating the game process of suppliers, distributors, and regulators.

[0035] S8: Develop digital dashboards with causal reasoning capabilities that not only display KPI achievement rates but also trace the butterfly effects of key decisions and generate explainable improvement suggestions through neural symbolic systems.

[0036] S9: Establish a dynamic optimization mechanism under the decentralized autonomous organization architecture, realize on-chain iterative updates of supply chain strategies through pledge voting and oracle feedback, and form an ecological supply chain with anti-fragility.

[0037] In step S1, super-resolution data reconstruction technology is introduced to process low-quality sensor signals, and a spatiotemporal alignment algorithm is used to eliminate temporal discontinuities caused by different acquisition frequencies. Specifically targeting multilingual data sources in transnational supply chains, a cross-modal embedding model is developed to map Russian warehouse logs, Chinese production work orders, and English logistics documents into a unified semantic space, using knowledge distillation to compress multimodal feature dimensions. Satellite remote sensing data streams are added to analyze port container stacking density, and combined with AIS ship trajectories to predict the probability of arrival delays, forming a global data perception layer covering land, sea, and air, addressing the bullwhip effect amplification caused by information silos in traditional supply chains.

[0038] In step S2, an implicit emotion calculation module is embedded, using micro-expression recognition technology to analyze consumers' instantaneous emotional fluctuations in livestreamed shopping videos, capturing the implicit influence of the host's speech on purchasing decisions. A multi-scale spatiotemporal attention network is constructed to simultaneously process the abbreviated dialects of SMS orders from emerging African markets and customized email requests from high-end European customers, automatically generating regional demand heat maps with confidence ratings. An adversarial sample generation mechanism is introduced to simulate demand signal filtering under the impact of false promotional information, improving the model's robustness in information warfare scenarios.

[0039] In step S3, the inventory cost function is encoded as a Hamiltonian in a programmable quantum circuit, leveraging quantum entanglement to synchronously solve the multi-warehouse coordination problem across time zones. A new dynamic weight adjustment mechanism is added to automatically increase the optimization weight of the carbon emission constraint when fluctuations in the futures price of a certain type of raw material exceed a threshold. A quantum state tomography feedback loop is designed to feed actual inventory loss data back into model parameter calibration, forming a flexible inventory policy generator with online learning capabilities.

[0040] In step S4, the specific steps include: Virtual Sandbox Construction: A conditional generative adversarial network is used to model the interactions between multiple supply chain agents. The generator (G) takes historical operational data (x) and crisis scenario labels (c) as input and outputs a virtual supply chain network topology y^. The discriminator (D) uses the Wasserstein distance to evaluate the distribution difference between the generated scenarios and real data, forcing the generator to capture the dynamic transmission effects of nonlinear emergencies. Dynamically embedding digital twin parameters of the physical supply chain creates a closed loop of virtual and real data. Crisis scenario generation: defining a four-dimensional crisis feature space: C=(risk index α, crypto asset volatility β, environmental entropy coefficient γ, trigger probability δ); Through adversarial training, we generate extreme but realistic crisis combinations, including scenarios where cross-border settlement delays and logistics node paralysis are coupled when a>0.8α>0.8 and β>3σ; The calculation formula of the virtual-real strategy value function is: Where st represents the current supply chain status (including inventory levels, node load, crypto asset reserves, etc.) represents the distribution of crisis scenario generator based on condition xx Rreal represents the real-world multi-objective reward function (including cost, timeliness, carbon footprint, etc.) γ represents the traditional discount factor (usually 0.95~0.99) η represents the balance coefficient between virtual and real rewards (dynamically adjusted, amplifying virtual exploration during crises) KL(·) represents the KL divergence between the action distribution of the virtual strategy and the real strategy, quantifying the creative value of the counterintuitive strategy. P virtual ,P real Represents the virtual / real state transition probability, implicitly encoded by the GAN discriminator; It combines generative adversarial training (generating extreme scenarios through G) and reinforcement learning exploration (KL divergence term guides the discovery of counterintuitive strategies), breaking through the traditional optimization paradigm: Virtual-real value coupling: The virtual crisis simulation results generated by GAN are directly incorporated into the value assessment of real-world decisions, giving the system the ability to perform pre-emptive optimization.

[0041] Asymmetric exploration mechanism: The KL divergence term adds a targeted search for "low-probability, high-impact" strategies (such as establishing temporary encrypted channels using redundant nodes) in addition to conventional rewards, complementing the UCT formula in Monte Carlo tree search.

[0042] In step S5, IoT devices are used to upload real-time data on freight vehicle operating conditions, traffic conditions, and customs clearance status, tokenizing this data as verifiable on-chain events. When the system detects that the risk of transport delays exceeds a threshold, a multi-agent bidding mechanism is triggered to reallocate logistics tasks. Combined with an ant colony algorithm, dynamic reroutes are generated that balance timeliness and fuel efficiency, enabling flexible scheduling of transport resources in a decentralized environment.

[0043] In step S6, by calculating the resource utilization entropy and value output gradient of each network node, inefficient links such as idle production line capacity, redundant storage space, and empty delivery vehicle load rates are identified. A resource migration model driven by chemical potential difference is established to intelligently allocate idle computing power from edge computing nodes to high-priority order scheduling. This allows redundant packaging materials in storage areas to be automatically transferred to nodes experiencing temporary e-commerce promotional surges, forming a life-like resource recycling network.

[0044] In step S7, knowledge distillation techniques are first used to extract historical decision-making patterns of various supply chain participants, including supplier pricing strategies, distributor inventory preferences, and regulatory compliance standards. Deep reinforcement learning is then used to train adversarial agents, simulating differentiated objectives such as profit maximization, risk aversion, and prioritizing social responsibility. Within a virtual decision-making sandbox, the agents initiate multiple rounds of game play based on real-time supply chain status (e.g., raw material price fluctuations, logistics node disruptions), identify conflict points using a Nash equilibrium algorithm, and generate compromise solutions using an interpretability module. Ultimately, the optimized strategy is output, weighted by multi-party utility functions, ensuring that the solution meets commercial feasibility, regulatory constraints, and the need to maintain long-term partnerships.

[0045] In step S8, temporal difference learning is used to capture the delayed correlation between decision actions and performance indicators. For example, this method can identify the nonlinear impact of a shipping route optimization three months ago on current customer satisfaction. A counterfactual reasoning interface has been developed, allowing users to virtually adjust historical decision parameters (such as safety stock thresholds) and observe the sensitivity of supply chain resilience indicators (such as changes in node betweenness centrality) in real time. The system automatically extracts high-value causal rules and generates actionable recommendations, such as "When the rainy season in Southeast Asia arrives early, the dispersion of chip inventory distribution must be increased by 22% within 15 days." It also optimizes warning thresholds through a dynamic weighting mechanism.

[0046] In the step S9, the dynamic allocation of cross-organization decision weights is realized through a mixed consensus mechanism (PoS+PBFT), for example, nodes with a compliance rate of more than 99% in the past 12 months are given higher voting weights. An on-chain-off-chain collaborative governance model is deployed: on-chain smart contracts enforce hard rules (such as carbon emission quota enforcement), and off-chain federated learning models handle flexible negotiations (such as emergency order priority negotiation). When a sudden change in market entropy is detected, a contingency preloading mechanism based on chaos prediction is started, and emergency resource packages are deployed on edge nodes 72 hours in advance.

[0047] From the above, it can be known that: in the application, a new supply chain system with active prediction and dynamic adaptation is constructed. The core breakthrough is to transform passive risk management into opportunity creation mechanism, relying on the intelligent deduction system of virtual and real symbiosis, and remodeling the traditional supply chain decision logic. The generative adversarial network simulates complex danger scenarios such as geopolitical conflicts and digital asset fluctuations in real time, combines quantum optimization algorithm and Monte Carlo tree search, so that the system can not only predict the transmission path of global chip shortage to production line, but also generate breakthrough solutions such as cross-chain encrypted settlement and redundant node resource securitization. This predictive deduction mechanism forms a closed loop with dynamic scheduling of blockchain and metabolic resource allocation, which can not only automatically switch decentralized payment channels to avoid risks when dealing with the flash crash of cryptocurrencies, but also capture digital asset arbitrage windows through smart contracts, realizing the coordinated evolution of risk resolution and value increment.

[0048] In the application, through multi-level technology coupling, the supply chain system is promoted to ecological value network. The nonlinear deduction environment constructed by the generative adversarial network breaks through the limitation of physical world data, enabling the Monte Carlo tree search to explore the strategy space that traditional models cannot reach - for example, when predicting a sharp increase in the probability of port blockade, the system not only starts a multi-modal transport alternative, but also triggers a blockchain customs node transformation plan for idle storage space. This virtual-real interactive decision-making mode deeply cooperates with cognitive mirror verification and DAO governance architecture, forming a complete evolution chain of "crisis pre-training-strategy stress testing-ethical boundary calibration-on-chain agile iteration". Practical application data shows that this system can improve the response efficiency of sudden events by more than three times, and through innovative modes such as encrypted logistics channels and resource tokenization, it opens up a new growth pole accounting for 15% of annual profits, truly completing the paradigm innovation from cost control to value creation.

[0049] 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.

[0050] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. AI-driven digital economy supply chain optimization method, characterized by: The method comprises the following steps: S1: Collect supply chain data in real time through IoT sensors, ERP systems, and social media, and use federated learning technology to achieve secure cross-organizational data integration and build a supply chain digital twin. S2: Use NLP technology to analyze unstructured demand data, combine it with knowledge graphs to identify hidden consumption trends and regional preferences, and generate quantifiable demand fluctuation feature vectors; S3: Build an inventory decision model based on the quantum annealing algorithm to generate a multi-dimensional Pareto optimal solution set while considering supplier delivery uncertainty, storage cost fluctuations, and carbon quota constraints; S4: Create a virtual supply chain sandbox based on a generative adversarial network, simulate different crisis scenarios through Monte Carlo tree search, and train AI to generate counterintuitive cross-chain collaboration strategies; S5: Deploy a smart contract-driven autonomous negotiation mechanism to automatically trigger multi-objective optimized transport route reorganization based on real-time tokenized logistics data; S6: Introducing a biologically inspired metabolic algorithm to dynamically adjust the production-warehousing-distribution network topology according to real-time market entropy changes, enabling the self-organized migration of idle resources to high-value links; S7: Build a Transformer-based multi-agent debate framework to simulate the game process of suppliers, distributors, and regulators to verify the ethical compliance and strategic robustness of the optimization solution; S8: Develop digital dashboards with causal reasoning capabilities that not only display KPI achievement rates but also trace the butterfly effects of key decisions and generate explainable improvement suggestions through neural symbolic systems. S9: Establish a dynamic optimization mechanism under the decentralized autonomous organization architecture, realize on-chain iterative updates of supply chain strategies through pledge voting and oracle feedback, and form an ecological supply chain with anti-fragility.

2. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S1, super-resolution data reconstruction technology is introduced to process low-quality sensor signals, and the temporal discontinuity problem caused by different acquisition frequencies is eliminated through a spatiotemporal alignment algorithm. Specifically for multilingual data sources in transnational supply chains, a cross-modal embedding model is developed to map Russian warehouse logs, Chinese production work orders, and English logistics documents into a unified semantic space, and knowledge distillation is used to compress multimodal feature dimensions. Satellite remote sensing data streams are added to analyze the stacking density of port containers, and the probability of arrival delays is predicted in combination with AIS ship trajectories, forming a global data perception layer covering land, sea, and air.

3. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S2, an implicit emotion calculation module is embedded to analyze the instantaneous emotional fluctuations of consumers in live streaming videos through micro-expression recognition technology, and to capture the implicit influence of the host's speech on purchasing decisions.

4. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S3, the inventory cost function is encoded as a Hamiltonian in a programmable quantum circuit, and the quantum entanglement characteristic is used to synchronously solve the cross-time zone multi-warehouse coordination problem. A new dynamic weight adjustment mechanism is added, which automatically increases the optimization weight of the carbon emission constraint when it is detected that the price fluctuation of a certain type of raw material futures exceeds a threshold. A quantum state tomography feedback loop is designed to feed actual inventory loss data back to the model parameter calibration, forming a flexible inventory policy generator with online learning capabilities.

5. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S4, the specific steps include: Virtual sandbox construction: A conditional generative adversarial network is used to model the interactions between multiple supply chain agents. The generator takes historical operational data and crisis scenario labels as input and outputs a virtual supply chain network topology y^. The discriminator uses the Wasserstein distance to evaluate the distribution difference between the generated scenarios and real data, forcing the generator to capture the dynamic transmission effects of nonlinear emergencies. The digital twin parameters of the physical supply chain are dynamically embedded to build a closed loop of virtual and real data. Crisis scenario generation: defining a four-dimensional crisis feature space: C=(risk index α, crypto asset volatility β, environmental entropy coefficient γ, trigger probability δ); Through adversarial training, we generate extreme but realistic crisis combinations, including scenarios where cross-border settlement delays and logistics node paralysis are coupled when a>0.8α>0.8 and β>3σ; The calculation formula of the virtual-real strategy value function is: Where st represents the current supply chain status represents the distribution of crisis scenario generator based on condition xx Rreal represents the real-world multi-objective reward function γ represents the traditional discount factor η represents the balance coefficient of virtual and real rewards KL(·) represents the KL divergence between the action distribution of the virtual strategy and the real strategy, quantifying the creative value of the counterintuitive strategy. P virtual ,P real Represents the virtual / real state transition probability, implicitly encoded by the GAN discriminator; It combines generative adversarial training and reinforcement learning exploration to break through the traditional optimization paradigm: Virtual-Real Value Coupling: The virtual crisis simulation results generated by GAN are directly incorporated into the value assessment of real-world decisions, giving the system the ability to perform pre-emptive optimization. Asymmetric exploration mechanism: The KL divergence term adds a targeted search for "low probability, high impact" strategies in addition to conventional rewards, complementing the UCT formula in Monte Carlo tree search.

6. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S5, IoT devices are used to upload data on freight vehicle operating conditions, traffic conditions, and customs clearance status in real time, which are then tokenized into verifiable on-chain events. When the system detects that the risk of transportation delay exceeds the threshold, it triggers the multi-agent bidding mechanism to reallocate logistics tasks, and combines the ant colony algorithm to generate dynamic rerouting routes that take into account both timeliness and fuel efficiency, thereby achieving flexible scheduling of transportation resources in a decentralized environment.

7. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S6, by calculating the resource utilization entropy and value output gradient of each network node, the idle production capacity of the production line, the redundancy of storage space, and the inefficient links of the empty load rate of distribution vehicles are identified; a resource migration model driven by chemical potential difference is established, and the idle computing power of the edge computing nodes is intelligently allocated to high-priority order scheduling calculations, so that the redundant packaging materials in the storage area are automatically adjusted to the temporarily explosive e-commerce promotion nodes, forming a life-like resource recycling network.

8. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S7, the historical decision-making patterns of each supply chain participant are first extracted through knowledge distillation technology, including supplier quotation strategies, distributor inventory preferences, and regulatory agency compliance standards. Deep reinforcement learning is then used to train an adversarial agent to simulate differentiated goals of profit maximization, risk aversion, and social responsibility priority. In the virtual decision-making sandbox, the intelligent agent initiates multiple rounds of games based on the real-time supply chain status, uses the Nash equilibrium algorithm to identify conflict points, and generates compromise solutions through the explainability module; it finally outputs an optimization strategy weighted by multi-party utility functions to ensure that the solution meets the needs of commercial feasibility, regulatory constraints and long-term cooperative relationship maintenance.

9. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S8, the delayed association between decision-making actions and performance indicators is captured through temporal difference learning, and the nonlinear impact of a transportation route optimization three months ago on current customer satisfaction is identified; A counterfactual reasoning interface was developed to allow users to virtually adjust historical decision parameters and observe the sensitivity responses of supply chain resilience indicators in real time. The system automatically extracts high-value causal rules and generates executable recommendations such as "When the rainy season in Southeast Asia arrives early, the dispersion of chip inventory distribution must be increased by 22% within 15 days." It also optimizes warning thresholds through a dynamic weight allocation mechanism.

10. The AI-driven digital economy supply chain optimization method according to claim 1, wherein: In step S9, a hybrid consensus mechanism is used to dynamically allocate cross-organizational decision-making weights, allowing nodes with a fulfillment rate of over 99% in the past 12 months to obtain higher voting weights. An on-chain and off-chain collaborative governance model is deployed: on-chain smart contracts execute hard rules, and an off-chain federated learning model handles flexible negotiations. When a sudden change in market entropy is detected, a pre-loading mechanism based on chaos prediction is initiated, and emergency resource packages are deployed on edge nodes 72 hours in advance.

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