Distributed intelligent warehouse scheduling system based on artificial intelligence

Abstract

The invention discloses a distributed intelligent warehouse scheduling system based on artificial intelligence, and belongs to the technical field of warehouse scheduling. Comprising a multi-source environment sensing module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a man-machine interaction and visualization module. A warehouse digital twinborn model is constructed, immersive display of a storage state and a scheduling strategy is realized, an AR scene is superposed through a color coding path, a thermodynamic diagram and a particle flow form, a manager can intuitively master inventory distribution, task progress and an abnormal region, eye movement tracking and a gesture recognition technology support an interactive decision, and the workload of the manager is reduced. The AR marking function can mark an abnormal area and synchronize the abnormal area to a decision making system, and through combination of AR and AI, a brand new interaction normal form is provided for intelligence and humanization of warehouse management.

Description

Technical Field

[0001] The present invention belongs to the technical field of warehouse scheduling, and specifically refers to a distributed intelligent warehouse scheduling system based on artificial intelligence. Background Art

[0002] Traditional warehouse scheduling systems have many problems. Centralized systems struggle to cope with the real-time dynamic demands of large-scale warehouses, have limited response speed, and lack the integration of artificial intelligence algorithms to optimize resource allocation. This leads to redundant picking routes, high equipment idle rates, and insufficient emergency response capabilities to sudden anomalies.

[0003] However, the existing distributed intelligent warehouse scheduling system based on artificial intelligence still has certain defects. The existing GNN and DRL collaborative optimization requires high computing power support, and path planning delays are prone to hardware performance bottlenecks in high-density AGV scenarios; federated learning relies on cross-warehouse data consistency, and heterogeneous data or noise may weaken the generalization ability of the pattern library. AR interaction is easily affected by sensor accuracy and environmental interference; digital twin models need to be continuously updated, and transfer learning under new working conditions may fail due to insufficient data. The coupling of rule engines and AI decision trees is difficult to cope with flexible responses in complex scenarios. For this reason, a distributed intelligent warehouse scheduling system based on artificial intelligence is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed intelligent warehouse scheduling system based on artificial intelligence to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: an artificial intelligence-based distributed intelligent warehouse scheduling system, comprising a multi-source environment perception module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a dynamic resource allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module, and a human-computer interaction and visualization module;

[0006] The multi-source environment perception module collects and uniformly processes heterogeneous warehouse environment data in real time through the Internet of Things and multimodal data fusion algorithm;

[0007] The dynamic inventory management module dynamically adjusts inventory strategies based on time series forecasting models and reinforcement learning, optimizes safety stock thresholds, and issues early warnings for slow-moving products.

[0008] The distributed task scheduling module uses a hybrid genetic algorithm reinforcement learning framework to decompose tasks and assign them to the optimal execution nodes, performing global scheduling and local adaptive collaborative optimization;

[0009] The intelligent path planning module combines deep reinforcement learning and graph neural networks to dynamically model warehouse topology and generate the optimal path for AGV robots with obstacle avoidance and low conflict in real time;

[0010] The resource dynamic allocation module dynamically allocates storage resources by integrating efficiency, energy consumption and equipment life indicators through a multi-objective optimization model and a multi-agent game algorithm;

[0011] The anomaly detection and emergency response module uses federated learning and transfer learning to build a cross-warehouse anomaly pattern library, combined with a rule engine and AI decision tree to identify faults in real time and generate emergency response plans;

[0012] The energy consumption optimization module uses digital twin simulation and reinforcement learning to analyze the energy consumption of equipment operation and generate a low-power scheduling strategy;

[0013] The supply chain collaboration module uses blockchain smart contracts to share supply chain data in a trustworthy manner, coordinates warehousing and transportation resources based on a multi-objective optimization algorithm, and reduces the cost of the entire supply chain.

[0014] The human-computer interaction and visualization module integrates AR technology and a three-dimensional visualization interface to display warehouse status, scheduling strategies, and prediction results in real time, supporting interactive decision-making by managers using AR glasses.

[0015] The multi-source environmental perception module deploys temperature and humidity sensors, RFID tags, high-definition cameras, and gas detection equipment to collect heterogeneous data on warehouse temperature, cargo location, personnel activities, and air quality in real time. It uses data cleaning and standardization techniques to eliminate noise and unify the data format. It uses multimodal feature extraction methods to construct a cross-modal feature space. It fuses data through a multimodal attention mechanism and a graph neural network to generate a unified structured input. It combines online learning algorithms to dynamically optimize model parameters, achieving the following formula:

[0016]

[0017] In the formula, e ij represents the edge weight, W (l) represents the weight matrix of the lth layer, σ represents the activation function, represents the feature vector of node i in layer l, N (i) represents the set of neighbor nodes of node i, Represents the updated feature vector of node i at layer l+1.

[0018] Among them, the dynamic inventory management module collects historical sales data, seasonal fluctuations and market trend time series data, uses the LSTM model to predict future demand, and combines the reinforcement learning algorithm to dynamically optimize the safety stock threshold to balance the risk of out-of-stock and inventory costs; the system monitors inventory levels in real time, identifies slow-moving products through anomaly detection and triggers early warnings; based on the deviation between the predicted results and actual sales, the reinforcement learning model continuously adjusts the replenishment strategy and inventory allocation rules for closed-loop optimization; the module synchronizes the dynamic strategy to the ERP system for automated inventory adjustments.

[0019] Among them, the distributed task scheduling module initializes the population through a genetic algorithm, breaks down tasks into subtasks and encodes them into chromosomes, and uses crossover and mutation operations to generate an initial scheduling plan; introduces reinforcement learning, dynamically adjusts the subtask allocation strategy based on the real-time environmental status, and optimizes the selection of local execution nodes; by combining the global search of the genetic algorithm with the local adaptation of reinforcement learning, the module continuously updates the scheduling strategy during the iteration process, balancing the global optimality and dynamic response capabilities; the system comprehensively evaluates multi-objective functions, outputs the optimal task allocation plan and synchronizes it to the execution node.

[0020] Among them, the intelligent path planning module dynamically constructs a warehouse topology map through a graph neural network to capture the spatial relationship between nodes and dynamic obstacle information; combined with the deep reinforcement learning framework, the topological state output by GNN is used as input to train the AGV's path decision strategy, and the objective function comprehensively considers path length, obstacle avoidance safety and conflict probability; the module continuously receives environmental perception data, updates the topology map through GNN and passes it to the DRL model to generate the optimal path; introduces a conflict detection mechanism and priority scheduling rules to dynamically adjust the paths of multiple AGVs to reduce conflicts; and continuously optimizes the DRL strategy through online learning to adapt to changes in warehouse layout and sudden obstacles.

[0021] Among them, the dynamic resource allocation module quantifies three types of indicators, namely efficiency, energy consumption and equipment life, through a multi-objective optimization model, and constructs a mathematical model with the goal of minimizing total cost and maximizing system robustness; introduces a multi-agent game framework, models warehouse resources as autonomous decision-making units, designs collaborative strategies based on Nash equilibrium theory, and dynamically adjusts the resource request and allocation strategies of each agent through reinforcement learning; the system continuously monitors equipment status and task priority, combines an online multi-objective optimization algorithm to generate a Pareto optimal solution set, and negotiates conflicts through a game mechanism, outputting a resource allocation plan that takes into account efficiency, energy consumption and life, and performs flexible scheduling and dynamic optimization of warehouse resources.

[0022] Among them, the anomaly detection and emergency response module aggregates the local anomaly feature data of each warehouse through the federated learning framework, and builds a shared anomaly pattern library across warehouses while protecting data privacy; uses transfer learning to migrate the high-precision model parameters of the core warehouse to the new warehouse, improving the detection capability in small sample scenarios; the system performs preset threshold detection and pattern matching through the rule engine to quickly identify preliminary anomalies; the AI ​​decision tree generates dynamic emergency plans based on the feature vector output by the federated learning model, combined with historical response strategies and current context; the module performs end-to-end closed-loop through the streaming computing engine, continuously updates the pattern library and rule library, and completes real-time optimization of anomaly identification, strategy generation and execution feedback.

[0023] Among them, the energy consumption optimization module builds a high-fidelity equipment operation model based on digital twin technology, collects equipment status, load and environmental parameters in real time, simulates dynamic energy consumption scenarios and quantifies key influencing factors; designs a multi-objective reward function through a reinforcement learning framework, takes minimizing energy consumption and maximizing equipment life as optimization goals, and combines algorithm training to generate a low-power scheduling strategy; the module continuously receives real-time energy consumption data output by the digital twin model, dynamically adjusts the strategy parameters using an online learning mechanism, and improves adaptability under new working conditions through transfer learning and migration of historical scenario optimization experience; the system integrates digital twin prediction results and reinforcement learning decisions, outputs a dynamic equipment scheduling plan, and conducts coordinated optimization of energy consumption reduction and equipment operation efficiency.

[0024] Among them, the supply chain collaboration module designs a data sharing protocol through blockchain smart contracts, defines the data access rights and synchronization rules of each participant, and ensures that the data of warehousing and transportation links are uploaded to the chain in real time and cannot be tampered with; constructs a multi-objective optimization model, takes transportation cost, warehousing utilization rate and delivery time as optimization goals, and dynamically adjusts the resource allocation strategy based on real-time data on the blockchain; the system continuously collects logistics status and demand fluctuation data, triggers the optimization algorithm through smart contracts, generates the globally optimal warehousing allocation and transportation route plan, and uses blockchain to record the execution results and cost changes; through the automatic execution of smart contracts and algorithm iterative optimization, the cost minimization and collaborative efficiency of the entire supply chain are improved.

[0025] Among them, the human-computer interaction and visualization module builds a three-dimensional warehouse digital twin model based on the AR development framework, integrating the warehouse status data collected in real time by IoT sensors; dynamic visualization is performed through the rendering engine, and the scheduling strategy is superimposed on the AR scene in the form of color-coded paths and prediction results in the form of heat maps and particle flows; when managers wear AR glasses, the system combines eye tracking and gesture recognition technology to allow users to view detailed data layers through gestures, trigger strategy comparison and analysis through voice commands, mark abnormal areas through AR annotations and synchronize them to the decision-making system; the module interacts in real time through low-latency streaming data transmission, and uses tools to quickly iterate visualization content to perform an immersive visualization closed loop of the overall warehouse and decision-making strategies.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. This invention uses a graph neural network to dynamically model warehouse topology and combines it with deep reinforcement learning to generate obstacle-avoiding and low-conflict AGV paths. GNN captures the spatial relationship between nodes and dynamic obstacle information, providing accurate environmental state input for DRL, significantly improving the real-time and safety of path planning. The conflict detection mechanism and priority scheduling rules further reduce the probability of multi-AGV path conflicts, while the online learning mechanism enables the strategy to adapt to changes in warehouse layout and sudden obstacles. Through the coordinated optimization of reinforcement learning and GNN, the system achieves adaptability and global optimization of path planning, providing key technical support for high-density AGV cluster operations.

[0028] 2. This invention builds a cross-warehouse anomaly pattern library through federated learning and transfer learning, enabling the sharing and generalization of anomaly patterns while protecting data privacy. The combination of the rule engine and AI decision tree enables the system to quickly identify initial anomalies and generate dynamic emergency plans. The end-to-end closed-loop mechanism of the streaming computing engine continuously updates the pattern library and rule library, ensuring real-time optimization of anomaly detection and response strategies. Through cross-warehouse knowledge transfer, the system's detection capabilities in small sample scenarios are enhanced, providing reliable protection for the stability and security of distributed warehousing systems.

[0029] 3. This invention uses digital twin technology to construct an equipment operation model and combines it with reinforcement learning to design a multi-objective reward function, achieving coordinated optimization of equipment energy consumption and lifespan. Digital twins simulate dynamic energy consumption scenarios, quantify key influencing factors, and provide accurate training data for reinforcement learning. The online learning mechanism dynamically adjusts strategy parameters, while transfer learning improves adaptability under new operating conditions. The system uses low-power scheduling strategies to reduce equipment idling and redundant operation, while extending equipment lifespan. Dynamic optimization capabilities enable the system to adapt to energy consumption requirements under different load conditions, providing a key technical path for green warehousing and sustainable development.

[0030] 4. This invention integrates AR technology with a three-dimensional visualization interface to construct a digital twin model of the warehouse, enabling an immersive display of warehouse status and scheduling strategies. By overlaying AR scenes with color-coded paths, heat maps, and particle flow, managers can intuitively understand inventory distribution, task progress, and abnormal areas. Eye tracking and gesture recognition technologies support interactive decision-making, and the AR annotation function can mark abnormal areas and synchronize them with the decision-making system. Low-latency streaming data transmission and rapid iteration tools ensure a real-time interactive experience. By combining AR and AI, the system transforms abstract data into intuitive operations, providing a new interactive paradigm for intelligent and user-friendly warehouse management. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the structure of the distributed intelligent warehouse scheduling system based on artificial intelligence of the present invention;

[0032] Figure 2 The operation process of the distributed intelligent warehouse scheduling system based on artificial intelligence of the present invention Figure 1 ;

[0033] Figure 3 The operation process of the distributed intelligent warehouse scheduling system based on artificial intelligence of the present invention Figure 2 ;

[0034] Figure 4 The operation process of the distributed intelligent warehouse scheduling system based on artificial intelligence of the present invention Figure 3 . DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] Example

[0037] See also Figure 1-Figure 4 As shown, the present invention provides a technical solution: including a multi-source environment perception module, a dynamic inventory management module, a distributed task scheduling module, an intelligent path planning module, a resource dynamic allocation module, an anomaly detection and emergency response module, an energy consumption optimization module, a supply chain collaboration module and a human-computer interaction and visualization module;

[0038] The multi-source environment perception module collects and uniformly processes heterogeneous warehouse environment data in real time through the Internet of Things and multimodal data fusion algorithm;

[0039] The dynamic inventory management module dynamically adjusts inventory strategies based on time series forecasting models and reinforcement learning, optimizes safety stock thresholds, and issues early warnings for slow-moving products.

[0040] The distributed task scheduling module uses a hybrid genetic algorithm reinforcement learning framework to decompose tasks and assign them to the optimal execution nodes, performing global scheduling and local adaptive collaborative optimization;

[0041] The intelligent path planning module combines deep reinforcement learning and graph neural networks to dynamically model warehouse topology and generate the optimal path for AGV robots with obstacle avoidance and low conflict in real time;

[0042] The resource dynamic allocation module dynamically allocates storage resources by integrating efficiency, energy consumption and equipment life indicators through a multi-objective optimization model and a multi-agent game algorithm;

[0043] The anomaly detection and emergency response module uses federated learning and transfer learning to build a cross-warehouse anomaly pattern library, combined with a rule engine and AI decision tree to identify faults in real time and generate emergency response plans;

[0044] The energy consumption optimization module uses digital twin simulation and reinforcement learning to analyze the energy consumption of equipment operation and generate a low-power scheduling strategy;

[0045] The supply chain collaboration module uses blockchain smart contracts to share supply chain data in a trustworthy manner, coordinates warehousing and transportation resources based on a multi-objective optimization algorithm, and reduces the cost of the entire supply chain.

[0046] The human-computer interaction and visualization module integrates AR technology and a three-dimensional visualization interface to display warehouse status, scheduling strategies, and prediction results in real time, supporting interactive decision-making by managers using AR glasses.

[0047] The multi-source environmental perception module deploys temperature and humidity sensors, RFID tags, high-definition cameras, and gas detection equipment to collect heterogeneous data on warehouse temperature, cargo location, personnel activities, and air quality in real time. It uses data cleaning and standardization techniques to eliminate noise and unify the data format. It uses multimodal feature extraction methods to construct a cross-modal feature space. It fuses data through a multimodal attention mechanism and a graph neural network to generate a unified structured input. It combines online learning algorithms to dynamically optimize model parameters, achieving the following formula:

[0048]

[0049] In the formula, e ij represents the edge weight, W (l) represents the weight matrix of the lth layer, σ represents the activation function, represents the feature vector of node i in layer l, N(i) represents the set of neighbor nodes of node i, Represents the updated feature vector of node i at layer l+1.

[0050] Among them, the dynamic inventory management module collects historical sales data, seasonal fluctuations and market trend time series data, uses the LSTM model to predict future demand, and combines the reinforcement learning algorithm to dynamically optimize the safety stock threshold to balance the risk of out-of-stock and inventory costs; the system monitors inventory levels in real time, identifies slow-moving products through anomaly detection and triggers early warnings; based on the deviation between the predicted results and actual sales, the reinforcement learning model continuously adjusts the replenishment strategy and inventory allocation rules for closed-loop optimization; the module synchronizes the dynamic strategy to the ERP system for automated inventory adjustments.

[0051] Among them, the distributed task scheduling module initializes the population through a genetic algorithm, breaks down tasks into subtasks and encodes them into chromosomes, and uses crossover and mutation operations to generate an initial scheduling plan; introduces reinforcement learning, dynamically adjusts the subtask allocation strategy based on the real-time environmental status, and optimizes the selection of local execution nodes; by combining the global search of the genetic algorithm with the local adaptation of reinforcement learning, the module continuously updates the scheduling strategy during the iteration process, balancing the global optimality and dynamic response capabilities; the system comprehensively evaluates multi-objective functions, outputs the optimal task allocation plan and synchronizes it to the execution node.

[0052] Among them, the intelligent path planning module dynamically constructs a warehouse topology map through a graph neural network to capture the spatial relationship between nodes and dynamic obstacle information; combined with the deep reinforcement learning framework, the topological state output by GNN is used as input to train the AGV's path decision strategy, and the objective function comprehensively considers path length, obstacle avoidance safety and conflict probability; the module continuously receives environmental perception data, updates the topology map through GNN and passes it to the DRL model to generate the optimal path; introduces a conflict detection mechanism and priority scheduling rules to dynamically adjust the paths of multiple AGVs to reduce conflicts; and continuously optimizes the DRL strategy through online learning to adapt to changes in warehouse layout and sudden obstacles.

[0053] Among them, the dynamic resource allocation module quantifies three types of indicators, namely efficiency, energy consumption and equipment life, through a multi-objective optimization model, and constructs a mathematical model with the goal of minimizing total cost and maximizing system robustness; introduces a multi-agent game framework, models warehouse resources as autonomous decision-making units, designs collaborative strategies based on Nash equilibrium theory, and dynamically adjusts the resource request and allocation strategies of each agent through reinforcement learning; the system continuously monitors equipment status and task priority, combines an online multi-objective optimization algorithm to generate a Pareto optimal solution set, and negotiates conflicts through a game mechanism, outputting a resource allocation plan that takes into account efficiency, energy consumption and life, and performs flexible scheduling and dynamic optimization of warehouse resources.

[0054] Among them, the anomaly detection and emergency response module aggregates the local anomaly feature data of each warehouse through the federated learning framework, and builds a shared anomaly pattern library across warehouses while protecting data privacy; uses transfer learning to migrate the high-precision model parameters of the core warehouse to the new warehouse, improving the detection capability in small sample scenarios; the system performs preset threshold detection and pattern matching through the rule engine to quickly identify preliminary anomalies; the AI ​​decision tree generates dynamic emergency plans based on the feature vector output by the federated learning model, combined with historical response strategies and current context; the module performs end-to-end closed-loop through the streaming computing engine, continuously updates the pattern library and rule library, and completes real-time optimization of anomaly identification, strategy generation and execution feedback.

[0055] Among them, the energy consumption optimization module builds a high-fidelity equipment operation model based on digital twin technology, collects equipment status, load and environmental parameters in real time, simulates dynamic energy consumption scenarios and quantifies key influencing factors; designs a multi-objective reward function through a reinforcement learning framework, takes minimizing energy consumption and maximizing equipment life as optimization goals, and combines algorithm training to generate a low-power scheduling strategy; the module continuously receives real-time energy consumption data output by the digital twin model, dynamically adjusts the strategy parameters using an online learning mechanism, and improves adaptability under new working conditions through transfer learning and migration of historical scenario optimization experience; the system integrates digital twin prediction results and reinforcement learning decisions, outputs a dynamic equipment scheduling plan, and conducts coordinated optimization of energy consumption reduction and equipment operation efficiency.

[0056] Among them, the supply chain collaboration module designs a data sharing protocol through blockchain smart contracts, defines the data access rights and synchronization rules of each participant, and ensures that the data of warehousing and transportation links are uploaded to the chain in real time and cannot be tampered with; constructs a multi-objective optimization model, takes transportation cost, warehousing utilization rate and delivery time as optimization goals, and dynamically adjusts the resource allocation strategy based on real-time data on the blockchain; the system continuously collects logistics status and demand fluctuation data, triggers the optimization algorithm through smart contracts, generates the globally optimal warehousing allocation and transportation route plan, and uses blockchain to record the execution results and cost changes; through the automatic execution of smart contracts and algorithm iterative optimization, the cost minimization and collaborative efficiency of the entire supply chain are improved.

[0057] Among them, the human-computer interaction and visualization module builds a three-dimensional warehouse digital twin model based on the AR development framework, integrating the warehouse status data collected in real time by IoT sensors; dynamic visualization is performed through the rendering engine, and the scheduling strategy is superimposed on the AR scene in the form of color-coded paths and prediction results in the form of heat maps and particle flows; when managers wear AR glasses, the system combines eye tracking and gesture recognition technology to allow users to view detailed data layers through gestures, trigger strategy comparison and analysis through voice commands, mark abnormal areas through AR annotations and synchronize them to the decision-making system; the module interacts in real time through low-latency streaming data transmission, and uses tools to quickly iterate visualization content to perform an immersive visualization closed loop of the overall warehouse and decision-making strategies.

[0058] Working principle: Real-time collection of heterogeneous data through multi-source environmental perception modules provides a decision-making basis for dynamic inventory management and distributed task scheduling; intelligent path planning and dynamic resource allocation collaboratively optimize AGV paths and equipment resources; anomaly detection builds a cross-warehouse pattern library and triggers emergency response in real time; energy consumption optimization dynamically generates low-power strategies; supply chain collaboration realizes trusted data sharing and global resource allocation, and human-computer interaction supports immersive decision-making; each module is linked through a closed-loop data loop to achieve intelligent, dynamic and efficient collaboration of the entire warehousing process.

[0059] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0060] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. Distributed intelligent warehouse scheduling system based on artificial intelligence, characterized by: It includes multi-source environment perception module, dynamic inventory management module, distributed task scheduling module, intelligent path planning module, dynamic resource allocation module, anomaly detection and emergency response module, energy consumption optimization module, supply chain collaboration module and human-computer interaction and visualization module; The multi-source environment perception module collects and uniformly processes heterogeneous warehouse environment data in real time through the Internet of Things and multimodal data fusion algorithm; The dynamic inventory management module dynamically adjusts inventory strategies based on time series forecasting models and reinforcement learning, optimizes safety stock thresholds, and issues early warnings for slow-moving products. The distributed task scheduling module uses a hybrid genetic algorithm reinforcement learning framework to decompose tasks and assign them to the optimal execution nodes, performing global scheduling and local adaptive collaborative optimization; The intelligent path planning module combines deep reinforcement learning and graph neural networks to dynamically model warehouse topology and generate the optimal path for AGV robots with obstacle avoidance and low conflict in real time; The resource dynamic allocation module dynamically allocates storage resources by integrating efficiency, energy consumption and equipment life indicators through a multi-objective optimization model and a multi-agent game algorithm; The anomaly detection and emergency response module uses federated learning and transfer learning to build a cross-warehouse anomaly pattern library, combined with a rule engine and AI decision tree to identify faults in real time and generate emergency response plans; The energy consumption optimization module uses digital twin simulation and reinforcement learning to analyze the energy consumption of equipment operation and generate a low-power scheduling strategy; The supply chain collaboration module uses blockchain smart contracts to share supply chain data in a trustworthy manner, coordinates warehousing and transportation resources based on a multi-objective optimization algorithm, and reduces the cost of the entire supply chain. The human-computer interaction and visualization module integrates AR technology and a three-dimensional visualization interface to display warehouse status, scheduling strategies, and prediction results in real time, supporting interactive decision-making by managers using AR glasses.

2. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The multi-source environmental perception module deploys temperature and humidity sensors, RFID tags, high-definition cameras, and gas detection equipment to collect heterogeneous data on warehouse temperature, cargo location, personnel activity, and air quality in real time. It uses data cleaning and standardization techniques to eliminate noise and unify data formats. It also uses multimodal feature extraction methods to construct a cross-modal feature space. Through multimodal attention mechanism and graph neural network data fusion, a unified structured input is generated; combined with online learning algorithm to dynamically optimize model parameters, the implementation formula is: In the formula, e ij represents the edge weight, W (l) represents the weight matrix of the lth layer, σ represents the activation function, represents the feature vector of node i in layer l, N (i) represents the set of neighbor nodes of node i, Represents the updated feature vector of node i at layer l+1.

3. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The dynamic inventory management module collects historical sales data, seasonal fluctuations, and market trend time series data, uses the LSTM model to predict future demand, and combines the reinforcement learning algorithm to dynamically optimize the safety stock threshold to balance out-of-stock risk and inventory costs; The system monitors inventory levels in real time, identifies slow-moving items through anomaly detection, and triggers alerts; Based on the deviation between the predicted results and actual sales, the reinforcement learning model continuously adjusts the replenishment strategy and inventory allocation rules for closed-loop optimization; the module synchronizes the dynamic strategy to the ERP system for automated inventory adjustments.

4. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The distributed task scheduling module initializes the population through a genetic algorithm, breaks down tasks into subtasks and encodes them as chromosomes, and uses crossover and mutation operations to generate an initial scheduling plan; introduces reinforcement learning, dynamically adjusts the subtask allocation strategy based on the real-time environmental state, and optimizes the selection of local execution nodes; by combining the global search of the genetic algorithm with the local adaptation of reinforcement learning, the module continuously updates the scheduling strategy during the iteration process, balancing global optimization and dynamic response capabilities; the system comprehensively evaluates multi-objective functions, outputs the optimal task allocation plan, and synchronizes it to the execution node.

5. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The intelligent path planning module dynamically constructs a warehouse topology map through a graph neural network to capture the spatial relationship between nodes and dynamic obstacle information; Combined with the deep reinforcement learning framework, the topological state output by GNN is used as input to train the AGV's path decision strategy. The objective function comprehensively considers path length, obstacle avoidance safety, and collision probability. The module continuously receives environmental perception data, updates the topological map through GNN, and passes it to the DRL model to generate the optimal path. It introduces a conflict detection mechanism and priority scheduling rules to dynamically adjust the paths of multiple AGVs to reduce conflicts. The DRL strategy is continuously optimized through online learning to adapt to changes in warehouse layout and sudden obstacles.

6. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The dynamic resource allocation module quantifies three indicators: efficiency, energy consumption, and equipment lifespan through a multi-objective optimization model, and constructs a mathematical model with the goal of minimizing total cost and maximizing system robustness. It introduces a multi-agent game framework, models warehouse resources as autonomous decision-making units, designs collaborative strategies based on Nash equilibrium theory, and dynamically adjusts the resource request and allocation strategies of each agent through reinforcement learning. The system continuously monitors equipment status and task priorities, combines online multi-objective optimization algorithms to generate a Pareto optimal solution set, negotiates conflicts through a game mechanism, and outputs a resource allocation plan that takes into account efficiency, energy consumption, and lifespan, enabling flexible scheduling and dynamic optimization of warehouse resources.

7. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The anomaly detection and emergency response module aggregates the local anomaly feature data of each warehouse through a federated learning framework, and builds a shared anomaly pattern library across warehouses while protecting data privacy; it uses transfer learning to migrate the high-precision model parameters of the core warehouse to the new warehouse to improve detection capabilities in small sample scenarios; the system uses a rule engine to perform preset threshold detection and pattern matching to quickly identify preliminary anomalies; the AI ​​decision tree generates dynamic emergency plans based on the feature vector output by the federated learning model, combined with historical response strategies and current context; the module performs end-to-end closed-loop through a streaming computing engine, continuously updates the pattern library and rule library, and completes real-time optimization of anomaly identification, strategy generation, and execution feedback.

8. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The energy consumption optimization module builds a high-fidelity equipment operation model based on digital twin technology, collects equipment status, load and environmental parameters in real time, simulates dynamic energy consumption scenarios and quantifies key influencing factors; designs a multi-objective reward function through a reinforcement learning framework, takes minimizing energy consumption and maximizing equipment life as optimization goals, and combines algorithm training to generate a low-power scheduling strategy; the module continuously receives real-time energy consumption data output by the digital twin model, uses an online learning mechanism to dynamically adjust strategy parameters, and improves adaptability under new working conditions through transfer learning and migration of historical scenario optimization experience; the system integrates digital twin prediction results and reinforcement learning decisions, outputs a dynamic equipment scheduling plan, and conducts coordinated optimization of energy consumption reduction and equipment operation efficiency.

9. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The supply chain collaboration module designs a data sharing protocol through blockchain smart contracts, defines the data access rights and synchronization rules of each participant, and ensures that the data of warehousing and transportation links are uploaded to the chain in real time and cannot be tampered with; constructs a multi-objective optimization model, takes transportation cost, warehouse utilization rate and delivery time as optimization goals, and dynamically adjusts the resource allocation strategy based on real-time data on the blockchain; the system continuously collects logistics status and demand fluctuation data, triggers the optimization algorithm through smart contracts, generates the globally optimal warehousing allocation and transportation route plan, and uses blockchain to record the execution results and cost changes; through the automatic execution of smart contracts and iterative optimization of algorithms, the cost minimization and collaborative efficiency of the entire supply chain are improved.

10. The distributed intelligent warehouse scheduling system based on artificial intelligence according to claim 1 is characterized by: The human-computer interaction and visualization module builds a three-dimensional warehouse digital twin model based on the AR development framework, integrating warehouse status data collected in real time by IoT sensors. Through the rendering engine, dynamic visualization is performed, and the scheduling strategy is superimposed on the AR scene in the form of color-coded paths and prediction results in the form of heat maps and particle flows. When managers wear AR glasses, the system combines eye tracking and gesture recognition technology to allow users to view detailed data layers through swiping gestures, trigger strategy comparison and analysis through voice commands, mark abnormal areas through AR annotations and synchronize them to the decision-making system; the module interacts in real time through low-latency streaming data transmission, and uses tools to quickly iterate visualization content, creating an immersive visualization closed loop of the entire warehouse and decision-making strategies.

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