9127results about "Logistics" patented technology

An emergency response and crisis management system comprises a computing system which performs steps comprising: aggregating real-time and historical data from multiple data sources; processing the data using artificial intelligence techniques; analyzing the processed data to detect and classify active crises to predict the likely development and trajectory of the ongoing crises, and assess potential short-term and long-term impacts across one or more sectors comprising one or more of public health, economy, infrastructure, environment, and social stability; generating multi-dimensional, interactive visualizations of the analysis results comprising one or more of geospatial maps, time series charts, and network diagrams; providing recommendations for crisis management and response tailored to different stakeholder roles; facilitating integration with external systems and applications through a comprehensive application programming interface (API) layer; and continuously adapting the system's models and algorithms based on one or more of real-world outcomes, expert input, and user feedback.

A generative AI-supported system (100) for real-time demand forecasting and inventory optimization in supply chain management, which includes the following: a data collection and integration module configured to aggregate and preprocess structured and unstructured data from multiple sources, including historical sales records, IoT sensors, external market trends, and environmental factors; a generative AI-based forecasting module configured to process input data, recognize complex patterns, and generate highly accurate real-time demand forecasts using deep learning techniques; an inventory optimization module configured to dynamically calculate and adjust inventory levels based on demand forecasts, replenishment plans, lead times, and safety stock requirements; a decision support and analysis module configured to generate actionable insights, provide simulations of multiple scenarios, and recommend cost-effective strategies for supply chain operations; a real-time warning and notification module configured to monitor supply chain activities and inform stakeholders of potential disruptions, anomalies, or demand spikes; a user interface and integration module configured to provide outputs via a user-friendly dashboard and seamlessly integrate with existing enterprise resource planning (ERP), warehouse management systems (WMS), and logistics platforms.

A system may include an Al convergence system of systems for a value chain network environment.

A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

The invention provides an intelligent logistics optimization method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion processing of the Internet of Things sensor data and historical business data of a multimodal transportation reloading node in a logistics process, and obtaining a spatial-temporal feature vector; performing operation adaptability evaluation on the reloading nodes by using a P median model according to the spatial-temporal feature vectors to obtain a comprehensive score reflecting the node operation condition; a Tabu search algorithm is adopted to carry out combinational optimization on operation parameters of the reloading nodes based on the spatial-temporal feature vectors and the comprehensive scores, and an initial reloading scheme is obtained; and according to the initial reloading scheme, performing multi-agent cooperative scheduling on the reloading nodes on the digital twin platform to obtain a logistics resource optimal allocation scheme. Through combination of multi-source fusion and an efficient optimization algorithm, dynamic operation analysis and collaborative scheduling of multimodal transport reloading nodes are realized, logistics timeliness and resource utilization rate are improved, and the method is suitable for a complex logistics network and a changeable market environment.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

A system for receiving and temporarily maintaining a delivery item and dynamically initiating a dispatched logistics operation for a storage receptacle. The storage receptacle has an entrance opening for receiving the delivery item, a temporary storage area for temporarily maintaining the delivery item once deposited with the storage receptacle, and a retrieval door providing selective access to the temporary storage area. The system includes a backend server that maintains a management profile related to the storage receptacle, a bridge node mounted to the storage receptacle, and a wireless accessory sensor node having at least one sensor that monitors for a change in state of the storage receptacle. The wireless accessory sensor node broadcasts an updated advertising signal. The bridge node transmits retrieved event information to the backend server at a reporting time, and the backend server initiates the dispatched logistics operation from comparing the retrieved event information to location information.

An enhanced multi-compartment connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with a plurality of sensors. A first subset of the sensors is disposed relative to a first monitored receptacle compartment and a second subset of the sensors is disposed relative to a second monitored receptacle compartment. The plurality of sensors monitor for a change in state of the storage receptacle. The wireless accessory sensor node detects the storage receptacle's state change based upon sensor data and broadcasts a signal with a flag set indicating there is event information available for upload. The bridge node detects the flag set, retrieves the event information available for upload from the wireless accessory sensor node, and transmits the retrieved event information to a backend server to cause the backend server to initiate a dispatched logistics operation related to the storage receptacle.

The present invention provides a system and method for data processing in procurement and supply chain application developed by codeless platform. The invention includes one or more large language model (LLM) agents configured for processing one or more input received on an electronic user interface. The invention includes selecting a tool selection agent from a tool repository and invoking the selected tool by a tool execution agent for processing at least one task to be executed.

A mattress assembly includes a tub including rails and a base with the tub defining an interior cavity bound by the rails and the base, a plurality of non-laminated and rearrangeable layers positioned within the interior cavity to provide both support and comfort levels within the mattress assembly with each layer including at least one of a layer cover or a layer foam, and a cover system including a removable cover allowing selective access to the plurality of layers and the interior cavity and selectively compressing the plurality of layers within the tub, where the cover system includes a first zipper and a second zipper coupling the removable cover to the tub, and the first zipper and the second zipper meet at a central portion of a front side of the mattress assembly.

Systems and methods can enhance efficiencies of order fulfillment processes. For example, this document describes systems and methods for efficiently processing direct-to-customer shipments at a last mile delivery (“LMD”) facility. The systems and methods are focused on rapid loading, unloading and cycling of LMD delivery vehicles through the operation in a space-efficient manner. In some embodiments, the delivery vehicles are cycled through the LMD facility in organized batches to allow high throughput with fast cycle time.

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

A healthcare supply management system that uses machine learning for predictive supply chain optimization, comprising: a) a data collection and integration module configured to collect and integrate real-time and historical data from multiple sources, including electronic health records, vendor databases, hospital inventory logs, and external market trends. b) a predictive analytics and demand forecasting module that uses machine learning algorithms to analyze data and generate accurate demand forecasts for medical supplies. c) an inventory optimization and automation module that dynamically adjusts inventory levels based on predictive insights and automates orders. d) a supplier management and logistics module that can be used to evaluate supplier performance, track shipments and predict delivery delays. (e) a risk management and disruption mitigation module that identifies potential risks in the supply chain and proposes emergency strategies. f) a decision support and reporting module that provides real-time insights, AI-driven recommendations, and customizable reports through a user-friendly dashboard. (g) a processing unit and a cloud-based database to store and process supply chain-related information and to facilitate seamless data access and communication between the modules.

The invention discloses an intelligent logistics supply chain management data analysis system based on a cloud platform, and relates to the technical field of intelligent logistics, the intelligent logistics supply chain management data analysis system comprises a supply chain management platform, and the supply chain management platform is in communication connection with the following modules: a data acquisition and processing module, which is used for acquiring logistics resource data from each link in a supply chain, the collected logistics resource data are preprocessed; and the digital twinborn simulation module is used for constructing a digital twinborn model of the supply chain and carrying out real-time dynamic simulation on the logistics process. According to the invention, through the resource optimization configuration model of federated learning training and in combination with an optimal scheme of digital twinborn simulation, global scheduling is carried out on logistics resources, and through a knowledge graph, potential conflicts in goods allocation are identified, the warehouse space utilization rate is optimized, and resource allocation at a global perspective is realized, so that the optimization limitation of a single node is broken through; the vehicle utilization rate, the warehouse space utilization rate and the equipment utilization rate are remarkably improved, and the overall operation cost is reduced.

A centrally managed system for tuning a plurality of enhanced node-based logistics receptacles includes a backend server, a first enhanced node-based logistics receptacles, and a second node-based logistics receptacles. The backend server transmits a first setup message to a first bridge node, transmits a second setup message to a second bridge node, receives retrieved first event information from the first bridge node and retrieved second event information from the second bridge node, compares the retrieved first event information with a management profile and compares the retrieved second event information with the management profile, revises the management profile based upon the comparison of the retrieved first event information with the management profile and the comparison of the retrieved second event information with the management profile, and transmits an adjustment message that is based upon the revised management profile to the first bridge node.

An enhanced node-based connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with at least one sensor monitoring for a change in state of the storage receptacle. The system's wireless accessory sensor node detects the storage receptacle's state change based upon sensor data, records timestamped information reflecting the detected change, and broadcasts a signal with a flag set indicating there is event information available for upload. The system's bridge node is operative to detect the flag set, retrieve the event information available for upload from the wireless accessory sensor node, and transmit a message related to the retrieved event information to a backend server to cause the backend server to initiate the dispatched logistics operation related to the storage receptacle.

An enhanced connected logistics receptacle system that selectively retrieves and reports event information to a backend server to initiate a dispatched logistics operation related to a delivery item. The system includes a storage receptacle for receiving the delivery item, a bridge node mounted to the storage receptacle, and a wireless accessory sensor node coupled to a plurality of sensors. The wireless accessory sensor node is operative to detect a change in state of the storage receptacle based upon sensor data, record timestamped information reflecting the detected change in state, and broadcast an updated advertising signal having a data available flag set within the updated advertising signal. The bridge node processor is operative to detect the data available flag, selectively retrieve event information from the wireless accessory sensor node, and transmit the retrieved event information to the backend server to cause the backend server to initiate the dispatched logistics operation.

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention discloses an intelligent logistics scheduling method and device based on a dynamic weight and a medium, and the method comprises the steps: carrying out the fusion processing of the operation environment data of a logistics carrier, so as to construct a dynamic environment model, and carrying out the fusion processing of the operation environment data of the logistics carrier based on a reinforcement learning model according to a real-time environment state and a multi-target optimization demand. Dynamically adjusting the weight vector of each logistics parameter in the logistics carrier; predicting the space-time trajectory of each logistics carrier based on the kinematic model of the logistics carrier, mapping the space-time trajectory to a three-dimensional grid, identifying a potential conflict area between the logistics carriers, and calculating a corresponding conflict severity index; based on the dynamic weight vector and the conflict severity index, generating an obstacle avoidance path of the logistics carrier by adopting a global path re-planning algorithm, and optimizing the obstacle avoidance path through a local track to generate a corresponding smooth track; and executing a scheduling instruction corresponding to the smooth trajectory, and collecting an execution feedback result in real time to update the reinforcement learning model parameters and the dynamic weight strategy.

The present disclosure relates to the field of intelligent warehouse management, and provides a path planning method, apparatus, and system, and a storage medium. The path planning method comprises: constructing a route network model of a warehouse; and, on the basis of real-time environment information of the warehouse, dynamically setting a weight of each passage in the route network model of the warehouse, so as to obtain a dynamically weighted route network model, and, on the basis of requirement information of a task of an automated guided vehicle in the route network model, planning a path for the automated guided vehicle in the dynamically weighted route network model. Thus, in a warehouse environment changing in real time and under task requirements changing in real time, a good path can be found for the automated guided vehicle, so that the operating efficiency of the automated guided vehicle is improved.

Systems and methods for automating the management of a warehouse are disclosed. The method includes monitoring. in real-time, a plurality of items in the warehouse, a plurality of tasks associated with the warehouse, and / or incoming vehicles to the warehouse. The method also includes receiving, via a plurality of sensors, image data and / or video data associated with the plurality of items, the plurality of tasks, and / or the incoming vehicles. The method further includes inputting the image data and / or the video data into a machine learning model to automate the management of the warehouse, wherein the machine learning model has been trained based on training data to generate predictions for the inputted data, and wherein the machine learning model utilizes the predictions for the inputted data for retraining to generate predictions for newly inputted data and improve accuracy of error handling.

The invention discloses an intelligent logistics system based on optimal scheduling, and relates to the field of logistics management, and the system comprises the steps: arranging environment sensors at key nodes of a logistics network, and transmitting collected real-time data to edge computing equipment for preprocessing; analyzing the preprocessed data by using a deep learning model, and predicting an environment change trend in a non-period of time; dynamically adjusting a logistics scheduling strategy based on the prediction result; after receiving the adjusted scheduling strategy, defining a state space, an action space and a reward function of the logistics system; training a reinforcement learning algorithm model, and continuously optimizing a decision strategy through interaction with the environment; and calling the reinforcement learning model through an API interface to make a real-time decision, and generating an optimal scheduling scheme. According to the invention, by integrating environment perception, deep learning prediction, adaptive scheduling and reinforcement learning, the defects of an existing intelligent logistics system in the aspects of environment perception, prediction and dynamic scheduling optimization are overcome, and the intelligent level and operation efficiency of logistics management are significantly improved.

A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

An AI-based inventory optimization system implemented as a device for pharmaceutical supply chains. The system includes: a structural frame defining a plurality of modular storage compartments, each compartment equipped with robotic racks mounted on motorized drive assemblies, the drive assemblies configured to translate drug containers along three-dimensionally guided rails; an array of environmental sensors arranged in the compartments, the sensors comprising temperature sensors, humidity sensors, and vibration sensors, each sensor electrically connected to an edge processing module housed in the frame, the edge processing module configured to pre-process sensor data using convolutional neural network models to detect deviations in cold chain compliance; a central processing unit comprising at least one GPU or neural processing accelerator, the central processing unit executing a recurrent neural network-based demand forecasting engine and a reinforcement learning agent-based optimization engine, the processing unit generating control signals for redistribution policies that minimize predicted shortages, waste, and regulatory violations; a conveyor-driven dispensing unit operatively connected to the actuator units, the dispensing unit comprising belt actuators, RFID tag readers, and load sensors, and configured to redistribute goods between compartments or to external dispensing points; a communications gateway that supports both 5G and LPWAN connectivity protocols for real-time synchronization with distributed supply chain nodes and cloud servers; and A blockchain-integrated cryptographic module embedded in the central processing unit, configured to digitally sign and record every inventory movement, environmental condition, and redistribution command in a distributed ledger, thus ensuring immutable traceability of the drug inventory.

A method of creating a SMARTPASS with computed estimated value including, a SmartHub computer processor system and data network, method including: receiving electronic requests to validate / authorize members and a purchase reporting request from a buyer and a financing request from a supplier engaged in a trade transaction over the network; determining if the electronic requests from the buyer and supplier are authorized, if so, creating the SMARTPASS with computed estimated value on the purchase reporting request; saving / minting the SMARTPASS as a non-fungible token, assigning an initial NFT value; computing the estimated value, tariff, taxes, or other fees; updating a database of the SMARTPASS, NFT, and initial NFT value with updated value; determining if financing requested, and if so: posting an NFT proxy to a second blockchain public NFT repository, notifying parties of financing options, and notifying FinTech, DeFi or decentralized Lenders; or if not notifying FinTech, DeFi or decentralized Lenders.

The invention discloses a logistics data credible evidence storage and tracing method based on a block chain, and the method comprises the following steps: collecting original logistics data, and executing data standardization processing; constructing a layered Merkle mountain structure, and calculating to obtain a main root commitment of the layered Merkle mountain; dividing a layered Merkle mountain range structure, and calculating to obtain a windowed subtree root set; configuring a revision branch record structure, generating a collision pair by using chameleon hash, and generating a revision proof record; a multi-index root structure is constructed, a cross-root sharing mechanism is established, and a multi-dimensional tracing index set is generated; and based on the hierarchical Merkle mountain range main root commitment, the windowed subtree root set, the multi-dimensional traceability index set and the revision proof record, executing anchoring consistency verification, and outputting a credible evidence storage result and a traceability verification result. According to the invention, the logistics data evidence storage integrity and the multi-dimensional tracing efficiency are improved.

The present invention relates to the technical field of warehousing control. In particular disclosed are an automatic cargo loading and unloading warehousing control system and a cargo loading and unloading device therefor, the system being provided with a compartment parking information acquisition and processing module, an intelligent cargo conveyance control module, a warehousing management information comprehensive determination module, and a data control library. First, compartment parking information acquisition and preprocessing is performed, and it is determined whether a cargo loading and unloading AGV can be started, and next, a first channel is screened out to convey a goods in a compartment. Hence, a control anomaly value of warehousing management information of an intelligent warehousing control platform is comprehensively calculated, and the warehouse management information is subjected to autonomous early warning control and indication, thus facilitating rapid operations of cargo loading and unloading, and greatly improving the efficiency of warehousing intelligentization. Meanwhile, the labor costs of warehousing operations can be reduced, and the quality and accuracy of cargo processing can also be improved.

The invention relates to the technical field of logistics distribution data fusion, in particular to a data fusion method and system based on a logistics distribution knowledge graph. The method comprises the following steps: extracting original data and carrying out independent representation learning and unified representation processing; extracting uniformly expressed context information and carrying out dynamic perception embedding to obtain semantic enhancement features; fusing time-space and dependency features and physical characteristic features based on semantic enhancement features, performing message passing and relation inference by using a graph neural network, and finally determining a multi-mode relation set; global entity clustering and local feature fine matching are carried out based on semantic enhancement features, hierarchical entity alignment is carried out in combination with the multi-mode relation set, and an aligned entity set is obtained; and constructing an entity filling graph according to the aligned entity set, supplementing multi-dimensional relation attributes, and finally generating a logistics distribution knowledge graph. According to the invention, through a logistics distribution data fusion technology, a more comprehensive and more intelligent logistics distribution knowledge graph is realized.

The invention relates to the technical field of medicine sales data analysis and supply chain management, in particular to a data analysis method and system for medicine sales, and the method comprises the steps: obtaining multi-source data, constructing a multi-layer causal network through causal reasoning and quantum causal reasoning, and carrying out the multi-layer causal analysis and optimization; dynamic scene simulation and hybrid augmented reality technologies are combined to generate a market change scene and optimize a decision framework; and finally, automatic execution and real-time monitoring of the supply chain are realized through an intelligent contract and a block chain technology, and continuous optimization is performed through a feedback mechanism. According to the invention, the response speed and market adaptability of drug sales are remarkably improved, and the defects of multivariate analysis, real-time monitoring and supply chain optimization in the prior art are overcome.