3430results about "Logistics" patented technology

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.

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.

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.

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.

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 invention discloses an AGV path planning algorithm and a conflict-free strategy based on a Dlite algorithm. The algorithm and the strategy are suitable for multi-AGV collaborative operation in dynamic complex environments such as track equipment assembly workshops. The method comprises the following steps: (1) adopting an improved Dlite algorithm to realize dynamic path planning, and shortening the time consumed for updating a single AGV path through an incremental re-planning mechanism to adapt to the real-time change of dynamic obstacles such as personnel flow and equipment displacement; (2) constructing a space-time joint constraint model, dividing a workshop channel into limited grid units, and accurately marking the space-time occupancy state of the AGV through a time window; and (3) designing a hierarchical conflict resolution strategy, and when path crossing is detected, dynamically allocating priorities based on the task emergency degree and the AGV cargo carrying value, and realizing conflict avoidance through a speed adjustment window and path fine adjustment.

Methods of performing a dispatched logistics operation for delivery / pickup of items using a modular autonomous bot apparatus assembly (with a modular autonomy module, cargo storage system, auxiliary power module, and mobility base) and a dispatch server. The modular autonomy control module receives a dispatch command from the dispatch server and authenticates that each of the modular components in the assembly are compatible with the particular dispatched logistics operation. The bot assembly receives the transported item, autonomously moves from an origin location to a destination per the dispatch command using the mobility base autonomously controlled by the modular autonomy control module, receives authentication input that at least correlates to authentication information in the dispatch comment to allow selective access to the modular cargo storage system of the bot assembly, and then autonomously returns to the origin after the item is detected removed from the cargo storage system.

A system for self-executing enhanced interaction with a node-based logistics receptacle. The system includes a wireless accessory sensor node disposed on the node-based logistics receptacle to monitor storage receptacle components of the node-based logistics receptacle to generate sensor data. The system includes a bridge node disposed on the node-based logistics receptacle that uploads information related the sensor data, detects an external device separate from the node-based logistics receptacle, communicates with the external device to establish a smart contract based connection that provides an interaction privilege, interfaces with the external device according to the interaction privilege, and transmits an update message to the backend server that corresponds to at least a portion of the uploaded information related to the sensor data and information related to interfacing with the external device.

The invention discloses a supply chain multi-level relation identification method based on a graph neural network. The method comprises the following steps of S1, collecting supply chain data and performing preprocessing to generate an original graph structure; s2, constructing a three-dimensional node feature vector according to the enterprise industry type, the legal person property and the transaction frequency; s3, extracting in-degree, out-degree, path depth and direction of the nodes, generating hierarchical position codes, and splicing the hierarchical position codes with the feature vectors to obtain a structural hierarchical embedded vector set; s4, inputting a Geom-GCN model, selecting neighbor nodes based on graph distance and direction consistency, and generating a structure perception embedding representation set; s5, constructing a node pair input classification network, and outputting a relation type label; s6, complementing the supply edge shielded by the agent node according to the label, and constructing a repaired graph structure; and S7, executing path hop count and upstream role verification, and generating a multi-level relation identification result. According to the invention, accurate identification of the supply chain multi-level relationship and efficient repair of the path structure are realized.

The invention relates to the technical field of logistics park management, and particularly discloses a logistics park intelligent early warning and dynamic evaluation method, and the method comprises the steps: carrying out the time-space alignment of vehicle trajectory data, cargo pressure distribution data and environment data collected by a heterogeneous sensor network, and inputting the data into a dynamic threshold adjustment module to generate a self-adaptive warning threshold; the dynamic threshold adjustment module establishes a dynamic calculation model containing the equipment utilization rate and the vehicle density based on the matching relationship between the historical operation mode and the real-time operation state; based on a multi-dimensional evaluation system constructed based on a self-adaptive warning threshold, generating a comprehensive evaluation index through collaborative analysis of three groups of indexes including transportation efficiency, safety risk and resource utilization; and when the comprehensive evaluation index deviates from the preset range, triggering a grading early warning mechanism associated with the deviation degree. According to the invention, dynamic evaluation and intelligent early warning are realized, and real-time monitoring and intelligent evaluation regulation and control of the operation state of the logistics park are realized through space-time correlation analysis and an adaptive decision-making mechanism of multi-source heterogeneous data.

A dynamic learning server-based logistics system includes a node-based logistics receptacle operative to receive a delivery item as part of a logistics transaction and also includes a backend server maintaining a management profile related to operation of the node-based logistics receptacle. The node-based logistics receptacle includes a plurality of monitored receptacle components, a wireless accessory sensor node having a plurality of sensors, and a bridge node operative to retrieve event information from the wireless accessory sensor node. The backend server receives the retrieved event information, compares the retrieved event information with the management profile, identifies a threshold change condition from the comparison, dynamically revises the management profile when the threshold change condition is identified, and transmits an adjustment message to the bridge node. The adjustment message is based upon the revised management profile, and the adjustment message initiates a timing change to operation of the bridge node.

The invention provides a manufacturing logistics intelligent matching recommendation method and system based on multi-modal data, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining the logistics demand data of a manufacturing enterprise and the service capability data of a logistics supplier; constructing a multi-level feature attention network to perform feature extraction on the logistics demand data and the service capability data, and performing screening to obtain multi-modal feature representation; constructing a positive and negative sample pair based on the multi-modal feature representation, obtaining an inter-modal semantic mapping matrix through a hierarchical contrast learning mode, and projecting the multi-modal feature representation to a unified semantic space to obtain a fusion feature representation; the fusion feature representation and the historical cooperation record are constructed into a state vector, an instant reward function and a long-term reward function are constructed, and a matching degree score between the manufacturing enterprise and the logistics supplier is obtained through optimization of a deep reinforcement learning algorithm; and recommending a logistics supplier to the manufacturing enterprise based on the matching degree score.

The invention discloses a multi-system collaborative data cross validation management system, and relates to the technical field of logistics data management, and the system comprises an initial credibility grading module which is used for obtaining a data stream and a preset static source reliability factor, and calculating an initial credibility score; the evidence weight dynamic quantification module is used for calculating a timeliness attenuation factor, matching a current service scene, distributing a service rule adjustment factor and determining a final evidence weight score of the data point; the gold record automatic judgment module is used for sequencing the data points; obtaining a highest score and a second high score; and the judgment confidence evaluation module is used for calculating a judgment confidence margin between the highest score and the second highest score, and automatically routing the conflict data set to a manual auditing queue or automatically confirming a golden record. Through quantitative evaluation, dynamic weighting and automatic judgment, the credible gold record is efficiently generated, and refined risk control and non-tampering decision traceability are realized.

A node-enabled logistics receptacle system with a deployable storage element for receiving and temporarily maintaining a delivery item. The system includes a storage receptacle with an entrance opening for receiving the delivery item and a deployable autonomous mobile container as the deployable storage element. A first node is mounted to the storage receptacle and includes a first node processor, a first node memory, a communication interface, and a plurality of sensors including an interaction sensor and a storage sensor. The first node detects a change in the state of the entrance opening, determines a current condition of what is presently maintained within the deployable autonomous mobile container, generates a deploy activation signal, and transmits the deploy activation signal to the deployable autonomous mobile container to initiate movement by the deployable autonomous mobile container from the host position within the storage receptacle to a position external to the storage receptacle.

Apparatus and associated methods relate to an artwork authenticity owner validation system. In an illustrative example, an artwork remote identification system (ARIS) may include an artwork companion chip (ACC) and a certificate of authenticity (COA). The ACC, for example, may be physically attached to an artwork. The ACC and the COA, for example, may each include a unique identifier. For example, a centralized server may be configured to automatically authenticate an ownership of the artwork attached to the ACC by validating the ACC and the COA. For example, the user may scan the COA and then the ACC within a predetermined time limit to gain access to modify an ownership record in an artwork profile of the artwork. Various embodiments may advantageously allow a modification to the ownership information only when the centralized authentication server validates the COA and the ACC within the predetermined time period.

Some embodiments provide a system to control retail product allocation, comprising: an anomaly detection system applying a series of anomaly detection models to business metric data to identify an anomaly of a category of products; a contextualization detection system applying contextual models to data relative to the anomaly and identifying contextual factors; a causal detection system applying causal inference and determination models to sets of relevance data as a function of the contextual factors to determine influence attribution factors that are predicted to have been factors in causing the threshold variation, and apply attribution prioritization models to define relevancy scores to the influence attribution factors and prioritize the influence attribution factors; a personalization recommendation system applying personalization models to the prioritized influence attribution factors and the contextual factors as a

A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.

The invention discloses an order logistics timeliness management system, and relates to the technical field of logistics intelligence, and the system comprises a risk prediction module which collects order logistics full-link data, carries out the preprocessing of the data, calculates the logistics node delay risk probability through a virtual intervention scene and a causal graph model, and obtains a delay risk prediction result; the visualization module is used for performing overlay analysis on the delay risk prediction result and the preprocessed order logistics full-link data to generate a delay risk thermodynamic diagram; the destroy-resistant module is used for carrying out vulnerability evaluation on the delay risk thermodynamic diagram to generate a vulnerability analysis report, and carrying out multi-objective optimization evaluation through a k-shortest path algorithm to generate a destroy-resistant strategy table; the scheduling module is used for carrying out transport capacity resource matching and scheduling scheme selection by utilizing a multi-target resource optimization model based on the survivability strategy table to obtain a scheduling instruction set; according to the invention, causal traceability and probability quantification of logistics delay risks are realized by constructing a collaborative prediction mechanism of a virtual intervention scene and a causal graph model.

The invention relates to the technical field of artificial intelligence, and discloses a supplier dynamic risk assessment method and device, and the method comprises the steps: obtaining the multi-modal data of a supplier, carrying out the cross-modal alignment processing of the multi-modal data, and generating a unified multi-modal feature representation; performing multi-modal semantic conflict detection based on unified multi-modal feature representation, performing risk assessment in combination with a dynamic compliance rule base, and generating comprehensive risk score and risk traceability information; and performing risk decision reasoning according to the comprehensive risk score and the risk traceability information through a preset large language model, and generating a customized risk coping strategy for the supplier. According to the method, the comprehensiveness, the dynamic adaptability and the accuracy of supplier risk assessment can be improved, the targeted risk coping strategy is quickly generated, and the supply chain stable management and control requirements in scenes such as cross-border e-commerce and the like are effectively met.

The methods and systems described herein can provide a control tower and / or mini-control tower and / or digital twin for managing a value chain network (VCN). Information related to a set of value chain network entities in the VCN may be received from various sources. That information may be provided to a set of artificial intelligence (AI)-based learning models. The AI-based learning models can use that information in conjunction with the control tower and / or mini-control tower and / or digital twin to determine potential risks in the VCN, determine potential actions to take in the VCN, configure intelligent procurement in the VCN, configure predictive sourcing in the VCN, automate VCN entities, direct smart machines in the VCN, and configure robotic process automation in the VCN.

A computer-implemented system (100) for real-time detection of supply chain disruptions and forecasting of the financial impact in a computer-driven ERP environment, wherein the system (100) comprises: a data acquisition interface (1) configured to continuously capture structured and unstructured operational data from one or more ERP modules, supply chain execution systems, and enterprise event streams; a data management and harmonization engine (2) configured to validate, normalize, deduplicate and semantically map the captured data into a unified, time-aligned canonical enterprise schema; a disturbance signal fusion and anomaly detection engine (3) configured to fuse signals from multiple sources and detect disturbance events using one or more machine learning models to output a disturbance value, event class and a list of affected nodes; a supply chain dependency graph and a digital twin builder (4) configured to create and update a dynamic dependency graph representing suppliers, facilities, transportation routes, SKUs, orders, contracts and lead times, and propagate disruption effects across the graph; a financial impact forecasting engine (5) configured to estimate the real-time and forward-looking effects of the disruption on one or more financial measures, including sales, margin, working capital, cash flow, service level penalties and inventory holding costs, and furthermore the effects on the cost of goods sold (COGS) and / or the landed costs using a Kl-based forecast with a range of uncertainty; a module for coordinating corrective actions and automating workflows (6) configured to generate ranked corrective actions and initiate ERP workflow steps, including reordering, reassignment, alternative sources of supply, production rescheduling and logistics diversions, wherein the ranked corrective actions include recommendations to resolve the disruption by using available stock, reassigning low-priority shipments or diverting shipments that can be delivered later within a lead time window; an explainability, audit trail and compliance logging module (7) configured to record model inputs, feature assignments, decision justifications, scenario assumptions and action results as an immutable audit trail; and a visualization, alerting and collaboration interface (8) configured to output real-time alerts, dashboards and scenario comparisons to authorized users and downstream systems via APIs and role-based access controls, wherein the interface (8) generates a quick report view that displays early disruption signals and impact on manufacturing costs, delivers notifications to mobile devices, triggers a special urgent notification if the disruption affects a high-priority trading partner, and accepts user responses on mobile devices that automatically and without delay trigger corrective actions or updates in the ERP system.

A trained model is used to predict and prevent a failed delivery of an order placed by a user of an online system. The online system accesses a delivery prediction model trained to predict a likelihood of a delivery for the order ending up as a failed delivery as the order would not be delivered at a location associated with the user. The online system applies the delivery prediction model to predict, based on order data, user data and fulfillment data, the likelihood of the failed delivery for the order. Responsive to the predicted likelihood of the failed delivery being greater than a threshold value, the online system identifies one or more actions associated with the order to prevent an occurrence of the failed delivery for the order. The online system applies the one or more actions to prevent the occurrence of the failed delivery for the order.

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.

A system of warehouse orchestration for inventory picking and fulfilment comprising a controller configured to receive order information from warehouse management system (WMS), allocate and distribute a plurality of orders across a plurality of virtual pick zones based on real-time or near real-time demand, pick capacity and received order information. Cause first autonomous mobile robot to move to first virtual pick zone of plurality of virtual pick zones, communicate guidance instruction to first operator to guide first operator to be available at first virtual pick zone and determine pallet loading pattern indicative of distribution of plurality of inventory items in one or more cases and stacking of the one or more cases in one or more layers based on a set of criteria and communicate pick instruction to first operator to pick one or more inventory items and place onto the pallet of the first autonomous mobile robot.

The invention provides a port logistics intelligent simulation and decision optimization platform based on a digital twin technology, and the platform comprises a basic layer which provides calculation, storage, network and basic software support; the resource layer integrates data resources, model resources and algorithm resources and is used for managing port multi-source data, business process models and intelligent algorithms; the digital twinborn layer is used for constructing and maintaining a high-fidelity digital twinborn body synchronous with a physical port and realizing virtual-real mapping, dynamic data fusion and model self-evolution; the simulation layer is used for deducing based on the digital twin and comprises a simulation engine and an event driving and data interaction module; and the application layer is used for providing a user interface, analyzing decision and optimizing functions and supporting scheme simulation, evaluation and closed-loop optimization. According to the method, the high-fidelity digital twinborn body of the port can be constructed, real-time mapping, accurate simulation, prospective prediction and intelligent decision making of the whole process of port logistics are achieved, and a technical closed loop of data acquisition, digital twinborn, intelligent decision making and virtual-real linkage is formed.

An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

The invention discloses a single gantry crane dispatching system based on an intelligent optimization algorithm. The single gantry crane dispatching system comprises an urgency degree calculation module, a task urgency degree calculation module and a task urgency degree calculation module, the future task prediction module is used for determining a prediction task set through a sliding time window and calculating the discount urgency degree of a prediction task; the path planning and task sorting module is used for modeling a task scheduling problem into a state transition problem on a search tree, and gradually exploring a combined space of a task execution sequence and a moving path in the search tree by adopting an improved A * algorithm under the guidance of urgent degree discount cost to obtain an optimal task sequence; the self-adaptive parameter adjusting module is used for optimizing parameters on line based on reinforcement learning; and the event-driven control module is used for responding to task arrival, periodic refreshing, abnormal events and task completion events through the message queue and triggering dynamic rescheduling. According to the method, the problems of dynamics, diversity and burstiness of tasks in actual operation can be effectively solved, and the scheduling efficiency of large-scale logistics equipment is improved.

Exemplary embodiments of the present disclosure are directed towards a method for detecting and identifying container number in real-time. Monitoring vehicle carrying containers and triggering first camera, second camera, third camera, fourth camera, fifth camera, and laser sensors to capture container views by pre-processing module. Transmitting containers image data to computing device by the pre-processing module. Detecting container number region in container image frames by visual object detection module. Cropping container number region by visual object detection module. Applying two-dimensional Fast Fourier Transform on cropped container number region. Segmenting each character situated in container number region by segmentation and character classification module. Classifying each character situated in container number region by segmentation and character classification module. Arranging characters in order based on relative positions of characters to obtain container number information by segmentation and character classification module. Aggregating container image frames and generating container number by post-processing module.

The invention discloses an intelligent management and control method, system and device for unloading of a large carrying vehicle in a factory and a storage medium. The method comprises the steps that vehicle carrying information is acquired, and the starting time, the cargo emergency degree, the transportation distance S, the cargo total weight W and the cargo total value M are extracted; the transportation duration T is calculated by combining the current time and the starting time, and the transportation efficiency Q is calculated based on the transportation duration T and the transportation distance S; distributing an efficiency coefficient based on the size of the transportation efficiency Q; distributing an emergency coefficient b based on the cargo emergency degree; and calculating a vehicle unloading priority value F, and arranging an unloading sequence in a descending order according to the size of the vehicle unloading priority value F. According to the method, the unloading priority value of the to-be-unloaded vehicle is calculated by integrating parameters such as the transportation duration, the transportation distance, the total cargo weight and the total cargo value, and unloading is arranged in sequence from large to small; the unloading sequence of vehicles in the factory is intelligently controlled, and collaborative optimization of safety, cost and management is achieved.