4455results about "Logistics" patented technology

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

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.

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.

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.

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.

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 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 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.

According to some embodiments, a method by a computing system includes accessing a plurality of images captured by an imaging device. The imaging device and the computing system are coupled to a vehicle that moves within an intermodal container yard. The method further includes determining, by analyzing the plurality of images using a machine-learning module, that a shipping container is depicted within at least one of the plurality of images. The method further includes determining, using a map of the intermodal container yard, a parking location of the shipping container. The method further includes electronically communicating, in response to determining that the shipping container is depicted within at least one of the plurality of images, a message comprising data about the shipping container. The data includes the determined parking location of the shipping container and one or more identification markings of the shipping container.

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.

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.

A system includes: an instrumentation unit (IU) that includes: an IU processor configured to generate an analog signal (AS) and transmit the AS to a first converter the IU; the first converter configured to convert the AS into energy and transmit the energy towards a communications unit (CU); the CU that includes: a second converter configured to: receive the energy, in which the energy includes a form of data generated in the IU; convert the energy into a second AS, in which the second AS is transmitted to a modem of the CU, in which the modem sends the second AS to a CU processor; and the CU processor includes circuitry and is configured to process the second AS to extract the data based on a start of a data frame and an end of the data frame.

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.

Methods of generating upstream cradle-to-gate carbon footprints for physical products based on minimal user inputs, and associated systems and devices are disclosed herein. In some embodiments, a representative method of generating a carbon footprint for a product to be manufactured at a source location and transported to a destination location includes decomposing the product into constituent materials, components, and / or processes. The method can further include matching the constituent materials, components, and / or processes to at least one database of emission factors, and extracting the emissions factors for the constituent materials, components, and / or processes. The method can further include generating a probabilistic supply chain mapping of the constituent materials and / or components from upstream source locations to the source location, and adjusting the emissions factors based on the probabilistic supply chain mapping. Finally, the method can include aggregating the adjusted emissions factors to generate an emissions output for the product.

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.

Systems and methods for tracking inventory items in an area of real space are disclosed. The method includes receiving a signal generated in dependence on sensors. The signal indicates a change to a portion of an image of an area of real space. The method includes, in response to receiving the signal, implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. The method includes implementing a trained item classification model to determine a classification of the inventory item. The method includes updating an inventory database with inventory item data determined in dependence on the classification of the inventory item to provide an updated map of the area of real space as a result of the received signal indicating the change to the portion of the image.

A method and system for calculating and optimizing carbon footprint of a product life cycle are disclosed. A graph method is used to account for the carbon emission on the complicated connecting the product life cycle. A well formulate set of equations are used to optimize the process to reduce overall carbon footprint. Unlike the traditional accounting method, in which human beings are needed to understand fully regarding each of the details of the carbon accounting method, the present system uses an assistant AI to compensate for the insufficient knowledge in the carbon accounting.

Disclosed are systems, methods, and apparatuses for automatically and optically determining structural integrity and catch weight information for a pallet. The apparatus can include: a conveyor belt to route a pallet having cases in a facility once the pallet is ingested into the facility, a turntable to receive the pallet from the conveyor belt and rotate the pallet, at least one imaging device proximate the turntable to capture optical data of the pallet, a partial enclosure to enclose the turntable, the at least one imaging device, and at least a portion of the conveyor belt, and an edge computing device in network communication with the at least one imaging device. The edge computing device can: receive the optical data from the at least one imaging device, process the optical data to determine structural integrity and catch weight information for the pallet, and determine routing or storage information for the pallet.