26 results about "Logistic process" patented technology

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 discloses a fresh food quality monitoring method fusing fresh food after-ripening characteristics and logistics data, and relates to the field of food science and technology, and the method comprises the steps: collecting logistics environment data, logistics process data and fresh food ontology data in real time through a distributed sensor network; time stamp synchronization and space coordinate mapping technologies are adopted, space-time alignment is carried out on heterogeneous data with different sampling frequencies, and a unified data matrix is established; the method comprises the following steps: establishing a variable parameter dynamical model comprising variety difference factors, harvest maturity and environment interaction coefficients based on post-harvest physiological mechanisms: dynamically updating model parameters of various varieties in different logistics scenes through a federated learning framework crossing supply chain nodes; when the quality attenuation curve predicted in real time deviates from a preset threshold value, graded early warning is triggered, and logistics parameter adjustment suggestions are generated. According to the invention, full-link accurate management and control of fresh products from a supply chain to a consumption terminal is realized through multi-dimensional technology collaboration.

The invention relates to the technical field of logistics management, in particular to a digital logistics management system and method. The method comprises the following steps: collecting multi-dimensional environmental parameter field data around a cargo and biochemical index change data of a cargo body; constructing a permeability coefficient matrix of the cargo based on the multi-dimensional environmental parameter field data and the biochemical index change data according to a preset material library, wherein the permeability coefficient matrix is obtained by quantifying the interaction rate between different environmental factors and the material of the cargo; and calculating the current environmental influence intensity based on the permeability coefficient matrix and the multi-dimensional environmental parameter field data, and performing thermodynamic coupling analysis in combination with the biochemical index change data to obtain quality entropy increase rate data. According to the method, the problem that the cargo quality change is difficult to accurately monitor, predict and actively protect in the logistics process is solved through a method of combining the establishment of the permeability coefficient matrix and thermodynamic coupling analysis, and the whole-course digital management from environmental perception to quality guarantee is realized.

The invention provides a multi-AGV path planning method in a flexible manufacturing workshop complex environment, and the method comprises the steps: firstly carrying out the modeling of a logistics process of a flexible manufacturing workshop based on a Petri net theory, and then designing a single-AGV path planning algorithm considering the congestion degree based on a constructed Petri net model of the logistics process of the flexible manufacturing workshop, a multi-AGV collision deadlock prevention strategy based on a Petri net is designed, finally, a multi-AGV path optimization algorithm considering an AGV motion state uncertainty scene is designed, a collision conflict prevention method is expanded, and the AGV is constrained by constructing a probability motion model according to a station arrival prediction probability. According to the method, the stability and the transportation efficiency of the multi-AGV system in a complex dynamic environment are remarkably improved, and an innovative solution is provided for realizing intelligent and efficient logistics in a flexible manufacturing workshop.

A method includes determining a correlation feature shared among at least a portion of a plurality of tracking documents including a first tracking document and a second tracking document. The method includes receiving a first batch of tracking documents associated with a logistics process. The tracking documents includes the correlation feature and logistics information associated with the logistics process. The method includes generating a first correlation between the first tracking document and the second tracking document. The method includes receiving a second batch of tracking documents. The second batch includes a third tracking document including a change in the logistics information indicated by the first tracking document. The method includes generating a second correlation between the third tracking document and the second tracking document. The method includes sending a status of the logistics process. Related systems and computer program products are also provided.

1. Name of the product in this design: Warehouse Logistics Robot. 2. Purpose of this design: Its main function is to improve warehouse automation and operational efficiency, reduce labor costs, and optimize logistics processes. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application provides a method for evaluating matching degree of logistics resources and demand in a sharing mode, and relates to the field of information technology.The method first determines the characteristics of logistics resources according to the key resources of each link of express logistics and the resources required by logistics tasks, then classifies the resource characteristics according to the nature, divides them into fuzzy characteristics and containing characteristics, determines the fuzzy characteristic boundary by using normal distribution, then calculates the combination value of different logistics resource characteristics by using Euclidean distance, Manhattan distance and Chebyshev distance, and puts different characteristics into the same measure, and finally comprehensively evaluates the matching of shared logistics resources and demand by using function matching degree, capacity matching degree and transaction matching degree.The method realizes the integration of different logistics links and different function parameters, can evaluate the whole logistics process or a certain link in the logistics process, realizes comprehensive analysis of the matching of logistics resource supply and demand, and is beneficial to improving the operation efficiency of the logistics network.

The invention discloses a production and logistics integrated decision-making method for improving distributed manufacturing efficiency. The method comprises the following steps: S1, obtaining process information of a workpiece to be processed, processing workshops specified by the process, processing machines of the process, corresponding processing time, the number of logistics vehicles, the maximum cargo capacity of each logistics vehicle and logistics time between the workshops; s2, the production and logistics integrated decision model of the distributed manufacturing system is utilized, decision variable values of the production and logistics process are obtained through an improved teaching optimization algorithm, and the decision variable values comprise the machining sequence of all procedures on a machine and the goods taking and delivering sequence of all workpieces. According to the method, production and logistics completion time is taken as an optimization target, a multi-load logistics vehicle is taken as a logistics tool, a processing sequence and a logistics task execution sequence are optimized in a collaborative manner, and an improved teaching optimization algorithm is designed, so that the solvability of the model is ensured, and the distributed manufacturing efficiency is effectively improved.

The application discloses a kind of factory bolt automation logistics method and logistics process layout method, belong to factory logistics technical field, including, S1, bolt is classified, divided into A class bolt, B class bolt and C class bolt;S11, the classification evaluation score of bolt is calculated;S12, according to bolt type determination rule is classified;S2, according to the different types of bolt, select corresponding logistics mode;For A class bolt, adopt logistics mode one: pull type logistics mode according to demand;For B class bolt, adopt logistics mode two: replenishment pull type logistics mode, based on replenishment type distribution, according to whether the quantity of line edge bolt surplus reaches safety stock, system automatically triggers distribution task;For C class bolt, adopt logistics mode three: push type logistics mode.The application has the advantages that: adopt push-pull combined logistics mode, solve the problem of warehousing pressure and distribution not timely.

The present invention provides an industrial digital twin optimization process evaluation method and system, including: step S1: obtaining a simulation business material data set and simulation business parameters; step S2: inputting the obtained simulation business material data set and simulation business parameters into a hot rolling heating furnace logistics process optimization verification model to perform heating furnace production process optimization simulation calculations, and obtain a slab heating production assignment plan and a slab production scheduling plan; step S3: displaying, summarizing and analyzing the simulation results and heating furnace simulation process data through the industrial digital twin system; wherein, the simulation business material data set includes: slab out-of-furnace sequence, slab information already in the furnace, and slab information to be entered into the furnace; the simulation business parameters include: heating furnace specification parameters and heating furnace simulation setting parameters.

The invention provides a method and device for supply chain warehouse logistics decision making, and relates to the technical field of supply chain warehouse logistics decision making, and the method comprises the steps: predicting supply demand fluctuation through employing a first machine learning model, and generating a to-be-executed replenishment plan in combination with the supply demand fluctuation; predicting a transportation route suggestion by using a second machine learning model, and generating an optimized transportation route suggestion in combination with the transportation route suggestion; using a third machine learning model to predict an equipment fault occurrence probability, generating an optimized equipment fault occurrence probability in combination with the equipment fault occurrence probability, and generating a preventive maintenance plan based on the optimized equipment fault occurrence probability; and generating risk early warning information, and generating an emergency response mechanism based on the risk early warning information. The target demand of risk control by comprehensively considering the inventory cost, the transportation efficiency and the equipment fault of the supply chain is met, the risk in the supply chain warehouse logistics process is reduced, and the efficiency and the success rate of the supply chain warehouse logistics process are improved.

The invention provides an electronic commerce product logistics tracing analysis method and device based on big data, and relates to the technical field of logistics data analysis. The historical electronic commerce product logistics tracing data comprises environment perception data, logistics process data, electronic commerce platform data and external environment data; based on historical electronic commerce product logistics traceability data, aiming at different categories of electronic commerce products, respectively constructing judgment standards for describing normal logistics states; receiving real-time logistics data; and based on the judgment standard, performing anomaly detection on the real-time logistics data, and when the real-time data deviates from normal distribution, judging that an abnormal event occurs and giving out an early warning. The electronic commerce product logistics traceability analysis method and device based on big data provided by the invention have the advantage of overcoming the problem that the judgment standard setting is subjective and static and the actual condition of the electronic commerce product logistics is difficult to accurately reflect in the prior art.

The application discloses a kind of intelligent logistics package monitoring systems and methods based on degradable new material, it is related to logistics packaging technical field, method includes: on the main body of logistics package made of degradable material, sample block is formed with material same batch;Through accelerated degradation experiment, the corresponding relationship of sample block physical property change rate and the remaining strength of package main body is established;At the first node of logistics process, the first remaining strength estimated value is obtained by measuring the first pair of sample blocks;At the second node, the second remaining strength estimated value is obtained by measuring the second pair of sample blocks;Whether the change trend of two estimated values is consistent with preset degradation law is compared, if it is consistent, then according to the second estimated value, the package state is judged, if it is not consistent, then trigger exception handling.The application replaces environmental data estimation by sample block measurement, and verifies the reliability of monitoring result by the trend self-consistency determination between two nodes, solve the fundamental problem that estimation result is not verifiable in prior art.

This invention discloses a method, system, and medium for dynamic orchestration, strategy binding, and canary execution of business processes in industrial logistics, belonging to the field of industrial internet and intelligent manufacturing technology. It includes the following steps: constructing a logistics process model; bidirectional binding of process nodes and business strategies; canary release and isolated execution of process versions; context-aware dynamic routing execution; and end-to-end execution tracking and visualization analysis. Through technologies such as visualized process orchestration, dynamic strategy binding, canary release, and context awareness, it solves the technical problems of rigid business processes, difficult changes, and downtime required for version updates in traditional warehousing systems, achieving flexible configuration and efficient execution of industrial logistics business processes.

The embodiment of the invention provides a digital factory logistics tracing method and system based on a digital thread, and the method comprises the steps: building real-time mapping connection between a physical logistics process and a virtual information system in a digital factory, and generating a virtual-real mapping result; a logistics tracing model covering the whole logistics process is constructed based on the result in combination with a distributed account book strategy; then establishing a logistics network topology model for describing a logistics path and node connection state according to the tracing model; and finally, responding to a tracing instruction, carrying out reverse tracing marking on the flow path of the target logistics unit by utilizing the topology model, generating a logistics tracing report containing a complete tracing path, and updating the topology model according to path deviation information in the report, and the method realizes logistics full-process tracing and dynamic optimization management.

The embodiment of the invention provides a logistics information dynamic tracking method and device, equipment and a storage medium, and belongs to the technical field of logistics information processing. The method comprises the following steps: obtaining logistics information formed in a fresh food logistics process, and constructing a disturbance fragment used for representing an environmental parameter deviation process; identifying the logistics operation behavior of the fresh food in the time range corresponding to the disturbance fragment, and generating an operation behavior time window aligned with the disturbance fragment; disturbance morphological characteristics used for representing the environmental parameter change process are extracted from the disturbance fragments; and on the basis of the time coverage relationship between the disturbance fragment and the operation behavior time window, and in combination with the disturbance morphological characteristics, determining an abnormal type corresponding to the disturbance fragment, and carrying out association recording on the abnormal type and the corresponding operation behavior information to form a logistics information dynamic tracking result. According to the scheme of the invention, the operation stage positioning and type determination of the abnormal environment in the fresh food logistics process are realized, and the logistics information dynamic tracking result capable of being continuously traced is formed.

The invention discloses a discrete scheduling-oriented hierarchical timing Petri net deduction simulation and conflict resolution method. The method comprises the following steps of: realizing hierarchical modeling of a discrete task; specifically, a hierarchical timing Petri net model is composed of a place, transition, a flow relation, place time delay, transition time delay, an identifier and a mapping function, hierarchical modeling of discrete tasks is achieved, a subtask hierarchical timing Petri net and a meta-task hierarchical timing Petri net are included, the former shows operation logic of a composite task on a subtask level, and the other shows operation logic of the composite task on a meta-task level. The latter shows specific execution steps of the composite task at the meta-task level; an execution library in the sub-task level timing Petri net can be mapped into a meta-task timing sub Petri net in the meta-task level through a mapping function; the hierarchical timing Petri net model takes a discrete task scheduling result as input, realizes dynamic conflict resolution of the discrete task scheduling result in a deduction simulation process, and is particularly suitable for simulation of discrete event systems such as intelligent manufacturing tasks, intelligent logistics processes, industrial automation system cooperative control and the like.

This invention discloses a dynamic warehousing and logistics route planning method for relays that considers oxidation factors, relating to the field of logistics and supply chain management technology. By constructing a dynamic corrosion dose field, this invention transforms the intangible oxidation risk into a quantifiable and predictable decision-making basis that runs through the entire warehousing and logistics process, realizing full life-cycle management of relay product reliability. This method not only optimizes a single link, but also coordinates and connects preventive protection at the warehousing end with in-transit risk avoidance at the logistics end, ensuring quality consistency from warehousing to delivery, guaranteeing the product's factory quality and long-term performance. Furthermore, it proposes and applies dynamic adjustment mechanisms such as adaptive drift and risk momentum, thereby achieving proactive and preventive protection of product quality in uncertain environments, improving the supply chain's response speed and management efficiency in the face of environmental changes.

The invention relates to a browning index prediction method based on vibration of picked litchis under different loading capacities, and belongs to the technical field of food logistics and quality safety. According to the method, a browning index prediction model of the litchis is established by simulating logistics processes of the litchis at different loading capacities (30%, 50%, 70% and 100%) and combining change data of browning indexes during storage. According to the method, the coupling effect of vibration, loading capacity and storage time is comprehensively considered, and the browning index of the litchis under the actual logistics condition can be rapidly and accurately predicted. According to the method, equipment is conventional, operation is easy and convenient, and the method is suitable for browning index prediction and logistics optimization of litchis and other fresh fruits sensitive to vibration.

The present application relates to the field of production line design, and particularly relates to a production line design simulation system using digital twinning technology, which comprises an industrial modeling unit, a logistics simulation unit and a digital twinning unit. The industrial modeling unit establishes a logistics model and a passability model based on production line design input. The logistics simulation unit calculates logistics process data using the logistics model according to initial logistics parameters. The digital twinning unit establishes a simulation production line according to the passability model, and then receives the logistics process data to display the logistics state on the simulation production line in the form of simulation animation. Design personnel generate new logistics parameters according to the logistics state, and perform a new round of iteration until the design requirements are met. The present application also comprises a simulation method. The present application combines passability simulation and logistics simulation of the production line through the digital twinning unit to cooperatively complete the simulation process. The present application can also more realistically restore the on-site process, reduce performance overhead costs and improve the efficiency of the simulation.

We provide the system. [Solution] Means for receiving multiple commands, A means of analyzing received instructions and breaking them down into detailed work items, A means of assigning subdivided work items to human resources or artificial intelligence, A means of monitoring the progress of work items and adjusting their priorities, A method for automating completed work items and maintaining them as templates for future use, A system that includes means for interpreting instructions in the logistics process and for executing the optimal allocation of logistics equipment and personnel.

The present invention relates to a system for certifying and sorting packages in reverse logistics processes comprising: - a subsystem for capturing images (B) of the products housed in each package; - a subsystem where the captured images (C), and the frames, are displayed; - a subsystem for automatic timed recording (D); - a subsystem for storing (E) the recording made; - an alphanumerical recognition subsystem (F); - a subsystem for comparing (G) the recorded images with original images and descriptions, as well as for comparing the recorded packaging codes with the original ones; and - a subsystem for sorting videos of suitable and unsuitable packages for reverse logistics. The system can be further provided with: - a subsystem for conveying and forwarding (A) packages (18); and - a subsystem for sorting out (H) the packages into packages that are inappropriate for reverse logistics and packages that are suitable for reverse logistics, depending on whether the package is properly framed or not, whether the codes are identical or different, and whether the images are identical or different.

This invention discloses a method for automatically carrying and maintaining synchronized product identity information during logistics: Production instructions are handled by the ERP system, while production planning, task allocation, task execution, monitoring and quantity statistics during production, and data uploading are handled by the MES system. Product identity information, batch size, and quantity are assigned by the ERP system. Throughout the production process, regardless of the product's location, its identity information is continuously tracked by the MES system until the product becomes a finished product and is stored in the warehouse, at which point the product's identity information is transferred to the warehouse management system. Because the product's identity information is automatically maintained in the MES system without manual marking throughout the logistics process, and the logistics system can also upload the product's real-time location along the logistics path to the MES system, this achieves the binding management of product identity information and location information, enabling highly efficient intelligent logistics for multiple products in highly complex environments.

A computer-implemented method of scheduling jobs of a manufacturing or logistics process using a priority function. The priority function is evaluated on multiple to be scheduled jobs to obtain multiple respective priority values. The priority function is defined to invoke a kernel function. The kernel function is defined to compare representations of two respective jobs. Evaluating the priority function on a selected job comprises evaluating the kernel function on representations of the selected job and one or more reference jobs. The schedule for the multiple jobs is determined based on the priority values and is then output to enable the multiple jobs to be carried out according to the schedule.

The invention belongs to the field of resource planning and scheduling, and provides a deep learning-based warehouse logistics system resource planning method and system, and the method comprises the steps: obtaining warehouse whole-process operation data in a to-be-subjected resource planning scene; the method comprises the following steps: performing standardization processing on a time dimension, a warehouse logistics process dimension and a feature channel dimension based on warehouse full-process operation data, and constructing a multi-channel three-dimensional input feature tensor; and based on the multi-channel three-dimensional input feature tensor, performing resource planning by using a pre-trained space-time attention resource planning network model to obtain a resource planning allocation scheme. According to the method, by introducing the deep learning technology, normal form transformation from experience leading to data driving is achieved, the input amount of various resources in the warehouse logistics system is adaptively planned and allocated, dynamic changes of a system design scheme and an operation stage are coped with, and the calculation and decision-making efficiency is improved.

This invention belongs to the field of resource planning and scheduling, and provides a resource planning method and system for warehousing and logistics systems based on deep learning. The method includes: acquiring full-process operational data of the warehousing system under the resource planning scenario; standardizing the full-process operational data from the perspectives of time, warehousing and logistics process, and feature channels to construct a multi-channel three-dimensional input feature tensor; and performing resource planning using a pre-trained spatiotemporal attention resource planning network model based on the multi-channel three-dimensional input feature tensor to obtain a resource allocation scheme. This invention, by introducing deep learning technology, achieves a paradigm shift from experience-driven to data-driven approaches, enabling adaptive planning and allocation of various resource inputs in the warehousing and logistics system, responding to dynamic changes in system design and operational phases, and improving computational and decision-making efficiency.