772 results about "Smart logistics" patented technology

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

The invention relates to the technical field of logistics management, in particular to an intelligent logistics terminal equipment collaborative management and control system based on AI edge computing, and the system comprises an edge data fusion and state recognition module which is deployed at an edge node, collects multi-source data of an operation state, environment perception, a communication link and the like, and generates an equipment state representation vector through fusion; the intelligent prediction and task scheduling decision module uploads the state vector to a cloud, predicts task completion capability and fault probability, and generates a task scheduling decision packet; the scheduling strategy issuing and edge execution collaboration module issues a scheduling packet through a multi-protocol gateway, and an edge node completes task distribution, communication switching and resource scheduling and caches a key strategy. According to the invention, the real-time sensing of the state of the logistics terminal equipment, the intelligent prediction and resource optimization of task scheduling, and the quick response and fault-tolerant control in a fault scene are realized, and the operation efficiency, the intelligent level and the stability of the system are remarkably improved.

The invention relates to the technical field of intelligent logistics scheduling, in particular to a logistics scheduling planning method and system based on a graph neural network and reinforcement learning, and the method comprises the following steps: S1, constructing a dynamic graph structure of a logistics network; s2, carrying out embedded learning on the dynamic graph structure through a graph attention network, and extracting a multi-dimensional feature vector of each node; s3, inputting the multi-dimensional feature vector into a multi-agent reinforcement learning framework to generate an initial vehicle path planning scheme; s4, dynamically correcting the road section traffic state in the initial vehicle path planning scheme; s5, iteratively updating the vehicle path planning scheme through local reinforcement learning; and S6, outputting a final collaborative optimization cargo transportation track and a vehicle driving path. According to the method, dynamic modeling and multi-agent path collaborative optimization of a logistics network structure can be realized, and the method has adaptive adjustment capability on real-time traffic and environment change, so that the overall scheduling efficiency is improved.

The invention discloses an intelligent logistics path optimization system based on big data, and relates to the technical field of logistics, the system comprises a data acquisition module, a data analysis module, a path planning module, a feedback adjustment module and a user interface module; according to the method, the driving style and behavior characteristics of each driver can be deeply analyzed by comprehensively collecting the historical driving data of the driver, such as the driving speed, the braking frequency and the turning habit, and by means of the detailed driver driving habit analysis, the path planning better fits the personal preference of the driver, for example, for the driver with aggressive driving style, the driving habit of the driver can be better optimized. The system intelligently recommends routes with excellent road conditions and less traffic restrictions, so that not only can a driver give full play to the driving speed advantage, but also the driving experience that the driver pursues speed and passion can be met to a certain extent, and for drivers with careful driving styles, the system tends to recommend routes with small traffic flow and high road safety.

The invention relates to the technical field of intelligent logistics and path planning, and discloses a digital twinborn technology-based logistics robot path planning method, which comprises the steps of constructing a digital twinborn model consistent with a storage environment, identifying logistics path key nodes and obstacles, generating a plurality of candidate paths and carrying out simulation verification, and screening an optimal path according to simulation feedback, and finally issuing the path to the robot for execution. By establishing a virtual and physical two-way synchronous environment model, efficient simulation and prediction of a logistics path are realized, so that the intelligent level of robot scheduling and the stability of path advancing are improved, and the method is suitable for complex logistics scenes such as multi-robot cooperation and dynamic path optimization.

The invention relates to the field of intelligent recommendation, and particularly discloses an intelligent logistics distribution system and method based on an Internet of Vehicles system, which obtains goods label information, position information of a plurality of preset time points in a preset time period and vehicle sensing data information, and deep learning and cloud computing technologies are adopted to carry out semantic analysis and time sequence association on the cargo label information, the position information and the vehicle sensing data information, so that a reasonable vehicle speed value is generated and recommended to a driver. Through the method, the relationship among various data can be better understood by adopting deep learning and cloud computing technologies, so that the recommended vehicle speed value is more accurately generated, intelligent scheduling of logistics distribution is realized, the cargo safety and the vehicle stability are ensured, the distribution route is optimized, and the distribution efficiency is improved.

The invention provides an intelligent logistics distribution path optimization method under multiple constraint conditions, and relates to the technical field of logistics distribution, and the method comprises the steps: constructing a constrained graph model according to a time window, vehicle information and real-time traffic API data; performing initial path planning by adopting an improved genetic algorithm; performing dynamic path adjustment according to the initial path set and the real-time traffic API data; and in combination with the optimized path set and customer preference information in the order information, screening an optimal solution through a plant rhizome growth optimization algorithm, and then outputting a final path optimization strategy according to a preset rule engine. According to the method, a plant rhizome optimization algorithm is adopted, a biological growth rule is simulated to realize multi-target co-evolution, and customer preference and task migration flexibility are considered while premature convergence is avoided; through flexible adjustment of constraint conditions and algorithm parameters, the method can adapt to more scenes, and can still maintain stable solving capability under the condition of continuous congestion or sharp increase of orders, thereby remarkably improving the anti-risk capability of a logistics system.

The invention discloses a receiving information real-time synchronization and abnormity early warning method and system, and relates to the technical field of intelligent logistics information processing and data security, and the method comprises the steps: collecting logistics track data, constructing a space-time diagram model, and carrying out the track feature extraction through a space-time diagram convolution network; performing trajectory time sequence prediction by adopting a Transform model, and judging an abnormal event based on an abnormal detection threshold value; in combination with zero-knowledge proof and a block chain technology, encryption verification and evidence storage are performed on logistics data, and a logistics scheduling optimization and dynamic exception recovery strategy is constructed. According to the method, through space-time diagram modeling, ST-GCN trajectory feature extraction, Transform prediction analysis, block chain evidence storage and reinforcement learning scheduling optimization, a complete logistics information real-time synchronization and abnormity early warning system is constructed; by introducing the block chain and the ZKP technology, the integrity and privacy security of the data are guaranteed, and the risk of logistics fraud and information tampering is reduced.

The invention relates to the technical field of intelligent logistics and supply chain management, and discloses a cross-border e-commerce logistics intelligent storage scheduling and transportation cooperation method based on AI path planning, and the method comprises the steps: building a monitoring module, a dynamic information capturing module, a data classification module, a data analysis module, a storage scheduling and transportation cooperation module and a management module; the monitoring module monitors storage and transportation information in real time through a sensor and monitoring equipment, the dynamic information capture module collects external environment data and internal abnormal events, the data classification module classifies the monitored data and the captured information, and the data analysis module constructs a path planning model according to the stored data of the data classification module. The system comprises a storage scheduling and transportation collaboration module, a storage scheduling and transportation collaboration module and a management module, the storage scheduling and transportation collaboration module generates an optimal path and calculates a storage efficiency index # imgabs0 #, a transportation path lifting coefficient # imgabs1 # and a storage-transportation collaboration index # imgabs2 #, the storage scheduling and transportation collaboration module optimizes the path according to a calculation result and sets an early warning mechanism, and the management module assists management personnel in monitoring and decision making through a visual interface.

The invention relates to the technical field of intelligent logistics scheduling optimization, and discloses a logistics resource intelligent scheduling optimization system based on big data analysis, and the system comprises a data collection module which is used for collecting basic data, and the basic data comprise temperature sensing data, transportation load data, path parameter data, and vehicle operation state information; the data fusion module is used for fusing the temperature sensing data and the transportation load data and outputting a temperature zone label and a temperature control load characteristic; the index construction module is used for calculating thermal exposure time and generating a thermal exposure intensity index and a quality risk index; the energy efficiency modeling module is used for calculating path cost values corresponding to the plurality of paths and vehicle combinations; and the path energy efficiency optimization module is used for constructing a multi-target path optimization model and outputting an optimal transportation path combination. According to the invention, efficient energy consumption control and quality risk collaborative optimization of resource scheduling in a cold-chain logistics scene are realized.

The invention relates to the technical field of intelligent logistics, and discloses a logistics supply chain optimization system and method based on artificial intelligence, and the method comprises the steps: constructing a heterogeneous vehicle characteristic vector, and building a digital twinborn model; calculating a matching relationship between the task and the vehicle based on a graph attention network; decomposing the scheduling problem into multi-level sub-problems by adopting a hierarchical reinforcement learning algorithm; constructing a decentralized collaborative decision-making system; a task exchange protocol based on game equilibrium is realized; according to the method, the problems of heterogeneous vehicle resource mismatching, centralized decision response lagging, low multi-level decision main body cooperation efficiency and insufficient large-scale task cooperation are solved, and the logistics distribution efficiency and the service quality are improved.

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

The invention relates to the technical field of intelligent logistics and unmanned shipping, in particular to an unmanned shipping system based on intelligent weighing, which comprises a multi-modal data acquisition module, a distributed weighing and dynamic optimization module, a loading execution module, a detection and verification module and an electronic shipping order generation module. The system calculates the loading sequence and path of goods through a dynamic loading optimization model, and monitors the gravity center change and load distribution of the vehicle by using an inertial sensor and a piezoelectric sensor to realize real-time adjustment and optimization; through the combination of an electronic shipping order and a digital twin entity, the system supports whole-process state tracking and abnormal early warning, and the transportation safety and the management efficiency are ensured; according to the invention, the intelligence and efficiency of logistics operation are obviously improved, and comprehensive technical support is provided for modern logistics and factory dispatching.

The invention discloses a logistics transportation personalized recommendation path planning method and system based on a cloud platform, and belongs to the technical field of logistics transportation, and the method comprises the steps: constructing a heterogeneous traffic map model fusing road sections, geographic interest points and traffic event information; collecting historical transportation task records of a plurality of users, generating user semantic intention tags based on delivery behaviors, and mapping the user semantic intention tags to traffic map nodes and edge attributes to form a semantic constraint graph structure; performing joint modeling on the user intention and the path reachability by using a heterogeneous graph neural network to generate a path scoring sequence; after the transportation task is completed, abnormal events are collected, and the semantic intention label mapping relation is dynamically updated based on reverse alignment errors; the model and the mapping module are deployed on a cloud platform, the traffic state is obtained in real time in combination with edge equipment, and path recommendation online generation and rapid pushing are achieved; according to the method, the personalized matching degree and the real-time response capability of path recommendation can be effectively improved, and the method is suitable for intelligent logistics path planning in a complex scene.

The invention provides a vulnerable cargo transportation scheduling method and device based on intelligent logistics and a medium, and belongs to the technical field of intelligent logistics. The method comprises the following steps: according to multi-dimensional transportation environment data collected by preset transportation environment monitoring equipment in real time, determining transportation cargo state information corresponding to a corresponding in-transit logistics transportation vehicle group; based on the transported cargo state information, the historical driving record and the planned driving path, generating a corresponding cargo loss prediction curve; determining a cargo demand quantity corresponding to each arrival storage node, and determining a cargo reissuing risk value of the corresponding arrival storage node according to the cargo loss prediction curve and the cargo demand quantity; and on the basis of each cargo reissuing risk value and the position information of each arrival storage node, generating an undetermined replenishment path corresponding to the storage node needing replenishment, and sending the undetermined replenishment path to a corresponding transportation scheduling management terminal so as to schedule a corresponding schedulable in-transit vehicle to replenish the storage node needing replenishment.

The invention provides an air hospital intelligent logistics distribution method based on deep reinforcement learning, and the method comprises the steps: receiving all target tasks, determining the priorities of the tasks through a hierarchical task decision model, sorting all the tasks according to the priorities, and entering a scheduling stage; constructing a task and aircraft resource matching model to perform single-task and single-aircraft matching between tasks and aircrafts, performing multi-aircraft cooperative scheduling task allocation in combination with the cooperation cost of the aircrafts, and performing optimization adjustment on the priority based on real-time monitoring; generating a flight instruction based on the optimized and adjusted priority; an optimized flight instruction is generated by adopting a fuzzy logic control method, and dynamic path adjustment is performed on the total flight path to ensure that the aircraft always keeps the optimal flight path in the task execution process; feedback of the flight instruction for executing optimization is collected, and dynamic optimization is conducted on the flight task.

The invention discloses a time-dependent vehicle path planning method, device and equipment and a storage medium, and relates to the technical field of intelligent logistics, the time-dependent vehicle path planning method comprises the following steps: obtaining vehicle information and time-varying road network information, and constructing constraint conditions and a target function according to the vehicle information and the time-varying road network information; obtaining a nectar source set according to the constraint condition, the time-varying road network information and the vehicle information, and constructing a hybrid artificial bee colony strategy according to the nectar source set; and obtaining a target value of a target function according to the constraint condition, the nectar source set and the hybrid artificial bee colony strategy, and performing path planning on the vehicle according to the target value. An objective function value is minimized by constructing an optimization model and adopting a hybrid artificial bee colony strategy, and optimal path planning is carried out on the vehicle according to the objective function value, so that the distribution efficiency is improved, and the cost is reduced.

The invention provides a railway digital intelligent logistics station platform and a storage medium. The platform comprises a digital intelligent sub-platform and a business application sub-platform. The data intelligent sub-platform comprises a data service system which is used for generating an operation service which can be applied to the business application sub-platform based on station data, a freight operation plan and a business service engine which are obtained by the Internet of Things sensing system; the artificial intelligence model system is used for storing and managing an artificial intelligence model, and the digital twin system is used for constructing a three-dimensional station model; the business application sub-platform comprises an automatic production operation system used for executing production operation and feeding back an operation state, an intelligent logistics service system used for vehicle passage reservation, information guidance and cargo storage, a safety management system used for realizing operation safety protection including crossing safety identification and field safety management, and a control system used for controlling the operation safety protection and the field safety management. And the green park management system is used for counting energy consumption. According to the invention, human, cargo, vehicle and yard total factor perception and intelligent management can be realized.

The invention discloses an intelligent optimization system and method for logistics transportation cost management, and relates to the technical field of intelligent logistics management, and the system comprises a data collection and analysis module which is responsible for collecting basic data of goods and analyzing the data; the constraint condition and transportation characteristic analysis module analyzes the characteristics of various constraint conditions and actual transportation environments in the cargo transportation process; the initial loading scheme generation module is responsible for generating an initial loading scheme according to characteristics and constraint conditions of goods; when an initial loading scheme is generated, three heuristic methods of first adaptation, optimal adaptation and worst adaptation are adopted, historical data are combined to guide generation of the scheme, and meanwhile, a genetic algorithm and a simulated annealing algorithm are introduced to further optimize the scheme, so that an approximate optimal solution is efficiently searched in a solution space.

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

In some embodiments, a first indication that a first antenna, of multiple antennas, has received data from a first tag coupled to an asset is received. The antennas are coupled to an interior housing within a logistics vehicle. A second indication that a second antenna, of the antennas, has received the data from the first tag is also received. Based at least in part on the receiving of the first indication and the receiving of the second indication, a location within the logistics vehicle that asset is located in is detected. In response to the detecting of the location, a control signal is transmitted that causes a user device or apparatus to indicate that the asset is located within an area associated with the location.

The invention discloses an e-commerce supply chain intelligent scheduling optimization system and method based on big data, and relates to the field of intelligent logistics scheduling, and the system comprises a demand prediction module, a balance optimization module, a path optimization module, a collaborative replenishment module and a feedback driving module. According to the method, the deep learning model and the time sequence decomposition method are combined, and multi-level modeling is performed on the historical order data and the real-time sales data, so that supply chain imbalance caused by prediction deviation is avoided. A dynamic replenishment plan is generated based on the prediction result and the inventory early warning information, real-time matching of the inventory and the demand is achieved, the stockout rate and inventory redundancy are effectively reduced, and the storage resource utilization rate is improved. By establishing the multi-constraint path optimization model and comprehensively considering order distribution, vehicle load and real-time traffic data, the distribution path can be dynamically adjusted, the vehicle utilization rate and the distribution time efficiency are improved, and the energy consumption and the cost caused by empty driving and detour are reduced.

The invention relates to the field of industrial automation and intelligent logistics, and discloses a vision-based operation management system, which comprises a visual perception module, a traffic modeling module, a path optimization module, a scheduling optimization module and a feedback adjustment module, the visual perception module obtains image or video data in the operation area and extracts the dynamic state of the target equipment; the traffic modeling module constructs a dynamic weighted graph model based on the equipment state data; the path optimization module generates an optimal path of the equipment by solving a dynamic Hamiltonian path problem; the scheduling optimization module optimizes the passing sequence and speed of the equipment based on a game theory model; and the feedback adjustment module monitors the running state of the equipment in real time, detects abnormal conditions and dynamically adjusts modeling and optimization results. The method can effectively solve the problems of path conflict and traffic jam in multi-device collaborative operation, improves the operation efficiency and the resource utilization rate, has high real-time performance and adaptability, and is suitable for various scenes such as logistics storage, industrial production and intelligent transportation.

The invention discloses an intelligent express parcel automatic sorting method based on deep learning, and the method comprises the steps: collecting a multi-view image, carrying out the standardization enhancement processing, carrying out the bidirectional space sequence coding through a Vision Mama structured state space model, obtaining a fusion visual feature map, inputting a preliminary classification network, and carrying out the primary classification processing, and according to the initial classification category, dynamically selecting expert sub-networks of the MoE-Mama hybrid expert network to complete deep feature reconstruction and fine classification, and finally, according to the final classification category, generating a control instruction to realize automatic guide positioning and accurate sorting. According to the method, the parcel sorting accuracy and efficiency are remarkably improved, and the method is suitable for intelligent logistics sorting operation in a complex environment.

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

The invention belongs to the technical field of intelligent logistics, and particularly relates to an intelligent distribution system and method based on artificial intelligence and an unmanned aerial vehicle. According to the invention, a three-dimensional intelligent distribution system with cooperation of a cloud decision center and edge calculation is constructed. The system dynamically divides a three-dimensional grid airspace through a space-time resource scheduling engine, and generates a flight path considering both efficiency and safety in combination with a multi-objective optimization algorithm; a standardized connection interface and a self-adaptive grabbing mechanism are adopted to achieve seamless connection of multiple goods, and a cloud-edge-end three-level disaster recovery system is constructed through a 5G-MEC network. According to the method, millimeter wave environment perception, model prediction control and reinforcement learning mechanisms are creatively fused, and full-process autonomous decision making and anomaly self-healing are achieved. According to the invention, the distribution efficiency, the equipment compatibility and the system reliability of urban unmanned aerial vehicle logistics are remarkably improved, and a core technical support is provided for constructing an air logistics network of a smart city.

The invention provides an intelligent logistics management system based on a large language model, and relates to the field of intelligent logistics management. According to the method, a low-rank adaptation technology general large language model is adopted in advance for fine tuning, and an external LLM service obtained through fine tuning is innovatively and deeply fused into a task decision process, so that deep understanding of user intentions and accurate analysis of complex environment situations can be realized, and an optimal distribution scheme is autonomously generated based on the deep understanding of the user intentions and the accurate analysis of the complex environment situations. Besides, the urban environment management module, the logistics management scheduling module, the simulation engine module and the large language model interface module included in the system do not work independently, but closely cooperate through an event-driven and message-passing mechanism, so that a complete unmanned aerial vehicle logistics management closed loop from environment perception to decision making and execution to event feedback is formed. The architecture can reflect and control the distribution task of the unmanned aerial vehicle in real time, and carries out self-correction on a set scheme, thereby finally remarkably improving the intelligent level and operation efficiency of logistics management.

The invention belongs to the technical field of information logistics, and particularly relates to an intelligent logistics control system and method based on machine vision and a storage medium, and the intelligent logistics control method based on machine vision comprises the steps: obtaining discharging information; controlling a visual identification module to run to a photographing position according to the discharging information so as to identify material grabbing position information and identification code information of an identification code; matching a discharging drawer of the storage cabinet module according to the identification code information, and generating matching information; and a robot carrying module is controlled to be matched with a transfer module according to the matching information and the material grabbing position information, so that the robot carrying module places the grabbed packaging parts on the transfer module, and the packaging parts are stored in a storage cabinet module through the transfer module. According to the invention, the classification, storage and taking efficiency of the packaging parts is improved, and the complexity and errors of manual operation are reduced.

The invention relates to the technical field of logistics distribution, in particular to a logistics distribution method and system based on intelligent logistics equipment, and the method comprises the steps: receiving logistics order information in real time, converting the logistics order information into digital logistics tasks, constructing a logistics distribution task pool, and carrying out the classification and priority setting of the logistics tasks according to the logistics distribution features; the state of the intelligent logistics equipment is monitored in real time, and an intelligent logistics equipment resource pool is constructed; a dynamic logistics distribution task allocation algorithm is combined with logistics distribution task features and intelligent logistics equipment resource pool information to match most suitable equipment for logistics distribution tasks and perform multi-equipment cooperation path planning; in the distribution process, the equipment is monitored in real time, data generated in the distribution process is analyzed, and optimization is carried out according to an analysis result. According to the invention, the intelligent logistics equipment is cooperatively combined with a dynamic task allocation mechanism, so that the efficiency of logistics distribution is improved, and the intelligentization of the whole flow of logistics distribution is realized.