631 results about "Smart logistics" patented technology

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 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 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 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 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.

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 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 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.

The invention provides an article access method, device and equipment applied to an unmanned express station, and a medium, and can be applied to the technical field of intelligent logistics. The method comprises the steps that a first article image of a target article in a target lattice is acquired in response to the fact that the target lattice of the unmanned express cabinet is detected to be in an open state, and the first article image is acquired in the process that a first user takes out the target article; determining article transportation information of the target article according to the first article image; the article transportation information is matched with the pick-up information, a matching result is obtained, and the pick-up information comprises information used for opening a target cell; and when it is determined that the matching result represents that the article transportation information is not matched with the pick-up information, a first alarm instruction is sent to the unmanned express cabinet, so that the unmanned express cabinet indicates abnormal pick-up to the first user by executing the first alarm instruction.

The invention discloses a marine assembled box shipping management method based on an Internet of Things platform, and belongs to the technical field of logistics management. The method comprises the steps of obtaining a cargo list, extracting cargo attributes, analyzing chemical attributes and physical characteristics, and generating a conflict risk assessment report; constructing a time limit prediction model based on the multi-link time data, and optimizing the loading priority; a loading scheme is optimized through a space distribution and dynamic adjustment algorithm, so that the compatibility and the transportation timeliness of goods are ensured; and the shipping state is monitored in real time, emergency adjustment is triggered according to the delay risk, and execution of a transportation plan is ensured. The loading efficiency can be effectively improved, the transportation safety is guaranteed, the transportation risk and delay probability are reduced, and the logistics management level is comprehensively improved. The method is suitable for the fields of intelligent logistics, shipping assembly box transportation and the like, and efficient and accurate decision support is provided for shipping management.

The invention relates to a hanging box pose sensing method and system based on multi-sensor fusion, and belongs to the technical field of industrial automation and intelligent logistics. According to the method, a staged multi-mode sensing strategy is adopted, and high-precision positioning of the hanging box is achieved: in a non-working area, IMU and UWB tight coupling positioning is utilized, the IMU collects 6D pose data in real time, and coarse positioning is achieved in combination with absolute position information provided by UWB; and after entering a working area, starting a visual fine calibration system, acquiring pixel and depth information of the corner fitting of the hanging box by using a depth camera, extracting feature points based on an improved YOLOv5 algorithm, and obtaining a 6D pose with millimeter-level precision through pose calculation. And finally, dynamically fusing IMU, UWB and visual data by adopting an adaptive Kalman filtering algorithm, adaptively adjusting the weight according to environmental noise, and outputting stable and reliable pose information. The system comprises a multi-sensor module, a pose resolving module and a multi-mechanical-arm cooperation module, the positioning precision and the logistics system efficiency are remarkably improved, and the system is widely suitable for scenes such as mine material transportation.

The present application relates to the technical field of intelligent logistics, and in particular to an automatic warehouse-in and warehouse-out method for intelligent shared pallets, which comprises collecting pallet operation state data through a multi-source sensing module and preprocessing, generating task decomposition parameters based on a task decomposition model, constructing a multi-objective planning model for global scheduling, and outputting execution instructions through a hierarchical execution control model. The present application can realize efficient and accurate warehouse-in and warehouse-out management of shared pallets, optimize the flow efficiency and resource conflicts, and improve the intelligent level and operation efficiency of the logistics system.

The invention discloses a waste recovery vehicle dynamic scheduling system and method based on an AI algorithm, and belongs to the field of intelligent logistics. The system comprises a task analysis module, a vehicle state sensing module, an AI scheduling core module and the like. The task analysis module identifies waste categories through an attention mechanism U-Net algorithm, and generates a task parameter set containing priorities; the vehicle state sensing module outputs accurate vehicle data through a Kalman filtering algorithm; the AI scheduling core module takes the task completion rate, the energy consumption and the carbon emission reduction as targets, and outputs a scheduling instruction through a multi-target optimization model and an improved Dijkstra algorithm; the data feedback module calculates the carbon emission reduction according to the Sichuan local standard and updates the model; the problems of poor scene adaptation and high energy consumption in the prior art are solved, the task completion rate is increased by 25%, the empty driving rate is reduced by 18%, and technical support is provided for the dual-carbon target.

The invention relates to the field of intelligent logistics optimization, and discloses an unmanned aerial vehicle transportation route distribution method for logistics multi-point transportation, which comprises the following steps: carrying out coordinate-weight joint standardization on a distribution point set to generate a three-dimensional feature vector; constructing a topological complex based on the standardized data, and extracting a key ring structure through continuous coherence; encoding the ring structure into a topological constraint term of a quantum model, and constructing Hamiltonian containing distance, load and topological constraint; dividing quantum sub-blocks according to the topological ring, and executing block annealing solution through chain coupling constraint; and carrying out topology-guided privacy fine tuning under a federated learning framework by using gradient information of a quantum solution. According to the method, a complex path optimization problem is decomposed into sub-problems capable of being processed in parallel through a quantum topological coding and block annealing strategy, quantum bit grouping is guided through a topological ring structure, the calculation complexity is reduced, efficient solving of a large-scale logistics network is achieved, and the calculation speed is increased compared with a traditional optimization method.

The invention provides a logistics transportation path intelligent planning system and method, and the method comprises the steps: obtaining an initial logistics transportation path through the planning of a transportation path of a target jurisdiction; determining node aggregation density corresponding to the transportation path nodes according to neighborhood association characteristics of the transportation path nodes; determining a node distribution association entropy of the target jurisdiction based on the node aggregation density and the node distribution rate corresponding to each transportation path node, and determining crowdsourcing distribution times of the target jurisdiction and node distribution gravitation corresponding to each transportation path node according to the node distribution association entropy; according to a mode of performing track redistribution on an initial logistics transportation path based on crowdsourcing distribution times of a target jurisdiction and node distribution gravitation of each transportation path node to obtain an intelligent logistics transportation track, distribution path redistribution can be performed based on node distribution association entropy and node distribution gravitation of the target jurisdiction; and the balance of path resource allocation in the crowdsourcing distribution mode is improved.

The invention relates to the technical field of intelligent logistics, in particular to an intelligent shared tray automatic warehouse-in and warehouse-out method, which comprises the steps of collecting and preprocessing tray operation state data through a multi-source sensing module, generating task deconstruction parameters based on a task deconstruction model, and constructing a multi-target planning model for global scheduling. And outputting an execution instruction through the hierarchical execution control model. According to the invention, efficient and accurate warehouse-in and warehouse-out management of the shared tray can be realized, the circulation efficiency and resource conflicts are optimized, and the intelligent level and the operation efficiency of a logistics system are improved.

The invention relates to the technical field of logistics transportation, in particular to a loading and unloading device for intelligent logistics and a using method of the loading and unloading device. A connecting frame is fixedly connected to one side of the bearing vehicle body, a bearing penetrates through the inner wall of the connecting frame, and a first threaded rod is fixedly connected to the inner ring face of the bearing. According to the loading and unloading device for intelligent logistics and the using method thereof, through cooperation of multi-direction movement adjustment of the adsorption assembly and the auxiliary assembly, flexible adjustment of the cargo loading and unloading direction is achieved, the adsorption assembly detects the cargo height in real time through a laser detector, and the position and height of the adsorption assembly are adjusted in real time; according to the method, the position accuracy of goods in the loading and unloading process is ensured, the stacking height of subsequent goods is adjusted according to the detected goods height, the goods are stacked in order, automatic operation of goods loading and unloading is achieved, manual intervention is reduced, the loading and unloading efficiency is improved, the labor cost is reduced, and the method is suitable for batch goods processing in logistics storage scenes.

The invention belongs to the technical field of intelligent logistics, and aims to provide a multi-AGV path planning method, system, equipment and product. The method comprises the following steps: establishing an environment map of a warehousing operation area; a multi-AGV path planning problem based on the environment map is converted into a Markov decision model; obtaining current environment state information of each AGV based on the Markov decision model, and obtaining an initial path of each AGV based on a deep reinforcement learning method according to the current environment state information of each AGV; performing path conflict detection on the initial path of each AGV, and when a path conflict occurs, performing path updating processing on the initial path of the corresponding AGV based on a priority obstacle avoidance strategy so as to obtain a final path of each AGV; and generating a motion instruction according to the final path of each AGV, and controlling each AGV to run according to the corresponding final path based on the motion instruction. The method can be applied to a multi-AGV collaborative operation scene, and the task execution efficiency and safety are higher.

The invention discloses a charging pile dynamic distribution method in a multi-forklift collaborative operation scene, and relates to the technical field of intelligent logistics dispatch, and the method comprises the steps: S1, based on a current traffic flow prediction result, generating the predicted arrival time of a forklift at each charging pile, and S2, according to the predicted arrival time, determining the charging pile according to the predicted arrival time. The method comprises the steps of S1, calculating the actual waiting duration and the corresponding priority weight of each forklift, S3, dynamically regulating and controlling the charging queuing sequence of the forklifts based on the priority weights, and S4, sending the regulated queuing sequence to a target forklift to guide the target forklift to complete charging pile distribution. According to the charging pile dynamic allocation method in the multi-forklift collaborative operation scene, the charging queuing sequence is regulated and controlled according to the current traffic flow prediction result, so that the problems of congestion and overlong waiting time are solved.