508 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 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 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 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 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 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 provides an intelligent logistics vehicle scheduling system driven by user demands, and relates to the technical field of logistics vehicle scheduling, and the system comprises a region division module which is used for dividing a logistics distribution space into a plurality of distribution time and space; the demand prediction module is used for predicting future demand space-time distribution; the vehicle management module is used for classifying logistics vehicles by adding feature tags; the vehicle scheduling module is used for performing initial logistics vehicle scheduling; and the change scheduling module is used for carrying out scheduling change on the associated vehicles in the initial logistics vehicle scheduling strategy according to the order change demand. According to the method, the future demand is predicted by establishing the demand prediction model, the prediction precision of future demand space-time distribution is ensured, and a reliable basis is provided for dynamic scheduling; by changing a DynNom dynamic scoring mechanism of the scheduling module, the state of the vehicle can be evaluated in real time, the vehicle with the optimal comprehensive score is scheduled preferentially, resource conflicts caused by sudden demands are effectively dealt with, and the overall scheduling robustness is improved.

The invention relates to a digital twin-driven distributed warehousing system and method, and belongs to the technical field of intelligent logistics. The system comprises a sensing execution layer, a network transmission layer, an edge computing layer, a cloud platform layer and an application service layer. The method comprises the following steps: acquiring storage physical entity data through a perception execution layer; the data is transmitted to the edge computing layer and the cloud platform layer through the network transmission layer; in the cloud platform layer, a high-fidelity virtual warehouse is constructed and synchronized through a digital twin engine; using a multi-agent reinforcement learning algorithm to perform centralized training in a digital twin environment to generate a distributed cooperative scheduling strategy; and issuing the strategy to an edge computing layer and a sensing execution layer, and driving an autonomous mobile robot cluster to execute a cooperative task. According to the invention, step-type improvement of the warehousing system in flexibility, intelligence and cooperative capability is realized, and the problems of rigid bottleneck, non-optimal decision and low cooperative efficiency of a traditional system are solved.

The invention discloses a logistics distribution deployment and rider operation management system, which relates to the technical field of intelligent logistics, and comprises a time sequence observation module, a causal analysis module, a conflict extraction module, a sequence construction module, a scheduling agent module and a beat regulation and control module, a scheduling beat, resource lock delay and a memory access sequence are collected, and a priority drift distribution diagram is generated; and the causal analysis module is used for constructing a time-space mapping structure dependent on topology based on the priority drift distribution diagram, performing time sequence correlation analysis of task paths, identifying a causal chain and determining race trigger nodes. According to the method, scheduling abnormal root cause identification is realized through time sequence observation, causal analysis and conflict fingerprint extraction, the consistency of a task sequence and resources is guaranteed in combination with monotonic task sequence construction and double-track scheduling control, dynamic balance of scheduling priorities is realized through beat reconstruction, and the stability and controllability of logistics scheduling in a high-concurrency environment are remarkably improved.

The invention relates to the technical field of intelligent logistics, in particular to a canteen people flow intelligent scheduling and dish recommendation system, which comprises a people flow monitoring module used for collecting people flow data of each region of a canteen in real time; the dish database is used for storing dish components, tastes, nutrition data and historical sales volume; the central processing unit is used for dynamically analyzing the data and generating a scheduling strategy; the user terminal is used for receiving the recommendation information and displaying a navigation path; according to the invention, the multi-mode sensing network is used for monitoring the distribution of people flow in the canteen in real time, the dynamic queuing model and the collaborative filtering algorithm are combined, the optimal distribution of window resources and personalized dish recommendation are realized, the average waiting time is reduced, the dining order in the peak period is ensured through the digital twin technology and the emergency response mechanism, and the dining efficiency is improved. The operation efficiency and the service quality of the canteen are integrally improved, and meanwhile, the dining path selection of the user is optimized through AR navigation, and the problem of local congestion is relieved.

The invention relates to an intelligent logistics log anomaly detection method based on a block chain and a space-time compression traceability graph, and the method specifically comprises the following steps: S1, collecting multi-source heterogeneous log data in an intelligent logistics ecosystem, S2, employing a traceability graph space-time compression algorithm based on space-time position coding and abnormal chain mapping, and carrying out abnormal chain mapping on the multi-source heterogeneous log data; the method comprises the steps of S1, obtaining an initial full-amount traceability graph, S2, processing the initial full-amount traceability graph to construct a compressed traceability graph, S3, calculating a topological Hash fingerprint based on a space-time compressed traceability graph feature tensor, S4, inputting the space-time compressed traceability graph feature tensor into a pre-trained space-time twinning anomaly detection model, and S5, responding to a detected anomaly root cause, and obtaining a time-space twinning anomaly detection result. Triggering a multi-dimensional reputation evaluation mechanism in the smart contract; according to the method, the entity alignment error rate in the graph construction stage is remarkably reduced, the causal integrity of the initial traceability graph is ensured, and the problem that heterogeneous logs are difficult to analyze is solved.

The invention discloses a freight logistics track abnormity processing method and system, and belongs to the technical field of intelligent logistics monitoring, and the method comprises the steps: obtaining a track original data set of a target freight vehicle; constructing a behavior evolution model based on the historical trajectory data, wherein the behavior evolution model is used for describing conventional operation boundaries of the vehicle under different road types and time periods; performing multi-source fusion correction on the trajectory data to generate a trajectory point sequence; calculating credibility factors of the track points based on GPS stability, historical track goodness of fit and road matching degree; constructing an anomaly judgment graph, and generating an anomaly distribution function by adopting a recursive scoring mechanism; identifying a track abnormal fragment based on a dynamic adaptive threshold value and outputting an abnormal label; generating a scheduling suggestion according to the abnormal label and a vehicle scheduling rule, and pushing the scheduling suggestion to a vehicle-mounted system in real time; according to the invention, individuation, high precision and real-time performance of track abnormity identification are realized, and the method is suitable for an intelligent logistics management and dynamic scheduling system in a complex scene.

The invention relates to the technical field of intelligent logistics and operation planning optimization, and discloses an import and export multimodal transport intelligent stowage service method based on a hybrid planning algorithm, which comprises the following steps: establishing an intermodal transport node operation impact database and an intermodal transport network diagram, and defining a node stacking operation state set; according to the force transmission attribute of the corner column of the container, the discrete stacking hierarchy is mapped into vertical pre-pressure, and the node working condition inherent frequency of the container-cargo combination is calculated through a pre-pressure and rigidity correlation model; in combination with node pulse dominant frequency data, constructing a response gain index including resonance interval intensity and parameter fluctuation sensitivity; and establishing a multimodal transport stowage optimization model, bringing the index into a target function, and carrying out collaborative solution on a cargo loading layout, a transport link and a node stacking level. Resonance risks caused by discrete stacking and lateral impact coupling can be effectively recognized and avoided, and a stowage scheme with high structural safety is output.

The invention relates to the technical field of radio frequency identification and intelligent logistics management, in particular to a multifunctional cargo tracking method based on RFID, which comprises the following steps: step 1, cargo binding and data synchronous acquisition: acquiring cargo state parameters through an RFID tag integrated with multiple sensors, and when a reader-writer transmits a query instruction, sending a query instruction to the reader-writer; synchronously acquiring a temperature value, a humidity value and a three-axis vibration acceleration, and packaging the temperature value, the humidity value and the three-axis vibration acceleration with a cargo identity code and a timestamp into a combined data unit; 2, dynamic environment interference suppression; step 3, mobile scene routing optimization: acquiring the moving speed of the carrier in real time, dynamically calculating an optimal query interval by combining with an environment interference coefficient, and transmitting a query instruction according to an interval period; and 4, multi-dimensional state fusion verification is carried out. Through innovative measures of multi-sensor integration, dynamic environment compensation, mobile scene optimization and the like, the accuracy, stability and intelligent level of cargo tracking are effectively improved.

The invention relates to the technical field of robots, and discloses an intelligent logistics sorting robot for carton packaging, which comprises a driving vehicle body, a supporting table fixedly arranged at one end of the driving vehicle body, a rotating table rotationally arranged on the supporting table, and a first driving assembly fixedly arranged on the rotating table, the first driving assembly is provided with a clamping assembly used for clamping packages. According to the device, the multiple mounting frames are arranged from bottom to top, so that the device can temporarily store many boxed packages, the boxed packages in the mounting frames can horizontally move through the arrangement of the conveying belt, even if the length of the mounting frames is large, the clamping assemblies can also clamp the boxed packages on the conveying belt, and the conveying efficiency is improved. And the boxed parcels are placed at the inlet of the mounting frame, and the boxed parcels can also move to the deep position in the mounting frame under the action of the conveying belt, so that the mounting frame can be set to be sufficient in length, the device can temporarily contain more boxed parcels, and the sorting efficiency is improved.

The invention relates to the technical field of intelligent logistics storage, in particular to a tray box target detection and quantity statistics method based on a multi-view depth camera, and the method comprises the following steps: 1, building a system architecture; a pretreatment process; step 3, a target identification process; step 4, a depth camera continuous image acquisition process; and step 5, an output process: adopting the training model to carry out tray box body identification on a plurality of visual angles, and carrying out fusion and three-dimensional positioning on detection result information of the plurality of visual angles to complete quantity statistics. The positions, the depths and the stacking layers of the box bodies on the tray and the number of the box bodies on each layer can be quickly and accurately identified, and the cargo management efficiency in logistics storage and industrial production is improved.

The invention discloses a truck-unmanned aerial vehicle cooperative distribution and scheduling optimization method for unmanned logistics, relates to the technical field of intelligent logistics scheduling, and solves the technical problem that the potential of an existing logistics station in the aspect of a transfer function is not fully utilized in the prior art. A distribution main body is composed of a plurality of groups of homogeneous automatic driving trucks equipped with unmanned aerial vehicles, and each group of automatic driving truck-unmanned aerial vehicle combination starts from a warehouse, loads to-be-distributed packages and carries out a distribution task; the RP selects a candidate transfer point from existing logistics stations, and deploys a plurality of unmanned aerial vehicles for transfer and continuous delivery of packages; the parcel receiving mode of the client point comprises the steps of automatically driving a truck for direct delivery, and completing parcel delivery by a paired unmanned aerial vehicle; transferring to the selected RP by the automatic driving truck, and then carrying out tail end distribution through the unmanned aerial vehicle deployed by the RP; according to the invention, transfer delivery and continuous delivery of the parcels are realized, the delivery range of the unmanned aerial vehicle is expanded, and the overall customer coverage rate and delivery flexibility are improved.

The invention discloses an unmanned aerial vehicle relay cooperative control method and system based on a smart lamp pole network, and relates to the technical field of smart city low-altitude logistics, and the method comprises the steps: constructing a smart lamp pole holographic node map comprising a parking space, a charging state and a smart logistics transfer unit state; performing flight flow field inversion by using an unmanned aerial vehicle as a mobile probe, generating a dynamic energy potential topology network containing an environmental impedance factor, and realizing self-evolution of a road network weight; asynchronous relay planning is executed based on space-time decoupling, and a long-distance task is decomposed into independent short-distance sub-tasks; the upstream unmanned aerial vehicle is controlled to deliver the goods to the transfer unit through cooperative scheduling and then leave the field, the downstream unmanned aerial vehicle carries out asynchronous extraction, transport capacity time sequence decoupling is achieved by introducing the intelligent logistics transfer unit, and the chain type waiting effect is eliminated; and a micro-meteorological flow field is inverted by utilizing crowd sensing, a high-energy-consumption risk path is automatically avoided, and the throughput and the safety of a low-altitude logistics network are remarkably improved.

The invention provides a material collaborative distribution method in an electric forklift assembly process, and relates to the technical field of intelligent logistics and production management, and the method comprises the steps: obtaining the material demand prediction data of each station through employing a time-phased material consumption prediction model based on the material demand information of an electric forklift assembly station; and in combination with the material storage position information, a cooperative distribution path of multiple distribution vehicles is calculated by adopting an improved ant colony algorithm, the cooperative distribution path is decomposed according to the material distribution emergency degree, a material distribution task sequence is generated, and distribution task adjustment is performed in a task interaction area by adopting a bidirectional matching algorithm. According to the method, distribution path planning and task dynamic adjustment are optimized, and task interaction area resources are fully utilized, so that the problems of insufficient prediction precision, poor path optimization effect and low distribution resource utilization rate in the prior art are effectively solved.

The invention discloses a warehouse logistics AGV and unmanned aerial vehicle cooperative scheduling system, and relates to the technical field of intelligent logistics scheduling, the system comprises a plurality of AGVs, unmanned aerial vehicles and a warehouse internal communication network, an intelligent docking platform is integrated at the tops of the AGVs, the AGV comprises an LED array beacon, a UWB accurate positioning module, a mechanical self-locking or electromagnetic adsorption mechanism and a wireless communication module, and the AGV and the unmanned aerial vehicle are connected through a distributed negotiation decision protocol. The AGV and the unmanned aerial vehicle can perform autonomous cooperative scheduling, after a demand side broadcasts a cooperative request data packet, an idle device calculates cooperative cost based on the state of the idle device and replies an intention response, the demand side selects a cooperative partner according to a preset rule, the cooperative partner and the cooperative partner determine a temporary convergence point through a dynamic convergence point algorithm, and the AGV drives to the convergence point and lights an LED array beacon. The unmanned aerial vehicle flies to a convergence point, visual identification and UWB positioning are fused to achieve accurate approaching, and finally butt joint and fixing are completed through a mechanical self-locking or electromagnetic adsorption mechanism, and cargo transfer is executed.