3393 results about "Storage garage" patented technology

To predict the working time of sorting work for taking out merchandise and sorting it for each shipping destination.SOLUTION: The prediction device 100 includes a prediction unit 113 that predicts a work time of sorting work, which is work of taking out and sorting articles from a storage that stores the articles, based on a plurality of orders related to the articles (commodities), and calculates the work time as a predicted work time. The prediction unit 113 calculates the predicted working time based on the input information including the type of article to be taken out from the storage, the number of articles to be taken out from the storage, and the number of articles included in each order. The storage may be a storage shelf that the transport robot carries to a work place of the sorting work. The item may be transported along a predetermined path to a work location of a sorting operation. Further, a worker who performs the sorting work may move to a place where the storage is placed and perform the sorting work.SELECTED DRAWING: Figure 3

The invention discloses a storage AGV dynamic path planning system based on multi-objective optimization, and relates to the technical field of storage logistics, and the system comprises a multi-source sensing and data collection module which is used for collecting the operation state, operation environment and external traffic information of an AGV and generating a standardized feature vector; and the cross-modal digital twinning and risk simulation module is used for constructing a virtual twinning body of a warehouse and external traffic and carrying out risk prediction and simulation under the driving of cross-modal sensing data. According to the invention, through the multi-source sensing and data acquisition module, the system can comprehensively acquire the AGV operation state, the operation environment and the external traffic information, and through combination with an advanced data fusion technology, a high-dimensional standardized feature vector is generated, so that an accurate and comprehensive data basis is provided for subsequent path planning and risk prediction; the cross-modal digital twinning and risk simulation module constructs a virtual twinning body of a warehouse and external traffic, and can reflect the dynamic change of the physical world in real time.

The invention discloses a cargo warehouse-in and warehouse-out management method and management system, and relates to the technical field of information management. Through dynamic storage position optimization, the warehouse-in and warehouse-out frequency, the associated sales relationship and the storage demand characteristics of cargos are combined with a warehouse three-dimensional space model; and generating a cargo storage location allocation scheme by using a multi-objective optimization algorithm. According to the scheme, the picking time of high-frequency warehouse-in and warehouse-out commodities is remarkably shortened, the average time consumption of single-time commodity picking is reduced, meanwhile, the combined picking error rate of associated commodities is reduced, the problem that in the prior art, storage positions are disjointed with warehouse-in and warehouse-out requirements is solved, through predictive process scheduling, cross congestion of operation channels is effectively avoided, and the working efficiency is improved. Therefore, smooth operation of the in-out warehouse process is realized, the occurrence frequency of congestion events of the in-out warehouse channel is reduced, the delay time is shortened, and the problem of congestion of the in-out warehouse process in the prior art is solved.

The invention discloses an automated guided vehicle scheduling optimization method and device, a medium and a terminal, and the method comprises the steps: generating fleet heterogeneous data based on parameter data of a plurality of automated guided vehicles in an intelligent warehouse, and fusing the fleet heterogeneous data into an undirected graph model obtained through undirected graph modeling based on storage environment topological data in advance, the method comprises the steps of obtaining an environment topological graph model, then carrying out graph topology and heterogeneous feature extraction on the environment topological graph model to obtain a multi-constraint path planning model, and dynamically adjusting a single-vehicle route of each automated guided vehicle by utilizing the planning model and real-time storage field state data. And then performing scheduling process simulation on the intelligent warehouse based on the dynamic routing table formed by adjustment and historical scheduling index data of the intelligent warehouse, so that a scheduling system model learns a mapping strategy from a state to a behavior and outputs an optimization decision strategy. The method is used for automatic guide vehicle scheduling, is suitable for storage environments of different scales and complexities, and improves the scheduling efficiency and accuracy.

The invention discloses a warehouse cargo supply chain management method and system based on AI big data, and relates to the technical field of warehouse management, and the method comprises the steps: collecting and preprocessing warehouse multi-source data, and predicting the cargo delivery frequency based on an LSTM neural network; and a multi-target scheduling model combining pickup path time consumption and storage gravity center control is constructed based on a prediction result, a cargo task and AGV path planning are jointly modeled into an open path multi-starting-point asymmetric TSP problem, an FWA-GA hybrid optimization algorithm is used for model solving, and an optimal cargo allocation matrix and an optimal AGV scheduling path table are obtained. Therefore, integrated joint optimization of task allocation, path planning and goods allocation layout is achieved, a supply chain platform is further linked to complete stockout early warning and supplier intelligent selection, and multiple limitations of a traditional method in the aspects of prediction precision, scheduling coupling, structure control, supply collaboration and the like are effectively broken through.

The invention discloses an intelligent warehouse management method and system for hazardous wastes, and the method comprises the steps: carrying out the real-time monitoring of the state of an object in a hazardous waste warehouse through a deployment sensor network, obtaining the basic data of the position of the object and the environment parameter, and transmitting the basic data to a central processing platform, thereby obtaining a preliminary data set; performing integration processing on multi-source information by adopting a data fusion technology according to the preliminary data set, eliminating redundancy and noise, and determining a standardized data record; aiming at the standardized data record, dynamically analyzing the distribution of goods locations in the warehouse by utilizing a path optimization algorithm, and if the change of the occupation state of the goods locations is detected, adjusting a goods location allocation scheme in real time, and outputting an optimal storage location combination; according to the optimal storage position combination, a dynamic route planning scheme is generated in combination with warehouse layout information, and the optimal moving track of the operation equipment is obtained; according to the invention, intelligent management of the hazardous waste warehouse is realized, and the storage efficiency and safety are improved.

The invention discloses a warehouse-in and warehouse-out path planning system and method applied to stored goods, and relates to the technical field of intelligent storage. The method comprises the steps of establishing a three-dimensional space structure of a warehouse, generating a menu to be stored and matching various storage schemes, carrying out importance degree assignment and priority ranking on goods, calculating the residual space and the total weight of the warehouse, judging and optimizing the space and the weight, and planning and optimizing warehouse-in and warehouse-out paths. Through the mathematical planning method, the total cost is minimized, and the space utilization rate and the access efficiency are maximized. And meanwhile, the operation condition in the warehouse is monitored in real time, the in-out path and the in-out path are dynamically adjusted, and congestion and conflicts are avoided. In addition, a high-definition camera and a sensor are further installed, and safety management in the warehouse is ensured. According to the invention, intelligent warehouse-in and warehouse-out path planning of the stored goods is realized, the warehouse management efficiency and the space utilization rate are improved, and safe storage and efficient warehouse-in and warehouse-out of the goods are ensured.

The invention discloses an intelligent logistics digital warehouse management method based on deep learning, and relates to the technical field of warehouse management, and the method comprises the steps: calculating the correlation of SKUs through Graph SAGE, constructing an SKU incidence matrix, optimizing an SKU combination based on an SKU co-occurrence frequency, calculating an SKU storage optimization strategy through Transform, analyzing the storage relation between the SKUs through an attention mechanism, and optimizing a storage layout in combination with GNN. A high-frequency SKU can be located in an optimal storage area, in a sorting task scheduling link, a sorting robot is trained by adopting reinforcement learning, an optimal sorting path is autonomously learned in a warehouse environment, optimal distribution of AGVs and manual tasks is calculated in combination with GNN, collaborative operation of automatic equipment and manual operation is balanced, and path conflicts and invalid movement are effectively reduced; and after the task is executed, recording the sorting time and a path optimization result, and applying feedback data to self-supervised learning to optimize a warehouse management strategy, so that SKU storage, a sorting path and task scheduling can be dynamically adjusted.

The invention relates to the technical field of warehouse management, in particular to an unmanned warehouse management method and system with a real-time inventory function, and the method comprises the steps: building a signal strength-probability statistical model through return signal data generated in a historical inventory process, and carrying out the stability verification of each RFID tag return signal in a current inventory period, and under the condition that the stability is confirmed, carrying out preliminary positioning based on the time difference of arrival and determining the probability of the storage unit. And finally, the preliminary positioning is verified in combination with the probability of the storage unit and the checking information of the last checking period, so that the situation that whether the position of the RFID tag is changed really cannot be confirmed due to the fact that verification cannot be carried out when the positioning jumps is avoided. According to the invention, low-cost and high-reliability material positioning can be realized by using an existing RFID system, so that real-time and accurate checking is realized, and an information basis is provided for management of unmanned warehouses.

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The embodiment of the invention discloses a path planning method and device, electronic equipment and a storage medium, and relates to the technical field of electric power, and the method comprises the steps: determining a starting point and an ending point of a to-be-planned path in a preset warehouse; based on the current scene information of the preset warehouse, determining the current traffic obstruction level of each sub-region in the preset warehouse; calculating the actual driving cost of each candidate node, and determining a corresponding first target weight based on the current traffic obstruction level of the sub-region where each candidate node is located; calculating the estimated driving cost of each candidate node, and determining a corresponding second target weight based on the current traffic obstruction level of the sub-region where the terminal point is located; and weighting and summing the actual driving cost and the estimated driving cost of the corresponding candidate node based on the first target weight and the second target weight to obtain the comprehensive evaluation cost of the corresponding candidate node, and determining the intermediate node of the to-be-planned path based on the comprehensive evaluation cost, so that the path planning function is realized, and the working efficiency of the robot is improved.

Disclosed in the present invention are a warehousing transportation resource scheduling method and system based on goods quantity prediction, solving the problems of planning and scheduling of warehousing transportation resources in a warehousing environment. The technical solution comprises: collecting data to form a data set; preprocessing the collected data set to ensure the quality and accuracy of the data; constructing seasonal data in the preprocessed data set; introducing a bidirectional LSTM model, an input being a historical feature sequence, and an output being a predicted inbound quantity; using a deep learning framework to build a CTA-LSTM model used for predicting the inbound quantity; using a grey wolf optimizer to initialize parameters in the CTA-LSTM model, and using the grey wolf optimizer to perform parameter optimization to determine an optimal parameter configuration in the optimizer; using an adversarial mechanism to perform improvement and adversarial training on the built CTA-LSTM model so as to predict a warehouse goods quantity; building a warehousing simulation model; solving the built warehousing simulation model; and performing transportation resource scheduling on the basis of an obtained optimal solution.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

The invention discloses a path planning method and system based on an automatic three-dimensional warehouse four-way shuttle vehicle, and the method comprises the steps: building a storage environment model through a grid method, making traffic rules according to a grid map, and building a map table; based on the map table, generating a task list by using a data memorization processing technology, and sorting according to task priorities; the distributor performs task distribution on the shuttle vehicle according to the task list, performs path planning by using an improved A * algorithm, divides an obtained path into a plurality of road sections, generates time window list information of the road sections, checks whether a time window conflicts with a time window of a planned path node or not, shields the road section corresponding to the time window if the time window conflicts with the time window of the planned path node, and does not shield the path corresponding to the time window if the time window conflicts with the time window of the planned path node. Re-planning the path; otherwise, time window dynamic adjustment is carried out, and the path is executed. According to the invention, conflict judgment is carried out through the time window table of the shuttle vehicle, the possibility of conflict occurrence is further reduced, and the operation efficiency is improved.

The invention discloses a logistics information management method and system based on the Internet of Things, and relates to the technical field of logistics intelligent management, and the method comprises the steps: collecting historical logistics data and real-time logistics data, storing the data on a block chain, carrying out the preprocessing, generating real-time data feedback based on the preprocessing data, and carrying out the management of the logistics information. The method comprises the steps of performing priority classification on storage tasks on the basis of real-time data feedback, adjusting warehouse storage layout on the basis of real-time logistics data, distributing the storage tasks to corresponding areas on the basis of storage task priorities and warehouse storage layout adjustment results, and controlling task execution in real time through remote monitoring. According to the method, intelligent classification and priority evaluation of the storage tasks are realized in combination with the environment characteristics and the change trend of the task priorities, the problems of lag and low efficiency of task allocation and priority judgment in existing logistics management are solved, the flexibility and timeliness of task execution are effectively improved, and the accuracy of task scheduling is enhanced.

The invention discloses an agricultural product supply chain abnormal event tracing method and system based on a knowledge graph, and relates to the technical field of agricultural product supply chain management, and the method comprises the steps: constructing a supply chain knowledge graph, and enabling graph nodes to cover agricultural product batches, production subjects, processing equipment, transport vehicles, storage warehouses and other entities; when an anomaly is detected, performing reverse traversal from an abnormal node based on a graph neural network to determine a candidate propagation path; calculating an abnormal source probability score by integrating the historical abnormal record, the time window goodness of fit and the association strength, and outputting a responsibility subject sequence; the method supports hypothetical reasoning to predict the downstream influence range, and solves the problems that a traditional traceability scheme is difficult to efficiently associate and analyze cross-link data and lacks abnormal path intelligent reasoning ability.

A material transfer machine comprises a material container containing material and a material transfer system capable of transferring the material from the material container to another location, wherein the material transfer system comprises a discharge pipe movable between an operating position and a storage position and an actuator driving the movement of the discharge pipe between the operating position and the storage position.The material transfer machine further comprises one or more processors, a memory, and computer-executable instructions stored in the memory which, when executed by the one or more processors, configure the one or more processors to: receive an input indicating that the unloading tube should be moved from the operating position to the storage position; identify a pivot zone that defines an area in which the unloading tube will move when moved by the actuator from the operating position to the storage position; and generate a control signal based on the determination.

The invention discloses a multi-warehouse logistics path optimization method and related equipment. The method comprises the following steps: acquiring warehouse data and customer data; constructing a distribution graph model according to the obtained data, and defining a Markov decision process; extracting homogeneous features and heterogeneous features of nodes in the distribution graph model, and fusing the extracted features and initial features of the nodes to form super-relation features; inputting the super-relation features into an encoder for feature extraction, and outputting a high-dimensional vector of each node; and the decoder gradually constructs a path decision sequence of vehicle distribution according to the vector provided by the encoder. According to the method, fine description of a heterogeneous relationship is introduced into a neural network model, and end-to-end automatic solution is realized in combination with reinforcement learning. According to the method, the warehouse distribution and path optimization strategy can be automatically learned, the warehouse distribution and path planning strategy can be automatically learned without manual parameter adjustment, a better transportation cost result can be obtained in a large-scale multi-constraint distribution scene, and the method has intelligent and practical values.

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 relates to a warehouse article management method, system and device based on industrial Internet of Things and a medium, and the method comprises the steps: building a warehouse three-dimensional model, updating the state of a goods location in real time, and combining the basic information of to-be-stored goods and the predicted storage time, and determining a candidate storage region and the priority of the candidate storage region; and determining an optimal storage area according to the area priority and the real-time storage state, planning an optimal storage path, and finally controlling the goods moving equipment to complete the storage operation of the stored goods. The technical problem that in the prior art, a high-density stereoscopic warehouse lacks a dynamic optimization capability based on goods basic information and predicted storage duration, and consequently storage space distribution is unreasonable is solved, and the purposes of monitoring the storage state in real time based on the industrial Internet of Things and improving the storage efficiency are achieved. And intelligent storage position optimization distribution is carried out according to the basic information of the goods and the predicted storage duration, so that the technical effect of reasonable distribution of the storage space is realized.

The invention relates to the technical field of cross-border logistics, in particular to an artificial intelligence algorithm for cross-border overseas warehouse location storage and picking path optimization, which comprises the following steps: S1, obtaining order details, commodity characteristics and warehouse environment data, and monitoring warehouse states and cargo storage conditions in real time; s2, data preprocessing is carried out, features are extracted, clustering analysis is carried out, and association rules are mined; s3, performing rule matching, constructing a mixed integer programming model, performing constraint processing, and outputting a commodity storage strategy; and S4, converting the storage strategy into a warehousing operation instruction, and executing the warehousing operation instruction. The method has the beneficial effects that the accuracy of goods classified storage and the space utilization rate of the warehouse location can be improved, and the storage cost of the warehouse location is reduced.

The invention discloses an automatic three-dimensional storing and taking warehouse which comprises a high-precision linear guide rail embedded in a stand column of a gantry lifting guide frame. The transverse conveying pick-and-place table drives a second chain through a servo motor, and the second chain drives the moving plate to move; the conveying, taking and placing platform is located between the gantry lifting guide frames, and the conveying, taking and placing platform moves up and down through the two symmetrical gantry lifting guide frames. In the using process, the dynamic avoiding module carries out calculation through data of a radar and infrared distance measuring sensors on the two sides of the conveying, taking and placing platform, so that the moving obstacle module is automatically monitored, and the situation of motion interference occurring when the conveying, taking and placing platform moves out a moving plate of a storage interlayer is avoided; collision accidents caused by the fact that the movable plate does not completely move out of the storage partition layer are avoided, and damage to the plates is avoided; and meanwhile, the optimal storage position and the fastest processing time are obtained according to the cooperation of the path planning module and the distribution of the collected plate quality.

The invention provides a railway automatic goods yard loading and unloading operation method, and relates to the technical field of railway goods yards, and the method is characterized in that a railway automatic goods yard goods loading and unloading operation system comprises a loading and unloading machine tool positioning system, a goods positioning system and a loading and unloading machine tool management system; the railway freight yard is provided with a freight car loading and unloading operation area, a warehouse operation area and a railway freight car loading and unloading operation area, and goods are loaded and unloaded in different areas. The loading and unloading machine tool management system controls the loading and unloading machine tools to carry out truck unloading operation and truck loading operation in the truck loading and unloading area, carry out warehousing operation and ex-warehouse operation in the warehousing operation area and carry out railway loading operation and railway unloading operation in the railway loading and unloading area. According to the method, rapid loading and unloading, efficient stacking and intelligent management of goods can be achieved, and the efficiency and competitiveness of railway transportation are improved; meanwhile, labor cost can be reduced, operation risks are reduced, and the automation degree of the railway freight yard is improved.

The invention relates to the technical field of radio positioning monitoring, in particular to a multi-warehouse cooperative state monitoring method and system for intelligent warehouse logistics docking. Comprising the following steps: acquiring real-time state data of a plurality of warehouses, including warehouse cargo position data, equipment state data, logistics vehicle position data and personnel distribution data; analyzing the data by adopting a multi-warehouse collaborative state evaluation model, and identifying potential logistics bottleneck points and risk points; and according to an identification result, generating a multi-warehouse collaborative optimization scheme including logistics path planning, a resource allocation strategy and an emergency processing suggestion. Wherein the evaluation model adopts a multi-level architecture, and comprehensive and accurate risk identification is realized through combination of static environment monitoring and dynamic environment monitoring. The system forms a complete optimization-implementation-evaluation closed loop, so that the multi-warehouse cooperative operation efficiency is improved, the logistics cost is reduced, the ability of the system to cope with demand fluctuation and emergencies is enhanced, and a reliable guarantee is provided for upgrading and optimization of the intelligent warehouse logistics system.

The invention relates to a commodity picking order generation system and method of a supplier end, and relates to the technical field of commodity logistics management, and the system comprises a path optimization module which combines similar orders through an AI optimization algorithm, segments tasks, and selects a shortest picking path; the placement recommendation module calculates associated commodities by using collaborative filtering and a depth algorithm, and realizes aggregation placement and one-commodity multi-position shelf recommendation; the inventory optimization module adopts a multi-level model, comprehensively associates warehouse inventory and automatically triggers a deployment instruction; the goods allocation optimization module dynamically allocates goods allocation according to commodity characteristics, and arranges a goods picking sequence according to a shortest path; the data prediction module integrates multi-source data to predict order trends, and pre-picking orders are generated in advance, correspondingly, the invention further provides a commodity picking order generation method for realizing the system, corresponding electronic equipment and a computer readable storage medium, and an efficient and intelligent picking order generation solution is provided for suppliers.

The invention discloses a multi-warehouse multi-logistics vehicle path planning method based on a self-adaptive neighborhood ant colony system, provides a self-adaptive warehouse selection strategy based on historical evolution information, autonomously selects a proper warehouse for each logistics vehicle, achieves the purpose of optimizing the position of the warehouse, and improves the path planning efficiency. A customer selection mechanism based on a self-adaptive neighborhood is designed for the ant colony system, so that the complexity of customer selection is effectively reduced; through continuous iterative optimization, a high-quality and high-precision multi-logistics-vehicle service path is finally output, and the service cost of each logistics vehicle is balanced as much as possible while the total service cost of all the logistics vehicles is reduced. According to the invention, solution is carried out through an ant colony optimization algorithm, and a self-adaptive warehouse selection strategy, a customer selection mechanism based on a self-adaptive neighborhood, an ant selection technology based on path estimation service cost and a path combination and segmentation strategy fused with 2-opt are designed, so that the quality of a path planning scheme is remarkably improved; and meanwhile, the time consumed for constructing the planning scheme is greatly shortened.

The invention relates to intelligent warehousing, and provides an intelligent warehousing system and a warehouse guiding control method, and the system comprises a cargo storage unit, a cargo conveying unit, a cargo sensing unit, a light-emitting positioning guiding device group, a positioning terminal, and a warehouse information processing system in communication connection with the cargo storage unit, the cargo conveying unit, the cargo sensing unit, the light-emitting positioning guiding device group and the positioning terminal. The positioning terminal collects signal light for matching the associated information set to determine the position of the terminal; a warehouse information processing system obtains cargo attribute information of cargos, and generates a planned route of warehouse-in / warehouse-out of the cargos based on a destination position corresponding to the cargo attribute information; and determining a group of target light-emitting positioning guiding devices according to the matching relationship between the position information of the light-emitting positioning guiding devices and the planned route, and instructing the target light-emitting positioning guiding devices to execute a light-emitting guiding action.

The invention discloses an overseas warehouse operation intelligent wave order management method and system, and the method comprises the steps: constructing a commodity incidence matrix, mining a frequent item set, determining the commodity correlation degree, carrying out the centralized picking of high-correlation commodities, reducing the round-trip times, shortening the walking distance, improving the picking efficiency, planning a picking path in combination with the warehouse layout and inventory information, and achieving the intelligent wave order management of the overseas warehouse operation. The method comprises the following steps: preferentially selecting short-distance commodities in the same area, further reducing the picking time, allocating priorities according to order emergency degrees and customer levels, ensuring that important orders are processed in time, improving customer satisfaction, dynamically adjusting the sequence in the picking process, avoiding invalid walking caused by stockout, enhancing path flexibility and adaptability, and collecting warehouse-in and warehouse-out information in real time. Inventory is updated immediately, data are ensured to be accurate and timely, labor intensity and operation time of picking personnel are reduced, labor cost is reduced, and storage equipment loss and maintenance cost are reduced.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.