116 results about "Logistics center" patented technology

The invention discloses a logistics center intelligent optimization scheduling system based on data analysis, and relates to the technical field of intelligent optimization scheduling, during operation of the system, key data in logistics center operation is collected and processed, after preprocessing, the inventory state of a logistics center is managed and optimized, and based on real-time inventory data and order demand information, the logistics center intelligent optimization scheduling system based on data analysis is obtained. Based on real-time traffic data, order demands and vehicle positions, calculation is carried out to obtain a comprehensive optimization index SOF, according to the order demands, the cargo capacity, the vehicle capacity and the real-time positions, vehicles are intelligently distributed, the loading sequence and the loading rate are optimized, and through the Internet of Things technology and a data analysis tool, the real-time traffic data and the real-time traffic data are analyzed. And the vehicle position, the transportation state, the environment change and the order processing progress are monitored in real time, the scheduling optimization level of the logistics system is evaluated in real time by comparing the comprehensive optimization index SOF with a preset threshold value, and an abnormal early warning and feedback mechanism is provided.

The invention relates to the technical field of data processing, in particular to a three-dimensional logistics center modeling method and system based on digital twinning, and the method comprises the steps: carrying out the voxelization of a point cloud of a logistics center to obtain the initial features of all voxel grids, constructing a multi-layer point cloud pyramid, determining the fusion features of the voxel grids, and carrying out the modeling of the three-dimensional logistics center based on digital twinning. Calculating feature similarity based on the fusion features and the interlayer distance, constructing a mapping relation based on the feature similarity, updating the fusion features of the corresponding voxel grids of the voxel grids and calculating interlayer propagation factors, screening significant feature points based on the interlayer propagation factors, determining an initial matching point pair set according to the significant feature points, and performing matching on the initial matching point pair set according to the initial matching point pair set; an iterative nearest point algorithm is used to initialize and register the three-dimensional point cloud to obtain a three-dimensional point cloud model, and a dynamic target object in the video data of the logistics center and the three-dimensional point cloud model are registered and fused, so that the actual change state of the logistics center can be reflected in real time, the point cloud registration precision is improved, and the real-time performance of the logistics center is improved. And the modeling accuracy of the three-dimensional logistics center is improved.

The invention discloses a logistics center flame smoke detection method, apparatus and device, and a storage medium. The method comprises the steps of obtaining a training data set; an initial YOLOv12 model is constructed, the model comprises a multi-scale large kernel attention mechanism module embedded in front of the detection head, and the multi-scale large kernel attention mechanism module is used for performing convolution processing on the fusion feature map through a multi-scale convolution kernel to obtain an enhanced feature map highlighting key features; performing optimization training on the initial YOLOv12 model based on the training data set to obtain a target YOLOv12 model, and in the optimization training process, adopting a Focaler-IoU loss function as a loss function; and inputting the target to-be-detected image into the target YOLOv12 model for detection, and determining the flame smoke type of the target to-be-detected image based on the output type of the target YOLOv12 model. According to the scheme, the recognition accuracy of the small target is improved.

The invention relates to the technical field of logistics personnel safety protection methods, and discloses an intelligent identification method for personnel tumbling in a logistics center. Comprising the following steps: acquiring a picture sample monitored by a logistics transfer center camera, pre-processing the picture sample to generate a pre-processed picture sample, and labeling the pre-processed picture sample through LabelImg; the method comprises the following steps: training a YOLOv11 model through an IoU-based losses loss function; training a human body posture estimation OpenPose model, and adjusting the architecture of the OpenPose model according to personnel characteristics; the trained YOLOv11 model and the trained OpenPose model are fused, and a personnel wrestling identification model is obtained; pictures monitored by a logistics transfer center camera are input into a personnel wrestling recognition model frame by frame, whether personnel wrestling exists in the pictures or not is judged through the personnel wrestling recognition model, if yes, an alarm is triggered, monitoring personnel are notified through an audible and visual alarm, and related images and video clip storage records are stored; the system has the advantages that the working state of the employee is monitored in real time, wrestling events are found in time, and therefore the safety management level and the response speed are improved.

The invention relates to an arrangement for introducing a roll container into a roll container loading station, in particular for filling the roll container with sorted packages in a logistics centre, wherein the roll container is, for example, a forwarding container or an intermediate container, wherein the arrangement has a transport system for transporting roll containers, wherein the transport system connects a receiving location for empty roll containers to the roll container loading station, wherein the transport system has an end position for roll containers transported with the transport system in a loading position of the roll container in the interior of the roll container loading station, and the arrangement has a sensor system with which, in the loading position of the roll container in the interior of the roll container loading station, at least one relative position between the roll container and at least one reference point in the interior of the roll container loading station is determined. Furthermore, a corresponding method is described.

The invention discloses an intelligent supply chain logistics management method, and the method comprises the steps: generating a multivariate supply feature vector corresponding to a predicted supply time period based on the logistics information of each to-be-scheduled logistics order, inputting the multivariate supply feature vector into a pre-trained supply time prediction model, and outputting the target supply time of the to-be-scheduled order; obtaining a difference value between the order maximum time limit of the to-be-dispatched logistics order and the target supply time of the to-be-dispatched logistics order, taking the difference value as a supply emergency value, and if the supply emergency value is smaller than a preset time threshold, marking the to-be-dispatched logistics order as an abnormal order; and carrying out descending sorting to obtain an abnormal order sequence, and based on the abnormal order sequence, notifying a logistics center to adjust logistics information of the abnormal order sequence, and carrying out distribution on the abnormal order sequence. Therefore, the multivariate supply feature vectors of the to-be-dispatched logistics orders are introduced, the factors influencing the logistics supply time are considered in multiple dimensions, the accuracy of the prediction model is improved, and the prediction model can dynamically adapt to the actual change of supply chain logistics.

The invention provides a multi-point navigation and target detection algorithm based on laser radar and point cloud image processing fusion of an ROS operating system, and relates to the technical field of mobile robot navigation and target detection. The algorithm is based on an ROS operating system and is composed of a laser radar, a data storage and management module, a point cloud image processing module, a traversal and path planning module and a navigation and target detection module. The laser radar scans the environment in real time and transmits data to the ROS operation system to generate point cloud map data; the image processing module is used for filtering the point cloud image stored in the navigation process, so that the accuracy of the map is improved; and the traversal and path planning module calculates the parking position and direction of the robot according to the processed point cloud map data and performs path planning, thereby realizing accurate navigation and target detection. The method has the advantages of real-time map generation and storage, point cloud image processing and calculation, accurate path planning, strong adaptability and the like, and is suitable for industrial automation, unmanned warehouses and logistics centers, search and rescue, agricultural application, intelligent inspection, unmanned driving and other complex or dynamic unknown environments.

An inventory management system for a fulfillment center that supports rapid scanning of inventory items using a handheld electronic device. The system comprises a processor, a non-transitory memory storing a plurality of inventory transaction tables, a data verification table, and a committed transaction table, an inventory application stored in the non-transitory memory that, when executed by the processor receives a plurality of scanned code inputs from a mobile application executing on a handheld scanner, writes each received scanned code input into an inventory transaction table, processes each received inventory transaction table, writes processed asset transactions to the data verification table, and in response to receiving a command to commit processed asset transactions, committing processed asset transactions to the committed transaction table.

The invention provides a cold storage type multi-temperature co-transportation path optimization method considering carbon emission under a time-varying road network, and the method comprises the steps: obtaining vehicle resources of a logistics center, and the product quantity and position information of a pickup place, and building a time dependence function of travel speed and a calculation function of carbon emission cost based on a current pickup transportation network; analyzing the satisfaction degree of the product quality based on the value loss generated in the process of driving from each pickup place to the logistics center; based on the calculation function of the carbon emission cost, a multi-objective optimization model with the purpose of minimizing the total transportation cost and maximizing the product satisfaction degree is constructed, and constraint conditions of a multi-temperature co-transportation path planning model are constructed based on vehicle resources and goods taking place information; and solving the multi-objective optimization model by using a two-stage optimization method combining K-means multi-dimensional clustering and a fast non-dominated sorting genetic algorithm to obtain the optimal departure time and transportation path. According to the method, the energy efficiency utilization rate in the cold chain transportation process is improved, and the distribution path planning is more scientific and reasonable.

The invention discloses a logistics park distribution service method. The method comprises the following steps: constructing a path initial solution; acquiring a closed loop around the logistics center; a minimum vehicle type service method is adopted to obtain a plurality of path initial solutions which take the logistics center as a starting point, pass through at least one service site and return to the starting point, and the service sites are adjacent in the sequence of the closed loop; carrying out boxing design on each path initial solution to obtain a path intermediate solution; when boxing is feasible, outputting the path intermediate solution; when encasement fails, increasing the vehicle type for encasement design until encasement is feasible, and outputting the path intermediate solution; if boxing is still failed when the maximum vehicle type is reached, the service place where boxing is failed is removed, the path intermediate solution is output, and the removed service place independently sets the distributed path initial solution; and optimizing the path intermediate solution by adopting a self-adaptive large neighborhood search algorithm in combination with a simulated annealing algorithm to obtain a final path solution. The vehicle distribution and loading efficiency is improved, and the distribution cost is reduced.

The invention discloses a tobacco retail terminal dynamic sorting priority generation method, and belongs to the technical field of logistics sorting optimization, and the method comprises the steps: carrying out the multi-dimensional feature coupling analysis of the geographic coordinates, order time windows and real-time traffic flow data of a retail terminal through an improved space-time clustering algorithm, and obtaining a dynamic sorting priority of the retail terminal; dynamically generating a sorting batch matched with the topological structure of the distribution road network; evaluating sorting priorities by combining a multi-objective optimization function: constructing a dynamic priority evaluation model, calculating a sorting sequence in real time, and synchronously generating a global optimal distribution path planning scheme by combining a business rule; and closed-loop parameter optimization is realized through a reinforcement learning framework: the reinforcement learning framework is introduced to carry out iterative optimization on a sorting strategy, clustering parameters and priority weights are dynamically adjusted through historical operation data feedback, and a perception-decision-execution closed-loop optimization link is formed. According to the invention, global dynamic optimization of the sorting sequence and the distribution resources is realized, and the sorting efficiency of the logistics center and the utilization rate of the distribution resources are improved.

The embodiment of the invention relates to the technical field of sorting, and discloses a sorting management method based on artificial intelligence, and the method comprises the steps: obtaining the real-time operation data of a physical logistics center; inputting the real-time operation data into an operation and maintenance large model, and generating a first operation and maintenance instruction by the operation and maintenance large model based on semantic understanding of the operation data; in the digital twin, executing a simulation operation based on the first operation and maintenance instruction to generate simulation operation and maintenance information; calculating systematic deviation based on the simulated operation and maintenance information and actual operation and maintenance information acquired from a physical logistics center; and generating and outputting a second operation and maintenance instruction subjected to deviation correction to the physical logistics center by the target large model so as to carry out sorting management operation. According to the method provided by the embodiment of the invention, the decision-making process is processed by the operation and maintenance large model and the target large model. The models perform semantic understanding and decision generation based on mass data and complex algorithms, so that sorting management is based on objective data analysis and model deduction, and the decision is more scientific, stable and quantifiable.

To provide an item location setting device, an item location setting method, a logistics system, a program, and a recording medium which can contribute to improvement of work efficiency as the whole of a system while flexibly coping with variation of items being objects of logistics in a process preceding article collection after arrival of items at a logistics center or the like before transfer to an item collection process.SOLUTION: The item location setting device comprises: an item attribute definition unit which defines an item attribute having an order attribute including an order period with respect to an item to be collected; and a location setting unit which refers to the defined item attribute to set a location to which the item is to be supplied.SELECTED DRAWING: Figure 4

Disclosed are an electronic apparatus for managing logistics information and a method therefor. A logistics information management method performed by an electronic apparatus, according to one disclosed embodiment, may comprise the steps of: transmitting, to a first terminal of a seller, item order information comprising information about an ordered item and an intermediate logistics center; checking, through communication between the first terminal, and a second terminal of the intermediate logistics center, whether there is an error in a first procedure for item delivery from the seller to the intermediate logistics center; and when there is no error in the first procedure, checking, through communication between the second terminal, and a third terminal of the final logistics center, whether there is an error in a second procedure for item warehousing from the intermediate logistics center to the final logistics center.

The invention relates to the field of logistics, and discloses a logistics center network fault automatic diagnosis method, device and equipment and a storage medium, and the method is used for improving the accuracy and robustness of network fault diagnosis. The method comprises the following steps: preprocessing collected logistics center network data; extracting samples from the preprocessed network data set by adopting a self-service sampling method in advance to form a preset number of sub-data sets, constructing a decision tree for each sub-data set, and randomly selecting part of features from numerous dimension features for splitting at each node of the decision tree; traversing each decision tree, introducing a long short-term memory network model on each node of the decision tree, and constructing a decision tree-LSTM model cluster; each decision tree-LSTM model in the decision tree-LSTM model cluster is trained, and a trained decision tree-LSTM model cluster is obtained; and calling the trained decision tree-LSTM model cluster to diagnose and predict currently input logistics center network data, and outputting a diagnosis and prediction result.

A system for managing a motor vehicle parking area comprises a plurality of physical media bearing a unique identifier selected from among the group consisting of a barcode optical identifier, a QR code, or an RFID tag. Each physical medium is arranged on or in a motor vehicle. The system further comprises at least one camera for filming the vehicles, an analysis means running a program for analyzing the images sent by the camera to extract the unique identifiers, and a means for wireless communication between the analysis means and at least one human or robotic conveyor moving or transporting the vehicles associated with the unique identifiers, so as to send to the at least one conveyor a digital message indicating the destination address of the vehicle. A method may be employed using the system to manage the motor vehicle parking area.

The invention relates to the technical field of application of recuperating medicine boxes, in particular to a portable recuperating medicine box which comprises a tool storage layer and a medicine storage layer, the tool storage layer of the portable recuperating medicine box plays a role of a'logistics center 'in the recuperating medicine box, and tool grids and cup grooves are formed in the tool storage layer at the same time; the two requirements of'recuperating equipment 'and'medicine taking and water drinking' are integrated on the same horizontal level. The tool grid is a square deep cavity and exactly contains the tool carrying box, and the tool carrying box can be integrally drawn out and independently carried, so that required instruments only need to be placed in the box at one time before going out, repeated rummaging is not needed during subsequent multiple times of going out, and omission is avoided. The cup groove is located beside the tool grid, a user can directly take the cup and take medicine on the same side after completing operations such as blood pressure measurement and blood sugar measurement, the moving line is shortened, and the risk of instrument pollution caused by walking back and forth is reduced. Positioning sleeves are reserved at the four corners of the top face of the tool storage layer, and an inserting reference is provided for the upper medicine storage layer.

The invention relates to the technical field of controlled atmosphere transportation, and particularly discloses a controlled atmosphere transportation storage box which comprises a box body, a sealing door is arranged on the front face of the box body, a containing frame is arranged in the box body, an automatic feeding and discharging mechanism is arranged on the front face of the containing frame, and a plurality of containing plates are arranged in the containing frame. And automatic detection mechanisms are arranged at the inner top of the placement frame and the bottoms of the multiple placement plates. According to the food packaging box, through the arranged automatic feeding and discharging mechanism, food placed in the box body can be rapidly fed, the food in the box body can be rapidly taken out and discharged, fast feeding and fast discharging of the food are achieved, the opening time of a sealing door is shortened to the maximum extent, and the stable air conditioning environment is maintained; when the box body is used on a logistics center or a production line, the time consumption of single operation is reduced, the turnover frequency of a single box body is increased, and therefore the transportation or storage amount in unit time is increased.

The application discloses a method for selecting a logistics center based on a hybrid clustering algorithm, which comprises the following steps: step 1, combining the process of finding density extreme points of a MeanShift algorithm with the process of distributing non-density extreme points of a DPC algorithm to control the number of clustering; step 2, performing data cleaning on logistics center data; step 3, obtaining a real road site distance data matrix according to application data and by means of a third-party map platform; step 4, combining the real road site distance data matrix A according to step 1; step 5, establishing a joint model of clustering density extreme points and maximum express quantity data, and maximizing the express quantity within a threshold T and minimizing the sum of distances from sites to the density extreme points by means of a maximization algorithm; and step 6, fusing the processed MS-DPC algorithm with the joint model, and finally obtaining a prediction result of the address of the logistics center by means of model training and testing. The application solves the misclassification problem that may occur in the current hybrid clustering algorithm.

The invention relates to the technical field of logistics management, and particularly discloses a logistics order delivery management method and system based on data processing, and the method comprises the following steps: S1, obtaining a delivery time period preset by a receiver through a user terminal connected with a logistics center, and recording the delivery time period as a door-to-door time period; s2, sorting and numbering the receiving addresses according to the distance between the receiving addresses and the logistics center; obtaining a path segment and a real-time delivery speed, and sequentially delivering each receiving address; s3, setting a distance threshold value, and recording that a plurality of receiving addresses of which the lengths of path sections between every two path sections belong to the distance threshold value are located in the same area; the receiving addresses in the same area are divided into non-key addresses and key addresses, and the key addresses are distributed preferentially; s4, for each key address, preferentially distributing the key address meeting the constraint; telephone communication between the courier and the recipient is reduced, and the efficiency of logistics distribution is improved.

The present invention discloses a logistics intelligent management system and method based on data analysis, which relates to the field of logistics intelligent management technology. The present invention comprises: S10: obtaining logistics demand data of a logistics platform and logistics transportation data of each logistics company, analyzing the spatial and temporal fit between the logistics demand data and each logistics transportation data, and conducting preliminary screening of logistics centers that meet the requirements of the logistics platform; S20: analyzing the execution strength of each logistics center retained after the preliminary screening on each logistics order; S30: intelligently allocating logistics orders of the logistics platform. The present invention intelligently generates allocation decisions for logistics orders corresponding to the end points of various transportation routes based on the execution strength of each logistics center on logistics orders. This process does not require manual docking with logistics companies to obtain the transportation costs and transportation efficiency provided by the logistics companies. While improving the intelligent effect of the system, it also improves the system's efficiency in allocating logistics orders.

Provided is an energy management system for enhancing energy efficiency in a logistics center based on accuracy of forecast data including: a data collection server configured to collect data related to energy management of the logistics center; and an energy management server configured to manage energy within the logistics center using the collected data.

An inventory management method and a system are provided. An inventory management method according to one embodiment of the present disclosure is a method performed by a computing device, comprising: receiving a first request order quantity of a first logistics center for a product of a first inventory management unit and a second request order quantity of a second logistics center for a product of the first inventory management unit; receiving the storage capacity status of each of the first logistics center and the second logistics center and identifying the first logistics center whose storage capacity has been exceeded; subtracting a multiple value of the minimum transfer unit quantity of the product of the first inventory management unit from the first request order quantity and obtaining a first quantity; summing the first quantity and the second request order quantity and obtaining a second quantity that can be allocated to the second logistics center; and rounding the second quantity to a multiple value of the minimum transfer unit quantity and obtaining a final transfer quantity to be transferred to the second logistics center, wherein the second logistics center may be a logistics center whose storage capacity has not been exceeded.

This application discloses a box-packing device based on logistics transfer, including a conveying assembly. The conveying assembly includes a conveying mechanism at the top, with multiple sets of conveyor belts below the conveying mechanism moving in the opposite direction. A box-stabilizing mechanism is provided at the discharge port of the conveying mechanism. The device also includes a transfer assembly, which includes a transfer tray positioned near the discharge port of the conveying mechanism. A receiving rack is arranged in a circular array on the transfer tray, with a receiving groove on one side of the receiving rack. A limiting mechanism is provided inside the receiving groove, and a pushing mechanism and a lifting mechanism are provided inside the receiving rack. Thus, by placing the conveying mechanism at the top, combining it with multiple sets of conveyor belts below for flow diversion, and using a transfer tray with a circular array of receiving racks for box transfer, vertical space is effectively utilized. This device can significantly reduce the floor area occupied by traditional horizontal conveyor belt layouts, improve site utilization, and thus optimize the overall operational efficiency of the logistics center.

A logistics center system is provided. A logistics center system according to one aspect of the present specification comprises a first space in which an internal temperature is set to a first temperature, a second space in which an internal temperature is set to a second temperature which is higher than the first temperature, a control space formed between the first space and the second space, and a conveying means for conveying logistics between the first space, the control space, and the second space, wherein the internal temperature of the control space is set to a third temperature which is a temperature between the first temperature and the second temperature.

The present invention provides a method and device for classifying hanging clothes in a logistics center based on a video stream. The steps of the method include receiving the input video stream, identifying the clothes in the video stream based on a preset deep learning model, and marking the bounding boxes of the clothes in the image data of the video stream; sequentially numbering the bounding boxes of the clothes based on the appearance order of the clothes in the image data of the video stream; dividing the image data of the video stream into multiple first image frames, and cropping the first image frames based on the bounding boxes to obtain boundary images; for the boundary images with the same bounding box number, extracting the image features in the boundary images, assigning different weights to the image features in the boundary images of different scales based on the scale of the boundary images, and performing feature fusion on the boundary images with the same bounding box number based on the attention mechanism to obtain a fused image; inputting the fused image into a preset convolutional neural network classifier to obtain the category of the fused image.

The present invention relates to a side loading / unloading dock leveler that is connected to a wing body truck in a logistics center or warehouse to compensate for the height difference between the loading space and the building, thereby increasing the efficiency of loading and unloading during logistics operations. This side loading / unloading dock leveler includes a fixed frame part installed in a groove of a side wall forming a floor surface on the upper side, a movable plate part pivotably hinged to the upper side of the fixed frame, a cylinder part that makes the movable plate part parallel to the fixed frame part when the piston rod advances and makes the movable plate part perpendicular to the fixed frame part when the piston rod retracts, and a control part connected to the cylinder part to supply hydraulic pressure to the cylinder part to advance or retract the piston rod.

1. The name of this design product: Cage truck handling robot. 2. Application of this product design: It is used in logistics cage truck handling scenarios and is applied to the main line distribution and branch line distribution links of logistics centers such as parcels, express delivery, and e-commerce items. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The invention relates to the technical field of new energy system optimization, and discloses a new energy station and a hydrogen energy coordination method. The new energy station comprises a hydrogen production power generation center, a renewable energy power generation center, a logistics center and a control center. The control center is connected with the hydrogen production and power generation center, the renewable energy power generation center and the logistics center; and the hydrogen production power generation center is also connected with the renewable energy power generation center. Operation of the hydrogen production and power generation center, the renewable energy power generation center and the logistics center is coordinated through the control center, uncertainty of renewable energy output and truck demand is considered at the same time, and the energy utilization rate and the income of a new energy station are improved.

Disclosed is an item allocation method of an electronic device. The item allocation method comprises the steps of: acquiring, from a user terminal, information about a first order including information about one or more items; on the basis of inventory information for zone groups in a plurality of fulfillment centers, identifying, among the zone groups, a plurality of zone groups in which there is inventory of one or more SKUs corresponding to the one or more items; determining candidate zone groups corresponding to each of one or more SKUs among the plurality of zone groups on the basis of information about an expected arrival time corresponding to the plurality of zone groups, inventory information for the plurality of zone groups, information about the location of a fulfillment center including the plurality of zone groups, and information about a cost according to a packaging method set for the plurality of zone groups; and determining an allocation method for minimizing the number of parcels packed with the one or more SKUs and allocating the one or more SKUs to one or more of the candidate zone groups.