19 results about "Container port" patented technology

The application discloses an intelligent filling system and filling method, and relates to the technical field of intelligent filling, which adopts a pressure sensor to monitor whether a container enters a filling table, triggers a terminal to generate a detection instruction, uses a camera cluster to collect images, extracts the positional relationship between a filling port and a container port, fits a standard and a to-be-filled plane graph and checks them to determine a filling condition, constructs a container panoramic image to determine the capacity and the filling amount if the filling condition is normal, and executes filling, wherein the scheme comprises a data acquisition, storage and processing module; the pressure sensor and the camera are cooperated to accurately control the filling process through an image processing technology, the filling precision and efficiency are improved, the scheme is suitable for various containers, manual intervention is reduced, and the production automation level is improved.

The invention belongs to the technical field of port vehicle scheduling, and discloses a dynamic scheduling and path planning method, device and equipment for trailers in a container port, and the method comprises the steps: carrying out the denoising of original positioning data, obtaining a lane-level continuous track based on the denoised positioning data, and carrying out the positioning of the trailers in the container port; setting a sliding scanning window along the road based on a lane-level continuous track, calculating a vehicle density index and a speed loss rate index in the window, judging the congestion level of each window, finally outputting real-time congestion level information, and carrying out path optimization based on the real-time congestion level information to obtain predicted arrival time; and a scheduling instruction is generated and issued by fusing the lane-level continuous trajectory, the real-time congestion level information and the predicted arrival time so as to realize scheduling and path planning. According to the mode, through dynamic identification and prediction of the lane congestion state and batch planning of the navigation optimal path and the estimation time, intelligent issuing of the scheduling instruction is guided, the inner trailer scheduling effect is optimized, and the operation efficiency of the trailers in the harbor district is remarkably improved.

The utility model relates to the technical field of channel construction, in particular to a container port channel construction water depth monitoring device which comprises an installation frame, a floating platform frame, a limiting assembly, a monitoring assembly and an assembly, the bottom of a monitoring probe and a clamping ring are installed and placed, a second magnet ring makes contact with a first magnet ring in a magnetic attraction mode, and a signal line passes through an inner through hole in the bottom end of an L-shaped plate. An inserting rod is installed in a through hole in the bottom end of an L-shaped plate, a threaded block is assembled with a thread in the top end of a column casing, the interior of the bottom end of the L-shaped plate and the interior of a semi-sector disc are assembled through external studs in a threaded mode, and the interior of an upper flexible plate, the interior of a wall plate and the inner wall of a channel are assembled through external anchoring studs. When the depth of the channel water body changes, the floating platform frame and the limiting assembly integrally move vertically, through the arrangement, the monitoring probe of the water depth monitoring equipment can be conveniently installed and placed through the device body, and the device body is convenient to assemble and disassemble.

The application provides a kind of pan-port area collection and distribution automatic driving vehicle formation task scheduling method and system, it is related to intelligent transportation system and port logistics technical field.The method includes system initialization and data acquisition to construct pan-port area digital twin model and global state space;Based on K-means clustering, task grouping and preliminary allocation are completed;Fusion multi-agent deep reinforcement learning MADRL model and auction mechanism realize vehicle collaborative bidding and decision optimization;Based on the action sequence output by auction, dynamic formation and path planning are carried out;Based on the gate reservation decision output by MADRL model, gate intelligent allocation and execution are carried out;Through performance evaluation and safety guarantee by executing monitoring and adaptive optimization.The application effectively promotes vehicle collaborative formation, realizes peak shaving in peak period, significantly improves the efficiency and safety of pan-port area collection and distribution, reduces energy consumption and carbon emissions, and is suitable for large container port pan-port area scene.

The application relates to a port scheduling simulation system and method considering automatic driving precision regulation and control, and the method comprises the following steps: S1, setting a vehicle detector in the simulation system, the vehicle detector being used for detecting vehicles entering a work point in real time; S2, setting a vehicle queue of a quay crane / yard crane loading and unloading service, and simulating a first-come-first-served service mode; S3, starting simulation, recording vehicle states and work equipment states at the end of each simulation step; S4, setting or removing a vehicle path in the simulation system; S4, setting / removing a parking line in the simulation system, so that parking work simulation is realized; S5, taking the vehicle states and the work states as inputs, and performing single-step solving of a model; and S7, taking a scheduling algorithm to be evaluated as an input of the simulation system, and performing joint simulation with the simulation system. Compared with the prior art, the application can accurately simulate an automatic container port work scene, so that the scheduling algorithm can be evaluated and tested, work efficiency is improved, and operation cost is reduced.

The application discloses a port loading and unloading efficiency prediction method based on deep learning and Internet of Things data fusion, and relates to the technical field of intelligent logistics, and comprises the following steps: S1, through deploying an Internet of Things terminal and a data transmission channel, synchronously collecting equipment operation state data, container attribute data, yard layout data, meteorological environment data and operation scheduling data; S2, pre-processing the collected data; S3, constructing a CNN-LSTM fusion model based on an attention mechanism and training; the model dynamic adaptation module is arranged, the CNN convolution kernel size and the LSTM hidden layer neuron quantity can be automatically adjusted, different scenes such as a container port and a bulk cargo port can be adapted, for different scales and different equipment configurations of the port, without additional customization and development, the existing Internet of Things terminal and the scheduling system can be directly connected, and the technical landing threshold and cost can be greatly reduced.

The embodiment of the invention provides a container port pressure rate analysis method and system, electronic equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: determining each evaluation index based on an evaluation index system of the container port pressure rate, determining an index value of each evaluation index according to ship data and port information, and obtaining an index weight distribution result of the evaluation index system; the index weight distribution result is obtained by performing weight distribution on each evaluation index through a combined weighting method of objective weighting and subjective weighting, subjective and objective factors are considered in the weighting process, and the rationality of the index weight distribution result is improved. And a fuzzy logic method and an evidence theory method are adopted to perform fusion calculation on the index values of the evaluation indexes according to the index weight distribution result to obtain the container port pressure rate, so that the container port pressure condition can be accurately quantified, and the reliability of quantitative calculation of the container port pressure rate is improved.

The present invention discloses a method, apparatus, device, medium, and product for calling an application, relating to the field of computer technology. The method comprises: if it is determined that the number of system users is at least two, obtaining candidate data source information associated with the candidate system user and first application container port information individually allocated for the candidate system user; updating the current container context parameters based on the candidate data source information and the first application container port information to generate first container context parameters corresponding to the candidate system user; generating a corresponding user sub-application container for the candidate system user based on the current container context type and the first container context parameters; wherein the candidate system user calls the application through the corresponding user sub-application container. The present invention achieves the effect of isolating the processes of system users calling applications from each other, reducing the risk of user data leakage when system users call applications, and improving the data security of the application system.

The embodiment of the present application provides a starting method and device of a container, comprising: detecting whether the network mode of the container is a host network mode; if the network mode is the host network mode, obtaining a plurality of container ports of the container; obtaining a host port which is not occupied; establishing a mapping relationship between the plurality of container ports and the host port which is not occupied, and starting the container based on the established mapping relationship. By using the technical scheme provided by the embodiment of the present application, the plurality of container ports of one container will not be mapped to the same host port, thereby reducing the degree of port conflict when a plurality of applications run in the container in the host network mode.

The application provides a construction method of a yard intelligent container receiving attraction model, comprising: establishing a UCT-MCTS framework; performing symbolic modeling based on the UCT-MCTS framework; performing target driving design based on the UCT-MCTS framework, and constructing a single-outlet container positioning total target function related to maximum container attraction, minimum repulsion and maximum yard tool attraction; performing hypothesis verification based on the UCT-MCTS framework, and determining the applicable conditions of the corresponding model; and performing constraint design based on the UCT-MCTS framework. The application also provides a scheduling method, which optimizes the positioning of outlet container storage of a container terminal through a yard intelligent container receiving attraction model, wherein the yard intelligent container receiving attraction model is a UCT-MCTS framework constructed by the construction method described above. The application also provides a computer readable storage medium for executing the above method. Through the construction of a multi-dimensional attraction model and an optimization algorithm, the application realizes accurate and efficient container positioning decision-making, and improves the operation efficiency of a container port.

The invention discloses a vacuum sealing cover compatible with multipath containers, which relates to the technical field of sealing covers and comprises a sealing assembly for sealing containers with different calibers. The sealing assembly comprises an end face sealing piece, a radial sealing edge and a compensation part. Through the cooperation of the end face sealing piece, the radial sealing edge and the compensation part, the caliber difference in different containers is matched, when the inner wall of a container port does not make contact with the outer wall of the radial sealing edge, the compensation part arranged on the radial sealing edge makes contact with the outer wall of the container port, the compensation part bends and deforms towards the end face sealing piece, and therefore the container port is sealed. The radial gap between the radial sealing edge and the outer wall of the container port can be filled through elastic deformation of the compensation part, so that radial sealing can be formed on the outer wall of the container port through the compensation part, the sealing cover can be directly used for existing containers with different calibers, direct vacuum preservation can be achieved without matching with a matched storage container, and the service life of the sealing cover is prolonged. And the method is high in expansibility, high in adaptability and very convenient.

The invention relates to the technical field of port logistics optimization, in particular to a bulk grain wharf continuous berth allocation and ship unloader joint scheduling method, which is characterized in that a hybrid intelligent optimization system is constructed by fusing a multi-objective evolutionary algorithm and the semantic reasoning capability of a large language model, and multi-objective collaborative optimization of berth allocation, ship unloader scheduling and energy consumption control is realized. And the method is suitable for complex logistics scenes such as bulk grain wharfs and container ports which need to consider both operation efficiency and energy consumption.

The invention belongs to the technical field of underwriting risk control, and particularly relates to a real-time underwriting risk control method, system, equipment and medium based on containerized deployment, and the method comprises the steps: packaging a trained underwriting risk control model, a preprocessing module and an interface service script into a Docker mirror image; the method comprises the following steps: deploying a Docker mirror image as a container, and configuring: when each container is started, a FastAPI service is automatically bound to a port; mapping a container port into a cluster unified entry; an external request is distributed to the container through load balancing and routed to a corresponding FastAPI interface, the preprocessing module is triggered to analyze data, and an underwriting risk control model is called to calculate a risk score; the returned risk score dynamically adjusts the underwriting strategy; when the risk score exceeds a threshold value, an underwriting rejection decision is returned; and when the risk score is lower than a threshold value, a low insurance premium or standard insurance premium suggestion is returned. The concurrent processing capability and the response speed of the system are improved, and the stable operation of the real-time underwriting risk control service is ensured.

The invention discloses a container terminal multi-ship collaborative scheduling automatic container distribution method based on a CWP algorithm, and relates to the technical field of container terminal automatic container distribution, and the method comprises the following steps: S1, a bridge crane resource arrangement model: based on a principle of maximizing bridge crane resource utilization and avoiding resource waste, reasonably arranging bridge crane resource planning information of a whole terminal, and establishing a bridge crane resource arrangement model; comprising time plan details of a bridge crane used by a ship, and a suggested maintenance plan of the bridge crane is given; s2, a multi-ship CWP algorithm model is established, on the premise that bridge crane resources in the step S1 are determined, graph box information of inlet and outlet ships, bridge crane performance and ship structures are comprehensively considered, and CWP plans of multiple ships are calculated at the same time; s3, deducing an algorithm model in real time through CWP, and deducing a follow-up plan in real time according to the actual operation efficiency and the actual completion amount of the bridge crane after the ship berths and starts operation; s4, a storage yard thermal prediction algorithm model; and S5, establishing a multi-ship automatic box distribution algorithm model.

The application discloses a self-recovery fault-tolerant emergency regulation method for an automated container port. The method comprises the following steps: combing port production operation business association and process paths, and constructing a multi-priority task sequence of a current shift of the port; collecting historical data of a port operation system and equipment, and realizing state feature extraction and hierarchical classification of a port emergency; constructing a port state transition event flow model and a port system T-S fuzzy fault tree, and evaluating and identifying a current port fault state; based on multi-priority task driving of the shift, combining a feedforward-feedback control thought, and according to the identified state of the fault point and emergency information, self-recovery fault-tolerant emergency regulation is carried out at the system and equipment levels. The application effectively improves the self-recovery fault-tolerant capability and online regulation capability of the automated container port, and comprehensively improves the decision-making level, operation efficiency, production income and core competitiveness of the port.

The present invention discloses a cluster-based strategy-based joint planning method and system for container port berth and yard allocation. The method comprises: collecting information on ships, container trucks, and yards during a target time period, including the planned arrival time of ships, the number and type of containers on board, the stockpiling status of the yard, and the planned arrival time of external container trucks; inputting the collected information into a pre-established multi-objective mixed integer programming model to calculate an optimal planning strategy, including a berthing plan for all ships and a loading and unloading plan for container batches during the specified time period; wherein the pre-established multi-objective mixed integer programming model includes three objective functions, namely, minimizing the expected turnaround time for ship berthing, minimizing the total container transport distance between berths and container areas, and minimizing the workload imbalance in the container areas of the yard; and executing ship and yard operations according to the optimal planning strategy. The present invention can improve the overall operational efficiency of the terminal.

The application relates to the technical field of electronic atomization, in particular to a liquid storage container and an atomization device. The liquid storage container comprises a container main body, a piston, a liquid guide structure and a driving module. The container main body has a container inner cavity and a container port. The piston is movably arranged in the container inner cavity and is sealingly connected with the cavity wall of the container inner cavity to divide a liquid storage space in the container inner cavity, and the liquid storage space is communicated with the container port. The liquid guide structure is communicated with the liquid storage space and the container port. The driving module comprises an elastic member arranged in the container inner cavity and used for providing elastic force for driving the piston to move in the direction of compressing the liquid storage space, a locking unit configured to lock the piston at an initial position in an initial state, and an operating member at least partially exposed to the container main body and linked with the locking unit and used for responding to a trigger action to release the locking of the piston by the locking unit, so that the piston is driven by the elastic member to move to compress the liquid storage space, and the atomization liquid is pressed out from the container port through the liquid guide structure, which helps to improve the atomization liquid supply speed at the initial use.

This invention relates to the field of port automation technology and discloses an intelligent scheduling and navigation method and system for unmanned vehicles in container ports. The invention utilizes a console system for unified access control and map data management; a monitoring system for real-time collection and display of vehicle status, trajectories, and operational statistics; a path planning system based on the A* algorithm and dynamic scene parameters to plan optimal paths for vehicles and manage long and short paths; a PB scheduling system for dynamically prioritizing vehicle passage at key intersections; a charging management system for predictive charging scheduling; a parking management system for intelligent allocation of parking spaces; and a lock station scheduling management system for coordinating lock loading / unloading tasks. This achieves unified scheduling and navigation of all elements of unmanned vehicles within the port. This invention effectively solves the problems of poor system coordination, insufficient intelligent scheduling, and inefficient resource management in existing technologies, significantly improving port operational efficiency, resource utilization, and system scalability.

The present application relates to the field of path planning technology, and discloses a path planning and control method and system for autonomous container port vehicles. The method comprises: collecting environmental data through multi-source sensors and fusing them through spatiotemporal registration to obtain a digital twin model of the port area; optimizing and generating primary and backup path plans based on the model using a three-layer progressive planner; predicting dynamic target trajectories using a temporal attention network to obtain situation assessments; processing assessment data using a distributed predictive control algorithm to generate control instructions; and forming a safety strategy through adaptive safety constraint monitoring of motion states and continuously optimizing it using deep reinforcement learning. The present application enables the safe and reliable operation of autonomous vehicles in complex and changing port environments, and is particularly capable of making accurate path planning and control decisions when faced with dynamic obstacles.