1381 results about "Wharf" patented technology

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention discloses a wharf shoreline terrain reconstruction method based on remote sensing data, and belongs to the technical field of three-dimensional terrain reconstruction. The method comprises the following steps: acquiring a multi-temporal multispectral remote sensing image, a synthetic aperture radar image, a laser radar point cloud and synchronous tidal water level data of a target wharf area; processing the multispectral image to generate an initial land and water segmentation map; carrying out tide correction by utilizing the tide water level data, fusing the multi-polarization characteristics of the synthetic aperture radar image, and correcting the ground feature type to obtain an accurate land and water boundary diagram; processing the laser radar point cloud to generate a digital surface model, extracting a shoreline contour with sub-pixel-level precision from the digital surface model, and endowing the shoreline contour with an elevation value; and superposing the shoreline contour with the elevation with the multispectral image to generate a three-dimensional terrain model. According to the method, the problems of low terrain reconstruction precision, large tide influence and insufficient automation degree of a traditional method in a complex wharf environment are solved, and high-precision and high-efficiency wharf shoreline three-dimensional automatic reconstruction is realized.

The invention belongs to but is not limited to the technical field of wharf dispatching, and particularly relates to a dry bulk wharf whole-process multi-operation-line equipment cluster collaborative configuration method and system. The operation time of a ship and a train at a wharf is associated with collected ship attributes, train attributes, berthing position attributes, train operation line position attributes and equipment attributes; establishing a feature extraction model, and defining an underlying rule base as a basic tool; then designing a hyper-heuristic double-layer framework, and generating or selecting a bottom-layer rule by using a deep Q network as a decision-making tool at a high layer; the model is dynamically trained and optimized, so that the model is more adaptive to a wharf scene, and the stability is enhanced. And outputting an equipment cluster coordination configuration scheme by taking the minimum sum of the actual operation time of the ship and the train and the predicted operation time difference as a target according to the model and the rule, and updating in real time to realize efficient coordination in the dynamic wharf environment.

The invention relates to a ship berthing position detection method and system based on shore end laser radars, and the method comprises the steps: deploying a plurality of laser radars at a shore end, and collecting original point clouds in parallel through an asynchronous thread model; constructing a rigid transformation matrix based on the radar installation attitude parameters, and further generating fusion point cloud data under a unified world coordinate system; then extracting 3D geometric edge points in a preset range of the front edge of the wharf as near-shore point cloud data, and further calculating the minimum ship-shore distance; based on the ship bow direction and a preset segmentation threshold value, the coastal point cloud data are segmented into ship bow area point cloud and ship stern area point cloud in the x-axis direction, the coordinate mean value of the ship bow area point cloud and the ship stern area point cloud is calculated and compared with an expected position in a wharf operation system, and berthing accuracy is judged; and the drift distance is calculated in real time, the drift distance, the attitude angle and the change rate of the attitude angle are compared with corresponding preset threshold values respectively for graded early warning and drift alarm, and high-precision ship berthing position dynamic detection is achieved.

The invention provides a light-weight aerial ship image target detection method and device based on yov8n and a storage medium. According to the scheme, the method comprises the following steps that an image data set of an aerial ship is obtained and preprocessed; the method comprises the following steps of: constructing a network model of the improved yov8n, replacing part of an original CBS module with a GAC module, replacing part of an original C2f module with a C2fADWC module, modifying a detection head and a loss function, and selecting a rotating frame OBB; the training set is input into the improved yolov8n-obb model to be trained; a trained yolov8n-obb improved model is obtained; and inputting the test data set into the trained improved lightweight yolov8n-obb model to obtain a ship detection and identification result. According to the method, the ship in the aerial ship image can be more accurately identified, the wharf and the ship can be better distinguished, and the detection precision is remarkably improved while extremely little parameter quantity is increased.

The invention discloses an automatic wharf operation delay propagation prediction method fusing anomaly perception and transfer entropy analysis, which comprises the following steps: on the basis of an operation time sequence of operation equipment, introducing anomaly characteristics of the operation equipment, constructing an enhanced time sequence set, and enhancing the response capability to sudden disturbance in a prediction process; a candidate condition variable set is selected through a recursive feature elimination algorithm, so that the calculation complexity and the over-fitting risk caused by a high-dimensional variable space are effectively reduced, and the problem of curse of dimensionality is relieved; by constructing the delay propagation atlas, the mutual relation between operation equipment can be indicated, and intuition is achieved; a multi-stage operation delay conduction model is constructed according to the transformation probability of the operation equipment in different states, so that the influence of the equipment in different states on the port transportation process can be reflected more accurately; by using the causal relationship between the operation devices in the delay propagation map, how to propagate between the operation devices during delay propagation can be more clearly understood.

The invention provides a container shipping stowage method and system based on a multi-stage optimization algorithm, is applied to the technical field of operational research and port logistics, and decomposes a complex stowage process into a plurality of optimization stages based on a four-stage mixed integer programming method. The method comprises the following steps: firstly splicing 20-foot boxes into 40-foot equivalent boxes, quickly locking a shipping space by taking the minimum box area-ship-shellfish connection number and weight deviation as targets, then eliminating vertical overweight and transverse unbalance layer by layer, and finally finely adjusting the shipping space through local neighborhood search, thereby reducing the box turnover rate and improving the dispatching efficiency. Therefore, higher efficiency, lower conflict rate and better process matching degree of container space distribution are realized, the adaptability and efficiency of actual wharf operation are remarkably improved while the safety of a ship structure is guaranteed, and intelligent, systematized and efficient development of a port operation system is promoted.

The invention belongs to the technical field of ship management, and provides a combined intelligent ship water diversion system integrating differential positioning, a laser radar and a camera. In the shore end RTK-inertial navigation depth fusion positioning module, extended Kalman filtering fusion is carried out on data acquired by shipborne equipment, so that continuity of accurate positioning is realized; the construction of a color high-precision map is realized through a laser radar-monocular vision color high-precision modeling module, details such as wharf fender materials and signboard characters can be better distinguished, and the problem of shoreline modeling defects is solved; visualization of obstacles is achieved through the multi-scene dynamic obstacle detection and fusion module, meanwhile, through cooperation of multi-source data, the precision and stability of ship positioning are greatly improved, higher precision and reliability are achieved, and the precision of a guide instruction is improved; through cooperation of the 5G private network and the visualization module, the data transmission efficiency is improved, the timeliness is improved, and transmission delay can be avoided.

The invention discloses an operation method and device for a dry bulk cargo shipping wharf and electronic equipment, and relates to the technical field of port logistics, and the method comprises the steps: carrying out the unit grid processing of a bulk cargo storage yard, and updating the storage yard state of each storage yard basic unit in real time through laser point cloud scanning, according to the state of the storage yard and the working state of each unloading device, executing a wharf unloading operation process on the storage yard basic unit; according to the state of the storage yard and the idle state of each ship loader, a material taking operation process is executed on the stacked goods of the storage yard basic unit; and dynamically adjusting stockpiling and material taking operation states of the storage yard basic units by adopting a pre-constructed simulation model framework of the dry bulk cargo shipping wharf, and adjusting wharf berth distribution and storage yard cargo stockpiling and taking strategies. The technical problems that the real-time state of the storage yard cannot be reflected in real time and the scheduling response speed is reduced due to the fact that a dry bulk cargo shipping wharf operation management system only depends on static resource management in the prior art are solved.

The invention relates to the technical field of intelligent wharfs, in particular to a wharf container truck dynamic optimization scheduling method and system combining machine learning and path planning. Comprising a behavior data acquisition and feature coupling unit; a learnable incentive and behavior guide unit; a scheduling demand prediction unit; and a path planning and scheduling unit. According to the method, on the basis of the coupling characteristics, the excitation coefficient is optimized through reinforcement learning, the excitation instruction is dynamically pushed, and targeted guidance of the non-operation staying behavior of the container truck is achieved; according to the method, based on standardized time series data, an association rule of a historical staying period and a working condition is learned through an LSTM model, a prediction result is optimized in combination with real-time data, a prospective constraint basis is provided for scheduling, and meanwhile, a time, space, resource and priority multi-dimensional path constraint system is constructed through a structural causal model; container truck-berth matching and dynamic path planning are completed by matching with an improved A * algorithm fused with dynamic weights, and scheduling conflicts are effectively avoided.

The invention discloses an automatic shipping operation scheduling method for a container wharf, and the method is realized through the following steps: determining a current workable candidate container list based on a preset on-board position dependence relation and a storage yard real-time state through feasibility screening; performing execution cost evaluation and opportunity utility evaluation on the candidate jobs respectively, quantifying the costs of equipment movement, overturning and ramming operation and the like by the execution cost evaluation, and actively and quantitatively unlocking the key jobs by the opportunity utility evaluation, thereby forming earnings brought by double-box collaboration or matching of wharf resources; and calculating a final decision score through a weighting algorithm fusing utility and cost, and selecting an optimal job generation instruction according to the final decision score. According to the method, a complex scheduling problem is decomposed into a plurality of evaluation levels, refined and intelligent management of ship loading operation decisions is realized, and the overall efficiency, balance and economical efficiency of automatic wharf ship loading operation can be effectively improved.

The invention relates to the technical field of container terminal intelligent scheduling, in particular to a container terminal intelligent management method and system based on space-time coordinate mapping, and the method comprises the steps: obtaining ship operation associated multi-source data, and carrying out the data fusion; establishing a control management coordinate system; generating a ship distribution thermodynamic diagram, and mapping each ship into a rectangular color block; loading container operation feature data, embedding the container operation feature data into color blocks, and performing partition identification; analyzing container space distribution characteristics from the partition identifiers; generating a crane deployment scheme and issuing the crane deployment scheme to the wharf operation terminal; determining whether to trigger an early warning mechanism; if not, the current crane deployment scheme is maintained; if yes, an early warning mechanism is triggered, and the current crane deployment scheme is dynamically optimized; and pushing the optimized crane deployment scheme to an operation terminal in real time. The method provided by the invention has the effect of improving the problems of poor dynamic environment adaptability and low multi-target cooperation efficiency of the wharf scheduling method.

The invention relates to the technical field of structure health monitoring, in particular to a wharf structure state judgment method based on time sequence image and vibration data collaboration, which comprises the following steps: synchronously acquiring a time sequence image set and a vibration signal set of a wharf structure, and generating an original double-source data set with time-space alignment; respectively extracting a structure displacement sequence and a vibration mode parameter set through sub-pixel displacement tracking and variational mode decomposition; fusing the two to construct a spatial-temporal characteristic matrix, introducing a temperature field and a water level height to construct an environmental interference compensation factor, performing characteristic enhancement and standardization processing, and outputting a damage sensitive matrix; constructing a hybrid discrimination model by using a physical mechanism constraint layer fusing a structural mechanical control equation and a deep learning classification layer formed by one-dimensional convolution-LSTM, and outputting a structural damage probability value; the method has the advantages of space-time cooperative sensing, environmental interference robustness, high damage discrimination precision, high result interpretability and the like, and is suitable for real-time monitoring and damage identification of a wharf structure in a complex marine environment.

The invention relates to the field of automatic container wharf operation optimization, in particular to a container wharf automatic remote control quay crane one-control-two operation control system and a quay crane operation control method, and the system comprises a remote console which is used for receiving an operator instruction and displaying real-time operation data; at least two quay crane devices, wherein each quay crane device comprises a crane and a container spreader; the automatic remote control system is used for analyzing the operation instruction and controlling the quay crane to execute automatic loading and unloading operation; the dynamic switching module is in communication connection with the automatic remote control system, so that the control rights of the two quay crane devices are alternately switched under the monitoring of a single operator; and the safety monitoring module is used for monitoring the operation state of the double-shore bridge in real time and ensuring the safety of the system when the control right is switched. According to the one-control-two operation control system for the automatic remote control quay cranes of the container wharf, the operation of managing and switching the two quay cranes at the same time by a single operator through the remote control console, and the problem that manpower resources of the wharf are insufficient is effectively solved.

The invention discloses an automatic shipment balance control method, which belongs to the technical field of automatic shipment control, and comprises the following steps: scanning a cabin of a berthing empty ship, establishing a cabin three-dimensional model, constructing a wharf coordinate system and a ship coordinate system, and obtaining ship type data; planning a shipping strategy according to the ship type data, setting shipping turns and blanking point coordinates and target material height of each turn, and determining safe operation parameters of the chute tube; according to the coordinates of the blanking point of the current turn and the safe operation parameters of the chute tube, the outlet of the chute tube is controlled to be aligned with the coordinates of the blanking point of the current turn based on the wharf coordinate system by adjusting the motion parameters of the ship loader, and blanking is conducted when it is monitored that the material height of the current turn reaches the target material height; controlling the outlet of the chute tube to transfer to the blanking point coordinate of the next round; refreshing the cabin three-dimensional model in the loading process, obtaining the posture of the ship body, and updating the coordinates of the blanking point; and stopping the shipping operation until the shipping volume reaches the standard. The method can improve the automatic shipment balance control precision.

A permanent-temporary combined square block and caisson combined gravity type wharf comprises a land area rear storage yard, a temporary prefabricating yard used for prefabricating square blocks and caissons is arranged on the land area rear storage yard, the temporary prefabricating yard is communicated with square block structural sections through caisson shipping channels, and the square block structural sections are arranged along the coast and serve as caisson temporary shipping wharfs. The caisson shipping channel is used for transporting prefabricated square blocks and caissons; and after the caisson is transported out through the caisson shipping channel and the square structure section, the caisson is arranged to be connected with the square structure section into a whole, and the caisson structure section wharf is formed. According to the wharf, a site does not need to be additionally found to serve as a prefabrication yard and a temporary wharf as a construction site, and the temporary shipping wharf and part of the prefabrication yard are combined permanently and temporarily, so that wharf construction and dismantling cost can be saved.

The invention relates to the field of port energy management, and provides a bulk cargo wharf production operation process-based energy consumption prediction method and system, and the method comprises the steps: obtaining a cargo attribute information set and an equipment parameter set, and constructing a linkage database through context fusion, time sequence storage and data cleaning; based on a cargo attribute information set splitting process, performing verification and collaborative division in combination with an equipment archive and an operation emergency degree coefficient, and performing risk assessment according to theoretical and actual parking duration to generate a link list; according to the link list and the real-time working condition, a candidate device set or a compensation scheme is output in combination with the mapping table, a collaborative operation trigger signal is generated, and a device allocation list is generated through resource allocation when a collaborative scheme is started; constructing an initial energy consumption prediction model, and carrying out online iterative optimization; and calculating each operation link and a final energy consumption prediction result, and generating a final energy consumption prediction report. According to the method, dynamic planning of a collaborative operation process and adaptive prediction of energy consumption are realized, and the method is suitable for port smart energy management and automatic scheduling.

The invention belongs to the technical field of stress corrosion tests, and particularly relates to a dry-wet alternating environment stress corrosion test device and method.The test device is simple in structure, convenient to operate and suitable for stress corrosion tests of metal materials of dry-wet alternating parts of ships, wharfs and other equipment facilities. Therefore, the corrosion performance and the residual life of the metal and the component thereof under the stress action of pulling, bending and the like in the dry-wet alternate harsh marine environment are measured, and a basis is provided for material selection, structural design and the like of key stress parts such as related equipment facilities. The test method is based on corrosion equivalent conversion, the seawater medium flow velocity is controllable, stress corrosion test parameters of the dry-wet alternating environment can be reasonably determined, and corrosion damage of the dry-wet alternating part of the equipment under the long-term stress service condition can be rapidly reproduced under the condition that the stress corrosion mechanism and the damage development process are not changed; the test time is greatly shortened, the test cost is reduced, and rapid screening and examination verification of novel structures / materials of equipment are realized.

The invention discloses a wharf ship docking intelligent detection method and system, and the method comprises the following steps: S1, collecting a video image of a wharf operation region, carrying out the synchronous registration through combining a berth direction template, and constructing an image-structure combined input tensor; s2, inputting a visual center guide detection network, and generating a structure perception fusion feature map; s3, on the basis of the structure perception fusion feature map, adopting a berth direction adaptive anchor frame transformation strategy to construct head and tail direction vectors; s4, establishing a direction matching relation between the head-tail direction vector and the berth direction vector, and inputting the direction matching relation to a course deviation dynamic regression module; s5, in a course deviation dynamic regression module, outputting a berthing posture state mark value based on the course deviation angle and the vertical deviation value of the current candidate detection frame; and S6, whether an abnormal condition exists or not is determined according to the berthing posture state mark value, and if yes, abnormal berthing early warning information is generated. According to the invention, the accuracy and response efficiency of ship berthing state identification are effectively improved.

The invention discloses an intelligent management and control system and method for energy and carbon emission of a container wharf, and relates to the technical field of container management and control, and the method comprises the steps: configuring a sensor interface, collecting the multi-source energy consumption data of a wharf operation site, carrying out the time-space segmentation processing of the collected data based on a preset time window, and generating a segmented energy consumption data set; the data analysis module is used for calculating an energy consumption coupling coefficient among different operation links by adopting a user-defined multi-dimensional dynamic correlation model based on the segmented energy consumption data set; constructing a rule base based on historical energy consumption fluctuation characteristics and a multi-dimensional coupling coefficient variation trend, and automatically generating an abnormal early warning signal by adopting nonlinear logic inference; outputting a regulation and control instruction according to the early warning signal, dynamically adjusting operation parameters of the energy consumption unit, and periodically backtracking historical data to adjust parameters of the association model; a wharf three-dimensional digital environment is constructed, energy consumption and carbon emission indexes are mapped in real time, and three-dimensional dynamic display of abnormal early warning and regulation feedback is achieved.

The invention relates to the technical field of wharf automation, in particular to a wharf automation task collaborative allocation processing method and system, and the method comprises the following steps: obtaining a wharf multi-source basic data set, and obtaining the dynamic environment information of a wharf based on the wharf multi-source basic data set; obtaining a task demand list and an equipment resource list according to the wharf dynamic environment information so as to establish a task-equipment preliminary matching pair set; constructing a bidirectional optimization mechanism, and performing global optimization iteration on the task-equipment preliminary matching pair set according to the bidirectional optimization mechanism to obtain an optimal task-equipment matching pair; and in the execution process of the optimal task-equipment matching pair, regulating and controlling the optimal task-equipment matching pair based on real-time feedback data so as to realize collaborative allocation processing of the wharf automation tasks. According to the method, the rationality of task allocation and the collaboration efficiency of equipment are improved, execution conflicts are reduced, and efficient collaboration processing of the automatic tasks of the wharf is realized.

The invention relates to an unmanned ship dynamic channel modeling method in an offshore water surface environment, and the method comprises the steps: constructing a three-dimensional offshore water surface simulation scene which comprises a sea surface, a wharf facility, an unmanned ship cluster and a communication node; establishing a wireless channel propagation model based on a ray tracing method, and simulating a propagation mechanism of direct, reflection and diffraction paths; establishing a motion characteristic model of the unmanned ship under the action of waves, and obtaining six-degree-of-freedom pose data of the unmanned ship; generating multiple frames of scene copies in batches by adopting an intelligent batch processing dynamic reconstruction method; dynamically correcting an antenna pattern in each frame of scene copy according to the pose data so as to couple the influence of hull motion on antenna radiation characteristics; importing the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response; simulation is executed, and channel data obtained through simulation are collected and analyzed. According to the method, the motion characteristics of the unmanned ship in waves and the wireless channel propagation mechanism can be accurately coupled, and the accuracy of dynamic channel prediction is improved.

The invention relates to a wharf safety intelligent monitoring method, device and equipment based on digital twinning and a medium. The method comprises the following steps: firstly, carrying out space-time alignment fusion on multi-source heterogeneous original data of a wharf site to generate a dynamic digital twinborn scene; extracting and matching entity relationships and events based on the dynamic digital twinborn scene and a pre-constructed wharf operation knowledge graph, and generating a dynamic knowledge situation sub-graph; performing graph neural network coding processing on the dynamic knowledge situation sub-graph to obtain coding features of nodes in the graph; risk and conflict analysis is carried out based on the coding features, and a current risk list and a potential conflict prediction list are generated; and generating security alarm information based on the list. By adopting the method, safety monitoring can be improved from a perception level based on simple rule matching to a cognition level fusing semantic understanding and context association reasoning, so that false alarm and missing alarm are remarkably reduced, and accurate early warning of potential risks in a complex working environment is realized.

The invention provides a cam limiting external fixing spiral clutch device for lifting opening and closing of a grab ship unloader, and relates to the technical field of bulk cargo wharf grab ship unloaders, which comprises a pinion in clearance fit with a planetary reducer shaft, a bull gear connected with a cam limiting switch through a bolt, and an adjusting bolt in threaded fit with a shaft hole at the end part of the pinion. A shaft shoulder unthreaded hole is formed in the center of the planetary reducer shaft to limit axial movement of the adjusting bolt. The small gear is driven to slide in the axial direction by twisting the adjusting bolt so as to be meshed with or separated from the large gear, the adjusting bolt is reversely rotated before rope replacement to enable the gear to be separated, the large gear is prevented from being driven by rotation of the small gear, and therefore the cam limiting switch shaft is fixed; after the rope is replaced, the gears are meshed through reverse rotation again, and connection is recovered. The device is simple in structure, realizes fixation and reconnection of the limit switch shaft through mechanical transmission, does not need manual disassembly, assembly and debugging, saves manpower and time cost, is low in failure rate, and is suitable for the scene of replacing the steel wire rope of the four-drum grab ship unloader.

The invention discloses a wharf risk assessment and management method based on artificial intelligence, particularly relates to the field of wharf risk assessment and management, and is used for solving the problems that an existing wharf is difficult to quantify and delay influence on a supply chain network level, and prospective intervention cannot be performed on supply chain risks. According to the method, a comprehensive data platform of wharf operation and supply chain cargo linkage is constructed, and the bottleneck time point of a future delivery window is analyzed and predicted based on the ship arrival density, the operation bearing capacity and the stockpiling structure; searching a historical supply chain interruption event and extracting corresponding cargo and node information; tracking a conduction path of an interruption event in the supply chain network, quantifying the influence of a bottleneck on a downstream node, and establishing a supply chain risk influence model; generating a supply chain risk influence map in combination with future bottleneck prediction; and finally, a composite risk portrait is constructed, and targeted risk intervention is carried out on the wharf and the supply chain node. And intelligent prediction and management of wharf and supply chain full-chain risks are realized.

The invention discloses an automatic dry bulk cargo wharf energy-saving dispatching method and system considering dynamic switching of belt conveyors, and belongs to the field of port operation dispatching. The method comprises the following steps: firstly, collecting automatic dry bulk cargo wharf storage yard characteristic data, material stacking operation task information, material taking operation task information and operation equipment parameter information; secondly, constructing an automatic dry bulk cargo wharf energy-saving scheduling optimization model considering dynamic switching of the belt conveyors; and finally, solving the automatic dry bulk cargo wharf energy-saving scheduling optimization model, and generating a job scheduling scheme in a planned period based on a solving result. According to the invention, integrated overall optimization of storage yard position distribution, task transportation path selection and belt conveyor start-stop time sequence under a multi-task and multi-transportation path scene can be realized; the phenomena of no-load operation and repeated start and stop are effectively reduced, the operation energy consumption is remarkably reduced while the operation efficiency is guaranteed, and technical support is provided for intelligent cooperation and green efficient operation of the dry bulk cargo wharf.

The invention discloses a constant tension mooring control integrated simulation method and a digital prototype system. The system comprises an integrated simulation model, a three-dimensional visualization system, a QT interaction interface and an AQWA verification model. According to the integrated simulation model, sub-simulation models of ships, constant tension winch control, mooring and environment are constructed and integrated through OpenModelica, a multi-physics field coupled integrated simulation model is established, and integrated simulation of the constant tension mooring control system is achieved; the three-dimensional visualization system builds a three-dimensional virtual model of a ship, a winch, a mooring rope, a wharf and a marine environment based on a Unity 3D virtual engine, and three-dimensional visual dynamic display of a simulation result is realized; the QT interaction interface is combined with visual dynamic presentation, and ship motion, winch state and other parameters and control PID parameters are displayed in real time. According to the invention, joint dynamic analysis and control optimization of the ship mooring system in a complex marine environment are realized.

The invention relates to a deep sea island tough wharf capable of resisting bidirectional earthquake action, comprising: a panel, the bottom of which is provided with a panel beam; the supporting piles are arranged in a scattered mode in the extending direction from the sea face to the land and comprise straight pile bodies and crossed pile bodies, the straight pile bodies comprise the first straight pile bodies and the second straight pile bodies, the first straight pile bodies are arranged on the offshore side in rows, and the second straight pile bodies are arranged on the near-land side in rows; the crossed pile bodies are arranged between the first straight pile body and the second straight pile body in rows; the first bidirectional damping device is arranged at the top of the first straight pile body, one end of the first bidirectional damping device is connected with the first straight pile body, and the other end of the first bidirectional damping device is connected with the panel beam; the second two-way damping device is arranged at the top of the cross pile body, one end of the second two-way damping device is connected with the cross pile body, and the other end of the second two-way damping device is connected with the panel beam. By optimizing the arrangement of the pile bodies of the wharf and arranging the damping device in a combined mode, the energy dissipation capacity and the overall stability of the wharf under the bidirectional earthquake effect are effectively improved.

The invention provides a dynamic detection management method and system applied to an intelligent port, and relates to the field of industrial Internet of Things information awareness, and the method comprises the steps: obtaining the modeling information of a wharf; building a BIM model of the wharf according to the modeling information of the wharf; determining a plurality of energy monitoring points based on the BIM model of the wharf; setting a plurality of energy monitoring sensors according to the plurality of energy monitoring points; acquiring real-time energy supply monitoring data acquired by a plurality of energy monitoring sensors; predicting an energy supply problem according to the real-time energy supply monitoring data collected by the plurality of energy monitoring sensors; the energy supply problem is visualized on the BIM model of the wharf, and the method has the advantage of improving the intelligent level of energy management of the port.

The invention relates to the technical field of intelligent management of wharf accessories, in particular to a wharf accessory scheduling management method and system fusing the Internet of Things and image big data analysis. Comprising a multi-agent sensing unit; an image big data acquisition and analysis unit; the multi-agent reinforcement learning scheduling decision-making unit is used for realizing dynamic scheduling decision-making and defining the accessories, the crane and the automatic guided vehicle as collaborative operation agents; and executing a feedback and model iteration unit. According to the method, the dynamic weight coefficient is output through the LSTM network, a state interaction mechanism between agents is constructed in combination with GNN, a scheduling scheme adaptive to a dynamic environment is generated, ship berthing time and weather parameter changes are monitored in real time, scheme recalculation is triggered when a threshold value is exceeded, the problem of decision logic staticization is solved, the no-load rate of accessories and operation conflicts are reduced, and the working efficiency is improved. And the adaptability of scheduling to complex scenes is improved.