1875 results about "Wharf" patented technology

The invention provides a wharf facility health monitoring method and system based on digital twinning and machine learning, and relates to the field of wharf facility health monitoring, and the method comprises the steps: building a weight coefficient of a monitoring point network topological graph according to a synchronism change value of the same type of sensor data corresponding to adjacent monitoring points; determining an abnormal transmission path based on the weight coefficient; determining an abnormal state propagation rule of each abnormal transmission path in each preset abnormal state according to the historical monitoring data corresponding to each preset abnormal state; after abnormal sensor data of any monitoring point is detected, parameter predicted values of other monitoring points in the corresponding abnormal transmission path are calculated based on an abnormal state propagation rule; and if the parameter prediction values of the preset proportion number deviate from the corresponding actual monitoring values, generating early warning information. By implementing the method, false alarms caused by single-point data fluctuation summarizing in a traditional health monitoring mode can be dealt with, and the accuracy and reliability of abnormal state early warning are improved.

The invention relates to a wharf sensing method and system based on data fusion of a laser radar and a camera, and the method comprises the steps: firstly employing a fisheye camera to shoot a checkerboard calibration plate at multiple angles, employing a Zhang's calibration method to calculate an internal reference matrix and a distortion coefficient of the fisheye camera, and synchronously collecting the data of the laser radar and the camera in real time; the method comprises the following steps: performing distortion removal processing on a camera image, calculating an external parameter matrix between a laser radar and a camera through a feature extraction algorithm, a feature point matching algorithm and a nonlinear optimization algorithm, and performing data fusion through an internal parameter matrix, the external parameter matrix and a bilinear interpolation method to generate fusion point cloud data with color information; and finally, identifying a key target of the wharf by marking and training a CNN model, and calculating three-dimensional parameters of the key target by adopting a specific calculation method, so that a sailor can quickly distinguish the target in the fused point cloud data, the accuracy and efficiency of wharf environment perception are remarkably improved, and accurate environment perception and decision support are provided for ship berthing and departing operation.

Provided in the present invention is a ship hatch data management method based on point cloud technology. The method is implemented by means of a constructed ship hatch data management system, and the system comprises a ship scanning module, an automatic hatch identification and calculation module, a ship attitude data compensation module, and a hatch data calibration and accumulation module. In the present invention, by means of processing data of a scanner and analyzing point cloud data, a point cloud model of a ship to be subjected to operation is generated in combination with mechanism parameters of a ship loader, the position of a ship hatch is automatically identified, the spatial coordinates of the ship hatch in a rectangular coordinate system of wharf space are automatically calculated, hatch identification data for current berthing is calibrated on the basis of historical hatch data records from the same hull number after berthing in a database, and the ship loader is guided to realize unmanned and automated operation modes by means of the calculated hatch position, thereby enhancing the level of port automation.

The invention discloses a ship shore-end intelligent management method, system and device and a storage medium, and the method comprises the steps: carrying out the ship position prediction based on an electronic chart, the current navigation state data of a ship, and the current environment data, and obtaining the information of a to-be-docked ship according to the future position information of the ship, constructing a port operation knowledge graph based on the information of the ships to arrive at the port, the equipment data of the ships approaching and leaving the port, the historical tug operation data and the port data, and predicting the tug workload demand based on the port operation knowledge graph; taking tug operation data as a state space, taking a tug scheduling strategy and a cargo stacking layout adjustment strategy as an action space, taking maximization of wharf operation efficiency as a reward target, and adopting a reinforcement learning method and a simulated annealing method to carry out wharf operation scheduling optimization to obtain an optimal wharf operation scheduling strategy. According to the method, the port working efficiency is improved, manual interference is not needed when the wharf operation scheduling strategy is obtained, and autonomous decision-making can be completely achieved.

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 provides a bulk cargo wharf intelligent equipment scheduling system and method, equipment and a storage medium. The scheduling system comprises a data acquisition module; an information analysis and strategy generation module; a joint scheduling calculation module; a scheduling execution module; an execution feedback module; a model updating module; and a rule induction module. According to the method, a closed-loop liberalization system fusing semantic understanding, strategy generation and scheduling execution is constructed, so that the problem that unstructured information cannot be effectively utilized by a scheduling system is solved; scheduling parameters can be accurately extracted from unstructured data, even scheduling characteristic parameters are directly extracted from the unstructured data and structured scheduling strategy suggestions are generated, the data threshold of system intelligent transformation is reduced, a scheduling model is driven together with the structured parameters, a feedback learning mechanism is introduced, and the scheduling efficiency is improved. And efficient, intelligent and self-learning management of equipment scheduling is realized, so that the system can adapt to complex and variable port operation environments, and has good expansibility and generalization ability.

The invention discloses a method and a device for searching a fastest driving path of a wharf vehicle. The method comprises the following steps: storing and acquiring a corresponding alternative path list based on an directed graph and an Actor corresponding to an intersection; dynamically refreshing the intensive quantity fluctuation time sequence record list and the feature combination counting record list according to the driving-in vehicle message and / or the vehicle driving-out message; acquiring a vehicle path planning instruction, searching a plurality of alternative paths between the starting intersection and the destination intersection from the corresponding alternative path list, and sequentially simulating the plurality of alternative paths after the paths are arranged in an ascending order according to the path lengths; sequentially triggering all Actor instances included in each alternative path in the forward direction or the reverse direction according to information in the vehicle path planning instruction, and measuring and calculating path predicted driving duration of the vehicle on the alternative paths; and taking the alternative path corresponding to the predicted driving time length of the shortest path as the fastest driving path. According to the method, the fastest driving path can be selected through the corresponding Actor instance, and the wharf loading and unloading operation efficiency is improved.

The invention relates to a ship shoreline illegal invasion and occupation identification method and system based on AIS big data, and the method comprises the steps: obtaining the AIS data of a ship, and carrying out the preprocessing; obtaining geographic information data, and constructing geometric representation of geographic information; performing superposition matching on the preprocessed AIS data and the geometric representation of the geographic information to obtain a ship behavior occurrence area, and combining a set threshold to obtain a ship abnormal behavior identification result; and based on the ship abnormal behavior identification result, adopting a multistage clustering algorithm to perform coarse clustering on ship berthing points, then performing fine clustering on each coarse clustering group, and performing statistical analysis in combination with the preprocessed AIS data to obtain a ship shoreline illegal invasion and occupation identification result. Compared with the prior art, the method has the advantages of improving the capability of identifying shoreline resources and illegal wharfs and the like.

The invention relates to the technical field of collaborative management and control, and particularly discloses a river-sea combined transportation transfer operation collaborative management and control method and system, which can monitor the navigation state of an inland ship in real time, and perform time sequence modeling deduction on the real-time navigation state data of the ship by using a time sequence analysis technology based on deep learning. The method comprises the steps of intelligently predicting the remaining navigation time of an inland ship arriving at a transfer wharf, triggering a transfer operation management and control process when the remaining navigation time of the ship is smaller than a preset time threshold value, and calling available berths of the transfer wharf and occupation state information of each berth based on the ship specification of the inland ship; and the berths of the inland ships are intelligently distributed based on the estimated time when the inland ships arrive at the transfer wharf and the estimated idle time window of each berth, so that collaborative management and control of ship transfer operation are realized. In this way, the sailing state of the ship can be monitored in real time, the cooperative efficiency of transfer operation is improved, and the phenomenon that the ship is congested at a transfer wharf is effectively avoided.

The invention relates to a buoyancy tank wharf real-time assembly quality control method, and belongs to the technical field of port engineering automatic construction. According to the method, aiming at the problems of large geometric positioning deviation, uneven contact surface stress and difficulty in real-time control of attitude instability in buoyancy tank assembly, laser range finders are mounted at four corners of a buoyancy tank through a geometric positioning layer to monitor horizontal deviation, and pressure sensors are arranged on a butt joint surface through a mechanical sensing layer to generate a pre-tightening force curve; the attitude monitoring layer adopts a bottom inclination angle sensor to detect inclination; according to the horizontal deviation, the curve similarity deviation and the inclination angle deviation, the real-time management and control platform triggers a hydraulic adjusting mechanism, a torque wrench and a balance weight drive to conduct adjustment; meanwhile, data are stored through a buoyancy tank number, a timestamp and a sensor type three-dimensional index, and a rechecking instruction is generated based on the difference between the current adjusting quantity and the first three mean values. According to the invention, real-time control and dynamic adjustment of the assembly process are realized, and the modular assembly precision and safety of the floating wharf are improved.

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 discloses a container port berth and storage yard allocation combined planning operation method and system based on a cluster strategy, and the method comprises the steps: collecting the information of ships, container trucks and storage yards in a target time period, including the planned arrival time of the ships, the number and types of containers carried by the ships, the stockpiling state of the storage yards, and the planned arrival time of outer container trucks; the collected information is input into a pre-constructed multi-target mixed integer programming model, and an optimal programming strategy is calculated and comprises berth plans of all ships in a specified time period and loading and unloading operation plans of container batches; wherein the pre-established multi-target mixed integer programming model comprises three target functions, and the targets are the shortest ship berthing deviation expected turnover time, the minimum container transportation total distance between a berth and a storage yard container area and the minimum workload imbalance of the storage yard container area respectively; and executing ship and storage yard operation according to the optimal planning strategy. The overall operation efficiency of the wharf can be improved.

A method and system for discharge operation of a ship loader at a bulk cargo terminal. The method comprises: acquiring initial ship point cloud data; establishing a three-dimensional coordinate system, and acquiring an initial frame ship hold center line and a discharge planning point; continuously scanning a ship to be unloaded, acquiring real-time ship point cloud data, and acquiring a current frame ship center line and a normal vector of a ship deck on the basis of the real-time ship point cloud data; obtaining a real-time ship body deviation value on the basis of the current frame ship center line and the initial frame ship hold center line; obtaining a real-time ship body inclination angle on the basis of the normal vector of the ship deck; correcting coordinates of the discharge planning point on the basis of the real-time ship body deviation value and the real-time ship body inclination angle to obtain coordinates of a real-time discharge planning point, and acquiring coordinates of a current discharge point of a chute mechanism (5); a superordinate computer (1) controlling, on the basis of the coordinates of the real-time discharge planning point and the current discharge point, a ship loader to complete current discharge operation. The present application enables a ship loader to complete operation accurately, and enhances the safety and efficiency of operation of the ship loader.

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 relates to the technical field of solid waste standardized management, in particular to a solid waste management system for port and wharf companies, which collects a shore bridge current peak value, box body corrosion and bulk cargo water content, judges physical properties according to standards to generate identifiers, monitors load track deviation and generates compliance results. And a scheduling instruction is generated by combining container truck positions and empty warehouse matching optimal warehouse positions, loading, unloading, transportation and warehousing node verification timeliness is extracted, batch container truck warehouse position certificates are associated, and a whole-process supervision chain is constructed by serial nodes. According to the method, classification precision is improved by fusing multi-dimensional parameters and dynamic rules, misjudgment of a single index is eliminated, a load track is analyzed in real time, compliance judgment timeliness is enhanced by a dynamic threshold value, missed detection is reduced, a scheduling optimization path and resource utilization are dynamically generated, whole-process timeliness verification guarantees connection of loading, unloading, transportation and warehousing, interruption is avoided, and the method is suitable for large-scale popularization and application. And the unique code series nodes realize complete tracing, and mixed loading and mixed transportation are restrained through penetrating supervision.

The invention provides a reverse hanging bottom die structure for wharf cast-in-place pile cap construction and a mounting method of the reverse hanging bottom die structure, and belongs to the technical field of wharf construction. The reverse hanging bottom die structure comprises a steel pipe pile, a stand column support arranged on the steel pipe pile, a transverse steel carrying pole arranged on the stand column, a hanging rod system, a limiting system, a supporting main beam structure and a die frame used for pile cap pouring. The supporting performance of the steel pipe cast-in-place pile is fully utilized, the structural steel structure is used for building the reverse hanging bottom die supporting structure for the cast-in-place pile cap, the defects of a welding construction technology of a traditional bracket supporting system are overcome, the supporting structure facilitating cast-in-place construction of the pile cap is provided, and the construction efficiency of a wharf cast-in-place project is effectively improved.

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 relates to a prefabricated square block mounting method, belongs to the technical field of wharf building construction, and aims to solve the problems of insufficient flatness of a foundation bed, large alignment deviation of prefabricated square blocks and poor seam sealing performance in a traditional mounting method. A trapezoidal tenon is pre-embedded in the bottom surface of the prefabricated square block and is matched with a groove in the top surface, and accurate embedding of the tenon and the groove is realized by combining sectional descending speed control of a crane ship, total station and RTK positioning data fusion deviation correction and underwater camera alignment confirmation; and after installation, scanning is conducted through a depth sounding system, a secondary leveling program is started, the wharf structure installation precision and the overall stability are improved, and the later settlement risk is reduced.

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 discloses a quantitative evaluation method for energy conservation and carbon reduction levels of dry bulk cargo ports and piers. According to the method, evaluation indexes and calculation methods are constructed for five key factors which influence the energy-saving and carbon-reducing level of the dry bulk cargo port and wharf, such as clean energy and renewable energy application, shore power facility construction and application, energy-saving technology application, energy efficiency and carbon dioxide emission level, and resource intensive and cyclic utilization. According to the evaluation method, a quantitative calculation method is constructed by combining 14 indexes such as a clean energy ratio, renewable energy increase, shore power facility use, a unit energy consumption level and a unit throughput carbon dioxide emission, the energy-saving and carbon-reducing level of a dry bulk port wharf is evaluated, and the construction level of a green port is promoted to be continuously improved.

According to the caisson wharf and the construction method thereof, a layered structure of a steel slag-rubber particle composite lower layer and a regenerated resin-basalt fiber composite upper layer is adopted for a base, and interface bonding is enhanced in combination with a silane coupling agent; the foundation bed is assembled by precast concrete modules and is poured by nano mortar; the caisson main body is provided with a positioning plate which is inserted into the guide groove, the caisson main body is filled with multi-graded steel slag fragments, and an independent airbag cavity is pre-buried in a bottom plate; the buoyancy control system dynamically adjusts the water injection rate based on multi-sensor data; the ecological permeable pipeline network adopts a diatomite-zeolite coating; the prestressed steel strand towing system is regulated and controlled in real time through the friction damper. The caisson wharf and the construction method thereof have the remarkable advantages in the aspects of structural stability, ecological friendliness, construction efficiency, intelligence and automation, the problems existing in a traditional caisson wharf can be effectively solved, and the requirements of modern port construction are met.

The invention relates to the field of long-piled wharf engineering construction, and discloses an overwater pile sinking pile body construction sequence dynamic optimization method which comprises the following steps: collecting four-dimensional dynamic multi-source data in a construction process; constructing a real-time changing construction environment model based on the fused data; building a pile body construction sequence mathematical model according to the construction environment model and the wharf structure parameters; performing multi-objective optimization on the construction sequence mathematical model by adopting an intelligent algorithm, and dynamically generating a construction sequence and a ship moving path scheme; and a digital twinborn model of the pile driving barge and the pile body is constructed, the minimum distance between the barge body and the constructed pile body is predicted through a collision detection algorithm, and the construction sequence and the barge moving path are dynamically adjusted according to the detection result. According to the method, the construction safety, efficiency and global resource cooperation capability under complex working conditions are remarkably improved, and technical support is provided for intelligent construction of ocean engineering.

The invention provides a wharf and foundation integrated coupling modeling and simulation method considering large deformation and hysteretic characteristics of a soil body, comprising the following steps: step 1, constructing a wharf and foundation integrated simulation analysis model for processing real data of a wharf foundation construction site and outputting characteristic parameters; 2, constructing a multi-modal data fusion network, and performing fusion correction on the feature parameters output in the step 1 according to real data; and step 3, establishing a wharf foundation design monitoring closed-loop control mechanism, and optimizing the wharf foundation integrated simulation analysis model and the multi-modal data fusion network in combination with the real data in the step 1 and the data fused and corrected in the step 2, thereby realizing wharf and foundation integrated coupling modeling and simulation considering the large deformation and hysteresis characteristics of the soil body.

A full-unit lifting method for a quayside container crane includes calculating an overall center position of gravity of the quayside container crane full-unit; estimating a lifting height and a floating amplitude required by a main hook of a floating crane according to a water level differential at a dock and a height of the quayside container crane full-unit; selecting the floating crane and obtaining detailed parameters thereof according to basic lifting conditions of the floating crane; selecting a type of the main hook according to a weight of the quayside container crane full-unit, checking whether an interference between the boom of the floating crane and the front boom occurs, and determining an inclination angle of the floating crane during lifting; calculating a load on the lifting wire rope and selecting the lifting wire rope and a shackle; making preparations before lifting; and performing lifting at the dock.

The invention provides a high-pile wharf anti-seismic toughness evaluation method and system based on finite elements, and relates to the technical field of high-pile wharfs, and the method comprises the steps: firstly constructing a finite element soil body model and a finite element wharf model at a high-pile wharf, and forming a finite element coupling model after coupling for simulating an earthquake situation under a real condition; after simulated seismic waves are applied to the finite element coupling model, the damage condition of the soil body, the damage index of the wharf, the energy dissipation of the wharf and the ductility coefficient of the wharf under the action of the seismic waves are analyzed respectively; wherein the damage index, the energy dissipation coefficient and the ductility coefficient are used for evaluating the damage degree, the energy absorption and consumption capacity, the deformation capacity and the ductility of the wharf respectively, fitting analysis is conducted on analyzed parameters, a comprehensive toughness evaluation index for reflecting the anti-seismic capacity of the high-pile wharf is generated, and finally the anti-seismic grade of the wharf structure is output. Workers are assisted to complete shock resistance toughness evaluation of the wharf, and the working efficiency is greatly improved.

The invention discloses a production simulation prediction optimization system based on artificial intelligence. The system comprises a data acquisition module, a data processing and analysis module, a model construction module, a prediction and optimization module and a decision support module. The data acquisition module is responsible for acquiring various data in a dry bulk cargo wharf production process; comprising cargo types, production equipment operation states, storage yard use conditions, ship docking information, berth occupation conditions, transportation equipment operation data, production personnel work records, data of all links in the production process, production activity data and market analysis data, and the collected data are stored in a database; and the data processing and analyzing module is used for cleaning, sorting and analyzing the acquired data, removing noise data and abnormal values and extracting valuable information. According to the invention, intelligent management and optimization of wharf production are realized, and the operation performance and core competitiveness of enterprises are improved.