527 results about "Bulk cargo" patented technology

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention discloses a bulk cargo supply chain data transmission method and system based on an intelligent transportation box, and relates to the technical field of logistics data transmission, and the method comprises the steps: collecting multi-modal sensing data in real time through a multi-source data collection interface of the intelligent transportation box; the intelligent transportation box edge calculation module performs cleaning, compression aggregation and key event intelligent identification on the data; a transmission strategy is adaptively selected according to the data type and the network state, a key event alarm message is immediately sent, data are locally cached when the network is interrupted, and subsequent transmission is recovered; transmitting the data to a cloud by adopting a secure communication protocol; the cloud receives management data and provides remote configuration, alarm notification and data visualization functions, the system comprises an intelligent transportation box terminal unit and a cloud service platform unit, data transmission reliability and efficiency are improved, bandwidth and cloud cost are reduced, cargo transportation safety is guaranteed, and the system is suitable for bulk cargo cross-regional transportation scenes.

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 utility model relates to the technical field of belt conveyors, in particular to a movable high-precision intelligent belt constant feeder which comprises a conveying support, a conveying belt and a weighing assembly, the conveying belt is rotationally arranged on the conveying support, and the weighing assembly comprises a weighing roller, a weighing sensor, a speed sensor, a controller and a digital display dial plate. The weighing roller is arranged in the conveying belt and abuts against the conveying belt, the weighing sensor is connected with the weighing roller and the conveying support and used for outputting weight signals, the speed sensor is arranged in the conveying belt and used for outputting speed signals, and the controller is electrically connected with the weighing sensor, the speed sensor and the digital display dial plate. The controller responds to the weight signal and the speed signal and is used for controlling the digital display dial plate to display the weight value in real time. The bulk cargo transfer weight measuring device has the effect that the bulk cargo transfer weight can be accurately measured in the conveying process of the conveying belt.

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 belongs to but is not limited to the technical field of port loading and unloading operation, and discloses a hyper-heuristic dry bulk cargo cabin cleaning and ship unloading equipment cluster cross-cabin collaborative operation method and system based on large language model-Q learning dual drive. Acquiring information such as historical data and equipment attributes of cabin clearing and ship unloading operation of the dry bulk cargo port; determining the cabin cleaning and ship unloading operation process and task information of the dry bulk cargo port; establishing a cabin-cleaning and ship-unloading equipment cluster cross-cabin collaborative operation model for determining an equipment collaborative operation plan; acquiring and utilizing a large language model expansion training sample; training and adjusting a Q learning model based on the sample; based on a hyper-heuristic algorithm, LLM and QL are fused, an LQ-HH algorithm is designed and used for optimizing and solving the equipment collaborative operation model, and the operation sequence and starting time of cabin cleaning and ship unloading equipment are obtained. According to the method, the operation efficiency, the equipment cooperation capability and the anti-interference capability can be effectively improved, and intelligent transformation and upgrading of the dry bulk port can be promoted.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are 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 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 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 present invention discloses an intelligent control method for sewage treatment equipment at dry bulk cargo terminals, relating to the technical field of sewage treatment equipment control, and is used to address the problem of poor control of sewage treatment equipment at terminals. The method comprises following steps: installing multiple types of sensors at key locations on dry bulk cargo terminals, utilizing edge computing nodes for real-time data acquisition and preprocessing, combining historical and temporal features with machine learning models to classify sewage types, achieving efficient dynamic adjustment of sewage treatment equipment operation parameters, then using weighted voting and confidence assessment to integrate multiple classification results to ensure optimal treatment results, and analyzing the actual sewage treatment situation to optimize equipment control continuously, thereby preventing failures, extending equipment life, and improving sewage treatment effectiveness.

The invention discloses a dust suppression method and system for railway track dry bulk cargo loading. The method comprises the steps that if it is detected that loading operation is started on an iron road trunk line, an area to be subjected to dust suppression is determined based on loading operation field data; acquiring environment information corresponding to the region to be subjected to dust suppression, wherein the environment information comprises wind direction information; determining a to-be-loaded article based on the loading field operation data, and determining the moisture content corresponding to the to-be-loaded article; and a control strategy corresponding to the spraying system is determined based on the wind direction information, the water content and the area corresponding to the area to be subjected to dust suppression, and operation parameters corresponding to the spraying system are adjusted based on the control strategy so as to perform dust suppression treatment on the loading operation process. According to the invention, accurate treatment of railway track loading flying dust pollution is realized, limitation of traditional fixed dust suppression is broken through, and pollution prevention and control efficiency and resource utilization efficiency are remarkably improved.

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.

The invention provides a railway bulk cargo discharge hopper identification and positioning method based on machine vision, and relates to the technical field of railway bulk cargo discharge hopper tracking and positioning. The method comprises the following steps: collecting a depth map sample of a target discharge hopper; preprocessing the depth map sample to generate a sample data set; extracting geometric features of the discharge hopper from the sample data set; taking the geometric features of the discharge hopper as input, and performing target identification and positioning training of the discharge hopper by using a CNN (Convolutional Neural Network) to obtain a discharge hopper identification model; and the real-time depth map captured by the camera is sent to the discharge hopper recognition model for real-time recognition and positioning of the discharge hopper, the discharge hopper recognition model outputs three-dimensional space information and the existence probability of the discharge hopper, the result is transmitted to the control system, and the mechanical arm is guided to execute discharge operation. The method is suitable for real-time monitoring in the unloading operation, the current position of the bucket is accurately positioned in real time, impact damage between the bucket and a cargo hold is avoided in time, and the unloading efficiency and safety are improved.

The invention discloses a port bulk cargo storage location intelligent distribution method and system, and belongs to the technical field of port storage location management and intelligent distribution. The method comprises the following steps: collecting historical data related to port cargo scheduling, and defining a storage location distribution demand; designing constraint conditions for a storage location distribution process; constructing a multi-objective optimization model based on a genetic algorithm according to the constraint condition; generating an initial feasible solution meeting all hard constraints by adopting a greedy pre-screening algorithm; taking the initial feasible solution as the input of a multi-objective optimization model, and solving the optimal solution of storage location allocation by using an improved NSGA-II algorithm; and according to an optimization result, distributing the goods to the optimal storage location. By adopting the technical scheme of the invention, the problems of low storage location utilization rate, load imbalance and high delivery cost in port multi-type cargo scheduling can be effectively solved, so that the port resource utilization rate and the operation efficiency are improved.

The invention provides a coal bulk loading and unloading efficiency optimization method and system based on machine learning, and relates to the technical field of coal bulk loading and unloading, and the method comprises the steps: recognizing key factors and quantitative association rules between the key factors and the coal bulk loading and unloading efficiency through a large language model; the storage yard decision topological graph is input into a reinforcement learning model, a coal bulk cargo loading and unloading scheme is determined by executing the process of state space coding, action selection and reward function calculation, and a reward function is obtained by taking a predicted value of the coal bulk cargo loading and unloading efficiency in the current period as a target and being constructed according to key factors and quantitative association rules; after the coal bulk cargo loading and unloading scheme is completed, the actual value of the coal bulk cargo loading and unloading efficiency in the current period is collected, the efficiency difference value is calculated, and the coal bulk cargo loading and unloading efficiency in the next period is optimized based on the efficiency difference value. According to the method, the efficiency can be effectively improved while various necessary conditions of loading and unloading operation are accurately met.

The invention relates to the technical field of truck loading platforms, in particular to a multi-stage telescopic truck loading platform which comprises a multi-stage overhanging assembly, the multi-stage overhanging assembly comprises at least three stages of overhanging arms telescopic in the horizontal direction and a first driving mechanism, every two adjacent overhanging arms are arranged in a sliding mode, and every two adjacent overhanging arms are in transmission connection. The first driving mechanism is used for driving the multiple stages of overhanging arms to synchronously stretch out or retract and make contact with one another all the time when the overhanging arms are located in a truck loading area, and a bearing platform used for bearing goods is arranged on the last stage of overhanging arm. During use, the overhanging arms of the multiple stages of overhanging assemblies are driven by the first driving mechanism to stretch out or retract in the horizontal direction; the loading platform on the last stage of overhanging arm is conveyed to the needed position in the compartment in a long distance, the loading platform is completely suitable for being used in an ultra-long compartment and is also suitable for a compartment with a common length, the applicability is greatly improved, the loading platform can be used in compartments with different lengths, and the loading efficiency of bulk cargoes is also improved.

The invention relates to the technical field of ship performance prediction, and discloses a marine bulk carrier navigational speed prediction method and system, and the method comprises the steps: obtaining route planning data of a target bulk carrier, dividing the route planning data into different route segments, obtaining route segment sea area feature data and route segment navigation time sequence data for each route segment, and building a sea condition space-time sequence data set, performing multi-dimensional time sequence feature extraction to obtain a sea condition feature vector of each leg; determining a current working condition according to ship design parameters and cargo loading information of the target bulk cargo ship, and calculating a ship body wet surface area and a drainage volume under the current working condition; analyzing the total resistance value and the effective propulsion power of each leg in combination with the sea condition feature vector of each leg; and based on the total resistance value and the effective propulsive power of each leg, solving the predicted navigational speed value of the leg, and combining with the voyage distance of each leg to generate a segmented navigational speed prediction curve and full voyage number voyage time along the route. According to the method, the reliability of forecasting the long-voyage shipping speed is improved.

The invention discloses a grab ship unloader man-machine anti-collision system and method based on the UWB technology, and the system comprises a UWB positioning unit which comprises UWB labels, a UWB base station and a UWB positioning server, the UWB labels are installed at the top of a cabin cleaning machine and safety helmets worn by personnel in a cabin, and send nanosecond pulse signals to the surroundings in real time; the PLC control unit comprises a PLC module and a switch, the PLC module receives real-time positioning data, transmitted by the UWB positioning unit, of the cabin cleaning machinery and personnel and then monitors the collision risk of the cabin cleaning machinery and personnel in the grab bucket descending process in real time, and the mechanism control unit comprises a lifting opening and closing frequency converter and a lifting opening and closing motor. And after receiving the lifting opening and closing control instruction transmitted by the PLC control unit, the lifting opening and closing frequency converter controls the operation action of the lifting opening and closing motor. The system eliminates the risk hidden danger that the grab bucket hits and touches the cabin cleaning machine, and improves the safety and reliability of bulk cargo wharf operation. According to the UWB technology-based man-machine anti-collision system of the grab ship unloader, the market core competitiveness of grab ship unloader products is greatly improved.

The invention discloses a large-scale bulk cargo equipment motion control optimization method based on deep learning, and relates to the field of computer systems, deep learning, intelligent control and industrial automation based on specific calculation models. Aiming at the problems of incomplete environment perception, poor dynamic environment adaptability and the like in a bulk material equipment control system, the method comprises the following steps: firstly, acquiring data and preprocessing through a multi-source sensor; secondly, constructing a cross-modal attention fusion model to realize feature alignment; and finally, an LSTM-PPO hybrid network architecture is designed, an LSTM layer processes time sequence state characteristics, and a PPO algorithm realizes control strategy optimization. Compared with the prior art, the precision and controllability in motion control of traditional large bulk cargo equipment can be improved, the operation efficiency and robustness of the system can be improved more easily, and the method can be widely applied to the fields of logistics, bulk cargo loading and unloading, industrial intelligent manufacturing and production.

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 relates to the technical field of material transfer, and discloses an unmanned automatic stacking and transferring method and system for bulk materials, and the method comprises the steps: collecting the three-dimensional point cloud data of a bulk material storage yard, carrying out the splicing of the three-dimensional point cloud data, obtaining the overall point cloud of the storage yard, and building an elevation map according to the overall point cloud of the storage yard; collecting body attitude data of the bulk cargo stacking transfer truck, planning a material taking motion path on the basis of the elevation map according to the body attitude data, updating the three-dimensional point cloud data in real time, and correcting the material taking motion path; and communicating with rear-end transfer equipment, performing cooperative control on a material taking motion path, dynamically calculating the quantity of remaining materials in a storage yard according to the three-dimensional point cloud data updated in real time, and completing unmanned automatic stacking, taking and transferring of the bulk materials. Through unmanned and intelligent control, efficient, safe and stable operation of the bucket-wheel stacker-reclaimer is achieved, manual intervention is reduced, the equipment operation efficiency is improved, and the intelligent operation and maintenance requirements of a large bulk material storage yard are met.

The invention belongs to the technical field of bulk cargo wharf operation and maintenance situation awareness, and relates to a bulk cargo wharf operation and maintenance situation awareness method and system. The residual life of the equipment is calculated through the residual life probability distribution of the equipment predicted by the constructed residual life probability distribution prediction model, and the failure rate of the equipment is predicted through the constructed situation awareness model according to the health degree index of the equipment and the residual life of the equipment, so that equipment alarm signals of different grades are obtained. An auxiliary management scheme is generated and executed according to the equipment alarm signal, the equipment user maintenance time is reasonably planned, and the equipment utilization rate and the field production efficiency are improved. Health state monitoring and operation and maintenance situation awareness of the bulk cargo wharf are achieved, the equipment utilization rate and the field production efficiency are improved, and the turnover rate and turnover efficiency of wharf cargos are improved.

The invention relates to the technical field of environment monitoring and automatic control, and specifically provides a dust monitoring analysis control system based on a port bulk cargo storage yard electronic greenhouse. The system comprises an environment multi-dimensional sensing module, a dust field dynamic reconstruction module, a dust suppression demand prediction module and a precise execution control module, and through multi-dimensional data acquisition, dynamic three-dimensional dust field modeling, future dust suppression demand prediction and space and time sequence optimization regulation and control of spraying resources, the dust suppression demand prediction and the precise execution control module are optimized. Accurate monitoring and prediction of dust concentration and on-demand throwing of dust suppression resources are achieved, and control timeliness, foresight and operation energy efficiency are effectively improved.

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 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 belongs to the field of green port planning and operation, and discloses a resource-saving type spraying operation intelligent control method considering dynamic change of stacking of a storage yard in a dry bulk port district, which comprises the following steps of: calculating spraying operation resource consumption, including power consumption of a water supply pump station and total water consumption of spraying operation of all material piles in the storage yard; and establishing a storage yard distribution model, wherein an objective function comprises the storage delay time cost of the material pile, the power consumption cost of the spraying operation and the water resource consumption cost of the spraying operation. The model is linearized, and part of constraints are linearized. The method comprises the following steps: firstly generating a material pile problem instance for a subsequent experiment, then introducing three rule-based scheduling methods as references, and determining the material pile distribution efficiency by testing a storage yard distribution model, a rule-based method, different stacking densities and different material piling and taking efficiency conditions and comparing with a meta-heuristic algorithm. The effectiveness and adaptability of the storage yard space distribution model are verified.

The invention relates to the technical field of conveying equipment, and particularly discloses a bulk cargo dust raising prevention suspension type conveying device which comprises a guide rail and a stock bin, guide seats are fixedly connected to the front side and the rear side of the outer surface of the upper end of the stock bin, a fixing seat is fixedly connected between the two guide seats, and a sealing assembly is arranged on the inner side of the fixing seat; the sealing assembly comprises a storage groove formed in the inner side of the fixing base in an embedded mode, a coil spring is fixedly connected to the inner surface of the storage groove, the storage groove is rotationally connected with a reel through the coil spring, and a sealing curtain is wound around the outer surface of the reel. By arranging the sealing assembly and covering the upper side of the stock bin with a sealing curtain, the upper end of the stock bin can be sealed, so that the probability that bulk cargoes in the stock bin are scattered in the hanging and conveying process is reduced to a certain extent, and meanwhile the risk that the bulk cargoes raise dust due to wind blowing can be effectively reduced; and the environment-friendly effect of the suspension conveying device can be further improved.

The invention discloses a deep learning-based fault prediction method for a bulk cargo port area belt type conveying system, and belongs to the field of fault prediction for the bulk cargo port area belt type conveying system. The method comprises the following steps of: 1, cleaning fault shutdown data of a harbor area belt type conveying system, complementing and extracting key component data of the belt type conveying system to obtain original data; step 2, carrying out belt conveying system degradation curve fitting based on Weber distribution; and 3, constructing a deep learning model, excavating the relationship between the deterioration trend of the key components of the belt conveying system and the fault of the belt conveying system, and accurately predicting the fault-free working time of the belt conveying system. And 4, designing an experiment and analyzing a result. According to the invention, several different deep learning models are utilized to predict faults of the bulk cargo port area belt conveying system, and a Weber distribution method is adopted to make a data set. Through the method, a decision can be provided for making a maintenance plan of the belt type conveying system, and potential safety hazards caused by faults of the belt type conveying system are reduced.

The invention provides a cam limiting internal fixing spiral clutch device for lifting opening and closing of a grab ship unloader, which relates to the technical field of bulk cargo wharf grab ship unloaders and comprises a pinion in clearance fit with a planetary reducer shaft, a cam limiting switch in bolted connection with a bull gear and an adjusting bolt. An end shaft hole of the pinion is provided with internal threads, a threaded hole is formed in the center of a planetary reducer shaft, and the adjusting bolt is matched with the threaded hole; the small gear is driven to slide in the axial direction to be engaged with or disengaged from the large gear by rotating the bolt cap. When the steel wire rope is replaced, the disengagement gear can avoid contact deviation caused by rotation of the cam limit switch shaft; the gear is directly meshed after the rope is replaced, and secondary contact adjustment is not needed. The structure of the device adopts a pure mechanical mode, operation is simplified, stability is high, and debugging time of about one day is saved for single maintenance.