11008 results about "Truck" patented technology

The invention relates to the technical field of software systems, and particularly discloses a vehicle matching and positioning system and method based on comprehensive characteristics of a vehicle and a container, and the system comprises a multi-mode perception and intelligent identification module, a dynamic task matching and scheduling optimization module, an intelligent decision and automatic execution module, and a user interaction and operation module. According to the system, the states of a container and a container truck are sensed in a fusion mode through multiple sensors, cross-modal data feature learning is carried out through a Transform-VIM self-attention mechanism, and high-precision container recognition in a complex environment is achieved; meanwhile, based on multi-dimensional feature matching and a Hungary optimization algorithm, a dynamic task matching mechanism is constructed, and it is ensured that the container trucks and the containers are in accurate butt joint; the system optimizes a scheduling strategy through reinforcement learning, dynamically adjusts an operation process, and is linked with automatic equipment, so that intelligent and efficient port container transportation management is finally realized, the risks of wrong loading, neglected loading and operation delay are effectively reduced, and the overall throughput and operation efficiency of a port are improved.

The invention discloses a long downhill section truck brake pad high temperature detection and cooling method and system, and the method comprises the steps: collecting the surface temperature data of a truck brake pad in real time according to high-precision infrared temperature sensor arrays and thermal imagers disposed at the two sides of a long downhill section, and generating a time-space correlation temperature field distribution diagram; based on the temperature field distribution diagram, dynamically adjusting a temperature safety threshold curve through reinforcement learning, and outputting a dynamic early warning trigger signal; according to the dynamic early warning trigger signal, a corresponding spraying instruction is generated so as to drive a piezoelectric ceramic microvalve array of a roadside spraying device to conduct spraying cooling on the brake pad; and according to the temperature attenuation rate and infrared thermal image feedback data of the brake pad after spraying, the temperature safety threshold curve and the spraying instruction are corrected through a Bayesian optimization algorithm, and closed-loop safety regulation and control are formed. According to the embodiment of the invention, the temperature of the brake pad can be monitored in real time, accurate spraying cooling control is realized, and safe and stable braking performance of a truck is ensured.

The invention provides an air-ground cooperative intelligent emergency material scheduling and electric power configuration optimization method and system, and the method comprises the steps: constructing an air-ground cooperative emergency resource joint scheduling frame which integrates heterogeneous resources, such as future emergency trucks, unmanned aerial vehicles, unmanned and manned new energy vehicles; the objective of the invention is to jointly optimize post-disaster emergency material distribution and power recovery processes so as to improve the overall emergency response efficiency and cooperative capability. Secondly, aiming at the complexity of air-ground cooperative scheduling under the framework, a two-stage optimization model is designed, and the objective of the two-stage optimization model is to maximize the emergency power supply capability of the key area in the first stage and minimize the material distribution time of the unmanned aerial vehicle taking off from the large power supply vehicle based on the two-stage optimization model in the second stage. And finally, in order to efficiently solve the model, a GRASP algorithm and an ALNS algorithm are respectively developed. Through simulation analysis based on actual scene data, the feasibility and potential of the whole method for realizing quick response of emergency materials and electric power under a real disaster background are proved.

The invention provides a heavy truck battery compartment guiding method and system based on visual perception. The method and system are used for automatic battery replacement operation in a complex industrial environment. According to the system, a multi-camera fusion visual perception platform is constructed, a plurality of industrial cameras arranged on the ground or ceiling of a battery swap station are used for collecting local images of different visual angles of a battery compartment, and a complete visual field image is generated through feature matching and image splicing. A battery compartment is coarsely positioned by adopting a YOLO series model, a bounding box region is extracted, pixel-level contour segmentation is realized by introducing SAM, and the complex background and multi-interference environment recognition capability is enhanced. And after segmentation, calculating a minimum enclosing rectangle of the battery compartment, obtaining a center coordinate and a deviation angle, and transmitting a pose parameter to an upper computer control system. According to the method, the defects of the laser radar are avoided, image processing, the deep neural network and multi-view information are fused, the recognition precision and stability are improved, the battery replacement efficiency and the unmanned level of the electric heavy truck can be remarkably improved, and reliable support is provided for green traffic.

Provided are an electric wagon and an electronically power assisted wagon. The electric wagon includes a wagon body including at least a foldable frame; a handle device connected to the wagon body; a walking assembly including at least driving wheels; a drive motor configured to drive the driving wheels to rotate; a power supply connection portion configured to be detachably connected to a battery pack for supplying power to the drive motor, and a controller electrically connected to at least the drive motor to control the drive motor to rotate.

The invention discloses an engineering vehicle multi-area environment control system based on AI, and particularly relates to the technical field of engineering vehicle environment control. Humidity, electric field, air pressure and dust multi-mode sensing arrays are arranged in multiple areas of an engineering vehicle, dust particle deposition distribution time sequence characteristics are extracted, and a deposition acceleration coefficient and a humidity response coefficient are calculated; calculating a dust deposition risk coefficient, comparing the dust deposition risk coefficient with a preset value, and marking a region exceeding a threshold value as a region in a dust deposition abnormal state; analyzing a deposition trend based on a time sequence prediction model, inputting environmental parameters, and predicting a dust deposition velocity curve of each area of the engineering vehicle in a future set time; according to the method, the static electricity, humidity, air pressure and dust coupling deposition risk is assessed in a prospective mode, a collaborative intervention strategy is triggered through a dynamic early warning threshold function, the effect of rapidly restraining the risk is achieved, and the problem that traditional single-parameter monitoring lags behind, and ventilation efficiency is suddenly reduced due to the fact that a coupling mechanism cannot be captured is effectively solved.

The invention discloses a port container transportation full-process simulation and space planning quantification system and method under random event deduction. The system comprises a data acquisition module, a data processing module, a process simulation module, an efficiency quantification index generation module and an efficiency quantification module. The data acquisition module is used for acquiring basic data in port operation; the data processing module is used for processing the basic data; the process simulation module is used for constructing port container transportation process simulation covering the whole process of ship navigation, ship berthing, quay crane loading and unloading, storage yard storage and container truck outward transportation based on the processed basic data, and obtaining a simulation result; the efficiency quantitative index generation module is used for obtaining an efficiency quantitative index according to the container transportation characteristics and the port operation experience; and the efficiency quantification module is used for quantifying the simulation result by utilizing the efficiency quantification index. According to the invention, the current operation efficiency level of the port can be measured comprehensively and objectively.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention relates to the technical field of data processing, and discloses a truck scale anomaly detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining the operation state data and peripheral image data of a truck scale when a to-be-weighed vehicle drives onto the truck scale; processing the operation state data and the peripheral image data by using a preset target detection model, and performing cross validation and correlation analysis on a processing result to obtain a preliminary abnormality diagnosis result and a potential fault prediction result; and generating a target anomaly detection report according to the preliminary anomaly diagnosis result and the potential fault prediction result. And when the to-be-weighed vehicle is driven into the truck scale, the running state data and the peripheral image data are processed by using a preset target detection model, then cross validation and correlation analysis are performed, and a target anomaly detection report is generated. Abnormal conditions in the running process of the truck scale can be found more timely and accurately, a detailed basis is provided for subsequent maintenance and repair, and stable running of the truck scale is effectively guaranteed.

The invention provides a multi-dimensional data fusion system for operating truck risk rating, and relates to the technical field of data processing, and the system comprises a data collection module which is used for obtaining the dynamic operation data and historical static information of a target operating truck; the data preprocessing module is used for performing field alignment, abnormal value elimination and format standardization; the feature analysis module is used for extracting dynamic behavior features of vehicle operation and performing statistical calculation in a preset time window; the event identification and risk assignment module is used for identifying a single event or a combined event, assigning an event risk weight to the single event or the combined event, and determining a corresponding road condition risk level in combination with the vehicle position information; the multi-dimensional fusion module is used for fusing features and generating time serialized multi-dimensional risk feature vectors; the risk rating module is used for scoring the multi-dimensional risk feature vector and outputting a corresponding risk rating result; according to the invention, the accuracy of the multi-dimensional data fusion system is improved.

The invention relates to the technical field of electric workover rig thermal management, and discloses a workover pump truck electric drive thermal management method and system, and the system comprises a multi-source temperature collection module, an operation state monitoring module, a thermal management analysis module, a strategy matching engine module, a dynamic execution control module, a feedback optimization module, and a prediction decision module. The method comprises the following steps: acquiring environment temperature, motor temperature, battery temperature and cooling liquid temperature data; obtaining motor power, battery current and a vehicle operation mode; generating a thermal management demand level based on the temperature and the operating parameters; matching natural cooling, air cooling or liquid cooling strategies according to grades; cooling operation is executed, the temperature change rate is calculated in real time, and the cooling intensity is dynamically adjusted; when the threshold value is exceeded, liquid cooling cooperative control is started; predicting a thermal load trend through an LSTM model, generating a dynamic cooling plan, and optimizing an execution priority; and a thermal management efficiency evaluation report is output, so that the operation reliability and energy efficiency of the electrically-driven workover pump truck are improved.

The invention discloses a truck-mounted crane group operation safety monitoring system and method based on multi-source perception and collaborative decision, and particularly relates to the field of safety monitoring, the truck-mounted crane group operation safety monitoring system comprises a distributed perception module, an edge calculation module, a graded early warning and execution module, a digital twin platform module and a man-machine interaction module; collected multi-source data are fused through Kalman filtering and then transmitted to an edge computing node; generating a dynamic safety envelope region based on an adaptive kinematics model, predicting a group conflict risk through a graph theory algorithm, and calculating collision time; the digital twin platform fuses a BIM model and real-time data, hoisting path rehearsal, load swing prediction and foundation bearing capacity visualization are carried out, an improved RRT * algorithm is adopted to plan a collision-free path, and a Lagrange dynamics model is adopted to predict a swing track; the grading early warning module triggers differential response according to the risk grade; and risk warning and path guidance are provided through the AR-HUD and the three-dimensional situation billboard.

A handling truck comprises a mast assembly, a base, a moving assembly, and a connecting member. The base is fixedly disposed at the bottom end of the mast assembly, and the base is provided with a socket portion. The moving assembly includes a leg assembly and a connecting portion, wherein one end of the connecting portion is connected to the leg assembly, and the other end of the connecting portion is configured for socket engagement with the socket portion. The moving assembly is configured to drive the mast assembly to move on a plane. The connecting member is configured to pass through the socket portion and the connecting portion to achieve a detachable connection between the moving assembly and the base. The present application provides a handling truck capable of reducing packaging costs and transportation costs.

The invention discloses a heavy truck charging health degree evaluation method based on an optimized graph convolutional network, and particularly relates to the technical field of electric power operation and maintenance. Multi-source heterogeneous operation data of the charging pile are collected, and unified time window segmentation is carried out; constructing a dynamic multi-graph structure fusing space, electrical and behavior coupling relations; extracting a node local state evolution trend and a neighbor influence factor, and generating a joint feature vector; inputting the features under multiple time windows into a multi-scale attention enhanced graph convolutional network to obtain joint potential health degree representation; calculating a health score based on the potential representation, identifying a high-risk node, and constructing a potential failure propagation path map; generating a maintenance priority table in combination with the centrality index and the degradation rate, and outputting an optimized inspection path; according to the invention, time-space correlation modeling and intelligent maintenance path planning of the health state of the charging pile are realized, and the method has the advantages of high accuracy and strong applicability.

The invention discloses a truck hub maintenance-free bearing all-condition simulation testing machine, which comprises a tool table, a servo driving system, a servo hydraulic system and a temperature control simulation system, and is characterized in that the tool table is provided with a tool seat and a main shaft movably arranged on the tool seat, and a to-be-tested bearing is detachably connected between the main shaft and the tool seat; the servo driving system is in linkage fit with the main shaft to simulate the actual operation condition of the bearing, the tool table is provided with a special-shaped load plate and a mandrel, the special-shaped load plate is in linkage fit with the mandrel, and the mandrel is inserted into an inner ring shaft hole of the bearing to be tested and is detachably connected with the inner ring shaft hole of the bearing to be tested. The servo driving system is in linkage cooperation with the special-shaped load plate to simulate the load working condition of the bearing during actual operation, and the temperature control simulation system is in linkage cooperation with the bearing to be tested to simulate the actual temperature working condition of the bearing. According to the invention, the problem that a test device for carrying out a multi-working-condition simulation test on a truck hub bearing is lacked in the prior art is solved.

A method and system are provided for identifying time-varying suspension characteristics of heavy-load vehicles. The method includes collecting sequential control state data of a mining truck using sensors, predicting parameter-related factors through a deep learning network, estimating suspension stiffness and damping coefficients via a linear dynamic model considering longitudinal-vertical coupling, and predicting future system states through a nonlinear dynamic model based on the estimated parameters and learned factors. According to the method, a deep learning network is integrated into a physical model of the mining truck, an accurate longitudinal-vertical dynamical model of the mining truck is established, accurate suspension parameters are identified, the stiffness damping time-varying characteristics of the suspension of the mining truck are given through a physical model-data driving method, and the model has certain interpretability and generalization; the rigidity and damping of the four suspensions can be obtained only through sprung information.

The invention relates to a quay crane RTK positioning precision evaluation method and system and a storage medium, and the method comprises the steps: L1, when an unmanned container truck carries out the container loading and unloading operation under a quay crane, the quay crane RTK feeds back the positioning value of a vehicle according to a fixed frequency, and obtains the data information of the positioning value of the vehicle within a fixed time; l2, based on the data information of the positioning value of the vehicle within the fixed time, predicting the positioning value of the vehicle by adopting an improved ELMAN dynamic recurrent neural network prediction algorithm optimized based on a particle swarm optimization algorithm to obtain the data information of the predicted positioning value of the vehicle; and L3, based on the predicted data information of the positioning value of the vehicle, optimizing the positioning value of the vehicle by adopting a zebra optimization algorithm of a self-adaptive dynamic learning factor based on global search to obtain the optimized data information of the positioning value of the vehicle. The alignment success rate of the unmanned container truck can be increased, and harbor area operation can be prevented from being affected.

The invention discloses a truck-unmanned aerial vehicle cooperative path optimization method and program product for a heterogeneity inspection task, and the method comprises the following steps: (1) setting a target function of a truck-unmanned aerial vehicle cooperative path optimization model as the minimum total cost, (2) adding routing constraints, heterogeneity constraints of different types of inspection tasks on equipment, unmanned aerial vehicle energy consumption constraints and time constraints to the truck-unmanned aerial vehicle cooperative path optimization model; and (3) solving the truck-unmanned aerial vehicle cooperative path optimization model to obtain an optimized decision variable so as to obtain an optimal path and an optimal unmanned aerial vehicle-truck configuration scheme for a heterogeneity inspection task. According to the invention, heterogeneity of inspection tasks is considered, and the planned path scheme is higher in inspection efficiency.

The invention discloses a mine card simulation method and system based on log data, and relates to unmanned driving. The method comprises the following steps: obtaining operation log data of a mine card under multiple working conditions; extracting nonlinear response feature data according to the running log data; taking the historical control instructions and historical vehicle states of continuous N periods as input characteristics, taking the vehicle state of the current period as an output label, and constructing a training set; using the training set to train an LSTM model, inputting a current control instruction, a historical vehicle state and a current load value, and outputting a predicted vehicle response state; and inputting the predicted vehicle speed, acceleration, front wheel turning angle or hinge angle into a vehicle kinematic model, and calculating the position and attitude of the vehicle under the current load condition. Aiming at the problem that the time delay between a control instruction and an actual response in a super-large inertial system cannot be processed due to the fact that a non-linear dynamic response caused by the fact that mine truck large load change cannot be accurately simulated by adopting fixed dynamic parameters in mine truck simulation, the simulation precision is improved.

Apparatus for real time water content and water cement monitoring on a ready-mix concrete truck includes at least one real time moisture measurement module or sensor configured tosense continuous real time moisture measurements of a batch of wet cement loaded into a rotating drum of a ready-mix concrete truck; andprovide signaling containing information about the continuous real time moisture measurements sensed.The apparatus may include either a hatch door of the rotating drum of the ready-mix concrete truck, or the rotating drum of the ready-mix concrete truck, or the ready-mix concrete truck itself.

A chassis frame welding apparatus with a turning-over function comprises a welding device (1), a support frame (2), and two rotating supporting apparatuses, wherein the support frame is located between the two rotating supporting apparatuses; the welding device is disposed adjacent to the support frame; and each rotating supporting apparatus comprises a lifting device (4), a rotating device (5) and a clamping device (6), the rotating device comprising a rotating shaft (51), a one-way bearing (52), a first gear (53), and a first toothed plate (10). The chassis frame welding apparatus is provided with the one-way bearing and the first gear, which fits with the first toothed plate, and during the process in which the lifting device is used to lift by means of the rotating device and the clamping device a workpiece to be separated from the support frame, the first gear interacts with the first toothed plate, and the rotating shaft is then driven by means of the one-way bearing to rotate by 180 degrees; thus, the spacing between the workpiece and the support frame is adjusted by means of the lifting device, and the turning over of the workpiece under driving is completed, without providing a plurality of driving systems, thereby simplifying a turning-over supporting apparatus.

The invention provides a light-weight aluminum alloy truck hub forging spinning device and belongs to the technical field of hub production.The light-weight aluminum alloy truck hub forging spinning device comprises a machine body, an upper die base and a lower die base, the upper die base is provided with a first pressing die, the lower die base is provided with a second pressing die, the first pressing die comprises a first fixing piece, and the second pressing die comprises a second fixing piece; the first pressing die comprises a first fixing piece, a positioning assembly is arranged on the outer wall of the first fixing piece in a sliding mode and used for fixing the top of the blank hub, the second pressing die comprises a second fixing piece, a plurality of jacking pieces are evenly arranged in the second fixing piece and used for limiting the inner wall of the blank hub, and a plurality of supporting pieces are arranged at the bottom of the second pressing die in a sliding mode and used for supporting the blank hub. The support is used for supporting the bottom of a blank hub; the moving distance of the connecting piece can be adjusted by adjusting the extrusion degree of the piston piece, the device can adapt to various hub models with different inner diameters, demolding is convenient, and the production efficiency is improved.

The invention provides a new energy light truck energy distribution method and system, and the method comprises the steps: obtaining the position and track of a vehicle to obtain the road section data of a current driving road section, carrying out the data preprocessing of the road section data, and carrying out the fusion of the preprocessed data, so as to obtain the current load and driving state of the vehicle; inputting the preprocessed data into an initial energy consumption model, introducing a deep learning algorithm to dynamically adjust model parameters of the initial energy consumption model to obtain an iterated target energy consumption model, and obtaining energy consumption coefficient data according to the target energy consumption model; and a motor torque demand curve is generated in real time according to the current load, the road slope and the predicted endurance mileage, torque distribution data are adjusted in combination with an adaptive algorithm, the adjusted torque distribution data are transmitted into a vehicle power control system to obtain real-time operation data of the vehicle, and then energy distribution is conducted on the new energy light truck. According to the invention, the endurance prediction precision is improved, and the real-time adaptive optimization of torque distribution control is realized.

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 a container logistics ecological chain large model system based on a joint model, and relates to the technical field of container logistics, and the system comprises real equipment, a Tos system, a real-time data concentrator, a virtual port, a database, and an LLM. The real equipment is key operation objects of a port site and comprises quay cranes, wheel cranes, inner container trucks, storage yards, ships and the like, and each object continuously outputs real-time operation state data; and the Tos system is responsible for receiving and processing real-time data from real equipment. According to the container logistics ecological chain large model system based on the joint model, a scheduling strategy with sensing, decision-making and self-adaption capabilities is formed by training an agent in a virtual port environment, and real-time deployment and application are performed in an actual port, so that multi-type equipment and multi-task concurrent scheduling can be supported; efficient, real-time response and dynamic rescheduling capability are realized, and the overall intelligent level, loading and unloading efficiency and handling capacity of port operation are improved.

The invention discloses a photovoltaic photo-thermal coupling hydrogen production system control method for a hydrogen-burning heavy truck traffic network. The method comprises the following steps: S01, collecting operation data of a photovoltaic array, an electrolytic cell and a hydrogen storage tank; s02, based on a model prediction control algorithm, establishing a multi-objective optimization model containing an objective function and constraint conditions; s03, merging the sub-objective functions into a comprehensive objective function through a linear weighting method; s04, applying constraint conditions; S05, performing rolling optimization calculation by adopting a model prediction control algorithm; s06, outputting an optimal control variable; s07, adjusting corresponding parameters in the system; s08, monitoring the running state of the system; s09, whether the system meets constraint conditions is judged, according to the control method, through multi-target collaborative optimization and precise constraint control, the performance, efficiency, economical efficiency and safety of the photovoltaic photo-thermal coupling hydrogen production system can be remarkably improved, and clean, efficient and stable hydrogen energy supply is provided for a hydrogen combustion heavy truck traffic network.

“THREE-DIMENSIONAL MONITORING SYSTEM FOR KINGPIN ON FIFTH WHEEL” describes an invention patent, for a monitoring system of Kingpin / Fifth Wheel articulation presented on trucks, with a great applicability for autonomous steering systems and vehicle dynamics control. This system allows the precise monitoring of translational, rotational movements and the relative accelerations of the king pin (5) in relation to the fifth wheel (3) on the X, Y and Z axes, totaling measurements in six degrees of freedom. The present patent presents two ways to make this monitoring of the three-dimensional position of the kingpin (5): The first through a set of sensors (6) accelerometers and gyroscope integrated to an electromagnet (4) that when electrified, is coupled to the king pin (5) by magnetic attraction. The second mode through a reference device (20) that is magnetic, identifiable, independent and easy to apply to the kingpin (4).

The invention provides an intelligent heavy truck end-to-end driving method based on focus attention and probabilistic game, and belongs to the technical field of intelligent driving. The method comprises the following steps: firstly, acquiring multi-source data such as a laser radar, a look-around camera, an IMU (Inertial Measurement Unit) and a load signal, and generating an anti-jitter space-time BEV feature through cross-dimensional position coding based on motion compensation, near-field non-uniform sampling, focus attention and time sequence fusion; then, vectorization decoding is carried out on the features to obtain obstacle vehicle movement and map element vector information. And finally, on the basis of the expert track prior space, probability game interaction is carried out through multi-round iterative prediction and planning, a self-vehicle track conforming to heavy truck variable load dynamics constraints is generated, and the self-vehicle track is output after safety verification. According to the method, the problems of heavy truck high-position sensor vibration distortion, hinge structure blind area and trajectory planning under the large-inertia variable-load working condition are effectively solved, and the driving safety and robustness are remarkably improved.