4216 results about "Transportation technology" patented technology

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention discloses a railway freight train data acquisition method and system based on multi-sensor fusion, and relates to the technical field of railway transportation, and the method comprises the steps: detecting the operation state and position of a train in real time through wireless communication, and carrying out the self-inspection of each sensor when the train is detected to be about to enter a monitoring region; constructing a three-dimensional health state model of the train through three-dimensional reconstruction and synchronous positioning according to the comprehensive state data of the train; in combination with a three-dimensional health state model and a side cloud collaborative architecture, layered processing and dynamic optimization of train comprehensive state data are accelerated by using a quantum algorithm, and abnormal features are identified in real time; the abnormal features are analyzed through deep learning and a time sequence analysis algorithm, and alarm information is generated and fed back to the edge computing terminal; according to the method, the running state of the train and the cargo state data are deeply fused through the convolution operation of the three-dimensional health state model and the space-time diagram, so that the global perception capability of the data is improved.

The invention discloses an enclosure system of a low-temperature storage tank and transportation equipment, and relates to the technical field of fluid transportation, the enclosure system is installed on the inner wall of the low-temperature storage tank, the enclosure system comprises a plurality of heat insulation modules installed on the inner wall of the storage tank, the heat insulation modules are spliced and cover at least part of the inner wall of the storage tank, and the heat insulation modules are arranged on the inner wall of the storage tank; assembling grooves are formed in the splicing positions of the heat insulation modules; the bridging blocks are installed in the assembling grooves, and every two adjacent heat insulation modules can be connected together through the corresponding bridging blocks; the shielding module is connected with the heat insulation module, the shielding module comprises a first shielding layer, a second shielding layer and a third assembly plate, the first shielding layer and the second shielding layer are arranged in a spaced mode, and the third assembly plate is located between the first shielding layer and the second shielding layer. According to the invention, a better shielding effect can be realized by arranging two or more shielding layers, and the first shielding layer and the second shielding layer are arranged at intervals, so that the capacitive coupling effect between the shielding layers is reduced, and the penetrating power of electromagnetic waves is reduced.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention relates to the technical field of intelligent traffic, and discloses a road transportation key operating vehicle intelligent supervision system, which comprises a data acquisition and fusion module used for acquiring multi-source heterogeneous data of a vehicle terminal, an external environment and the like in real time and carrying out standardization processing; the cognitive digital twinning construction module is used for constructing a dynamic heterogeneous graph representation human-vehicle-road-environment system and outputting a cognitive state vector through a graph neural network; the risk prediction and evaluation module is used for predicting a risk evolution trend based on the state vector sequence and quantifying the uncertainty of prediction by using a Monte Carlo discarding method; and the self-adaptive intervention decision module is used for combining risk prediction and uncertainty, making a decision through a reinforcement learning model and executing an optimal active intervention instruction. By constructing cognitive digital twinning and introducing uncertainty quantification and closed-loop self-checking, prospective prediction and self-adaptive intervention of driving risks are realized, and the accuracy and robustness of supervision are improved.

The invention provides an inter-fleet dynamic electric quantity balance and collaborative replenishment system, which relates to the technical field of shipping transportation, and comprises an intelligent scheduling module used for obtaining a data set containing electric power conditions, navigation parameters, sea condition influences and task progress requirements of each ship based on ship motion states, ship equipment is monitored in real time to obtain data and analyze environmental influence to obtain adjustment parameters, then the total energy consumption contribution of a single ship is calculated, the total loss of a fleet is quantified to fuse to obtain the initial amount of total energy consumption and correct to obtain the navigation efficiency, and then a power output adjustment strategy is formulated for a main propulsion ship according to the navigation efficiency and an electric power balance target. And finally, a collaborative supply path is planned by taking the low-power ship as a core and combining multi-party information. According to the invention, efficient coordination of deployment and supply of the fleet on power management is realized, the transportation efficiency is improved, the cost is reduced, and the navigation safety is enhanced.

The invention discloses a low-altitude logistics unmanned aerial vehicle path planning method and system, and relates to the technical field of unmanned aerial vehicle logistics transportation, and the method comprises the steps: firstly, carrying out the optimization through calculating a preliminary path and combining the terrain and weather information; a sensor is used for detecting and dynamically avoiding obstacles; secondly, data transmission between the unmanned aerial vehicle and the ground control center is achieved, and the ground control center carries out monitoring, control and data processing. According to the low-altitude logistics unmanned aerial vehicle path planning system, efficient path planning is realized through multi-module cooperation. According to the invention, the flight safety, path planning accuracy and energy utilization efficiency of the unmanned aerial vehicle can be improved, the operation cost is reduced, and the system adapts to complex environmental conditions.

The invention discloses an intelligent sensing system for multi-modal data fusion, and relates to the technical field of intelligent traffic. The multi-modal data fusion intelligent sensing system comprises a data acquisition and preprocessing module, a multi-modal data fusion module, an intelligent sensing module, a target detection and behavior prediction module and a system verification and optimization module, and high-precision and high-stability target sensing and tracking are realized by integrating millimeter wave radar and camera data. The multi-modal data fusion module effectively combines physical features and visual features, and improves the accuracy and robustness of perception. The intelligent sensing module adopts a volume Kalman filter and a Hungary algorithm, state estimation and target association are optimized, and continuous and accurate tracking of the target is ensured. The target detection and behavior prediction module utilizes an advanced YOLOv5 model and a DeepSORT algorithm to realize multi-target accurate detection and trajectory prediction, and the intelligent level of the system is further improved.

The invention discloses a vehicle-road cooperation path planning decision-making system based on deep learning, and belongs to the technical field of intelligent traffic. According to the system, global traffic flow data, vehicle positions and vehicle driving destinations are collected, traffic flow prediction of each road section is carried out in combination with a GNN-LSTM fusion model, and a basis is provided for global path planning; the motion trails of the participants are predicted through a Transform model, and real-time interactive decision making between the vehicles and the participants is achieved through a DQN algorithm; vehicle state data, traffic light time sequence data and intersection geographic parameters are fused based on an MPC algorithm to generate a vehicle intersection passing track, whether a dangerous space-time window exists or not is detected through an IoU algorithm, whether the MPC algorithm with constraints is adopted to correct the vehicle intersection passing track or not is determined, and intersection collaborative decision making is achieved. According to the invention, the dynamic adaptability of vehicle path planning is improved, the safety of vehicle-road cooperation and interaction with surrounding participants is enhanced, and the traffic efficiency is effectively improved.

The invention relates to the technical field of lithium ore conveying, and discloses a lithium ore hopper energy-saving conveying system based on intelligent self-adaptive control. The system comprises a data acquisition module, a self-adaptive control module, an energy-saving optimization module and an execution adjustment module. The data acquisition module acquires multi-dimensional operation data of the lithium ore hopper through a distributed sensor network, and a hopper conveying state database is constructed after standardization processing; the adaptive control module performs dynamic feature extraction on the database, generates a real-time control parameter matrix, establishes an association rule graph of each parameter, and constructs an intelligent adjustment decision model based on the association rule graph; an energy-saving optimization module extracts a key energy consumption feature vector from the real-time control parameter matrix, calculates an energy consumption influence coefficient of each operation parameter, and screens out an optimal energy-saving control sequence in combination with an intelligent adjustment decision model; and the execution adjustment module drives an execution mechanism according to the optimal energy-saving control sequence, and self-adaptive adjustment of hopper conveying parameters is completed.

The invention relates to the technical field of cold-chain commodity transportation, in particular to a temperature monitoring and control method in a cold-chain commodity transportation process, which comprises the following steps: acquiring real-time temperature data, and generating a thermal response digital file of a cold-chain commodity according to the real-time temperature data and a cold-chain commodity category database; real-time positions and external air temperature data of the cold chain commodities are collected, and a temperature threshold value of the next time period is predicted according to the heat capacity characteristic parameters reflected by the thermal response digital archive; a three-dimensional temperature field distribution diagram is constructed based on the temperature threshold value of the next time period and the vibration data of the compartment where the cold-chain commodities are located; generating a control instruction according to the coordinates of the temperature anomaly region in the three-dimensional temperature field distribution diagram; after the control weight coefficient is calculated according to the control instruction and the preset energy consumption optimization objective function, the temperature control operation is executed according to the control weight coefficient and the control instruction, and balance temperature monitoring and control and energy consumption optimization are effectively achieved.

The invention provides a pneumatic conveying system optimization method and system based on a multi-target particle swarm, and relates to the technical field of pneumatic transportation, and the method comprises the steps: collecting key transportation data affecting the conveying efficiency, the energy consumption and the system stability, employing a particle swarm algorithm, regarding each particle as a group of optimizable parameter combinations, and obtaining a particle swarm optimization parameter combination; the method comprises the following steps: calculating a pneumatic transport performance index based on an engineering formula, generating a fitness value by adopting a weighted summation method, comparing the fitness value with a preset threshold value, determining whether optimization is needed or not, carrying out iterative optimization by updating particle positions and speeds by taking minimization of pipeline wear, energy consumption and pressure fluctuation as targets, and when the fitness value meets a threshold value condition, carrying out iterative optimization on the particle positions and speeds. And selecting an optimal solution based on a minimum deviation method. The pneumatic conveying system is optimized based on the multi-target particle swarm algorithm, and the conveying efficiency and the system stability are improved under the target of minimizing pipeline abrasion, energy consumption and pressure fluctuation.

The invention discloses a high-density stereoscopic warehouse ex-warehouse scheduling and transportation system and method based on RGVs, and relates to the technical field of warehouse ex-warehouse scheduling and transportation, and the system comprises a scheduling management center which is in communication connection with a virtual model construction module, a task analysis and planning module, a task distribution module and a real-time monitoring intervention module. Wherein the modules are connected through electric signals; and the virtual model construction module is used for constructing a virtual model of the warehouse and the RGV. By constructing the virtual models of the warehouse and the RGVs and combining the digital twinning technology, the state of a physical entity can be synchronized in real time, the space-time paths of the multiple RGVs can be accurately simulated, path conflicts and traffic congestion are effectively avoided, the efficiency of warehouse-out operation is remarkably improved, task delay caused by the path conflicts is greatly reduced, and the working efficiency of warehouse-out operation is improved. And the accuracy and timeliness of warehouse-out operation are ensured, so that the overall logistics efficiency is improved.

The invention relates to the technical field of intelligent traffic, and discloses an expressway parking risk dynamic early warning method and system based on spatial-temporal characteristics and an adaptive threshold, and the method comprises the steps: collecting vehicle driving data through a multi-type sensor, obtaining vehicle internal data through 5G communication, and employing a D-S evidence theory for fusion; performing spatio-temporal feature processing on the fused data, constructing a spatio-temporal matrix, and extracting features by using a spatio-temporal convolutional neural network; constructing a parking risk assessment model by combining a gating circulation unit of an attention mechanism based on the extracted features, and outputting a parking risk probability in a period of time in the future; according to road sections, time periods and weather conditions, an early warning threshold value is dynamically adjusted by using a reinforcement learning algorithm, multi-channel early warning is triggered when the threshold value is exceeded, and the system comprises a data acquisition and transmission subsystem, a data processing and analysis subsystem and an early warning decision and release subsystem. The objective of the invention is to realize prediction and timely and effective early warning of the parking risk of the high-speed vehicle.

The invention relates to the technical field of intelligent traffic, in particular to an urban traffic flow prediction method fusing a dynamic graph convolutional network and a Transformer, which comprises the following steps: firstly, collecting traffic data in a road network to form a data set, then constructing a space-time dynamic GCN unit to capture dynamic evolution of road network topology, and extracting multi-scale space-time characteristics through expansion time convolution; then, constructing a Transform unit for modeling a global time sequence dependency relationship of the traffic flow; finally, in combination with a gating fusion prediction unit, spatial-temporal features are fused through a spatial-temporal cross attention mechanism, and the prediction precision and robustness are effectively improved. Through verification and evaluation of real data, the method is suitable for a traffic flow prediction scene in an urban road network dynamic environment, and especially has good performance in sudden road conditions and peak hours.

The invention provides a stock matching method and device and electronic equipment, and relates to the technical field of cargo transportation. The shipping matching method comprises the steps of determining shipping information corresponding to a to-be-matched cargo source, wherein the shipping information comprises the to-be-matched cargo source, a to-be-selected driver corresponding to the to-be-matched cargo source, a first shipping score, a driver cargo source pool and a second shipping score corresponding to the driver cargo source pool; determining the goods source selection probability of each goods source selected by the to-be-selected driver based on the to-be-matched goods source, the first shipping score, the driver goods source pool, the second shipping score and the historical transaction order of the to-be-selected driver; and determining a target driver matched with the to-be-matched goods source from the to-be-selected drivers by taking the maximum sum of the transaction probabilities of the to-be-matched goods source and all goods sources in the driver goods source pool as a target based on the shipping information and the goods source selection probability. According to the scheme, invalid exposure of the goods source can be reduced, and exposure resources of the goods source are saved.

The invention discloses an intelligent parking space guiding system for a hospital parking lot, relates to the technical field of intelligent traffic, and is used for solving the problems that the hospital parking lot is seriously jammed in peak hours and special vehicles lack exclusive passing authority. The system comprises a parking space monitoring module, a vehicle identification module, an intelligent guiding module and a user interaction module. The parking space state is monitored in real time through the multi-mode sensor array; recognizing entering and exiting vehicles through a deep learning recognition algorithm based on cameras deployed at entrances and exits, and obtaining corresponding vehicle information; based on parking space state information and vehicle identification information, a route optimal value is dynamically calculated, and a whole-course navigation service from an entrance to a target parking space is provided for a driver, so that accurate detection of the parking space state, special vehicle priority distribution, high-low peak dynamic route planning and whole-process intelligent guidance are realized, the hospital parking efficiency is remarkably improved, and the hospital parking experience is improved. And the congestion condition in the peak period is relieved.

The invention provides a path planning method and system based on four-AGV cooperative carrying, and relates to the technical field of automatic logistics transportation. The method comprises the steps that a high-precision grid map is constructed, a global path is generated in combination with a simulation improved A * algorithm, dynamic weight factors are adaptively adjusted according to the obstacle density, and the path search efficiency is optimized; a time window constraint method is adopted to divide path segments and dynamically allocate time windows; real-time sensing of dynamic obstacles is fused, and a local path is adjusted based on Kalman filtering and a dynamic priority scheduling algorithm; a one-master and three-slave cooperative control architecture is adopted, a master AGV tracks a simulation path, and slave AGVs dynamically adjust control parameters through a fuzzy adaptive PID algorithm; the synchronous jacking operation of the four AGVs feeds back the state to the central server in real time through the Ethernet, so that the synchronism and safety of collaborative carrying are ensured. According to the invention, the technical problems of high path conflict rate, low trajectory tracking precision and slow obstacle avoidance response of a multi-AGV system in a complex environment are solved.

The invention discloses a large-load unmanned aerial vehicle group structure, a control method, system and device and a storage medium, and relates to the technical field of material transportation in the power industry, and the method comprises the steps: selecting a proper unmanned aerial vehicle connection mode according to the working environment of transportation and the load requirement, and constructing an unmanned aerial vehicle group and a material carrying platform; based on the constructed unmanned aerial vehicle group and the material carrying platform, synchronous take-off is achieved in combination with environment and load data; the horizontal attitude of the material carrying platform is controlled through a hierarchical cooperative control architecture based on the synchronous take-off unmanned aerial vehicle group data; on the basis of unmanned aerial vehicle group data after horizontal attitude flight is controlled, the height and the attitude are combined, and a multi-mode hierarchical control strategy is adopted, so that the unmanned aerial vehicles safely land and unload materials; the method can effectively guarantee the safety, stability and high efficiency of transportation in the whole process of cargo transportation, and has important practical application value.

The invention discloses an intelligent scheduling and transportation system for a port unmanned flat car, which belongs to the technical field of port unmanned transportation, and is technically characterized by comprising a cloud intelligent scheduling platform, an unmanned flat car transportation system and a car cloud collaboration module, the cloud intelligent scheduling platform comprises a vehicle cloud control system, an operation task management system, an automatic driving operation monitoring system, an intelligent network connection operation scheduling system and a port scheduling collaborative management system; the unmanned flat car transportation system comprises intelligent sensor hardware, global fusion situation awareness, multi-redundancy fusion positioning, high-precision motion control and anthropomorphic driving decision planning. And the vehicle cloud cooperation module comprises task instruction interaction and vehicle real-time state reporting, and has the advantages that through seamless butt joint with automatic container hoisting equipment, the whole-process unmanned operation of transportation and loading and unloading operation is realized, the port operation efficiency and the intelligent level are improved, and the intelligence and the high efficiency of the port unmanned transportation system are greatly improved.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a road intersection perception and scene understanding method combining a deep learning algorithm and a large language model, and the method comprises the steps: obtaining traffic visual data; performing deep learning target detection based on the traffic visual data to obtain an object corresponding to the traffic visual data, and recording related dynamic information to form a prompt label; based on a traffic monitoring image and a prompt label of a certain view angle of the intersection, scene description of a corresponding view angle of the intersection is obtained through processing of a large language model; and based on the intersection single-view scene description and the prompt label, enhancing the chained thinking fine-tuning large language model to obtain the traffic scene understanding and semantic description of the whole intersection.

The invention discloses an intelligent suspension conveying system for a marine large valve body, and relates to the technical field of valve body conveying. Comprising a conveying track and a suspension frame, a U-shaped frame plate is fixedly installed on the upper end face of the suspension frame, symmetrically-arranged track wheels are rotationally installed on the inner side face of the U-shaped frame plate, and a transmission chain is fixedly installed on the U-shaped frame plate; a cross plate is fixedly installed at the bottom end of the hanging frame, four hanging ropes are fixedly installed on the lower end face of the cross plate, a connecting clamping plate is fixedly installed at the bottom ends of the hanging ropes, and a hook is fixedly installed on the connecting clamping plate; an anti-shaking assembly is arranged on the hanging frame, and the valve body hung on the hook is in a stable state through the anti-shaking assembly. According to the ship valve body anti-shaking device, through cooperative use of the anti-shaking assembly and the pressing and fixing assembly, it is guaranteed that when the valve body is conveyed on the ship, the phenomenon that the valve body shakes greatly and falls off during conveying due to shaking of the ship body is avoided, and the stability and firmness of the valve body on the ship are guaranteed.

The invention relates to the technical field of intelligent traffic, in particular to a dynamic road network collaborative capacity expansion decision-making system based on federated learning-digital twinning, which comprises the following steps of: calibrating pulse type and periodic type data flow weights through a dynamic weight distributor, and generating a normalized feature vector; fusing the normalized feature vectors of all the domains through a privacy protection aggregation engine to obtain a passenger flow pressure distribution prediction matrix; the reconstruction module is used for constructing a digital twinborn body based on the prediction matrix and initializing the digital twinborn body; and based on the initialized twinborn environment, recognizing and sensing a missing region through a blind area data reconstructor, and fusing historical features and real-time data streams of adjacent nodes by adopting a space-time correlation algorithm to reconstruct a complete road network state. According to the method, the dynamic road network collaborative expansion decision system is constructed by fusing federated learning and digital twinning technologies, and the expansion decision efficiency of the traffic road network is improved.

The invention provides a vibration reduction buffering and posture self-adaptive adjusting method in wind turbine generator cabin transportation, and relates to the technical field of wind turbine generator transportation, which comprises the following steps: collecting transportation vibration time sequence data, after wavelet denoising and normalization processing, adopting synchronous compression decomposition and nonlinear cross-scale feature mapping to extract a dominant frequency component, and then extracting the dominant frequency component; synchronously calculating time domain parameters (a root mean square value, a peak factor and a waveform factor) and frequency domain parameters (a frequency centroid and bandwidth energy distribution); inputting the multi-dimensional features into a graph convolutional neural network to realize vibration mode classification, constructing an attitude optimization model based on a classification result, performing cooperative solution through a multi-agent depth deterministic strategy gradient algorithm, and outputting an optimal attitude adjustment parameter; after the adjustment is implemented, new vibration data are collected to update network parameters, and closed-loop control of vibration feature recognition and attitude optimization is formed. Transportation vibration impact is effectively reduced, and the transportation stability and safety of wind power equipment are improved.

The invention is suitable for the technical field of marine transportation, and provides a container marine transportation network climate adaptability recovery method fusing a toughness theory, and the method comprises the steps: obtaining liner plan data, ship dynamic data, route adjustment data, port geographic coordinate data and multi-scale geofence data, introducing time dimension parameters, and obtaining a time dimension parameter; a space-time weighted container shipping dynamic network is constructed; the cascading failure strength of the space-time weighted container shipping dynamic network is quantified, and then the comprehensive efficiency index of the space-time weighted container shipping dynamic network is calculated; quantifying a toughness index of the space-time weighted container shipping dynamic network under an extreme weather event; and the cost effectiveness of the climate suitability improvement strategy of the space-time weighted container shipping dynamic network is evaluated. According to the method, the influence mechanism of the extreme weather event on the network efficiency can be quantitatively analyzed, and the recovery capability and adaptability of the network under the extreme weather event can be comprehensively evaluated.

The invention discloses a thin film tank pump tower base device, and belongs to the technical field of LNG storage and transportation, the thin film tank pump tower base device comprises a base main body, the base main body is cylindrical, the periphery of the base main body is provided with a first connecting part, and the first connecting part is connected with a first shielding layer; wherein the first connecting part is annular, the first shielding layer is formed by splicing two corrugated plates, the two corrugated plates are integrally formed, a circular through hole is formed in the connected end face of the two corrugated plates, and the two corrugated plates are directly connected with the first connecting part. According to the pump tower base, the corrugated plate is integrally formed, the base body is directly connected with the specially-made corrugated plate, the problem that a conventional pump tower base needs to be in transition connection through a connecting plate is solved, the problem that the connecting plate and the corrugated plate are not smoothly connected is solved, the installation procedure of the pump tower base can be simplified, and the preparation efficiency of a thin film tank is improved.

The invention discloses a thin film enclosure system of a low-temperature storage tank and fluid transportation equipment, and relates to the technical field of fluid transportation, the enclosure system is installed on the inner wall of the low-temperature storage tank, the enclosure system comprises a plurality of heat insulation modules installed on the inner wall of the storage tank, and the heat insulation modules are spliced and cover at least part of the inner wall of the storage tank; assembling grooves are formed in the splicing positions of the heat insulation modules; the bridging blocks are installed in the assembling grooves, the bridging blocks can connect the two adjacent heat insulation modules together, and the bridging blocks are fixedly connected with the heat insulation modules through first assembling studs; wherein an expansion stud is arranged on the inner wall of the storage tank, the expansion stud penetrates through part of the heat insulation module and is locked with the heat insulation module through a nut, and the projection position of the arrangement position of the first assembly stud and the projection position of the arrangement position of the expansion stud on the heat insulation module are mutually staggered. According to the heat insulation module, the thickness limitation of an existing heat insulation module of a bridging block can be broken through, and the problem of cold leakage of a gap between every two adjacent heat insulation modules can be solved through arrangement of the bridging block.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent network connection vehicle driving risk assessment method and a visualization system based on multi-source data fusion. According to the method, multi-source data such as vehicle perception, communication and maps are fused, an artificial potential field theory is combined, a gravitational force risk potential field function, a repulsive force risk potential field function and a road boundary repulsive force potential field function are constructed respectively, and a unified total risk potential field TPF is formed by a gravitational force risk potential field, a repulsive force risk potential field and a road boundary repulsive force potential field; according to the method, the driving risk of the intelligent networked vehicle is obtained, risk grade division and real-time visual display are realized through clustering analysis, the accuracy and dynamic response capability of risk assessment are improved, the method is suitable for safety decision support of advanced driving assistance and automatic driving systems, real-time assessment of the driving risk can be realized more accurately and efficiently, and the risk assessment efficiency is improved. And the risk assessment precision and the dynamic response capability are improved.

The invention relates to the technical field of transportation, in particular to a vehicle emission anomaly detection and tracing method and device and electronic equipment, and the method comprises the steps: inputting a current operation condition and current operation data into a dynamic threshold model to obtain a first detection result, and inputting the first detection result into an emission prediction model to obtain a second detection result; fusing the first detection result and the second detection result to obtain a final detection result when the first detection result and the second detection result do not meet a preset vehicle emission abnormal judgment condition, judging that the vehicle emission is abnormal when the final detection result is greater than or equal to a preset threshold value, and judging that the vehicle emission is abnormal based on a vehicle emission abnormal knowledge graph database. And a Cosine similarity calculation strategy is utilized to obtain reasons and probabilities of abnormal emission. The problems that a traditional vehicle emission anomaly detection and tracing method is low in emission anomaly detection efficiency, high in misjudgment rate and difficult in tracing are solved, accurate detection of emission anomaly is achieved, closed-loop management from anomaly triggering to root cause tracing is completed, and operation and maintenance intelligence is improved.