484 results about "Dangerous goods" patented technology

The invention discloses a visual and three-dimensional modeling luggage automatic identification method, equipment, a medium and a product. The method comprises the following steps: firstly, carrying out dynamic multi-view scanning on luggage entering a scanning area of a conveyor belt to obtain a multi-view image, carrying out deep learning to obtain three-dimensional point cloud data, and fusing the three-dimensional point cloud data with X-ray transmission density data to form a preliminary three-dimensional model; whether the model is complete or not is judged by combining geometric closure and material density continuity, and if not, features are extracted through an adversarial generative network, and occlusion area point cloud data are predicted for complementation; and finally, matching the complementation model features with a dangerous goods feature library, and judging whether the dangerous goods are dangerous goods or not. By implementing the technical scheme provided by the invention, the information of the articles in the luggage can be comprehensively obtained, the complete and accurate three-dimensional model can be constructed, whether the articles are dangerous articles or not can be efficiently and accurately judged, and the efficiency and the safety of aviation security inspection are improved.

The invention discloses a dangerous goods transportation charging method based on risk assessment, and relates to the technical field of dangerous goods transportation charging, and the method comprises the following steps: collecting the data of traffic flow, accident occurrence frequency, environmental factors and the like of each road segment in a dangerous goods transportation network, and calculating a dangerous goods transportation charging result based on a population coverage risk model and an accident expected value calculation method; and evaluating the potential risk level of each road section. Through multi-dimensional data fusion and an advanced algorithm, real-time assessment and path optimization of transportation network risks are realized. A population coverage risk model and an accident expected value calculation method are adopted, high-risk road sections are accurately recognized, transportation tasks are reasonably distributed, and safety is improved. Meanwhile, the genetic algorithm is utilized to optimize the transportation path, balance the cost and risk, reduce the transportation cost and guarantee the safety. Global path distribution is optimized through a simulated annealing algorithm, excessive load of high-risk road sections is avoided, task distribution is dynamically adjusted according to risk prediction, reasonable resource allocation is ensured, and transportation efficiency and safety are improved.

The invention discloses a logistics transportation platform monitoring system and method based on big data, and relates to the technical field of logistics monitoring. Based on historical road conditions, traffic flow, accident distribution and high-risk areas, combined with physicochemical properties of dangerous goods and real-time weather conditions, comprehensive risk values and adaptive values of paths are calculated; the route safety is guaranteed, and meanwhile, the transportation environment is adapted; meanwhile, based on the GPS signal intensity, the interruption area is marked, and a standby path is planned, so that the path continuity is improved; in the transportation process, a vehicle sensing module collects position information, environmental parameters and cargo state data in real time, integrates a GPS, inertial navigation and a historical track prediction model, dynamically corrects the position of a vehicle, verifies the accuracy of the position in combination with road signs, and enhances the monitoring precision; in addition, based on multi-source data, abnormal conditions are judged in real time, grading processing is carried out according to the severity of anomalies, and transportation safety is effectively guaranteed.

The invention discloses a traffic safety early warning method and device for a dangerous cargo transport vehicle, and the method comprises the steps: S1, monitoring the operation state of the dangerous cargo transport vehicle in real time, and collecting environment data in real time; and S2, based on the collected data, establishing a risk assessment model by using a convolutional neural network algorithm, analyzing the influence of extreme weather and complex terrains on the safety of the dangerous cargo transport vehicle, developing a risk assessment algorithm, and according to a risk assessment result, the risk assessment model established by using the convolutional neural network algorithm in the invention can be used for evaluating the safety of the dangerous cargo transport vehicle. And specific influences of extreme weather and complex terrains on vehicle safety can be deeply analyzed. The risk assessment model can learn various risk factors in a complex environment and comprehensively assess the risk factors, so that the accuracy and reliability of risk assessment are improved. The system can continuously monitor the change condition of the risk, timely adjust the early warning strategy according to the actual condition, and continuously improve the adaptive capacity and the early warning effect.

The invention provides a dangerous goods container yard stockpiling operation planning and scheduling method and system, and relates to the technical field of dangerous goods container stockpiling intelligent decision-making, and the method comprises the steps: constructing a risk data set of dangerous goods of a current shift; determining dangerous goods container grouping and stacking safety distance; constructing a dangerous goods container stockpiling decision optimization model; and inputting the data obtained in real time into the dangerous goods container stockpiling decision optimization model to obtain an optimal dangerous goods container stockpiling scheme. The technical problems that in the prior art, a traditional stockpiling planning method mainly depends on artificial experience, operation efficiency is low, the utilization rate of the storage yard is low, and even potential safety hazards exist are solved, and under the condition that the safe stockpiling distance of the dangerous cargo container in the storage yard is guaranteed, the safety of the storage yard is guaranteed. The technical effects that the assigned number of the moving carrying machines is optimized, the operation driving cost and the follow-up box turning cost of the moving carrying machines are reduced, the stockpiling operation efficiency is improved, and the safety risk is reduced are achieved.

The invention provides a three-dimensional intelligent sorting method and system for dangerous goods logistics. Multi-frequency-band sound wave signals are emitted in a storage space through a sound wave standing wave array, a three-dimensional frequency coding grid is formed through standing wave interference, millimeter-level accurate positioning of a dangerous goods container is achieved, and a sorting execution instruction is generated. Airfoil piezoelectric ceramics of the aircraft receive sound wave signals and convert the sound wave signals into flight attitude parameters, a sorting path is planned in combination with a path tracking algorithm, meanwhile, a dynamic sorting channel opening is generated through airfoil flutter frequency and sound wave standing wave resonance, and a leakage container is accurately guided to be transferred to an isolation area. According to the method, the accuracy and efficiency of dangerous goods sorting can be remarkably improved, the risk of manual intervention is reduced, and a whole-process intelligent solution is provided for storage safety.

The invention discloses a highway hazardous chemical substance liquid leakage accident monitoring method and a highway hazardous chemical substance liquid leakage accident monitoring system based on computer vision. The method comprises the following steps: arranging and installing an identifier configured for hazardous chemical substance vehicle identification on a vehicle for loading and transporting hazardous chemical substances; the identifier comprises an optical identifier configured to be used for improving the visual identification degree of the dangerous goods vehicle and a digital identifier configured to realize encrypted storage and quick reading of vehicle information; on the basis of an existing road monitoring system, a lightweight identification model configured for detecting the states of an optical identifier and a digital identifier in real time is deployed and loaded in the existing road monitoring system; performing visual positioning on the road surface projection pattern through a camera, and calculating the real-time position of the vehicle by combining map coordinates so as to supplement the positioning blind area of the GPS in the tunnel mountain area; accident probability calculation is carried out based on the identification state to determine whether the vehicle is suspected to have an accident; and when the accident is judged to be suspected, triggering an accident response process. And the accident response efficiency is obviously improved.

The invention belongs to the technical field of data analysis, and relates to a port ship berthing dynamic risk prediction and pilot intelligent matching method. According to the invention, by quantitatively calculating the comprehensive qualification score and dividing the grade, dynamic matching of the pilot and the task risk grade is realized, an evaluation mode dominated by artificial experience is broken, the accuracy of pilot qualification evaluation is improved, and the operation error rate caused by insufficient personnel qualification is reduced; by dynamically simulating the berthing process and calculating risk indexes such as the offset rate and the collision energy, the berthing risk is pre-judged and graded, the berthing risk identification accuracy is improved, equipment operation parameters, dangerous goods states and meteorological data are analyzed in real time, risks such as faults, leakage and extreme weather are accurately identified, corresponding measures are triggered, and the safety of berthing is improved. Targeted management of berthing risks is facilitated, and accident loss and port operation interruption risks are effectively reduced.

The invention provides a port dangerous cargo risk assessment method and system based on a V-HAZOP model, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining the feature information and additional information of port dangerous cargos, and building a risk assessment basic data set; preprocessing the risk assessment basic data set based on an HAZOP method, and establishing a risk data index library; analyzing the risk data index library based on a correlation analysis algorithm, and updating the risk data index library; obtaining final risk index data on the basis of a Bayesian network conversion method based on the fault tree analysis model; performing visual data modeling according to the final risk index data, and constructing a V-HAZOP model; displaying the V-HAZOP model by using a VIEW-Glass multi-dimensional visualization technology so as to obtain data; according to the invention, the management difficulties of various types of dangerous goods, different physicochemical properties, high operation requirements and the like in the existing port can be solved, the safety production operation of the dangerous goods storage yard is optimized comprehensively, and the professional storage yard intelligent operation management of the dangerous goods in the port is realized.

The invention provides a dangerous goods risk detection method and device, equipment, a medium and a program product, and the method comprises the steps: constructing a graph structure according to the interaction behavior data generated in the import and export process of dangerous goods, and obtaining an original structure graph of the dangerous goods in an import and export scene; performing feature enhancement processing on the original structure diagram based on a diagram diffusion algorithm to obtain a feature-enhanced high-order structure diagram; and based on the original structure chart and the high-order structure chart, adopting a pre-trained anomaly prediction model to detect the abnormal risk of the dangerous goods in the import and export process, and performing risk early warning when the abnormal risk of the dangerous goods in the import and export process is detected. According to the embodiment, the complex interaction mode of the dangerous goods and the interaction participants in the entrance and exit process can be comprehensively described through the static graph structure and the high-order structure graph, the accuracy of abnormal condition recognition in the entrance and exit process of the dangerous goods is improved, and therefore the abnormal risk in the entrance and exit process of the dangerous goods can be accurately recognized.

The invention relates to the technical field of dangerous cargo management, in particular to an intelligent compliance verification system for dangerous cargo boxing operation. Comprising a data acquisition preprocessing unit; the law and regulation graph construction iteration unit is used for extracting a'law and regulation term number-verification dimension-constraint condition 'triple of the law and regulation in the dangerous cargo boxing field and introducing an improved three-dimensional dynamic weight conflict resolution algorithm according to the efficacy level, the release subject and the application range indexing; a multi-agent collaborative verification unit; a risk early warning report generation unit; and a man-machine cooperation closed loop unit. According to the method, the problems of lagging of laws and regulations and clause conflicts are solved by adopting triple extraction of'laws and regulations clause numbers-verification dimensions-constraint conditions', a three-dimensional dynamic weight conflict resolution algorithm based on laws and regulations effectiveness-scene matching degree-release time and a technical means of periodically optimizing weight parameters through a reinforcement learning model.

The invention discloses an automatic wharf cold dangerous cargo storage yard layout simulation method and system considering manual and unmanned mixed operation, and belongs to the field of automatic wharf cold dangerous cargo storage yard layout. Firstly, data are collected to generate an overall layout set A; secondly, randomly selecting one overall layout from A to obtain a first-stage optimization model, and solving the first-stage optimization model by using a genetic algorithm; then, constructing a second-stage physical model, and recording the second-stage physical model as a simulation model set; and finally, evaluating, comparing and selecting different simulation models to obtain an optimal layout and scale combination. The automatic wharf cold dangerous cargo storage yard layout simulation system comprises a data acquisition module, a layout mode generation module, a scale optimization module, a simulation model construction module, a comprehensive evaluation module and a judgment module. According to the method, layout and scale collaborative optimization is achieved through two-stage optimization and simulation, the port operation efficiency, the resource utilization rate and safety can be improved, meanwhile, energy consumption and carbon emission are reduced, and scientific decision support is provided for port design.

The invention discloses a metro dangerous goods terahertz detection method based on adaptive downsampling and a multi-dimensional attention mechanism. The metro dangerous goods terahertz detection method comprises the following steps: acquiring a terahertz image data set; building a deep learning network model, wherein the deep learning network model comprises a backbone feature extraction network, a check feature extraction network, a down-sampling module, a fusion channel and space attention module, a space attention module and a YoloHead detection head which are connected in sequence; performing dual-path processing on the input feature map through parallel pooling and convolution operation; in the fusion channel and space attention module, global context modeling is carried out on the feature map; in the space attention module, long-range context dependence is captured through a strip-shaped pooling layer; inputting the multi-scale feature map output by the check network into a YoloHead detection head, and outputting a target detection frame; a redundant detection frame is removed through a non-maximum suppression algorithm, and a detection result with the highest confidence coefficient is reserved.

The invention relates to the technical field of X-ray hazardous article detection, and discloses an X-ray hazardous article detection algorithm based on improved YOLOv8-seg, which can detect the types and specific areas of hazardous articles in disordered security check articles. The method comprises the steps that a multi-channel interactive convolution module is designed, and based on a channel decoupling and recombination mechanism and a heterogeneous convolution kernel collaborative optimization strategy, the multi-scale feature discrimination capability is enhanced while calculation redundancy is reduced; the method comprises the following steps: constructing an SPPF-LSKA (Space Purpose Pooling Function-Learning SKA) composite module, and effectively improving the boundary retention degree of an occluded target through deep fusion of a large-kernel space attention mechanism and adaptive pyramid pooling.

The invention relates to a dangerous goods identification and detection system based on an intelligent AI algorithm. The system comprises an environment required for deploying YOLOv8s development training; the attention of the EMA is added to a check layer of a YOLOv8s algorithm; a deformable convolution DCNv2 is added to the backbone part of the YOLOv8s, and the deformable convolution DCNv2 is added to the backbone part of the YOLOv8s; an improved pigeon inspired algorithm is introduced to automatically update and adjust the learning rate, and finally the final detection precision of the improved model is improved on the hyper-parameter optimization level; carrying out an experiment on the improved YOLOv8s model, and carrying out comparative analysis on the improved YOLOv8s model and an experiment result obtained by an original YOLOv8s algorithm; an improved YOLOv8s model is deployed on a PyQt5 platform, a visual interface is constructed, and early warning can be achieved while prohibited goods are detected. The system can automatically give an alarm to remind security inspectors while detecting contraband goods, and can assist the security inspectors in daily work, so that the detection precision of the security inspectors on dangerous goods is improved, the false detection rate is reduced, the working efficiency of the security inspectors is improved, and the waiting time of passengers is greatly shortened.

The invention discloses a container terminal truck intelligent scheduling method and system based on multiple driving modes. The method comprises the steps that a whole-process system architecture integrating a central scheduling platform, a vehicle-mounted intelligent terminal and a sensing and communication network is constructed; the method comprises the following steps: acquiring basic data of vehicles, tasks, road networks and environments, constructing a multi-objective optimization function, establishing a dangerous goods risk integral model, respectively setting mathematical constraints for task assignment, path planning, hybrid driving coordination, dynamic path tracking and mode switching, and forming a multi-dimensional mathematical model; an improved non-dominated sorting genetic algorithm is adopted to solve the model, and a personalized scheduling scheme including vehicle-task assignment, path planning, time requirements and risk control rules is generated; through cooperation of the central scheduling platform and the vehicle-mounted intelligent terminal, MPC dynamic path tracking is adopted for the unmanned vehicle, visual and voice dual-mode guidance is provided for the artificial vehicle, and hybrid driving cooperative management and control are implemented, so that the vehicle operation efficiency and the dangerous goods transportation safety are guaranteed.

The invention discloses a security declaration file generation method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: receiving a dangerous goods transportation voucher of a target dangerous goods provided by a freight station; carrying out digital preprocessing on the dangerous goods transportation voucher to obtain a standardized digital file; extracting a plurality of dangerous article field information from the standardized digital file; based on the OCR recognition accuracy, the semantic matching degree and the field integrity, determining the confidence score of the field information of each dangerous article; key field information is determined according to the confidence score; generating a potential risk score based on the key field information and the aviation dangerous goods knowledge graph; generating a comprehensive evaluation value according to the confidence score and the potential risk score; and when the comprehensive evaluation value is higher than or equal to a preset evaluation value threshold value, generating a security declaration file based on a preset airline template and the standardized digital file. The problem that the security declaration file generated by the related technology is not accurate and reliable enough can be relieved.

The invention discloses a toll station special situation emergency processing system integrated with a localized AI large model, and belongs to the technical field of toll station management and control. Comprising an edge computing device, a multi-element sensing module, a localization AI large model engine, a dynamic rule engine and an execution control interface. The edge computing device is responsible for being deployed in a toll station site and is embedded hardware with AI acceleration computing power, and the multi-source sensing module is responsible for being connected with a camera, a millimeter wave radar and an ETC-RSU device. According to the invention, by deploying the lightweight large model in the edge server, millisecond-level identification and automatic disposal of special situations such as vehicle card punching and dangerous goods leakage are realized. The system adopts a multi-modal data fusion technology, and is combined with a dynamic plan engine, so that the operation of core functions can still be ensured in an off-network environment, and the emergency response efficiency and safety of the toll station are remarkably improved.

The invention discloses an automatic container terminal quay crane operation mode intelligent switching system and method, and the system comprises a mode management unit which is used for defining an automatic mode, a manual intervention mode and a special mode, and configuring mode switching conditions; the dynamic safety detection unit is used for monitoring the lifting appliance height and container operation area parameters in real time, the types of containers comprise tanks, overrun boxes and dangerous goods boxes, and the types of operation areas comprise an on-board area, a bridge bottom IGV operation area and a bridge rear container truck operation area; the task coordination engine unit is used for responding to the mode switching request and executing a corresponding task; and the abnormity recovery unit is used for triggering a task recovery or manual intervention mode based on the container carrying state of the lifting appliance and the ID of the container when the special mode is switched back to the automatic mode. According to the embodiment of the invention, while the cost advantage of a single-trolley automatic system is maintained, the system safety and the operation efficiency are optimal, and a brand new solution is provided for automatic modification of a wharf.

The invention discloses a digital dangerous cargo transportation supervision system and method, belongs to the technical field of vehicle supervision, and aims to solve the problems that in digital dangerous cargo transportation, loading and unloading links are insufficient in authenticity verification, abnormal fluctuation in the transportation process is difficult to recognize in real time, and abnormal handling response is delayed. After a dangerous cargo transportation electronic waybill is obtained, loading and unloading scene verification is completed by delimiting a compliance area, shooting an initial scene image and dynamically constructing an image flow sequence, and a carrying password is generated for cargo encryption storage and unloading unlocking; a passing route is segmented, a vehicle operation base line is established based on historical data, data delay and loss are distinguished by monitoring the conformity and transmission interval of transportation data and the base line, abnormal fluctuation is recognized, and early warning is triggered; and during early warning, a target verification position is selected based on positioning, and the vehicle is guided to the verification area to complete abnormity verification. And cheating behaviors in loading and unloading links are avoided, transportation risks are identified, abnormities are quickly handled, and dangerous cargo transportation safety and supervision efficiency are effectively improved.

The invention discloses an intelligent paid waste recovery system, which relates to the technical field of waste recovery and comprises an intelligent recovery box main body, a central control module, a user interaction module, a settlement module and a user side applet, the intelligent recycling bin main body comprises a single recycling bin door, an independent identification channel, a sorting execution module and a double-category sorting bin body; the double-category sorting cabin body comprises a paper-plastic cabin body and a fabric cabin body, electric sorting gates are arranged at the tops of the paper-plastic cabin body and the fabric cabin body, and weighing settlement units are arranged at the bottoms of the paper-plastic cabin body and the fabric cabin body; a dangerous goods detection module, a delivery quantity limiting module, an overlapping recognition module, an overlapping goods dispersing module and a self-adaptive illumination category recognition module are sequentially integrated in the independent recognition channel in the goods conveying direction; according to the scheme, identification precision fluctuation can be improved, and a stable and reliable classification effect is formed.

The invention discloses a road transportation informatization safety supervision method and system, and relates to the technical field of route planning. Acquiring a road network of the target vehicle in the transportation area to obtain a route distribution map; for each section of route in the route distribution map, determining a transportation score, a time score and a danger score corresponding to the section of route according to historical data; determining a total transportation score of each route according to the transportation score, the time score and the danger score; determining an optimal path of the target vehicle from the starting point to the terminal point according to the total transportation score of each section of route; and for each section of route in the optimal path, determining the vehicle detection frequency of the route according to the danger score. The optimal path is determined by comprehensively considering the transportation score, the time score and the danger score, then the vehicle detection frequency of the route with the high danger score is determined, the monitoring strength can be dynamically adjusted, for the route with the high danger score, the vehicle detection frequency is increased, accidents or other safety events are effectively prevented, and the safety of dangerous goods transportation is improved.

The invention discloses a hazardous chemical detection method based on a terahertz lightweight capsule network, and relates to the technical field of terahertz spectrum detection.The method comprises the steps that target absorption spectrum data of a to-be-detected article are obtained, and target absorbance characteristics of the target absorption spectrum data are extracted; inputting the target absorbance characteristics into a terahertz lightweight capsule type network model trained based on a sample data set; classifying the target absorbance characteristics through the terahertz lightweight capsule type network model, and outputting a prediction probability value that the to-be-detected article corresponding to the target absorbance characteristics is classified into each type of dangerous chemical article; and outputting the type of the dangerous chemical with the maximum prediction probability value as a detection result of the to-be-detected article. According to the method, the problem that the detection accuracy is low when an existing deep learning model is used for detecting dangerous chemicals is solved.

The invention belongs to the technical field of target detection, and particularly relates to an X-ray security check image hazardous article segmentation and detection system based on improved YOLOv8. The method sequentially comprises the following steps: acquiring an X-ray security check image data set, and dividing the X-ray security check image data set into a training set and a test set according to a proportion; according to the method, a YOLOv8-seg architecture is improved, convolutional layers of a backbone network and a neck network are optimized, and the extraction capability of features of the backbone network and the neck network is enhanced by combining space-to-Depth Conversion (SPD-Conv), so that the segmentation precision of small targets, occluded objects and dangerous goods under a complex background is improved; and the detection capability of the model on the X-ray security check image dangerous goods is improved. According to the method, the YOLOv8-seg algorithm is improved by combining the SPD-Conv module, the dangerous goods in the X-ray security check image of the transportation junction can be accurately segmented and detected under the complex background, the detection capability is remarkably improved, and powerful technical support is provided for optimizing the security check process.

The invention discloses a dangerous article detection method and system for optimizing feature CBAM-YOLOv10, and relates to the technical field of dangerous article detection, and the method comprises the steps: obtaining a dangerous article X-ray image, and carrying out the preprocessing of the dangerous article X-ray image; a YOLOv10n network model is constructed in combination with bidirectional feature pyramid network optimization features, a backbone feature fusion part of YOLOv10n is improved by fusing a CBAM attention mechanism, and a CBAM-YOLOv10 inspection model is generated; a small target detection head is added to the check part of the inspection model, and the inspection model is optimized; and inputting the preprocessed X-ray image of the dangerous goods into the inspection model to complete the detection of the image of the dangerous goods. The method can focus on the most important characteristics of the dangerous goods, enhances the expression ability of the characteristics of the dangerous goods, improves the detection accuracy of the dangerous goods, and improves the security check efficiency of urban subways and interurban railways.

The invention belongs to the technical field of computer vision and target detection, and particularly relates to an X-ray security check image hazardous article detection system combining multi-scale attention to improve YOLOv8, and the system comprises the steps: firstly carrying out the data preprocessing and enhancement of an X-ray security check image data set, so as to improve the number of data set samples; according to the method, a dangerous article detection framework based on YOLOv8 is modified, a Multi-scale Enhancement Attention Module module is combined, a YOLOv8-Multi SEAM network is constructed, the improved model can focus feature information of targets of different scales more accurately, and therefore the detection precision of dangerous articles is effectively improved. According to the method, the dangerous goods in the X-ray security check image are detected by using the YOLOv8-MultiSEAM algorithm, so that the detection precision and efficiency are improved, the security check work can be better served, and the public security is guaranteed.

The invention provides explosion-proof danger-eliminating disposal equipment. The equipment comprises a three-layer annular protection framework and an intelligent mobile platform framework, wherein the three-layer annular protection framework comprises a sensing layer, an execution layer and a protection layer; the intelligent mobile platform architecture comprises a multi-mode mobile chassis and an environment sensing system; the sensing layer comprises a detection equipment module and an intelligent identification and evaluation module; the execution layer comprises an emergency disposal equipment module; the protective layer comprises a protective storage and transportation equipment module; the sensing layer detects and senses data and information of dangerous goods in a field environment through a detection equipment module, and carries out risk assessment judgment and execution coping strategy formulation through an intelligent identification and assessment module; and the execution layer, the protection layer and the intelligent mobile platform operate the emergency disposal equipment module to perform danger elimination action according to the execution coping strategy. According to the method, through the whole-process closed-loop design of'perception-decision-disposal-protection ', the equipment damage rate is greatly reduced while the disposal efficiency is ensured, the risk elimination success rate of a complex binding scene is improved, and the single disposal cost is remarkably reduced.

The utility model relates to the field of dangerous goods transportation protection, and discloses a protective device for chemical liquid storage tank transportation, which comprises a fixed plate, a vehicle plate, a fixed rod, an air cylinder, a rotating piece, a rotating rod, a long connecting rod, a short connecting rod and a protective plate, when the protective device is used, the fixed plate is fixed on the vehicle plate, and when chemical liquid is infused or sundries fall on the protective plate, the air cylinder is started to stretch a piston rod; the lower end of the rotating rod rotates in the direction away from the air cylinder to drive the two long connecting rods to move and drive the rotating piece to rotate, and the short connecting rod rotates upwards and drives the two protection plates to rotate upwards along the fixing column. When transportation is started, the piston rod of the air cylinder is shrunk, the lower end of the rotating rod is driven to rotate reversely, the two connecting rods move reversely, and therefore the rotating piece is driven to rotate reversely, the short connecting rod is driven to rotate downwards, and the two protection plates rotate downwards along the fixing columns till being flatly placed above the storage tank. The storage tank has the advantages of being not prone to water accumulation, convenient to use and easy to clean, and the service life of the storage tank is prolonged.

The invention provides a ship lock intelligent scheduling method and system considering ship classification and dangerous goods isolation, and the method comprises the steps: S1, obtaining the information of a to-be-scheduled ship and the information of a lock chamber, and constructing a conventional ship information list ListN and a dangerous ship information list ListD based on the danger level of the to-be-scheduled ship; s2, according to the management method of the hydro-junction where the lock chamber is located, the danger level of independent lockage is obtained, and the safety size of the ship needing independent lockage in the ListD is modified so that the lock chamber can be fully occupied by the ship; and S3, respectively taking the ListN and the ListD as an input list ListI, calculating a global highest scheduling score of the ListI based on the safety size, the arrival time and the type of the ship, and selecting a global optimal scheduling scheme corresponding to the higher global highest scheduling score in the ListN and the ListD as a final scheduling scheme. The united targets of improving the utilization rate of the lock chamber, reducing the waiting time of the ship, weighing the priority of the ship and isolating the dangerous goods ship are achieved.

The invention discloses a temperature on-line monitoring and early warning system for fire protection clothing, which relates to the technical field of fire protection monitoring and early warning and comprises a data collection module, a data preprocessing module, a calculation processing module and an early warning execution module. Wherein the calculation processing module comprises a dynamic temperature risk unit, a comprehensive environment risk unit and a safety prediction unit. The dynamic temperature risk unit fuses temperature, a safety threshold value, a change rate, material aging, humidity and a heat reflection attenuation coefficient, outputs a dynamic temperature risk coefficient and reflects a safety margin, and the comprehensive environment risk unit introduces gas concentration, the number of hazardous articles, smoke attenuation and an action intensity coefficient based on the dynamic temperature risk coefficient, constructs an environment coupling risk index, and finally performs the safety analysis on the environment coupling risk index. And the safety prediction unit integrates a basic safety time constant, an environment coupling risk index, a dynamic temperature risk coefficient, a heat flux density and an oxygen concentration correction coefficient, outputs a residual safety time prediction value, and guides an evacuation decision of a fireman.