614 results about "Dangerous goods" patented technology

The invention provides a management method and system for hazardous chemicals in a laboratory, and relates to the technical field of hazardous articles, and the method comprises the steps: obtaining hazardous article operation video data of an operator through an image collection device, extracting key node coordinates and skeleton movement track information, and mapping the information into standardized operation behavior sequence data; and a time sequence behavior evaluation model constructed based on a long and short term memory network is used to identify the non-standard operation fragments, a risk level coefficient is calculated in combination with a dangerous goods attribute information base, and a multi-dimensional evaluation report is generated. And according to the evaluation report, calling a correction strategy library to generate an improvement suggestion containing characters, videos and three-dimensional models, and pushing the improvement suggestion to an operator. By monitoring the behavior of the operator after executing the improvement suggestion and dynamically updating the risk score and the time sequence behavior evaluation model, the dynamic optimization of the evaluation standard is realized, and the safety of the laboratory hazardous chemical substance operation is effectively improved.

The invention relates to the technical field of multi-source information integration, and discloses an intelligent fire-fighting command method and system based on a multi-modal AI large model.According to the intelligent fire-fighting command method and system, fire scene data are obtained in real time through multi-terminal information collection, scene reconnaissance feedback is conducted through flame and smoke recognition and dangerous goods recognition through an image recognition algorithm, and the fire-fighting commanding efficiency is improved. A pre-trained multi-mode live restoration model is utilized to carry out restoration simulation on a fire scene, a multi-source live simulation model is generated to analyze and formulate a personalized rescue plan, positioning and state information of a rescue unit is collected in real time, a rescue unit simulation model is constructed, and specific rescue execution steps are analyzed according to the personalized plan. The steps help commanders to realize accurate commanding and efficient rescue, the rescue decision accuracy is improved, the response time is shortened, the resource allocation is optimized, the emergency response capability and the successful rescue rate of a fire scene are remarkably improved, and the problem that in the prior art, the efficiency is low when multi-source information is manually integrated to restore the fire situation is solved.

The invention belongs to a target detection technology in the field of computer vision, and particularly relates to an X-ray security check image hazardous article detection system based on improved YOLOv11. Firstly, an X-ray security check image data set is divided into a training set and a test set according to the proportion of 6: 4, the system is based on a YOLOv11 architecture, a space and channel cooperative attention mechanism (SCSA) is introduced into a backbone network structure, the SCSA comprises two parts, namely sharable multi-semantic space attention (SMSA) and progressive channel self-attention (PCSA), and the SCSA is used as a main network of the system. The objective of the invention is to improve the detection performance of hazardous articles in an X-ray security inspection image, effectively enhance the understanding ability of a model for complex scenes, and improve the detection precision and speed of small targets, shielded objects and low-contrast hazardous articles. According to the method, the YOLOv11 algorithm is improved by combining the SCSA module, the detection precision and speed of the dangerous goods in the X-ray security check image are remarkably improved, and powerful technical support is provided for improving public safety and optimizing the security check process.

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 ship dangerous goods transportation safety control method and system, and relates to the field of marine transportation, and the method comprises the steps: obtaining dangerous goods parameter data of a dangerous goods monitoring sensor, the dangerous goods parameter data comprising gas concentration, temperature and smoke concentration; under the condition that the sensor parameter data exceeds a preset parameter threshold value, acquiring motion attitude data and inclination angle data of the current ship and pressure distribution data of the cargo on the bottom and the side surface of the cargo hold; calculating the center-of-gravity offset of the current ship based on the motion attitude data, the inclination angle data and the pressure distribution data; calculating a fixed adjustment parameter of the cargo fixing device based on the gravity center offset; and sending a device adjustment instruction containing the fixed adjustment parameter to the target client. By implementing the method, the efficiency of adjusting the cargo fixing device can be improved.

The invention relates to the technical field of dangerous goods storage, and discloses a data twinborn early warning system and method for a dangerous goods storage area. The model establishing module is configured to establish a data twinborn model of the storage area according to the image information; the acquisition module is configured to acquire the environment information and the dangerous goods information of the storage area and display the information on the data twinborn model; the environment evaluation module is configured to obtain an environment evaluation value according to the temperature information and the humidity information and adjust the environment evaluation value according to the change value amplitude; the dangerous goods evaluation module is configured to obtain a dangerous goods evaluation value according to the complete state of the dangerous goods and adjust the dangerous goods evaluation value according to the gas leakage information; and the early warning module is configured to judge the early warning level of the storage area according to the comprehensive evaluation value and perform early warning. Storage of dangerous goods is monitored in multiple aspects, and the safety of storage materials is effectively evaluated.

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 dangerous goods expressway transportation green path planning method based on multi-source data, and relates to the field of traffic transportation planning and management, and the method comprises the steps: obtaining and processing multi-source data related to a dangerous goods expressway transportation green path planning process; calculating an alarm rate, a dangerous goods transportation accident consequence and a risk mitigation factor of each highway section, and constructing a risk assessment model; calculating greenhouse gas and atmospheric pollutant emissions in the vehicle operation process, and constructing an external cost evaluation model in combination with a unit gas monetization price; constructing a dangerous goods expressway transportation green path planning model by taking the lowest transportation risk and the lowest external cost as double targets; and designing a mixed algorithm combining an ant colony algorithm and a simulated annealing algorithm to solve the model, and outputting a result. According to the method, the highway transportation risk of the dangerous goods can be comprehensively evaluated, and a group of path planning schemes in which the transportation risk and the external cost are balanced can be obtained.

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 safety monitoring method and system for a dangerous goods storage tank group, and the method ingeniously carries out the monitoring of the corrosion condition and crack initiation and expansion conditions of a storage tank bottom plate of the storage tank group through a corrosion monitoring sensing module and an acoustic monitoring sensing module, so that the storage tank is not liable to crack when being used for a long time. The thickness of the bottom plate and the acoustic abnormal condition can be monitored in time, so that a background manager can timely and reliably know the corrosion condition and the stress change condition of the storage tank body; according to the scheme, corrosion rate data, acoustic event characteristics, environmental parameters, inter-tank position parameters, storage tank bottom plate thickness difference data and other data of different dimensions are fused to generate multi-source fusion data, and on the basis, independent and combined judgment is conducted on the multi-source data through a corresponding algorithm so as to analyze whether the storage tank bottom plate is abnormal or not; and reliable mechanism guarantee is provided for safety monitoring of the storage tank group.

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.

A separator system and method may provide a four-way separator that may separate a material and remove a hazardous material. The hazardous material may include gas and sand that may be removed by the four-way separator. The separator system and method may further provide a main unit that may include three chambers or recirculation hoppers, an auger sand extractor, and a strap tank. The separator system and method may provide a faster rig-up time and may be exclusively driven by hydraulics.

The invention discloses a dangerous cargo risk early warning method and system and a computer program. The dangerous cargo risk early warning method comprises the steps of obtaining multi-source monitoring data; analyzing the multi-source monitoring data through a preset rule engine and a trained dangerous cargo risk prediction model to obtain a risk prediction result; and performing early warning according to a risk prediction result. According to the technical scheme provided by the invention, the historical and real-time multi-source monitoring data is acquired, and the rule engine and the machine learning algorithm are used for predicting dangerous cargo risk factors, so that dangerous cargo risk early warning is more real-time and accurate; compared with manual inspection and video monitoring, data can be acquired more frequently, and key information is synchronized in a data center; multiple data sources are covered, and the influence of various factors on a monitoring station can be considered more comprehensively; by means of automatic data collection and intelligent data analysis, manpower resources can be saved, problems and risks can be found more timely, and therefore potential loss and cost are reduced.

The invention relates to the technical field of transportation supervision platforms, in particular to a dangerous goods transportation vehicle positioning system based on the 5G Internet of Vehicles, which comprises a management unit, a monitoring unit and an early warning unit. According to the invention, the early warning mechanism construction module constructs an early warning trigger mechanism and sets a dynamic early warning threshold value, the dynamically adjusted early warning threshold value can be matched with vehicle transportation data under various conditions, and the monitoring data acquisition module can acquire accurate dangerous goods parameter information and vehicle information. According to the system and the method, accurate hazardous article parameter information can be compared with a threshold value, accurate triggering of early warning can be ensured when an abnormity occurs, timely sending of an alarm is ensured, a subsequent emergency processing effect is ensured, multi-source data acquisition can be realized through a multi-source data acquisition module, and the system and the method are suitable for popularization and application. Data can be cleaned, fused and synchronized through the data correction processing module, rapid processing of the data can be achieved, and errors generated when the sensor is used can be reduced through sensor calibration.