28 results about "Potential accident" patented technology

Smart vehicle-to-everything (V2X) preemptive connections for emergency and / or potential accident scenarios. A V2X-enabled vehicle may determine a current route of the vehicle and high-risk areas through which the vehicle may travel (e.g., a hospital zone, high pedestrian traffic area, etc.). The vehicle may determine a distance (d) from a beginning of the high-risk area and begin establishing a V2X connection upon reaching the determined distance. The V2X-enabled vehicle may also, or alternatively, pre-emptively establish a V2X connection upon detecting an event initiative of an emergency, a hazardous situation, or a potential emergency. Examples of such events may include a sharp turn, a vehicle speed exceeding a speed threshold, a sudden acceleration or declaration, etc. A preemptive V2X connection may, for example, enable vehicles to begin communicating with one another faster in case of a threat, emergency, or unsafe conditions for vehicles in the area.

The invention discloses a parking lot accident cause determination method, and relates to the technical field of parking lot safety management, and the method comprises the following steps: a data collection step: obtaining multi-dimensional data in real time through a sensor network deployed in a parking lot, the multi-dimensional data including human behavior data, environment state data, engineering compliance data and equipment operation data; and a parameter calculation step based on human behavior data. According to the invention, the multi-dimensional data sensing network covering people, vehicles, roads, environment and equipment is constructed, real-time risk assessment and accurate cause positioning are carried out based on the dynamic weight and interaction item analysis model, advanced early warning and active prevention and control of potential accidents in the parking lot are finally realized, the safety management level of the parking lot can be significantly improved, and the safety of the parking lot is improved. The traditional passive post-event disposal is converted into active pre-event prevention, and the method is particularly suitable for high-end parking lot scenes with extreme requirements for safety, privacy and asset protection.

The application discloses a kind of method and system of active identification traffic accident based on Internet of Vehicles, it is related to accident identification technical field.By confirming and testing current vehicle operating state gradually, the accuracy and reliability of traffic accident detection can be significantly improved.Real-time acquisition vehicle operating state data and preliminary abnormality judgment based on preset basic threshold, by combining historical operating state data to calculate trend difference, and set standard threshold to further verify the authenticity of preliminary abnormality event, significant test is carried out on trend difference, its statistical significance is evaluated by error probability, ensure that only truly meaningful abnormal event is marked as potential accident, to avoid misjudgment caused by random noise or short-term fluctuations.Integrated analysis trend difference and operating state data using machine learning model, output specific traffic accident type, so that the system can not only confirm abnormal state, but also accurately classify different types of traffic accident.

This invention belongs to the field of bridge traffic risk assessment technology, and specifically relates to a method for assessing traffic risk on downhill bridge sections based on reliability analysis. The method comprises the following steps: using driving speed as a random variable, constructing a vehicle braking traffic risk function for the downhill bridge section based on the sight distance required for vehicles going downhill and the emergency stopping distance provided by the bridge deck; analyzing reliability indices and failure probabilities to determine traffic risk level classification standards; determining reliability indices based on the longitudinal force coefficient detection value of the bridge deck, thereby determining the traffic risk level of the section and assessing the risk of the downhill bridge section. This invention quantifies the impact of decreased bridge deck anti-skid performance on driving safety, predicts potential accidents from a probabilistic perspective, and thus achieves the purpose of timely risk warning and timely hazard elimination.

Disclosed are an accident detection method and apparatus, and a storage medium. The method comprises: receiving an input video; identifying a plurality of objects in the video; when a specific object from the plurality of objects complies with a predetermined rule of transitioning from a moving state to a stopped state and finally remaining in the stopped state, determining that the occurrence of a potential accident is experienced by the specific object; determining a frame image in the video where the occurrence of a potential accident is experienced by the specific object; and instructing a trained image processing model to analyze and process the frame image, so as to determine whether the occurrence of an accident is experienced by the specific object.

This invention relates to the field of bridge engineering technology, specifically disclosing a steel wedge slope adjustment device in a cap beam support system. It includes a main body comprising an upper wedge and a lower wedge; an adjusting component is provided between the upper and lower wedges; the adjusting component includes a threaded steel bar with nuts at both ends; a support plate for supporting the casting mold is provided at the end of the upper wedge away from the lower wedge, and an adjusting mechanism is provided on the support plate for adjusting the tilt angle of the support plate. This device can prevent the support plate from becoming detached from the contact area of ​​the cap beam formwork, thus preventing slippage of the casting mold during demolding and potential accidents.

The utility model relates to the technical field of AGVs (Automatic Guided Vehicles), in particular to a fork arm guide rail protection structure of an AGV, which effectively limits the movement range of a fork arm and reduces equipment damage and potential safety hazards caused by excessive stretching through the cooperative work of a limiting assembly and a guide rail assembly. And the special limiting separation blade and the guide block are adopted, so that potential accidents caused by deviation or collision in the operation process of the fork arm are avoided, and the movement safety of the whole fork arm is improved.

Disclosed in the present invention are a vehicle instrument screen operating state monitoring method, a smart cockpit, and a vehicle having same. The steps of the method comprise: using a microcontroller unit (MCU) in a cockpit-domain system controller (CSC) to periodically query an operating state of a vehicle instrument screen MFD module by means of an IIC communication protocol, and determining whether an interactive message between the MCU and the vehicle instrument screen MFD module is successful; and when it is detected that a heartbeat signal is lost, the MCU performing a hardwire reset on the vehicle instrument screen MFD module by means of a hardwire IO. In summary, according to the present invention, requirements of an automotive safety integrity level are considered, and by means of precise fault detection and timely recovery measures, the safety of the vehicle during operation is significantly improved. The present invention not only meets functional safety requirements but also provides a higher level of safety assurance for drivers and passengers by reducing potential accidents caused by instrument screen failures.

This invention proposes a method for assessing driving risks by integrating an independent climate field. The method mainly consists of three parts: First, based on a three-field model of kinetic energy field, potential energy field, and behavioral field in human-vehicle-road collaboration, an independent climate field model is innovatively defined and constructed to characterize the additional driving risks caused by abrupt changes in the environmental medium. Then, by acquiring environmental information such as road surface adhesion coefficient and rainfall intensity, the risk equivalent mass of the climate field source is calculated, and the field strength distribution and attenuation function of the climate field are established based on spatial distance. Finally, the climate field is vector-superimposed with the original three fields to calculate the total driving risk field strength across the entire region. Under the premise of ensuring driving safety, the method achieves advanced warning and risk avoidance decisions for potential accident hazards based on the dynamic change trend of the total field strength. This invention does not change the internal structure of the original three-field model, but supplements the influence of meteorological and road factors on risk characterization through the principle of physical field superposition, effectively improving the safety of intelligent vehicles in complex and extreme environments.

This utility model discloses a seismically resistant and stable oil drilling platform, relating to the field of oil extraction technology. The platform includes a support seismic-resistant component fixedly installed at its four lower corners, and anti-collision and shock-absorbing components fixedly installed on all four sides. This seismically resistant and stable oil drilling platform, through its support seismic-resistant components, can effectively cope with earthquakes, wave impacts, and vibrations generated by drilling operations in the marine environment, reducing wear and fatigue damage to the platform structure. Furthermore, the seismic-resistant frame evenly distributes the platform's weight to the connecting columns at the four corners, preventing excessive local stress that could lead to structural deformation, thus providing seismic resistance and further extending the platform's service life. The anti-collision and shock-absorbing components effectively protect against potential accidents such as ship collisions and ice floe impacts during offshore operations, reducing platform structural damage, lowering the risk of equipment failure, and ensuring personnel safety.

The invention discloses a dangerous chemical storage tank area risk assessment method and device based on comprehensive risks, and relates to the technical field of chemical accident risk analysis, and the method comprises the steps: constructing a basic database of a dangerous chemical storage tank area; performing failure probability analysis on the potential accident source according to the basic database to determine an initial accident frequency; calculating a first economic loss expectation based on the initial accident frequency; performing upgrade probability analysis on each affected device, and determining the domino accident frequency of the affected device in combination with the initial accident frequency; calculating a second economic loss expectation according to the domino accident frequency; calculating a comprehensive risk probability according to the initial accident frequency and the domino accident frequency; calculating an accumulated economic loss expectation according to the first economic loss expectation and the second economic loss expectation; and performing risk assessment according to the comprehensive risk probability and the accumulated economic loss expectation of each device. According to the scheme, the accuracy of performing economic loss and risk assessment on the dangerous chemical storage tank area can be improved.

The application belongs to the technical field of geographic information services, and discloses a mountaineer anti-lost early warning and tracking system, comprising the following modules: a map display module, an information reporting module, a data management module and a system interaction module. By constructing the four core modules of map display, information reporting, data management and system interaction, the pain points of inconvenience of carrying traditional paper maps, limited coverage and insufficient flexibility of fixed signboards can be effectively solved. The design of the information reporting module further strengthens the safety guarantee capability. When the mountaineer cannot judge the position or is in a positioning dilemma, the mountaineer can initiate help by uploading the photos of the surrounding environment to the system. In addition, if it is found that other personnel walk to a deep and undeveloped area or a high-risk area, the on-site photos can be taken and reported in time to avoid potential accidents such as getting lost or being in danger in advance, thereby playing the functions of active early warning and risk preposition intervention.

The invention relates to the technical field of constructional engineering protective sheds, and discloses a big data-based steel bar protective shed intelligent management system, which comprises a data acquisition module, a big data analysis module, an intelligent control module, an early warning module, a maintenance prediction module and a cloud platform interface, when real-time monitoring management of the steel bar protective shed is carried out, structure state data, environment data and operation data of the protective shed are collected in real time through a distributed sensor network, and a risk prediction model is established based on a big data analysis module, so that the operation state of the protective shed can be mastered, and early recognition and early warning of structure safety risks are realized; according to the system, the potential safety hazard discovery capability is improved, potential accidents caused by environmental changes and structure aging are prevented, auxiliary facilities of the protective shed are automatically adjusted through the intelligent control module according to a risk assessment result, the system can quickly respond when abnormal conditions are detected, delay and errors of manual intervention are reduced, and the system safety is improved. And the management timeliness and accuracy are improved.

In a vehicle control device, environmental information is acquired which is information about an environment in which a vehicle is placed. The environmental information precludes an obstacle around the vehicle. A possibility is estimated for potential proximity of the vehicle to the obstacle. A behavior of a predicted target moving object including the vehicle and at least one moving object around the vehicle is predicted based on the estimated possibility. A responsibility is determined for a potential accident that is assumed in response to the vehicle traveling on a candidate route that the vehicle travels.

The utility model discloses a lifting mechanism and a roller changing system, and belongs to the technical field of battery production. The lifting mechanism comprises a tray suitable for being arranged opposite to the roller in the vertical direction, and a groove used for containing the roller is formed; the hydraulic cylinder comprises a cylinder barrel, at least one stage of moving cylinder slidably mounted in the cylinder barrel and a piston rod slidably mounted in the innermost stage of moving cylinder, the cylinder barrel is suitable for being mounted on the roller press, the tray is supported on the piston rod, and every two adjacent stages of moving cylinders are in sliding fit; the hydraulic cylinder is configured to drive the moving cylinder and the piston rod to reciprocate by injecting oil into or returning oil to the cylinder barrel so as to drive the tray to ascend and descend. By means of the roller changing mode design that the hydraulic cylinder drives the tray to ascend and descend, uncertainty and potential accident risks of manual operation are reduced, safety of roller changing operation is improved, roller changing time is greatly shortened, production line rhythm and production capacity are improved, input of manpower and material resources is greatly reduced, and roller changing cost is reduced.

The application belongs to the technical field of engineering cost, and relates to an AI-based engineering cost big data intelligent analysis system and method, wherein a ground pile coupling model is constructed, precipitation prediction parameters in a construction period are input as time-varying boundary conditions, transient seepage changes of foundation soil under the action of precipitation are analyzed, the distribution of pore water pressure increment in the influence domain of each supporting pile is quantified, the pile body inclination probability, embedded failure probability and soil loss probability between piles of the supporting pile are predicted based on the reduction relationship of the pore water pressure increment and the effective stress of the soil body, directional parameter adjustment is triggered according to the probability threshold value condition, a supporting pile design parameter adjustment scheme and a corresponding supporting structure incremental cost are formulated, high precipitation risk working conditions are identified in advance to output risk control supporting cost, the possibility of supporting structure failure is effectively reduced, the safety and stability of the engineering structure are enhanced, and the economic loss caused by potential accidents is reduced.

The invention discloses a method and a system for actively identifying a traffic accident based on the Internet of Vehicles, and relates to the technical field of accident identification. The accuracy and reliability of traffic accident detection can be remarkably improved by gradually confirming and checking the current vehicle running state. The method comprises the following steps: acquiring vehicle running state data in real time, carrying out preliminary abnormity judgment based on a preset basic threshold value, calculating a trend difference by combining historical running state data, setting a standard threshold value to further verify the authenticity of a preliminary abnormal event, carrying out significance test on the trend difference, and evaluating the statistical significance through an error probability. And it is ensured that only real meaningful abnormal events are marked as potential accidents, so that misjudgment caused by random noise or short-term fluctuation is avoided. And a machine learning model is utilized to comprehensively analyze trend difference and operation state data, and a specific traffic accident type is output, so that the system not only can confirm an abnormal state, but also can accurately classify different types of traffic accidents.

The utility model relates to the technical field of high-speed motor train units, and discloses a high-speed motor train unit front section opening and closing mechanism which comprises a motor train unit head shell and a closing shell, an opening and closing mechanism is arranged at the front end of the inner bottom face of the motor train unit head shell and comprises a bottom fixing plate, and a fixing frame is fixedly connected to the middle of the upper surface of the bottom fixing plate. The rear ends of the outer walls of the two sides of the fixing frame are each fixedly connected with two mounting plates. According to the front section opening and closing mechanism of the high-speed motor train unit, the locking mechanism is arranged on the opening and closing mechanism, and under the action that the sector gear is meshed with the limiting stop lever, a user can adjust the position of the limiting stop lever through the exposed rotating block; therefore, locking and limiting of the opening and closing mechanism are completed by blocking the first positioning wheel and the second positioning wheel, the opening and closing mechanism can be effectively prevented from being accidentally opened or closed in the operation process through the design of the locking mechanism, safety is improved, and potential accident risks are reduced.

Systems and methods in accordance with the present disclosure include techniques for predicting an accident, comprising: receiving monitoring data from a user device associated with a user, the monitoring data indicative of movement of the user; determining a user action based on the monitoring data using a first artificial intelligence model; and determining a probability of occurrence of one or more potential accidents based on the determined user action and the monitoring data using a second artificial intelligence model.

Rotor with improved airtightness and venting for tire calibration assemblies that prevents the entry of water and / or liquids into the rotor, thus improving the functionality and lifespan of the components, providing improved venting for the axle hub, maintaining oil retention, and preventing premature wear of internal parts and potential accidents during operation.

The invention discloses an electric vehicle transportation accident situation prediction method and system based on a time sequence evolution model, and the method comprises the steps: collecting battery data and transportation vehicle data in the historical transportation process of an electric vehicle, and obtaining a historical dynamic risk sequence; processing parameter changes at continuous time points in the historical dynamic risk sequence, and determining historical risk evolution vectors; constructing a long short-term memory network, and training the long short-term memory network by using the historical risk evolution vector and the historical accident condition to obtain an accident evolution model; acquiring a current dynamic risk sequence, inputting the current dynamic risk sequence into the accident evolution model to simulate the risk state evolution of a future time step, and judging a potential accident trigger point; determining a specific accident type based on the potential accident trigger point; and generating a prediction precision evaluation report according to the determined accident type and outputting a trend intervention signal to obtain a final accident situation prediction result.

This application relates to the field of automotive safety technology, and in particular to a method, device, vehicle, and storage medium for assisting novice drivers. The method includes: identifying whether the vehicle has entered a novice mode; when the vehicle is identified as having entered novice mode, determining the vehicle's drive torque limit curve based on the actual driving scenario, wherein the drive torque limit curve is a curve of accelerator pedal opening versus drive torque, and when the accelerator pedal opening is the same, the drive torque corresponding to the drive torque limit curve is less than the drive torque corresponding to the drive torque curve in normal driving mode; and controlling the vehicle to perform driving actions corresponding to the actual driving scenario based on the actual accelerator pedal opening and the drive torque limit curve. This solves the problems in related technologies such as novice drivers lacking vehicle awareness and difficulty in controlling the vehicle, low vehicle universality and driving error tolerance, potential accident safety hazards, and poor driver experience.

To provide a travel storage system and a travel recording method that make it possible to confirm the reliability of potential accident responsibility information.SOLUTION: A potential accident responsibility determination device includes: a target vehicle behavior determination unit (141) configured to acquire a sensor value (S) from a sensor (111) indicating behavior of surrounding vehicles present around a host vehicle (1) and sequentially determine a relative behavior (V) of a target vehicle selected, from the surrounding vehicles, with respect to the host vehicle (1), based on the sensor value (S); a rule acquisition unit (142) configured to acquire an accident responsibility rule at a current position of the host vehicle (1); a potential accident responsibility value determination unit (151) configured to sequentially determine potential accident responsibility values (AL), based on the relative behavior (V) of the target vehicle and the accident responsibility rule, each of the potential accident responsibility values (AL) indicating a degree of responsibility of the host vehicle (1) for an accident assumed between the target vehicle and the host vehicle (1); and a potential accident storage unit (152) configured to store the potential accident responsibility value (AL) and responsibility value determination information (R), which is information used for determining the potential accident responsibility value (AL), in association with each other.SELECTED DRAWING: Figure 1

This invention provides an elastic mold sleeve, the inner surface of which forms a cavity with a mandrel inserted inside. A shielding tube colloid is injected into the cavity and cured to form a shielding tube with no mold seam on its outer surface. The elastic mold sleeve and the shielding tube colloid are two incompatible materials, and the elastic mold sleeve is a soft elastomer to facilitate peeling from the shielding tube. This invention replaces traditional parting molds, resulting in a shielding tube with no mold seam on its outer surface, eliminating the need for flash polishing and preventing quality problems and potential accidents such as discharge and breakdown caused by polishing. The use of a material incompatible with the shielding tube colloid in the elastic mold sleeve satisfies the requirements for forming the shielding tube and facilitates subsequent peeling. Furthermore, the elastic mold sleeve of this invention is reusable, and the resulting shielding tube, intermediate joint prefabrication body, and intermediate joint are of high quality and safe to use.

Systems and methods in accordance with the present disclosure include techniques for predicting an accident, comprising: receiving monitoring data from a user device associated with a user, the monitoring data indicative of movement of the user; determining a user action based on the monitoring data using a first artificial intelligence model; and determining a probability of occurrence of one or more potential accidents based on the determined user action and the monitoring data using a second artificial intelligence model.

The application relates to the technical field of ship safety monitoring, in particular to a city inland river cruise ship driving dynamic safety monitoring method and system based on multi-modal sensing, which comprises the following steps: based on multi-source heterogeneous data collected by a multi-modal sensor network in real time, performing data preprocessing and synchronization, and outputting time-space aligned multi-source heterogeneous data; based on the time-space aligned multi-source heterogeneous data, performing cross-modal attention model feature fusion, and outputting fused multi-dimensional state features; based on the fused multi-dimensional state features, performing dynamic risk assessment, and outputting a final risk level; based on the final risk level and historical risk data, judging a risk type and whether to activate an emergency response; when the emergency response is activated, performing a risk avoidance operation and generating a feedback report. The application has the effect of effectively preventing navigation risks and reducing potential accident losses.

The invention discloses a safety monitoring method, system and device for storage carrying equipment and a storage medium, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring a target detection image collected in real time; constructing a target detection model; the target detection model comprises a target identification module and a distance detection module; the target identification module adopts a YOLOv5 detection model; the distance detection module adopts a DeepSORT target tracking algorithm based on vision; and inputting the target detection image into the target detection model for processing to obtain a distance state of the target, and performing safety early warning according to a third-level early warning criterion. According to the invention, early warning can be carried out on personnel and objects separately, the personnel and object identification accuracy in a complex environment is improved, and driver behaviors can be standardized to prevent potential accidents.