660 results about "Accident risk" patented technology

A method for determining an accident risk of a first object with at least one second object is proposed, a collision probability and a hazard probability of the at least one second object being determined in a predefined region around the first object, the collision probability and the hazard probability being determined as a function of motions and object classes of the first and the at least one second object. The accident risk is then determined as a function of the collision probability and the hazard probability.

An approach is provided for determining vehicle accident risk. The approach determines a current vehicle state of the vehicle during operation of the vehicle. The current vehicle state is based on one or more intra-vehicle factors and one or more extra-vehicle factors that pertaining to the vehicle. The current vehicle state is compared to a number of historic accident-related vehicle states that correspond to a vehicle accidents previously recorded for a number of vehicles. Based on the comparison, the process detects an accident risk that pertains to the vehicle. The driver of the vehicle is then notified of the detected accident risk.

The invention relates to an inland river bridge area ship safe navigation precontrol system which comprises a radar station, an information processing center and an AIS base station. The radar station and the AIS base station are respectively in communication connection with the information processing center. The information processing center comprises a data processing and recording unit, a track fusion unit, an electronic river map unit, a precontrol decision-making unit and a precontrol information sending unit, wherein the data processing and recording unit obtains data from the radar station or/and the AIS base station and processes and records the data, the track fusion unit carries out fusion on the obtained radar or/and AIS data to obtain ship tracks, the precontrol decision-making unit carries out prediction according to the data processed and recorded by the data processing and recording unit and judges whether alarm instructions are sent out or not, and the precontrol information sending unit controls the AIS base station to send out alarms according to the instructions sent out by the precontrol decision-making unit. According to real-time traffic situations, the inland river bridge area ship safe navigation precontrol system can automatically identify navigation risks of the ships, automatically carry out early warning on the ships, guide the ships to pass through a bridge area safely, and reduce accident risks.

The invention discloses an automatic electric pole climbing operating platform comprising a gear rack sliding climbing mechanism, a buckle locking mechanism, a work platform, and a hoop rotating mechanism. The gear rack sliding climbing mechanism comprises an inner sliding arm, an outer sliding sleeve, a driving device, and climbing unidirectional guide wheels. The inner sliding arm is inserted into the outer sliding sleeve from the top of the outer sliding sleeve and is in sliding connection with the outer sliding sleeve. The driving device drives the inner sliding arm to slide. The top of the inner sliding arm, the top end and the bottom end of the outer sliding sleeve are all equipped with the climbing unidirectional guide wheels. The climbing unidirectional guide wheels are in sliding connection with an electric pole. The buckle locking mechanism locks the inner sliding arm and the outer sliding sleeve or separates the inner sliding arm and the outer sliding sleeve from the electric pole. The work platform is fixed to the outer sliding sleeve and tightly holds or loosens the electric pole by means of the hoop rotating mechanism. The automatic electric pole climbing operating platform helps a worker improve power equipment installation and maintenance work efficiency, and reduces the labor intensity and the accident risk of the worker.

The invention discloses a driving risk evaluation method and a driving risk evaluation system and relates to the field of traffic risk evaluation. The driving risk evaluation method comprises the following steps: acquiring driving information of a vehicle; processing driving information so as to obtain abnormal driving information of the vehicle; acquiring accident information, and establishing amachine learning model for evaluating driving risks according to the abnormal driving information and the accident information; evaluating the driving risks of the vehicle according to the machine learning model. By adopting the driving risk evaluation method and the driving risk evaluation system disclosed by the invention, driving risk evaluation results with very high precision can be acquired,risks of an evaluated vehicle can be accurately and objectively evaluated, fine requirements on risk evaluation are met, adverse driving behaviors of drivers can be corrected in time, and accident risks can be reduced.

The invention discloses a method and a system for diagnosing and responding and supporting a nuclear power plant severe accident. The method comprises steps of obtaining accurate information needed by accident management by combining an instrument usability database through accident-correlated-instrument monitoring and correlated important parameter evolvement, realizing simulation of server accident development and an emergency state through the combination of a sever accident simulation module, predicting the development tendency of the server accident and predicting expected effects after various possible intervention measurements, integrating and electronizing the server accident management guidance (SAMG) and giving advices on the following measurements. The invention can start up the corresponding measurement and the emergency plan on the short notice, maximally reduces the accident risk, provides the guarantee and further enhances the safety of the nuclear power plant.

The invention discloses a safety management method based on dynamic quantitative accident risk prediction, and the method comprises the following steps: an accident predicting platform is provided, and a basic event dynamic predicting model and an accident analyzing model are established; the accident predicting platform detects state information of production units in real time, and failure information is obtained; then the basic event dynamic predicting model predicts the failure information to obtain basic event dynamic probability; then the accident analyzing model calculates the basic event dynamic probability to obtain the current accident risk probability of the production unit; then the current accident risk probability and a preset risk probability control criterion are compared, if the accident risk probability is more than the risk probability control criterion, a preset emergency plan is started to assist an executor to tackle the accident; if not, a preset standard working flow is started, and a warning message is emitted so that the executor can correct a mistake. The method and the system can reflect the influence of dangerous factor change on probability dangers in real time, thus really realizing the safety management idea of prevention first.

The invention discloses a petrochemical port area accident risk area quantitative assessment method comprising the following steps: (1) putting forward a petrochemical port area accident integrated risk area quantitative assessment mode; (2) building an improved accident probability calculation model based on an accident domino effect; (3) building an improved consequence intensity calculation model based on uncertainty analysis; and (4) getting the formation mechanism and the overall distribution characteristics of accident risk in a petrochemical port area according to the corrected accident integrated risk area quantitative assessment mode, the improved accident probability calculation model and the improved consequence intensity calculation model, and putting forward petrochemical port area accident risk control measures. By employing the method of the invention, the accuracy of accident probability calculation and consequence simulation is improved, and the guiding significance of risk assessment to risk prevention and control is enhanced.

The invention discloses a system and a method for linkage of an electric power tunnel inspection robot and a fire extinguishing system. The method includes: a control platform sends fire disaster signals to the robot and controls fireproof doors to close after detecting a fire disaster in a tunnel; after receiving the signals, the robot moves towards the fire disaster, controls the corresponding fireproof door to open and closes the fireproof door after passing through; the robot verifies the fire disaster and calculates the distance between a fire origin and the robot after reaching a highest-temperature zone; the robot aims at a target in the fire zone and controls nozzles to release fire extinguishing materials to the target; fire extinguishment is finally realized by linkage control of the control platform, the fireproof doors and the robot. The robot is capable of freely passing through fire compartments of the electric power tunnel, once the fire disaster is detected, all of the fireproof doors are closed in a linkage manner, the robot quickly arrives at the fire point to check fire conditions, and firefighting steering engines are directly adopted for aiming at fixed points to extinguish fire. The system and the method for linkage of the electric power tunnel inspection robot and the fire extinguishing system have the advantages of quickness in reaction, information accuracy and flexibility, and accident risks can be greatly reduced to guarantee tunnel environmental safety.

A method for outputting alert messages of a driver assistance system in a vehicle in which data for moving the vehicle and data regarding accident risks in the surroundings of the vehicle are detected, the detected data is evaluated and attributed to a specific accident risk category and an alert message is output subject to the attributed accident risk category. The method according to the invention is characterized in that from the captured movement of the vehicle a planned movement of the vehicle is derived and it is tested whether without modification of the planned movement of the vehicle a collision of the vehicle with another object will occur, and the manner of outputting the alert message is carried out in dependence on the result of said collision testing. The invention further relates to an associated driver assistance system and to a vehicle having such a driver assistance system.

The invention relates to a burning and explosion accident sensing and risk predicting early-warning intelligent fire fighting helmet implementing method. A fire fighting helmet, a long-distance combustible gas detector, a thermal imaging camera module, a head-mounted near-eye display module, a positioning instrument, a data communication and transmission module, an embedded core board, an earphone, a base station, and a fire fighting command centre are involved. According to the burning and explosion accident sensing and risk predicting early-warning intelligent fire fighting helmet implementing method, the real-time position of a fireman in a building can be displayed for the fireman by combining a fireman positioning technology, a building information model and real scene thermal image information, and projecting and displaying information through the head-mounted near-eye display module, so that the environment burning and explosion dangerousness and risk thereof are predicted in real time. When the fireman enters an unknown environmental place, a potential fire disaster explosion accident risk can be effectively predicted quickly; quick sensing and information communication ofthe fireman to a fire disaster accident scene can be realized through an advanced video and thermal imaging technology and an indoor positioning and communication transmission technology, so that thefire fighting rescuing scientific level and the capacity of the fireman are improved.

The invention discloses a method for online estimating and pre-warning the grid safety accident risk levels, and belongs to the technical field of power system automation. The method comprises the following steps: on the basis of a grid current operation manner provided by an EMS and a control strategy of a second defensive line and a third defensive line, calculating and simulating the action of a control device of the second and third defensive lines through time-domain simulation in allusion to a preconceived fault set of assessments of the second and third defensive lines, so as to obtain a total fault load reduction quantity, a loss user quantity and a voltage loss station quantity; dividing the grid safety accident risk levels of the preconceived faults according to the requirements of ''Regulations on the Emergency Response to and Investigation and Handling of Electric Power Safety Accidents '' and ''Rules for the Investigation of Electric Power Accidents of China Southern Power Grid Company Limited''; and determining the important levels of the faulted equipment according to the accident risk levels. According to the method, the online estimation and pre-warning of the grid safety accident risk levels are realized, a technological foundation is laid for the real-time pre-warning and pre-control of the grid safety accidents.

The invention provides a real-time prediction method for an urban road traffic accident risk. Calculation is carried out by extracting geometric alignment data of all observation objects in an observation set and historical traffic flow essential data and historical weather condition data n minutes before a traffic accident, the grade of traffic flow characteristic parameters and the weather condition data n minutes before the traffic accident converted into classified variables and the distribution probability of the grade are obtained, an urban road traffic accident real-time prediction model based on poisson distribution is built, the prediction model is calibrated through the determined grade of the traffic flow characteristic parameters and the weather condition data and the distribution probability of the grade, when the traffic accident risk of the object needing to be predicted is predicted, only the grade of the real-time traffic flow characteristic parameters and the weather condition data, converted into the classified variables, of the object needing to be predicted and the distribution probability of the grade need to be calculated in real time, and the traffic accident risk of the object needing to be predicted can be predicted through a calibrated formula.

Provided are an apparatus and a method that chooses an object to be preferentially controlled based on an accident risk degree and hazard and controls the chosen object. In the present invention, it is possible to choose and control a ship having high hazard calculated with trajectory prediction, current operation state information, and past history information under a traffic situation in which the ships are concentrated, by priority. The ship having high hazard is chosen by calculating and applying various history information including an accident record, an entry history of an operator, an aging degree history of the ship, and a steering feature history by using a collision risk on a real-time trajectory collision risk and past history information and different warnings are given during a control for each level.

The invention discloses a residual battery capacity estimation management system and method. Application condition of open-circuit voltage capacity estimation is fully taken into consideration, and under the condition of meeting the use condition, fine multi-temperature-range OCV data accuracy is utilized to make up for the error due to long-time ampere-hour integration, and thus estimation precision is improved; meanwhile, under the condition of not meeting the OCV condition, platform period and mutation period judgment is introduced; in the platform period, estimation is carried out through an ampere-hour integration method; and in the mutation period, estimation is carried out through an interpolation method, and corresponding early warning processing is carried out, thereby preventing overcharging/overdischarging of a battery effectively, prolonging the recycle service life, and reducing accident risk. The two cases above are measured through test, and influence of different temperatures and aging factors is obtained; and corresponding correction processing is carried out on estimated value, and thus estimation precision is further improved.

The invention discloses a shield tunneling machine cutter state monitoring system and method. The system comprises a data acquisition module, a control and transmission module and a data processing module, wherein the data acquisition module comprises a temperature sensor, an abrasion loss sensor and a rotation speed sensor; the temperature sensor is used for acquiring temperature information of acutter of a shielding tunneling machine; the abrasion loss sensor is used for acquiring abrasion loss information of a hob on the cutter; the rotation speed sensor is used for acquiring rotation speed information of the hob; the control and transmission module is used for controlling each of the temperature information, the abrasion loss information and the rotation speed information, and respectively transmitting the information to the data processing module through a plurality of wireless links; the data processing module is used for analyzing an operation state of the cutter of the shieldtunneling machine according to preset geological information, received tunneling parameter information, the temperature information, the abrasion loss information and the rotation speed information; and the abrasion loss sensor and the rotation speed sensor are magnetic sensors. According to the system and method, accident risk of shield tunneling construction can be reduced and the comprehensivebenefit of shield tunneling construction can be improved.

The invention discloses a risk monitoring system of automobile insurance. The risk monitoring system of the automobile insurance comprises a vehicle-mounted risk monitoring device and a risk monitoring center which are accessed to the internet. The vehicle-mounted risk monitoring device is used for collecting risk data of an assembled vehicle and sending the data to the risk monitoring center which comprises a risk assessment server and a risk database server which is used for storing the risk data of the assembled vehicle, wherein the risk data is sent by each vehicle-mounted risk monitoring device. The risk assessment sever is used for outputting an insurance accident risk index of the vehicle and/ or outputting premium pricing of the vehicle to the vehicle-mounted risk monitoring device assembled by the vehicle according to the risk data which is collected in a real time mode. The risk monitoring system of the automobile insurance is capable of monitoring and evaluating the risk of the automobile insurance.

The invention relates to a method for assessing and forecasting marine traffic risk on the basis of the geographic information system; the method comprises the steps of: taking the present and future situations of navigation risk in a certain water area as a research object, analyzing the features of navigation environment in the water area according to the past and present traffic situation of the water area, establishing a model for assessing and forecasting the risk of collision and stranding accidents by combining the features of marine traffic accidents in the water area, and assessing the influence of the change of navigation conditions in the water area on the navigation environment in the water area, as well as the corresponding navigation risks, thereby visually manifesting the geographic distribution of the risks in the water area and carrying out the dynamic forecasting of the marine traffic risk in the water area in future. The method avoids the simplification in assessments only by using qualitative evaluation and has the features of gridding and dynamic development. The application of the method can not only visually reflect the geographic distribution of the marine traffic risks, but also forecast the situation of navigation environment risks in future.

The invention discloses a road traffic accident data mining server, a method and a system. The analysis module is used for carrying out cross-correlation analysis on the pre-processed road traffic accident data according to the designated statistical index, and obtaining the analysis result of multi-level and multi-dimension; The mining module is used for mining accident influence factors, accident casualty characteristic factors and accident risk degree corresponding to each stage of the road traffic accident according to the pretreated road traffic accident data, and obtaining the mining results; A visualization module for outputting the analysis result and the mining result to a geographic information system for display on the geographic information system. Accident visualization and accurate positioning are realized, and the causes and mechanism of accidents hidden in accident data are mined, which provides reference for road traffic accident prevention and traffic safety management.

The invention discloses an identification method of atmosphere pollution risk source based on the analysis of accident overall process, which comprises the following steps that: (1) critical value is identified according to environmental pollution accident risk source, and an atmosphere pollution accident risk unit is preliminarily selected; (2) maximum credible accident source intensity of the atmosphere pollution is determined; (3) accident danger range of risk source is calculated; (4) harmful consequences such as personnel injury, public scare, ecological loss and the like caused by the atmosphere pollution risk source is evaluated; (5) comprehensive harmful consequence of the atmosphere environmental pollution accident is normalized and superposed; (6) risk value of atmosphere pollution accident risk source is calculated; (7) grade of atmosphere pollution accident risk source is determined. The method solves the problems of the traditional environmental pollution accident risk source identification method that the pertinence is poor, the precision is low, the risk level of the atmosphere pollution accident cannot be effectively determined, and the like, establishes an atmosphere pollution accident risk source identification method based on the analysis of the accident overall process which has strong universality, and provides a technical method for the circumvention and emergency of atmosphere pollution accident.

The invention discloses a personalized traffic accident risk prediction recommendation method based on depth learning, which comprises the following steps: dividing a city into grid regions; calculating traffic accident data, traffic flow data and weather characteristic data of each grid region and each period; using the depth learning method to train the model, the traffic accident risk prediction model being obtained. According to the traffic accident data, traffic flow data and weather characteristics data input at the present time, the traffic accident risk prediction model is used to calculate the traffic accident risk prediction at the next time. The invention utilizes the depth learning method to learn the non-linear, high-dimensional and complex correlation relationship between thetraffic accident influence factor and the traffic accident, predicts the traffic accident risk at the city level, and improves the accuracy of the prediction result.

The invention discloses a city region traffic accident risk prediction method. A future traffic accident risk is predicted through adoption of a coder-decoder deep learning frame with a space-time attention mechanism. On the basis of a great deal of multi-source heterogeneous data related to traffic accidents, the future traffic accident risk is predicted relatively effectively and accurately through fusion of the multi-source heterogeneous data. A main data set comprises traffic accident quantity and traffic flows of different vehicles. External data is external environment data such as weather and street design. According to the space-time attention mechanism provided by the scheme of the invention, space-time characteristics of local and global regions can be grasped at the same time, precision of models is improved, explainability of the models is also improved, and important degree of different influence factors on the predicted value is explained according to the attention value.The frame provided by the invention can be expanded to the other similar fields of space-time data, and the frame has universality value.

The invention provides a natural gas pipeline tour-inspection system based on a drone. The system comprises a drone body, a flying control sub-system, a data collecting sub-system and a ground monitoring sub-system; a high-definition sport camera and a gas detector of the data collecting sub-system are connected below the drone body; the flying control sub-system and other data collecting sub-system devices are disposed inside the drone body; the flying control sub-system and the ground monitoring sub-system are wirelessly connected with each other through a first data transfer radio station and a second data transfer radio station; and the data collecting sub-system is communicated with the ground monitoring sub-system through wireless connection between a first image transfer radio station and a second imagetransfer radio station and wireless connection between a first wireless communication module and a second wireless communication module. The natural gas pipeline tour-inspection system has the following beneficial effects: performing tour-inspection by means of the drone, and using image information and gas information captured in real time as an inspection result, thus improving working efficiency of the natural gas pipeline tour-inspection, reducing working difficulties at the same time, and reducing accident risks.

The invention belongs to the field of traffic safety and intelligent traffic management control, in particular to a real-time estimating method for high-grade road traffic flow running risks. The real-time estimating method for the high-grade road traffic flow running risks considers the problem that a high-grade road short of fixed-point traffic flow collecting facility is incapable of acquiring the road traffic flow rate, occupancy and the like traffic parameters. The real-time estimating method for high-grade road traffic flow running risks includes that using traffic flow speed data acquired through different traffic information collecting technologies to build a real-time accident forecasting model, using a dynamic Bayesian network model to consider the speed state data of several time periods, building relationships between the traffic flow state and accident risks, and estimating the accident in real time so as to early warn or regulate a vehicle to avoid an accident. The real-time estimating method for the high-grade road traffic flow running risks has good forecasting precision for the high-grade road accident risks through speed data, and the real-time estimating method for the high-grade road traffic flow running risks has broad practical application value.

The invention provides a method for enhancing traffic hazard personnel accident risk prediction precision. According to the method, traffic violation data and accident data samples are obtained via anoptimized sampling method; an integrated learning algorithm is used for training a traffic participator traffic accident risk prediction model; and a genetic algorithm is used for optimizing the model. According to the method, the integrated learning algorithm is used for mining safety characteristics of passengers from traffic violation data, the initial model performance is improved in the sampling link of the model construction by adoption of the optimized sampling method, and the genetic algorithm is used for optimizing model parameters, so that the high-risk personnel accident risk prediction precision is effectively enhanced.

The invention discloses an urban oil and gas pipeline major accident risk early warning and assessment method, and belongs to the technical field of pipeline risk early warning and assessment. The method comprises the steps that a factor set is determined according to risk factors in the pipeline operating process; weight matrixes of all levels of factor indexes are determined by the use of an interval analytical hierarchy process relative scalar quantity method; risk assessment is conducted on the indexes, so that a warning degree division result is obtained; and risk assessment results of various factors are obtained on the basis of risk quantity indexes, and relative risk values are calculated through the combination of leakage influence coefficients. According to the urban oil and gaspipeline major accident risk early warning and assessment method, a kent pipeline risk assessment method and a risk early warning method are combined, and therefore the purposes that the pipeline warning degree is judged, and an early warning response is conducted can be achieved on the basis of risk assessment of all risk factor warning omens and overall consideration of the pipeline accident occurrence probability and the consequence severity.