94 results about "Potential accident" patented technology

The invention relates to the technical field of engineering early warning, in particular to a drilling engineering early warning method and system, and the method comprises the following steps: collecting a plurality of operation parameters of drilling equipment in real time based on a drilling engineering environment, screening the data, carrying out the comparative analysis of the parameters, evaluating the deviation between the current operation parameter and the conventional operation data, and carrying out the early warning of the drilling engineering. And obtaining equipment operation deviation information. According to the method, the operation parameters of the drilling equipment are collected and processed, key parameters such as the drill bit speed and torque are monitored in real time, deviation with conventional data is found in time, the abnormal condition of the equipment can be quantitatively evaluated by analyzing the frequency and duration of the operation deviation of the equipment, and the stratum data aggregation and trend analysis are combined. The safety of drilling operation is enhanced, the stability of a drilling site can be evaluated by extracting key features such as stratum density and rock hardness, risk classification is performed accordingly, risk early warning is achieved, and potential accidents are effectively avoided.

The invention provides a crane hoisting operation process risk early warning method and system, and relates to the technical field of crane hoisting risk early warning, and the method comprises the steps: obtaining actual data through a pre-operation test, correcting a load in combination with a wind speed, a load matrix and a dynamic factor, and generating a fitting equation to predict a future load condition. Dynamic monitoring and accurate evaluation of the crane jib load are realized, and the accuracy of risk management is remarkably improved. Compared with a traditional method, the scheme can predict the operation condition in advance and carry out risk assessment, so that potential accidents are prevented, and reliable safety guarantee is provided for operators. Therefore, the operation safety of the crane is improved, the equipment management and maintenance process is optimized, and an effective solution is provided for a high-risk operation scene.

The invention relates to an alarm information processing system and method for a technological process, and the system comprises a data collection module which is used for collecting the real-time parameter data of each equipment node in the technological process; the knowledge base module is used for storing a risk knowledge graph of the technological process, and the risk knowledge graph comprises a plurality of accident chains representing abnormal propagation paths and alarm signals among equipment nodes; the risk diagnosis module is used for positioning a potential accident chain from a risk knowledge graph according to the received alarm signal and the real-time parameter data; and the alarm deduction module is used for deducing the potential influence of the alarm signal on the technological process according to the potential accident chain. According to the method and the device, the potential influence possibly caused by the alarm event can be automatically deduced, the severity and the influence range of the accident consequence can be effectively pre-judged, a scientific basis is provided for emergency decision making, and the problem of how to improve the safety of the technological process in industrial production in related technologies is at least solved.

The invention discloses a traffic accident risk prediction method and system inspired by driving attention, and the method comprises the steps: obtaining a driving scene image, preprocessing the image, and converting the image into continuous image frames; based on attention mechanism text-to-video attention transfer fusion, text information is converted into video representation, and cross-modal features with coherent semantics are generated; semantic context migration is realized on the cross-modal features through a graph convolutional network, and a dual-path model is constructed on the basis of a generated semantic context feature matrix; and a multi-task learning strategy is adopted, and a potential accident risk caused by distraction of the driver is identified and predicted through a dual-path model. According to the invention, key semantic clues are identified in each module by using an attention mechanism, so that the robustness of accident prediction is enhanced, and the driving safety is remarkably improved.

The invention discloses a method for evaluating the operation risk of an unmanned distribution vehicle, and the method comprises the following steps: dividing an operation region: dividing the operation region of the unmanned distribution vehicle into a plurality of honeycomb-shaped land parcels, and guaranteeing that the land parcels are not overlapped and completely cover the operation region; key nodes are determined; land block congestion probability calculation: for each honeycomb land block, calculating a truncation mean value of congestion probabilities of all key nodes in the honeycomb land block, and selecting a maximum value from the truncation mean value as an overall congestion probability of the land block; and distribution resource configuration: according to the congestion probability of each cellular land parcel, determining the suitable delivery amount of unmanned distribution vehicles in the land parcel and the number of safety officers responsible for handling potential accidents. According to the method, key nodes of risks in urban traffic operation can be identified, the regional risk assessment method for unmanned distribution vehicle operation is provided from the two dimensions of congestion and accidents, and unmanned distribution vehicle delivery strategy constraints are formed.

The invention relates to the field of hydrogen energy conveying safety, in particular to a method, device and equipment for estimating the explosion damage range of a hydrogen conveying pipeline. The method for estimating the explosion damage range of the hydrogen conveying pipeline comprises the following steps: acquiring physical parameters and environmental conditions of the hydrogen conveying pipeline; based on the physical parameters of the gas conveying pipeline, the isentropic expansion theory is adopted to calculate the explosion energy of the hydrogen conveying pipeline in unit length; calculating initial peak overpressure by adopting an exponential decay model on the basis of the explosion energy of the hydrogen conveying pipeline per unit length; based on environmental conditions, correcting the initial peak overpressure to obtain corrected peak overpressure; and determining a damage range based on the corrected peak overpressure. According to the method, the shock wave overpressure generated by explosion of the hydrogen conveying pipeline can be rapidly calculated, efficient, accurate and real-time damage range estimation is achieved in combination with environmental factor correction, and personnel injury and property loss caused by potential accidents are reduced.

The invention discloses a chemical production area environment safety early warning system based on big data analysis, and relates to the technical field of intelligent prediction, the system comprises an acquisition module, an analysis module and an early warning module, an LSTM model is trained, a prediction model is obtained, a predicted expected output matrix is obtained, the prediction model is updated, and current monitoring parameters and current prediction nodes are obtained. And calculating a second prediction value of the current prediction node, obtaining a standard monitoring parameter range, carrying out safety level division on the second prediction value according to the standard monitoring parameter range to obtain a safety level, and sending an early warning signal according to the safety level. According to the invention, a more accurate basis is provided for accident prediction and prevention through a big data analysis technology, equipment abnormity, production line fluctuation and emergencies are identified by adopting a deep learning technology, the risk of potential accidents is reduced, sudden environmental events occurring in a chemical industry park can be quickly responded, invalid alarms are greatly reduced, and the safety of the chemical industry park is improved. And the accuracy of risk hierarchical management and control is effectively improved.

The invention relates to the technical field of ship safety monitoring, in particular to an urban inland river pleasure boat driving dynamic safety monitoring method and system based on multi-modal sensing, and the method comprises the steps: carrying out the data preprocessing and synchronization based on multi-source heterogeneous data collected by a multi-modal sensor network in real time, and outputting the multi-source heterogeneous data with time-space alignment; performing cross-modal attention model feature fusion based on the multi-source heterogeneous data of space-time alignment, and outputting fused multi-dimensional state features; based on the fused multi-dimensional state features, dynamic risk assessment is executed, and a final risk level is output; based on the final risk level and the historical risk data, the risk type is judged, and whether emergency response is activated or not is judged; when the emergency response is activated, risk avoiding operation is executed, and a feedback report is generated. The method has the effects of effectively preventing navigation risks and reducing potential accident loss.

The invention discloses a mass concrete temperature monitoring method. The method comprises the following specific steps: step 1, planning a monitoring point position; step 2, selecting and installing equipment; step 3, installing and pre-burying a measuring point, step 4, installing and pre-burying an optical fiber, step 5, laying temperature measurement layers, step 6, collecting and monitoring data, and step 7, ending data analysis. According to the invention, through a mode of combining a wireless temperature measurement process and manual measurement, the frequency of manual point measurement can be reduced, manpower consumption is reduced, the comprehensiveness of monitoring data is improved, the monitoring blind area of a traditional point type sensor is avoided, and the possible equipment fault problem of a temperature measurement system is avoided; and data comparison of manual monitoring and wireless monitoring is adopted, so that the reliability of temperature measurement data can be determined, and accident hidden dangers are reduced.

The invention discloses an operation real-time monitoring system based on risk factor dynamic assessment, and relates to the technical field of desilting operation risk assessment, the system can comprehensively assess various potential risks in the desilting operation process through multi-dimensional environmental data acquisition and real-time monitoring, and the risk factor dynamic assessment is realized. Comprise dam instability, water flow shearing force, sediment disturbance, soft foundation stability and the like. The system calculates comprehensive risk factors according to the data, timely identifies risks and triggers early warning, and ensures the safety of an operation area. The system can dynamically evaluate the soil liquefaction risk by monitoring the soil liquefaction factor and the volume change of the workboat in real time, and prevents the workboat from inclining or sinking. In addition, the system generates and implements specific emergency strategies according to different early warning instructions, and takes effective protective measures to deal with sudden risks. The safety and efficiency of the dredging operation are improved, the potential accident risk is reduced, and the sustainability of the dredging operation is guaranteed.

The invention discloses a road accident warning method and system based on image recognition, and relates to the technical field of road accident warning, and the method comprises the following steps: firstly, analyzing a road traffic image through an image recognition technology, detecting various traffic elements in the image, and recognizing abnormal conditions in the image; through an image recognition technology, dynamic feature extraction and deep learning evaluation, the problem of misjudgment of a traffic monitoring system caused by field factor interference is solved. Image data are processed in real time, different traffic situations are adapted, and the accuracy of feature extraction is ensured. By analyzing the characteristic change trend and capturing abnormal fluctuation, the system can accurately identify potential accidents and avoid false warning. The low-level early warning and verification stage ensures that no formal alarm is given before no high-level confirmation, so that false alarms and excessive alarms are effectively reduced, the alarm fatigue is reduced, and the accuracy and efficiency of system response are improved.

The invention relates to a vehicle accident handling method, device and equipment and a medium, and the method comprises the steps: obtaining vehicle driving data; performing accident prediction on the vehicle driving data based on a pre-trained identification model to obtain an accident prediction result; the accident prediction result represents whether a potential accident exists or not; acquiring sensor data under the condition that the accident prediction result is that a potential accident exists, and matching with a preset accident condition according to the sensor data to determine an accident identification result; and when the accident identification result is that the vehicle accident occurs, generating an accident report of the vehicle accident. According to the technical scheme, the vehicle accident can be accurately recognized, the accident report can be generated, and the vehicle accident handling accuracy and the accident handling efficiency are improved.

The invention relates to the technical field of road construction safety protection, and particularly discloses a road construction protection method, system and device. The method comprises the following steps: acquiring multi-modal sensor data and performing multi-source data fusion processing to obtain an environment data sequence of a construction site; extracting feature information from the environment data sequence, and performing risk identification based on a lightweight deep learning model in the edge calculation unit to obtain a potential risk identification sequence; uploading the identification sequence to a cloud for model optimization, and generating an updated identification model parameter; and finally, the optimized model parameters are applied to an edge calculation unit, dynamic risk identification of real-time construction environment data is carried out, and a real-time early warning signal is generated. According to the invention, through fusion of the multi-modal data and real-time optimization of the deep learning model, accurate safety risk identification can be realized, false alarm or missing alarm can be effectively avoided, the safety of a construction site is improved, and potential accidents are reduced.

The invention provides a construction safety early warning method for a construction site, a terminal and a storage medium. The method comprises the following steps: constructing a physical model of an obstacle in a construction site based on a physical engine simulation algorithm; based on the physical model of the construction site and the state information of all obstacles in the construction site at the current moment, performing mixed collision detection, determining a target obstacle, screening out obstacles which do not collide, and reducing the calculation amount; physical motion simulation is carried out on the target obstacles to obtain state information of each target obstacle in a future time period; further collision prediction is carried out on the target obstacle based on the state information of the target obstacle in the future period, and a collision detection score is determined; and performing safety early warning on the construction site based on the collision detection scores of any two target obstacles in the future time period. According to the method, early warning can be carried out on possible accidents in advance, so that the occurrence of safety accidents is reduced, and the safety warning effect of a construction site is improved.

The invention relates to the technical field of special equipment accident prevention, and discloses a special equipment accident prevention optimization potential analysis method and system based on a TRIZ functional model. Comprising a data collection module, a function analysis module, an optimization potential analysis module, a decision support module, a system operation module, a user interface module, a data storage module, a system maintenance module and a user management module. A data collection module, a function analysis module, an optimization potential analysis module, a decision support module, a system operation module, a user interface module, a data storage module, a system maintenance module and a user management module are integrated, so that potential accident risks of the special equipment can be effectively identified and prevented, and the safety of the special equipment can be improved; the risk of accident occurrence is reduced, a scientific basis can be provided for continuous improvement of equipment by optimizing potential analysis, and the efficiency and effect of accident prevention are improved, so that the safety management level of the whole industry is improved.

The invention discloses a control system for blast furnace top gas recovery. The control system comprises a period classification module, a threshold parameter acquisition module, a threshold parameter analysis module, a prediction parameter acquisition module and a prediction parameter analysis module. The method comprises the following steps: analyzing pressure data of monitoring points in a plurality of monitoring periods during gas recovery, and classifying the monitoring periods; calculating a threshold coefficient and an accident pressure threshold of the same type of cycle after classification; whether the accident pressure threshold needs to be adjusted and optimized or not is judged, and if yes, threshold optimization is carried out; analyzing the real-time change rate of the pressure value of the monitoring point and the historical pressure change curve of the same type of historical period to obtain a deviation degree value; comparing the deviation degree value with a deviation degree threshold value to generate a prediction signal; a prediction time value is obtained and analyzed, and whether a potential accident risk exists or not is judged; the flexibility of setting the pressure threshold is improved, and the method is more suitable for complex and changeable operation states in the blast furnace production process.

The invention relates to a method for identifying illegal behaviors in a crane scene, which comprises the following steps of: acquiring at least two adjacent frames of first images acquired by a first camera and a second image acquired by a second camera; generating a first recognition result based on the first image, wherein the first recognition result comprises whether a crane of the crane is in a working state, whether a hook is in an inclined state and whether the hook is in an auxiliary state; generating a second identification result based on the second image, wherein the second identification result comprises whether a person enters the target risk area and whether the person wears a safety helmet; and judging an identification result and generating an alarm corresponding to the preset alarm condition. Through carrying out video snapshot on a crane scene, by virtue of related technical means such as artificial intelligence video behavior analysis and the like, production field safety visual management is realized, the effect of realizing intelligent identification and control on safety violation behaviors is achieved, the production field safety management capability is improved, and accident hidden dangers are eliminated in the bud by utilizing informatization and intelligent technologies.

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.

Method for preconditioning protective measures for at least one occupant (10) in an interior (12) of at least a semi-autonomous motor vehicle (14), in which at least one simulation of a potential accident situation is simulated by means of a simulation device (16) and, depending on the simulation, at least one signal for controlling a control device (18) of the motor vehicle (14) is generated and transmitted to the control device (18),wherein, by means of a detection device (20), predefined parameters of protection-relevant control devices (18) within the interior (12) are first detected and transmitted to the simulation device (16), and depending on the parameters, a simulation is carried out to calculate specific protective measures for the protection of the at least one occupant (10) in the event of an actual accident situation, and wherein, in the actual event of an accident situation, one of the protective measures is loaded from the database (22), and depending on the loaded protective measures, the at least one signal for controlling the at least one control device (18) is generated and transmitted to the control device (18), characterized in that, by means of an artificial intelligence arranged in the simulation device (16), a clustering of detected data from the simulations is carried out.in which, based on previously known parameters and data from the database (22), it is calculated which protective measures and output signals are suitable for potential accident situations, whereby parameter values ​​of the parameters are also taken into account by means of the simulation device (16) to determine efficient specific protective measures.

The utility model relates to an electromagnetic wire defect cutting device, which belongs to the technical field of electromagnetic wire defect cutting and comprises a mounting base, an optical axis support is slidably mounted at the top of the mounting base, and a cutting assembly is arranged on the optical axis support and comprises a cutting base arranged on the optical axis support. And a partition plate is fixedly mounted on one side of the cutting base. According to the electromagnetic wire defect cutting device, the cutting assembly is arranged at the terminal of the copper wire production path, the two tungsten steel cutter strips are controlled by the cutting assembly to synchronously get close to the surface of the flat copper wire, large particle defects on the surface of the copper wire are cut off in the copper wire conveying process, and the problems that due to the limitation of the production technology, the paint surface is not uniform in the painting process, and the product quality is poor are solved. In the process production requirements, particles lower than 200 microns are called small particles, particles larger than 200 microns are called large particles, the defects of the large particles are the fatal injury that coils are prone to being short-circuited and burnt after being electrified, and potential accidents exist at any time.

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.

A method for a motor vehicle for controlling an occupant protection component of the motor vehicle, the method comprising: detecting a potential accident opponent of the motor vehicle; detecting accident opponent data relating to the potential accident opponent and motor vehicle data relating to the motor vehicle; determining, based on the accident opponent data and the motor vehicle data, acceleration information relating to a possible accident between the potential accident opponent and the motor vehicle, wherein the acceleration information characterizes an acceleration of the motor vehicle during the possible accident; and outputting a control signal for controlling the occupant protection component of the motor vehicle taking into account the acceleration information.

The present invention belongs to the technical field of electronic rearview mirrors, and specifically relates to an electronic rearview mirror system, an electronic rearview mirror image jam detection method and a device. During the operation of the electronic rearview mirror system, the method selects at least one state from the video data stream state, the connection state of the video transmission link, and the working state of the image display module to determine whether the electronic rearview mirror image is jammed, and the selected state at least includes the video data stream state. When determining the video data stream state, different time nodes of the video stream are extracted, and the differences between the extracted image data are compared. Based on the differences, it is possible to accurately determine whether the video data stream is normal. Moreover, during the comparison process, the selected area is generally not the complete image data, which can meet the judgment accuracy while reducing the amount of calculation. When selecting three states for judgment, it is possible to detect whether the video data stream is normal from the entire video transmission link, thereby reducing the occurrence of potential accidents.

The invention provides a method and a system for determining a protection signal of a heat pipe micro-stack. The method comprises the following steps: obtaining safety criteria of the heat pipe micro-stack, wherein each safety criterion relates to at least one protection signal; determining a first potential protection signal list comprising at least one potential protection signal according to a safety criterion; obtaining a heat pipe micro-reactor accident list comprising at least one accident category, wherein each accident category comprises one or more accidents; determining a second potential protection signal list comprising at least one potential protection signal according to the heat pipe micro-stack accident list; implementing optimization is carried out on the first potential protection signal list and the second potential protection signal list according to legal protection signal requirements and design constraint conditions, and a preliminary protection signal list is determined; and carrying out key accident verification on the protection signals in the primary protection signal list to determine a protection signal setting value meeting a safety criterion so as to obtain a final protection signal list.

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.

The invention relates to an input parameter influence analysis method and system for nuclear power plant accident consequence evaluation, a medium and equipment. The method comprises the following steps: acquiring parameters required for evaluation; screening and setting parameters required by evaluation to generate a parameter information table; performing random sampling based on the input parameters to generate a random sampling matrix; calculating by using a random sampling matrix to obtain an accident consequence evaluation result; analyzing uncertainty and sensitivity based on the accident consequence evaluation result, and obtaining an uncertainty analysis result and a sensitivity analysis result of the input parameter; performing comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result; and determining an influence result of the input parameters according to the comprehensive analysis result. The uncertainty and sensitivity of the input parameters are analyzed, the influence degree of each parameter on the accident consequence evaluation result is effectively quantified, it is ensured that the nuclear power plant can make an accurate decision in time when a potential accident occurs, and the public and environment safety is effectively protected.

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.

The invention relates to a method for preventing a CAS dipping cover lifting trolley steel wire rope from being damaged, and belongs to the technical field of automatic control systems and mechanical safety. In order to solve the problem of frequent rope breakage accidents caused by lack of real-time monitoring of steel wire rope tension of a converter CAS dipping cover lifting system, a strain gauge tension sensor is installed on a steel wire rope to collect tension data in real time, a dynamic protection threshold value is set through a PLC program, and a multi-stage linkage protection mechanism is established. And when the tension is detected to exceed the limit, sound-light alarm, equipment sudden stop and data recording are triggered. According to the method, millisecond-level abnormal response is achieved, the steel wire rope jumping accident rate is reduced by 90% or above, the non-planned shutdown duration is shortened by 75%, the service life of equipment is prolonged, and the vicious accident potential is eliminated.

The present invention provides a flexible water-stop device, including a flexible water-stop sleeve, a first connection assembly, a second connection assembly and a universal pull rod assembly, wherein the flexible water-stop sleeve is sleeved on the outer periphery of a concrete pipe, one end of the flexible water-stop sleeve is connected and fixed to the first connection assembly at the starting well entrance, the other end of the flexible water-stop sleeve is connected to the second connection assembly, the second connection assembly is sleeved on the end of the concrete pipe away from the starting well entrance, and the two ends of the universal pull rod assembly are respectively connected to the first connection assembly and the second connection assembly. During the pipe jacking operation, the present invention tightens the second connection assembly through the universal pull rod assembly to balance the axial pressure of the flexible water-stop sleeve. When the curved pipe jacking is performed, the concrete pipe at the starting well entrance will deviate. At this time, the end of the universal pull rod assembly away from the starting well entrance can be deviated with the concrete pipe at the same time as the corresponding end of the flexible water-stop sleeve, which can avoid the failure of the water-stop seal of the flexible water-stop device, thereby ensuring construction safety and reducing potential accidents.