39results about How to "Efficient early warning" patented technology

The invention discloses a cooperative vehicle infrastructure-based curve speed warning method, which comprises the following steps: putting a road side unit at a curve, broadcasting out curve information periodically, and judging whether a vehicle heads for the curve or not in advance by a vehicle terminal according to the information of the vehicle and the received curve information; giving out the distance to the curve and the speed-limit prompt of the curve in advance if the vehicle heads for the curve; giving out the speed-limit prompt of the curve and detecting whether the vehicle is overspeed or not in real time if the condition that the vehicle drives into the curve is detected, and giving out overspeed warning if the vehicle is overspeed to remind a driver of driving at a safety speed; relieving the warning when the condition that the vehicle puts off the curve is detected. According to the method, the limiting speed of the curve can be given out in advance, and the condition that the vehicle drives into the curve and puts off the curve can be efficiently and reliably judged; written or audio warning is given out when the vehicle is overspeed, and therefore, a selectable simple scheme is provided for the auxiliary driving safety application of Internet of vehicles.

The application relates to a method, a device, and a system for monitoring driving behavior, a vehicle and a storage medium. The method comprises the following steps: performing detection on video information which is collected in a vehicle to judge whether the current driving state of a driver is an abnormal driving state; wherein the detection comprises a distracted driving detection and a fatigue driving detection; and performing an early warning operation corresponding to the abnormal driving state if the current driving state is an abnormal driving state. The method for monitoring the driving behavior can realize all-round monitoring for the abnormal driving states of the fatigue driving state and the distracted driving state, and realizes a targeted and effective early warning by performing the early warning operation corresponding to the abnormal driving state, thereby reducing driving risk.

The invention provides an electromagnetic radiation detection device and method for potential fire danger of a mine and belongs to a fire detection device and method. According to the electromagnetic radiation detection device and method, electromagnetic radiation signals generated in the temperature rising or burning process of a coal body are detected and analyzed, and then the potential fire danger of the mine is detected. The detection device comprises a perpendicular directionally receiving antenna set and a monitoring host. The detection method includes the steps that the effective receiving direction of the perpendicular directionally receiving antenna set faces a detection area, the value and the main direction of electromagnetic radiation are measured and calculated; a crossed position of main directions of different measuring points is a potential fire danger area; it is judged that fire danger exists in the area according to the average value of electromagnetic radiation indexes in the potential danger area or a dynamic change trend of the electromagnetic radiation indexes in multiple measurements in a critical value mode and a dynamic change trend mode when the electromagnetic radiation indexes or the dynamic change trend exceeds a corresponding critical value. Through the electromagnetic radiation detection device and method, potential fire risks and the potential fire danger area can be detected in time and space; the electromagnetic radiation detection device and method have the advantages of being efficient, fast and convenient.

The invention discloses a system for monitoring overall tilting of a high-rise structure through laser projection, which relates to the technical field of laser. The invention particularly relates to a system for monitoring the overall tilting of the high-rise structure, which combines a laser projection technique, a photographic technique and an image processing technique. The system comprises a laser projector (1), a laser projection point receiving sighting board (2), a picture pick-up device (3), a computer (4) and a power supply unit (5), wherein the picture pick-up device (3) has high pixels, comprises image transmission and is connected with the computer (4) through electric signals; and the computer (4) comprises an image acquisition card, software for performing operation according to an inclination angle formula theta=arctg delta / H, and a display used for real-time monitoring. The system can display monitoring data and imitate a dynamic tilting state of a monitored structure in real time, provide early warnings, and can notify a construction unit in time to take preventive measures for nipping in the bud once the overall tilt displacement or the inclination angle of the structure is close to or exceeds a designed given alarm actuation value.

The embodiment of the invention discloses an early warning method for a logistics transportation vehicle, which comprises the following steps of: evaluating each influence factor influencing the logistics transportation safety in the vehicle operation process by utilizing an expert scoring method, obtaining a judgment matrix of the influence factors, and calculating an absolute weight vector of each influence factor based on an analytic hierarchy process; obtaining a secondary influence factor danger level fuzzy evaluation matrix and a corresponding absolute weight vector according to the analysis of the real-time condition of each influence factor, and calculating to obtain a fuzzy membership vector; according to each fuzzy membership vector, generating early warning level information based on a maximum membership principle, wherein the influence factors comprise a first-level influence factor and a second-level influence factor, and the first-level influence factor is the driving behavior of the driver, the vehicle performance, the road condition, the environmental factor or the type of the transported object, wherein the second-level influence factors are sub-evaluation factorsof the first-level influence factors. Early warning level information can be accurately and timely provided for a driver, and logistics transportation safety is improved.

The invention provides an edge computing intelligent sensing system building method oriented to Ming Great Wall ruins protection. The edge computing intelligent sensing system based on a terminal-edge-cloud three-layer architecture is built by using a cloudlet open source technology, which solves the problems that 1), the computing power of monitoring equipment is weak, and 2) the cultural relic protection response delay is high and the field network stability is difficult to be guaranteed in the existing Ming Great Wall ruins protection, achieves a better effect, and plays a demonstration role for the protection of field earthen ruins.

The invention belongs to the field of access control of vault doors and particularly relates to a short message-authorized door opening alarm system for a vault door. The system comprises at least two door locks arranged on the vault door, wherein at least one of the door locks is connected with an authentication application device; and the authentication application device is connected with an authorization terminal through a signal. In the alarm system, the opening and closing situations of the vault door can be monitored through a short message and management personnel far away from the vault door field can receive an alarm signal in time and remotely control the monitoring functions, such as the opening or closing of the vault door lock as required.

The invention belongs to the technical field of food safety, and particularly relates to a multi-factor food safety risk comprehensive evaluation method. The method comprises the following steps: (1)determining a food full-chain process; (2) carrying out full-chain hierarchical treatment on the food; (3) determining food quality safety key control points; (4) identifying the types of risk factorsand measuring the content; (5) determining a key control point risk factor limit value; (6) calculating a relative risk index and a chain risk index; (7) determining an early warning coefficient; and(8) dividing risk levels. Different processing bodies, different processing nodes and related different materials in the food processing process are inspected, evaluation is more comprehensive. The early warning coefficient is determined, early warning in advance can be achieved, and the risk position and the risk magnitude are responded in time. According to the method, different food processingchains, different enterprises and different regions can be taken as the same benchmark for investigation according to specific requirements, and the method is convenient, rapid and efficient.

The invention discloses an expressway vehicle group operation risk state warning system. The expressway vehicle group operation risk state warning system includes a central processing unit, a wireless communication device, a loudspeaker, an intelligent watch and a display; the wireless communication device, the loudspeaker and the display are all connected with the central processing unit through cables; the central processing unit is used for obtaining processing information according to acquired vehicle information and driver information, wherein the processing information can indicate whether vehicles are in operation risk states; the wireless communication device includes an information receiving terminal and an information transmitting terminal; the intelligent watch is used for transmitting the acquired driver information to the central processing unit; and the loudspeaker and the display are used for broadcasting and displaying. The invention also discloses an expressway vehicle group operation risk state warning method. According to the expressway vehicle group operation risk state warning system and method of the invention, vehicle groups which are in operation risk states can be warned in advance scientifically and reasonably, and the rear-end and pile-up accidents of vehicles can be avoided. The expressway vehicle group operation risk state warning system and method are of great practical significance for reducing the safety accidents of expressways and improving the safety level of the expressways.

The invention discloses a security early-warning model based on a genetic wavelet neural-network. The model includes the following steps: 1) carrying out fuzzification of input data; 2) determining the layer number of the genetic wavelet neural-network and the node numbers of an input layer, an output layer and hidden layers; 3) carrying out information encoding; 4) carrying out population initialization; 5) carrying out fitness function calculation; 6) carrying out a selection operation; 7) carrying out a genetic operation; 8) selecting an optimal individual for decoding; 9) carrying out training according to parameters of the BP network set in the first step; 10) optimizing the wavelet neural-network; and 11) optimizing a genetic function. According to the security early-warning model ofthe invention, a structure of the wavelet neural-network is simpler, a convergence speed is higher, the neural network is enabled to have higher learning ability through using wavelet basis functionsas neurons of the neural network, precision is higher, immediate, highly-efficient, scientific, accurate and early warning on a chemical production system is realized, and chemical production accident appearing is avoided.

The invention discloses an early warning method and device for the epidemic disease propagation situation of foreign livestock and poultry. Specifically, the early warning method comprises the steps of obtaining quarantine data of foreign livestock and poultry, and obtaining feature data of a to-be-predicted epidemic disease based on the quarantine data; determining disease probability of the to-be-predicted epidemic disease of the foreign livestock and poultry according to the feature data of the to-be-predicted epidemic disease through a disease probability model, wherein the disease probability model is obtained by training the quarantine data of historical foreign livestock and poultry; based on a preset probability and the disease probability of the to-be-predicted epidemic disease ofthe foreign livestock and poultry, obtaining a group risk coefficient of the to-be-predicted epidemic disease of the same kind of foreign livestock and poultry; and determining a risk level of the whole batch of foreign livestock and poultry based on the feature data and the group risk coefficient of the to-be-predicted epidemic disease according to graded early warning conditions, determining anearly warning scheme according to the risk level, and pushing the early warning scheme. By means of the early warning method and device, effective prediction and early warning can be conducted on thespreading situation of foreign livestock and poultry epidemic diseases.

The invention provides a GIS mountain torrent prevention and early warning system based on big data, thereby solving the problems that an existing mountain torrent prediction system is limited in monitoring range, insufficient in reference data and low in prediction precision. The system comprises a cloud computing platform, an environment monitoring module, a terrain monitoring module, an image processing module, a neural network module, a data query module, a data storage module, an alarm driving module and an intelligent terminal module. According to the invention, the neural network moduleis arranged, and the design constructs a prediction model based on existing data, so that the overall prediction precision of the system can be improved; the environment monitoring module, the terrain monitoring module and the image processing module are arranged, rich data are provided for the mountain torrent early warning system, and the mountain torrent early warning precision of the mountaintorrent early warning system is guaranteed; the data storage module is arranged in the system, and the design aims to overcome the defects of the storage capacity of a cloud computing platform, so that the system is more complete and stable.

The invention relates to the technical field of information, and discloses a dynamic placeholder grid model construction method and an application architecture design method thereof, and the method comprises the following steps: 1) constructing a real-time dynamic digital visual region grid block diagram model associated with region mapping of reality description according to an application scene; and (2) constructing a real-time dynamic digital visual grid placeholder structure diagram or a diagram block set which is mapped with a real moving or static entity and is associated with the regional grid block diagram based on a sensor system. According to the dynamic placeholder grid model construction method and the application architecture design method thereof, the problem that various types of information management systems in the prior art cannot well achieve digital and intelligent management such as real-time, efficient and accurate monitoring, searching, tracking, scheduling, early warning and the like on various types of vehicles, personnel and moving objects, and have the problems of difficulty in automatic monitoring management and weak performance and efficacy exist can be solved.

A community mountain disaster early warning device is disclosed by the present invention, and comprises a base station column and a monitoring rod arranged on a mountain, a central control module is arranged on the base station column, and the input end of the central control module is connected with a wireless receiving module, a monitoring module, a rainfall monitoring module, a river channel water level monitoring module and a picture editing comparison module. The output end of the central control module is connected with the display module, an alarm module and a voice broadcast module, adata processing module is arranged on the monitoring rod, the input end of the data processing module is connected with a data acquisition module and a power supply module connecting block, the outputend of the data processing module is connected with a wireless transmission module, and the wireless transmission module is connected with the wireless receiving module. The data acquisition module on the monitoring rod monitors the data of each aspect of the mountain body in real time, if the data is abnormal, the alarm module and the voice broadcast module give an alarm to prompt community people to evacuate quickly, and the device has the characteristics of intelligent, efficient and quick early warning of mountain disaster monitoring.

The invention discloses an online detecting device and a detecting method for integrity of a hollow fiber membrane module. A shading shell is internally provided with a laser transmission system, an optical detection system, a laser receiving system and a display alarm system; the laser transmission system comprises a first laser transmitter and a second laser transmitter; the optical detection system comprises a first optical detector and a second optical detector; the laser receiving system comprises a first signal receiver and a second signal receiver; the display alarm system comprises an alarm and a display; the alarm comprises a third variable resistor and a fourth variable resistor; the fourth variable resistor is connected with an LED (Light Emitting Diode) and a buzzer in parallel; the display comprises a display screen. According to the online detecting device disclosed by the invention, a photovoltaic conversion technology is flexibly used by utilizing the characteristics of good laser directivity, high brightness, strong monochromaticity, high energy density and the like, and the online detecting device has the characteristics of low cost and high sensitivity.

The invention discloses an automatic fire extinguishing device of a power battery system of a new energy automobile based on multi-source information detection and a control method. The automatic fireextinguishing device is internally provided with a controller, is connected to an integrated sensor to input data and is connected to an electromagnetic valve to output a command. The integrated sensor detects temperature data, gas data and smog data in a battery pack. The controller has two stage threshold values. When the thermorunaway state of a battery reaches the first stage threshold value,a manual alarm switch starts to alarm. When the thermorunaway state of the battery reaches the second stage threshold value, a general electromagnetic valve and thermorunaway corresponding branch electromagnetic valves are started. An extinguishant passes through a fire extinguishing bottle to extinguish a copper tube and arrives at a collector and a three-way valve and is finally conveyed to thebattery pack through a fire extinguishing pipe. The automatic fire extinguishing device has the advantages of high thermorunaway pre-judging precision, long-term early warning and efficient control.

The invention discloses an advanced forecasting method and system for soil mass collapses in aeration zones. The system comprises a plurality of soil mass moisture content sensor and an underground water level sensor, wherein the soil mass moisture content sensors are pre-buried from bottom to top from an interface between a soluble rock stratum and a soil mass layer; the underground water level sensor is arranged below the underground water level in the soluble rock stratum; the soil mass moisture content sensor and the underground water level sensor are respectively connected with a data receiver; the data receiver is connected with a data transmission unit; the data transmission unit is connected with a data processor; and the data processor is connected with an alarm. The method and system are capable of realizing advanced warning of soil mass collapses, so that the warning is efficient, timely and accurate.

The invention relates to the field of artificial intelligence, and discloses an urban inland inundation early warning method, device and equipment and a storage medium, and the method comprises the steps: obtaining first real-time monitoring data and first optimization data in a first time period; analyzing the first real-time monitoring data and the first optimization data by using an urban inland inundation risk analysis model to obtain corresponding first inland inundation early warning data and second inland inundation early warning data; acquiring second real-time monitoring data in a second time period based on the effectiveness of the first waterlogging early warning data, and checking the first waterlogging early warning data to obtain second optimized data; judging whether the second optimization data is in a preset error range or not; and if yes, performing waterlogging early warning on the corresponding area based on the second waterlogging early warning data. According to the method, the monitoring data are input into the model to obtain two early warning data, early warning of the urban waterlogging risk is realized based on the other waterlogging early warning data after verification of one early warning data, and the urban waterlogging risk early warning can be realized efficiently.

The invention discloses an NB-IoT intelligent water purifier which comprises a water purifier main body. A serial number and a two-dimensional code are arranged on the water purifier main body, a single chip microcomputer is arranged in the water purifier main body, a device power supply, a positioning unit, a timing unit, a detection unit set and an NB-IoT communication module are arranged at theconnecting end of the single chip microcomputer, a big data background is arranged at the connecting end of the water purifier main body, a background display, a could computing server and a WeChat official account are arranged at the connecting end of the big data background, and a user mobile phone and a maintenance worker mobile phone are arranged at the connecting end of the WeChat official account. The NB-IoT intelligent water purifier pushes information to the user mobile phone through the WeChat official account, displays the pushed information for workers in the big data background through the background display, accordingly intelligently reminds customers and manufacturers to replace filter elements in time and ensures the experience of the customers. In addition, when the waterpurifier needs updating or maintenance, an operator can also push information for the user mobile phone through the WeChat official account and achieve good early warning.

The invention relates to a safe early-warning system for a train station platform, and belongs to the field of safe early warning. The safe early-warning system involves automatic lifting rods arranged inside a waiting area, and an image acquisition device arranged at the edge of the waiting area, wherein the waiting area is arranged on the side edge of a train rail, and infrared emitters and infrared sensors are arranged on the automatic lifting rods; and the infrared emitters, the infrared sensors, the automatic lifting rods and the image acquisition device are in wireless signal connectionon a remote system platform. The front ends of the infrared emitters, the infrared sensors and the image acquisition device are adopted, the scheme of the remote system platform is combined, and a worker can know the situation of the safe area of the waiting area through the remote system platform, so that efficient early warning is carried out.

The invention belongs to the technical field of condition monitoring of a continuous casting machine, and discloses a monitoring system for the large ladle turret of the continuous casting machine. The monitoring system comprises a vibration monitoring unit, a displacement monitoring unit, communication buses, an industrial personal computer and a sound and light alarm, wherein the vibration monitoring unit comprises a plurality of vibration sensor sets; the vibration sensor sets are arranged around the rotating tower bearing of the continuous casting machine to form a plurality of condition monitoring points; the displacement monitoring unit comprises a plurality of displacement sensors; the displacement sensors are arranged at the condition monitoring points; the vibration sensors and the displacement sensors at the condition monitoring points are all connected with the industrial personal computer through the communication buses; the sound and light alarm is connected with the industrial personal computer. According to the monitoring system, the vibration parameters and displacement values of point monitoring pints are obtained by setting the condition monitoring points; the monitoring system is convenient for recording and maintenance, logical analysis and judgment are carried out at the same time to give an alarm signal, and the production safety is maintained.

The invention discloses an advanced forecasting method and system for soil mass collapses in aeration zones. The system comprises a plurality of soil mass moisture content sensor and an underground water level sensor, wherein the soil mass moisture content sensors are pre-buried from bottom to top from an interface between a soluble rock stratum and a soil mass layer; the underground water level sensor is arranged below the underground water level in the soluble rock stratum; the soil mass moisture content sensor and the underground water level sensor are respectively connected with a data receiver; the data receiver is connected with a data transmission unit; the data transmission unit is connected with a data processor; and the data processor is connected with an alarm. The method and system are capable of realizing advanced warning of soil mass collapses, so that the warning is efficient, timely and accurate.

The invention provides a battery fault detection method. The battery fault detection method comprises the following steps: collecting monomer voltage and monomer current of each battery monomer in a charge-discharge cycle; calculating a first correlation coefficient of each battery monomer and a second correlation coefficient of the battery pack based on the monomer voltage and the monomer current of each battery monomer in the charge-discharge cycle, and calculating a first slope of each battery monomer and a second slope of the battery pack; when the first correlation coefficient and / or the second correlation coefficient meet a preset condition, determining whether each single battery has a fault in the charge-discharge cycle based on a first slope, and determining whether the battery pack has a fault in the charge-discharge cycle based on a second slope; wherein the first correlation coefficient and the first slope of each single battery are both related to the voltage and the current of the single battery; the second correlation coefficient and the second slope of the battery pack are both related to the total voltage and current of the battery pack.

The invention belongs to the technical field of vehicle engineering, and discloses a vehicle-mounted power supply monitoring device, which comprises a power supply detection controller module, an output control module, a fault indication module, an Internet of Things communication module and a cloud diagnosis server, wherein the power supply detection controller module detects detection parametersincluding working parameters of a vehicle-mounted power supply, environmental parameters of the vehicle-mounted power supply and running state parameters of a vehicle in real time and sends a vehicle-mounted power supply on-off control signal, the output control module is connected with the power supply monitoring controller module, the fault indication module is connected with the power supply detection controller module, the cloud diagnosis server is connected with the power supply detection controller module through the Internet of Things communication module, and the cloud diagnosis server performs data classification and combination on the detection data according to modes of different devices at the same time and the same device at different times, and performs outlier detection byadopting a clustering analysis method to determine the vehicle-mounted power supply with safety risks. The vehicle-mounted power supply monitoring device provided by the invention can realize high-timeliness fault detection.

The application relates to a method, a device, and a system for monitoring driving behavior, a vehicle and a storage medium. The method comprises the following steps: performing detection on video information which is collected in a vehicle to judge whether the current driving state of a driver is an abnormal driving state; wherein the detection comprises a distracted driving detection and a fatigue driving detection; and performing an early warning operation corresponding to the abnormal driving state if the current driving state is an abnormal driving state. The method for monitoring the driving behavior can realize all-round monitoring for the abnormal driving states of the fatigue driving state and the distracted driving state, and realizes a targeted and effective early warning by performing the early warning operation corresponding to the abnormal driving state, thereby reducing driving risk.

The embodiment of the present invention discloses an early warning method for logistics transportation vehicles, which includes evaluating various influencing factors affecting logistics transportation safety in the process of vehicle operation by using the expert scoring method, obtaining a judgment matrix of the influencing factors, and calculating each influencing factor based on the analytic hierarchy process According to the analysis of the real-time status of each influencing factor, the fuzzy evaluation matrix of the risk level of the second-level influencing factors and the corresponding absolute weight vector are calculated to obtain the fuzzy membership vector; according to each fuzzy membership vector, based on the principle of maximum membership Generate warning level information; among them, the influencing factors include first-level influencing factors and second-level influencing factors, the first-level influencing factors are driver's driving behavior, vehicle performance, road conditions, environmental factors or types of transport goods; the second-level influencing factors are each one The sub-evaluation factors of level influencing factors. This application can accurately and timely provide warning level information to drivers, and improve the safety of logistics transportation.

The invention discloses an enterprise intellectual property safety risk early warning method and a management system, and the method comprises the steps that enterprise intellectual property information and contents are digitalized, and the digitalized information is input into the management system; a retrieval program is pre-installed in the management system, and enterprise intellectual property information and content are compared; through the preprocessing of the early warning method, the intellectual property information of the enterprise can be well retrieved, the early warning capability of the intellectual property of the enterprise is better, the management and control of the risk of the intellectual property of different enterprises can be met, and the early warning can be more efficient.

The invention discloses a smog monitoring and early warning broadcasting machine which includes a solar photovoltaic panel and a pillar. The broadcasting machine is characterized in that: the top part of the pillar is connected to the solar photovoltaic panel. The middle position of the pillar is provided with an early warning broadcasting machine. One side on the upper part of the pillar is connected to a smog detector through a connecting lever. The other side on the upper part of the pillar is connected to a smog collector through the connecting lever. The connecting lever is provided with a monitor thereabove. The bottom part of the smog collector is connected to a storage box through a collecting pipeline. The interior of the pillar is provided with a battery and a control circuit board. The control circuit board is separately connected to the battery, the solar photovoltaic panel, the smog detector, the smog collector and the early warning broadcasting machine. According to the invention, the smog monitoring and early warning broadcasting machine has the advantages of effective monitoring smog, excellent provision of smog early warning, and capability of collecting smog. The smog monitoring and early warning broadcasting machine has simple operation, easy mastering, low cost, easy popularization, easy promotion, easy industrial production, and easy marketization.

The invention discloses a wind and solar complementary natural disaster early warning broadcasting machine which includes a windmill, a solar photovoltaic panel, and a pillar. The top part of the pillar is connected to the windmill. The two sides of the upper part of the pillar are connected to the solar photovoltaic panel through a support rack. A connecting pole is arranged on the pillar and below the support rack. The connecting pole has one end connected to the pillar and the other end connected to a haze monitor. The middle part of the pillar is provided with an early warning broadcasting machine. The upper part of the pillar is provided with a sensor. An alarm button is disposed on the pillar and below the early warning broadcasting machine. The bottom part of the pillar is provided with a detecting valve. The interior of the pillar is provided with a control circuit board, a battery and a power generation apparatus. The control circuit board is separately connected to the battery, the power generation apparatus, the solar photovoltaic panel, the haze monitor, the early warning broadcasting machine, the sensor and the early warning button. The power generation apparatus is connected to the windmill. According to the invention, the broadcasting machine has the advantages of energy conservation and environmental protection, better early warning of haze, and better early warning of natural disasters, etc..

The invention discloses a shield tunnel alignment early warning method, comprising the steps of: 1. calculating the actual accumulated rotation angle of the current segment ring I; 2. calculating the actual base distance of the current segment ring I; 3. calculating the base support Four, calculate the standard deviation of the residual error of the distance deviation of each ring of the segment ring; five, calculate the residual coefficient of each ring of the segment ring; six, calculate the shock value of the residual coefficient; seven, Shield tunnel linear warning. The present invention calculates the included angle between the axes of two adjacent two-ring segments and accumulates them to obtain the actual accumulated rotation angle of the current segment ring, and then calculates the actual distance of each ring segment to obtain the rotation angle. Calculate the residual standard deviation and residual coefficient of the offset deviation, obtain the shock value of the residual coefficient and give the early warning signal of the shield tunnel alignment. High, to reduce accidents caused by severe deviation of the shield segment assembly route.