581results about How to "Timely warning" patented technology

The invention discloses a vehicle driving state evaluating method based on road-switching behavior detection, comprising the steps of: carrying out video acquisition and real-time processing on front road conditions by using a camera so as to achieve purposes of monitoring dangerous driving behaviors of road-switching, long-time line-pressing driving and the like of a vehicle and immediately warn, i.e. arranging a camera facing the front direction inside the vehicle to obtain front road condition information; detecting and tracking a lane line; judging the current position state of the vehicle, if the vehicle is in a line-pressing state, carrying out state tracking to judge whether the vehicle is subjected to lane-switching operation or not; if the vehicle is subjected to the lane-switching operation, judging the behavior danger level of the vehicle according to the lane-switching times within a recent period of time; and if the total time in the line-pressing state within the recent period of time is m percent, making an alarm of the long-time line-pressing driving. For the vehicle active safe driving, by adopting a computer monocular vision and image processing technology, the invention has characteristics of simple system configuration, low cost, good real-time and capability of being applied in various climatic environments in the daytime and at night.

The invention discloses an integrated horizontal well geosteering method. The integrated horizontal well geosteering method includes the steps of establishing a precise three-dimensional geologic model by geologic data, well logging data, well measuring data and geophysical prospecting data before drilling; designing a three-dimensional track according to a stratigraphic dip and reservoir stratum properties extracted from the three-dimensional model; during drilling, guaranteeing accurate landing and continuously revising the geologic model via tracking while-drilling records and controlling the track; during target drilling, according to combination between while-drilling well recording, while-drilling well measurement combined geological research, seismic interpretation, drilling technology, horizontal well controlling technology, performing while-drilling tracking and explanation evaluation, immediately regulating and forecasting next steps in a horizontal well construction scheme, and realizing field and remote visualized geosteering via an integrated geosteering software system platform.

The invention provides a personnel detection and counting system based on a millimeter wave radar, wherein the millimeter wave radar is installed at a set position of a monitoring area, the millimeterwave radar transmits millimeter wave band radio frequency signals to the monitoring area through a multi-transmitting and multi-receiving antenna of the millimeter wave radar and receives echo signals at the same time, the echo signals are subjected to frequency mixing with the transmitted signals and then subjected to down-conversion to obtain beat intermediate frequency signals, and the beat intermediate frequency signals are sampled to obtain an echo sampling sequence. A digital signal processor reads the echo sampling sequence and performs signal processing to obtain a detected point cloud data set. An ARM processor reads the point cloud data set, meanwhile, Kalman filtering tracking is carried out on the point cloud data, continuous observation of states and the number of a pluralityof human body moving targets is achieved, the positions and the number of people in the monitoring area are obtained, and the number of people in the monitoring area is counted. The personnel detection and counting system based on the millimeter wave radar can track at least 20 targets simultaneously, provide the position and speed information of the targets, effectively judge the state of the targets and give early warning in time.

The invention relates to a mine water bursting disaster monitoring and early-warning system. The system comprises an in-situ measurement subsystem, a data acquisition subsystem, a data transmission control subsystem and a data and warning release subsystem. The in-situ measurement subsystem is connected to the data acquisition subsystem by a communication system, data input and output ports of the data acquisition subsystem are connected with the data input and output ports of the data transmission control subsystem by the communication system to realize bidirectional data transmission, and the data transmission subsystem is connected with the data and warning release subsystem by a computer network. The invention also relates to a control method of the mine water bursting disaster monitoring and early-warning system.

The invention belongs to the field of communication technology and relates to a smart old-people caring system. The main structure of the system comprises an information collection subsystem, an information processing subsystem, a cloud platform and a command center, wherein the cloud platform is a data storage system and is used for storing information collected by the information collection subsystem and transmitting the information to the information processing subsystem for analytical processing; the information processing subsystem serves as an information processing pivot, is provided with an intelligent AI analytical algorithm module, performs intelligent analytical sort-out on collected big data and sends the processed information to the command center; and the command center can store all the information and provide information query and transmission services. Through the smart old-people caring system, 24-hour old people behavior analysis in videos is provided, a reasonable and effective judgment is made on suspicious abnormal behaviors of old people through bidirectional linkage in combination with sign data of old people, and abnormal behavior categories, confidence ofabnormal behaviors, risk levels, abnormal staff identities and first-aid projects are provided.

The invention belongs to the field of mobile communication, which provides a wireless base station network quality monitoring and automatic evaluation analysis device and a realization method which are used for different application scenarios of the mobile communication network. The invention can simulate the behaviours of subscribers by a wireless preamble which can be controlled in real-time, automatically measures and stores plenty of wireless testing data, and uniformly updates all data to a background control center which achieves automatic and concentrative analysis and pre-alarm of the index defects to the data. The invention realizes continuous monitoring to the wireless network with large range, effectively achieves objective evaluation to the network operation quality, greatly reduces the daily measurement cost of the network, effectively evaluates the project construction at the same time, and objectively reflects the improvement of the network quality after the project is newly built. The system also provides the monitoring analysis and pre-alarm function of the network operation quality and various analysis reports can be output to the maintenance optimization personnel according to the requirement of the user.

The invention relates to the network safety, and provides an enterprise network safety event management system comprising safety devices, a safety event data acquisition module, a safety event data standardization module, a safety event situation assessment and prediction module, a safety device management linkage defense strategy generation module, a state protection module and a system management module. The method comprises the following steps: the system automatically collects safety event information generated by the safety devices in the enterprise network; the system carries out standardization format transition for the collected safety event information; the system analyzes the safety event information after standardization format transition so as to obtain network information safety situation, and predicts network safety trends; the system carries out association analysis for safety devices disposition safety application strategy according to the network information safety situation and the predicted network safety trends, thus generating the linkage defense strategy and sending the strategy to the safety devices. The enterprise network safety event management system and method are suitable for enterprise network safety event management.

The invention relates to a strip steel quality forecasting, furnace condition early-warning and fault diagnosis method, in particular to a strip steel quality forecasting, furnace condition early-warning and fault diagnosis method based on partial least square, comprising the following steps: model selection: multiple models are adopted to describe the process characteristics of corresponding steel types; data preprocessing: data alignment based on the model is carried out, synchronization relation of process input and quality output is built and data dimensionless treatment is carried out to eliminate effect of process data on modeling precision owning to non-unity of physical units; an off-line model building; a PLS (partial least square) model for strip steel quality and process variable is built by utilizing a great amount of historical data in normal working conditions; determining control limit of an monitor-control index; determining variable quantity control limit; and on-line forecasting and on-line detection and fault diagnosis. In the invention, the model for the strip steel quality and the process variable is built by PLS algorithm, so as to realize real-time quality forecasting, process monitoring and fault diagnosis.

The invention discloses an online health monitoring system and method for a composite material. The system comprises an optical fiber grating demodulator, an optical fiber coupler, a processor and a double-lead array optical fiber grating pair sensor. A light source emits light signals, and the light signals are divided into two ways through optical fibers, and connected to the two ends of the double-lead array optical fiber grating pair sensor through one optical fiber coupler. All optical fiber gratings are embedded into the composite material. After the light signals and the optical fiber gratings are mutually affected, reflected light enters the optical fiber grating demodulator through the optical fiber coupler. The optical fiber grating demodulator is connected with the processor. The processor calls a damage database of the composite material, analyzes the damage type, degree and distribution of the fiber composite material to set up a three-dimensional model, outputs the damage distribution of the composite material, calculates residual intensity of the composite material by means of finite element analysis, and evaluates the health condition of the composite material. The system monitors the damage state of the composite material and gives out early-warning in real time.

The invention discloses a vehicle rear-view monitoring alarm system and method based on multiple rotating zooming cameras, and the system comprises a rotating zooming device, an image collection module, an image collection module, an image processing and analysis module, a vehicle information extraction module, a warning prompt module, a warning module, and an auxiliary module. The invention discloses an alarm method employing the above alarm system. The system can monitor the regions at different distances behind a vehicle, can timely transmit the warning information to a driver under various conditions, achieves the classified warning, achieves the prompt in a highlighted manner under dangerous conditions, can effectively reduce the occurrence rate of traffic accidents, and protects the personal safety of persons and the safety of property in a vehicle.

The invention discloses a mechanical equipment fault intelligent early warning method based on multivariate estimation and prediction. The method comprises the following steps: step 1, modeling a datasubset; step 2, preprocessing the modeling data subset; step 3, constructing a prediction model of the mechanical equipment state parameters and the working condition parameters; step 4, estimating and predicting the state parameters of the mechanical equipment corresponding to the actually measured state parameters in a normal operation state; step 5, subtracting the actual measurement result ofthe state parameter from the prediction result to obtain a residual value of the state parameter, judging whether the absolute value and the growth trend of the residual value exceed corresponding threshold values or not, further detecting the fault abnormality of the equipment and giving an alarm; the intelligent early warning model of the mechanical equipment is established based on the multivariate estimation prediction method, and then intelligent early warning of the variable working condition mechanical equipment fault is achieved. Compared with a traditional mechanical equipment faultearly warning method, the method has the advantages of being high in prediction precision, high in early warning accuracy and more timely in early warning.

The invention relates to the technical field of machining, in particular to a cutter wear estimating and monitoring method and system based on characteristic fusion. The method comprises the steps ofdata flexible acquisition, working condition division, data preprocessing, characteristic extraction, characteristic fusion and estimation and monitoring. The system comprises a working condition division module, a data preprocessing module, a characteristic extraction module, a characteristic fusion module, an estimation and monitoring module and a self-learning module serially connected in sequence. The method and the system can realize multi-signal, multi-working-condition and multi-dimension fusion of original signals to obtain characteristic indexes capable of comprehensively reflecting the cutter wear state for estimating cutter real performance state and change rules to give more precise timely prewarning, alarm and feedback optimization policies. Through improvement of cutter wearonline estimation and monitoring performances, the machining problems caused by cutter wear are gradually solved, and the cost reduction, the quality improvement and the efficiency improvement in themachining process are finally achieved.

The invention discloses a vehicle-mounted fatigue early warning device and a method thereof. The vehicle-mounted fatigue early warning device comprises an image acquisition unit, an image processing and positioning unit, a fatigue detection unit, a control unit used for controlling the coordinated operation of all functional units and a warning unit. The method comprises the following: the first step, acquiring pupil image data; the second step, position-processing the acquired pupil image data; the third step, working out a pupil area, and judging whether a driver is tired according to the percentage of eye-closing time against unit time; and the fourth step, sending out a warning signal according to a judging result. The device uses an FPGA chip, and realizes the eye positioning algorithm in a mode of digital circuit system. Compared with the prior art, the device has the advantages of high real-time response, quick and timely early warning on fatigue, small volume, low power consumption, convenient assembly and the like, so that the device is quite suitably applied to a vehicle-mounted electric system.

The present application relates to a database verification method and device, a computer device and a storage medium. The method comprises the following steps: obtaining a verification request, wherein the verification request carries the database identifier; obtaining a verification configuration file in response to the verification request and creating a verification task according to the verification configuration file; searching for a target database corresponding to the database identifier based on the verification task; acquiring online data corresponding to the target database and indexdata corresponding to the online data; executing calibration tasks based on online data and indicator data to obtain a calibration result; when the verification result is abnormal, generating alert information according to the verification result. The method can be used for early warning of database abnormity.

The invention provides an automatic vehicle alarm terminal and method. The automatic vehicle alarm terminal comprises an information acquisition module, an information processing module, a local vehicle alarm module and a vehicle communication module. The automatic vehicle alarm terminal can automatically determine whether a local vehicle is a dangerous vehicle or not, when the local vehicle is determined to be the dangerous vehicle, alarm is given to the local vehicle, and early warning information is sent to other vehicles. The automatic vehicle alarm terminal can process the received early warning information, whether the local vehicle is threatened by dangers of a vehicle which sends the early warning information according to the early warning information or not can be automatically determined, and if the local vehicle is threatened by dangers of the vehicle which sends the early warning information, alarm is given to the local vehicle. Thereby, when faults or accidents happen to the vehicles on a road, an owner of the local vehicle and drivers of surrounding vehicles can be timely warned.

The invention belongs to the technical field of three-dimensional detection of subway tunnels, and discloses a vehicle-mounted measurement device of three-dimensional deformation monitoring of subwaytunnels based on reference transmission. A rotatable laser distance measurement system for high-speed distance measurement and angle measurement is integrated by a laser range finder, an inclination angle sensor, a high-precision turn table and a PLC controller together; a tunnel section outline is measured in a discrete multi-point mode; meanwhile, in combination with a photoelectric encoder installed on a distance measurement wheel at the bottom of a tunnel detection vehicle, a fixed displacement amount of the detection vehicle between a starting position and a travelling position on an axial direction of the tunnel is obtained, a program is triggered, and the detected data of the specific position in the tunnel are transmitted to an upper computer by a wireless communicator. The vehicle-mounted measurement device disclosed by the invention makes breakthroughs in the practical technical innovation of the online monitoring of the tunnel structure safety, lays a foundation for forminga technical system for the reliability design, analysis, test and evaluation of rail transit facilities, and has practicality in many fields.

The invention provides a monitoring device and a monitoring method for a mining FBG roof separation layer. The device comprises a vertical measurement barrel and a packaging casing, wherein fixed pulleys, tape pulleys and constant-strength cantilever beams are symmetrically arranged in the packaging casing; a steel wire rope penetrates through the tape pulleys and the fixed pulleys and penetrates out from a steel wire rope guide hole in the top part of the vertical measurement barrel; one end of the steel wire rope is connected with an anchor head; a shallow part measurement point and a deep part measurement point are distributed inside a drill hole; the anchor head is pushed to the measurement points in the drill hole; when the stress of the steel wire rope changes, the stress change is displayed through wavelength values of an FBG A and an FBG B; a wavelength demodulation device is used for demodulating the wavelength values into digital signals, the digital signals are sent to a computer for real-time processing, and a separation layer numerical value is combined for drawing a separation layer quantity changing curve and a set separation layer boundary value; whether the separation layer exists is judged; and the data change of the roof separation layer is monitored in real time. Temperature compensation is performed with a differential form, on-line measurement of the roof separation layer is realized, the reliability and the accuracy are high, an early warning signal can be issued, and roof fall accidents can be avoided.

The invention discloses a BIM-based steel structure net rack hydraulic lifting time-varying finite element analysis method. The BIM-based steel structure net rack hydraulic lifting time-varying finite element analysis method comprises the steps that 1, a real-time steel structure hydraulic lifting BIM three-dimensional model is imported into finite element software, model properties are set according to a construction drawing, and a time-varying finite element model is established. 2, finite element numerical simulation is verified by utilizing actually measured stress-strain detection results to generate a time-varying construction process mechanics cloud picture; 3, the time-varying construction process mechanics cloud picture is fedback to the BIM three-dimensional model, a stress state of steel structure rack hydraulic lifting at a corresponding stage is displayed; 4, analysis and early warning are conducted on the zone which is not in a stable state in the model to facilitate timely adjustment in a field. The project department can make corresponding emergency measures in time if major security hidden dangers occur. The BIM-based steel structure net rack hydraulic lifting time-varying finite element analysis method has the advantage of achieving timely monitoring and early warning of the stress situation of a steel structure net rack.

The invention provides a driving safety detection method and device which are applied to an automobile equipped with a camera, wherein the camera is used for acquiring image information inside the automobile. The driving safety detection method comprises the steps of switching on the camera when the automobile detected to be electrified; acquiring video information acquired by the camera, analyzing each image contained in the video information according to a first algorithm, and recognizing face images in each image; counting the number of face images in each image, and when the number of faces in any image is greater than a first preset value, marking the image to be a target image; counting the number of target images contained in the video information, analyzing each target image according to a second algorithm when the number of the target images reaches a second preset value, and recognizing face images in each target image again; determining the automobile to be overloaded when the number of recognized face images in any target image is greater than the first preset value, prohibiting startup of the automobile and alarming.

The invention discloses an online analysis and early warning method for low-frequency oscillation of electric power system, relating to the safe operation of electric power system. The method comprises the steps that: oscillation frequency is obtained by carrying out K point median filtering over collected relative power angle in a power grid wide-area measurement system and via Teagar energy operator; and when power oscillation with the oscillation frequency between 0.1-0.2 Hz exists in power grid, the damping ratio dominating oscillation mode can be worked out through a signal energy analysis method based on EMD, thus to realize online analysis and early warning for low-frequency oscillation. The advantages of the invention include that the Teagar energy operator can rapidly work out the frequency of the relative power angle in the power grid; the algorithm of the damping ratio is simplified; large amount of integral operation can be avoided; the calculation is much simpler and morerapid; and the online analysis and early warning for low-frequency oscillation in power grid is realized.

The invention relates to an early warning method based on rule engine and intelligent prediction. The method comprises steps as follows: various monitoring data sensing gateways wirelessly upload monitoring data to an early warning analyzer, the early warning analyzer analyzes the monitoring data in combination with two modes and finally judges whether the monitoring data meet the early warning condition or not, and the early warning analyzer judges whether early warning is performed or not according to an early warning rule which is analyzed by a rule engine and flexibly configured by a user or according to current monitoring data and future monitoring data predicted from historical data with a neutral network method in combination with the early warning rule or according to historical monitoring data and historical early warning data trained models. Early warning is performed timely if the early warning analyzer gives an early warning. The method is used for solving problems that existing early warning rules in all industries are diversified and complex to manually maintain, heavier in workload, low in early warning timeliness and short in handling and protection time.

The invention discloses a smoke detecting method based on random forest characteristic selection. The method comprises the following steps of (1), determining four features of the smoke in a smoke image as input of a random forest model; (2), synthesizing the smoke image by means of a no-smoke image, and constructing a smoke block sample and a non-smoke block sample by means of an image segmentation method; (3), performing dimension-reducing feature selection through random forest model training for obtaining a regression feature; (4), by means of a support vector machine (SVM) and the regression feature, training a smoke block sample and a non-smoke-block sample, obtaining a classifier; and (5), performing real-time smoke detection, namely performing smoke detection on the image in a video according to the classifier which is obtained by the SVM. The smoke detecting method has advantages of performing in-time smoke early-warning and reducing error warning rate in fire early-warning, obtaining a smoke area without setting an experiment threshold, and automatically performing smoke feature analysis by means of random forest feature selection.

The invention provides an unmanned police patrol vehicle and a patrol method. The unmanned police patrol vehicle comprises a vehicle-mounted monitoring system, a violation judgment system, a police platform interaction system, a positioning patrol system and a warning system; the vehicle-mounted monitoring system can collect the driving state information of surrounding vehicles, the environmentalinformation of driving, as well as the basic information of the vehicles and the information of persons in the vehicles; the violation judgment system includes an information classification module, aviolation comparison module, and an information interaction module, and performs classification analysis on the vehicles according to collected image information so as to determine whether the vehicles violate laws; the police platform interaction system can realize the information exchange between the unmanned police patrol vehicle and a police platform, and timely report the conditions of the vehicles and patrol conditions; the positioning patrol system is used for the navigation and positioning of the vehicles, planning the patrol path of the police vehicle and positioning the vehicles violating laws; and the warning system is used for emitting warning prompts to the vehicles violating laws.

The invention discloses an early warning method and an early warning system for vehicle abnormal aggregation. Early warning regions are set in a GIS platform, and the set early warning regions are transmitted to GPS equipment of a vehicle; the GPS equipment judges whether to enter the early warning regions through acquiring longitude and latitude information of the vehicle, and transmits the current longitude and latitude information of the vehicle to a GIS platform after entering the early warning regions; and the GIS platform looks up the corresponding early warning region according to the current longitude and latitude information of the vehicle, adds the vehicle to vehicle information of the corresponding early warning region, and performs statistical analysis on the vehicle information of the early warning regions to generate corresponding early warning level. The early warning method and the early warning system have high universality, can send out early warning for vehicle abnormal aggregation situations caused by various reasons, and have low false warning rate and higher accuracy.

The invention discloses a hybrid digital eye ground image recording and sharing system based on a blockchain. Through a blockchain technology, eye ground images are stored and shared in a distributedmode. For a current state that chronic disease patients in a current community and a remote area can not receive regular eye ground examination, a portable digital eye ground lens is used to achieve patient eye ground image acquisition, complete eye ground standardization image format conversion work, and store in an eye ground mage transmission and storage system. And then, image fingerprint characteristic extraction is performed and an image address is encrypted to the blockchain for recording. And through applying an intelligent contract, an eye ground image can be shared after the authorization of each patient. Through applying the blockchain technology, the patient has the initiative of sharing the eye ground image data of the patient himself or herself, and a workflow that the eye ground image is stored in a dispersive system and is difficult to share is improved. Through data encryption and an eye ground image recording block, safety and a data anti-tampering characteristic in amedical data sharing process can be effectively increased, and the eye ground image is safely recorded and is effectively used.

The invention discloses a resistivity real-time imaging monitoring method and a resistivity real-time imaging monitoring system for a water-bursting geological disaster in a construction period of underground engineering. The method comprises a data acquisition method, a data transmission method, a data real-time inversion and imaging method and a catastrophe premonition identification and early warning method. The invention also relates to the resistivity real-time imaging monitoring system for the water-bursting geological disaster in the construction period of the underground engineering. The system consists of a data acquisition subsystem, a data transmission subsystem, a data processing (data real-time inversion and imaging) subsystem and an early warning subsystem. The invention has the advantages that: data are acquired, transmitted, inverted and imaged in real time; a real-time dynamic image of crack generation, expansion and penetration can be effectively obtained; information such as the generation position and formation process of main cracks and burst water seepage channels is obtained; a series of premonition information can be successfully obtained; and geological disasters can be forecast and timely warned.

The invention discloses a project data management platform for a power supply company. The project data management platform comprises a mobile client, a WEB client and a server, The mobile client is used for logging in the server to perform project management work and realize project initialization information acquisition, information acquisition in project construction and project display; The WEB client is used for logging in the server and carrying out management of platform user permission, data import, data export and summarization; And the server is used for providing storage and management services of project data of a power supply company for the mobile client and the WEB client. According to the platform, unified supervision of a decentralized development system can be realized, aplatform for effective utilization of data is provided, and multi-dimensional analysis of data integration is realized, so that advanced practicability of deep mining of the data and flattening monitoring of the data in an abnormal state are achieved.

A method for classifying underwater original position of polytoplankton by detecting concentration of polytoplankton includes setting a groove at one side of closed pressure box and a light shading cover at external side of said box; erecting LED array formed by parallel-arranged LED with center wavelength of 450nm, 525nm, 570nm 590nm and 620nm in light path at side surface of said groove; dividing polytoplankton to be five light spectrum set as green, blue, brown, red and mixture; measuring 685nm florescent intensity of five different exciting wavelength to calculate out chlorophyll concentration of each light spectrum according to known coefficient.

The invention belongs to the technical field of image recognition and computer vision, and discloses a traffic abnormal picture recognition method based on a focus loss function. The method comprisesthe following steps: a convolution neural network based on a point loss function is constructed to establish a picture data set; the image data set is preprocessed and the convolutional neural networkis trained with the processed image data set. The trained convolution neural network is used to recognize the input picture, and the detection result of the traffic anomaly category is obtained. Theinvention uses focus loss function instead of original loss function to smoothly train the convolution neural network and improve recognition accuracy in view of the situation that the road abnormal and normal data set types are not balanced, which leads to difficult training of the convolution neural network; the invention uses focus loss function instead of the original loss function to smoothlytrain the convolution neural network. The identification method provided by the invention can be deployed in the monitoring system of each traffic section, and can automatically identify whether thetraffic anomaly occurs in the current road section, so as to provide timely early warning for the traffic department.