139results about How to "Real-time warning" patented technology

The embodiment of the invention provides a vehicle auxiliary driving system. The vehicle auxiliary driving system comprises a vehicle terminal, a road side unit and a central control system installed on a vehicle, wherein the vehicle terminal comprises a data acquisition module, a data processing module, a first communication module and a second communication module. First vehicle travelling data of the vehicle is collected by the data acquisition module, travelling environment information is collected by the first communication module, and the first vehicle travelling data and the travelling environment information are sent to the data processing module. The data processing module generates operating instructions according to the first vehicle travelling data and the travelling environment information, and sends the operating instructions to the central control system. The central control system controls driving of the vehicle based on the operating instructions, achieving the goal of controlling the vehicle to drive according to the first vehicle travelling data and the travelling environment information. The vehicle auxiliary driving system can real-timely predict and warn probable dangers existing in the current driving environment, thus improve safety during vehicle driving.

The invention relates to a real-time deformation monitoring pre-warning system based on an inertia-assistance positioning receiver and a real-time deformation monitoring pre-warning method based on the inertia-assistance positioning receiver. The real-time deformation monitoring pre-warning system is characterized by comprising a GNSS (global navigation satellite system) receiver, an accelerometer, a deformation monitoring device including a data interface and a time synchronization module, and a GNSS correction information obtaining device connected with the GNSS receiver and the deformation monitoring device respectively, wherein the data interface is connected with the time synchronization module in sequence, and the GNSS receiver and the accelerometer are connected with the data interface simultaneously. The GNSS receiver outputs the precise positioning result in real time; meanwhile, the accelerometer is adopted to help to capture dynamic deformation signals, the sampling rate stored by data is adjusted reasonably, and the storage space is saved; and due to the arrangement of a buffer area, the high-sampling-rate data of foreboding period of the severe deformation can be stored completely. The system and the method can be widely applied to the monitoring and the pre-warning of the geological disasters such as earthquake, landslide and the like, and applied to the deformation monitoring of dams, bridges, high-rise buildings and the like.

The invention provides a foggy day detecting method based on an image color spatial feature. The method comprises the following steps of first step, obtaining a background picture through a video image or a single image; step 2, performing color space conversion for the background picture, and extracting the color space feature: firstly performing color space conversion for the background picture, converting from an RGB (red-green-blue) color space into an HSV (hue, saturation, value) color space, and extracting the features of various components of the HSV according to the information included image; and step 3, dividing the weather information included image into a non-foggy day, a little foggy day and a dense foggy day according to the identification conditions: at first, if the identification condition 1 is met, the weather is the dense foggy day, otherwise the identification is continued; secondly, if the identification condition 2 is met, the weather is the non-foggy day, otherwise, the identification is continued; and finally, if the identification condition 3 is met, the weather is the little foggy day, otherwise, the weather is the dense foggy day. The method provided by the invention is suitable for being used for monitoring the foggy day in expressways, particularly pre-warning the emergency of agglomerate fog in partial road segments, thus, the safe road driving can be ensured.

The invention relates to an energy-saving wireless multi-hop agricultural remote intelligent monitoring and early warning system, which includes a plurality of data collection devices, the signal input terminals of the data collection devices are connected with sensors, the data collection device communicates wirelessly with the data collection device, and the data collection device communicates with the data collection device. Background computer wireless communication. The present invention uses sensors to collect environmental parameters such as temperature, light, and soil pH, and transmits these parameters to a data acquisition device. Multiple data acquisition devices use wireless transmission modules to transmit environmental parameters to a data collection device, and the data collection device collects all data The parameters collected by the device are collected and encoded, and the encoded parameter information is sent to the background computer through the wireless transmission module, and the background computer performs real-time monitoring and early warning of the agricultural field growth environment information.

The embodiment of the invention discloses an on-board battery failure early warning system based on Internet of Vehicles. An Internet of Vehicles OBD terminal acquires voltage information of the on-board battery, and the voltage information is transmitted to an Internet of Vehicles server; the Internet of Vehicles server stores the received voltage information and the time for receiving the voltage information; and according to a preset algorithm, the voltage information and the time for receiving the voltage information, whether the on-board battery fails is judged, and when the on-board battery is judged to fail, early warning information is transmitted to a user terminal. The embodiment of the invention also discloses an on-board battery failure early warning method based on the Internet of Vehicles.

The invention provides an intelligent air conditioner. The intelligent air conditioner comprises a main unit, a thermal imaging module and an image processing module. The thermal imaging module is used for obtaining an environment thermal image. The image processing module is connected with the thermal imaging module and used for processing the received environment thermal image to obtain environment parameters. The main unit is connected with the image processing module and used for regulating the temperature, the air volume and the air outlet direction according to the environment parameters. The invention further provides a temperature regulation method of the intelligent air conditioner. The temperature regulation method comprises the steps that firstly, the environment thermal image is obtained through the thermal imaging module; secondly, the environment thermal image is processed through the image processing module, so that the environment parameters are obtained; and thirdly, the main unit regulates the temperature, the air volume and the air outlet direction according to the environment parameters, so that intelligent temperature regulation is achieved, the comfort of a user is improved, and meanwhile, the purpose of saving energy is achieved. In addition, real-time monitoring and early warning of an abnormal heat source can be achieved, and thus risks are effectively avoided.

The invention discloses a bridge dynamic weighing method based on the long-gauge fiber grating sensing technology and a bridge dynamic weighing system thereof. The dynamic weighing method comprises the steps that long-gauge sensors are arranged: the long-gauge sensors are arranged on the initial entering and final exiting side span units of a vehicle and the part between the two side span units along the length direction of the bridge; load condition monitoring of a vehicle to be identified and load condition monitoring of a standard vehicle are performed: the vehicle of which various parameters are known acts as the standard vehicle, the strain response data of each sensing unit under the load condition of the standard vehicle are acquired, and the maximum strain of each sensing unit only caused by the axle load is calculated; and vehicle load identification under the load condition of the vehicle to be identified is performed. The maximum bending strain value only caused by the axle load under the load condition of the standard vehicle is analyzed and compared with the corresponding value under the load condition of the standard value so that the total weight of the driving load on the bridge and the load of each vehicle can be identified, the overload vehicle can be effectively pre-warned and thus damage to the big long-span bridges can be avoided.

The invention discloses an equipment abrasion state wireless monitoring device based on the internet of things. The equipment abrasion state wireless monitoring device based on the internet of things comprises a wireless monitoring and transmitting module and a wireless monitoring system module. The wireless monitoring and transmitting module comprises a sensor, a signal amplifier, an analog-digital converter, a microprocessor and a wireless transmitter, wherein the sensor, the signal amplifier, the analog-digital converter, the microprocessor and the wireless transmitter are sequentially connected; the wireless monitoring system module comprises a wireless receiver, a level translator and a PC, wherein the wireless receiver, the level translator and the PC are sequentially connected. According to the equipment abrasion state wireless monitoring device based on the internet of things, a wheel bushing of a traction wheel rope groove reaching to an abrasion critical value is diagnosed in advance through the detection of contact fatigue contacts of the surface of the wheel bushing of the traction wheel rope groove, abrasive wear state and oxidative wear degree, the collected abrasion state information is transmitted to an enterprise management platform or relevant management personnel in a wireless transmission mode, working personnel do not need to descend to a well to detect and eliminate potential safety hazards any more, early warning of abrasion situations of the wheel bushing of the mine traction wheel rope groove is accurately and intelligently carried out in real time, and accordingly safety coefficient of the mine traction wheel is greatly increased in the operation process.

The invention provides a digital twin-based cloud medical simulation platform building method and a cloud medical system. The method comprises the steps that medical data of physical objects is obtained, and a digital twin medical model is built; the physical objects comprise medical resources, medical capacity and patients; the digital twin medical model comprises a medical resource model, a medical capacity model and a patient model; interconnection among the medical model, the physical object and the cloud medical service system is built; a cloud medical simulation platform is built on thebasis of the digital twin medical platform. Accordingly, precise modeling is conducted on the patients and medical equipment through a digital twin technology and a cloud architecture, the medical process can be subjected to real-time simulation, medical service can be subjected to optimal management, and the patients can be monitored and pre-warned in real time.

The invention discloses an intelligent landslide catastrophe monitoring method based on L-type liquid metal antennas. The intelligent landslide catastrophe monitoring method based on the L-type liquid metal antennas includes the following steps that first, a certain number of L-type liquid metal antennas are arranged in a landslide body to be monitored; second, the natural frequency of the L-type liquid metal antennas is measured; third, data of the natural frequency of the L-type liquid metal antennas are transmitted; fourth, the data are analyzed and a landslide alarm is given out. The intelligent landslide catastrophe monitoring method based on the L-type liquid metal antennas has the advantages that the anti-interference capacity of a circuit, the automation degree and the measurement precision are high, and the changes of the state of the landslide can be remotely monitored in real time.

The invention relates to a bridge structure early warning method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring weight information and a vehicle identifier of a vehicle in a driving state; inputting the weight information of the vehicle into a bridge load transverse distribution model to obtain response data of the bridge; when the responsedata of the bridge exceeds a set threshold value, acquiring the position information of the vehicle on the bridge through the vehicle identifier, and sending out alarm information; wherein the response data comprises at least one of a cable force value, a deflection value, a strain value, an acceleration value, a crack width value and an inclination angle value. By adopting the method, early warning can be carried out on the bridge structure in real time.

The invention belongs to the field of power industry, particularly relates to the field of power industry material supplier management and supervision, and more particularly relates to a material supplier production capacity monitoring and abnormity early warning method based on power consumption analysis. Monthly production capacity monitoring and abnormal early warning are carried out on material suppliers. Power consumption analysis model is established, the historical power consumption of the single sample of the supplier is analyzed; electricity consumption data are separated into trendsand fluctuations through wavelet analysis; abnormal data points in the historical electricity consumption data are mined by using a low-order differential denoising method; Lagrangian interpolation method replacement is carried out on the abnormal data points to obtain a new fluctuation trend; scientific methods such as a neural network algorithm, a re-sampling method and the like are adopted, themonthly electricity consumption data and reasonable fluctuation intervals of the supplier are predicted by utilizing a plurality of groups of trend charts processed by different methods, and a monthly electricity consumption early warning interval is set, so that the real-time monitoring of the electricity consumption of the supplier and the timely early warning of abnormal production conditionsare realized. According to the method, the material categories of the power consumption standard values within the year and the season of the supplier are analyzed, and industrial standard setting suggestions are given.

The invention discloses an anti-external force monitoring early warning method of an electric transmission line based on image recognition. According to the method, on the basis of an original electric transmission line remote monitoring system, an image recognition analysis module, an early warning information processing module and an early warning information display module are additionally arranged, an image recognition technology is used for analyzing data collected by a monitoring device, and the analysis result is transmitted to monitoring personnel in time in an early warning mode. The monitoring early warning method includes the following steps of (1) receiving an image, (b) preprocessing the image, (c) setting a warning trigger rule, (d) detecting a target, (e) analyzing the target, (f) conducting automatic alarm, (g) processing alarm information and (h) previewing a warning image and examining the image. The anti-external force monitoring early warning method of the electric transmission line based on image recognition has the advantages that cost is low, the image recognition method is simple, anti-external force monitoring data of the electric transmission line can be analyzed and calculated in real time, the abnormal condition on site can be subjected to quick early warning, and thus good economic benefits can be achieved.

The invention discloses a smoke detection method. The smoke detection method comprises the following steps of: video stream access: reading a video stream from a front-end camera device or a local video file, and decoding the accessed video stream to obtain a frame video image; video image preprocessing: preprocessing the acquired video image by using an image preprocessing technology to improve the accuracy of later-stage smoke detection; detection of a moving object based on background modeling; target classification based on a deep convolution neural network; and video smoke target calibration: recording the position of an image block determined to be smoke, and marking the area of the smoke in the video. The method integrates the advantages of a deep convolution neural network and a background difference method, so that the smoke is accurately and real-timely detected and positioned, the false detection rate is effectively reduced, the smoke in the video can be early warned in realtime, and the method has an important significance and an application value.

The invention discloses a water pollution monitoring and early warning device based on fish behaviors, and an early warning method of the water pollution monitoring and early warning device based on the fish behaviors. The early warning device comprises an experiment reaction box and a control device, wherein the experiment reaction box is provided with a biological early warning unit and a waterquality checking unit in the water flow direction in sequence, and the biological early warning unit is separated from the water quality checking unit through a water blockage device; the control device comprising a central processing unit and a collection unit connected with the central processing unit, the collection unit is composed of a fin sensor, a CCD camera, a photoelectric sensor and an impacting sensor, the collection unit transmits a collected data signal to the central processing unit, and the central processing unit contrasts the collected average swimming speed, average turning frequency, average activity water depth and total impacting frequency of fishes in the biological early warning unit with a constructed fish behavior reaction-water pollution degree black-box model, and therefore the degree of water pollution is judged quantitatively.

The invention provides a bioaerosol monitoring and early-warning method. The bioaerosol monitoring and early-warning method comprises the following steps: subjecting a bioaerosol to filtration so as to convert the bioaerosol into an aqueous solution sample; and then detecting biological particles non-specifically adsorbed on the sensitive surface of a sensor from the aqueous solution sample in real time by using the sensor which is sensitive to surface mass change. With the method, monitoring and early warning of the bioaerosol are realized, and bases are provided for subsequent identity detection and protection of the biological particles; and the method has wide application prospects.

The invention relates to a tunnel ground surface settlement monitoring device based on distributed optical fiber sensing and a monitoring method. The tunnel ground surface settlement monitoring device based on distributed optical fiber sensing comprises distributed optical fibers, a datum point arranged at the position 3-5 times of the tunnel hole diameter away from the horizontal distance of the tunnel axis and multiple monitoring points which are symmetrically distributed at equal intervals by taking the tunnel axis as an axis; the datum point and the monitoring points are each provided with an installation base, the distributed optical fibers are arranged in the installation bases of the datum point and the monitoring points and communicate the datum point with the monitoring points. The tunnel ground surface settlement monitoring device based on distributed optical fiber sensing and the monitoring method are simple in step, high in operability and lower in cost.

The invention relates to the field of airborne weather radars, in particular to a wind shear identification method. The method includes the steps of firstly, preprocessing radial speed data detected at a preset position to remove noise points and isolated points in the radial speed data; secondly, calculating spectral width according to the preprocessed radial speed data, and judging whether the spectral width is larger than a preset value or not; if so, determining that wind shear possibly exists at the preset position, and performing the third step; if not, then determining that wind shear does not exist; thirdly, performing wind shear identification on the preprocessed radial speed data through a region growing method. The wind shear identification method has the advantages that a dangerous resultant wind shear region can be positioned fast, accurately and clearly, dangerous weather can be pre-warned in real time on an airborne weather radar, and the flight safety of an airplane can be guaranteed.

The invention discloses a traffic conflict prediction method based on space-time proximity, which belongs to the technical field of road traffic safety prediction. In view of limitations of the existing road traffic accident prediction method, in order to avoid the problem that the prediction method based on traffic accident data statistics does not have sufficient basic data, the traffic conflict prediction method based on space-time proximity is thus brought forward. The method comprises the following steps: 1, based on the space-time proximity, a speed difference and a vehicle-following distance serve as conflict severity two-dimensional evaluation indexes; 2, according to the two-dimensional evaluation indexes of the vehicle-following distance and the speed difference, the conflict severity is determined; 3, according to the two-dimensional conflict evaluation indexes and the determined actually-measured construction area data, VISSIM simulation software is applied to building a conflict simulation model, and the traffic flow operation condition in the construction road section is simulated; and 4, according to the simulation result, the traffic conflict prediction based on the space-time proximity is obtained. The method of the invention is used for traffic conflict prediction.

The invention discloses an environment safety detection assessment method and system based on environment microorganism data and belongs to microorganism detection technologies. The assessment methodcomprises a detection step and an assessment step, wherein according to the detection step, sampling is performed on a to-be-assessed environment, and high-throughput undifferentiated biological detection is performed on samples obtained through sampling to obtain environment microorganism detection data; and the assessment step is executed in a calculation system, wherein the calculation system contains a database storage medium storing a database, a storage device and a processor. Through sampling and undifferentiated comprehensive detection on the environment of organisms, the biological safety level of the environment is assessed based on a microorganism data model, a biological disease data model and a biological internal and external microorganism environment correlation data model,and the technology is a noninvasive, efficient, comprehensive and accurate biological disease/epidemic prevention and early-warning technology.

The invention discloses a power grid project implementation phase risk early warning method based on the extreme value theory, and belongs to the technical field of power grid construction project management and control. Under the condition that the progress and the quality standard are met, the whole life cycle of the power grid project is divided into different phases, aiming at the fund utilizing plan and the actual investment plan in the power grid project implementation phase, an investment monitoring point set is established; by means of an exponential regression model, the optimal threshold is directly estimated in an asymptotic representation manner; by means of the extreme value theory method, dynamic tracking, calculating and deviation early warning are carried out to the investment of the power grid project implementation phase, and the aim of monitoring the investment risk is achieved. Aiming at the characteristic and the fund investment mode and the like of the power grid project implementation phase, fund monitoring points are selected, early warning and management and control can be carried out to the fund utilizing risk in real time, and the early warning and management and control of the current power grid project investment risk are achieved.

The invention provides a community security situation awareness and early warning method, a community security situation awareness and early warning system and a storage medium. The method comprises the following steps: acquiring current community data detected by a multi-source heterogeneous sensor; inputting the current community data into a pre-established behavior detection model based on dynamic network space-time, performing data feature fusion, and extracting behavior feature data; sending the behavior characteristic data to a central analysis platform for processing and analysis; and performing community security situation awareness and early warning according to processing and analysis results fed back by the central analysis platform. Compared with the prior art, real-time situation awareness and early warning are realized, and false alarm in community activities is avoided.

The invention discloses an online examination invigilation system based on big data analysis. The system includes several examinee clients connected through network, a Web server, a detection unit, acentral processor, a database unit, cloud storage and one or more management clients. The online examination invigilation system based on the big data analysis is simple in design, can effectively detect online cheating behaviors such as potential proxy examination and plagiarism during the online examination, and can promptly warn examinees or terminate the examination in real time, thereby solving the problem of difficult detection of network cheating behaviors, greatly improving the efficiency of the invigilation work of the online examination and ensuring the fairness of the examination, and the system has good use and promotion value.