257results about How to "Effective early warning" patented technology

The invention discloses an intelligent monitoring system and an on-site violation monitoring method for personnel violating rules and regulations on a working site. The system comprises a data storagelibrary and a plurality of video acquisition units arranged on a working site. The video acquisition units are in communication connection with the data storage library. The data storage library comprises a personnel information data unit, a face detection unit, a personnel information matching unit and a violation behavior detection unit, the personnel information data unit is in communication connection with the face detection unit, the face detection unit is in communication connection with the personnel information matching unit, and the personnel information matching unit is in communication connection with the violation behavior detection unit. Also provided is an onsite violation monitoring method. Through the machine vision learning network, different violation behaviors of the operating personnel can be early warned rapidly and effectively, factors such as weak personnel safety awareness, behavior habits and difficulty in traditional video monitoring and recognition can be effectively avoided, potential safety hazards caused by the violation behaviors are effectively prevented, and the safety of site construction is improved.

The invention discloses a method and system for detecting the fatigue driving of a driver, and belongs to the technical field of image processing and pattern recognition. The method comprises the steps: collecting a head image of the driver; positioning an eye area of the driver; processing a positioned eye image, and obtaining the contour of an eye; calculating the eye height according to the obtained contour of the eye, and obtaining the opening degree of the eye; Judging whether the driver is fatigued or not according to the comparison of the opening degree of the eye with a set threshold value; and giving an alarm for the reminding of the driver when the driver is fatigued. The method and system can detect whether the driver is fatigued or not under the condition of difference light intensities and the condition that the driver wears sunglasses, obtain more accurate detection results, and achieves the effective prompt and warning for the driver against the fatigue driving.

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

The invention discloses a vehicle driving risk evaluation method considering a driving style, which overcomes the problems that the prior art does not take into account the differences of driving habits of drivers with different styles, and the evaluation method is not personalized and humanized. The method comprises the following steps: 1) evaluating the driver's radical degree by using historical driving control data; 2) predicting that driving state base on the real-time vehicle running data and using the hidden Markov model; 3) for different driving states, establishing the fuzzy controller with different characteristic parameters to evaluate the driving risk; 4) using the driver's radical degree to correct the drive risk degree to obtain the final driving risk degree: carrying out thesuperposition correction of the driver's radical degree on the value of the drive risk degree in the range of [0.3, 0.7], and obtaining the final driving risk degree output result; therefore, for drivers with high or lower aggressiveness, the driving risk value within [0.3, 0.7] will increase or decrease.

The invention discloses an e-commerce website cheat user identification method and system based on a random forest algorithm. The system comprises an e-commerce website user data processing module, an e-commerce website user data storage module, an e-commerce website user data analysis module and a result display module, wherein the e-commerce website user data processing module, the e-commerce website user data storage module, the e-commerce website user data analysis module and the result display module are connected in sequence. The method includes the steps of data collection, data pre-processing, data conversion, data analysis, data display and the like. On the basis of the random forest algorithm in a machine learning method, static data and dynamic data of a user are collected, data mining and data analysis are carried out fast, and not only are completeness, continuousness and effectiveness of information of the user guaranteed, but also a good classification result is obtained.

The invention discloses a geology calamity early warning analysis system and method; the system comprises a database, a server, a data analysis module, a data acquisition module and an alarm; the dataacquisition module is linked to the data analysis module and the database through the server; the data analysis module comprises a classification analysis unit, a cluster analysis unit, an association rule analysis unit, a regression analysis unit, a vector similarity analysis unit and a time sequence analysis unit; the database comprises a basic geography database, a geology calamity core thematic database and a dynamic monitoring database; the system and method use the data analysis module to effectively analyze the calamity frequently occurring region geology information, thus obtaining the current calamity possible generation probability, and using the alarm to send alarms so as to remind work personnel if the probability reaches certain level. The system is easy to use, simple and fast, and the method can effectively analyze the geology calamities and warn people in advance.

The invention discloses a mountain torrent disaster on-line monitoring and identification early warning method. The method includes the following steps that: a data acquisition subsystem acquires data at a site: an automatic rainfall monitoring station acquires and processes disaster field rainfall information and transmits the information to the system platform of an early warning index analysis model through a wireless mode and/or a wired transmission mode, a water level and/or mud level monitoring station acquires and processes field debris flow and water level information and transmits the information to the system platform of the early warning index analysis model through a wireless mode and/or a wired transmission mode, and a video monitoring station acquires and processes disaster field image information and transmits the information to the system platform of the early warning index analysis model through a wireless mode and/or a wired transmission mode; an identification early warning subsystem performs early warning judgment and issues internal early warnings; personnel are determined, and formal early warnings are issued; real-time monitoring is performed; and information query is performed. The method has the advantages of rapid identification and predictive early warning, and can assist in avoiding loss. The invention also discloses an early warning system using the mountain torrent disaster on-line monitoring and identification early warning method.

The invention discloses an analysis system and an evaluation method for power telecommunication business risks. The analysis system comprises a business impact analysis unit, a risk probability analysis unit and a communication risk evaluation unit. The business impact analysis unit is used for quantitatively assigning values for different types of power telecommunication network management businesses according to importance degrees and social impact effects of the businesses. The risk probability analysis unit is used for analyzing channel organization factor, route association factor, history data statistics factor and channel risk factor, and determining a probability value of each factor. The communication risk evaluation unit is connected with the business impact analysis unit and the risk probability analysis unit, and is used for obtaining a risk value of a power telecommunication business according to the quantitatively assigned value and the probability value. Various factors impacting the power telecommunication network communication business risks are comprehensively considered, so that the risk warning and evaluating index system of the power telecommunication network is more stable, more reliable, and applicable to making determination to achieve an effective warning result.

The invention, which relates to the technical field of the power transmission equipment, provides a protection method and a system of a power transmission tower. The protection system of the power transmission tower comprises: a wireless sensor network, which is laid on the tower body and around the tower body of the power transmission tower as well as is used for detecting a signal that indicates the power transmission tower is threatened and converts the signal to data; a convergent node, which is used for connecting the wireless sensor network, obtaining data from the wireless sensor network, carrying out buffer memory and processing on obtained data and transmitting processed data through a mobile communication network; and a monitoring platform, which is used for obtaining data that is transmitted by the convergent node through the mobile communication network. According to another aspect of the invention, a cooperative and perceptive network is formed by a plurality of sensors, so that a target identification capability, a multi-point fusion capability and a cooperative and perceptive capability are realized; in addition, effective classification, area location and early warning of wrongful behaviors to the high-voltage and mainstay power transmission tower can be realized, and the overall protection of the high-voltage and mainstay power transmission tower can be realized.

The invention discloses a river way multi-sensor fusion upstream and downstream pollution early warning system and method. A river way part includes a water quality on-line monitoring buoy nodes deployed on multiple parts of the upstream and the downstream of a river way, and a shore part includes a wireless gateway and a cloud server. The water quality on-line monitoring buoy nodes are used for real-time collection, storage and transmission of water quality data; the system includes a water quality multi-sensor system, a water flow sensor, a real-time clock module, a microprocessor, a wireless communication module, an antenna, a storage module and a power source module. A river way multi-sensor data fusion analysis model is established by the cloud server, the multi-sensor data fusion method is used for analyzing whether the pollution exists in the river way, classifying pollutants and estimating the location of a pollution source, the upstream and downstream relations are combined for on-line monitoring and classified early warning on the river way water quality, whether the river way is invaded by the pollutants is judged, and the location of the pollution source is estimated. The system and the method perform on-line monitoring by multiple water quality parameters, and have the advantages of low energy consumption, high detection rate of the pollutants and wide applicationrange.

The invention provides an integrated cruise system exit method and device. The integrated cruise system exit method comprises the following steps: when the integrated cruise system is in an on state,obtaining driver state data; and if the driver state data indicates that the driver is unable to perform a safe driving operation, sending a warning signal for a preset time to remind the driver to drive safely. The above scheme provided by the integrated cruise system exit method can find the dangerous driving behavior of the driver when using the integrated cruise system in time by acquiring thedriver's state data to judge the driver's driving state, such as inattention, emotion and fatigue, thus being able to timely and effectively warn and ensure that the integrated cruise system is usedunder the premise of effective monitoring of the driver.

The invention discloses an abnormal fasting blood glucose level early-warning method based on an integrated learning fusion model. With combination of individual physical examination data of blood routine examination, liver function, blood fat, and renal function and the like, a fasting blood glucose level is predicted by fusing a gradient regression tree, a random forest, and a linear regressionmodel and the like based on an integrated learning method. The prediction model is trained by using lots of training data, so that the accuracy, universality and robustness of the prediction model areimproved. Fasting blood glucose level prediction is carried out on timely an individual without fasting blood glucose checking, so that the high-diabetes-risk patient is warned early and effectively.

The invention discloses an anti-surge early warning control method of a COREX gas compressor, and relates to control over gaseous fuel supply of a gas turbine and a device, in particular to an anti-surge detection, early warning and adjustment control method of the gas compressor of a CCPP (Combined Cycle Power Plant). The method comprises the following steps of: acquiring compressor inlet conditions through a CCPP monitoring system; determining safety margin, a surge line, a control line and a step protection line; if an operating point (RP) is positioned in a control region adjacent to the control line, linearly adjusting the opening of an anti-surge valve by using a PI adjustment algorithm; and if the operating point is positioned in a control region adjacent to the surge line SLL, performing step control over the opening of the anti-surge valve. According to the method, effective early warning and control adjustment functions can be effectively generated for the operating conditions of a low-voltage coal crusher and a high-voltage coal crusher of the CCPP and an operation state of the entire gas system, the problem of high failure rate of the COREX combined gas-steam combined cycle generating unit can be effectively solved, and the safety and the stability of the unit are improved.

The invention discloses a fatigue detection method based on face detection and eye state identification, comprising the following steps: first, sampling a monitoring video of a driver to get a face sample set; then, loading a to-be-detected image frame in the face sample set to a final classifier trained using an adaptive boosting algorithm, scanning image pixels to get a face image set, binarizing precise face areas under different light conditions based on different thresholds through use of an Otsu method, getting the optimal threshold, and precisely positioning the eye area through use of a vertical gray-level projection method to get the best eye shapes in the open state and in the closed state respectively; and finally, judging the fatigue state of the detected person through use of a PERCLOS method. The beneficial effects are as follows: the running speed is high, and the identification effect is good; and effective warning can be issued according to the state of a driver, and thus, effective help is provided for the avoidance of fatigue driving and traffic accidents caused thereby.

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 overflow water level warning method for glacial lake outburst, which comprises the following steps: (a) the thickness T of the top of an ice trace dam, the width b of the outburst mouth, the average width w0 of a glacial lake, the length-to-width ratio c of the glacial lake, the average slope Beta of the shore slope of the glacial lake before outburst, and the characteristic particle size d of incipient sediment are surveyed; (b) the critical overflow water depth H of glacial lake outburst is calculated; and (c) the water level of the glacial lake above the overflowmouth rises suddenly for more than 30s, and when HCr<0.6H, there is very small probability that the glacial lake bursts; when 0.6H<=HCr<0.8H, there is small probability that the glacial lake bursts; when 0.8H<=HCr<H, there is medium probability that the glacial lake bursts; and when the HCr>=H, there is large probability that the glacial lake bursts. The method fully considers the factors influencing the critical overflow water depth of glacial lake outburst. The calculated result is more in line with the actual situation. The accuracy of warning is high. The method is of higher applicability.

The invention provides an equipment fault three-level bidirectional early warning method and system based on edge computing, and the method comprises the steps: building a first-level bidirectional data sensing prediction model based on an adaptive exponential smoothing algorithm, predicting the data of a collection node, carrying out preliminary screening of a fault signal, uploading the fault signal, and reducing the cost of normal signal transmission; a second-stage bidirectional data perception prediction model of an autoregressive moving average algorithm based on extended Kalman filtering is constructed, and is used for further confirming the accuracy of a fault signal, reducing the false alarm rate and reducing the communication cost between a side end and a cloud end; creating a third-stage bidirectional data perception prediction model based on LSTM and BP neural network combination so that strong computing power is achieved based on edge equipment, he accuracy of data is enhanced, underlying requirements are timely responded, thus reducing time delay of cloud layer transmission. According to the invention, bandwidth and time delay consumed in a data acoustic wave communication transmission process are greatly reduced, and early warning is effectively carried out on a fault signal.

The invention relates to the field of communication, and discloses a method for predicting human flow and establishing a human flow prediction model and related equipment. Aiming to achieve accurate forecasting of the total flow of people in hot spots, the method comprises the following steps: determining a target area and at least one reference area which has influence on the total human flow ofthe target area; obtaining the total human flow of the at least one reference area at the first time based on the moving data; and predicting the total human flow of the target area at the second timein the future according to the target ARMA model; and determining the target area and at least one reference area which has influence on the total human flow of the target area. The target ARMA modelis based on the influence of the total flow of people in at least one reference area at each historical moment on the total flow of people in the target area at the corresponding subsequent historical moment. In this way, according to the historical data of the reference area, the total human flow at the future time of the target area can be accurately predicted, and at the same time, the problems of narrow source data coverage and high maintenance cost of the data acquisition equipment existing in the prior art can be effectively solved.

The invention discloses an intelligent fire hazard early warning system. The system comprises an image acquisition module used for obtaining image information in a monitoring scope in real time; a processing module used for correspondingly processing the image information obtained by the image acquisition module, obtaining flame information in the monitoring scope and determining whether a fire hazard is generated in the monitoring scope; an early warning module used for emitting early warning signals when the processing module determines that the fire hazard is generated in the monitoring scope; and a power supply module used for supplying power to the image acquisition module, the processing module and the early warning module. A fire hazard detection technology integrated with machine vision and image processing is realized, the structure is simple, the system has the feature of non-contact type detection, restrictions of such environmental conditions as space heights, air flow speeds, thermal barriers, dust, humidity and the like are eliminated, flame information can be intelligently detected in real time, the early warning signals can be emitted when the fire hazard is detected, effective monitoring of the fire hazard is realized, and the purpose of giving a prompt for performing fire fighting through early warning at the initial stage of the fire hazard is realized.

The invention discloses a method and device for predicting a group war negative and control equipment, belongs to the technical field of computers, and is used for predicting the group war negative before group war begins so as to provide group war early warning for users. The method comprises the following steps: when a target object is in a group battle confrontation scene, obtaining individualstate data of each object in the group battle confrontation scene, and obtaining environment description data of the group battle confrontation scene; according to the individual state data of each object and the environment description data, based on a pre-trained group battle negative prediction model, obtaining a group battle negative prediction result of group battle of the target object in the group battle confrontation scene; wherein the group war negative prediction model is obtained by training according to a plurality of group war training samples marked with group war negative results; and outputting group battle early warning information, the group battle early warning information being generated according to the group battle negative prediction result.

An early warning method for contract-type software fault based on program invariants includes 1), generating the program invariants for programs in need of early warnings by a Daikon tool, operating source programs to generate invariants on the Daikon tool according to a test case suite, and outputting the program invariants; 2), screening the invariants; 3), manually generating a data contract composed of Boolean assertions; 4), signifying the contracts acquired from the step 2) and the step 3) in a normalized manner, and inserting the contracts signified in the normalized manner into relevant positions of the source programs by annotations; 5), converting the annotations inserted into the relevant positions in the step 4) to codes with a fault detection function, and inserting the codes into corresponding target positions of the source programs; 6), operating the source programs processed from the step 1) to the step 5), if a program contract is violated during program operation, the codes with the fault detection function can automatically display monitored faults to users.