269results about How to "Improve the accuracy of early warning" patented technology

The invention relates to a system for power grid thunderstorm disaster warning based on recognition and forecast. The system is characterized by comprising a thunderstorm forecast information module, an early warning data analyzing module, a power grid thunderstorm risk early warning module and a display module all connected with one another in sequence. The invention further relates to a method which adopts the system to perform power grid thunderstorm disaster early warning. The system and the method utilize region thunderstorm characteristic and regional power grid characteristic, and combine the anti-thunderstorm design parameters and the on-site geographical conditions of power grid equipment including various transformer substations, electric transmission lines, pole and the like, so as to realize real-time automatic monitoring on the thunderstorm disaster, perform actual condition analysis quantitatively at a specific time and location and near-disaster early warning and perform fine instructional functions on production and scheduling of power grid.

The invention provides a fire early warning method based on an infrared thermal imager in a narrow environment. According to the method, infrared images in a narrow space acquired by the infrared thermal imager are pre-processed, image segmentation is realized by employing a connected domain manner, high-temperature objects, suspected high-temperature objects, and low-temperature objects are determined in a preliminary manner according to the absolute temperature, continuous multi-frame video images are acquired in the area of the suspected high-temperature objects whose temperature of segmented target images is from 70 to 100 DEG C, further image segmentation and binaryzation of the multi-frame video images are performed, the characteristics of the dispersion degree, the number of sharp corners, and height change of the segmented target area are calculated and regarded as flame shape characteristics, and flame monitoring determination of flames in a suspected high-temperature target area is performed based on a probability statistics model according to the flame shape characteristics. By employing the method, fire sources can be recognized more accurately, the influence of interference targets such as water is distinguished, and the accuracy of early warning is greatly improved.

The invention relates to a ransomware prevention method and system. The method comprises the steps of creating at least one bait file with the type conforming to a ransomware encryption type and inserting the bait file into an original file sequence of a to-be-protected disk; judging whether the bait file is changed or not; and prohibiting the to-be-protected disk to be subjected to a preset operation when the bait file is changed. According to the method and the system, the bait file with the type conforming to the ransomware encryption type is created by utilizing a characteristic that ransomware traverses disk files necessarily to search for a file type suitable for encryption, and the bait file is put in the original file sequence of the to-be-protected disk; the effects of prewarning the ransomware and protecting other disk files are achieved by monitoring the bait file; the prewarning accuracy is high; a targeted scheme is designed based on an inevitable common behavior of the ransomware, so that known and unknown ransomware can be detected; and the disk space occupied by the created bait file nearly can be ignored.

The invention provides an intersection collision prevention and warning apparatus, comprising a vehicular unit and a roadside unit. The vehicular unit is mounted on a vehicle; the roadside unit is mounted on the roadside of an intersection. The vehicular unit includes a vehicular V2X communication device, a navigation positioning device and a vehicle controller. The roadside unit comprises a roadV2X communication device and a target capture device. When a vehicle enters an intersection region, positional information and moving trajectory of the vehicle are sent out, positional information andmoving trajectories of other vehicles and moving targets are received, a predicted track is calculated according to the positional information and moving trajectories of the vehicles and the moving targets, whether this vehicle has a track crossing region with other vehicles or moving targets is judged according to the predicted track, if yes, collision time TTC and track crossing region occupation time difference PET are calculated based on the track crossing region according to the positional information and moving trajectories of this vehicle and other vehicles or moving targets, and collision warning is calculated according to TTC and PET.

The invention relates to an operation tunnel health monitoring and early warning system and method based on distributed optical fiber sensing. The system comprises an early warning subsystem, a data processing subsystem, a wireless transmission subsystem, a data collecting subsystem and an optical fiber sensing subsystem. The data collecting subsystem is used for carrying out real-time online monitoring on a tunnel field through the optical fiber sensing subsystem, obtaining tunnel real-time monitoring signals and converting the tunnel real-time monitoring signals into electric signals. The wireless transmission subsystem is used for transmitting the electric signals collected and converted by the data collecting subsystem to the data processing subsystem in a long-distance mode. The data processing subsystem is used for receiving the electric signals transmitted by the wireless transmission subsystem and then comparing the electric signals with a preset threshold value of the data processing subsystem, and carrying out an early warning task on the comparison result through the early warning subsystem. The operation tunnel health monitoring and early warning system and method based on distributed optical fiber sensing have the advantages of being low in cost, convenient to construct and high in anti-interference capability, space adaptability, automation degree and early warning accuracy.

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 an alarm prediction method, a system, a storage medium and a computer device. The method comprises the following steps: an initial model of a convolution neural network is established, and the initial model of the convolution neural network is trained offline according to the historical alarm data; Based on the trained convolution neural network model and real-time alarm data, the on-line real-time alarm prediction is carried out. The invention utilizes the historical alarm data to train the alarm prediction model so as to extract the inherent law derived from the alarm. In real-time prediction, according to the current alarm situation and alarm prediction model, the possible faults are early-warning to avoid serious equipment failures or service delays and ensurethe smooth operation of the system.

The invention provides a strip deviation monitoring and diagnosis method for a soaking furnace of a continuous annealing unit, comprising: establishing a monitoring model of a strip deviation; The strip steel of specification builds PCA model, and calculates the control limit of two statistics T2 and SPE, establishes LSSVM model for the strip steel of each specification; Monitoring: Select the online monitoring PCA model and the online monitoring LSSVM model, and use the online monitoring PCA model and the online monitoring LSSVM model to monitor according to the data samples of the current production process collected at regular intervals; When the LSSVM alarm is carried out again, it is judged that the strip steel in current production deviates. The invention also provides a strip deviation monitoring and diagnosis system for the soaking furnace of the continuous annealing unit. The invention improves the accuracy rate of early warning and the accuracy rate of fault diagnosis for strip deviation.

The invention provides a city inundation monitoring and early warning system. The system herein includes a fixed monitoring terminal, a mobile monitoring terminal, a network transmission module, a cloud server, a monitoring center and an early warning publishing platform. The fixed monitoring terminal is intended for conducting real-time monitoring of the inundation information of the easily-inundated places in the city. The mobile monitoring terminal is intended for conducting real-time acquisition of the position information and inundation information of the water-logged places. The cloud server is intended for identifying the identities of the easily-inundated places, and based on the position information of a place that is water-logged, determining whether the place is one of the easily-inundated places, and if the place is one of the easily-inundated places, giving up the position information and the inundation information of the place, and if the place is not one of the easily-inundated places, saving the position information, the inundation information of the place and the inundation information of the easily-inundated places. The monitoring center is intended for marking the easily-inundated places and the inundation information thereof and/or the place that is water-logged and the inundation information thereof in a GIS map, and also analyzing the inundation information of the easily-inundated places and the inundation information of the place that is water-logged, generating the inundation early-warning information, and publishing the information to the public onthe early warning publishing platform.

The invention discloses an on-line biological safety early warning and monitoring method for the water quality. By means of the method, continuous real-time on-line water comprehensive toxicity early warning according with a toxicology mechanism can be achieved. The method comprises the following steps that the absolute motion behavior changing process of a tested aquatic organism in a to-be-monitored water body is monitored by a biological behavior sensor on line in real time and compared with the motion behavior changing process of a same-variety tested aquatic organism in a pollution-free standard water body, normal motion behavior changes are filtered out to obtain the relative motion behavior changing process of the tested aquatic organism in the to-be-monitored water body, and qualitative and/or quantitative evaluation is conducted on the biological safety of the to-be-monitored water body accordingly. The method is particularly suitable for biological safety early warning on sudden pollution accidents of natural water bodies or drinking water sources and has the advantages that early warning is more precise, pollutant concentration early warning can be conducted on the monitored water body, the method is simple, and secondary pollution cannot be caused to the environment of the to-be-monitored water body.

The invention belongs to the technical field of safety warning and particularly relates to a child fall-off-bed warning system and method. The system comprises a covering layer and a human body communication transceiving device. A warning area forms around the covering layer. The human body communication transceiving device is disposed in the warning area of the covering layer. When a child arrives the warning area and touches the human body communication transceiving device in the warning area, a signal transceiving link of the human body communication transceiving device is closed through the human body to trigger warning; the covering layer is a mattress or other bedding articles. The child fall-off-bed warning system and method has the advantages that warning accuracy can be maximally improved and warning missing is avoided; the warning area is a certain distance away from the bedside, and full time is gained for guardians to take safeguard procedures; the human body communication transceiving device is integrated on the bedding and needs no extra space; signal transmission is performed based on human body communication, and system power consumption is low.

The invention relates to a big data processing technology in intelligent information processing of a computer and particularly relates to an intelligent analysis early warning method for a dangerousness tendency of prison persons serving sentences. The intelligent analysis early warning method comprises the following steps: carrying out efficient collection and extraction on information of persons serving sentences in a prison information system to obtain basic data of behavior characteristics of the persons serving sentences; carrying out analysis judgment and early warning on the dangerousness tendency of the persons serving sentences by adopting a cost sensitive multi-stage semi-monitoring analyzing method. The intelligent analysis early warning method has the beneficial effects that 1, a lot of monitoring data of the persons serving sentences, which are provided by an existing system, are sufficiently utilized to automatically find potential behavior characteristics and behavior models of the abnormal persons serving sentences; the construction cost of the system is smaller and the feasibility is strong; 2 the method considers a cost problem of incorrect judgment of an early warning system and the influences caused by the early warning incorrect judgment are reduced to the greatest extent so as to meet actual requirements; 3 the method provided by the invention has a stronger self-adaptive ability; the early warning accuracy of the established early warning system is higher and the influences caused by the incorrect judgment are small.

The invention discloses an automatic driving data labeling method, a cloud control platform and a storage medium. The labeling method of the automatic driving data comprises the following steps: dividing the automatic driving data into at least one data subset and a perception data set according to a data source of the automatic driving data so as to store the perception data set and the at leastone data subset in a classified manner; then performing data annotation on the perception data set so as to construct a perception data set related to the scene according to a data annotation result;and fusing the perception data set related to the scene with the at least one data subset and generating an annotation data set. According to the method and the device, automatic labeling of the automatic driving data is realized, a data basis is disclosed for testing of the automatic driving vehicle, and a driving scene is analyzed by utilizing related data, for example, whether the current automatic driving vehicle runs normally or not is judged according to the sensing data.

The invention provides a coal mill operation fault identification method. The coal mill operation fault identification method comprises the following steps: acquiring historical operation parameters of coal mill operation faults and corresponding fault types; standardizing the historical operation parameters; constructing a PCA (Principal Component Analysis) model and a corresponding T2 statisticmodel for the historical operating parameters corresponding to each fault type, and determining an early warning threshold of the fault; and substituting the to-be-identified parameters into T2 statistic models corresponding to different faults, and if the to-be-identified parameters exceed an early warning threshold, determining that a corresponding fault type exists. The invention further provides a coal mill operation detection system applying the method, and faults and reasons are rapidly determined by calculating real-time operation parameters and comparing the real-time operation parameters with early warning thresholds. The invention has the advantages that the contribution of the parameter to the abnormal reason is analyzed, so that the dependence on historical sample data is reduced; the recognition precision is improved; and online real-time monitoring and analysis of the running state are realized in combination with a monitoring model, so that the production safety is improved.

Theinvention discloses an automobile rollover warning algorithm based on an SVM (Support Vector Machine) model, belonging to the field of traffic security. The algorithm comprises the following stepsof: a, selecting an automobile model for design of a rollover warning controller, and creating a driver-automobile-road environment model; b, determining a rollover risk evaluation index; and c, building an automobile rollover risk prediction experience model by using an SVM algorithm, determining an explicit function of an automobile rollover limit state function with a deterministic variable anda random variable output by the driver-automobile-road environment model as inputs of the automobile rollover risk prediction experience model, and visibly dividing automobile state samples into a rollover insecurity area and a rollover security area by using a hypersurface. A probability method considering uncertainty is applied to automobile dynamic rollover warning algorithm design, an explicit function for automobile rollover security limit and gradient thereof is provided, external interference of system strong nonlinearity and uncertainty is lowered, and the automobile dynamic rolloverwarning accuracy is improved effectively.

The present invention discloses a sudden loess landslide early warning method and an application thereof, belonging to the technical field of landslide prevention and control engineering. The method comprises the following steps of: a, preprocessing of deformation monitoring data; b, determination of a multi-stage deformation rate threshold Vt; c, determination of a deformation rate increment [Delta]V; d, division of different landslide deformation rate curve features; and e, integration early warning through combination of the multi-stage deformation rate threshold Vt and the deformation rateincrement [Delta]V. The multi-stage deformation rate threshold is arranged to determine whether the landslide generate a large deformation or not in advance and track a deformation development trendin real time based on the deformation rate increment, and therefore, the combination thereof achieves total-process early warning of the sudden loess landslide, the early warning accuracy and the early warning efficiency are greatly improved, and the disaster prevention applicability is good.

The invention is applicable to the technical field of the Internet of Vehicles, and provides a vehicle driving early warning method and system, a server, an automobile terminal, and a memory. The early warning method includes: setting an early warning radius for a driving vehicle; acquiring driving information of the vehicle in real time, wherein the driving information includes positioning information; uploading an early warning request to the server, wherein the early warning request carries the early warning radius and the driving information of the vehicle; and emitting a driving safety prompt corresponding to the early warning radius when an early warning feedback given by the server based on the received early warning request is received. According to the method and system, the earlywarning radius is set in advance, the server performs early warning determination according to the driving information and the early warning radius uploaded in real time and obtains an early warningdetermination result of a driving environment corresponding to the early warning radius, the early warning accuracy can be improved, and the practical requirement of a user can be met.

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 relates to a gully debris flow monitoring and warning method and device; the method comprises: determining main control factors of forming gully debris flow according to principal component analysis; analyzing interactional relation of the main control factors according to an interactional relation matrix; constructing a judging module for the start of gully debris flow at an upstream section according to the interactional relation of the main control factors; based on such in connection with real-time monitoring data, judging whether debris flow occurs or not, if yes, by further acquiring midstream segment level monitoring data for the debris flow, dynamically calculating scales of the debris flow such as flow speed and quantity so as to provide full-flow-area full-process monitoring and warning for the debris flow. The judging model for the start of debris flow is constructed according to the interactional relation of the main control factors, mobile parameters of the debris flow are dynamically acquired, and the method may provide improved debris flow warning accuracy when compared to the single-threshold warning method of the prior art.

The embodiment of the invention provides a fault early warning method and device, and relates to the technical field of wind driven generators. The fault early warning method is used for carrying outfault early warning on the wind driven generator. The fault early warning method comprises the steps that various historical normal operation data and various historical fault operation data of the wind driven generator are acquired; Performing feature selection on the plurality of historical normal operation data and the plurality of historical fault operation data to obtain data features; Performing model training on the data features to obtain a training model; And inputting the real-time operation data of the wind driven generator into the training model to obtain early warning information. According to the fault early warning method, the fault early warning function of the wind driven generator is achieved through various data of the wind driven generator, and then the early warning accuracy is improved.

The invention discloses a video monitoring method for preventing the gathering of people, and belongs to the technical field of image processing. The method is applied to video monitoring for preventing the gathering of the people in special places such as drug rehabilitation places, prisons and the like. The method comprises the following steps of: 1) acquiring a video monitoring image; 2) determining the background of a current frame; 3) calculating and binarizing a differential image between the current frame and the background of the current frame; 4) morphologically processing the binarized differential image, and extracting moving objects; 5) determining the positions, sizes and geometric center positions of the moving objects; and 6) calculating and judging whether a distance between the geometric centers of any two moving objects is less than a distance threshold value L or not, and transmitting an early warning signal if the distance between the geometric centers of any two moving objects is less than the distance threshold value L. By the method, the video monitoring is performed on places to be prevented from the gathering of the people, and automatic detection and judgment in the gathering conditions of the people are realized by a computer image processing technology, so the shortcomings of judgment made by the subjective experiences of users are overcome, and early warning accuracy and the efficiency of video monitoring workers can be improved at the same time.

The invention discloses a shallow soil landslide early-warning method in the red-bed area and belongs to the technical field of landslide prevention and control engineering. The method comprises the steps of adopting a landslide mass and a non-landslide mass in the red-bed area as a respondent, measuring a gradients alpha of the landslide mass and the non-landslide mass, an area A of the landslide mass, an upper gradient beta, an upper area Au, a transverse left gradient theta 1, a transverse right gradient theta 2, a transverse left area AL, a transverse right area AR and a terrace area A' on the landslide mass; calculating a gradient factor S, an upper-side factor U, a two-side factor C, a terrain factor T, and a rainfall factor R; calculating a critical value Cr for inducing the shallow soil landslide in the red-bed area and conducting the condition judgment; determining the landslide level, and sending an early-warning signal. According to the technical scheme of the invention, the combined effects of two factors, namely the terrain factor and the rainfall factor, are fully considered to obtain the critical value Cr for inducing the shallow soil landslide mechanism. Therefore, not only the landslide early-warning is high in accuracy, but also the early-warning reaction time is greatly shortened. Therefore, the disaster prevention applicability is improved.