42results about How to "Good real-time detection" patented technology

The invention relates to an intelligent traffic signal lamp control device based on the comprehensive computer vision, which comprises a camera device and a microprocessor, wherein the camera device is installed above the middle part of an intersection and used for obtaining a traffic state panorama video image at the whole intersection; the microprocessor is used for detecting the queue length of vehicles waiting for passing through at the intersection and the congestion state of an outlet according to the panorama video data of the camera device and deciding the signal lamp control strategy and controlling the signal lamp switching action; the intelligent control carried out by the signal lamp control strategy of each phase to traffic signal lamps is determined through detecting the vehicle queue length at an inlet of each phase and the congestion state at the outlet, so that the sequence at the intersection is good, therefore, the conflict points where the traffic accidents are possibly caused are reduced or completely eliminated, and the operating indexes at the intersection are optimal.

The invention relates to a method for detecting Android malicious software by means of a random forest classifier in real time. The method comprises the steps of 1 collecting network data, 2 calling data flow through an application program interface for grouping, 3 extracting data flow minimum unit, 4 extracting calling sequence characteristics, 5 training a hidden Markov model, 6 training a random forest model, 7 extracting application program interface data flow characteristics of a to-be-detected sample, 8 inputting feature vectors of the to-be-detected sample into a random forest detection model and judging whether or not output of a field network characteristic detection model is the malicious software category and 9 outputting the malicious software category corresponding to the to-be-detected sample. Accordingly, the malicious software can be detected in real time, software transmitted in the network can be detected, and the good detection accuracy rate is achieved.

The invention discloses a fault online diagnosis method of uninsulated track circuit tuning zone, which relates to the online diagnosis of a track circuit. The method comprises the steps of obtaining cab signal inductive voltage amplitude envelope, shunt point positions, train speed and track circuit signal carrier frequency information, pretreating the data to obtain the length of the track circuit, the quantity and position of compensation capacitors and cab signal inductive voltage normalized amplitude envelope, performing segmented fitting on the cab signal inductive voltage normalized amplitude envelope and establishing corresponding feature vectors, establishing a neural network, training the neural network by utilizing the feature vectors and performing fault diagnosis on the uninsulated track circuit tuning zone equipment, and sending diagnosis results of the tuning zone equipment to a corresponding ground monitoring client-side through a cab signal remote monitoring system. The method has the characteristics of being complete in functions, high in detection accuracy, high in applicability, simple in method, good in real-timeness and the like, no any hardware equipment is added in the method based on the existing cab signal system, and the cost is low.

The invention discloses a double-threshold energy detection method based on history sensing information. According to the method, probabilities that states of occupying a licensed band by a licensed user are changed or not in adjacent sensing frames are estimated through an energy detection method; a high threshold and a low threshold satisfying constraint of a target false alarm probability are set according to a theoretical value of a false alarm probability and the probabilities; spectrum detection is carried out; and when an energy statistic value of a current sensing frame is greater than the high threshold, it is judged that the licensed user occupies the licensed band in the current sensing frame, otherwise, secondary judgment is carried out on the state of occupying the licensed band by the licensed user through utilization of the energy statistic values of the current sensing frame and a history sensing frame according to the high threshold and the low threshold. The method has the advantages that the constraint of the target false alarm probability is satisfied, a relatively high detection probability is obtained, the spectrum detection performance is improved, moreover, the complexity is low, the detection timeliness is relatively good, and the occupied system bandwidth is low.

The invention discloses an oil delivery pipeline leakage detection method based on information fusion of an extreme learning machine. The oil delivery pipeline leakage detection method comprises the following steps that S1, historical data of a pipeline leakage experiment are collected as an initial training sample; S2, after flow information and pressure information in the historical data are subjected to characteristic value extraction and calculation, information fusion characteristic data are obtained; S3, the obtained information fusion characteristic data serves as input information, anda pipeline leakage detecting model is established through an extreme learning machine method; S4, the accuracy of the detecting model is verified; and S5, the information fusion characteristic data obtained after the flow information and pressure information of a to-be-detected pipeline are subjected to characteristic value extraction and calculation are input the final detecting model, and the state classification result of the to-be-detected pipeline is output. According to the oil delivery pipeline leakage detection method, the flow information and the pressure information of the pipelineare collected and subjected to characteristic value extraction and calculation, thus information fusion characteristic data are obtained to serve as the training sample, the main characteristics of asignal are covered, and the computation amount of the extreme learning machine method is decreased.

The invention discloses a voltage fast detection algorithm suitable for a grid power system low voltage ride through function. The voltage fast detection algorithm suitable for the grid power system low voltage ride through function comprises the following steps: (1) acquiring amplitude value information under the normal operation of a power grid based on a phase-locked loop of a synchronous reference coordinate frame, (2) separating the voltage of a sampled power grid into three-phase of A, B, C, conducting a period of delay to the sampled power grid, and acquiring the absolute value of an difference value, (3) setting a voltage threshold value and comparing the difference value in the step (2) with the voltage threshold value, (4) if the difference value is smaller than the voltage threshold value, then judging that the voltage is normal, if the difference value is larger than the voltage threshold value, then turning to step (5), (5) sending real-time voltage signals into a maximum value tracking module and an over zero point tracking module through each of the phrases of voltage, after judging that the system enters into a low voltage status, and (6) recording the maximum value tracking module and the over zero point tracking module to acquire an amplitude value after the voltage of the power gird drops.

The invention discloses a roadside parking intelligent monitoring method on the basis of vacant parking space image detection. The roadside parking intelligent monitoring method includes an offline setting portion and an online monitoring portion. Corresponding irregular regions are enclosed for each parking space in manual modes on the basis of photographed single-frame photos of roadside parking places during offline setting, and unique parking space numbers are attached to the irregular regions; an image is acquired by a camera at the intervals of a time period t during online monitoring, the images are fed to control centers via wireless networks, and then states of the parking spaces are detected. The roadside parking intelligent monitoring method includes preliminarily processing the images in detection procedures and acquiring edge graphs of the images; acquiring corresponding regions of edge images according to predefined region templates and clipping the corresponding regions; judging whether the parking spaces are vacant parking spaces or not on the basis of the irregular regions; recording current parking space states and parking start time and end time of the parking spaces according to change of the states of the parking spaces.

The invention discloses an LED (Light-Emitting Diode) display module and an LED display screen. The LED display module comprises a base plate and one or a plurality of groups of RGB (Red Green Blue) LED chips packaged on the front face of the base plate through packaging glue, and further comprises a signal data acquisition module and a signal processing module which are electrically connected, and the signal data acquisition module is arranged on the base plate or is directly connected with anodes and cathodes of the LED chips. The LED display screen disclosed by the invention is formed by splicing the LED display modules; the LED display module and the LED display screen disclosed by the invention overcome the defects that the junction temperature detection of the LED chips is inaccurate and on-site real-time continuous monitoring and prewarning of LED products cannot be realized, and have the advantages of accurate temperature detection and good detection real-time property.

A method for detecting irregular defects of industrial products comprises the following steps: step 1, image enhancement processing, specifically comprising histogram equalization and histogram matching; step 2, constructing a network model; and step 3, setting relevant parameters of the network model, specifically including setting of a default box, a default box matching mode and composition ofa loss function. According to the invention, image enhancement processing is carried out on a collected sample image, so that defects are more obvious. Then, on the basis of a convolutional neural network CNN, a defect detection network model is constructed in combination with the SSD target recognition model, and model parameters are reasonably designed, so that the detection problem of irregulardefects can be effectively solved.

The invention discloses an estuary suspended sediment concentration real-time measuring method based on sound attenuation. The estuary suspended sediment concentration real-time measuring method basedon the sound attenuation comprises the steps that an underwater sound signal sending end and a receiving end are arranged in an estuary, sound attenuation is calculated, acoustic attenuation disturbance caused by suspended sediment is calculated, and the suspended sediment concentration is iteratively calculated. According to the estuary suspended sediment concentration real-time measuring methodbased on the sound attenuation, covering areas are wide, the expandability is high, obtained parameters are more continuous in space, the detecting instantaneity is good, and the measuring precisionis high. Sound propagation technology is adopted for measuring, and the performance is better and development and application potentiality are more achieved compared with light, electromagnetic wave and other modes.

An industrial product irregular defect detection method based on deep learning comprises the following steps: firstly, performing image enhancement processing on an acquired sample image to enable defects to be more obvious; then, on the basis of a convolutional neural network CNN, constructing a defect detection network basic model in combination with the SSD target recognition model, and reasonably designing model parameters; finally, reducing the number of prediction boxes by adopting a non-maximum suppression algorithm, expanding a data set by adopting a data enhancement operation, increasing the training amount of the network, effectively improving the network identification performance can be, and solving the detection problem of irregular defects. The method is superior to a traditional detection method in the aspects of irregular defect detection, interference defect detection, detection real-time performance and the like, and can meet the requirements of enterprises for visualdetection of general industrial products.

The invention discloses a vehicle detection method with granularity-level multi-scale characteristic based on asymmetric convolution. The method comprises the following steps: S1, obtaining a plurality of pictures containing vehicles through a video stream, and making a vehicle target detection data set which comprises a training set and a test set; S2, designing an asymmetric convolution AC unit;S3, embedding the asymmetric convolution AC unit into a Res2Net module to construct an asymmetric convolution ACB module with a granularity level multi-scale characteristic; S4, embedding an ACB module into a lightweight model ShuffleNetV2 to construct a vehicle target classification network ACBNet based on a convolutional neural network; S5, training a vehicle target classification network ACBNet by using the manufactured vehicle target training set; and S6, putting test set pictures into the classification network ACBNet for classification, then processing the test set pictures through a residual error prediction module to obtain category and position information of vehicles in the predicted images or videos, and framing out vehicle targets.

The invention discloses a converter system and a control method thereof. The converter system comprises a converter, a motor, a speed sensor and a control device. When the converter system works normally, the control device controls the converter by employing a rotor position signal sampled by the speed sensor; and the state of the speed sensor is monitored in real time according a rotor current error of the motor. When the signal of the speed sensor is abnormal, the control device employs a rotor position estimation signal to replace the rotor position signal sampled by the speed sensor to control the converter. According to the invention, with the system and the control method, a technical problem that the converter system shuts down due to fault occurrence after abnormity of the speed sensor signal can be solved; the shutdown time of the converter system before maintenance can be reduced; and the system stability can be improved under the circumstance that the speed sensor signal is abnormal.

The invention discloses a vehicle occlusion treating method based on the Markov random field. The method comprises step 10), establishing a background image; step 20), establishing a vehicle foreground profile; step 30), filling blocks in a vehicle outline; and step 40), performing occlusion treatment on the blocks, including step 401), in all the n blocks contained in a vehicle K, taking the i block in the (t-1) frame gray scale image as a template, and determining a the most matching block in the t frame gray scale image by using a three-step search method; step 402), measuring motion vectors; step 403), performing verification treatment on the matching blocks; step 404), performing occlusion judgment and occlusion treatment on the blocks; and step 405), returning to the step 401) until i=n to finish the verification treatment and occlusion treatment of all the blocks. The vehicle occlusion treating method is high in vehicle tracking accuracy, accurate in treatment and good in timeliness.

The invention discloses a flame identification algorithm of low-rank analysis. First, video images are acquired by an infrared camera and preprocessed by Gaussian smoothing filter, and grayscale images are binarized. The external rectangle of a suspicious area of the current frame is extracted and zoomed in. The data of the external rectangle is used as a column vector. The data of external rectangles of a flame area of the continuous n-1 frame behind the current frame is extracted and zoomed to the same size, and the data of the frame is taken as column vectors frame-by-frame. All column vectors form a matrix D which his decomposed into the sum of a low rank matrix A and a sparse error matrix E. The singular value decomposition for the low rank moment A is obtained, and respective contribution rate is calculated by the square of the singular values. The number of singular values is determined by that the cumulative contribution rate is greater than the given threshold. The determined number is new rank rank(D) of the matrix D, and the boundary point theta* is obtained through experiments. An alarm is given off when the number is greater than or equal to theta*. A lot of experiments show that the algorithm has high recognition rate and robustness, and the method is proved to be effective.

The invention is suitable for the technical field of artificial intelligence and behavior detection, and provides a smoking behavior real-time detection method based on deep learning. According to themethod, a data set is constructed through a data acquisition standard, and a set of smoking convolutional neural network for smoking behavior detection is constructed. The method promotes research ofdeep learning on smoking behavior detection, and has good detection accuracy, and by means of deep learning, the method has very high real-time performance and robustness and can adapt to the environmental change of an application scene.

With the practical application of developing an intelligent auxiliary driving system as the background, a top-speed lane deviation real-time early-warning system is designed and realized on a vehicle-mounted ARM platform, a top-speed lane line recognition algorithm for image W-r extension dynamic evaluation is provided, lane information is detected in real time, and the vehicle lane deviation early-warning system is obtained by considering the transverse speed of a motor vehicle in a lane. Two early warning strategies are combined, in the first mode, when the transverse speed is low, the convenient relative distance between a motor vehicle and a lane is adopted for early warning judgment, and under the condition that the transverse speed is high, lane departure judgment is conducted according to the deviation rate of a left lane line and a right lane line in an image; according to the method, perspective transformation of the image is not needed, external parameters and internal parameters of the monocular camera do not need to be measured, the requirement for the monocular camera is low, and great convenience is brought to a user. And meanwhile, the algorithm is low in time complexity, high in speed and high in practicability and applicability, and has huge value and wide market in real-time early warning of lane departure under complex road conditions.

The invention provides a coal plough planing blade mechanical characteristic on-line testing device, and fundamentally solves the problem that mechanical characteristic data under a coal plough condition fails to be detected. According to the technical scheme, a coal plough cutter head comprises a cutter seat, a cemented carbide cutter head arranged on the front end of the cutter seat, a protection cover assembled on the cutter seat, and a cutter rod arranged on the bottom part of the cutter seat; the middle part of the cutter seat is provided with a square hole, and the vibration sensor is placed in the square hole through a strong magnetic base; a gap exists between the tail part of the cutter seat and the cutter rod; the square hole and the front end of the cutter seat form a tetragonal column, strain gage grooves are machined in the four surfaces of the tetragonal column, and the strain gage grooves are respectively equipped with strain gages I-III; and the front inner wall of the square hole is provided with the strain gage III; the protection cover is a U-shaped housing provided with a hook-shaped structure, the protection cover is embedded in the outer wall of the cutter seat through the front part of the cutter seat, and the hook-shaped structure is clamped to the rear part of the cutter seat and is fastened through a sunk screw. The coal plough planing blade mechanical characteristic on-line testing device has the advantages of simple structure, convenient usage, easy control, visual data detection, reliable detection result and the like.

The invention discloses an application audit failure identification method, which is applied to a cloud. The method comprises the steps of obtaining update log data and audit log quantity data of eachapplication; loading historical statistical analysis data through a big data statistical analysis platform; performing classification calculation on each application, and calculating a log change trend of each application according to the audit log quantity data and the historical statistical analysis data; and judging whether the application auditing is invalid or not according to the log changetrend and the updated log data. The invention relates to an identification scheme based on big data statistical analysis. Through collecting the audit log quantity of various applications, and analyzing the change trend of logs in combination with historical data, whether audit fails or not is automatically judged. The whole process is automatically completed by equipment, the labor cost is avoided, the detection real-time performance is very good, the detection efficiency is improved, and meanwhile the detection effect is guaranteed. In addition, the invention further provides an applicationauditing failure recognition device and system with the technical advantages and a readable storage medium.

The invention discloses an intelligent monitoring method for roadside parking based on image empty parking space detection. The method is divided into two parts: offline setting and online monitoring. When setting offline, based on the single photo of the roadside parking lot, manually circle the corresponding irregular area for each parking space and assign a unique parking space number; when monitoring online, it is collected by the camera at intervals t An image is sent back to the control center through the wireless network, and then the state of the parking space is detected. The detection process first performs preliminary processing on the image to obtain the edge image of the image; then obtains the corresponding area of ​​the edge image according to the predefined area template and cuts it out; then judges whether it is an empty parking space based on the irregular area; finally records according to the state change of the parking space The current parking status and the parking start time and end time of the parking space.

The invention relates to the technical field of operation monitoring of coal mine belt conveyors, in particular to a mining belt conveyor conveying belt state detection method and system based on image recognition, and the method comprises the steps: installing a video sensor on a portal frame of a conveyor, firstly collecting a video of a belt surface area of a conveying belt in work in real time through the video sensor, screenshot is carried out on the video frame by frame to obtain multiple frames of continuous mining conveyor belt images; then, the video sensor analyzes and processes the images of the mining conveying belt, and finally, the video sensor displays the processed images of the mining conveying belt, and the processed images of the mining conveying belt comprise whether coal exists on the conveying belt or not and whether the conveying belt runs or not. The mining belt conveyor conveying belt state detection method based on image recognition is reliable in detection, good in detection real-time performance, convenient to maintain, low in cost and convenient for practical application in coal mine scenes.

The invention provides a coal plough planing blade mechanical characteristic on-line testing device, and fundamentally solves the problem that mechanical characteristic data under a coal plough condition fails to be detected. According to the technical scheme, a coal plough cutter head comprises a cutter seat, a cemented carbide cutter head arranged on the front end of the cutter seat, a protection cover assembled on the cutter seat, and a cutter rod arranged on the bottom part of the cutter seat; the middle part of the cutter seat is provided with a square hole, and the vibration sensor is placed in the square hole through a strong magnetic base; a gap exists between the tail part of the cutter seat and the cutter rod; the square hole and the front end of the cutter seat form a tetragonal column, strain gage grooves are machined in the four surfaces of the tetragonal column, and the strain gage grooves are respectively equipped with strain gages I-III; and the front inner wall of the square hole is provided with the strain gage III; the protection cover is a U-shaped housing provided with a hook-shaped structure, the protection cover is embedded in the outer wall of the cutter seat through the front part of the cutter seat, and the hook-shaped structure is clamped to the rear part of the cutter seat and is fastened through a sunk screw. The coal plough planing blade mechanical characteristic on-line testing device has the advantages of simple structure, convenient usage, easy control, visual data detection, reliable detection result and the like.

The invention discloses a fresh concrete thixotropic property evaluation device which comprises a collection hopper fixed on a fixed support, the collection hopper is connected with a diaphragm pump through a first stainless steel braided tube, the diaphragm pump is further connected with a horizontal conveying steel tube through a second stainless steel braided tube, and a pressure sensor is arranged in the horizontal conveying steel tube. The horizontal conveying steel pipe is connected with a discharging nozzle through a stainless steel bent pipe, a flowmeter is arranged on the discharging nozzle, a stainless steel frame is arranged below the discharging nozzle, and the stainless steel frame is placed on a glass plate. The invention also provides a method for evaluating the thixotropic property of the fresh concrete. By simulating the pumping and construction process of the fresh concrete, the pumpability of the fresh concrete can be evaluated, the thixotropic performance of the fresh concrete can be reflected in real time, and the evaluation method and device have the advantages of being easy and convenient to operate, efficient, good in detection real-time performance, wide in application range and the like.

The invention discloses an LED display module and a display screen. The display module includes a substrate and one or more groups of RGB LED chips encapsulated on the front of the substrate by encapsulation glue. The display module also includes signal data for electrical connection. The acquisition module, the signal processing module, and the signal data acquisition module are arranged on the substrate or directly connected with the positive pole and the negative pole of the LED chip. The LED display screen of the present invention is spliced ​​by the LED display modules. The LED display module and the display screen of the present invention overcome the defects of inaccurate detection of junction temperature of LED chips, incapability of real-time continuous monitoring and early warning at the LED product site, and have the advantages of temperature detection. Accurate, good real-time detection advantages.