31results about How to "Real-time detection results" patented technology

The invention discloses a playing state detection method and device, and the method comprises the steps: sampling occupancy information of a processor of a to-be-inspected channel when the live broadcast of the to-be-inspected channel is responded, and obtaining a sampling result; calculating an average sampling value according to a sampling result; and based on judging whether the average sampling value is greater than a preset occupancy threshold, obtaining a monitoring result of the playing state. Real-time comparison can be performed according to the sampling value of the processor and thepreset threshold in the live broadcast process of the channel. The real-time detection result is obtained. The detection method can be packaged in an applet, and the applet can run in the equipment for playing the live broadcast image, so that the detection of the playing state of the live broadcast process can be realized without third-party detection equipment, and the detection efficiency andthe detection convenience are improved.

The invention relates to a gas concentration detection device and a gas concentration detection method, comprising a vacuum detection chamber, a gas concentration detection sensor, a vacuum sampling element and a detection analyzer; the sampling element is connected to the detection chamber, and the detection chamber is connected to an external storage The sealed chamber of the gas to be detected is connected, the gas concentration detection sensor is arranged in the detection chamber, and the detection analyzer is respectively connected with the gas concentration detection sensor and the sampling element. The above-mentioned gas concentration detection device detects the gas concentration through the gas concentration detection sensor and the vacuum detection chamber, and can obtain real-time and accurate detection results. The gas in the sealed chamber is sampled through the sampling element, and analyzed by the detection analyzer to obtain More accurate detection results, and by comparing the two concentration detection results with the detection analyzer, it is judged whether the sensor is faulty, thereby avoiding the wrong concentration information obtained due to sensor faults, and making the detection results more accurate.

The present invention provides a human skeleton-based abnormal behavior detection method, system and medium, including: step S1: establish a training video sample database, and construct an original video database with a preset frame length according to the public violent behavior database; step S2: according to The obtained original video database uses the open source attitude estimation algorithm OpenPose to extract the human skeleton in all video samples in the original database to obtain the skeleton video database; Step S3: Utilize the FFmpeg tool to deframe all the video samples in the obtained skeleton video database into Skeleton image stream to obtain the skeleton image stream; step S4: Input the obtained skeleton image stream into the 3D-DenseNet network model for training in sequence, and obtain the trained 3D-DenseNet network model. The present invention uses the human skeleton and 3D-DenseNet to detect abnormal behaviors for the first time, with high detection accuracy, strong robustness, real-time output and updated detection results.

The invention discloses rapid microorganism detection equipment based on Mie scattering and a spatial light modulator. The equipment comprises a laser, an optical system, a detector spherical array frame, a cuvette and photoelectric detectors, wherein laser beam emitted by the laser forms parallel light after being collimated and is converged by the optical system and irradiated to a microorganism sample in the cuvette, a microorganism emits scattered light to the space after irradiated by the laser light, the scattered light is received by photoelectric detectors surrounding the sample pool, a computer performs analysis and calculation on received optoelectronic signals on the basis of the Mie scattering theory, and the form of the current microorganism can be detected in real time. Microorganisms are rapidly detected in real time by the device, the detection speed and accuracy are improved by means of the optical scattering principle, and signals are acquired, processed and analyzed in real time in the acquisition process.

The invention provides an online detection method for an oil pipe field. The method comprises the following steps: (1) equipment installation; (2) parameter setting; (3) instrument calibration; (4) simulation test; (5) oil pipe detection; (6) field evaluation; (7) field identification; (8) equipment detachment; and (9) detection report. An oil pipe in a well is detected on line by using a well mouth oil pipe detection instrument during drilling. The method is low in operation cost, detection results are obtained in real time, time apart of drilling is not occupied, drilling in an operation process is not different from conventional drilling, and additional operation risk is not added. The operation cost of oil pipe detection is reduced, and the construction safety and aging are enhanced.

The invention discloses a partition human body motion detection method based on continuous wavelet transformation. The partition human body motion detection method is characterized in that a receiver is used to receive a signal waveform after precoding of a transmitter; the received signal can be segmented into signal segments, and the continuous wavelet transformation of each signal segment can be carried out to acquire a transformation matrix, and then the variance vectors of the transformation matrix can be calculated; and at last, by calculating the maximum value of the variance vectors, and the maximum value can be compared with the threshold value to detect whether the human body motion occurs behind the wall. By adopting the continuous wavelet transformation, the weak target detection problem in the strong noise environment can be solved, and whether the human body motion occurs behind the wall can be effectively detected, and therefore the accuracy of the partition human body motion detection can be greatly improved.

The invention discloses a wall-across human body motion detection method based on short-time Fourier transform. The method comprises the following steps: firstly, receiving a signal waveform, which is pre-coded by virtue of a transmitter, by virtue of a receiver; then, segmenting the received signal into various sections of signals, and conducting short-time Fourier transform on each section of the signal so as to obtain a transformation matrix; then, computing a variance vector of the transformation matrix; and finally, by computing the range of the variance vector and comparing the computed range with a threshold, detecting whether human body motion occurs behind a wall or not. The detection method disclosed by the invention can solve the problem on weak target detection in a strong noise environment through the short-time Fourier transform, and the detection method can effectively detect whether the human body motion occurs behind the wall or not, so that the accuracy of detecting the human body motion is greatly improved.

The invention discloses a machine vision identification analysis and drug sensitivity identification system, comprising: an optical and digital image acquisition device, a digital image processing device and a drug sensitivity identification analysis device. The optical and digital image acquisition device is used to provide the light source and collect the digital image, send it to the digital image processing device, use the polarizer and analyzer set in it to enhance the digital image of the measured object, and use the lens to correct the stereoscopic image of the digital image Poor; the digital image processing device is used to receive digital images and perform motion capture. When the target object is perceived to be stacked, the image is filtered and the feature is enhanced. After the target sample image is obtained, the target sample type is determined according to the target sample characteristics and barcode information. The detection method is selected, the detection results are obtained, processed and then sent to the drug sensitivity identification analysis device; the drug sensitivity identification analysis device performs drug sensitivity identification through the pattern recognition algorithm combined with the preset CLIS identification standards, and gives the identification conclusion.