6results about How to "Strong interference ability" patented technology

This invention discloses a downconversion fluorescence thermometry method based on bilinear mode, implemented using a hardware system. The hardware system includes a 280nm light-emitting diode, two convex lenses, and a Tb... 3+ :Cs2Na 0.9 Ag 0.1 The system comprises InCl6 temperature-sensing material, a grating spectrometer, an oscilloscope, a signal generator, a computer, and a thermostat. The specific implementation steps are: signal excitation and acquisition; temperature calibration; actual temperature measurement; and high measurement accuracy: VPR mode linear correlation coefficient R² ≥ 0.998, FL mode linear correlation coefficient R² ≥ 0.992, avoiding measurement errors introduced by the exponential bias or correction term of traditional FIR technology, thus fundamentally improving temperature measurement accuracy.

This application provides a multimodal zoned induction and precise trapping method based on airflow field reconstruction, relating to the field of pest control technology. The method includes constructing a peripheral light-odor composite attraction zone and pre-optimizing the odor concentration using AI; forming a circumferential swirling locking zone using an annular guide hood and monitoring the residence time in real time; increasing the fan speed to form an axial laminar flow trapping zone after target detection and using a trajectory prediction algorithm; periodically and intelligently executing reverse micro-blowing to break up the stacked structure of the sticky traps; dynamically adjusting the light-attracting and airflow modes according to environmental parameters and handling external interference. This application achieves high trapping accuracy and low energy consumption.

The utility model discloses a kind of scaling coal powder cleaning devices on conveying belt, belong to scaling coal powder cleaning technical field, including rack, grinding mechanism, dust removal mechanism, driving mechanism and belt reversing mechanism;Grinding mechanism includes support wheel and grinding wheel, support wheel and rack rotation connection, grinding wheel is coaxially fixed in the outer ring of support wheel, grinding wheel includes grinding wheel outer frame, grinding wheel inner frame and grinding layer, grinding wheel outer frame and grinding wheel inner frame are coaxially arranged and elastically connected, and grinding layer is arranged in the outer layer of grinding wheel outer frame.The utility model realizes the effective cleaning and collection processing of scaling coal powder on conveying belt by the cooperation of grinding mechanism, driving mechanism, dust removal mechanism and belt reversing mechanism, without shutdown maintenance.

This invention relates to a triaxial gyroscope, specifically a topological ring-shaped monolithic triaxial MEMS gyroscope chip, comprising a substrate, a resonator portion, and an electrode portion. The resonator portion includes a central anchor point bonded to the upper surface of the substrate. A resonant ring is sleeved on the outer side of the central anchor point. Four resonant squares and four U-shaped sensitive mass blocks are connected around the outer side of the resonant ring. A square sensitive mass block is connected to the inner side of each resonant square. A straight beam A and two rows of detection comb teeth A are connected to the outer side of each resonant square. Two rows of detection comb teeth B are connected to the outer side of each straight beam A. Four rows of detection comb teeth C are connected to the inner side of each U-shaped sensitive mass block. This invention effectively solves the problems of low sensitivity, poor working stability, low measurement accuracy, and large size of existing triaxial gyroscopes, and is suitable for aerospace, autonomous vehicles, intelligent robots, consumer electronics, and other fields.

This invention relates to a deep learning-based method for angle detection and correction of industrial product images, comprising the following steps: Step 1, acquiring industrial product image information; Step 2, building an industrial product image angle detection neural network model and training the network model using the industrial product image information; Step 3, after the neural network model training is completed, loading the trained model parameters, obtaining the result feature map through model forward operation, restoring it according to the labeled format to obtain the network model prediction result; Step 4, correcting the angle of the industrial product image according to the target position and angle predicted by the neural network model, and correcting the detected industrial product image angle to a uniform orientation.

The application relates to a sea fog monitoring method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: acquiring image data of a target region to be identified; inputting the image data into a classifier to obtain a sea fog identification candidate result; determining a sea fog core pixel and a sea fog determination rule based on the sea fog identification candidate result; taking the sea fog core pixel as a starting point, judging whether adjacent pixels satisfy the sea fog determination rule one by one until all the pixels in the image data are traversed, and obtaining a sea fog identification target result. In this way, the classifier is used to quickly narrow the candidate range, and then the key region is focused for growth verification, so that the calculation efficiency and the identification accuracy are considered, invalid calculation is reduced, the local deviation of the classifier is compensated, and the problem that a single sub-growth is easily disturbed by noise is solved; the pixel-by-pixel verification of the core pixel not only naturally matches the continuous distribution characteristics of the sea fog, reduces the complete fog area from being split, but also can accurately depict a gradual edge and match the real shape of the fog area.