8results about How to "Easy to identify automatically" patented technology

The invention relates to the technical field of safety management, in particular to a safety management method of Internet of Things equipment, electronic equipment, a medium and a product, and the method comprises the steps: obtaining real-time operation data of each piece of Internet of Things equipment in an Internet of Things system; based on preset hidden danger features, whether abnormal operation data exists in all the real-time operation data is judged, and the abnormal operation data is the real-time operation data containing the preset hidden danger features; if yes, determining a hidden danger type corresponding to each piece of abnormal operation data based on a preset hidden danger feature corresponding to each piece of abnormal operation data; and based on each abnormal operation data and the corresponding hidden danger type, triggering an early warning mechanism of the abnormal Internet of Things equipment corresponding to each abnormal operation data. According to the application, the Internet of Things equipment with potential safety hazards can be found in time, and the Internet of Things equipment can be overhauled or maintained in time, so that the influence on other Internet of Things equipment is reduced.

The utility model discloses a kind of compressor identification detection structure and breast machine for breast machine.The described compressor identification detection structure includes photoelectric detection module and compressor, the photoelectric detection module is installed in the installation port, the compressor is provided with the lead-in seat compatible with the installation port, the lead-in seat is provided with locking module and detection identification board, the locking module is locked in the installation port when the lead-in seat is inserted into the installation port, the detection identification board is detected by the photoelectric detection module when the lead-in seat is inserted into the installation port;Breast machine includes breast machine host, the breast machine host is provided with riser and bearing table, the bearing table is located below the riser, the riser is provided with installation port, the installation port is provided with the described identification detection structure, and the breast machine host is electrically connected with remote control system. The utility model is applied to the technical field of breast machine.

The present application relates to a kind of defect identification method of deep learning in ultrasonic infrared technology combined with space-time domain features, belong to nondestructive testing field, comprising: S1: the test piece is applied excitation, obtains the infrared thermal image sequence of measured object surface;S2: select the optimal foreground image number and background image number, carry out background reduction processing to thermal image sequence data, obtain temperature rise thermal image sequence;S3: select the image at a moment to carry out threshold segmentation;S4: all test pieces are divided into training set, verification set and test set;S5: to binary image is marked;S6: in training set and verification set according to temperature rise thermal image sequence, threshold segmented binary image and the annotation file of binary image, extract the one-dimensional space-time domain signal of white pixel point;In test set, extract the one-dimensional space-time domain signal of all white region;S7: space-time domain signal is input into defect identification model, obtain the binary image containing white region, white is defect.

The utility model discloses a turnover device for gear ring processing, which comprises a turnover platform for placing a gear ring, the turnover platform is arranged on a support frame through a turnover assembly, and the turnover platform is provided with a clamping assembly for clamping the gear ring. The overturning platform is provided with a positioning assembly used for positioning during grinding of inner teeth of the gear ring through a jacking assembly, and the overturning platform is provided with a grinding hole allowing a grinding robot to stretch into the grinding hole for grinding after overturning. Automatic clamping of a gear ring placed on the overturning platform is achieved through the clamping assembly, overturning of the clamped gear ring is achieved through the overturning assembly, meanwhile, the positioning assembly is further arranged and used for guaranteeing that the gear ring is easily positioned when clamped, and a polishing robot can conveniently conduct automatic recognition to polish inner teeth, so that efficiency is improved, and labor cost is reduced.

The invention provides a data asset intelligent analysis method, which relates to a data processing technology, and comprises the following steps: receiving screening information of a user side, and screening a data asset library according to the screening information to obtain a first data set; when the first data set meets the interaction screening condition, an interaction screening interface is called and sent to the user side, and the interaction screening interface comprises an interaction area corresponding to the data asset specification; determining an element region based on the segmentation information of the user side on the interaction region, and determining a corresponding element type according to the filling information of the user side on the element region; and according to the element region, the element type and the position comparison information of the first data in the first data set, generating a data sequence, and sending the data sequence to the user side.

The utility model discloses a test device for test piece identification and density detection, which comprises a measuring module, a visual sensor, a mechanical grabbing module and a computer, and is characterized in that the measuring module is used for measuring the air mass, the water mass and the surface dry mass of a test piece; the visual sensor is used for acquiring a label-containing image of the test piece and reading size parameters of the test piece; the mechanical grabbing module is used for grabbing and placing a test piece, the measuring module, the visual sensor and the mechanical grabbing module are all connected with the computer, the visual sensor can collect images including serial numbers of the test piece and transmit the images to the computer, and the computer can automatically recognize the serial numbers on the test piece. According to the utility model, the serial number of the test piece can be identified, the density of the test piece can be obtained according to test data, and the problem of excessive repeated manual operation in the existing experiment can be solved.

A railway freight car customs lock detection device and method, belonging to the field of railway detection, the present invention is to solve the problem that the existing customs station uses manual detection of railway freight car customs locks with poor efficiency and poor inspection quality traceability. The present invention comprises a first image acquisition unit, a second image acquisition unit and an analysis unit; the first image acquisition unit and the second image acquisition unit are respectively used to capture image information of the customs locks located on the two end faces of the freight car; the two end faces of the car refer to the two surfaces of the freight car set opposite to each other along its forward direction: the front face A1 of the car and the rear face A2 of the car; the analysis unit, the analysis unit judges the state of the customs lock according to the image information.

The invention relates to a calcaneal measurement method and a method for digitized custom-made orthosis. The method comprises the following steps: using an electronic camera to photograph the calcaneal of a foot to be measured on a collection platform to obtain a calcaneal image, and then using a halcon algorithm to extract the contour of the calcaneal image. Then, the range of the inner and outer ankles is marked in sequence, and a computer automatically identifies and calculates the inner and outer ankle heights, the inner and outer ankle distances, the inner and outer ankle inclinations, and the inner and outer valgus degrees of the calcaneal. The development of the calcaneal is determined by the identified data, and a calcaneal orthosis is digitized and designed according to the development of the calcaneal, so as to customize a matching orthosis for a person to be measured. The customized orthosis has a higher adaptability to the person to be measured, can more accurately control and adjust the ankle joint force line of the person to be measured, and shortens the treatment time of the person to be measured.