34results about How to "There is no cumulative error" patented technology

The invention discloses a method for connecting flow-type 3D visual measurement based on two-sided target, including the steps of: A setting two digital cameras capable of observing the panorama of the measured object; B calibrating the coordinate conversion relations of the inner parameters of the two digital cameras and therebetween; C placing the two-sided target at the measuring area of the measured object, shooting at least three non-collinear feature points on the two-sided target separately by the two digital cameras and visual sensor; D moving the two-sided target, and performing 3D visual measurement to the measuring area by the visual sensor; E calculating the coordinate of the feature point in the global coordinate system and visual sensor coordinate system; F determining the conversion relation of visual sensor coordinate system to the global coordinate system based on at least three non-collinear feature points, thereby converting the 3D visual measurement data of the measuring area to the global coordinate system; G performing repeatly C, D, E and F, so as to realize the connection of the 3D visual measurement data. The invention improves the data connection precision and efficiency.

The invention relates to an absolute stroke detecting technology, in particular to an absolute stroke detecting method used combining with a ceramic piston rod and a device thereof. The detecting section of the piston rod is divided into a plurality of continuous segments along an axial direction and a group of marks are arranged within each segment. The group of marks can be identified and converted into a group of unique codes by a sensor which is used for reading the codes of varied marks of current positions, then decoding and identification is carried out to the codes read by the sensor to know the segment where the sensor is located and the offset of the position of a corresponding segment where the sensor is located and then the current absolute position of the sensor can be obtained by combining the segment where the sensor is located with the offset of the position of the corresponding segment where the sensor is located. The sensor is an initiative magnetic detecting type and the marks on the piston rod represent to be a series of well-regulated grooves and bulge loops. The absolute stroke detecting technology guarantees that no impact is caused to the read of the position of the piston rod during power-off period and no accumulated error exists.

The invention discloses a field positioning method based on a wireless positioning device for agricultural machinery. The field positioning method includes steps that (1), a positioning point and a reference points are set; (2), the positioning point transmits activation instructions to actuate the peripheral reference points; (3), position signals carrying corresponding position information of the reference points are transmitted by the activated reference points, the positioning point receives the position signals, and the distance from the positioning point to each reference point is computed according to the intensity of the corresponding signal; (4), the number of the reference points corresponding to the received positioning signals is judged, if the number of the reference points is smaller than 3, the step (2) is repeated, and all the other reference points are controlled to sleep except for three reference points with the shortest distances to the positioning point if the number of the reference points is equal to or larger than 3; and (5), coordinates of the position of the positioning point are computed according to the position information of the three reference points with the shortest distances to the positioning point. The field positioning method for the agricultural machinery is high in positioning precision and low in power consumption, the positioning precision is irrelevant to the driving distance of the machinery, and cost is saved.

The invention provides a digital display gradienter and a machine tool guiding rail leveling method. The digital display gradienter comprises a gradienter and a water channel, wherein the gradienter comprises a horizontal base, a vertical guiding rail, a ball screw, a vertical sliding block, a stepping motor, a stepping motor driver, a hand-cranking wheel, a digital display grating ruler, a static wire, an indicating lamp and a working power source. The stepping motor drives the ball screw which drives the vertical sliding block to move vertically. The digital display grating ruler comprises a grating ruler, a grating ruler reading head and a grating ruler displayer. Two ends of the working power source are respectively connected with a water tank and one end of the indicating lamp, the other end of the indicating lamp is connected with the upper end of the static wire, and the lower end of the static wire is arranged corresponding to the water tank. By means of the method, three gradienters simultaneously perform leveling on a machine tool guiding rail, a first gradienter is separately fixed and provides reference coordinates of the plane, and the other two gradienters are respectively placed on a level guiding rail and a V-shaped guiding rail for dynamically testing the relative horizontal degree of the guiding rails. The digital display gradienter is high in accuracy, and leveling through the method is high in efficiency.

The invention discloses a method for judging the degree of elevator strain. The steps include: step S1: obtaining the real-time acceleration a of the elevator; judging the number of floors moved by the elevator in a running process and the number of people loaded by the elevator; step S2: combining the moving The number of floors and the number of people loaded by the elevator are used to calculate the degree of fatigue of the elevator: Th=(W×num+δ)×H×n+Th 0 ; where Th is the elevator strain value, Th 0 is the previous accumulated strain value, W is the average weight of a person, num is the number of people loaded, H is the average height of a floor, n is the number of floors moved by the elevator, and δ is a coefficient that represents the loss when the elevator runs without load . The invention has the advantages of simple rationale, wide application range and high judgment accuracy.

The invention discloses a method for quickly acquiring an object surface model, which comprises the following steps of: (1) automatically controlling the movement of a bearing platform and acquiring qualified sequence images through software; (2) accurately marking a laser plane, a camera parameter and a bearing platform movement space through coordinate of each marking point of a marking block, position of a laser line on the image and relative relationship between an object space point and the laser line; (3) accurately extracting the image coordinate sequence of the laser line; (4) readingthe marking parameter, and obtaining a three-dimensional coordinate point cloud of an object by using forward intersection of triangulation principle; and (5) obtaining a complete three-dimensional model by using Polyworks equal point cloud processing software for editing. The method can quickly reconstruct the three-dimensional surface model of the object, and can judge whether to meet the requirements by using the quickly generated accurate three-dimensional model.

The present application discloses a method, device, device, and medium for intelligent classification of left ventricular magnetic resonance images. The method includes: acquiring a first word embedding vector corresponding to the first data of a detection object, a second word embedding vector corresponding to the second data, The numerical feature vector formed by splicing the third data; extracting the short-axis video and long-axis video in the heart magnetic resonance image of the detection object, and preprocessing to obtain the target video data; splicing and inputting the above three vectors into the first feature analysis model to obtain the second The first feature analysis result and the target video data are input into the second feature analysis model to obtain the second feature analysis result, and the third feature analysis result is obtained, and input to the third feature analysis model to obtain the classification probability of the left ventricle image of the detection object value. The method uses multi-sequence nuclear magnetic imaging data and clinical data to make classification predictions. The results are more reliable and accurate, and it does not require complicated post-processing procedures, and there is no cumulative error, which improves the robustness.