10 results about "Distance sampling" patented technology

The embodiment of the invention provides an outer wall hollowing and falling risk prediction method and system based on infrared detection, and the method comprises the steps: obtaining the wall materials of a to-be-detected building outer wall at different time points based on a preset time interval; acquiring a plurality of sampling distances corresponding to the wall material from a preset and determined mapping relation library according to the wall material of each time point in different time points; based on the plurality of sampling distances, performing physical sampling on the to-be-detected building outer wall to generate a multi-distance sampling image; based on the multi-distance sampling images, image fusion is carried out, and wall body temperature field images of the to-be-detected building outer wall at different time points are generated; determining a temperature field image sequence based on the wall temperature field images at different time points; based on the temperature field image sequence, outer wall risk detection and risk grade division are carried out, and the current risk grade of the to-be-detected building outer wall is determined. According to the scheme, the accuracy of outer wall hollowing and falling risk prediction can be improved.

The invention discloses a sight tracking method and system based on cascade deep learning and distance correction, and a storage medium. The method comprises the following steps: processing an eye image through a cascade deep learning model, and obtaining pupil center coordinates and cornea reflection light spot coordinates of two eyes; calculating a binocular pixel distance as a distance correction factor based on the pupil center coordinates, and calculating a relative position difference vector based on the pupil and light spot coordinates; establishing a three-dimensional nonlinear polynomial regression model taking the relative position difference vector and the distance correction factor as input; obtaining calibration data through a multi-distance sampling strategy and solving a model coefficient; and in the real-time tracking stage, calculating the coordinates of the fixation point of the screen by using the solved model. According to the method, the distance correction factor and the three-dimensional mapping model are introduced, so that geometric errors caused by forward and backward movement of the head of the user are effectively eliminated, meanwhile, the feature point positioning precision is ensured through cascaded deep learning detection, and the precision and robustness of a sight line tracking system are remarkably improved.

The invention discloses a mixed field channel representation and acquisition method based on a main lobe energy coverage criterion. The method comprises the following steps: S1, determining a Rayleigh distance and a minimum sampling distance based on communication system parameters; s2, determining an angle domain sampling interval, and generating angle sampling points according to 3dB power overlapping; s3, deriving and applying a 3dB distance recursion analytic expression, determining a self-adaptive distance sampling interval and iteratively generating distance sampling points at each angle according to a nonlinear attenuation rule of beam focusing, and ensuring that main lobe energy coverage overlapping of adjacent dictionary code words in a distance domain meets a preset threshold value; and S4, gathering the beam atoms corresponding to all angle and distance sampling points to obtain a final dictionary as mixed field channel representation. According to the invention, the design of the code words in the adjacent angle and distance domains meets the condition that the power overlap is not less than 3dB, so that the channel representation blind area is greatly reduced in the whole angle and distance domain range.

The invention relates to the technical field of rock sampling equipment, in particular to geological field grooving sampling equipment, which comprises a supporting guide rail, a sampling device, a sampling device, a sampling device, a sampling device, a sampling device, a control device and a control device, and is characterized in that the supporting guide rail is laid on a sampling surface in a mine and is used for limiting a cutting stroke; the movable cutting assembly is arranged on the supporting guide rail in a guiding mode and used for moving in a limited cutting stroke along the supporting guide rail, and the movable cutting assembly comprises cutting pieces of at least two sizes so as to meet different cutting requirements; the discharging assembly is arranged on the movable cutting assembly and used for sweeping sundries located on the supporting guide rail along with movement of the movable cutting assembly. The supporting guide rail is further provided with a discharging channel, and the discharging channel is used for discharging sundries cleaned by the discharging assembly. Through integration of the supporting guide rail, the movable cutting assembly of the multi-size cutting piece, the follow-up discharging assembly and the discharging channel, the problems that in the prior art, long-distance sampling is tedious, secondary cutting operation is complex, sundries are prone to clamping stagnation, and installation and movement are inconvenient are solved.

The invention discloses a surveying and mapping device and a surveying and mapping method, and the surveying and mapping device comprises a roller distance measuring device which comprises a roller and a handle and is used for obtaining the data of the distance that the roller travels; the RTK device comprises a receiving module and a base station module, the receiving module is fixedly installed on the handle, and the RTK device is used for obtaining three-dimensional coordinates of sampling position points on the roller advancing route at a first sampling frequency; and the data processing module is connected with the roller distance measuring device and the receiving module, and is used for sampling the distance data at a second sampling frequency so as to obtain distance sampling data between two adjacent sampling position points, and drawing the motion trail of the roller based on the three-dimensional coordinates of the sampling position points and the distance sampling data. According to the invention, the roller distance measuring device and the RTK device are combined to obtain the motion trail, and the measurement precision of the motion trail is improved to a millimeter level.

The application discloses a kind of multi-source information coordination's electric power equipment three-dimensional temperature field construction method.Firstly, precise external parameter relationship is obtained by joint calibration using infrared camera, IMU and laser radar, eliminate point cloud distortion and extract corner point and plane point, construct re-projection residual, distance sampling residual and IMU pre-integral residual, adopt error state iterative Kalman filter to optimize global pose, obtain positioning.Self-supervised depth completion network is proposed, combine infrared temperature image and sparse depth map generated by laser radar as input, adopt infrared image guided depth completion strategy, and use pose information to estimate relative motion of adjacent frame, introduce feature alignment module to reduce alignment error, combine depth map, infrared image and IMU data, realize the efficient construction of electric power equipment three-dimensional temperature field.The application more accurately constructs electric power equipment three-dimensional temperature field, improves the ability of substation inspection robot to electric power equipment state monitoring and fault diagnosis.

This invention provides a fully automated pretreatment method, system, and storage medium for soil samples. The method involves acquiring a three-dimensional environmental model of the soil sample pretreatment process, determining a task sequence consisting of a starting point, task points, and target points based on the process flow, and planning a segmented, collision-free path using adjacent point pairs as start and end points. A Gaussian mixture probability model is constructed based on the endpoints, generating sampling points in the probability space to guide random tree growth. The node closest to the sampling point is selected as an expansion node, and multiple alternative directions are generated around the expansion direction. The direction with the lowest energy consumption is selected using a joint energy consumption model. New nodes are generated with a preset step size, and these nodes are added only when the weighted sum of the angular acceleration and angular jerk of the path segment is below a disturbance threshold. When a new node enters the endpoint's attraction domain, an initial path is generated and locally optimized to obtain a segmented path. All segmented paths are then concatenated into a global path, and a robotic arm is controlled to perform the soil pretreatment operation.

The utility model discloses a long-distance safety analysis sampling device, relates to a sampling device, and in particular relates to a long-distance safety analysis sampling device which adopts a telescopic structure and can realize long-distance sampling and improve the safety of sampling personnel. The long-distance safety analysis sampling device is characterized in that the sampling device comprises a telescopic assembly, a sampling probe, a stepless motor and a take-up assembly, the telescopic assembly and the take-up assembly are both connected with a driving gear on a rotating shaft of the stepless motor, the sampling probe is installed at the end of the telescopic assembly, and the sampling probe is installed at the end of the take-up assembly. The sampling probe is connected with an analysis instrument through a data line, and the data line is wound on the take-up assembly. The long-distance safety analysis sampling device is simple in structure, scientific in design and convenient to use, the motor is adopted to drive the telescopic assembly and the take-up assembly to work synchronously, the threaded sleeve stretches out and draws back through meshed teeth between the telescopic assemblies so as to achieve the purpose of automatic stretching and retracting, sampling work can be conveniently carried out in a limited space, and the sampling efficiency is improved. And the sampling safety is improved.

The application relates to a deviation summation two-parameter fast estimation constant false alarm rate detection method, which realizes the following steps: 1) setting parameters such as a protection window, a reference window and a reference sub-window; 2) receiving logradar pulse echo data; 3) adopting a sliding window mode to sequentially count the background amplitude mean value of each distance sampling unit along the distance direction; 4) calculating the deviation square value of each echo amplitude and the background amplitude mean value; 5) adopting the sliding window mode to sequentially count the background amplitude standard deviation of each distance sampling unit along the distance direction; and 6) calculating a detection threshold according to the background amplitude mean value and the standard deviation, and sequentially performing threshold detection on each distance sampling unit. The application estimates two parameters of the background amplitude mean value and the standard deviation, so that the detector can adaptively cope with a non-uniform clutter environment, a fast parameter estimation means of approximate sorting is adopted, the multiple target environment can be effectively coped with, and the processing speed can be greatly improved.

The embodiment of the application provides a kind of based on infrared detection's outer wall hollowing out and falling risk prediction method and system, the method comprises: based on preset time interval, the wall material of the building outer wall to be detected at different time points is acquired;And for the wall material of each time point in different time points, from the mapping relationship library determined in advance, obtain the multiple sampling distances corresponding to wall material;Based on multiple sampling distances, the building outer wall to be detected is physically sampled, and multiple distance sampling images are generated;Based on multiple distance sampling images, image fusion is carried out, and the wall temperature field image of the building outer wall to be detected at different time points is generated;And based on the wall temperature field image of different time points, determine temperature field image sequence;Based on temperature field image sequence, outer wall risk detection and risk level division are carried out, and the current risk level of the building outer wall to be detected is determined.The above scheme can improve the accuracy of outer wall hollowing out and falling risk prediction.