5results about How to "Overcome incompleteness" patented technology

The invention relates to a digital measurement method for an assembly clearance of a large depth-to-width ratio assembly, and the method comprises the following steps: scanning to obtain point cloud data P at a cold plate and an assembly in a TR assembly assembly region, preliminarily dividing the point cloud data into a TR assembly point cloud Pz and a cold plate point cloud P1 through clustering, extracting the features of a mounting hole and the features of a positioning plane from the point cloud Pz and the point cloud P1, and calculating the assembly clearance of the TR assembly according to the features of the mounting hole and the features of the positioning plane; and aligning the corresponding theoretical models of the TR assembly and the cold plate to the point cloud data according to the extracted hole features and the positioning plane features, calculating the gap distribution between the transceiving assembly and the cold plate according to the relative poses of the theoretical models, converting the gap distribution into scalar quantities, and displaying the scalar quantities on the models in a color difference diagram form. In a word, the theoretical model is used for recovery of the assembly clearance through an efficient feature extraction algorithm and a registration method, so that calculation of the complete assembly clearance in an invisible area is achieved, and clearance calculation precision and data processing efficiency are greatly improved.

According to the machine defect trend and historical action rapid query method and device, the medium, the program product and the terminal, integrated query and analysis functions are provided, and the machine management and fault handling capacity is remarkably improved. A user can easily inquire detailed actions and states of a machine and obtain a closed-loop result of a case so as to evaluate the effect of improvement measures, and all information is presented through a visual chart. The machine state chart and defect data analysis are integrated, a user can comprehensively understand the historical operation condition of the machine, identify abnormal reasons and effective countermeasures, and provide a basis for quick response, so that the wafer influence quantity is reduced, and the yield fluctuation is controlled. And real-time and historical data query is supported, so that the working efficiency is greatly improved, and the problems of low speed and tedious process of manual query are avoided. Meanwhile, the database comprehensively records detailed action information of the machine, data incompleteness is overcome, and a user can analyze defect distribution and directivity problems in a specific time period.

The present application relates to the technical field of mine safety production and geological disaster monitoring, in particular to a kind of open pit slope floatstone dangerous intelligent sensing method and related equipment, method is, obtain target slope image, and reconstruct depth map.Prepreg floatstone region segmentation is carried out to slope image using pre-trained PSPNet image segmentation model, and floatstone segmentation map is generated.Pixel-level fusion is carried out to segmentation map and depth map, and three-dimensional point cloud data of floatstone is extracted.Convex hull algorithm is used to carry out three-dimensional grid reconstruction to point cloud, and the geometric characteristic parameter of floatstone is calculated, based on fuzzy comprehensive evaluation theory, in combination with above-mentioned geometric characteristic parameter, the danger level of floatstone is comprehensively evaluated, and scientific basis is provided for mine safety management.The method realizes the full-process automation from image acquisition to danger level evaluation, improves the evaluation efficiency and accuracy.

The invention discloses an infrared detector and an infrared detector preparation method, and belongs to the technical field of infrared detectors. The infrared detector comprises a detector and a reading circuit; the detector comprises a plurality of pixel structures arranged in an array; the pixel structure comprises an absorption layer, a first electrode and a second electrode; a first doped region and a second doped region are arranged on the surface of the absorption layer; the first electrode is arranged on the surface of the first doped region; the second electrode is arranged on the surface of the second doped region; a lead-out bonding pad and a grounding lead are arranged on the surface of the reading circuit; the first electrode is electrically connected with the lead-out bonding pad; the second electrode is electrically connected with the ground lead. According to the invention, the grounding lead is additionally arranged on the readout circuit to lead the electrode at the periphery of the detector to the single pixel structure, and the single pixel structure forms an independent closed-loop lead-out structure by changing the interconnection between the single pixel structure and the readout circuit, so that the complete isolation of the single pixel structure is realized, and the stress mismatch is fully eliminated.

A method for extracting triples from knowledge graphs in the mining industry based on ontology constraints and thought chain principles is proposed. The method constructs a highly complete mining domain ontology, acquires and preprocesses unstructured text data sources from the mining domain, and then constructs structured prompts containing ontology constraint information and thought chain reasoning instructions, transforming the triple extraction task into a constrained generative reasoning process. The structured prompts are input into a generative large language model. After receiving the input, the model, relying on its self-attention mechanism and pre-trained knowledge, strictly follows the pre-defined thought chain paths in the structured prompts to conduct reasoning. The output of the generative large language model is parsed using a parsing algorithm, separating the thought chain part and the final result part of the model output. Triple data structures are extracted from the final result part to form a candidate triple list, followed by ontology-based post-processing verification and optimization. This method enables efficient, accurate, and standardized extraction of knowledge in the mining domain.