7results about How to "Reliable basis for judgment" patented technology

The invention belongs to the technical field of swivel bridge detection, and particularly relates to a swivel bridge horizontal alignment detection device which comprises a mounting seat fixed to the upper end of an abutment, a controller is fixedly connected to the side wall of the mounting seat, and two guide boxes are symmetrically and fixedly connected to the upper side wall of the mounting seat. After the swivel bridge construction is completed, mechanical contact detection is arranged on the abutment, compared with a detection assembly arranged between two swivel bridges which are spaced by four meters, a fixed and accurate mounting reference can be provided for the detection device by means of a stable structural foundation of the abutment, and a detection structure does not need to be arranged between the bridges which are spaced by four meters; the problems of inconvenient operation, large assembly fixing difficulty and the like caused by cross-interval installation are avoided, the bending deformation of the detection rod due to the gravity of the detection rod can be effectively avoided, and the detection precision is guaranteed from the installation reference and the detection structure.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

The invention relates to the technical field of high-pressure water pumps, in particular to a multistage pressurization-based self-adaptive high-pressure water pump system, which comprises a data acquisition module, an intelligent analysis module, a state analysis module, a performance evaluation module and a self-adaptive decision module, water quality index parameters and operation performance index parameters of the target high-pressure water pump in the historical period are collected through a data collection module; the intelligent analysis module analyzes the water quality index characterization value and the operation performance index characterization value; the state analysis module determines whether the target high-pressure water pump operation process reaction environment stability meets the standard or not; the performance evaluation module determines whether the target high-pressure water pump system performance evaluation meets the standard or not; the adaptive decision module determines a processing strategy so as to determine the adjustment amplitude of the water quality index characterization threshold. The maintenance and operation efficiency of the self-adaptive high-pressure water pump system is improved.

This invention provides a packaging method, apparatus, and storage medium suitable for baseband modulation and encryption circuits. It relates to data processing technology, which involves feature processing of a substrate image corresponding to a circuit board to identify abnormal components located on the circuit board and determine the type of abnormality for each component. Based on the substrate image, it identifies an identification region with an abnormality identification relationship to each abnormal component and forms a pointing identifier based on the identification region. Furthermore, during the identification process, it analyzes the data in a circuit component database for any component, solder joint, pin interconnection relationship, pin surface features, and structural features. This invention, based on image recognition and big data technologies, offers advantages such as high packaging efficiency, high accuracy, and high yield rate during the packaging process, meeting the packaging characteristics of baseband modulation and encryption circuits and ensuring high bandwidth and good encryption during later use.

The invention discloses a thickness detection method for detecting the qualification rate of a woven valve bag, which relates to the technical field of industrial detection and comprises the step of dividing a flat surface area of a bag body and a non-flat surface area containing suture lines and creases. And plane fitting is carried out on the plane surface point cloud, and the material thickness deviation distribution is obtained by calculating the distance from a measurement point to an ideal plane. And for the non-flat surface, establishing a section contour along the extension direction, extracting a peak value and a valley value, calculating a height difference, and obtaining a structure thickness set. And finally, inputting the two types of data into an analysis module, and outputting a detection result of whether the thickness of each part is compliant or not according to a preset tolerance. According to the method, the material body thickness and the process structure size are quantified respectively, the problem that the material body thickness and the process structure size interfere with each other in detection is solved, and more accurate and comprehensive thickness quality evaluation is achieved.

The invention discloses an additive part durability evaluation method, device and equipment, a medium and a program. The method comprises the steps that a target part is printed into a durability limit evaluation sample, and a basic sample is obtained through heat treatment and linear cutting; dividing the basic sample into a reference group and at least two experimental groups to obtain a reference sample and a contrast sample group; performing a rotary bending fatigue test on each reference sample and each contrast sample to obtain fatigue limit data under different processes; calculating a surface durability limit reduction coefficient of the experimental group by taking the fatigue limit of the reference group as a reference; and substituting the surface durability limit reduction coefficient as a fatigue limit correction factor into a safety margin calculation formula, and evaluating whether the fatigue durability meets the design requirements or not according to the safety margin. According to the embodiment of the invention, the influence of different surface processes on the fatigue performance is quantified through contrast tests, and the actual working condition is associated with the safety margin, so that a reliable basis is provided for additive part surface process screening, and the part failure risk is reduced.

This invention provides a scheduling method and related equipment based on large model output length prediction. The method includes: acquiring a large model input request set, wherein the large model input request set includes multiple large model input requests within a time period; determining the corresponding predicted output length based on each input request; determining the scheduling priority of each input request based on each predicted output length; and controlling the output order of each input request in the large model input request set based on the scheduling priority. The method in this application solves the problem of head-of-line blocking that often occurs in current Model-as-a-Service platforms, improving the utilization rate of platform computing resources and the overall efficiency of request processing.