7results about How to "Solve blindness" patented technology

The invention discloses a liquid milk UHT cleaning model construction method based on multiple monitoring parameters, and the method comprises the steps: setting cleaning parameters, carrying out the multi-parameter monitoring through a plurality of monitoring probes in a CIP cleaning process, and carrying out the sampling at preset time intervals, so as to measure the protein concentration; by adopting a response surface analysis method, analyzing and fitting monitoring data, sampling measurement values and cleaning parameters of each probe to obtain an equation model of the cleaning time to the cleaning temperature, the cleaning flow speed and the cleaning liquid concentration; a symbolic regression method is adopted, and an equation model of the cleaning speed to the cleaning time, the cleaning temperature, the cleaning flow speed and the cleaning liquid concentration is obtained by analyzing and fitting the monitoring data, the sampling measurement values and the cleaning parameters of all the probes. The cleaning time equation and the cleaning rate equation adopt multiple parameters to carry out modeling research on the cleaning process, and reference is provided for optimization of the pipeline cleaning process in the dairy industry.

This invention belongs to the field of copper alloy material design and preparation technology, and discloses a new process for efficiently designing high-performance copper-iron alloys. Based on the 5% Fe content vertical cross-sectional characteristics of the Cu-Fe-Si ternary phase diagram, the Si content is designed in stages (0-4%) to achieve targeted performance optimization. Combined with a three-stage heat treatment process of "homogenization-basic aging-gradient aging", a closed-loop system of "phase diagram design-preparation-detection-optimization" is constructed. Through precise composition control by EPMA and real-time feedback of mechanical properties, the problems of blind composition design and insufficient precipitation of strengthening phases in traditional processes are solved. The resulting alloy has excellent tensile strength, yield strength, and total elongation at break, with synergistic improvement in mechanical and magnetic properties, and a design efficiency improvement of more than 40%, making it suitable for electronic, mechanical and other fields.

The invention discloses a sheet metal processing intelligent control method and system based on multi-source data feedback, and relates to the technical field of industrial digital control, and the method comprises the steps: obtaining the data of a to-be-processed sheet metal part, and according to the data of the to-be-processed sheet metal part, based on the geometric characteristics and deviation sensitive characteristics of a sheet metal part processing path, calculating the processing path of the sheet metal part; differentiated machining areas are divided, and a partition machining map is generated. According to the method, differentiated classified management and control of the processing areas are realized through the partitioned processing map, a regional basis is provided for subsequent differentiated process parameter configuration, accurate distribution of acquired resources is realized through configuring data acquisition frequency and precision parameters based on the regional deviation sensitivity level, deviation causes are quantified through a data model, and the accuracy of the data model is improved. And the technological parameter adjustment amount is deduced in combination with the equipment constraint range and the parameter sensitivity, so that the scientificity and accuracy of parameter adjustment are realized, the problems of insufficient blindness and adaptability of traditional manual experience adjustment are solved, and the precision consistency of sheet metal processing is improved.

The present application relates to the cross technical field of software testing and intelligent optimization algorithm, disclose a kind of unmanned plane training parallel program particle swarm test case generation method, comprising: first, based on the similarity of variation path determines convergent path pair and divergent path pair;Then, the above path pair is established to generate multiple task mathematical model for covering multiple path test case;Finally, based on the double-layer multi-population particle swarm algorithm collaborative evolution executes mathematical model solution.The present application is aimed at the multi-process feature of parallel program, through path similarity screening, multi-task modeling and the layered optimization strategy of double-layer particle swarm collaborative evolution, can efficiently generate test data covering multiple target paths, significantly improve the integrity and execution efficiency of software testing, especially suitable for the testing scene of complex software such as parallel program.

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 discloses an automatic test data generation method based on scene description template language driving, and relates to the technical field of automatic test. The method aims at solving the technical problems of low test data generation efficiency, poor accuracy and low cross-project reusability in automatic test of a rail transit signal system. According to the scheme, a rail transit field scene description template language fusing technical terms, basic scenes and logic symbols is constructed; performing semantic analysis on the natural language test case, extracting domain entities and matching key elements, and generating a standardized test scene language template by utilizing a logical relationship; and finally, automatically constructing a retrieval chain based on the template, and automatically searching, verifying and extracting target test data completely matched with the scene requirement from the original data. The full-process automation and precision of the test data generation are realized, the data generation efficiency and accuracy are obviously improved, and the efficient reuse of the test data is realized by relying on a standardized template.

The present application belongs to the technical field of abnormal alarm, and particularly relates to a feed production control abnormal alarm method and system, comprising the following steps: obtaining feed production historical parameter data, filtering parameter points located in the forbidden hyperspace based on a production process constraint model, and the remaining parameter points constituting a candidate set; for each candidate point of the candidate set, extracting parameter time series within a preset time window, calculating the Frobenius norm of the time gradient matrix as a state fluctuation measure; calculating the number of points within a specified neighborhood radius of each candidate point as a local density, and calculating the Euclidean distance from the candidate point to the nearest point with a local density higher than itself as a dispersion degree. The present application can trigger an alarm for malignant boundary crossing phenomena that violate the rules, and can also identify hidden deviations according to the boundaries set by each normal sub-working condition, reducing false positives and false negatives, and providing a guarantee for the safe and reliable operation of the feed production process.