10results about How to "Realize organic integration" patented technology

This utility model relates to a blood pressure monitor, disclosing a multifunctional blood pressure monitor including a main unit and a secondary unit. A cuff is installed at the lower end of the main unit, and a first main control board is installed inside the main unit. The secondary unit contains a second main control board and a battery, which is connected to and powers the second main control board. Electrodes are also installed on the secondary unit and connected to the second main control board. The main unit and the secondary unit are detachably connected. When the secondary unit is mounted on the main unit, the battery is connected to the first main control board via the second main control board. The battery in the secondary unit not only ensures the normal operation of its own ECG and heart sound measurement functions but also, when mounted on the main unit, powers the first main control board of the main unit via the second main control board. This power supply mode solves the problem of separate power supply for the main unit and achieves the organic integration of blood pressure, ECG, and heart sound measurement functions. Users do not need to prepare multiple power supply devices, reducing operating costs.

This invention relates to the field of NLP technology and discloses a plug-in function testing method for a pre-trained power system model based on NLP. The method includes: receiving a technical supervision query question and performing vector encoding and retrieval to obtain an initial retrieval result set; obtaining reference text with standard citation annotations through weighted sorting and concatenating it with multi-level prompt word templates to obtain domain-structured prompt words; performing a bundle search using a large technical supervision domain model fine-tuned with LoRA low-rank adaptation to generate candidate answer texts; and conducting quality assessments including citation integrity detection, logical consistency detection, and entity illusion detection to obtain a qualified technical supervision standard answer. This method solves the technical problems of low standard retrieval accuracy, poor answer accuracy, lack of citation tracing, and insufficient domain customization capabilities in existing technologies, providing a complete technical solution for intelligent applications in the field of power technical supervision.

The application relates to the technical field of liquid crystal display panel detection, and discloses a liquid crystal display panel visual detection system and method, which comprises the following steps: S1, acquiring real-time image information of a liquid crystal display panel and production parameters of a production line; S2, performing multi-dimensional analysis according to the real-time image information to acquire multi-dimensional evaluation indexes; and S3, establishing a panel qualification judgment model according to the production parameters, inputting the multi-dimensional evaluation indexes into the panel qualification judgment model, and obtaining a detection result. According to the multi-dimensional analysis of the liquid crystal display panel based on the real-time image information, the panel qualification judgment model is established according to the production parameters of the production line, the quality of the liquid crystal display panel is judged through the panel qualification judgment model and the multi-dimensional evaluation indexes, the 'apparent quality' and 'process health degree' of the panel can be comprehensively reflected, the misjudgment caused by single-dimensional information can be avoided, the organic fusion of multi-source heterogeneous information is realized, and the robustness of decision-making is improved.

The application relates to the technical field of data processing, and discloses a soil profile database-based organic carbon density prediction method and system. The method comprises the following steps: collecting soil profile data, obtaining soil attribute parameters and environmental parameters of each depth layer, and obtaining a profile data set; dividing samples into different groups according to pH values and texture parameters to obtain a classification matrix; training a bulk density prediction model in each group, interpolating the missing values of the bulk density, and obtaining complete bulk density data; establishing a transfer function, substituting the organic carbon content, the bulk density value and the layer thickness, and calculating the organic carbon density of each depth layer; and weighting and accumulating the organic carbon density of each depth layer according to the layer thickness to obtain the profile organic carbon storage. The application solves the problems that, in the prior art, a global unified model leads to low bulk density prediction accuracy, and the heterogeneity of soil types and the depth effect are not considered, resulting in inaccurate organic carbon density estimation.

This invention discloses a deep learning-based simulation method for scheduling electronic atomization production lines, comprising: S1, collecting and preprocessing historical production data from the electronic atomization production line to construct a standardized dataset; S2, constructing and training an electronic atomization production line scheduling model using a multi-dimensional spatiotemporal collaborative Transformer network; S3, inputting real-time production line status information into the trained electronic atomization production line scheduling model and evaluating production line operation indicators to generate an optimized set of candidate scheduling strategies; S4, constructing a multi-objective simulation environment for the electronic atomization production line and simulating the execution of the optimized set of candidate scheduling strategies; S5, using an improved multi-objective ant colony optimization algorithm to select the optimal scheduling strategy; S6, applying the optimal scheduling strategy to the actual production line and collecting feedback information in real time; S7, continuously iteratively optimizing the scheduling model based on the feedback data. This invention improves production line operating efficiency, resource utilization, and scheduling response capabilities.

This invention discloses a pollution source tracing method that couples water system topological constraints with multi-source tracing technology. This invention relates to the field of environmental monitoring and water pollution control technology, and includes the following steps: constructing a digital water system topology network for a watershed: based on digital elevation model data of the target watershed, after hydrological preprocessing, flow direction calculation, runoff accumulation analysis, and river network extraction, the natural river network is abstracted into a topological network graph composed of nodes and directed edges. A reachability matrix is ​​generated based on the directed edge connection relationships. The advantages of this invention are: by fusing four complementary chemical fingerprints—stable isotopes, biomarkers, three-dimensional fluorescence spectroscopy, and conventional water quality physicochemical parameters—a high-dimensional feature space is constructed. Cross-validation is performed using the distinguishing advantages of different fingerprints in different dimensions, effectively overcoming the source type confusion problem caused by insufficient dimensions of a single indicator, and significantly improving the specificity and accuracy of source identification.

This utility model relates to the technical field of underground communication and electrical explosion-proof equipment in coal mines, and particularly to a flameproof and intrinsically safe gateway for coal mines. It includes a housing assembly, a flameproof battery chamber, an intrinsically safe chamber, a battery module, a data exchange unit, a power supply unit, a battery information display unit, an intrinsically safe component, an observation window assembly, a light panel assembly, an explosion-proof cable entry device, and an explosion-proof cable connector. The housing is made of metal, and an internal partition divides it into the flameproof battery chamber and the intrinsically safe chamber. The flameproof battery chamber houses the battery module and a rotary switch that provide independent power to the equipment. The data exchange unit collects and converts multiple RS485 signals, and the power supply unit provides intrinsically safe power and monitors the status in real time. The intrinsically safe component in the intrinsically safe chamber converts the signals into standard Ethernet and optical signals for output to the ground system. The battery information display unit displays the equipment's operating status in real time through the observation window, and the light panel assembly provides auxiliary lighting. This gateway possesses excellent flameproof and intrinsically safe performance.

The application relates to a physical simulation experiment method of a caisson wharf under the combined action of earthquakes and waves, and relates to the technical field of coasts and oceans, and comprises the following steps: S1: experiment system construction and model preparation: a comprehensive experiment pool integrating a servo hydraulic vibration table and a wave making system is constructed; S2: load design: seismic motion input, wave load and loading working conditions are designed; S3: experiment loading and data acquisition: experiments are carried out according to the designed working conditions, and data are collected; S4: data processing and analysis, stability and damage mode of the caisson wharf are judged. The application provides a systematic, effective and repeatable physical simulation experiment method of a caisson wharf under the combined action of earthquakes and waves, and realizes real reproduction of the dynamic response of a caisson-ground system under complex environmental loads.

The application discloses a kind of in-service roadbed underlying ground permafrost probability distribution graph mapping method and system, the method includes: step S1, multi-source data acquisition and pretreatment;Step S2, permafrost indicating feature extraction;Step S3, permafrost existence probability model construction and training;Step S4, permafrost probability distribution calculation and mapping;Step S5, precision verification and engineering application.The application realizes the accurate diagnosis to underlying permafrost state under the premise of completely not destroying the integrity of roadbed structure by the application of multi-source data fusion and machine learning model.The method makes full use of multi-physical field coupling information, overcomes the limitation of single technical means, and the diagnosis accuracy is significantly better than single geophysical prospecting method, which provides reliable technical support for roadbed safety monitoring.The roadbed maintenance decision is changed from traditional experience-driven to quantitative data-driven, and the potential risk area is accurately positioned by fusing area, which effectively avoids blind excavation and ineffective maintenance, and reduces maintenance cost.

A cognitive impairment auxiliary analysis method based on multi-view brain network feature fusion, belonging to the field of medical information processing technology, solves the technical problem that existing technologies struggle to fully exploit the correlations and complementarities between multi-level structural features of patients' brain functional connectivity data, leading to low accuracy in assessing patients' brain functional status. This method uses rs-fMRI as input data, extracts multi-level brain connectivity information by constructing functional connectivity networks and higher-order functional connectivity networks, and builds a multi-view local-global graph neural network model (MVLG-GNN) to achieve collaborative modeling, adaptive fusion, and auxiliary analysis combining group relationships of subjects from multiple views of brain connectivity features. This invention is also applicable to the field of Alzheimer's disease brain data processing.