12results about How to "Efficient and accurate analysis" patented technology

The power distribution network reliability evaluation method based on load partitioning comprises the following steps: S1, establishing an improved power distribution network reliability evaluation model of a switch element-oriented load partitioning technology, and inputting a fault parameter, a load parameter, a system topology parameter and a load branch parameter; s2, forming a system topological structure diagram according to the branch numbers of the line nodes; s3, partitioning the power distribution network according to the system topological structure diagram; s4, introducing fuse factors, and calculating block fault parameters; s5, enumerating fault blocks to form a fault consequence mode table; s6, performing power supply reliability judgment on the areas divided in the step S3, setting fuses in the required areas, and supplementing the influence of fuse branches on the load at the same time; s7, enumerating all fault blocks; s8, adopting a Bayesian network method to obtain weak links of the power distribution network; and S9, calculating a system reliability index. The reliability evaluation precision of the power distribution network is comprehensively improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of mill lining plate wear monitoring, and discloses a mill lining plate intelligent wear monitoring system and method based on big data, the system comprises a mill lining plate wear monitoring parameter acquisition module, a mill lining plate wear failure state evaluation module, and a mill lining plate wear monitoring information feedback module; accurately matching the wear failure state spatial three-dimensional model library required by the monitored mill lining plate according to the type of the mill lining plate product by combining the wear failure state spatial three-dimensional model library of different types of mill lining plate products stored based on big data with an intelligent search algorithm and the type identification parameters of the mill lining plate product; intelligent evaluation of the wear failure state of the mill lining plate is carried out based on mill lining plate space three-dimensional model parameters in combination with an intelligent recognition algorithm and a mill lining plate product wear failure state space three-dimensional model library which is customized and matched according to the type of the mill lining plate product, and the wear failure state of the mill lining plate is efficiently and accurately analyzed.

The invention relates to the technical field of machine vision, in particular to a teaching project assessment system based on posture perception, which comprises a video acquisition module; the data extraction module is used for extracting examination images and key examination video segments; the data processing module is used for acquiring the outline and key assessment action point positions of the assessed person and constructing lines of the key assessment action point positions; the evaluation module is used for judging whether the key evaluation point position posture of the examinee meets a preset standard or not, verifying whether the key evaluation point position posture of the examinee meets the preset standard or not, determining an optimization mode of a key evaluation action standard image, judging whether the key evaluation action posture process in the key evaluation video segment meets the preset standard or not, and if yes, judging whether the key evaluation action posture process meets the preset standard. The attitude analysis module is used for checking whether the key assessment action process meets a preset standard or not; comprising a first storage unit and a second storage unit, the intelligent analysis of the examination posture is realized, and the accuracy of the examination system is improved.

The invention relates to the technical field of oil well program control systems, and discloses an off-grid oil well intelligent control system and method, the system comprising an oil well drilling behavior self-checking module, an oil well drilling fluid control parameter analysis module, and an oil well drilling control execution module; the system dynamically feeds back formation pressure parameters at the drill bit through a pressure measuring device, combined with an intelligent recognition algorithm and scientifically preset drilling fluid density parameters required for different formation pressures, to perform intelligent analysis of the drilling fluid density required for balancing the formation pressure at the drill pipe and drill bit position in the oil well; the system adopts a drilling pressure torque measurement pup joint to collect real-time formation working condition information at the drill bit online, combined with target drilling fluid density parameters, intelligent search algorithms and scientific storage of different drilling fluid densities and drilling fluid types corresponding to formation working conditions, to perform precise and efficient analysis of the drilling fluid type required for balancing the formation pressure at the drill pipe and drill bit position in the oil well, thereby improving the drilling fluid parameter control precision and accuracy of the oil well control system.

The present invention relates to the field of materials science and engineering, and discloses a big data driven intelligent gold bonding wire system performance prediction method, comprising: constructing a gold wire physical model of the gold bonding wire, defining a data driven network architecture of the gold bonding wire, and determining a data driven model of the gold bonding wire; defining a GANs network of the intelligent gold bonding wire system, analyzing the gold wire feature tuple of the gold bonding wire, training the gold wire physical model and the data driven model, and obtaining a training gold wire physical model and a training data driven model; analyzing the physical model performance and driving model performance of the training gold wire physical model and the training data driven model, coupling the training gold wire physical model and the training data driven model, and obtaining a hybrid gold wire analysis model; capturing the performance factor dependency of the gold bonding wire, analyzing the potential performance coefficient of the gold bonding wire using the hybrid gold wire analysis model, and constructing a performance analysis report of the gold bonding wire. The present invention can improve the performance analysis effect of the gold bonding wire.

The invention discloses an RGB-T semantic segmentation method and system based on multi-attention guidance and hierarchical fusion, and relates to the technical field of image processing. The system is composed of a double-flow encoder, a discriminative local texture perception unit, a semantic-driven cross-modal fusion unit, a semantic enhancement unit and a multi-scale layered refinement decoder, and efficient fusion and analysis of multi-modal features in a complex traffic scene are achieved. According to the discriminative local texture perception method, saliency features are learned through multi-attention guidance and a self-adaptive gating mechanism, accurate modeling of shallow texture information is focused, and the distinguishing ability of a region of interest and a target edge is improved; according to the semantic-driven cross-modal feature fusion method, efficient aggregation of global contexts is realized through high-level semantic guidance and cross-modal feature interaction, and feature complementarity is enhanced, so that the semantic-driven cross-modal feature fusion method has significant advantages in analysis of small targets, long-distance targets and boundary regions. The decoder adopts a progressive fusion mode, an additional edge detection module does not need to be added, and the overall segmentation precision is improved.

The invention provides a passive sonar network monitoring system based on an underwater unmanned aerial vehicle group. The passive sonar network monitoring system comprises a plurality of unmanned aerial vehicles distributed underwater and a sonar monitoring module for sonar monitoring underwater, the fixing module is used for matching and fixing the sonar monitoring module and the unmanned aerial vehicle; and the control station is arranged on the water and is used for analyzing and processing sound wave signals obtained by monitoring of the sonar monitoring module. The control station disclosed by the invention realizes efficient and accurate underwater sound source monitoring and analysis by receiving and filtering various sound wave signals, extracting a target frequency spectrum band by utilizing Fourier transform, analyzing the signals in detail to determine the type of a sound source, summarizing position information and monitoring time of different sensors, splicing the monitoring time and generating a moving track.

The invention discloses a hybrid expert multi-classification method and system combined with an embedded double-model organization structure, is applied to the technical field of data processing, and constructs a scientific value sentence recognition model by performing deep learning on a pre-training model based on a data distribution consistency threshold and a scientific value sentence training corpus. Based on a hybrid expert mechanism, a scientific value sentence multi-classification model is built, and the scientific value sentence multi-classification model comprises an academic value expert sub-model, an application value expert sub-model, an innovation value expert sub-model and a gating network. And packaging the scientific value sentence recognition model and the scientific value sentence multi-classification model into a scientific value sentence detection double model to obtain scientific and technical literatures. And inputting the scientific and technical literature into the scientific value sentence detection double model to obtain a scientific value sentence comprehensive classification result. The technical problems that in the prior art, when an existing scientific literature value sentence classification method is used for processing scientific literatures, the processing efficiency is low, and the accuracy is difficult to guarantee are solved.

This invention relates to the technical field of agricultural production management and discloses a collaborative management method and system for agricultural production processes based on multiple intelligent agents. The method intelligently and accurately selects the agricultural production guidance agents needed for user-side agricultural production based on user-side agricultural production stage location data, user-side agricultural production process analysis data, artificial intelligence algorithms, and standard agricultural production stage information for different agricultural production guidance agents. Furthermore, it intelligently formulates guidance suggestions for user-side agricultural production problems based on agricultural production problem characteristic data, user-side agricultural production stage location data, user-side agricultural production process analysis data, user-side agricultural production guidance agent characteristic data, and in conjunction with agricultural management platforms and internet platforms. This enables the comprehensive and reliable formulation of highly scenario-based and operable agricultural production guidance suggestion data based on multiple agricultural production guidance agents, achieving intelligent and convenient agricultural production management for users.

The application discloses a training method, a simulation method, equipment and a medium of a simulation model of combustion fracturing crack propagation. The training method of the simulation model of combustion fracturing crack propagation based on a graph neural network comprises the following steps: obtaining combustion fracturing crack propagation simulation parameters; generating a triangular mesh geometric model according to the geometric model parameters; performing numerical simulation of combustion fracturing crack propagation according to the generated triangular mesh geometric model and the combustion fracturing engineering parameters to obtain displacement data and crack state data of the triangular mesh geometric model at multiple time steps; constructing a graph structure and determining features according to the displacement data and the crack state data of the triangular mesh geometric model at the multiple time steps; taking graph data of the graph structure as a training data set according to the constructed graph structure; and training a first graph neural network model constructed in advance according to the training data set.

The invention provides a route generation method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the following steps: in the embodiment of the invention, performing data extraction on acquired tourism data by using a large model, and determining a plurality of routes; and determining a target route based on the similarity between the related data corresponding to the plurality of routes and the demand data corresponding to the current user. Through the scheme in the embodiment of the invention, a personalized target route can be provided.

Systems and methods for determining the concentration of a plant component in a developed food product prior to or during the manufacture of the food product are disclosed. The method may include: receiving, by a computing system, protein data of a plant component; retrieving at least one model, the at least one model having been trained to generate an output indicative of a desired concentration of a plant component to be used for developing the food product, the at least one model comprising at least one of a pH model, a temperature model, and a protein content model; providing the received protein data as input to the model; receiving an output from the model; and generating instructions for developing the food product, the instructions including a desired concentration range of the plant ingredient.