17results about How to "Improve forecast" patented technology

The invention discloses a medical data processing method and system combining big data and smart medical treatment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a historical anatomical record based on the big data, and carrying out the analysis to obtain an anatomical data set; calling a preset data processing mechanism for processing and analyzing to establish an anatomical medical database; calling a predetermined association analysis strategy to perform association analysis to obtain an association list; performing multi-dimensional feature collection on a target user of smart medical treatment to obtain target feature data; collaboratively forming target input data; and activating the intelligent prediction model for analysis to obtain a target output result and performing early warning intervention processing. The technical problems of low medical data processing efficiency, insufficient association between anatomical knowledge and patient individual features and limited intelligent early warning ability in the prior art are solved, and the technical effects of efficiently mining anatomical data values, accurately associating patient multi-dimensional features and improving intelligent medical prediction and intervention ability are achieved.

The present application provides a pore pressure prediction method based on rock physics modeling and multiple linear regression, relates to the oil and gas exploration and development technical field, and the method comprises the following steps: S1: selecting a plurality of reference wells in a secondary structural unit for overpressure analysis; S2: pre-processing the logging data of the reference wells and analyzing the overpressure causes; S3: performing fluid replacement by using the Gassmann equation, and performing solid replacement by using the Brown-Korringa theory, and calculating the rock elastic modulus; S4: selecting an anisotropic soft pore model to calculate the rock effective velocity; S5: performing sensitivity analysis on the elastic parameters and the pressure coefficient; S6: constructing a multiple linear regression model with the elastic parameters having the best correlation with the pressure coefficient, and predicting the pore pressure; and S7: comparing and verifying the prediction result with the Eaton method. The present application avoids the problem of errors caused by relying on the normal compaction trend line, fits the elastic parameters having good correlation with the pressure by using multiple linear regression, comprehensively considers the influence of multiple variables, and has higher prediction and interpretation capability.

A deformation-viewpoint dynamic coupling robot viewpoint intelligent planning method and system, the method comprising: constructing a deformation-view mapping model and training; constructing a space-time graph convolution network and training; constructing a multi-objective hierarchical decoupling optimization network, inputting the multi-modal information in the actual into the trained deformation-view mapping model, generating a coverage prediction value, generating a compensated view according to the deformation compensation layer; the motion optimization layer uses a deep reinforcement learning strategy network and combines an adaptive importance sampling strategy to screen the compensated view and generate a screened candidate viewpoint set; generating a space-time safety corridor according to the trained space-time graph convolution network, scoring the collision risk of the screened candidate viewpoint set according to the safety constraint layer, constructing a multi-objective fitness function and solving, obtaining the optimal viewpoint set. The present application is suitable for high-precision visual detection scene, which significantly improves the real-time performance and robustness of robot viewpoint planning.

The application discloses a mechanical drilling speed prediction method based on micro-inclination characteristics, comprising the following steps: acquiring real drilling wellbore logging while drilling and logging data as a first data set; preprocessing the first data set and extracting the best feature and the micro-inclination feature as input features; the preprocessing comprises data cleaning and correlation analysis; training a mechanical drilling speed prediction model by using the input features; and predicting the mechanical drilling speed of a target well by using the trained mechanical drilling speed prediction model. The micro-inclination feature introduced in the application can significantly improve the ROP prediction accuracy of the model. By introducing the micro-inclination feature in the MLP neural network, the model weight is optimized, and the prediction accuracy of the ROP is improved. By introducing the micro-inclination feature in the SVR model and selecting the optimal hyperparameter, the prediction effect is significantly improved. The method provides a new idea for improving drilling parameter prediction and control by using machine learning, and can be applied to the optimization of drilling speed and efficiency.

The invention relates to the technical field of laser welding, and discloses a laser welding seam tracking and quality real-time detection system and method based on OCT image guidance. According to the method, through system calibration and offline learning, a coordinate mapping and feature database is established; during welding, the coaxial OCT is used for synchronously scanning a front groove and a rear molten pool area; tracking and correcting a welding path in real time based on the front three-dimensional point cloud; meanwhile, a dynamic feature sequence of the molten pool is extracted and analyzed; inputting the features into a pre-training machine learning model, and judging quality and predicting defects in real time; according to the judgment result, parameters such as laser power and welding speed are adjusted in a self-adaptive mode, and closed-loop control is formed; and full-process data is fed back to optimize the model. According to the invention, synchronous closed-loop control of groove tracking and internal quality detection is realized, and the welding precision and the quality reliability are improved.

The application discloses an AI identification method, system and model training method for millimeter wave image overlapping targets based on a three-branch network, and solves the security target detection problem in millimeter wave images with low resolution, mutual interference or mutual overlap through collaborative work of three independent branch networks, namely, an analysis branch network, an aggregation branch network and a prediction boundary branch network. The analysis branch network saves the detailed information in a high-resolution feature map; the aggregation branch network realizes the feature position offset alignment of high and low resolution feature maps through a recursive fusion module; and the prediction boundary branch network extracts high-frequency features and enhances the perception and prediction of target edges. The multi-branch network structure effectively realizes the segmentation and identification of images through the fusion between branches. While improving the segmentation performance of the model, the model significantly reduces the calculation complexity and the detection error and missed detection probability in millimeter wave images, and finally realizes the accurate detection of overlapping objects.

The application provides a relative velocity analysis well-free constraint relative wave impedance inversion method, which comprises the following steps: step 1, velocity analysis is carried out by using seismic data to establish the time-depth relationship of a study area; step 2, seismic facies analysis is carried out on the seismic data to determine the geological model of the study area; step 3, velocity correlation analysis is carried out to determine the velocity migration amount; step 4, the velocity curve of a seismic trace is extracted; step 5, relative velocity normalization analysis of different lithologies at the same depth point is carried out; step 6, relative velocity wave impedance inversion processing is carried out; and step 7, lithology and reservoir identification, tracking and description are carried out. The relative velocity analysis well-free constraint relative wave impedance inversion method breaks through the limitation of conventional inversion which can only be used for single target layer and time domain wave impedance inversion, eliminates the influence of different velocities in time domain and depth domain of multiple target layers, and provides relative wave impedance inversion data for lithology or reservoir prediction under complex conditions in a well-free exploration area.

The application discloses a kind of based on multi-modal data fusion's injury condition early warning method and device, method includes: obtaining continuous physiological waveform data and relevant structured data;After waveform data is preprocessed, it is converted to two-dimensional image representation by time series topology enhancement algorithm;Structured data is standardized to extract structured features;Through heterogeneous feature correlation weight mechanism, two kinds of features are semantically aligned and complementary fusion;Based on multi-window risk aggregation algorithm, output injury condition early warning result.The application solves the problems of difficulty in waveform and structured data fusion, insufficient use of time series features, and poor early warning stability in the prior art by enhanced image representation of waveform data, deep multi-modal fusion, and multi-scale risk aggregation, improving the accuracy, generalization ability, and engineering practicality of injury condition early warning. It can be widely applied in clinical first aid, intensive care and trauma treatment scenarios.

The invention discloses a rapid expansion cloud chamber environment simulation method and system based on artificial intelligence, and the method comprises the steps: obtaining the experimental data and experimental conditions of a to-be-simulated expansion cloud chamber, carrying out the structural classification based on the experimental data according to the experimental conditions and expansion stages, and obtaining the feature vector of each stage, meta learning is carried out through a neural network according to the feature vectors, a cloud simulation model and simulation errors are obtained, a cloud chamber working condition interval is identified based on the simulation errors and experimental data, an output result of the cloud simulation model in a high-deviation interval is obtained according to the working condition interval, and an interval result is corrected through a CFD equation based on the output result. And obtaining a cloud room environment simulation result. According to the method, the cloud expansion stage is split, the fog drop growth process is fitted, CFD local correction is carried out on the simulated high-deviation interval, the prediction error control precision is improved, the prediction and collaborative analysis efficiency of the fog forming process in the complex environment is improved, and meanwhile good interpretability is achieved.

This invention discloses a method for detecting the opening and closing performance of circuit breakers based on dynamic resistance monitoring, belonging to the field of equipment performance testing technology. The method includes: acquiring resistance sensor sampling values ​​during the opening and closing processes of the circuit breaker; constructing a periodic resistance curve based on the resistance sensor sampling values ​​according to each opening and closing cycle; generating a periodic resistance data set; dividing each periodic resistance curve into multiple equally spaced fusion windows; extracting the resistance change slope value, resistance fluctuation amplitude value, and the number of second-order disturbance points in each fusion window to construct a fusion window feature sequence; constructing fitting functions for the resistance change slope value and fluctuation amplitude value based on the multiple fusion window feature sequences of the normal cycle, and superimposing a disturbance response function constructed based on the number of disturbance points; this invention achieves effective identification of performance degradation cycles throughout the entire opening and closing process of the circuit breaker, enhancing the ability to predict and intervene in advance regarding the circuit breaker's operating status.

The invention discloses a method for predicting soil heavy metal content by using spectral data and social environment factor data, which comprises the following steps: S1, acquiring a training sample set, each training sample comprises position information (longitude and latitude, region codes or other attributes capable of reflecting spatial positions) and spectral data and social environment data corresponding to the sample; s2, data fusion: based on the position information or other associable sample attributes, carrying out matching fusion on the spectral data and the social environment data to obtain fusion feature data; social environment factors are fused to supplement key information of pollution sources and migration, and model prediction precision and operation stability are effectively improved. By means of a spectrum dimension reduction and embedding technology, the problem of high-dimensional data colinearity is relieved, and the over-fitting risk of the model is reduced; a cross validation method based on geographic grouping is adopted, so that the model validation process is closer to a cross-regional actual deployment scene; meanwhile, the feature contribution degree is output, and the practical application value in environment supervision work is improved.

The invention discloses a method and system for calibrating the transient working condition exhaust temperature of a virtual engine model based on AVLCruiseM construction, and relates to the field of simulation calculation, and the method comprises the steps: obtaining engine physical structure parameters and performance test data, and carrying out basic engine model construction and steady-state model calibration processing to obtain a steady-state calibration model; performing transient condition decomposition presetting, constructing a step heat exchange control model and a preliminary sensor control model according to a condition classification result, performing simulation exhaust temperature precision judgment, and if the precision does not meet a preset requirement, performing adjustment processing according to a difference between transient test data and a simulation result to obtain an optimized transient model; fixing a code module, an interface and a signal flow to obtain a calibrated transient engine model; and integrating to a virtual development platform for processing to obtain a virtual engine system. The method is used for solving the problem that in the prior art, the exhaust temperature prediction deviation of a steady-state model under the transient working condition is large.

The application discloses an optimization control method for a ubiquitous heterogeneous network topology of panoramic monitoring of an extra-high voltage converter station, belongs to the technical field of panoramic monitoring of the extra-high voltage converter station, and solves the problem of congestion of panoramic monitoring data transmission caused by poor real-time performance and low reliability of the communication network reconstruction of the extra-high voltage converter station when a network fault occurs; a topology control algorithm based on deep reinforcement learning sequentially constructs a topology structure of the heterogeneous network; a framework combining deep reinforcement learning and Monte Carlo tree search is adopted to sequentially construct the network according to pre-defined topology rules; a deep convolutional neural network is trained to predict transmission flow of a partially established topology and guide the Monte Carlo tree to expand search in a more promising area in a search space; and the search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network so as to obtain more accurate prediction in the next iteration.

This invention discloses a method for predicting the melting settlement of ice-rich permafrost, relating to the fields of geotechnical engineering and disaster prevention and mitigation in cold regions. The method includes: constructing a layered parameter model of the permafrost; vectorizing the temperature field based on Fourier's law and the sensible heat capacity method; and automatically identifying the soil type based on an initial void ratio threshold. For ordinary permafrost, a large-strain consolidation model is used to calculate settlement; for ice-rich layers, a volumetric collapse model is used to directly convert the melted ice volume into settlement. The method combines Lagrange dynamic mesh technology to update node coordinates and physical parameters in real time, achieving bidirectional coupling of thermodynamic parameters, and introduces adaptive time step control to improve computational efficiency. This invention, through the coupling of consolidation and collapse mechanisms, solves the problems of inaccurate settlement prediction and low computational efficiency in traditional methods for high-ice-content permafrost. It can accurately simulate the settlement deformation of complex strata such as ice wedges and ice lenses under long-term thermal action, and is suitable for stability assessment and disaster prevention in cold region engineering.

The application discloses a rapid expansion cloud chamber environment simulation method and system based on artificial intelligence, comprising: obtaining experimental data and experimental conditions of a to-be-simulated expansion cloud chamber, structurally classifying according to the experimental conditions according to the expansion stages based on the experimental data, obtaining feature vectors of each stage, carrying out meta-learning through a neural network according to the feature vectors, obtaining a cloud and fog simulation model and a simulation error, identifying a cloud chamber working condition interval based on the simulation error and the experimental data, obtaining an output result of the cloud and fog simulation model in a high deviation interval according to the working condition interval, correcting interval results through a CFD equation based on the output result, and obtaining cloud chamber environment simulation results. The method splits the cloud and fog expansion stages, fits the fog droplet growth process, carries out CFD local correction on the simulation high deviation interval, improves the prediction error control precision, improves the prediction and collaborative analysis efficiency of the fog formation process in a complex environment, and has good interpretability.

The present application relates to the technical field of satellite communication, and especially relates to a spacecraft multi-standard wireless transmission link adaptation method and system, which first collects the space-ground channel state and service demand, constructs a laser / radio heterogeneous link dynamic model, and calculates the transmission capacity and interruption probability. Secondly, the adaptation scheduling weight is generated based on the above indexes, and is decomposed into a slow-changing steady-state component and a fast-changing transient component; the potential change characteristics of the transmission load are analyzed in combination with the steady-state weight and the defined link jitter waveform. Then, the self-organizing adaptation strategy is generated according to the load characteristics, and the quality of service guarantee demand distribution of the multi-standard service is analyzed. Finally, the adaptation coefficient of the distribution is calculated, and if it meets the preset threshold, the strategy is established as the target adaptation scheme. Through the weight analysis and closed-loop verification mechanism of static and dynamic separation, the intelligent self-organizing scheduling of the heterogeneous link in the complex space environment is realized, and the transmission robustness and service quality guarantee capability are significantly improved.