46results about How to "Efficient forecasting" patented technology

The invention provides a GNSS data quality prediction method and device based on a fisheye camera, and a medium, and belongs to the technical field of data processing, and the method comprises the steps: determining the satellite orbit information of a future time period with a to-be-evaluated observation station as a reference point based on ephemeris prediction; according to the fisheye sky image shot at the to-be-evaluated observation station and the satellite orbit information, determining satellite feature data of a future time period and the occlusion porosity of the future time period; respectively inputting the occlusion porosity of the future time period and at least part of satellite feature data of the future time period into a general prediction model and an observation station prediction model corresponding to the observation station to be evaluated for data quality evaluation, and obtaining a first data quality evaluation index and a second data quality evaluation index; and determining a data quality prediction result based on the first data quality evaluation index and the second data quality evaluation index. A general prediction model and an observation station exclusive prediction model are introduced to carry out double-model evaluation, and accurate prediction of GNSS observation data quality is realized.

This application belongs to the field of functional ceramic material performance optimization technology, specifically disclosing a method and system for predicting the performance of multiphase carbon ceramic resistors based on entropy descriptors. The method includes: determining the multi-source microstructure characteristic parameters of the carbon ceramic resistor to be tested; inputting the multi-source microstructure characteristic parameters of the carbon ceramic resistor to be tested into a carbon ceramic resistor performance prediction model to obtain the comprehensive performance entropy descriptor prediction information of the carbon ceramic resistor to be tested output by the carbon ceramic resistor performance prediction model, thereby determining the energy tolerance performance of the carbon ceramic resistor to be tested; the carbon ceramic resistor performance prediction model is trained based on the multi-source microstructure characteristic parameter samples of the carbon ceramic resistor sample and the label information of their corresponding comprehensive performance entropy descriptors; the comprehensive performance entropy descriptor is used to characterize the degree of energy dissipation order of the carbon ceramic resistor material. Through this application, accurate and efficient prediction of the performance of carbon ceramic resistor materials can be achieved.

The invention discloses a method for predicting capillary force of a liquid bridge between parallel cylindrical particles based on machine learning, which comprises the following steps: establishing a capillary force prediction model according to an original data set of the capillary force of the liquid bridge between the parallel cylindrical particles, the original data set being determined according to liquid bridge capillary force simulation results under various working condition parameters, and the capillary force prediction model being used for predicting the capillary force of the liquid bridge between the parallel cylindrical particles. The capillary force prediction model carries out training determination on a supervised learning model according to the original data set; and according to the capillary force prediction model, obtaining a capillary force prediction value under a to-be-predicted working condition. According to the method, the capillary force prediction model of the liquid bridge between the parallel cylinders is obtained by using a machine learning method, and accurate and efficient prediction of the capillary force can be realized.

This invention discloses an acidic oxidizing potential water generator and its preparation method for stable water quality control, belonging to the field of disinfection equipment technology. The device is designed with an inlet module, a brine preparation module, a mixing and control module, an electrolysis module, and a control system. Its core lies in employing a high-precision electric proportional control valve and a millisecond-level PID algorithm to achieve constant pressure and constant flow control of the inlet water flow. The brine preparation module uses a solid natural melting structure combined with closed-loop liquid level control, enabling long-term maintenance with a single salt addition lasting for six months. A built-in water quality prediction model based on electrolysis current (fma) is incorporated, using a specific algorithm formula to calculate pH, ORP, and available chlorine content in real time, achieving "soft measurement" and feedforward control of water quality. This solves the problems of high manual operation intensity and raw material concentration fluctuations, ensuring that the effluent water quality stably meets the high standard disinfection requirements of pH 2.5-2.6, ORP 1112-1115mV, and available chlorine 50-55mg / L.

This invention provides an optimization method for thermal deformation compensation of alloy plates based on digital twins, relating to the field of data processing technology. The method includes: collecting multi-source heterogeneous data of the alloy plate to be processed; performing finite element simulations on multiple processing conditions to obtain a residual stress field sample set; performing dimensionality reduction processing on the residual stress field sample set to determine a low-dimensional parameterized expression model; constructing a thermo-mechanical coupling deformation model of the alloy plate by combining the low-dimensional parameterized expression model and processing environment temperature data; mapping the residual stress field to the deformation of the alloy plate structure in the processing coordinate system through the thermo-mechanical coupling deformation model of the alloy plate; constructing a rapid prediction model of the machine tool temperature field; calculating the thermal displacement error of the machine tool structure through the rapid prediction model of the machine tool temperature field; fusing the deformation of the alloy plate structure and the thermal displacement error to obtain a comprehensive deformation error; and calculating the control variables in the processing process based on the comprehensive deformation error to obtain the optimal compensation parameters.

The invention discloses application of a molecular genetic marker in simultaneous prediction of intestinal immunity, egg weight at first laying and body weight at first laying. The molecular genetic marker disclosed by the invention can be used for simultaneously marking the intestinal immune function, the egg weight at the first laying time and the body weight at the first laying time; by means of the detection method, one-time detection can be achieved, and early, accurate and efficient prediction of the intestinal immune function, the first laying weight and the first laying egg weight of the chickens can be completed. The'one-point three-detection 'technology accords with the mainstream goal of laying hen breeding, and can save the detection cost: taking a single sample detection condition as an example, under the detection method of the same type of PCR principle, corresponding sites are respectively detected for three times if the three characters are detected, however, the prediction of the three characters can be realized by only detecting for one time in the invention, and the detection cost is greatly reduced. The detection cost is reduced by 2 / 3, and meanwhile, the important characters of a plurality of chickens can be predicted.

The application belongs to the technical field of image recognition segmentation, and discloses a medical image cell segmentation and tracking method, which comprises the following steps: step 1: data processing; feature extraction: the backbone part in the model is used to extract features from the preprocessed image, and the CSPDarknet structure is adopted in YOLOv8; step 3: FPN-PAN multi-scale feature fusion; step 4: Head prediction according to multi-scale features. The application realizes real-time tracking of the motion trajectory of cells by combining with a tracking algorithm such as deepsort. The method is mainly based on the YOLOv8 framework, and the Simam attention mechanism and the multi-scale proto method are adopted to optimize the model, so that the detection effect of YOLOv8 is further improved. The application can automatically complete the analysis and detection of medical images, is high in convenience and easy to use.

The invention provides a fatigue crack propagation prediction method and system based on incremental information machine learning modeling, and relates to the technical field of fatigue crack propagation prediction. The method comprises the following steps: firstly, acquiring an early-stage data sample of a sample fatigue crack propagation test as an original data set, and carrying out iterative expansion on the data sample through machine learning modeling interpolation prediction to obtain an expanded data set; and constructing an incremental information sample data set based on the expanded data set. And carrying out secondary machine learning modeling to obtain a fatigue crack growth rate prediction model. And finally, obtaining a crack length value and a confidence interval thereof in the later stage of the fatigue crack propagation test of the sample by utilizing the fatigue crack propagation rate prediction model through multi-path prediction weighted average, and finally obtaining a complete fatigue crack propagation curve of the sample. According to the method, through data expansion of interpolation prediction and machine learning modeling based on incremental information, efficient and accurate prediction of the fatigue crack propagation curve is realized through fusion of the incremental information.

This application provides an Alzheimer's disease prediction method, system, electronic device, and medium. The Alzheimer's disease prediction method includes: acquiring multimodal data of a subject; preprocessing the multimodal data and automatically quantifying and calculating the perivascular space analysis index based on diffusion tensor imaging to obtain processed test data; using an Alzheimer's disease prediction model to predict the processed test data to obtain the Alzheimer's disease risk level of the subject; and generating a visual decision report for the subject based on the Alzheimer's disease risk level. The Alzheimer's disease prediction method of this application can automatically fuse features of multimodal data to accurately and hierarchically predict early-stage Alzheimer's disease, improving the accuracy and precision of early warning.

The present application relates to the field of remote sensing data analysis, in particular to a kind of water bloom area prediction method based on remote sensing image, water quality prediction parameter is obtained by carrying out water quality parameter inversion to remote sensing image, based on water quality prediction parameter, water bloom area extraction is carried out to remote sensing image, and combined with hydrological data, water bloom position prediction is carried out, not only consider the local effect of time and space object, also consider the influence of water quality, hydrology, water temperature and other factors on water bloom, improve water bloom prediction accuracy;Realize the fine, efficient prediction of water bloom.

The invention relates to the technical field of oil and gas exploitation, and provides a gas well blockage prediction method and device. The method comprises the following steps: acquiring gas well production historical data; establishing a training sample according to gas well production historical data; training a gas well production prediction model by using the training sample to predict and obtain gas well production prediction data; based on the dynamic flow process of the gas well gas, gas well blockage historical parameters corresponding to the gas well production historical data and gas well blockage prediction parameters corresponding to the gas well production prediction data are calculated respectively; whether the gas well is blocked or not is judged according to the change trend of the gas well blocking historical parameters and the gas well blocking prediction parameters; and if the gas well is blocked, calculating and comparing the maximum productivity of the wellhead when the gas well is not blocked and the maximum productivity of the wellhead when the gas well is blocked by utilizing a stratum inflow dynamic relation in the gas well according to production prediction data of the gas well, so as to determine the blocking degree of the gas well according to a comparison result. According to the embodiment of the invention, accurate and efficient prediction of gas well blockage can be realized.

The invention belongs to the technical field of three-dimensional integrated circuit thermal management. The invention provides a TSV thermal modeling method based on a physical information neural network. According to the embodiment of the invention, the PINN model is constructed, and the multi-stage training strategy and the systematized sensitivity analysis experiment are combined, so that high-precision and high-efficiency prediction of the TSV temperature field is realized. According to the method, the precision better than that of a traditional machine learning method can be achieved only through a small amount of training data, the extrapolation capability is excellent, the prediction result strictly follows the physical law, a novel technical means is provided for thermal reliability design of a three-dimensional integrated circuit, and the design efficiency and the model credibility are remarkably improved.

The invention relates to an unsaturated soil tensile strength prediction method and system based on machine learning. The method comprises the following steps: acquiring basic physical property parameters and a tensile stress-displacement curve of a test soil body; constructing a data set based on the acquired data, and determining input features for model training; designing a hybrid evaluation index and a training and testing strategy; determining the vertical displacement of the unsaturated soil during failure in the tensile test by using a machine learning algorithm; performing supervised regression training and testing on a machine learning algorithm according to the data set to obtain a tensile stress-displacement curve prediction model; and obtaining a prediction result of the tensile stress-displacement curve, and determining the tensile strength based on the prediction result. The method has the advantages that the tensile stress-displacement curve can be efficiently predicted, then the tensile strength is accurately determined, the complexity and time cost of a traditional tensile test are remarkably reduced, and meanwhile the defects that an empirical model is insufficient in simulation in the post-peak stage and soil body type limitation is caused are overcome.

The present application relates to the technical field of mineral prediction, and discloses a mineral prediction method and system based on a multi-modal Transformer architecture. The method comprises collecting multi-modal data related to mineral prediction through multiple heterogeneous data sources; cleaning, aligning and standardizing the collected data to generate a multi-modal data set in a unified format; deeply encoding the data set using a multi-modal Transformer encoder to extract multi-modal feature representations; based on the feature representations, calculating the correlation weights between the features through the Transformer self-attention mechanism to construct a dynamic attention relationship graph; evaluating the importance of the features according to the correlation weights in the graph to filter key features to form a core feature set; and inputting the core feature set into a prediction model to generate a mineral prediction result. The method can mine the correlation information of multi-modal data, filter core features, and adapt to the demand for mineral prediction under complex geological conditions.

The application discloses a kind of tree sap flow prediction method and equipment combined with gradient promotion and time convolution, first acquisition includes the time series data of tree sap flow data, corresponding environmental factor and equipment parameter;And pretreatment is carried out;Then the key factor that is screened with the influence tree sap flow data using lightweight gradient promotion machine model;Then the multivariate time convolution prediction model of integrated gate feature fusion mechanism is constructed, and the multivariate time convolution prediction model constructed is trained;Again, the trained multivariate time convolution prediction model is used to carry out tree sap flow prediction;Finally, based on the post-processing of the prediction result of sliding quantile and environmental factor, the prediction result is converted into probabilistic output.The application realizes the efficient prediction of tree sap flow by the triple optimization strategy of "feature screening-model construction-postprocessing".The overall scheme considers accuracy and efficiency, and the supporting equipment is simple, low in power consumption, suitable for long-term monitoring and early warning application of tree sap flow.

The application discloses a multi-factor short-term power load prediction system based on adaptive weight sharing of a graph structure, and comprises the following steps: multi-dimensional power load data preprocessing, implicit graph structure and dynamic space correlation modeling, adaptive weight sharing graph convolution design, multi-factor enhancement module fusion, and end-to-end space-time graph network training.The application solves the problem of poor data quality after new energy access through multi-dimensional data preprocessing; through the cooperation of dynamic implicit graph and weight sharing graph convolution, the problem of the inability of a static graph to capture dynamic implicit correlation and the loss of space-time characteristics is solved; through weight sharing and multi-factor fusion, the problems of model parameter redundancy and insufficient feature fusion are solved, high-precision and efficient short-term power load prediction is realized, and the needs of users are met.

This invention belongs to the field of cutting process power consumption prediction technology, specifically involving a machine tool processing power consumption prediction method based on image encoding and convolutional neural networks. The method constructs a three-axis coordinate system and defines the machining tilt angle, then performs image encoding on the material removal volume, spindle speed, and feed rate. Next, it constructs a machining power consumption prediction model based on a convolutional neural network. By training the power consumption prediction model using a two-dimensional convolutional neural network, model verification and application of machining power consumption prediction are achieved. Simultaneously, the geometric and machining information of the cutting process is encoded into a three-channel RGB image. Combining the feature extraction capability of the two-dimensional convolutional neural network, a nonlinear mapping model between machining features and power consumption is established. This solves the problems of insufficient feature representation, weak adaptability across working conditions, and insufficient real-time performance of existing methods. It achieves high-precision and efficient prediction of machine tool processing power consumption under complex working conditions, providing reliable support for energy efficiency optimization and process parameter adjustment in the machining process.

The present application provides a traffic prediction system, a traffic prediction method and a computer readable medium, which efficiently predict the change in required time for a route in a case where the traffic volume of the route changes along with a change in action. A traffic volume data acquisition unit acquires traffic volume data. A required time acquisition unit acquires an actual value of required time. A traffic volume calculation unit calculates the inflow traffic volume of each route using the traffic volume data and the actual value of required time. A traffic state prediction unit calculates, for each route, a predicted value of required time for the calculated inflow traffic volume and a predicted value of required time in a case where the inflow traffic volume changes, using a traffic model generated in advance. A change amount calculation unit calculates, for each route, a change amount of the predicted value of required time in a case where the inflow traffic volume changes.

The invention discloses an academic paper reviewer recommendation method based on a dynamic balance edge graph, which accurately matches a reviewer through a dynamic balance edge graph technology, and efficiently predicts the reviewer in a complex scene through centroid contrast learning and a dynamic synthesis and deletion mechanism of a tail node. According to the method, through a dynamic balance edge graph technology, the defect that reviewer selection is inaccurate due to link category imbalance is effectively overcome, and the appropriate reviewer can be accurately matched; through centroid contrast learning and a dynamic synthesis and deletion mechanism of the tail node, the robustness of the prediction model in processing the class imbalance problem is significantly improved, efficient reviewer prediction in a complex scene is ensured, and the stability and the overall performance of the prediction model are enhanced at the same time.

This invention provides a thermal simulation method for analyzing thermoelectric cooling integrated packaging systems. The method includes: decomposing the computational domain into thermoelectric and non-thermoelectric regions based on the physical characteristics of the thermoelectric cooling integrated packaging system; employing a one-dimensional adaptive segmentation technique for the thermoelectric region to simplify the three-dimensional thermoelectric arm into a one-dimensional structure, and approximating the temperature-dependent parameters as constants within each segment to establish a port macromodel for the thermoelectric region; constructing a spatially perceptual neural network for the non-thermoelectric region, using a dual-branch architecture to extract spatial heat flow features and global features, and establishing a port macromodel for the non-thermoelectric region; establishing the connection between the thermoelectric and non-thermoelectric regions through the temperature and heat flow continuity conditions at the ports, and performing iterative solutions; and reconstructing the temperature distribution of the non-thermoelectric region based on the heat flow density at the ports. This invention significantly improves the simulation efficiency of thermoelectric cooling integrated packaging systems while ensuring computational accuracy.

The application discloses an accumulator piston displacement detection method, system, device and medium, and relates to the technical field of displacement detection. The method comprises the following steps: detecting a target accumulator piston by using a linear frequency modulation continuous wave radar to obtain a to-be-detected difference frequency signal; generating a to-be-detected frequency spectrum diagram according to the to-be-detected difference frequency signal; determining a frequency value corresponding to a maximum amplitude spectrum line and frequency values corresponding to left and right adjacent spectrum lines of the maximum amplitude spectrum line from the to-be-detected frequency spectrum diagram to obtain a group of to-be-detected frequency values; inputting the to-be-detected frequency values into an accumulator piston distance detection model for detection to obtain a predicted distance between the target accumulator piston and the linear frequency modulation continuous wave radar; and the accumulator piston distance detection model is obtained based on long short-term memory network training. The accumulator piston displacement detection method provided by the application comprehensively considers various errors and has the advantages of high efficiency, accuracy and intelligence.

The invention discloses an idiopathic atmospheric hydrocephalus biomarker, a diagnostic kit and application, belongs to the technical field of biological medicine, and particularly relates to the idiopathic atmospheric hydrocephalus biomarker which is a protein biomarker and comprises NBL1, TREM2, COL3A1, DPP7, PLA1A, SELENBP1, FGB, CCK, WFIKKN2, KNG1, HBA2, GFRA3 and CSF1. The invention relates to an application of an idiopathic atmospheric hydrocephalus biomarker in preparation of an idiopathic atmospheric hydrocephalus diagnostic kit. Differential proteins are screened through a DIA technology, protein expression is verified through a PRM technology, the idiopathic atmospheric hydrocephalus biomarker is obtained through screening and used for early screening, risk assessment and prognosis judgment of iNPH, early, accurate and objective diagnosis of iNPH is achieved, the clinical diagnosis period is shortened, and the diagnosis and treatment efficiency is improved.

The invention relates to the technical field of spectral signal processing, in particular to a fabric spectrum modeling method and system oriented to MOF spectrum regulation and control, and the method comprises the following steps: recombining structure parameters and spectrum data to generate a reference parameter set, analyzing load capacity and reflectivity correlation, recognizing abnormal fluctuation, and generating a diagnosis data set; and extracting a spectral distribution state to identify key regulation and control nodes to generate a distribution result, and identifying spectral offset of adjacent sections of environmental fluctuation to obtain an evolution model. According to the method, digital quantization of spectral response characteristics is achieved through structured recombination, energy exchange deviation is recognized through correlation analysis to eliminate interference, the radiation refrigeration mode recognition capacity is enhanced in combination with position coding, and the wave spectrum change trend is tracked in real time by building a dynamic evolution model; the regulation and control sensitivity under the complex microclimate is obviously improved, the self-adaptive cooperation of the refrigeration efficiency and the environmental fluctuation is ensured, and the efficient prediction of the spectrum behavior is realized.

The application discloses a kind of welding camera and radar monitoring method, system and readable storage medium, belong to image recognition technical field, this method includes: in the case where determining that the device to be welded is in the welding stage, the image group of the device to be welded is obtained;According to image group, the sharpness change rate is obtained using image analysis algorithm;The sharpness change rate is input into the sharpness stress mapping model to obtain the stress change of the device to be welded.The method does not require a large amount of computing resources, and is not limited by the complexity and accuracy of the simulation model, can accurately and efficiently predict the stress change in the welding process.

The application relates to an effluent BOD concentration prediction method based on a WSFA-AFE ILSTM neural network and relates to the field of artificial intelligence.The application proposes a WSFA-AFE method aiming at the problem that the input characteristic variable and input history step length are difficult to determine when a neural network is used to predict a multivariate time sequence of effluent BOD.The method can adaptively extract dynamic characteristic variables in the multivariate time sequence, so that the neural network can better predict the effluent BOD concentration.The application proposes an ILSTM neural network aiming at the problems that the standard LSTM neural network has a large number of structure parameters and the training process is time-consuming.The application simplifies the recursive term weight in the structure equation, reduces the number of required training parameters in the network, and accelerates the convergence speed through a parameter updating algorithm.The application realizes efficient, accurate and low-cost prediction of effluent BOD concentration at future time according to the data collected in the sewage treatment process.

This invention discloses a method for predicting the T2 spectrum of rock nuclear magnetic resonance (NMR) based on image feature transfer learning, relating to the field of oil and gas exploration and development technology. The method includes: S1. Standardization and enhancement preprocessing of rock casting thin section images; S2. Hybrid feature extraction fusing pre-trained deep features and handcrafted rock physical features; S3. Construction and training of a regression model from high-dimensional features to T2 spectrum under small sample conditions; S4. Direct prediction of rock NMR T2 spectrum. By employing image feature transfer and small sample learning strategies, this method overcomes the limitations of traditional NMR experiments, such as high requirements for core samples, long measurement cycles, and high costs. It enables rapid and efficient prediction of rock NMR T2 spectra using a small number of casting thin section images. Simultaneously, it significantly reduces the cost and time of rock property analysis, providing rapid support for rock property parameters in oil and gas reservoir evaluation, and has broad application prospects and economic benefits.

This application belongs to the field of hose whiplash warning technology for refueling and receiving aircraft docking, specifically involving a hose whiplash warning method based on multi-sensor fusion. A force sensor is installed at the connection point between the refueling aircraft and the hose to monitor the force on the hose in real time; an infrared vision sensor is installed at the receiving aircraft head, and multiple infrared markers are evenly distributed on the hose surface to capture images of the overall hose motion; an inertial measurement unit is installed at the end of the hose to collect the linear and angular accelerations at the hose end; a data processing and fusion module acquires real-time data from the force sensor, inertial measurement unit, and infrared vision sensor, performs data processing and fusion, and outputs a whiplash risk assessment result.

This application provides a method, apparatus, and equipment for predicting the impact of marsh vegetation change on land surface temperature, belonging to the field of ecological remote sensing and climate effect assessment technology. Based on long-term series leaf area index, land surface temperature, digital elevation model, and marsh wetland distribution data, it first determines the vegetation pixel distribution as the study area within the unchanged marsh wetland distribution range; it then constructs annual and multi-year average growing season datasets using the maximum value synthesis and arithmetic mean methods; within the grid cells, pixels are divided into high and low value groups based on the multi-year average growing season leaf area index, and the difference between the multi-year average growing season leaf area index and the average land surface temperature of all pixels in the high and low value groups is calculated to quantify the degree of land surface temperature change caused by marsh wetland vegetation cover change; finally, by combining the vegetation change trend at the pixel scale, it achieves accurate prediction of the future impact on land surface temperature.

The invention discloses a KASP molecular marker for identifying drought-tolerant phenotypes of soybeans in a germination period and application of the KASP molecular marker, and belongs to the technical field of molecular biology. According to the invention, 308 parts of re-sequenced soybean materials are subjected to drought tolerance phenotype identification, a molecular marker related to soybean drought tolerance is developed, and a KASP primer group related to soybean drought tolerance is also provided. The molecular marker can be used for rapidly identifying the drought-enduring phenotype of the soybean and assisting in screening out soybean lines with drought-enduring ability in the germination period. According to the invention, the process of soybean molecular breeding can be effectively promoted.

This invention proposes a cross-domain heterogeneous graph link prediction method based on structure awareness and a large language model. The method includes: constructing a text-attribute heterogeneous graph and defining the cross-domain inductive link prediction task; constructing a subgraph for target node pairs based on importance awareness, performing semantic encoding and topological position encoding from the endpoint perspective; injecting structural information into a frozen large language model using a contextual cue attention module to obtain hidden state features that fuse structure and semantics; optimizing the fused features using autoregressive cross-entropy loss to complete the cross-domain inductive heterogeneous graph link prediction. This invention designs awareness rules based on the topological structure of the heterogeneous graph and text semantics, making the link prediction decision traceable to specific graph structural and semantic features. This enhances the model's interpretability, provides an intuitive reasoning basis for graph analysis tasks, and addresses the shortcomings of traditional cross-domain link prediction models, such as poor generalization ability and inefficient structural-semantic fusion.