1020 results about "Predicting performance" patented technology

The invention relates to the technical field of computer science and artificial intelligence, and discloses a digital twin basin flood disaster early warning plan generation system based on deep learning, and the system comprises a data collection and processing module which obtains and standardizes multi-source data of basin meteorology, hydrology, terrain, remote sensing and the like in real time, and provides real-time data input; the digital twinborn modeling module is used for constructing a digital twinborn model of a watershed hydrological dynamic process according to the real-time data and outputting a simulation result; the deep learning prediction module is used for receiving a simulation result, training a prediction model and outputting space and time distribution prediction of flood disasters; the emergency response generation module is used for generating an emergency plan of the flood disaster according to the prediction result; and the system feedback module is used for evaluating an emergency plan and a prediction effect and providing an adjustment basis for future disaster early warning. According to the invention, accurate flood disaster prediction based on real-time data can be realized, the accuracy and response efficiency of an emergency plan are improved, and disaster loss is reduced.

The invention relates to the technical field of data processing, and discloses an anti-oxidation barrier layer performance degradation prediction method and system based on multi-source data. The method comprises the following steps: collecting and preprocessing multivariate data of a barrier layer environment, and constructing three-dimensional tensor data; indexes such as oxygen blocking efficiency and anti-permeability performance are calculated, and performance time sequence data are obtained; extracting multi-dimensional features and fusing the multi-dimensional features through an automatic encoder; applying a hybrid deep learning model to predict performance degradation; analyzing degradation curve characteristics, and executing mode clustering to obtain a risk matrix; and optimizing a maintenance decision scheme based on risk assessment. According to the invention, the hybrid deep learning model is utilized to capture the dependency relationship of time and space dimensions at the same time, and the prediction precision is significantly improved; based on degradation mode identification and risk level evaluation, accurate multi-scene maintenance decision is realized, environmental risk is reduced, and maintenance cost is optimized.

The invention relates to the technical field of cross-border payment optimization, and discloses a cross-border payment fund routing optimization method and system, and the method comprises the steps: receiving a cross-border payment transaction request, analyzing the transaction parameters of the cross-border payment transaction request, and querying and obtaining an initial available payment channel set and corresponding channel information according to the transaction parameters; according to the transaction parameters, identifying payment routing capability applicable to the transaction and matching a special routing rule of the transaction, and according to the payment routing capability and the special routing rule relationship, performing three-level routing rule filtering processing on the available payment channel set to obtain a remaining available payment channel set; three layers of historical performance index data are obtained, a three-layer performance index model is constructed by performing weighted fusion on the three layers of performance index data, performance indexes of all dimensions are predicted, comprehensive scores of all channels are calculated in combination with customer levels, and the optimal payment channel is selected for each cross-border transaction through the method. The transaction success rate is improved, the cost is reduced, and the user experience is optimized.

The invention provides a method and equipment for predicting the heat exchange performance of a buried pipe ground source heat pump thermal camouflage system. The method comprises the steps that S1, a multi-physics field coupling heat exchange mathematical model of a buried pipe heat exchange system is established; s2, based on the multi-physics field coupling heat exchange mathematical model, a control equation and boundary conditions are constructed; s3, on the basis of the control equation and the boundary conditions, a multi-physics field numerical model is constructed, and simulation and calibration are carried out; and S4, based on the calibrated numerical model, designing a simulation test and carrying out main control factor analysis to obtain a prediction effect of the heat exchange performance. By establishing a rock-soil body-fluid multi-physical field coupling model, the interaction mechanism of underground heat conduction, working medium flow and ground surface heat radiation is completely represented, and the hiding efficiency of the thermal camouflage system is remarkably improved.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

The invention provides a method for predicting the quality of a sparse small sample industrial process based on a stacked network model. The method comprises the following steps: collecting end point quality report data of an industrial production process; performing hierarchical processing on the acquired data according to the missing rate, and removing abnormal data in combination with a quartile method and production experience; generating a synthetic data expansion small sample data set by adopting a conditional generative adversarial network; obtaining a first-layer basic model based on an accumulated contribution rate screening method of an SHAP value; constructing a first layer of a stacked integrated learning model and adjusting hyper-parameters by using Bayesian optimization; constructing a Ridge meta learning device to integrate the output of the basic model and constructing a second-layer network; a six-fold cross validation training model is adopted; predicting performance through a multi-index quantitative model based on the test set; and verifying the prediction precision of the end-point phosphorus content and the temperature by using real converter production data. The method can realize high-precision prediction of the end point quality index of the complex industrial generation process, and is beneficial to ensuring the product quality and improving the production efficiency.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

The invention provides a reinforced concrete structure performance degradation intelligent prediction and durability limit state evaluation method. The method comprises the following steps: establishing a random variable probability model of environment and structure parameters; a convolutional neural network is fused to construct a chloride ion diffusion intelligent prediction model, and Monte Carlo sampling is adopted to analyze probability distribution of the initial corrosion time of the steel bar, so that prediction of the steel bar de-blunt time is realized; establishing a steel bar time-varying corrosion model to obtain the change condition of the steel bar corrosion loss rate along with time; further, an incremental static analysis method is adopted, and a multi-scale finite element model of the reinforced concrete structure under different corrosion rate conditions is established through random sampling; obtaining a critical load value in a limit state, and obtaining a failure probability curve under different corrosion degrees; finally, the bearing capacity failure time of the reinforced concrete structure is obtained by defining a bearing capacity reduction coefficient, and evaluation of the durability limit state of the reinforced concrete structure in the corrosion state is achieved.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

Discussed are systems, methods and computer readable media that use a segment routing system and compute a path for network traffic based on segment routing policies. Based on these computations, the system is able to determine a path, and if the path is unique. When the path is unique it is stored in a path database which includes the unique paths available to the segment routing system. Based on the path database and segment routing polices, the system will compute a best path from the available unique paths for the network traffic based on measured and predicted performance metrics of the unique paths.

A bridge scour formula optimized reconstruction method based on symbol regression. The method comprises: obtaining statistical data of bridge scour, and separately using the statistical data as a training set and a test set; determining an initial structure of a bridge scour formula; firstly, determining a candidate operator, then introducing a function group having significant physical importance and a statistical relationship in scour, and determining a candidate operation variable; by using a symbol regression method based on genetic programming, generating an optimization formula by using data of the training set; adjusting parameters and generating an alternative formula group; and, on the basis of the test set, performing formula effect evaluation and filtering out a formula form having an optimal predicted effect and the simplest formula structure. On the basis of existing standard formulas, by using a symbol regression method based on a genetic programming algorithm, a machine learning method having a nonlinear representation capability is fused, in order to more accurately mine rich features in data. Meanwhile, by combining prior knowledge of scour and a physical relationship, prediction performance and generalization of a scour calculation formula can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a multi-modal man-machine interaction interface and behavior prediction system, which comprises a multi-modal data acquisition module used for acquiring visual, auditory and tactile data of a user in real time; the data synchronization and preprocessing module is used for carrying out time synchronization on the collected multi-modal data and carrying out denoising and error correction on the data; the data fusion module is used for fusing different modal data and extracting core features of the data; the behavior prediction module is used for performing user behavior prediction based on the fused data; and the system optimization module is used for dynamically adjusting the interaction mode and function of the system based on the behavior prediction result so as to realize personalized user experience. By adopting a multi-modal data fusion technology, data from a visual sensor, an auditory sensor and a tactile sensor are efficiently fused, core features are extracted, and a more accurate user behavior prediction effect is achieved.

The invention relates to a fractured well output profile evaluation method using a carbon quantum dot tracer agent, which comprises the following steps: collecting and arranging historical data of a horizontal well output profile tested by the existing quantum dot tracer agent, including geological parameters, engineering parameters and output profile data corresponding to each well; preprocessing the data, establishing a fractured well output profile prediction model, inputting the preprocessed data, and training the fractured well output profile prediction model for later use; and comparing the output profile prediction data with the output profile fact data to evaluate the prediction effect of the current fractured well output profile prediction model, and adjusting the fractured well output profile prediction model to obtain the adjusted fractured well output profile prediction model. Compared with the prior art, the method has the advantages that the production profile of the newly developed horizontal well can be effectively predicted, and the exploitation efficiency and economic benefits of oil and gas exploitation can be improved.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction method based on Bayesian optimization XGboost-LSTM, and the method comprises the following specific steps: 1, inputting wind power data collected by a wind power plant and numerical weather prediction data of a corresponding time sequence; 2, data preprocessing, wherein missing value interpolation and data deduplication are carried out on input data; according to the method, an XGBoost feature optimization method is used for selecting key features influencing the wind power, the influence of irrelevant feature noise on the model prediction precision and accuracy is eliminated, then a Bayesian optimization algorithm is used for carrying out hyper-parameter tuning on an LSTM model, and finally an XGBoost-LSTM-BO model is constructed. XGboost feature optimization and Bayesian hyper-parameter optimization can obviously improve the prediction effect of the LSTM on future data and improve the model prediction precision, and compared with a traditional prediction model, the wind power data generalization ability of the model can be improved while the high prediction precision is kept, and higher prediction performance is achieved.

The invention discloses an underground target imaging method and imaging system based on variable scale deep learning, and belongs to the technical field of geophysical exploration, and the method comprises the following steps: S1, building a multi-scale magnetic survey data set; s2, constructing a UNet + +-based variable-scale deep learning network model, and performing feature fusion on pooled information and information subjected to split convolution by a dynamic information fusion layer so as to adapt to input data of any size and make up for information loss caused by pooling; s3, designing a multi-constraint loss function formed by mean square error and loss weighting; s4, migration learning is adopted, model parameters trained in a fixed size serve as initial weights, progressive training of multi-size data is achieved by freezing and fine tuning of decoder parameters, and finally the effectiveness of the whole network model is verified according to a test set prediction effect, so that the universality and flexibility of the network are improved when data of different sizes are processed, and the network performance is improved. And large-size data features in a specific area can be quickly adapted.

The invention provides an optimal process parameter prediction method, system and device and a storage medium, and the method comprises the steps: collecting semiconductor performance test data which comprises process parameters of a tested piece and performance data of the tested piece; training a machine learning model based on the semiconductor performance test data to obtain a trained process parameter prediction model, the process parameter prediction model being used for predicting performance requirements which can be met by various process parameter combinations, and selecting a process parameter combination which can meet specific performance requirements; setting a target performance requirement, and obtaining an optimal process parameter combination meeting the target performance requirement based on the process parameter prediction model; and verifying whether the optimal process parameter combination meets the target performance requirement or not. According to the method, a traditional trial and error experiment method or a simulation calculation method based on experience is abandoned, the optimal process parameter searching efficiency is improved, and the research and development period is remarkably shortened.

The invention relates to the technical field of batteries, in particular to an all-vanadium redox flow battery performance prediction method based on parameter compensation. The method comprises the following steps: firstly, according to structure parameters of an all-vanadium redox flow battery in an energy storage power station, constructing a multi-physical field coupling simulation model comprising electrochemical reaction, ion transmission, fluid dynamics and thermal management; operating the model according to the operating parameters within a preset operating time period, and obtaining galvanic pile performance index data; carrying out sensitivity analysis on the structural parameters by adopting an orthogonal test method, and obtaining a regression coefficient through multiple linear regression; sorting the regression coefficients, selecting a target regression coefficient, establishing a parameter mapping relation according to a performance index fluctuation value, and performing compensation when the fluctuation value change exceeds a threshold value; and finally, determining a weight factor based on an analytic hierarchy process and an entropy weight method, and screening key structure parameters. According to the method, the limitations of fixed parameters and single environment in a traditional method are overcome, and accurate prediction of the performance of the all-vanadium redox flow battery in different operating environments is realized.

The invention belongs to the technical field of edge computing in a distributed system and an internet of things environment, and discloses a software performance real-time monitoring system and method fused with edge computing, in particular to a technology for reflecting software performance by monitoring performance of edge nodes, which comprises the following steps: acquiring key data of each edge node, obtaining a preliminary performance evaluation vector of each edge node; dynamically selecting a reference node, and correcting the initial performance evaluation vector of each edge node to obtain a performance evaluation vector; grouping each edge node by using a community detection algorithm to obtain a comprehensive feature vector; using the comprehensive feature vector to train and obtain a performance fluctuation prediction model, and performing prediction based on the performance fluctuation prediction model to obtain a predicted performance value; and obtaining an actual performance monitoring result according to the predicted performance value, and judging to obtain an abnormal node according to the predicted performance value and the actual monitoring result.

The invention provides a power distribution network scheduling strategy generation optimization method and system, and the method comprises the steps: obtaining historical operation state data of all equipment in a power distribution network, and carrying out the preprocessing of the historical operation state data, and obtaining a target data set; establishing a deep learning network for predicting the performance of the power distribution network, inputting the target data set into the deep learning network, and predicting the performance of the power distribution network through the deep learning network; according to the predicted performance of the power distribution network and the current operation state of each device in the power distribution network, determining a scheduling strategy of the power devices in the power distribution network by adopting a genetic algorithm; establishing a simulation model of the scheduling strategy, wherein the simulation model is a mathematical model for describing the actual structure of the power distribution network; and inputting the scheduling strategy into a simulation model for a simulation experiment, and correcting the scheduling strategy according to a simulation result. Based on the method, the invention further provides a generation optimization system of the power distribution network dispatching strategy. The scheduling strategy can be continuously optimized and improved, and the overall performance of the system is improved.

The invention discloses a turboshaft engine dynamic process performance parameter prediction method considering thermal inertia, and belongs to the field of aero-engine fault diagnosis. According to the method, the influence of thermal inertia on gas path parameters in the dynamic process of the turboshaft engine is analyzed, a thermal inertia coefficient is designed and introduced, a hot end component heat exchange model is established, a component characteristic diagram is corrected in combination with real test run data, and performance parameters in the dynamic process are predicted in real time through a neural network prediction algorithm. According to the method, the hysteresis phenomenon of gas temperature change in the dynamic process can be effectively described, the accuracy of dynamic modeling of the hot end component is improved, the capability of predicting dynamic thermodynamic parameters of the engine in real time is improved, and a scientific basis is provided for formulating a prediction health management strategy of the turboshaft engine.

The invention discloses a task scheduling method and device, a storage medium and electronic equipment, and relates to the field of cloud computing. The method comprises the steps that a task model is constructed according to a target training task, and the task model is used for representing attributes and resource requirements of the target training task; on the basis of the task model, predicting performance results when the target training task is executed on the N computational nodes to obtain N performance results, N being an integer greater than 1, and the performance results comprising execution efficiency and resource utilization rate of the target training task on the computational nodes; filtering the N computing nodes according to the N performance results and the network environment information to obtain S first computing nodes, and determining a target node from the S first computing nodes; and executing the target training task through the target node. The technical problem of low deep learning training task scheduling efficiency in the prior art is solved.

The embodiment of the invention provides a traffic flow prediction method and device, and the method comprises the steps: carrying out the importance feature selection of traffic flow features in different time periods through employing a random forest, reserving key discrimination information through employing dimension reduction, taking the features with higher importance as output vectors, inputting the output vectors into a transformer network, and carrying out the prediction of the traffic flow. And the transform network is used as a traffic flow prediction model to carry out feature learning and prediction. According to the method, the data stability and representativeness are enhanced to a certain extent by using the feature selection of the random forest, and meanwhile, auxiliary information of different types and different granularities is ingeniously and effectively injected into each processing step of the model through feature fusion, so that the Transformer can comprehensively utilize all information to carry out more accurate traffic flow prediction. By combining the generalization ability of the transform network, the model has good adaptability in different time periods and different traffic scenes, can effectively cope with the uncertainty of traffic flow changes, and has a good prediction effect on new data and complex traffic conditions.

The invention relates to the technical field of gas turbines, and discloses a geometric parameter optimization method, system and equipment for turbine blades and a medium. The method comprises the following steps: modeling the turbine blade according to geometric parameters, working condition parameters and material parameters of the turbine blade to obtain a plurality of first input samples with first fidelity and a plurality of second input samples with second fidelity; obtaining performance parameters of the turbine blade corresponding to each first input sample and each second input sample as a first response sample and a second response sample; and training the gas turbine performance prediction network, and predicting the performance parameters of the target turbine blade through the trained gas turbine performance prediction network according to the geometric parameters, the working condition parameters and the material parameters of the target turbine blade. And optimizing the geometric parameters of the target turbine blade by taking the maximum reliability and robustness of the target turbine blade indicated by the predicted performance parameters as a target.

The invention provides a traffic control system based on multi-modal data fusion, and belongs to the technical field of traffic control. Comprising a multi-source data acquisition and self-adaptive preprocessing module, a modal difference identification module, a modal difference modeling analysis module and a strategy fusion module, the multi-source data acquisition and self-adaptive preprocessing module is used for constructing a multi-modal original data set, the modal difference identification module is used for constructing a difference index sequence, and the modal difference modeling analysis module is used for analyzing the multi-modal original data set. The modal difference modeling analysis module is used for predicting a position difference coefficient, a speed difference coefficient and a behavior state difference coefficient of the k-type traffic target, and the strategy fusion module is used for constructing a strategy fusion coefficient. According to the system, the position, speed and behavior state differences of the traffic participation targets are analyzed, and finally the prediction effect of the traffic condition is improved through strategy fusion.

In one embodiment, a current path of a mobile device is determined based on radio signals between the mobile device and a base station, which indicates a sequence of positions of the mobile device over a current time window. A future path of the mobile device is then predicted based on the current path, which indicates a sequence of predicted future positions of the mobile device over a future time window. A link performance prediction (LPP) is then generated for the mobile device based on the future path of the mobile device and a base station coverage map. The base station coverage map indicates a radio signal quality across a base station coverage area, which is represented as a three-dimensional (3D) coordinate space. Moreover, the LPP indicates a predicted performance of a radio link between the mobile device and the base station during the future time window.

The invention provides a bearing fault variable working condition diagnosis method based on a multi-scale convolutional network and MAML, and relates to the technical field of equipment fault diagnosis. The method comprises the following steps: firstly, introducing fast Fourier transform to pre-process an original time domain vibration signal; secondly, a fault diagnosis model based on a multi-scale convolutional network and MAML is applied; and then, an internal and external circulation updating method based on model-independent element learning is adopted, so that the model can quickly adapt to a new task, and a relatively good prediction effect can be achieved only through a small amount of fine adjustment. According to the method, multi-scale feature extraction and meta-learning are creatively combined, the generalization ability of the model under variable working conditions is remarkably improved, the problem that a traditional fault diagnosis method depends on a single working condition and a large sample size is effectively solved, and the method is particularly suitable for small-sample and multi-working-condition bearing fault diagnosis scenes on an industrial site.

The invention discloses a method and system for predicting the grouting effect of foaming type grout based on PSO-BP, and belongs to the field of grouting repair engineering.The method comprises the following steps that bedrock fracture characteristics are collected to obtain fracture opening, fracture roughness and hydrodynamic pressure, meanwhile, grouting influence factor data are obtained, the grouting influence factor data comprise the grouting amount, the grout component proportion and the grouting pressure, and the grouting influence factor data comprise the grouting amount, the grout component proportion and the grouting pressure; the method comprises the following steps: dividing fracture opening, fracture roughness, hydrodynamic pressure and grouting influence factor data into a training set and a test set by using a PSO-BP neural network model, generating a grouting diffusion distance prediction model and a grouting water plugging rate prediction model, predicting the test set through the generated models, obtaining predicted values of the grouting diffusion distance and the grouting water plugging rate, and predicting the grouting diffusion distance and the grouting water plugging rate according to the predicted values of the grouting diffusion distance and the grouting water plugging rate. And according to the two groups of predicted values and measured values, calculating to obtain a mean square error, a mean absolute error and a decision coefficient corresponding to each group to evaluate the prediction effect of the slurry grouting effect. According to the method, accurate prediction of the grouting repair effect of the foaming slurry is realized through the PSO-BP neural network.

The invention discloses a method and a device for predicting performance degradation of a PEMFC (Proton Exchange Membrane Fuel Cell), and the method comprises the steps: screening operation data of the PEMFC through employing a ReliefF feature selection algorithm, and screening out key features; using SVMD to decompose the data after feature extraction, and decomposing the data into a plurality of signal sequences with small volatility; a multi-scale Boltzmann-Shannon interaction entropy method is adopted to carry out complexity measurement on the decomposed signals on different time scales, and interdependence and information flow characteristics among data are analyzed; constructing a GCN-MG-TSD series model, and processing time series data by using a graph convolutional network to extract time dependence and space structure information; processing data of different time scales in combination with the model MG-TSD; and optimizing the parameters of the MG-TSD model by using an improved aurora optimization algorithm IPLO. According to the method, the performance degradation of the PEMFC can be effectively predicted, and a new technical means is provided for health management and maintenance of the battery.

The invention relates to the field of intelligent transportation, and discloses an intelligent monitoring and management system and method for an expressway and electronic equipment, and the system collects global spatial-temporal data of the expressway to construct digital twins synchronized with the physical world; in the twinborn body, performing prediction and deduction based on a space-time causal map to identify potential risks; responding to the risk, generating an optimal intervention strategy through anti-fact deduction and executing the optimal intervention strategy, and recording a predicted intervention effect of the optimal intervention strategy; and after intervention, comparing a real traffic state with a prediction effect, calculating an anti-fact error, and carrying out dynamic self-correction on the space-time causal map according to the anti-fact error. According to the invention, links of perception, prediction, decision making, execution and feedback are fused into a self-adaptive control loop, and a self-correction mechanism based on an anti-fact error is introduced, so that the system can continuously learn and self-evolve from interaction with the physical world, and the problems of model solidification and poor adaptability of a traditional traffic management system are solved.

The invention discloses a calculation method of a fan blade tip gap accelerated flow analytical model, and belongs to the technical field of fan speed field calculation. By analyzing the aerodynamic characteristics of a fan wake flow gap acceleration area, the correlation between the power increase of the side-by-side fan and the fan wake flow gap acceleration area is disclosed, and an analytical model is constructed according to the self-similarity of the gap acceleration area and an eddy current equation. According to the model, local accelerated flow caused by rotation of adjacent rotor blade tips under the same plane is considered, by adopting the method, the used model has high precision in the aspect of predicting the flow field characteristics of the blade tip wake flow gap acceleration area, the prediction effect is close to that of a high-fidelity numerical simulation method, but the calculation cost is far lower than that of traditional CFD simulation; the method is well applicable to processing and simulating wind power plant structures which are closely arranged along the y-z same plane, such as a double-head floating fan and a fan wall, and shows good engineering application value.