1302 results about "Predicting performance" patented technology

An example operation may include one or more of storing a current portfolio of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest of the user based on execution of a generative artificial intelligence (GenAI) model based on the received contextual data of the user and the current portfolio of the user stored in memory, predicting a performance of the current portfolio with the identified asset of interest included therein at a future point in time, and displaying the predicted performance of the current portfolio with the identified asset of interest included therein on a user interface.

The invention provides a switch cabinet solid insulating material performance detection method and system based on temperature detection, and relates to the technical field of temperature detection, and the method comprises the steps: constructing a grid detection matrix on the surface of a solid insulating material, collecting real-time temperature data through a passive radio frequency identification temperature sensor, and building an initial state model; adopting a deep learning algorithm to calculate a temperature gradient value and thermal stress distribution, and constructing a performance degradation index; and the recurrent neural network is combined to predict a performance evolution trajectory, and dynamic early warning and diagnosis are realized. The performance degradation state of the solid insulating material can be accurately evaluated, the detection precision and the prediction accuracy are improved, and intelligent monitoring and early warning are achieved.

The present invention belongs to the technical field of engineering monitoring, and relates to a method for monitoring construction of water conservancy projects, including: collecting monitoring data from a water conservancy project construction site; inputting the monitoring data into a structural stability prediction model for analysis to generate a safety factor assessment value; inputting the monitoring data into a seepage risk prediction model for analysis to generate a seepage risk probability; combining the safety factor assessment value and the seepage risk probability, presetting a threshold, and triggering a graded warning signal; in response to the graded warning signal, calling a multi-objective optimization algorithm to dynamically adjust the current construction parameters and generate an optimized construction plan; loading the optimized construction plan into a digital twin system for virtual construction simulation to obtain predicted performance parameters; comparing the deviation value of the predicted performance parameters with the measured monitoring data, and iteratively updating the parameters of the prediction model when the deviation exceeds the tolerance range. The present invention solves the problem that traditional methods are difficult to discover potential problems and make optimization adjustments before construction.

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.

In variants, the method can include determining training data, determining a router, and using the router. In variants, using the router can include receiving a runtime prompt, predicting performance scores for the runtime prompt for each of a set of candidate models, optionally predicting operational metrics for responding to the runtime prompt for each of the set of candidate models, selecting a candidate model based on the predicted performance scores and optionally the predicted operational metrics, and optionally determining a response based on the runtime prompt.

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 discloses an old people chronic disease risk prediction method and system based on a time sequence knowledge graph, and belongs to the technical field of medical health information, and the method comprises the following steps: S1, obtaining a health data set of an old people group; s2, on the basis of an ESOA-XGBoost algorithm, performing feature selection and sorting on each piece of health data in the health data set so as to determine correlation among variables, and generating a health data feature set; s3, constructing and training a time sequence knowledge graph prediction model including an STKGR-PR model and a T i PNN model, and predicting a relationship among the target entity, other entities and time sequence information through the time sequence knowledge graph prediction model; s4, predicting the old chronic disease risk of the old people group according to the trained time sequence knowledge graph prediction model; through the method, an elderly chronic disease risk prediction result with a good prediction effect and high accuracy can be obtained.

The invention discloses an electric power system time sequence prediction model training method, system and device based on large language model weight fine tuning and a storage medium. The method comprises the steps of obtaining time sequence data of different fields and performing data cleaning to obtain a data set; performing weight freezing on a feedforward neural network and multi-head attention of the large language model, and performing first-stage training on the large language model by using the data set; reconstructing an input layer of the large language model to enable the reconstructed input layer to generate a corresponding natural language instruction according to the time sequence data; and freezing weight parameters of the large language model, and performing second-stage training on the large language model by using source load data of the power system to adjust parameters of the reconstructed input layer so as to obtain a time sequence prediction pre-training model. According to the method, the prediction effect can be improved while the weight of the large language model backbone network adapts to the specific task of time sequence prediction.

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.

According to the combustion optimization method and device based on machine learning and the electronic equipment, the real-time operation data of the to-be-optimized system is input into the trained multi-target prediction model for data prediction processing, and the multi-target prediction result is obtained; the trained multi-target prediction model is a model which is established based on a graph neural network and is used for predicting performance indexes of the to-be-optimized system; according to a multi-target preset result and the real-time operation data, through the trained combustion optimization model, oxygen amount and air door opening adjustment processing is carried out on the to-be-optimized system, and the to-be-optimized system after combustion optimization is obtained; the trained combustion optimization model is a model which is constructed based on a distributed reinforcement learning algorithm and is used for carrying out combustion optimization on the to-be-optimized system by dynamically adjusting the oxygen content and the air door opening degree of the to-be-optimized system. Compared with the prior art, the embodiment of the invention can respond to the requirement of state change in real time in the combustion optimization process, improve the optimization effect and reduce the operation complexity.

The invention belongs to the field of wind power prediction, and provides an ultra-short-term wind power prediction method based on secondary decomposition and TCN-ECA-BiGRU by considering the strong volatility and randomness of wind power. The method comprises the following steps: firstly, constructing a secondary mode decomposition framework based on improved complete set empirical mode decomposition, sample entropy, K-means clustering and variational mode decomposition, adaptively selecting components with relatively high complexity for secondary decomposition, and weakening volatility and randomness of an original sequence; and then, on the basis of the time convolutional network, an efficient channel attention module (ECA) is introduced to mine a coupling relationship among components, weights are given to the components in a self-adaptive manner to highlight important information, and a long-term time sequence dependency relationship of the components is further captured by using a bidirectional gating cycle unit, so that the prediction effect of the ultra-short-term wind power is effectively improved.

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 production process optimization method of a surfactant, and relates to the technical field of surfactants, and the production process optimization method comprises the following steps: collecting historical production data of the surfactant; constructing a production process prediction model; performing performance prediction on the initial process parameters, and comparing the initial process parameters with a target performance value; when the predicted performance difference value is larger than a preset performance difference threshold value, technological parameter optimization is executed; producing a surfactant sample by adopting the optimized process parameters, obtaining an actual performance value of the sample, and comparing the actual performance value with a target performance value; when the sample performance difference value is smaller than or equal to a preset performance difference threshold value, the optimized process parameters serve as execution process parameters for machining. By means of the method and device, the technical problem that in the prior art, due to the fact that the influence of technological parameter changes on the product performance is large, the production efficiency of the surfactant is low can be solved, the prediction model is constructed through historical data, the technological parameters are optimized, and the production efficiency and the product quality of the surfactant are improved.

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.

The invention belongs to the technical field of water quality prediction, and particularly relates to a river basin water quality prediction and early warning method and device based on a multi-model processing technology and a medium, and the method comprises the steps: firstly obtaining and preprocessing historical water environment monitoring data and real-time water environment monitoring data of a river basin; then constructing a water quality prediction deep learning model, and outputting a river basin water quality prediction result after training verification; constructing a water quality index prediction mechanism model, and after training verification, outputting water quality index prediction results of the river basin according to water quality prediction results; and finally, performing threshold judgment based on the river basin water quality prediction result and the dynamic change result of each water quality index of the river basin, and calling an expert emergency early warning plan according to the judgment result. According to the invention, the problem of poor prediction effect in the prior art can be solved.

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.

The invention relates to the field of computer application, in particular to a semantic relation prediction method based on large language model multi-section prompt optimization. The method comprises the steps of obtaining a data set, defining an initial prompt as a current prompt of a first round of iteration, starting iteration, and inputting the current prompt and a training data set into a large language model for prediction to obtain an error case; obtaining a section set according to the error case; obtaining a candidate prompt set by using a second error analysis method; taking the candidate prompt set as the current prompt to participate in the next round of iteration; respectively inputting prompts in each candidate prompt set and statements in the test data set into a large language model for prediction, obtaining prediction results, evaluating the prediction results obtained by each prompt, and selecting k prompts; and the selected prompt is combined with the to-be-predicted statement to be input into the large language model for prediction. According to the method, prompts can be automatically constructed from multiple sections to utilize the large language model, so that the labor cost is reduced, and meanwhile, the prediction effect is improved.

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 provides a reliability test evaluation method and system for an integrated circuit chip, and relates to the technical field of integrated circuit chip test.The method comprises the steps that a test target and a test index are defined; constructing a multi-level test environment; performing a dynamic stress test in the constructed multi-level test environment; based on the historical test data and the monitoring data, establishing a prediction model by using a machine learning algorithm to predict performance changes and potential faults of the integrated circuit chip under different conditions; performing intelligent diagnosis on faults occurring in the test process by using the established prediction model, and positioning a fault source; and continuously monitoring the test process through the online remote monitoring platform, and dynamically adjusting the test parameters and the test strategy according to the monitoring data and the prediction result of the prediction model. The reliability of the integrated circuit chip is comprehensively and accurately evaluated, and the test efficiency and the fault prevention capability are improved through a multi-level test environment, machine learning prediction and intelligent diagnosis.

The invention provides a construction period highway carbon emission estimation method and system and electronic equipment, and relates to the technical field of carbon emission, and the method comprises the steps: collecting special data based on construction project information, setting a corresponding carbon emission factor based on the obtained data, carrying out the LCA evaluation modeling based on the processed data, and synchronously carrying out the input and output analysis modeling, thereby achieving the estimation of the carbon emission of a highway during a construction period. Carrying out fusion modeling on the two models, carrying out explanation and evaluation based on the fused model, combining the collected data with corresponding carbon emission factors, calculating the carbon emission of each link, considering key parameters in the model, carrying out sensitivity analysis, evaluating the influence degree of the key parameters on an evaluation result, optimizing and improving the model, and carrying out analysis on the carbon emission of each link based on the improved model. And performing prediction effect evaluation, verifying the actual prediction capability, performing fine adjustment and correction on the model in time, and performing carbon emission prediction on newly collected highway special sample data based on the fine-adjusted and corrected fusion model to obtain a final prediction result.

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.

A method for predicting rotating turbulent flows using a BSTMQ turbulence model is provided, which can improve the effectiveness of predicting the internal flow field of fluid machinery. The method includes the steps of performing solid modeling of a rotating turbulent flow prediction target by 3D modeling software to obtain a simulation model, meshing the simulation model, improving the eddy viscosity damping function of an existing STRUCT turbulence model to construct a BSTMQ turbulence model, and performing a numerical simulation calculation of the rotating turbulence using the BSTMQ turbulence model by a calculation program based on OpenFoam to obtain a prediction result of the rotating turbulence of the prediction target. The BSTMQ model can better simulate the rotating turbulent flow, improve the prediction effect of the internal flow field of fluid machinery, and provide a new idea for efficiently and accurately calculating the rotating turbulent flow in fluid machinery.

According to the Bayesian optimization CNN-LSTM-based piezoelectric actuator temperature rise prediction method provided by the invention, the prediction precision is improved by combining a convolutional neural network and a long-short-term memory network and through a prediction result of a Bayesian optimization model; according to the method, the convolutional neural network part can effectively extract local features of piezoelectric actuator data, the long-term and short-term memory network part is good at capturing the long-term dependency relationship of time sequence data, the design of the LSTM network structure enhances the anti-noise capability of the model, the parameters of the CNN-LSTM model are automatically adjusted and optimized through Bayesian optimization, and the accuracy of the model is improved. In the Bayesian optimization, an objective function is approached by constructing an agent model, and a search process is guided by using an acquisition function, so that the evaluation frequency of the objective function is reduced, the calculation efficiency is improved, and the prediction effect of the temperature rise phenomenon of the piezoelectric actuator is further improved. The method comprises the steps of temperature rise data collection, training data construction, CNN data input, LSTM data input and Bayesian optimization, and finally a final prediction result is obtained.