121results about How to "Reliable predictions" patented technology

The invention discloses a highway pavement temperature forecasting method based on physical constraints, which comprises the following steps of: collecting and preprocessing meteorological data of all stations in a region, historical observation data of pavement temperature and geographic data, and adding time information to construct time features; building a U-net neural network model, carrying out space field data correction on the multi-station multi-meteorological element forecast data, and outputting corrected meteorological data of each station; constructing a bidirectional LSTM network on the basis of the U-net model, processing time sequence data, and evaluating and optimizing model performance by using a loss function; physical constraints including the time relation of temperature change, seasonal and day and night change characteristics, material thermal characteristics, a thermal balance equation and the like are added into the model, and a model result is optimized; and evaluating the generalization ability and forecasting precision of the model by using a test set, and finely adjusting model parameters. According to the method, the precision and reliability of road surface temperature prediction are remarkably improved, and a reliable basis is provided for traffic management and safety early warning.

The invention relates to the technical field of marine ecology, in particular to a marine ecology multi-agent construction method and an interaction system thereof. The construction method comprises the following steps: a marine ecology large model construction step: constructing a fine adjustment data set, and training based on the fine adjustment data set to obtain a marine ecology large model; a parallel application module construction step: constructing a domain knowledge base and a marine ecological multi-intelligent body, constructing a multi-modal large model, and deploying the domain knowledge base, the marine ecological multi-intelligent body, the multi-modal large model and the marine ecological large model of the parallel application module in a GPU server, and the marine ecology large model is used for analyzing the user request and distributing the user request to the parallel application module, triggering the parallel application module to generate a response result, feeding the response result back to the marine ecology large model for semantic integration, and outputting the result. According to the invention, the intelligent level in the field of marine ecology is improved, the prediction capability of the model is enhanced, and the prediction result is more accurate and reliable.

The invention discloses a measuring system and a calculating method for the friction coefficient of a plunger pump flow distribution pair, and relates to the technical field of fluid machinery and intelligent measurement and control. The system comprises the following components: a multi-dimensional data acquisition module, a data processing and preprocessing module, an intelligent calculation model construction module, a friction coefficient prediction and optimization module and a visualization and report generation module. According to the invention, through the multi-dimensional data acquisition module and the intelligent calculation model construction module, accurate measurement and prediction of the friction coefficient of the plunger pump flow distribution pair are realized, the nonlinear relationship among the parameters is fully considered during design of the intelligent calculation model, and training is carried out through a large amount of experimental data, so that the accuracy and the accuracy of the friction coefficient of the plunger pump flow distribution pair are improved. According to the method, the complex influence mechanism of each parameter on the friction coefficient can be automatically learned and identified, the prediction precision of the friction coefficient is improved, and the generalization ability of the model is remarkably enhanced through wide-coverage operation conditions and design variants, so that the prediction result is more reliable and stable.

The invention provides a tourism management market demand prediction method and system based on deep learning. The method comprises the steps of obtaining tourism related information corresponding to a preset tourism management platform; the tourism management platform is used for managing a plurality of preset scenic spots; performing sentiment analysis on the tourism related information based on a natural language processing technology to obtain positive sentiment information, negative sentiment information and tourist interested information; obtaining travel information corresponding to the travel related information; the travel information comprises multiple pieces of travel preference information and a travel group type corresponding to each piece of travel preference information; acquiring scenic spot access information of each preset scenic spot corresponding to the tourism related information; generating market demand information in a preset time period according to the positive emotion information, the negative emotion information, the tourist interested information, the travel preference information, the travel group type and the scenic spot access information; the market demand information at least comprises tourist number information, popular time information and tourist preference information of the preset scenic spot.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a manned submersible observation window stress prediction method based on machine learning, which comprises the following steps: acquiring strain and pressure data, and establishing a finite element model of an observation window; constructing a training set based on the strain and pressure data, and correspondingly constructing a machine learning hybrid model; training the machine learning hybrid model through the training set, and obtaining a stress prediction model; and predicting stress data of the observation window of the manned submersible in different environments through the stress prediction model. The stress prediction method based on data driving does not depend on a complex physical modeling process, stress distribution under complex working conditions can be predicted more quickly and accurately, calculation time and resource consumption can be greatly reduced, and a robust stress prediction result can be provided in a complex load environment; the calculation time is shortened, the prediction precision is improved, and an efficient and reliable prediction result is provided for structural safety assessment.

The invention relates to the technical field of concrete performance prediction, in particular to a method for predicting residual compressive strength of ultra-high performance concrete after high temperature based on machine learning, which comprises the following steps: establishing a data set, establishing an optimal prediction model, dividing the data set and substituting the data set into the optimal prediction model, and performing parameter optimization on the optimal prediction model. And predicting the residual compressive strength. By comparing a plurality of different machine learning algorithms, the optimal machine learning algorithm suitable for predicting the residual compressive strength of the ultra-high performance concrete is found, the optimal prediction model of the residual compressive strength of the ultra-high performance concrete is obtained, and the residual compressive strength of the UHPC under the high-temperature condition can be predicted more accurately.

The invention relates to the technical field of environmental protection detection, and discloses an industrial park atmospheric pollution prediction method comprising the following steps: S1, collecting meteorological data, emission data, historical air quality data and geographic information data in an industrial park; s2, preprocessing the collected data, including data cleaning, normalization and feature extraction; s3, based on a time sequence analysis method and a machine learning algorithm, constructing a multi-level pollution source prediction model, and identifying and predicting emission characteristics of different pollution sources; s4, through a multi-scale modeling method, modeling is carried out on the emission behavior of a single pollution source on the microscopic scale; and S5, coupling and integration of the models are implemented, and cross-scale pollution prediction is realized. By combining a micro-scale long-short-term memory network model and a macro-scale Gaussian diffusion model, complex emission behaviors of a single pollution source and diffusion paths of pollutants in a regional range can be captured at the same time.

The invention relates to the technical field of immersed injection spray guns, and discloses a multi-channel rotational flow spray gun optimization method and system based on graph convolution, and the method comprises the steps: combining computational fluid dynamics and a graph convolution neural network technology, and rapidly predicting a velocity field and a pressure field of a spray gun through rotational flow spray gun grid coordinates and rotational flow blade boundary information; the design period is obviously shortened, the test cost is reduced, and the research and development efficiency is improved; the graph convolutional neural network model subjected to repeated training and verification has high generalization ability and high accuracy, and it is guaranteed that the spray gun can still provide reliable prediction results under different working conditions and variable industrial conditions. The system comprises a data acquisition and preprocessing module, a graph convolutional neural network model module and a rotational flow spray gun structure optimization module. The flow characteristics and local physical characteristics of the spray gun are further analyzed, efficient and stable performance can be kept in actual working conditions, the service life is prolonged, and the metallurgical reaction efficiency is improved.

The invention discloses a city-level decommissioned power battery quantity prediction method and system based on a multi-dimensional feature ML. The method comprises the following steps: obtaining a new energy automobile risk quantity and N city feature values in a preset time period to construct a data set; analyzing the N kinds of city characteristic values, and determining city key characteristic values and weight values of the city key characteristic values which influence the risk amount of the new energy automobile; screening a data set according to the urban key characteristic values and the weight values thereof, and training a plurality of optimized machine learning models by using the screened data set; evaluating the prediction performance of each machine learning model, and selecting an optimal model for predicting the insurance amount of the passenger vehicle and the commercial vehicle; inputting the future city key characteristic value into the optimal model to predict the risk amount of the new energy automobile; and based on the predicted risk amount of the new energy vehicle, predicting the urban-level decommissioned power battery amount by utilizing the battery type proportion and the battery characteristics of the new energy vehicle and combining Weibull life distribution. According to the method, the prediction accuracy and practicability of the urban-level retired power battery quantity can be realized.

The invention provides a hyperspectrum-based tea tree canopy leaf nitrogen estimation method, which comprises the following steps of: firstly, smoothing original spectral data of a tea tree canopy by using a Savitzky-Golay method so as to reduce random noise and improve a signal-to-noise ratio; then, the original spectrum is converted into 600 wave bands at the interval of 1 nm through spline interpolation, so that the continuity and convenience of calculation are ensured; the method comprises the following steps of: firstly, carrying out first-order derivative processing on a spectrum, then carrying out second-order derivative processing on the processed spectrum, calculating a normalized difference spectrum index (NDSI) by using a derivative spectrum, calculating a Pearson's correlation coefficient (PCC) between the NDSI and a measured nitrogen element, carrying out principal component analysis (PCA) on the highest 20 indexes of the correlation coefficient, and finally constructing a multiple linear regression model by using the principal components with the first 10 PCA scores as parameters. The method is used for predicting the nitrogen content (LNC) of tea tree canopy leaves. Prediction experiments of a plurality of tea gardens show that the model shows a good prediction effect which is close to the actual nitrogen content, and the generalization performance is good.

The invention discloses an intelligent multi-sensor fusion board separator fault prediction and diagnosis method and system. The method comprises the steps of collecting data of various sensors on a board separator; carrying out random smooth secondary sampling preprocessing on the sensor data; establishing a causal relationship model by using the causal depth set of the separation strategy evaluation under the space-time interference, and carrying out the separation strategy learning of the sensor data; based on a result of the separation strategy learning, extracting features of the sensor data and generating causal sequence data; and based on the extracted features and the causal sequence data, applying a hybrid model to monitor the state of the board separator in real time, predicting potential faults, and generating maintenance suggestions.

The invention discloses a pedestrian crossing tolerance time prediction method under a signal control condition. The method comprises the following steps: calculating a risk score of each pedestrian sample in a pedestrian crossing data set through a random survival forest model; carrying out tolerance grouping on the pedestrian samples by using a K-means clustering algorithm in combination with the risk score, and generating a data set of a plurality of tolerance; dividing the data set of each tolerance into a training set and a test set; a Stacking integration model is constructed; performing hyper-parameter tuning on each primary model through a Bayesian hyper-parameter optimization algorithm; inputting the training set into each primary model for training according to five-fold cross validation, and outputting a prediction result of each primary model; integrating the prediction result of each primary model and the true value of the tolerance time into a new training set and a new test set, inputting the new training set and the new test set into the meta-model, and outputting a corresponding tolerance time prediction value; and realizing tolerance time prediction of a single pedestrian through the trained Stacking integrated model. According to the invention, the accuracy of pedestrian crossing tolerance time prediction can be improved.

The invention discloses a distributed roof photovoltaic processing method and device based on Bayesian optimization, electronic equipment and a storage medium. Advanced data acquisition and preprocessing technologies, accurate grid division and feature extraction, comprehensive multi-dimensional evaluation index calculation and efficient Bayesian optimization modeling are combined. Through a Gaussian process model and a Bayesian optimization algorithm, accurate capacity prediction and resource allocation can be realized in a complex multi-target optimization space. The method has the advantages that multiple factors such as power generation potential, economic benefit and power grid load are comprehensively considered, and the method has dynamic adjustment capability to adapt to changes in actual operation. The invention discloses a distributed roof photovoltaic processing method and device based on Bayesian optimization, electronic equipment and a storage medium, and aims at remarkably improving the overall benefit and resource utilization efficiency of a photovoltaic system and promoting wider application of renewable energy sources.

The invention relates to the technical field of logistics management, in particular to a cross-border logistics demand prediction method and system, which can provide accurate and reliable prediction results and decision support for cross-border logistics enterprises. The method comprises the following steps: acquiring a destination cargo demand influence parameter set and a delivery place cargo supply influence parameter set; respectively performing feature analysis on the destination cargo demand influence parameter set and the shipping place cargo supply influence parameter set to obtain a destination cargo demand feature index and a shipping place cargo supply feature index; comparing the destination cargo demand characteristic index with the shipping place cargo supply characteristic index, extracting a minimum characteristic index, and marking the minimum characteristic index as a logistics demand characteristic index; obtaining the real-time cross-border transportation cost and the historical same-period cargo transportation characteristic quantity corresponding to the logistics demand characteristic index; and inputting the logistics demand characteristic index and the real-time cross-border transportation cost into a pre-constructed cross-border logistics demand influence analysis model as input.

The invention discloses a method for predicting the concentration of dissolved gas in transformer oil based on an EVO-VMD-LSSVM. An energy valley optimization algorithm EVO, variable mode decomposition VMD and a least square support vector machine LSSVM are combined and used. The method comprises the following steps: decomposing an original dissolved gas concentration signal into a plurality of intrinsic mode functions IMFs with different frequency characteristics through a VMD method, and extracting multi-scale characteristics of the signal; a plurality of factors such as transformer load, operating oil temperature, environment temperature and operating time are introduced into the model, an LSSVM prediction model is established for IMF in combination with characteristic parameter correlation, LSSVM model parameters are optimized by using an EVO algorithm, and prediction precision is improved; reconstructing the prediction result of each IMF to obtain a final dissolved gas concentration prediction value; the prediction performance is obviously better than that of a traditional method, and evaluation indexes such as mean square error (MSE), mean absolute error (MAE) and decision coefficient (R) show that the method has high prediction precision and robustness.

The invention relates to the field of building construction, in particular to a deep foundation pit construction intelligent monitoring and early warning method based on multi-source characteristic parameter fusion, and constructs a comprehensive foundation pit intelligent monitoring and early warning index system by fusing multi-source characteristic parameters including deformation characteristics, mechanical characteristics and technical characteristics. By means of the combination of a one-dimensional convolutional neural network (1DCNN) and a support vector machine (SVM), complex feature information in foundation pit deformation monitoring data can be deeply excavated and extracted, compared with a traditional prediction method depending on a single feature or a simple model, the foundation pit deformation prediction precision is remarkably improved, and the foundation pit deformation prediction method has the advantages of being high in practicability and the like. And comprehensive monitoring and accurate early warning of the construction process are ensured.

The invention discloses a groundwater environment quality prediction method and system, and relates to the technical field of prediction data processing, and the method comprises the steps: obtaining historical observation data and real-time observation data of a to-be-predicted solute; obtaining a boundary condition, dividing the prediction region into a plurality of sub-regions based on the boundary condition, and generating a plurality of three-dimensional grid points; acquiring geological data corresponding to the three-dimensional grid point, acquiring an influence factor between the solute to be predicted and the geological data based on the correlation model, judging whether the influence factor is within a preset threshold range, and if not, taking the three-dimensional grid point as a grid point to be processed; acquiring a first boundary value and a second boundary value; performing calculation processing on the first boundary value and the second boundary value based on a dispersion calculation model to obtain longitudinal dispersion and transverse dispersion; and obtaining prediction moment data, obtaining a grid prediction result, obtaining a sub-region prediction result, and obtaining a target prediction result. The method has the advantages of being comprehensive, accurate, high in prediction efficiency and high in adaptability.

The invention belongs to the field of plant disease prevention and control, and discloses a plant leaf spot diffusion prediction method and system based on a Markov model and a long-short memory time network, and the method comprises the steps: continuously collecting a grape leaf spot diffusion process image series, extracting the related leaf spot features of each image, and carrying out the prediction of the leaf spot diffusion; constructing features extracted from the series of images into a time sequence; training an LSTM (Long Short Term Memory) prediction model, so that the LSTM prediction model can predict a subsequent spot feature region; the method comprises the following steps of: converting a test picture into gray scale maps according to leaf surface features, regarding the change of pixels of each leaf surface gray scale map as a discrete Markov process, calculating a probability transfer matrix of the next step through leaf surface pixel features predicted by an LSTM network, calculating a next leaf surface gray scale map by adopting a discrete Markov model, and calculating a probability transfer matrix of the next step; and a final diffusion prediction map is obtained through image adjustment. The method predicts and simulates the diffusion process of leaf scabs to a certain extent, and the model has generalization and is easy to popularize to spot diffusion research of other plant disease species.

The invention relates to the technical field of tunnel construction intelligent supervision, in particular to a tunnel construction intelligent supervision system and method based on an intelligent safety helmet. The sensing unit is used for collecting data of positions and working environments of constructors; the communication unit is used for transmitting the collected data through a wireless communication protocol; the edge calculation unit performs edge calculation on the data transmitted by the communication unit for real-time analysis, analyzes positioning information through a centroid method, and introduces personnel density and material stacking weight to optimize calculation of centroid coordinates, so as to reduce the influence of signal change in positioning information calculation, the edge calculation unit comprises a trajectory prediction module, a contour construction module and a collision early warning module. According to the design, positioning information is analyzed through a centroid method, the positions of constructors can be tracked, and each worker can be rapidly positioned especially in a complex and changeable tunnel environment.

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

The invention relates to the technical field of electric power material distribution management, particularly discloses an electric power material distribution management system and method based on big data, and aims to solve the problems of dispersed material data, inaccurate demand prediction, low distribution efficiency, improper inventory management and the like in an existing system. According to the specific technical scheme, the method comprises the steps of integrating material names, material description information and supplier information, and establishing a unified electric power material database; carrying out preliminary classification and refined classification on the materials by using a clustering algorithm, and adding a label for each type of materials; historical demand data are analyzed, a demand prediction model is constructed, and the prediction precision is improved; dynamic optimal distribution path planning is realized by utilizing a geographic information system technology and a linear programming algorithm, and the distribution efficiency is improved; the stock is dynamically adjusted by calculating the safe stock and the reorder cycle, and stock overstock or shortage is avoided; the distribution plan is dynamically adjusted according to the inventory level and the demand frequency, the distribution process is monitored in real time, and on-time distribution is ensured.

The invention relates to a sewage plant odor intensity and odor type evaluation and prediction method, which comprises the following steps: sampling in a sewage plant, analyzing a gas sample, and performing olfactory discrimination analysis on the sample to obtain a GC-IMS spectrogram, odor intensity and odor type; processing the sample data, carrying out frame selection on peaks in the spectrogram, carrying out integration on the peak area of the selected signal peak, and exporting a csv file; the different csv files are arranged and combined into a dataset.csv file; performing data preprocessing on the file, and arranging data in the file obtained in the step 3 into an n * s matrix; selecting sample data as a training set and a test set, and establishing a random forest model; and applying the obtained data to a random forest model to obtain the odor intensity and the odor type. The method has the advantages of reasonable design and high reliability, and can more accurately predict the stink pollution characteristics of the specific point location and the surrounding environment of the sewage plant, thereby guiding the sewage plant to more effectively carry out stink pollution prevention and control.

The present invention provides a flood warning detection method and system based on a GCN graph convolutional neural network, the method comprising: S1, deploying multiple data acquisition devices in a target area; the data acquisition devices include online monitoring devices for real-time acquisition of original water flow data at the monitoring location; S2, preprocessing the original water flow data and processing outliers to obtain processed water flow data; S3, converting the processed water flow data into a vector form suitable for input into a neural network, and constructing a graph structure of the water flow data; S4, constructing a graph convolutional neural network architecture, training and optimizing the network; using an adaptive algorithm to dynamically adjust model parameters, and an output layer generates flood warning results; S5, training historical water flow data and real-time water flow data, and constructing a flood warning detection model; S6, using the flood warning detection model to perform flood warning prediction, and outputting the obtained real-time flood warning information to a user interface; the method can provide more accurate and reliable prediction results for flood warning.

The invention discloses a full-reference image quality evaluation method and system based on a local area mask, and relates to the technical field of image quality evaluation, and the method comprises the specific steps: carrying out the local area information extraction of a distorted image and a reference image, and obtaining an initial local area feature map; performing multi-stage feature extraction on the depth feature map to obtain an initial depth feature map; generating a local area mask based on the initial local area feature map of the reference image, and generating a local area depth feature according to the mask; utilizing a self-attention mechanism and a cross attention mechanism to generate enhanced local area depth features according to the local area depth features; and performing weighted aggregation on the enhanced local area depth features by using the weight matrix to generate weighted fusion local area depth features, splicing the weighted fusion local area depth features with the initial local area feature map, and calculating a quality score. According to the method, the predicted quality score is closer to a perception result of human eyes, and an evaluation standard which better conforms to human visual characteristics is provided for image quality evaluation.

The invention discloses a concrete faced rockfill dam deformation prediction method based on CBMA and DE-MCMC algorithms, and the method comprises the steps: constructing a concrete faced rockfill dam deformation monitoring model according to the water pressure, temperature and aging component, replacing the original uniform distribution assumption of averaging a Bayesian model with the Copula function joint distribution between the settlement amount of a measurement point and an influence factor, and predicting the deformation of the concrete faced rockfill dam. A concrete faced rockfill dam monitoring model and measuring point data are substituted into an average calculation system of a Copula Bayesian model, the model is improved in combination with a differential evolution Markov chain Monte Carlo algorithm, and the method is applied to deformation monitoring of the concrete faced rockfill dam. According to the prediction method based on the CBMA and DE-MCMC algorithms, the uncertainty of the model is subjected to better quantitative analysis in the process of predicting the deformation of the rockfill dam, the deformation rule and characteristics of the rockfill dam can be analyzed more effectively, and good interpretability and actual measurement fitting of a statistical model are reserved.

The invention provides a fermentation process prediction method and system based on a generative algorithm and a terminal, and the method comprises the steps: firstly, comprehensively collecting various fermentation parameters in the whole life cycle of biological fermentation, and carrying out the fine preprocessing of the parameters, thereby constructing a sample data set; then, aiming at the constructed sample data set, deeply mining domain features closely related to the fermentation process, and establishing a model to construct a data set according to the domain features; on the basis, one or more generative algorithms are used, and a staged generative model is constructed according to the model construction data set. Finally, the fermentation process is accurately predicted according to the input fermentation parameters by means of the constructed staged generation model, and then a reliable prediction result is obtained. According to the method, on the basis that the effectiveness of data set division and the stability of model training are not affected, domain features are effectively fused in the modeling process, and the prediction precision and generalization ability of the model are remarkably improved.

The invention provides a flange production cycle simulation prediction method and system, a program product and a medium, and relates to the field of production cycle measurement. An initial node and a plurality of production lines are constructed in a prediction model, and each production line is subdivided into a plurality of production nodes connected in series, so that a simulation model architecture matched with an actual production process is formed. By transmitting state information such as residual raw materials and residual output demands among production nodes, a constraint and coordination mechanism among the working procedures is established, so that the production rhythm of the next working procedure can be dynamically adjusted according to the constraint and coordination mechanism. Concepts of an intermediate production line and an ending production node are introduced, the starting time of the intermediate line is dynamically adjusted, and material consumption and output conditions of related production nodes are synchronously updated. The scheduling method can intuitively reflect product dependence and material constraint between procedures, and realizes time sequence optimization and beat synchronization of production activities. The accuracy of simulating and predicting the flange production cycle can be improved.

The invention discloses a method, a system, equipment, a medium and a program for predicting photovoltaic life, and belongs to the technical field of photovoltaic energy. The method comprises the steps of collecting historical electrical data and meteorological data of a photovoltaic array in a photovoltaic station, and preprocessing the historical electrical data and the meteorological data to obtain a historical degradation trend analysis result of the photovoltaic array; constructing a physical degradation model of the photovoltaic station based on the historical degradation trend analysis result of the photovoltaic array; and predicting the failure time and the residual life of the photovoltaic array by using the physical degradation model of the photovoltaic station. According to the method, the problem of difficulty in photovoltaic array life prediction is solved, the stability and reliability of a photovoltaic power generation system are improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of traffic carbon emission prediction, in particular to a traffic engineering construction carbon emission prediction method and system based on digital twinning. According to the method, sites are used as nodes, carbon emission data sequences are used as node values, distances are used as edge weights between the sites, and an initial graph structure is constructed; obtaining interference factors corresponding to every two nodes; analyzing the nodes to obtain affected nodes and affected nodes, updating the initial graph structure to obtain a directed graph, and correspondingly updating interference factors of the two nodes of the directed graph; selecting influence components from IMF components of the carbon emission data sequences of the nodes in combination with interference factors between the nodes; and according to the carbon emission data sequence of the node and the difference in the influence component, determining the weight value of the corresponding site as an interpolation weight, and predicting to obtain predicted emission data of each site by using a digital twinning technology. According to the method, the carbon emission data of each unknown point can be accurately obtained, and a more reliable prediction result can be obtained according to digital twinning.

The invention provides a rock mass structural surface strength prediction method driven by a ground vibration load, and relates to the technical field of rock mass mechanics. The method comprises the following steps: acquiring and integrating geometric and mechanical parameters, seismic oscillation parameters and test and numerical simulation data of a structural plane to form a standardized database; cleaning and standardizing database data, constructing a dynamic attenuation factor, and screening and enhancing key features to obtain initial input features; constructing a physical experience reference model and a machine learning predictor, and training a hybrid prediction model by combining prediction results of the physical experience reference model and the machine learning predictor; verifying the model and quantizing the output uncertainty; inputting the basic data calling model to complete calculation and outputting a result. According to the method, the reliability and adaptability of rock mass structural surface strength prediction are improved, and support is provided for seismic design of geotechnical engineering.