1365 results about "Predictive value" patented technology

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

The invention discloses a bridge detection method and system based on a digital twin technology, and relates to the field of bridge structure health monitoring. The method comprises the following steps: acquiring a strain distribution value, a vibration spectrum value and an environmental load spectrum value in real time through a sensor network deployed in a physical bridge, generating a structural response data set, and synchronizing the structural response data set to a digital twinborn body; calculating a damage index value and an accumulated damage quantity value based on the structural response data set; inputting the damage index value and the accumulated damage quantity value into a preset safety criterion, and calculating a safety margin coefficient value and a failure risk grade value; calculating a residual life prediction value based on the safety margin coefficient value and the environmental load spectrum value, and synchronously correcting a degradation rate value of the digital twin; and generating a priority maintenance instruction according to the failure risk grade value, the residual life prediction value and the safety margin coefficient value, and feeding back maintenance effect data to the digital twinborn body to complete updating after execution. The bridge operation and maintenance efficiency and safety are remarkably improved.

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses a whole course follow-up visit management system and method for gynaecology and obstetrics patients, and relates to the field of medical information management.The method comprises the steps that when a patient sees a doctor for the first time, basic information, medical related information, medical image data, gene detection data, sensor data and patient self-report data of the patient are input; preprocessing the input patient data, and integrating the preprocessed patient data into a personal health record; establishing a health assessment model of the patient based on the integrated personal health record, outputting a current health assessment score of the patient, and generating a follow-up visit plan according to the current health assessment score of the patient; and establishing an obstetrical complication prediction model based on the personal health record, and outputting a patient complication prediction value. Potential obstetrical complication risks are recognized in advance, especially when the health condition of the pregnant and lying-in woman changes, the prediction result can be dynamically updated, real-time monitoring and early warning of complication are achieved, and safety management of the lying-in woman is optimized.

The invention discloses a self-adaptive power matching method and device for a receiving system. The method comprises the steps of collecting a power signal, constructing a geodesic equation for decomposition, generating a geometric feature vector and calculating a stability index. Constructing a standardized feature matrix, obtaining an expansion coefficient sequence, and calculating to obtain a fusion entropy feature and an information completeness index; performing recursive decomposition to obtain a spectrum feature vector and a modal parameter set; constructing a hybrid prediction model to obtain prediction confidence and a prediction value sequence; performing multi-objective constraint optimization to obtain an optimal solution and a constraint satisfaction degree; feedback correction and stability analysis are carried out, and correction control quantity is output; carrying out self-optimization feedback regulation, and outputting an optimization parameter set; calculating a power adjustment control signal and executing power adjustment to obtain an adjusted actual power value; and closed-loop correction is carried out to realize adaptive power matching control. According to the invention, automatic, efficient and accurate power matching is realized, and the stability and reliability of the system are improved.

The invention provides a road slope stability prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: collecting the multi-dimensional environmental parameters of a road slope in real time, and forming an original parameter set; based on a Granger causal test algorithm, analyzing the original parameter set, determining a causal influence relationship among the parameters, constructing a parameter causal network, and screening out key parameters; calculating the similarity of each key parameter in time and space dimensions through a dynamic time warping algorithm, and generating a space-time incidence matrix; based on the space-time incidence matrix, performing weighted fusion on the key parameters to obtain fusion parameters; inputting the fusion parameters into a pre-trained slope stability prediction model to obtain a predicted value of the slope stability; and when the predicted value exceeds a dynamically updated early warning threshold value, outputting slope instability early warning information. According to the method, the defects of inaccurate prediction and data redundancy of a current road slope stability prediction method are overcome.

The invention discloses an interpretability prediction analysis system for multivariable time series data. According to the method, the prediction precision and the decision support capability of the complex time series data are remarkably improved through multi-module cooperation. Firstly, an adaptive learning optimization module dynamically adjusts model parameters and a prediction strategy, so that the model can quickly adapt to time-varying characteristics of data distribution, for example, when a causal relationship between variables suddenly changes, the weight of latest data is automatically enhanced, and historical noise interference is reduced. The dynamic causal interpretation engine tracks the influence intensity and hysteresis effect of key variables in real time, converts traditional black box prediction into a traceable causal relationship chain, and helps a user to intuitively understand driving factors of a prediction result, for example, it is identified that prediction value sudden increase in a certain period is mainly derived from hysteresis effect accumulation of an upstream variable A.

The invention provides a musculoskeletal disease risk assessment and early warning method and system based on multi-source data, and relates to the technical field of assessment and early warning of muscles and skeletons. Joint angles, myoelectricity and plantar load data are collected, a joint feature vector sequence consistent in time sequence is constructed, the limitation of a traditional single monitoring means is effectively made up, and the risk assessment and early warning of musculoskeletal diseases is achieved. K-means clustering is carried out on a joint angle sequence to form a standard attitude set with physiological significance, and a transition edge weight based on angle change, muscle activation and load fluctuation weighted calculation is constructed, so that quantitative expression of physiological costs of different attitude transfer paths is realized, a risk transition region is conveniently identified, and the risk transition rate is improved. According to the method, the sudden transition behavior in the movement process can be captured by calculating the deviation between the predicted value and the true value of the latent variable, and effective early warning of the musculoskeletal disease risk is realized by combining the edge weight strength of the sudden transition behavior in the directed graph and considering the transition strength and the abnormal behavior degree at the same time.

The invention provides a limb nerve rehabilitation state evaluation method based on multi-modal sensor data fusion. Motion inertia data, electromyographic signal data and text data of a corresponding limb nerve rehabilitation action manual of a patient during rehabilitation actions are obtained in real time; inputting the data into a designed and trained multi-modal data feature extraction network to obtain motion inertial data features, electromyographic signal data features and text semantic features; inputting the motion inertia data features, the electromyographic signal data features and the text semantic features into a designed and trained multi-modal data feature fusion network to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a trained prediction output network to obtain a limb nerve rehabilitation state prediction value of the patient. Through multi-modal data fusion and time-dependent feature extraction, the limb nerve rehabilitation state of the patient is evaluated more accurately, and a more reliable diagnosis basis is provided for doctors. Meanwhile, the labor cost is reduced and the evaluation efficiency is improved.

The invention discloses a shear capacity analysis method for a reinforced concrete beam with stirrups based on machine learning, and the method comprises the steps: constructing a training set and a test set based on a shear capacity database of the beam with stirrups; establishing an interpretable prediction model by utilizing machine learning, setting hyper-parameters for the interpretable prediction model, and performing training; obtaining a shear bearing capacity prediction value of the samples in the test set by using the trained prediction model; and quantifying the contribution of each key anti-shearing characteristic to the anti-shearing bearing capacity predicted value, and analyzing and judging the reasonability of the anti-shearing bearing capacity predicted value output by the prediction model according to the anti-shearing theory. According to the method, the problems of high prediction difficulty, high discreteness, lack of reliable physical models, large calculation amount of high-precision numerical simulation and the like in machine learning are solved for the first time; the prediction model can accurately reflect the complex nonlinear relation between the shear bearing capacity and the influence factors, and meanwhile, the model reasonability is verified and improved in combination with a prediction result and priori knowledge, so that the man-machine interaction trust in practical application is improved.

The invention relates to the technical field of power load prediction, and discloses a power load prediction method and system based on a VMD and MoE system, and the method comprises the steps: inputting original power load data into a trained VMD decomposer, dynamically adjusting a modal number K value and a penalty factor # imgabs0 # value according to a power load fluctuation level, and decomposing the data into K modal components; thirdly, inputting the modal components into the trained MoE system, distributing M groups of expert models and weights for the modal components through a gating network, and adjusting the number of the expert models of the high-frequency and low-frequency modal components according to the fluctuation level; and finally, at least one group of expert models is selected from the M groups of expert models, prediction results are fused through weighted summation based on the selected model and the weight thereof, and a final power load prediction value is obtained. Through the method, the precision and robustness of power load prediction are improved.

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

Disclosed in the present invention are a flocculant addition amount real-time optimization method and system based on multi-flocculation jar testing. The method comprises: obtaining an addition amount of a flocculant and a coagulant aid during first operation; on the basis of a flocculant basic addition amount in an r-th round of flocculation jar testing, respectively obtaining three sets of floc image and water quality index data corresponding to three intelligent JarTest modules which carry out flocculation testing at the same time in the r-th round of flocculation jar testing; respectively inputting the three sets of floc image and water quality index data into a pre-trained deep learning model, to correspondingly obtain a supernatant turbidity predicted value Td1(r), a supernatant turbidity predicted value Td2(r), and a supernatant turbidity predicted value Td3(r) in the r-th round of flocculation jar testing; and on the basis of the supernatant turbidity predicted value Td1(r), the supernatant turbidity predicted value Td2(r), and the supernatant turbidity predicted value Td3(r), obtaining a new flocculant actual addition amount, and taking the new flocculant actual addition amount as a flocculant basic addition amount in the (r+1)-th round. The present invention can achieve real-time adjustment and optimization of the flocculant addition amount.

The invention relates to the technical field of isolator service life prediction, and discloses an isolator remaining service life prediction method based on deep learning. The method comprises the following steps: acquiring a vibration signal and a temperature signal when the isolator operates, and constructing a multi-dimensional sensor sequence; dividing a plurality of sub-sequence units containing a fixed time window; extracting time domain and frequency domain features of each sub-sequence unit, and generating a fusion feature vector; calculating statistical distribution characteristics and variation trend characteristics of key indexes in the vector, and outputting a health state index; through a bidirectional recurrent neural network model containing an attention mechanism, taking the health state index and the historical degradation data as input, processing a time sequence dependency relationship through multi-layer residual error connection, and generating a residual service life prediction value; and updating the multi-dimensional sensor sequence according to the real-time sensor signal, and dynamically correcting the predicted value. According to the method, multi-dimensional data are integrated, degradation characteristics are deeply mined, and the accuracy and adaptability of prediction of the remaining service life of the isolator can be improved.

The invention provides a power grid equipment health state prediction method and system based on multi-modal data fusion, and the method comprises the steps: obtaining the multi-modal state associated data of target power grid equipment in real time; performing data feature extraction on the multi-modal state associated data according to a preset multi-modal feature extraction model to obtain a to-be-analyzed multi-modal state feature, and dynamically obtaining an optimal fusion weight of the to-be-analyzed multi-modal state feature according to the obtained historical cycle health index prediction error of the target power grid equipment and the equipment operation environment data; and according to the optimal fusion weight, carrying out feature fusion on the to-be-analyzed multi-modal state features to obtain a corresponding equipment health index prediction value. According to the method, rich equipment operation state characteristics are captured in real time through multi-source data, and a multi-modal characteristic fusion weight dynamic optimization strategy is combined, so that the timeliness, the high efficiency and the accuracy of equipment health assessment are effectively improved, the power production loss and the maintenance cost are reduced, and reliable guarantee is provided for stable and safe operation of a power system.

The invention discloses a fan operation real-time dynamic adjustment control method and system based on environmental data. The method comprises the steps that multi-dimensional dynamic parameters of the environment where a fan is located are collected in real time based on a distributed sensor network; constructing a dynamic coupling prediction model, inputting the dynamic coupling prediction model into a bidirectional verification module based on multi-dimensional dynamic parameters, and obtaining an environment state prediction matrix in a future time window; based on the environment state prediction matrix, a combined adjustment instruction set of the fan pitch angle, the yaw angle and the generator torque is obtained, and the instruction set comprises main strategy parameters and an emergency tolerance interval; based on an incremental learning mechanism, model parameters are dynamically updated according to the deviation between actual environment parameters and predicted values, and the updating period is adaptively adjusted along with the change of turbulence intensity. The method has the advantages that accurate prediction and optimal control of the fan in a complex environment are achieved, the limitation that a traditional data driving model lacks physical interpretability is broken through, and the running stability and efficiency of the fan are improved.

The invention relates to the technical field of unit detection, in particular to a unit magnetic variable on-line monitoring method and system, and the method comprises the steps: carrying out the multi-source data collection through a sensor, carrying out the correction of the multi-source data through a multi-dimensional calibration mechanism, synchronizing a timestamp through a dual-synchronization system, and carrying out the frequency-band-divided conditioning and standardization processing; environmental noise in the standardized multi-source data is eliminated through an intelligent algorithm, feature vectors are extracted, and purified feature vectors are obtained; screening effective abnormal features through an isolation forest algorithm; and inputting the effective abnormal features into an LSTM prediction model to obtain a corrected prediction value, substituting the corrected prediction value into a logistic regression formula to obtain a fault prediction probability of fault occurrence, calculating a health degree score, and performing graded early warning according to the health degree score. According to the scheme, through multi-dimensional calibration, working condition adaptive feature extraction and graded early warning, the unit monitoring data precision, the fault feature recognition accuracy and the operation and maintenance decision efficiency are improved.

The invention discloses a log abnormal behavior detection method and system based on periodic pattern mining and incremental learning, and relates to the technical field of system abnormality monitoring. The method comprises the steps of obtaining original log data and executing preprocessing operation; main periodic frequency components are extracted through time frequency analysis to form a periodic set, a periodic stable part sequence and a transient change part sequence are divided, and a periodic modeling mechanism and a transient modeling mechanism are used for modeling; a joint prediction model is constructed, Monte Carlo Dropout is introduced to estimate uncertainty, and Bayesian weighting is adopted to generate a prediction value; abnormity is judged through a periodic residual error, a transient residual error and an overall residual error, and an abnormal causal path is analyzed and identified in combination with transfer entropy; a memory sample driven playback and distillation mechanism is adopted to execute incremental training, and modeling structure parameters are dynamically adjusted. The method has the capabilities of periodic rule modeling, unsteady behavior expression, prediction fusion, abnormal causal identification and continuous model learning.

The subject matter described herein includes methods, systems, and computer readable media for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping. According to one method for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping includes obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device; generating, using the user related information and a machine learning based model, a user assessment report including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental / psychiatric) disorder and a prediction confidence value computed using relative contributions of the metrics to the prediction value generated using the machine learning based model; and providing the user assessment report to a display or a data store.

The invention discloses a blood pressure measuring method, and belongs to the technical field of medical health monitoring. Comprising the following steps: synchronously acquiring an ECG signal and a PPG signal, and preprocessing the ECG signal and the PPG signal; performing denoising and feature extraction on the ECG signal and the PPG signal; a weighted kernel function regression model fused with multi-modal features is constructed, and a blood pressure predicted value is generated in combination with random forest feature selection and a piecewise linear model dynamic switching mechanism; the model weight is updated based on a recursive least square method, dual-mode calibration is carried out by adopting an event triggering and period triggering mixed mechanism, and a final blood pressure value is obtained through global fine tuning. Compared with the prior art, the method has the beneficial effect that the accurate prediction of the blood pressure can be realized by combining the regression analysis principle with the individual physiological characteristics.

The invention relates to the technical field of power station equipment temperature prediction, and provides a power station key equipment temperature prediction method based on multi-source heterogeneous data fusion, and the method comprises the following steps: collecting multi-source heterogeneous data of a hydropower station, carrying out the preprocessing, feature extraction, feature selection and feature fusion of the multi-source heterogeneous data, and obtaining a temperature prediction fusion feature; performing hyper-parameter optimization on the initial back-propagation neural network through an improved underwater foraging optimization algorithm to obtain an optimized back-propagation neural network; inputting the temperature prediction fusion feature into an optimized back propagation neural network to obtain a temperature prediction value; and comparing the temperature prediction value with the actual temperature value, and when the error of the temperature prediction value is within a preset range, generating a temperature prediction table. According to the method, temperature abnormity can be found in time, early warning of equipment faults and generation of a temperature prediction table are facilitated, the method is suitable for processing complex multi-source heterogeneous data, and the accuracy of temperature prediction is enhanced.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

Example solutions for real-world evidence generation using artificial intelligence models and performing trial simulations include: training a large language model (LLM) to receive medical documents that include medical text associated with a patient output predicted values for medical attributes of the patient based on the medical text; performing attribute extraction from structured medical documents, including extracting values for a first plurality of attributes associated with the plurality of patients; performing attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients; and performing a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.

The invention relates to a mechanism data collaboratively driven dam safety state intelligent diagnosis method and system. The method comprises the steps of collecting environmental quantity data, dam deformation data and corresponding monitoring point coordinates; a PINN model is constructed, the PINN model comprises an input layer, a hidden layer and an output layer, the input layer receives the collected data, and the output layer outputs a predicted value of a deformation component; the PINN model is trained, the weight and bias of the PINN model are adjusted by minimizing a comprehensive loss function, and the comprehensive loss function comprises a data driving loss function, a physical constraint loss function and a boundary condition loss function; using the trained PINN model to predict a deformation field; and comparing the prediction result with the monitoring index to obtain an abnormal region, and performing abnormality diagnosis on the abnormal region. Through the mechanism data synergistic effect, it is guaranteed that model prediction follows the physical law, actual monitoring data can be better fitted, and high-precision dam safety state prediction and diagnosis are achieved.

The invention discloses a residual period prediction method, system and device for enhancing time sequence prediction and a medium, and aims to solve the problems of low efficiency and limited prediction precision in the prior art in a long time sequence prediction task. According to the method, the periodic residual error is predicted by explicitly modeling the periodic mode in the time sequence data and combining the prediction model, so that the prediction precision and efficiency of the time sequence prediction model are improved. The method comprises the steps of determining the period length of a data set, generating a learnable cycle period, separating a periodic component and a residual component of a time sequence, performing prediction based on a residual, and finally combining a prediction value of the residual component and the corresponding periodic component to form a final prediction result. The method can effectively capture the inherent periodicity in the time sequence, reduce the prediction error, significantly improve the precision of long-time sequence prediction, maintain the simplicity and high efficiency of the model, and is especially suitable for application scenarios with clear periodicity characteristics, such as power consumption, traffic flow, meteorological data and the like.

The invention relates to a multi-factor fusion Internet of Things outlier data identification method, and belongs to the technical field of Internet of Things, and the method comprises the steps: determining an identified target factor, fusing a plurality of influence factors associated with the target factor, obtaining corresponding historical data, and carrying out the preprocessing; performing correlation analysis on the specific indexes, and selecting index data having significant influence on target factors to form a final effective index combination; constructing a prediction model combining the LSTM with a feature attention mechanism, performing prediction by using the data after correlation analysis screening, outputting a prediction value in a future period of time, and obtaining an actual prediction value through reverse normalization; a prediction model is adopted, an outlier data identification method fused with multi-factor comprehensive analysis is adopted, an isolated forest algorithm is used for modeling, prediction residual errors and influence factors serve as input features, and the abnormal score of each data point is calculated; and based on the distribution of the abnormal scores, dynamically setting a threshold value by adopting a sliding window technology, and judging the data points exceeding the threshold value as outlier data.

The invention belongs to the field of safety and stability analysis of tailing dams, and discloses a tailing dam stability analysis method based on a GCN-AGRU fusion model and a related device, and the method comprises the steps: obtaining the monitoring data of a multi-source heterogeneous sensor of a tailing dam; preprocessing the monitoring data to obtain multi-source heterogeneous time series data; performing feature analysis on the multi-source heterogeneous time series data to obtain key factors of the stable state of the tailing dam; constructing a heterogeneous hierarchical causal graph by using the key factors; determining a key factor stable state index system according to the heterogeneous hierarchy causal diagram; a multi-source GCN-AGRU fusion space-time prediction model is utilized to obtain space information of the state of the tailing dam; performing time correlation modeling on the multi-source heterogeneous time sequence data to obtain a time sequence of the state of the tailing dam; calculating a predicted value of the multi-source heterogeneous sensor according to the spatial information and the time sequence; and determining the stability state trend of the tailing dam according to the predicted value in combination with a key factor stability state index system. The intelligent sensing of the situation of the tailing dam can be realized.

The invention relates to the technical field of tunnel engineering monitoring, in particular to a tunnel abnormal data early warning method based on an LSTM model. Comprising the following steps: S1, data preprocessing; s2, building a model, wherein the structure of the model comprises two layers of LSTM networks and one layer of full connection layer; s3, model hyper-parameters are selected; s4, network training: performing parameter iteration training through optimization calculation and loss calculation; s5, establishing a dynamic prediction model: continuously inputting measured data, and comparing the measured data with a mean square error standard value to judge whether parameters are retrained or not; and S6, early warning judgment: calculating a difference value between a predicted value and a measured value of the LSTM model, comparing the difference value with a preset threshold value, and carrying out graded early warning. Compared with the prior art, the method has the advantages that the prediction curve is more stable, the calculation standard deviation is larger, the prediction confidence interval range is wider, the change of the data trend can be predicted in advance, the change of the data trend can be identified in advance, and the early warning advancement is better.

The invention discloses a data prediction method based on a kernel regression primary function Bayesian dynamic linear model, and belongs to the technical field of bridge structure health prediction. A to-be-analyzed bridge is detected by using a structural health monitoring system (SHM), and a time series dynamic model KR-BDLM of a Bayesian dynamic linear model is established to describe a bridge structure strain response changing along with time, so that bridge structure health monitoring data prediction and missing value filling are realized; performing parameter optimization reasoning on parameters of the KR-BDLM model by adopting a maximum likelihood algorithm; providing a time-varying state parameter and an observation prediction value of a bridge structure through the optimized KR-BDLM model, and further realizing SHM monitoring data single-step prediction; and multi-step prediction is carried out on the basis of single-step prediction, so that longer-time prediction of bridge data is realized. The prediction result is high in precision, the engineering structure safety can be effectively guaranteed, and the leading-edge development of the structure health monitoring field is promoted.

The invention discloses a multivariable underground water level time sequence prediction method and device, equipment and a medium, and relates to the technical field of artificial intelligence. Performing feature variable contribution proportion calculation, feature screening and normalization processing on the processed data based on a random forest regression model by using an average impurity reduction method to obtain target data; constructing a prediction model based on a convolutional neural network and a long-short term memory network, and performing structure setting, cross validation, parameter setting and model training on the prediction model to obtain a target prediction model; the method comprises the following steps: inputting target data into a target prediction model to obtain a predicted value, generating an evaluation index based on the predicted value and an actual measured value, optimizing the target prediction model by using the evaluation index, improving the applicability, generalization ability and training speed of the model, optimizing the convergence of the model, and improving the accuracy and stability of multivariable groundwater level time sequence prediction. And the sustainable utilization efficiency of underground water resources is improved.