930 results about "Predictive value" patented technology

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 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 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.

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 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.

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.

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.

The invention discloses a three-level inverter dual-tracking model prediction control method and an inverter, and the method comprises the steps: constructing an integral sliding mode observer, and obtaining a current prediction value and a lumped disturbance estimation value through the integral sliding mode observer; calculating a voltage error of the inverter, and substituting the voltage error into the PI controller to obtain a voltage tracking control item; based on the voltage tracking control term, a switching state that minimizes the cost function is selected to control the inverter. The integral sliding mode observer is configured to calculate a current prediction error between a current prediction value of a current control period and a system output current, introduce an integrator to track the current prediction error, and generate an error function based on an output result of the integrator and the current prediction error. And feeding back a current predicted value and a lumped disturbance estimated value of the next control period through an error function. According to the invention, the method can achieve the quick estimation of the operation state of the inverter and the robust compensation of disturbance, reduces the steady-state tracking error, and reduces the total harmonic distortion of the inverter.

The invention provides an intelligent control method and system for the zinc layer thickness of continuous hot galvanizing, and relates to the technical field of data processing, and the method comprises the steps: collecting the historical production data of a production line, and constructing a zinc layer thickness and air knife pressure prediction model; the two models are trained according to historical data, and a setting table of the distance and height of the air knife is obtained through statistical analysis; acquiring real-time production data, determining air knife parameters according to the setting table, and inputting the air knife parameters into the trained air knife pressure prediction model to obtain an air knife pressure set value; and inputting the real-time data and the air knife set value into the zinc layer thickness prediction model to obtain a first zinc layer thickness prediction value. If the predicted value meets a preset standard, current air knife parameters are stored, otherwise, a feedback adjustment mechanism is used for adjusting air knife pressure, and a second zinc layer thickness predicted value is calculated; if the adjusted predicted value meets the requirement, air knife parameters are stored; if the requirement is not met, the adjusting step is repeated; and intelligent control is carried out through the stored air knife parameters.

The invention relates to the technical field of integrated supply chain plan collaboration, and discloses an integrated supply chain plan collaboration intelligent decision-making method and system, and the method comprises the steps: decomposing a single prediction value into a commitment layer signal and an option layer signal, generating a differential signal, generating an agility index through issuing a periodic probe signal, and carrying out the calculation of the agility index; the method comprises the following steps: establishing an agility index of each option signal, calculating a collaborative entropy index based on the time value of each option signal, and finally, carrying out closed-loop dynamic adjustment on a decision activation threshold according to the agility index and the collaborative entropy index. Therefore, the problem of plan vulnerability caused by the fact that a traditional planning system depends on a single deterministic predicted value is solved, the whole collaborative system internally accommodates uncertainty at an information source, the operation stability of the collaborative system does not excessively depend on prediction accuracy any more, and mode conversion from passive prediction execution to active preparation response is achieved.

The invention discloses a multi-scale time sequence prediction method and device, and belongs to the technical field of time sequence prediction analysis. According to the method, the fOU process, the LSTM model and the attention mechanism are combined, the long-term trend and the short-term fluctuation of the time sequence can be captured at the same time, and the prediction precision and the interpretability are improved. The method comprises the following steps: firstly, acquiring multivariate multi-scale time sequence data and preprocessing; then decomposing the target time sequence into a low-frequency component and a high-frequency component by adopting a moving average method and a residual error method; carrying out modeling and prediction on the low-frequency components by adopting an fOU-LSTM hybrid model; carrying out modeling and prediction on the high-frequency components by adopting a multi-scale LSTM model and combining an attention mechanism; and finally, fusing the low-frequency result and the high-frequency result by adopting a dynamic weighting method to obtain a final target variable prediction value. The method is suitable for time sequence prediction tasks with long memorability, multi-scale features and nonlinear features in the fields of finance, meteorology, medical treatment and the like.

The invention discloses an integrated wind power prediction method and system based on a multi-source data set. The method comprises the following steps: acquiring historical meteorological factors and fan operation data; preprocessing the historical meteorological factors and the fan operation data to obtain a data set; based on the data set, key features are obtained through a Boruta algorithm, the key features are processed through a sliding window mechanism and a VMD algorithm, and an enhanced feature matrix is obtained; inputting the enhanced feature matrix into a deep learning model for prediction, and obtaining a preliminary prediction value; and carrying out residual error correction and fusion on the preliminary prediction value to obtain a final wind power prediction result. The method effectively improves the capability of processing wind energy intermittency, volatility and randomness, avoids the defects that a physical model is complex in calculation and a statistical model is difficult to process nonlinear and non-stationary features, reduces the over-fitting risk of a single deep learning model, can improve the prediction accuracy and stability, and improves the prediction efficiency. And the method has better generalization ability in practical application.

The invention discloses an automatic dosing control method and device for removing hardness of industrial high-salinity wastewater. Comprising the following steps: collecting raw water quality parameters before medicament feeding, effluent water quality parameters after medicament feeding and medicament feeding amount as historical operation data; performing data cleaning and data standardization processing on the historical operation data to obtain a standard data set; training and testing the supervised learning model by adopting a standard data set to obtain an effluent quality prediction model; on the basis of the effluent quality prediction model, taking the raw water quality parameter and the medicament dosage as input quantities to obtain an effluent quality parameter prediction value; and based on the raw water quality parameter, the current agent dosage and the effluent quality parameter predicted value, dynamically adjusting the agent dosage by adopting a reinforcement learning model. According to the invention, full-automatic control from water quality monitoring to medicament adding is realized, manual intervention is not needed, the dosage can be automatically adjusted according to real-time water quality data, and the treatment efficiency and precision are remarkably improved.

The invention discloses an acupuncture stimulation evaluation method based on multi-physiological signal fusion real-time monitoring, relates to the technical field of acupuncture medical treatment, and aims to accurately judge a phase locking state through correlation analysis of an absolute value of a myoelectricity attenuation slope and a skin temperature phase difference standard deviation, break through the limitation of a single signal dimension and improve the judgment accuracy to 90% or above. A linear prediction model is constructed based on clinical data, and dynamic correction is performed in combination with a compensation coefficient when a beta wave ratio exceeds a standard, so that personalized needle retaining time prediction is realized, and the prediction deviation rate is effectively reduced; when the accumulated phase locking time reaches a predicted value, a needle retaining adjustment instruction is triggered automatically, a'monitoring-prediction-execution 'closed loop is formed, invalid treatment time is shortened by about 15%, and acupuncture operation efficiency is improved; model parameters are corrected in real time through beta wave anomaly monitoring, curative effect deviation caused by muscle tension or nerve imbalance is inhibited, the system false alarm rate is reduced to 3% or below, and robustness is remarkably superior to that of a traditional method.

The invention discloses a multivariate water quality monitoring data prediction method based on cross attention fusion. The method comprises the following steps: inputting water quality monitoring data collected by a monitoring station; constructing a multivariate water quality monitoring data prediction model; training a multivariate water quality monitoring data prediction model by using the water quality monitoring data; calculating the accuracy rate of model prediction, if the accuracy rate exceeds a preset threshold value, inputting the water quality monitoring data of the monitoring station into the trained prediction model to obtain a water quality monitoring data prediction value of the monitoring station, otherwise, continuing to train the multivariate water quality monitoring data prediction model; compared with the prior art, the method has the advantages of stable prediction result, good practicability and the like.

The invention discloses a rotating machine health state evaluation method based on an LSTM and Transform fusion network, and relates to the field of intelligent monitoring, and the method comprises the following steps: collecting and preprocessing fault data of a rotating machine, and dividing the preprocessed fault data into a training set and a test set; a fusion network model based on LSTM and Transform is constructed, and the fusion network model is trained; using the trained fusion network model to predict the health state of the rotating machine, and outputting the health degree score of the rotating machine; by comparing and analyzing the health degree score prediction value of the rotating machine and the actually collected health state data of the rotating machine, the fusion network model is retrained and subjected to parameter adjustment, and the prediction accuracy and stability are improved. According to the method, the advantages of the LSTM and Transform networks are combined, the comprehensiveness of feature extraction and the reliability of prediction results are ensured, the high prediction precision and robustness of equipment health assessment under complex working conditions are ensured, and the efficiency and stability of operation monitoring of industrial equipment can be effectively improved.

The invention relates to the technical field of disaster prevention and reduction of a power system, and discloses an icing risk early warning method based on multi-model fusion and residual time sequence characteristic analysis, which comprises the following steps: collecting meteorological data of a line area in real time, removing abnormal values through secondary judgment of a Pauta criterion and a trend, and standardizing; adopting a TEROL algorithm to screen high-weight key features; running SWD-BP, MUL-GRNN and ELM models in parallel, constructing a dynamic weight by combining DSI, an independence weight method and an entropy weight method, and calculating a final meteorological predicted value; generating a prediction residual signal, extracting time domain features such as a mean value and a peak value, and constructing a residual feature matrix through a sliding window; and inputting an LSTM model to process a time sequence dependency relationship, and judging an icing risk level. According to the method, meteorological prediction is optimized through multi-model dynamic fusion, and deviation is analyzed and corrected in combination with residual time sequence characteristics, so that the problem of weak generalization ability of a single model is effectively solved, and the accuracy of icing risk early warning is obviously improved.

The invention provides a rolling bearing digital twinning dynamic evolution method and system based on continuous learning, and belongs to the technical field of bearing life prediction. The method comprises the following steps: processing a bearing monitoring signal through short-time Fourier transform to generate standardized time-frequency data; constructing health indexes based on the index degeneration function and dividing health levels; training a life prediction model by using the extended LSTM network and taking the time-frequency data as input; real-time data is collected through a fixed time window to predict the life, and when the root-mean-square error of a predicted value and an actual value exceeds the limit, the edge device is triggered to upload new data; evaluating parameter importance in combination with a Fisher information matrix, and dynamically adjusting a regularization intensity updating model; and monitoring the data standard deviation in real time, triggering shutdown when the data standard deviation exceeds the limit, otherwise, predicting the remaining life by updating the model, and stopping when the remaining life reaches the threshold value. According to the method, dynamic evolution of the digital twin model is realized through continuous learning, and the industrial equipment state monitoring and predictive maintenance capability is effectively improved.

The invention discloses a control method and system for outlet steam pressure of a steam jet type mixer, and relates to the technical field of steam jet type mixers.The control method comprises the following steps that real-time operation parameters and structure parameters of the steam jet type mixer are obtained; constructing an outlet pressure prediction model based on the historical operation data, and inputting the real-time operation parameters and the structure parameters into the prediction model to obtain an outlet pressure prediction value; the method is technically characterized by comprising the steps that an outlet pressure prediction model comprising a working condition feature recognition layer, a pressure change trend prediction layer and a pressure deviation early warning layer is constructed, and the model reduces prediction errors through iterative training by collecting full-cycle operation data of past three years; real-time operation parameters and structure parameters are input into the prediction model, so that an outlet pressure prediction value and a change trend graph can be obtained in advance; and on the basis, the control adjusting quantity is obtained by combining the outlet pressure deviation value and the development trend, so that the adjustment is more predictive.

The invention relates to the technical field of medical data processing, in particular to a lung cancer risk early warning system based on remote four diagnosis information, which comprises an acquisition module, a generation module, a calculation module, an early warning module and a correction module. According to the method, through multi-modal time sequence fusion and causal map driven closed-loop regulation and control, the more stable, interpretable and adaptive early warning capability for the lung cancer risk is realized, heterogeneous evidences from observation, listening and interrogation are aggregated into a dynamic alarm index according to time and causal paths, instantaneous noise is filtered by using trend consistency and adversarial test, and the early warning effect is improved. Therefore, false alarms caused by short-time fluctuation are remarkably reduced while the sensitivity to real abnormity is kept; the method achieves balance among false alarm reduction, missed diagnosis risk reduction, positive prediction value improvement and user sampling burden optimization, and effectively solves the problem of insufficient early warning accuracy caused by risk identification lag due to dependence on single static medical record information.

The invention relates to the technical field of medical treatment, in particular to a multi-parameter collaborative infusion process intelligent monitoring and priority calling method, which comprises the following steps of: acquiring four-dimensional monitoring data through a liquid level pressure sensor array, an infrared dripping speed detection module, a flexible temperature patch and a photoelectric heart rate sensor, and combining an LSTM (Long Short Term Memory) anomaly prediction model, and outputting the pipeline state score, the allergy risk index and the infusion progress prediction value in real time. And generating a real-time risk value through a weight configuration mechanism and stage identification logic, judging a risk level according to a dynamic threshold value, and outputting a corresponding early warning level signal. And the system further maps the early warning level signals into common, emergency and critical call instructions, and distributes the common, emergency and critical call instructions to a multi-channel execution terminal to construct a multi-level linkage response mechanism of closed-loop feedback. According to the invention, the accuracy and response efficiency of infusion risk identification can be significantly improved, and the method is suitable for intelligent reconstruction and automatic early warning response scenes of a clinical nursing information system.

The invention belongs to the technical field of metallurgical industry process intelligent control and prediction, and discloses a self-adaptive mixed intelligent prediction method and system for smelting endpoint parameters of an electric arc furnace. Acquiring smelting process data of the electric arc furnace; constructing a dual-drive prediction system comprising a mechanism model and a data drive model; calculating a decision coefficient of a prediction value and an actual measurement value of the theoretical model, and counting an effective historical data volume; constructing a machine learning prediction model, and selecting a modeling algorithm according to the effective historical data volume; selecting a hybrid prediction strategy based on the decision coefficient and the effective historical data volume; predicting and outputting an end point carbon content predicted value and an end point temperature predicted value according to a hybrid prediction strategy; predictive deviation threshold value judgment and execution control are conducted, and the electric arc power, the oxygen blowing flow, the feeding speed or the cooling water flow are adjusted. Accurate prediction and dynamic optimization control of the electric arc furnace end point parameters are achieved, and the smelting quality, the energy utilization rate and the production stability are remarkably improved.

The invention discloses a power equipment health evaluation method and system based on time sequence analysis and probability modeling, equipment and a medium, and relates to the technical field of power equipment state monitoring and fault prediction, and the method comprises the steps: obtaining and preprocessing multi-source operation data of power equipment, outputting a predicted value and a confidence interval of a future parameter through a time sequence prediction model, calculating a residual sequence of an actual observation value and a predicted value, fitting distribution through a probability distribution model, establishing a statistical characteristic model of a normal operation state of the equipment, performing anomaly judgment, calculating a health degree index of the equipment based on a deviation degree and a dynamic weight of a monitoring parameter and weighted accumulation, and dividing equipment state grades according to the index. Quantitative evaluation of the health state of the equipment is realized. According to the method, accurate quantification and early abnormity identification of the health state of the power equipment are realized, a reliable basis is provided for predictive maintenance, and the intelligent level and the safety guarantee capability of power grid operation and maintenance are remarkably improved.

The invention discloses a bridge multi-source damage evolution prediction method based on a knowledge graph, and relates to the technical field of bridge loss prediction, and the method comprises the steps: employing the combined modeling capability of a multi-source monitoring feature set Mon, a damage evolution stage node set Dag, a bridge damage knowledge graph Gra and a causal chain Chain; the prediction process of bridge damage evolution is converted from single-moment numerical prediction to a structured reasoning process oriented to multi-dimensional features, multiple damage stages and a chain type trigger relation. By means of a time slice feature vector predicted value VecPred and a stage category identification predicted value LabPred obtained through time sequence diagram convolution reasoning, which stage the damage may evolve to at the next moment can be revealed in advance in the bridge operation process, and whether the damage may be further propagated along a chain path or not is clearly predicted in combination with a chain risk result Risk output by a causal chain Chain. The chain type damage evolution can be recognized in advance in the actual operation scene of the bridge.

The invention discloses a sand and dust pollution health risk early warning terminal and early warning method, and the method comprises the steps: obtaining multi-source input data, such as meteorological parameters, sand and dust pollutant concentration and geographical environment, in real time, and carrying out the preprocessing through a data fusion algorithm, thereby obtaining a standardized data set; based on the data set, utilizing a trained time sequence prediction model to output a sand and dust pollution level prediction value of a future preset time length; meanwhile, an exposed crowd sensitivity index and regional health baseline data containing the disease incidence rate and the meteorological sensitive crowd proportion are constructed; calculating a comprehensive health risk index through a dynamic health risk assessment model in combination with the predicted value, the sensitivity index and the baseline data; and when the index exceeds a preset threshold dynamically adjusted based on the incidence peak range in the baseline data, generating a graded early warning signal and issuing the graded early warning signal in real time, thereby realizing accurate early warning of the dust pollution health risk.

The invention discloses a nuclear leakage radioactive substance diffusion situation inversion method and device, and relates to the technical field of nuclear emergency response. The method comprises the following steps: acquiring initial source item parameters and meteorological data, and obtaining a nuclide concentration predicted value through a Gaussian puff model according to the initial source item parameters and the meteorological data; acquiring a nuclide concentration observation value corresponding to the nuclear leakage area, and performing Kalman filtering fusion processing according to the nuclide concentration prediction value and the nuclide concentration observation value to obtain a nuclide optimal concentration field and analysis error distribution; adjusting parameters of a Gaussian diffusion mode according to the analysis error distribution to obtain an adjusted Gaussian diffusion mode, and obtaining target source item parameters based on the adjusted Gaussian diffusion mode and the optimal concentration field; and performing iteration by taking the target source item parameter as an initial source item parameter until a preset requirement is met, and taking the source item parameter obtained when iteration is ended as a final source item parameter. The method can dynamically respond to the sudden change of atmospheric stability, and improves the prediction precision of the nuclear hazard area range.