857 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 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 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.

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 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 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 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.

The invention relates to the technical field of time sequence data processing and deep learning, in particular to a time sequence filling method and system based on a coarse-to-fine filling normal form. In the invention, a preprocessing module is used for sampling an input sequence to obtain a subsequence rk with the length of Lk, and a regression prediction module is combined with a causal mask to generate a prediction value r'k with the same structure and the same length as the rk based on all subsequences {r1... rk} generated by the preprocessing module; the correction module performs up-sampling on the predicted value r'k to obtain an up-sampling sequence r ''k with the length of T; supplementing missing data of the original sequence based on the r ''k to obtain a corrected sequence X (k + 1); and traversing k = 1... K to obtain a correction sequence X (K + 1) as a final repair completion time sequence. The method overcomes the defects that the time sequence filling mode in the prior art does not consider the unfixed missing rate and missing value block distribution, and is beneficial to improving the filling accuracy.

The invention relates to the technical field of medical image diagnosis, and discloses a cardiovascular disease diagnosis model construction method based on image processing. The method comprises the steps that cardiac medical image data of a target patient is collected, a standardized data set is generated through preprocessing, and a morphological and hemodynamic feature set is extracted; establishing a heart state evolution characteristic spectrum according to a characteristic dynamic evolution rule, dividing a pathological state space, and calculating the characteristic distribution density of a historically diagnosed case; acquiring real-time image data of a patient to be diagnosed, and constructing a real-time diagnosis feature vector; mapping the vector to a pathological state space, and calculating a space matching degree to generate a pathological association index; and combining the association index and the two types of feature sets to construct a heart pathology probability prediction model, outputting a pathology probability prediction value and generating a hierarchical diagnosis suggestion. According to the method, through multi-dimensional feature analysis and space matching analysis, precise and graded diagnosis of the cardiovascular diseases is realized, and an efficient and feasible technical path is provided for diagnosis of the cardiovascular diseases.

The invention relates to a multi-source data and physics combined driven drainage basin runoff uncertainty forecasting method. The method comprises the following steps: acquiring runoff sequence data and external forecasting factor data of a target drainage basin; constructing a runoff uncertainty generation model, performing probability diffusion on the runoff sequence data through a diffusion generator with constraints, and generating approximate runoff data; designing a joint loss function to update the weight and offset terms of the runoff uncertainty generation model; training loss convergence under joint guidance, and outputting a runoff prediction result. The method has the beneficial effects that a loss function based on Fourier transform and a physical theory guide item are combined, the randomness in a runoff physical system can be effectively quantified through introduction of physical constraints, more selectable predicted values can be provided while the physical consistency of prediction results can be ensured, and the prediction accuracy of the runoff physical system is improved. The inherent random uncertainty in the physical drought and flood process is effectively quantified, so that the physical consistency and prediction precision of the model are enhanced.

The invention provides an intelligent tailing dam displacement prediction and early warning method, and belongs to the technical field of safety prediction and early warning, and the method comprises the steps: obtaining online monitoring historical data of a tailing dam, and carrying out the preprocessing of the data; constructing a GRU main prediction model to predict main prediction displacement; obtaining an error sequence through the measured data and the main prediction displacement; decomposing the error sequence into a trend term error sequence and a noise term error sequence through PSO-VMD-TOPSIS, and respectively constructing a trend term error correction model and a noise term error correction model based on GRU; an error correction value and a standard deviation of a trend item and a noise item are obtained through an MC-dropout technology; a dynamic weight calculation mechanism is constructed, a final displacement prediction value and a confidence interval are obtained in combination with the main prediction displacement, and whether an alarm is given or not is judged; according to the invention, high-precision real-time prediction, uncertainty quantification and real-time early warning of the displacement of the tailing dam are realized, and intelligent decision support is provided for safety state evaluation and disaster early warning of the tailing dam.

The invention discloses a univariate ultra-short-term wind power prediction method based on two-stage trend decomposition, and the method comprises the steps: carrying out the multiple times of trend decomposition of historical wind power time series data, and generating a macroscopic trend component, a mesoscale trend component and a residual component; performing exponential distribution initialization causal convolution kernel extraction on the decomposed macroscopic trend component and the mesoscale trend component to extract multi-scale trend characteristics, and keeping time sequence causality by adopting a proportional normalized adaptive weight; residual modeling is enhanced by adopting a loop reconstruction attention mechanism, and residual component dynamic features are obtained through sequence splicing and double residual connection; and performing linear processing on each component and performing result fusion to generate an ultra-short-term wind power prediction value. And precise prediction of ultra-short-term wind power can be realized.

The invention provides an electric quantity prediction method and system fusing physical constraint factors, and relates to the technical field of electric quantity prediction. Historical load, weather, electricity price and calendar data are collected, and a key feature set is constructed through preprocessing and feature selection; a prediction model with the physical information neural network as the core is constructed, the prediction model comprises a recursion sub-module used for short-term prediction and a trend sub-module used for long-term prediction, and a physical constraint loss item based on a physical rule is introduced into model training so as to enhance the generalization ability; a multi-time granularity modeling framework is adopted, uncertainty quantization is achieved through a Monte Carlo Dropout or Bayesian neural network, and a confidence interval of a predicted value is output; and finally, causal reasoning is carried out through a Shapley value algorithm and anti-fact simulation, and key influence factors are identified. According to the method, the precision, stability and interpretability of electric quantity prediction are effectively improved, and reliable support is provided for power grid dispatching and decision making.

The invention provides a heart rate and breath prediction method, a program product, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original signal sequence acquired from a human body through an ultra-wideband radar; determining a thoracic cavity displacement signal according to a mapping relation between the phase change and thoracic cavity displacement; performing multi-domain feature extraction on the thoracic cavity displacement signal to obtain multi-domain features; the multi-domain features comprise time domain features, frequency domain features and time-frequency domain features; on the basis of the multi-domain features, prediction values are obtained through a pre-trained prediction model, and the prediction values comprise the heart rate and the respiratory rate in the future time period. The robustness of the algorithm is enhanced through multi-dimensional feature fusion, high precision is still kept in an electromagnetic interference or multi-target scene, and the reliability of heart rate and respiratory rate prediction in a complex environment is improved. The physical signal advantage of the ultra-wideband radar is complementary with the environmental adaptability of the neural network, and the prediction accuracy of the heart rate and respiratory rate is improved.

The invention discloses a foundation pit supporting strength self-adaptive adjusting method and system based on real-time monitoring data, and relates to the technical field of engineering control, and the method comprises the following steps: collecting monitoring data of a foundation pit supporting structure in real time; preprocessing the monitoring data to obtain standardized time series data; inputting the standardized time sequence data into a pre-trained LSTM prediction model, and outputting a displacement prediction value sequence; calculating a prediction state index based on the displacement prediction value sequence; with minimization of the maximum prediction state index and minimization of the total support strength adjustment cost as targets, a multi-target optimization model is constructed, and the optimal prestress adjustment amount of each intelligent support execution mechanism is solved; and the optimal prestress adjusting quantity is converted into a control instruction, and a corresponding intelligent supporting execution mechanism is driven to act. The foundation pit supporting strength can be automatically and accurately adjusted according to data monitored in real time.