235 results about "Long-term prediction" patented technology

The invention discloses a high and steep slope dangerous rock falling intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, collecting and preprocessing real-time monitoring data, and generating a standardized input sample set; s2, constructing a long-term prediction network model, and outputting a state prediction sequence of multiple time steps in the future; s3, optimizing structure parameters and training parameters of the prediction network model based on a bald eagle search algorithm; s4, executing multi-step state prediction by using the optimized model, and outputting a crack trend, a displacement trend and an abnormal probability value; s5, constructing an early warning risk scoring function, and fusing multiple prediction indexes to generate a risk scoring value; and S6, setting a multi-level early warning threshold value, outputting an early warning level, and issuing the early warning level in linkage with voice, a terminal and a platform. According to the invention, through constructing the intelligent monitoring and early warning method fusing the long-term prediction network model and the bald eagle search algorithm, accurate prediction and multi-stage linkage early warning of the high and steep slope dangerous rock falling risk are realized.

A method for multivariate time-series long-term forecasting based on multi-scale temporal feature enhancements, includes a time-series forcasting model TFEformer. The model utilizes a multi-branch structure and a patch-series attention mechanism to extract global and local time-series features at multiple temporal scales, and designs an adaptive feature fusion mechanism to achieve adaptive fusion of multi-scale temporal features. It employs an variate-wise attention mechanism and a redesigned gated feedforward network to perform feature fusion among multivariate variables and within the time-series, respectively. The time-series forcasting model TFEformer proposed by the present invention significantly improves the prediction of long-term trends in time-series and enhances the fitting ability for short-term local fluctuations, comprehensively increasing prediction accuracy across different prediction time lengths in multivariate time-series forcasting tasks.

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention belongs to the technical field of electric digital data processing, and discloses a dynamic remote sensing monitoring method and system based on land utilization. The method comprises the steps of obtaining a multi-source heterogeneous time sequence data set, and generating standardized spatio-temporal data through preprocessing; extracting space-time correlation features based on the dynamic graph convolutional network, and generating dynamic mode data through multi-dimensional feature fusion; performing classification decision and state transition analysis on the dynamic mode data, identifying a state change area and generating a dynamic analysis report; constructing an interactive visualization engine to map a space-time thermodynamic diagram and a trend graph; and in combination with the incremental learning optimization model, outputting a long-term evolution prediction result. Through the dynamic graph convolutional network and the adaptive cross-modal alignment technology, the problems that multi-source data space-time correlation modeling efficiency is low and the noise suppression capability is insufficient are solved, the dynamic mode recognition precision is remarkably improved, real-time interaction analysis and long-term prediction are supported, and the method is suitable for accurate decision making of industrial monitoring, traffic planning and other scenes.

The invention discloses a building structure crack intelligent detection method, which comprises the steps of S1, constructing a multi-dimensional intelligent sensing array, and obtaining four-dimensional spatio-temporal data including vision, stress, vibration and temperature; s2, performing feature enhancement processing on the multi-modal data; s3, realizing cross-modal crack identification and positioning based on a space-time attention neural network; s4, adopting a sub-pixel edge detection and ultrasonic genetic inversion algorithm; s5, constructing a coupling dynamics prediction model; s6, establishing a dynamic threshold evaluation and multi-dimensional self-calibration mechanism; 0.05 mm micro-crack identification and 3mm depth detection precision are achieved and are improved by 50% compared with a traditional method, the detection accuracy under the complex environment is larger than or equal to 98% through the multi-modal data fusion and GAN enhancement technology, the RMSE is smaller than or equal to 0.03 mm through short-term prediction, the accuracy is larger than or equal to 85% through long-term prediction, double guarantees of a physical mechanism and data driving are established, and the detection accuracy is improved by more than or equal to 98% through the multi-modal data fusion and GAN enhancement technology. And the system has self-calibration, self-adaptive sampling and multi-modal fusion decision-making capabilities, so that the manual intervention cost is greatly reduced.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention discloses a submarine cable insulation multi-field aging prediction method and system, and relates to the technical field of material science and engineering. An existing model has the problems that parameter interaction effect analysis is insufficient, feature weight distribution is not scientific, a data standardization method is insufficient, and the capacity of capturing dynamic response and nonlinear aging behaviors is weak. The method comprises the steps of multi-physics field accelerated aging experiment design and data acquisition, key characteristic parameter screening through grey correlation analysis, support vector machine modeling and optimization, dynamic aging behavior evaluation and service life prediction and feedback adjustment. According to the technical scheme, aging behaviors are comprehensively captured through a multi-physics field cooperation experiment, key parameters are scientifically screened in combination with grey correlation analysis, a nonlinear relation is optimized through support vector machine modeling, long-term prediction precision is improved through dynamic feedback adjustment, and reliability is enhanced through quantitative life evaluation; the aging characteristics of the submarine cable insulating material in multiple physical fields are comprehensively and accurately reflected, and a scientific basis is provided for multi-dimensional evaluation of the aging performance of a high polymer material.

The invention discloses a scene-level trajectory prediction method, system, medium and equipment based on a conditional diffusion model in the technical field of intelligent driving, and the method comprises the steps: obtaining a multi-agent trajectory feature collected at a current moment and a navigation constraint condition of agent motion extracted based on a vehicle position and urban high-precision map data, and inputting the trained conditional diffusion model, and outputting a future multi-modal trajectory prediction result. According to the method, multi-source heterogeneous data is deeply fused, so that the generalization ability of the model in a complex traffic scene is improved, particularly, high prediction reliability can be kept in a rare scene which is not covered by training data, and the bottleneck that a traditional method is difficult to cope with diversified scenes is solved; according to the method, the conditional diffusion model is adopted, the multi-modal trajectory distribution is learned through the iterative denoising process, the semantic probability and the direction matching probability are combined, the prediction trajectory is effectively restrained to conform to the road topology and traffic rules, and the error accumulation effect of long-term prediction is reduced.

The invention discloses a digital twin-driven bridge full life cycle damage prediction and evaluation method and system, and belongs to the field of bridge structure health monitoring, and the method comprises the steps: obtaining a monitoring data set of a bridge structure; an initial digital twinborn model embedded with a micro physical layer is constructed and trained, the micro physical layer constructs a damage evolution model applied with monotonic physical constraint based on multi-source monitoring data, and a damage evolution trajectory of the bridge in a future time period is predicted through the damage evolution model based on the physical parameter vector; in combination with an uncertainty quantification method, generating a time-varying reliability index of the bridge in a future time period; and based on the time-varying reliability index, constructing and solving a maintenance decision optimization model to generate a maintenance decision of the bridge. According to the invention, the physical authenticity and reliability of the long-term prediction result are ensured.

The invention relates to the technical field of charging management, in particular to a front-and-back stage cooperative control method and system for a direct-current fast charging module. Monitoring working parameters and environmental parameters of the front-stage converter and the rear-stage converter in real time, and constructing a multi-dimensional feature matrix in combination with a pre-acquired historical charging mode and user behavior data; performing multi-scale prediction on the charging energy demand by adopting the trained time sequence prediction model, and generating an energy demand curve comprising a short-term prediction window and a long-term prediction window; and dynamically adjusting modulation parameters of the front-stage converter according to the energy demand curve of the short-term prediction window, and adaptively switching a hybrid modulation mode of the rear-stage converter based on the energy demand curve of the long-term prediction window. According to the invention, working parameters and environmental parameters of the front-stage converter and the rear-stage converter can be monitored in real time, and comprehensive analysis is carried out in combination with a historical charging mode and user behavior data, so that more accurate and efficient charging control is realized.

The invention discloses a hybrid photovoltaic prediction method based on SARIMA-LSTM, and the method comprises the steps: carrying out the calculation according to historical photovoltaic power generation data in an SCADA system, wherein the historical photovoltaic power generation data comprise the power generation power and the accumulated power generation amount, and the meteorological data comprise the irradiance, the temperature, the humidity, the wind speed, the cloud cover and the like, and are obtained from a meteorological station or an open-source data platform; statistics is carried out by combining an SARIMA model with a seasonal prediction function and an LSTM model for processing a time sequence, and a more accurate photovoltaic power generation prediction value is obtained after combined prediction and evaluation. Compared with the prior art, short-term prediction and long-term prediction can be considered, and seasonal change data prediction is more accurate.

The embodiment of the invention discloses a power grid load prediction method and device based on fractal analysis, and the method comprises the steps: obtaining historical load time series data of a power grid, and carrying out the preprocessing, and generating a standardized load data set; fractal features of the load data set are extracted by using a fractal analysis algorithm; judging dynamic behavior characteristics of the load time sequence based on fractal characteristics, adaptively selecting short-term prediction or long-term prediction model parameters, and dynamically adjusting the time scale of a prediction model to adapt to load fluctuation characteristics; analyzing an abnormal mode in the historical load time sequence based on fractal features, and generating an abnormal detection result; constructing a load prediction model based on the fractal features, the anomaly detection result, the model parameters and the external features; and predicting the power grid load in the future time period by using the load prediction model to obtain a prediction result. According to the power grid load prediction method provided by the invention, the prediction accuracy and stability are improved.

The invention relates to the technical field of power load prediction, in particular to a long-term power system load prediction method and system based on multi-scale decomposition fusion. The method comprises the steps of performing data preprocessing based on time sequence data; performing multi-scale decomposition and feature embedding on the preprocessed data to obtain a multi-scale load feature vector set; performing gating adaptive filtering and attention double-path fusion under the multi-scale load characteristics based on the multi-scale load characteristic vector set; performing independent prediction and prediction fusion on a fusion result based on a space-time attention gating mechanism; and evaluating a result after prediction fusion. According to the multi-scale prediction result space-time attention fusion mechanism provided by the invention, prediction information on different scales can be adaptively integrated, deviation caused by a single scale is avoided, the comprehensive performance of long-term prediction is further improved, and the method is suitable for various power system planning and operation scenes.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention discloses a node trajectory prediction method and device for an air-sky-sea-diving unmanned cluster, and belongs to the technical field of mobile ad hoc networks, and the method comprises the steps: collecting data in real time, and carrying out the preprocessing of the data; constructing an unmanned node trajectory prediction model based on a gating circulation unit, a Transform and a width learning system, and inputting data into a gating circulation unit and Transform parallel cascade module; performing weighted fusion on module output results, and dynamically adjusting the weight by using a back propagation algorithm; and finally, the weighted fusion result and the dynamic environment index are input into a width learning system, the output of the width learning system outputs a final unmanned node trajectory prediction result through a full connection layer, and the width learning system can dynamically adjust the network width according to the data change. Based on the method provided by the invention, the gating circulation unit and the Transform are cascaded in parallel, the combination of real-time response and long-term prediction is realized, a wide learning system is introduced to quickly adapt to data distribution change through incremental expansion, and the environmental adaptability of a prediction model is improved.

The invention belongs to the technical field of new energy electric power, and discloses a new energy bearing intelligent evaluation and regulation system and method, the system is composed of a data acquisition module, an intelligent evaluation module, a prediction module, a regulation strategy module and an execution and feedback module, and the data acquisition module acquires meteorological, historical operation, load and power grid state data in real time; the time sequence of the prediction module and a deep learning algorithm are combined, short-term to medium-and-long-term prediction is carried out on new energy output and load change, output change is captured in real time, regulation and control lag is avoided, power grid risks are quantified through a multi-model fusion algorithm, and node accessible capacity and a risk assessment result are output; and the regulation and control strategy module generates a regulation and control instruction by utilizing optimization algorithms such as a genetic algorithm based on the evaluation result and the prediction data, and realizes closed-loop control through the execution and feedback module, so that the evaluation data directly guides regulation and control actions.

The invention particularly relates to a hydrogeological analysis system and method based on big data, and relates to the technical field of hydrogeological analysis, and the method comprises the steps: placing a new model and an old model in an A / B test stage, and carrying out the parallel processing of real-time data; performing automatic judgment according to a preset performance index, and deciding whether to upgrade the candidate model to a new production model; and applying the model selected after decision making to hydrogeological analysis to solve the problem of long-term prediction precision under dynamic change of the hydrogeological system. According to the method, dynamic threshold monitoring, K-S outlier sample examination, sub-model optimization and A / B test smooth iteration processes are constructed, and a mechanism and machine learning coupling model and parallel calculation are combined, so that the model drift identification delay is reduced, the prediction precision is improved, scenes such as underground water over-mining prevention and control and pollution emergency are effectively supported, and the prediction accuracy is improved. And an accurate and efficient scientific decision basis is provided for water resource management.

The invention relates to the field of power grid real-time monitoring and coordination control, in particular to a distributed photovoltaic grid-connected regional power grid real-time monitoring and coordination control system which comprises a sensing acquisition module, a multi-time scale prediction module, a self-evolution calibration module and a distributed coordination control module. The sensing acquisition module acquires wide-frequency-domain electrical quantity and multi-dimensional meteorological quantity through a double-layer sensing network, and restoration data is obtained through anomaly detection; the multi-time-scale prediction module integrates ultra-short-term, short-term and medium-and-long-term prediction, and realizes cross-scale collaboration based on a combined state space model and the like; the self-evolution calibration module performs online compensation on the multi-scale prediction residual error based on deep reinforcement learning; the distributed coordination control module is combined with an improved alternating direction multiplier method and a non-dominated sorting genetic algorithm to obtain an optimal power instruction meeting voltage, frequency and harmonic ternary constraints; according to the invention, the stability of power grid operation in a high-permeability photovoltaic grid-connected scene is improved.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention relates to the field of dam effect quantity monitoring, in particular to a dam monitoring effect quantity prediction method based on mechanism and data dual drive, which comprises the following steps of: performing noise reduction preprocessing on monitoring data by adopting a wavelet noise reduction method; constructing a monitoring effect quantity long-term prediction model; constructing a short-time proximity precise prediction model; a Newton-Raphson optimization algorithm (NRBO) is used for optimizing network hyper-parameters of a physical constraint radial basis function (PIRBF) to construct an inversion agent model of finite element model parameters, a finite element calculation result is calibrated, and finally a hybrid model is constructed to realize long-term prediction of monitoring effect quantity. A deep learning model is constructed through an aurora optimization algorithm (PLO), a Transform architecture and a gated cycle unit network (GRU) to correct a long-term prediction model error, a deep learning model result is coupled to construct a short-term approaching prediction model, and short-term approaching accurate prediction of the effect quantity is 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 relates to a mine gas emission quantity prediction method based on deep learning. The deep learning-based mine gas emission quantity prediction method comprises the following steps of (1) preparing a data set, (2) performing correlation analysis on the data set to determine main control elements of mine gas emission, and (3) constructing a VMD-WTC-PatchTST combined model according to time sequence data of the main control elements of mine gas emission. According to the VMD-WTC-PatchTST model prediction performance evaluation method, data and the model are improved through error analysis, and the final VMD-WTC-PatchTST model prediction performance evaluation result is that R2 reaches 0.894, RMSE is 1.80, and MAE is 1.38. Compared with an original PatchTST model, the fitting degree of the PatchTST model is improved by 2.6%, and due to the fact that the PatchTST is also suitable for long-term prediction, the performance of the model is expected to be further improved theoretically along with continuous supplementation of data.

The invention relates to the technical field of hydraulic engineering monitoring and control, in particular to a sluice pump station operation state and water regimen linkage monitoring system. The decision control unit is used for generating long-term prediction information and short-term prediction information based on hydrological observation data and generating control instructions of the opening degree of a water gate and the rotating speed of a water pump. The control instruction execution unit is used for executing the corrected control instruction of the water gate opening degree and the water pump rotating speed; the linkage safety interlocking unit is used for transmitting a standby pump starting instruction to the control instruction execution unit when the first condition and the second condition are met at the same time, sending a blocking signal to the tactical optimization module, and sending a release signal to the tactical optimization module after the standby pump starting instruction is executed. According to the system, low efficiency or safety risk caused by a fixed strategy and execution of wrong operation under the condition that the equipment has a fault or has a fault are avoided, and secondary damage to the equipment and interruption of system operation caused by improper control instructions are effectively prevented.

The invention provides a network demand prediction method based on multi-feature fusion. The problem that in the prior art, demand prediction research only pays attention to the single feature of time or space, the relevance of a user in time and space is ignored, and therefore prediction precision is poor is mainly solved. The method comprises the following steps: establishing a spatial-temporal feature model of historical demand data based on the historical demand data; and establishing a service type weight model through a complementary matrix, processing the spatial-temporal feature model by using an LSTM neural network model and a convolutional neural network CNN model, and fusing by using a weighted feature fusion method, thereby obtaining a final output accurate demand prediction result. Compared with a traditional method, dynamic adjustment is performed in combination with the spatio-temporal characteristics and the service type characteristics, the rapid change requirement can be met, long-term prediction error accumulation caused by a static model is avoided, and the requirement prediction precision and stability are improved.

The invention discloses a deep learning photovoltaic power generation long-term prediction method fusing domain knowledge, and the method comprises the following steps: constructing a meteorological feature fine classification module, carrying out the classification processing of original meteorological data, and generating discrete weather category labels; carrying out embedded representation on discrete weather category labels, and splicing the discrete weather category labels with original continuous meteorological features to form an enhanced input sequence; the enhanced input sequence is input into a Fusionform model, and photovoltaic power generation power prediction is carried out; introducing a theoretical power modeling module based on a photovoltaic physical mechanism, and calculating theoretical power generation power according to photovoltaic system parameters and meteorological data; performing weighted fusion on the output of the Fusionformer model and the theoretical generated power to obtain a final prediction result; according to the method, the physical consistency and interpretability are improved while the precision is ensured, and high-precision long-term prediction support is provided for power grid dispatching.

The invention discloses a control method of a mixed electrolytic cell cluster and a related device, relates to the technical field of intelligent control, and determines the initial starting number of AEL electrolytic cells by utilizing prediction data based on a long-term prediction period. And based on the prediction data of the first short-term prediction period, the initial starting number of the PEMEL electrolytic cell is determined. And based on the prediction data of the target short-term prediction period, predicting a wind-light power prediction value of the target short-term prediction period, and determining the operation state of the mixed electrolytic cell cluster according to the wind-light power prediction value of the target short-term prediction period, the initial starting number of the AEL electrolytic cells and the initial starting number of the PEMEL electrolytic cells. According to the method, long-term prediction and short-term prediction are combined, coordinated operation of the PEMEL electrolytic cell and the AEL electrolytic cell in the mixed electrolytic cell cluster is controlled, real-time performance and flexibility of electrolytic cell control are achieved, and therefore the control effect of the mixed electrolytic cell cluster is improved.

The invention discloses a lithium battery health state and residual life combined prediction large model and a prediction method, a bimodal image is generated through charging and discharging data of a lithium battery, and a lithium battery health state and residual life prediction result is output through a combined prediction model. In the training stage, an attention mask strategy is adopted, an original image and a mask image are predicted twice, key aging feature attention is dynamically enhanced, and noise is suppressed; the accuracy, robustness and generalization ability of lithium battery health state prediction are remarkably improved, and the problems that in the prior art, the data quality dependency degree is high, the model generalization is weak, and the long-term prediction error is large are solved.

The invention discloses a network fault report prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an alarm data set and a historical fault report list; associating the stock historical alarm data with a historical fault reporting list to serve as a training set to generate a pre-training model; inputting the real-time alarm data into a pre-training model for processing to obtain a fault prediction result; and periodically arranging the newest fault reporting list, and carrying out self-adaptive updating adjustment on the pre-training model according to a comparison result of a fault reporting prediction result and the newest fault reporting list. According to the invention, based on the stock historical alarm data and the corresponding historical fault reporting list, the mapping relation between the alarm data and the guarantee condition is learned through a deep learning strategy, and the latest fault reporting list is periodically arranged by using an adaptive adjustment strategy, the prediction result is fed back in real time, and the model is updated, so that a new alarm mode can be rapidly adapted; therefore, the method can be widely applied to the technical field of data processing.

The invention discloses an intelligent analysis system for the running state of a 3C vehicle-mounted contact network, and belongs to the technical field of crossing of intelligent operation and maintenance of rail transit and industrial artificial intelligence. And the system associates the multi-source heterogeneous observation data to a specific equipment unit through an equipment centralized data binding module. And the multi-modal feature extraction and state management unit processes the data and maintains a multi-dimensional state vector of the equipment by using the Kalman filtering updating unit. And the physical-data hybrid decision maker fuses the data driving rule and the simplified physical model to output a diagnosis result. The topology analyzer performs global verification based on a mechanical transfer rule. The system further comprises a long-term health state prediction and feedback module, early failure risks are predicted through a hidden Markov model, Kalman filtering process noise is dynamically fed back and adjusted, and cooperation of long-term prediction and short-term estimation is achieved. According to the method, the technical problems of multi-source data splitting, lack of physical basis in diagnosis and incapability of predictive maintenance are solved.

The invention discloses a global fracture conductivity ultra-long-term prediction method and system, belongs to the technical field of crossing of oil and gas reservoir engineering and artificial intelligence, and is used for solving the technical problem that the calculation cost is high when an existing physical model is used for predicting fracture ultra-long-term conductivity. The method comprises the following steps: generating a global fracture conductivity evolution data set covering multiple working conditions through a numerical simulation system; constructing a multi-channel input tensor containing dynamic fracture conductivity and static proppant concentration distribution; using the data set to train a plurality of Fourier neural operator models for different prediction time scales; and based on a hierarchical time sequence aggregation strategy, iteratively calling trained models of different time scales, and realizing rapid prediction of fracture conductivity in a few years to tens of years in the future. According to the method, on the premise of ensuring the prediction precision, the calculation time consumption of ultra-long-term prediction can be remarkably reduced, and decision support is provided for crack design and production strategy optimization.