896 results about "Failure probability" patented technology

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention discloses a wiring harness dynamic load and electromagnetic interference coupling test method, and relates to the technical field of wiring harness testing, and the method comprises the steps: S1, defining a plurality of environment parameter groups, and constructing a test platform; s2, dynamic current pulse and multi-axis vibration excitation are synchronously loaded; s3, applying broadband electromagnetic interference signals and monitoring signal crosstalk amplitude and bit error rate changes; s4, the sensor nodes are dynamically configured according to the topological structure of the wire harness; s5, generating a feature vector; s6, outputting a wire harness performance index; s7, outputting a wire harness fault probability prediction value; s8, obtaining prediction data; s9, triggering a high-speed data recording mode, and storing original sensor data 60 seconds before the fault; and dynamically adjusting the gradient step length and the loading sequence in the environment parameter group. Performance evaluation, fault prediction and service life management can be carried out on the wire harness under complex working conditions, and the reliability and safety of an automobile electrical system are improved.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention belongs to the technical field of electric power environment monitoring, and particularly relates to an intelligent terminal data acquisition control system and method based on an electric power environment. The intelligent terminal data acquisition control method based on the electric power environment comprises the following steps that S10, an intelligent terminal acquires environment data and operation data in a target object, associates the environment data with the operation data according to acquisition time, generates an associated data set with a timestamp and stores the associated data set to the local; and S20, the intelligent terminal calls the local historical fault data and the associated data set, and an analysis model is constructed based on a multi-factor coupling analysis model. According to the scheme, the problem that the comprehensive assessment accuracy of the overall operation risk of the target object is low due to the fact that the intelligent terminal cannot capture the hidden risk formed by multi-factor coupling is solved through multi-factor coupling analysis, and meanwhile, the fault probability is cooperatively reduced, the environmental adaptability is enhanced, decision-making intelligence is achieved by combining hierarchical response and dynamic self-adaptive adjustment, and the risk assessment efficiency is improved. And full-life-cycle intelligent management of the power equipment is achieved.

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention discloses a bridge network anti-seismic performance rapid evaluation method based on capability spectrum analysis. The method comprises the following steps: firstly, carrying out finite element modeling on bridges in a regional bridge network needing to be analyzed by using OpenSees, extracting piers, and carrying out vulnerability analysis on the piers; a bridge vulnerability curve is obtained based on a capacity spectrum method, then the failure probability of the bridge in a certain damage state serves as a post-earthquake damage index, and the bridge failure probability is replaced by the post-earthquake failure probability of the corresponding road section; after the post-earthquake failure probability of each road section exists, all paths between a starting point and an ending point, namely, to-be-researched OD pairs in a network area are analyzed, the passing probability of each path after the earthquake is calculated based on the Bayesian theorem and an event independence calculation method, all the paths under the OD pairs are sorted according to the passing probability, and therefore the optimal passing path is obtained; and the path with small passing probability is avoided. According to the invention, a scientific basis is provided for regional bridge network anti-seismic planning and emergency management.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The invention provides a production line equipment fault prediction method and system, and relates to the technical field of fault prediction.The method comprises the steps that multi-source time sequence signals are synchronously collected, a multi-dimensional health feature sequence is extracted, the comprehensive sensing capacity covering multiple states of equipment is established, and further, the fault prediction efficiency is improved. By selecting a key health feature sequence for each device and performing linear fitting to quantify the performance degradation severity, accurate description and early recognition of the performance degradation trend of the device are realized, and in addition, the causal influence intensity between the devices is quantified by adopting a transfer entropy algorithm, so that the accuracy of the performance degradation trend is improved. Scientific modeling and visual analysis of a fault propagation path and a linkage effect in a production line are realized, and a comprehensive risk index is generated through weighted fusion of performance degradation severity and a global impact factor, so that a comprehensive risk assessment index is formed. Based on the cumulative failure probability function, the comprehensive risk index is converted into a fault probability curve changing along with time, a visual mathematical expression of the risk trend is formed, and a scientific basis is provided for decision making.

The invention provides a task alarm processing method and system based on intelligent grading, and relates to the technical field of task scheduling alarm, and the method comprises the steps: collecting execution behavior data of each task in a task scheduling system in a plurality of time windows, carrying out the time sequence coding, and converting the execution behavior data into a state transition matrix, identifying an abnormal task and quantifying an alarm intensity value by analyzing non-stationary features; constructing a data flow direction and resource competition relation heterogeneous graph between the tasks, and mapping the abnormal tasks to corresponding nodes; generating a context sensing vector through multi-hop neighborhood aggregation, forming an alarm cluster based on semantic distance clustering, and determining a core node; and determining a control instruction according to the alarm intensity of the core node, performing conflict detection in combination with a resource competition relationship, adjusting a task execution time sequence, and predicting a failure probability output risk type based on a state transition path. According to the invention, intelligent grading and accurate processing of alarms can be realized, and the reliability and resource utilization efficiency of a task scheduling system are improved.

The invention provides a reinforced concrete structure performance degradation intelligent prediction and durability limit state evaluation method. The method comprises the following steps: establishing a random variable probability model of environment and structure parameters; a convolutional neural network is fused to construct a chloride ion diffusion intelligent prediction model, and Monte Carlo sampling is adopted to analyze probability distribution of the initial corrosion time of the steel bar, so that prediction of the steel bar de-blunt time is realized; establishing a steel bar time-varying corrosion model to obtain the change condition of the steel bar corrosion loss rate along with time; further, an incremental static analysis method is adopted, and a multi-scale finite element model of the reinforced concrete structure under different corrosion rate conditions is established through random sampling; obtaining a critical load value in a limit state, and obtaining a failure probability curve under different corrosion degrees; finally, the bearing capacity failure time of the reinforced concrete structure is obtained by defining a bearing capacity reduction coefficient, and evaluation of the durability limit state of the reinforced concrete structure in the corrosion state is achieved.

The invention discloses a double-rocker-arm derrick monitoring system based on multi-sensor data fusion, and particularly relates to the field of monitoring, which comprises a sensor module, a data preprocessing module, a data fusion analysis module, a state evaluation module and a visual terminal module, parameters of the double-rocker-arm derrick are monitored in real time through the sensor array, multi-source data synchronization and feature extraction are achieved in combination with the data preprocessing module, multi-modal data fusion is conducted through the improved D-S evidence theory, and the structure health index, the risk level and the fault probability are output; the state evaluation module generates three-level early warning signals based on a dynamic threshold model, historical data is compressed and stored and supports cloud synchronization, and the visual terminal renders a three-dimensional digital twinborn model through Unity 3D, dynamically displays postures, deformation fields and environmental factors, provides an interactive evaluation report and AR early warning annotation, and realizes whole-process real-time monitoring and intelligent decision support.

The invention relates to a reliability evaluation method and system for a flexible AC / DC power distribution network containing an optical storage system in the field of flexible AC / DC power distribution networks. The method comprises the following steps: collecting photovoltaic data, energy storage data, AC / DC bus data and the like in real time; constructing a multi-time scale dynamic topology model, and establishing a failure probability distribution function; load flow calculation based on an improved forward-backward substitution method; adopting an N-1 + N-1-1 composite fault screening strategy to simulate a typical disturbance scene; establishing a vulnerability evaluation matrix to calculate power failure probability and other indexes; designing an optimal scheduling strategy based on model predictive control; multi-time-scale reliability evaluation is carried out; and outputting an evaluation report and a topology reconstruction suggestion. The system comprises a data acquisition unit, a dynamic modeling unit and the like, and an evaluation and analysis unit is internally provided with an algorithm module and is configured with an FPGA acceleration card. The method breaks through the limitation of traditional steady state evaluation in the technology level, improves the load flow calculation speed in the calculation efficiency level, reduces the operation cost in the application value level, improves the energy storage efficiency, and provides support for reliability evaluation of a novel power system.

The invention discloses an NAND Flash bad block management method and system based on artificial intelligence, and belongs to the technical field of solid-state storage, the method comprises the following steps: S1, carrying out multi-dimensional data acquisition and preprocessing on a bad Page based on preset parameters and preset indexes; s2, inputting the preprocessed multi-dimensional data into a space-time diagram convolutional network model, and outputting the failure probability of each Page; and S3, according to the failure probability obtained in the step S2, a hierarchical isolation strategy is determined, and then error correction code dynamic loading and preventive data migration are carried out. Actual measurement shows that more than 85% of normal Pages can be recycled in a Block with a single bad page, and the effective capacity is improved by 10 times compared with that of a traditional scheme; the overall service life of the chip is prolonged by about 30% by reducing the discarding of the whole chip caused by local damaged pages. The method also has a real-time risk management and control function, the prediction accuracy of the AI model to a potential risk area reaches 92%, and the secondary failure rate is reduced by 70% compared with a static isolation scheme.

The invention discloses a power distribution equipment health assessment method and system based on multi-source data, and the method comprises the steps: mapping each modal feature into a comparable measure, constructing a learnable cost containing power flow and heat consistency, outputting a modal weight through scene gating, and forming a fusion representation of physical consistency; driving a neural differential equation by fusing the stress force obtained through representation decoding, adopting monotone weight parameterization and introducing equipment-level damage budget, and obtaining damage and health indexes which are irreversible along with time; under the constraint of physical baseline life, combining a working condition input time-scene gating danger rate model, performing causal consistency correction through virtual intervention, and outputting an interval failure probability and residual life; and calibrating a dynamic threshold value in the working condition cluster, and triggering routing inspection, sampling and load shedding based on the risk sensitivity and the topological linkage risk priority. According to the method, multi-source physical consistent fusion, individualized health modeling and dynamic closed-loop optimization can be realized, and the accuracy and interpretability of health assessment of the power distribution equipment are improved.

The invention relates to the technical field of fault diagnosis, in particular to an electromechanical equipment fault diagnosis method and system based on data analysis. The method comprises the following steps: extracting a historical operation log of a motor, analyzing a current overload state and constructing a current overload spectrogram; then, carrying out insulation layer performance loss gradient analysis based on the spectrogram, and calculating the failure probability of the motor; finally, a risk level is given to the overload current according to the failure probability, an equipment fault diagnosis framework is constructed, and the framework is sent to a control terminal to execute fault diagnosis. According to the invention, the electromechanical equipment fault diagnosis technology is optimized, so that the electromechanical equipment fault diagnosis technology is more accurate.

The invention relates to the technical field of electric digital data processing, and particularly provides a method for judging seepage failure of an immersed roadbed under the action of repeated seepage, which comprises the following steps of: constructing a water-force-chemical coupling test mechanism, monitoring dynamic change of a permeability coefficient, a fine particle loss rate and three-dimensional reconstruction of a pore structure in real time through a sensor, and determining seepage failure of the immersed roadbed under the action of repeated seepage. A scanning technology is combined to quantify an evolution rule of a seepage path; key parameters such as seepage path fractal dimension, fine particle mobility and dynamic porosity change are extracted on the basis of an evolution rule, the weight of each index is determined by introducing fuzzy analytic hierarchy process, and a seepage failure comprehensive index is constructed; and establishing a time sequence prediction model, inputting a seepage failure comprehensive index and an environment variable, outputting a seepage failure probability, and dividing safety-critical-danger three-level early warning. According to the method, the limitation of a traditional static evaluation method is broken through, and the whole-process monitoring and prediction of seepage failure under the dynamic action of repeated seepage are realized; and the discrimination precision is obviously improved.

The invention provides an AI-driven equipment health state assessment method and system, and relates to the technical field of intelligent operation and maintenance. The method comprises the following steps: acquiring equipment operation data, and performing time and dimension unification and quality control to form a multi-source operation data set and an environment context; generating an initial state feature based on the mechanism feature library, and obtaining a general representation through self-supervised pre-training; executing calibration learning by using a preset health label, and establishing a fusion evaluation model containing time sequence consistency and physical boundary constraint; carrying out distribution alignment and uncertainty estimation on the basis of scene differences to obtain alignment characterization and credibility scores so as to optimize a model gating strategy; performing joint mapping on the new data, outputting health index, fault probability and residual life estimation, and generating a root cause clue; lightweight online updating is executed under drifting detection, health indexes and root cause clues are written back to a mechanism feature library, early warning levels and maintenance suggestions are generated, and therefore complete-cycle intelligent sensing and self-adaptive optimization of the equipment state are achieved.

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention discloses a low-sample neural network structure reliability evaluation system and evaluation method, and relates to the technical field of engineering structure safety monitoring, and the evaluation system comprises a cloud server which is used for constructing a recurrent neural network model containing a static variable embedding mechanism, completing model training and converting a model format; the edge calculation terminal is used for receiving and preprocessing real-time data of the sensor, executing multi-step prediction to output a future time period response sequence, and calculating a future failure probability through virtual Monte Carlo simulation; the sensor assembly is used for collecting structure state time sequence data; and the communication module is used for realizing data interaction and alarm signal transmission operation. According to the method, collaborative modeling of time-varying and static uncertainty is realized by adopting a static variable embedded recurrent neural network model, failure probability distribution is generated at an edge computing terminal in combination with a virtual Monte Carlo technology, failure risk prediction in a future time period is supported, and real-time and accurate reliability early warning can be realized in a resource limited scene.

The invention provides an internal damage assessment method and system based on a multi-modal graphite component, and relates to the technical field of material deterioration assessment, and the method comprises the steps: generating a standardized multi-modal data stream through synchronously collecting five-modal data; extracting acoustic and electro-thermal physical features from the multi-modal data stream, and constructing a multi-dimensional damage sensitive feature vector; inputting the multi-dimensional damage sensitive feature vectors into a deep learning network corrected by a physical constraint layer, and extracting crack quantization parameters from the deep learning network; inputting the crack quantization parameters into a digital twin driven irradiation-creep-fatigue coupling damage model, and calculating the residual life and failure probability of the overall structure; and in combination with the space coordinate information of the three-dimensional crack probability field, a preventive maintenance instruction and schedule for the high-risk modular component are generated and optimized. According to the method, accurate perception, quantitative evaluation, accurate prognosis and optimized decision making of the internal damage of the graphite component are realized, the resource utilization efficiency is maximized, and unnecessary shutdown loss is reduced.

The invention relates to the technical field of structural reliability analysis, in particular to an adaptive structural reliability analysis method based on a Co-kriging proxy model, which comprises the following steps: determining a structural failure mode, acquiring corresponding high-fidelity and low-fidelity performance functions and inputting distribution information of variables; a high-fidelity sample set and a low-fidelity sample set are obtained through sampling; obtaining responses corresponding to the two fidelity samples, and constructing a Co-kriging agent model; carrying out structural reliability analysis by utilizing a Monte Carlo simulation method, and calculating a failure probability of each iteration; judging whether a convergence condition is met; adding new sample points into the training set by using a learning function, constructing a Co-kriging proxy model, and obtaining a final failure probability; according to the method, the Co-kriging proxy model is combined with the learning function MPO (x, m), multi-precision sample data can be fused, and on the premise that it is ensured that the failure probability estimation result has high precision, the calculation process is optimized, unnecessary calculation resource consumption is effectively reduced, and the overall calculation cost is remarkably reduced.

The invention relates to the technical field of intelligent substations, and discloses a safety measure checking method and system for an intelligent substation. The method comprises the steps of collecting multi-source real-time monitoring data of a target intelligent substation, and performing edge federation aggregation on the multi-source real-time monitoring data to obtain collaborative feature data; constructing a dynamic topological graph of the target intelligent substation based on the collaborative feature data, and performing short-term fault prediction on the target intelligent substation to obtain a short-term fault prediction result; performing probabilistic reasoning on each short-term fault event according to the fault probability propagation model to obtain a cascading fault posterior probability of the corresponding short-term fault event; performing multi-objective optimization sorting on each safety measure strategy to obtain a safety measure priority sequence; and performing simulation verification on the safety measure priority sequence according to the digital twin to obtain a check report. According to the method, a complete closed loop from risk analysis, rapid verification to strategy optimization is formed, and efficient and intelligent technical guarantee is provided for safe and stable operation of the intelligent substation.

The invention relates to the technical field of geotechnical engineering structures, in particular to a multi-factor coupling CFG pile full-life failure probability prediction and optimization method. Comprising the following steps; the method comprises the following steps: S1, integrating resistance degradation, pile-soil interaction, geological evolution and load time-varying characteristics to construct a limit state model; s2, on the basis of the limit state model constructed in the step S1, Monte Carlo simulation circulation is used, the failure probability is calculated, and a CFG pile system failure probability change graph is obtained, S3, parameters in the limit state model are optimized, after each parameter is optimized, the steps S1 to S2 are carried out again, and the optimized CFG pile system failure probability change graph is obtained; according to the method, the accuracy and the adaptability of full-life prediction and optimization of the CFG pile are improved.

The invention discloses a water and electricity intelligent management method and platform based on electric power prediction, and belongs to the technical field of water and electricity management, and the method specifically comprises the steps: collecting hydrological forecast data, electric power load data, equipment operation state data and historical scheduling records, building a unified data storage library, and guaranteeing the real-time updating and format standardization of various types of data; the method comprises the following steps: generating uncertainty quantitative indexes of each source by adopting a probability distribution model, error sequence analysis and fault tree analysis methods according to three sources of hydrological forecast errors, load prediction deviations and equipment fault probabilities; constructing a mapping model; according to the mapping result, establishing a risk grade evaluation system, and outputting the specific grade of each scheduling risk in real time; and according to the risk level, formulating a targeted scheduling strategy, executing the generated scheduling strategy, synchronously monitoring and predicting data change and risk level fluctuation, and immediately triggering dynamic adjustment of the scheduling strategy when an uncertainty index exceeds a threshold value.

The invention provides a training method of an equipment system fault early warning model, an equipment system fault early warning method and device, a computer readable storage medium and electronic equipment, and relates to the technical field of equipment system fault early warning, and the method comprises the steps: obtaining data of a plurality of key dimensions from data of a plurality of monitoring dimensions; preprocessing the data of each key dimension, and determining a plurality of samples and labels; determining new feature data corresponding to the feature data in each sample through a self-attention mechanism to obtain a plurality of training samples; and according to the plurality of training samples, through a fox-monkey optimization algorithm and a gradient optimization algorithm, parameters of the time domain convolutional network model based on the self-attention mechanism are optimized, and an equipment system fault early warning model is obtained and used for predicting the fault probability or residual life of the equipment system. According to the invention, the prediction accuracy of the equipment system fault early warning model is improved, and the technical effects of efficient and accurate equipment system fault early warning are achieved.

The present invention relates to an adaptive assessment method and apparatus for nuclear power plant component reliability, a storage medium, and an electronic device. The method comprises the following steps: constructing a Kriging initial model; gradually updating the Kriging initial model on the basis of first-layer samples of ICE to obtain a Kriging surrogate model; gradually updating the Kriging surrogate model on the basis of last-layer samples of the ICE; after the updating of the Kriging surrogate model is completed, obtaining a current Kriging model; and on the basis of the current Kriging model, assessing the reliability of a component to be assessed. In the present invention, first-layer adaptive Kriging gradually updates and explores a failure domain on the basis of the first-layer samples of the ICE until a set stopping criterion is satisfied, and on the basis of a currently constructed Kriging model, the last-layer samples of the ICE are also updated accordingly. Thus, it is ensured that an estimated failure probability converges unbiasedly to a real failure probability, and unnecessary updating of Kriging is avoided, thereby greatly improving calculation efficiency and saving calculation costs.