367 results about "Statistical Confidence" patented technology

The invention provides a method for predicting the residual life of a power transformer based on digital-analog fusion, and belongs to the technical field of transformer detection.The method comprises the steps that multi-dimensional sensor data of the power transformer is collected, wavelet transform preprocessing is conducted, a normalized data matrix is established, a physical equation is established, and a deterministic physical model is formed; a data-driven model is established based on an improved adaptive multi-scale network to realize multi-scale feature adaptive extraction, a topological phase change algorithm is introduced to identify key transition points in an aging process, and a deterministic physical model and the data-driven model are fused to establish a digital-analog fusion prediction framework. A generative adversarial network is adopted to perform data enhancement to solve the problem of scarcity of fault samples, a Bayesian neural network and a Monte Carlo random inactivation technology are utilized to construct an uncertainty quantization framework to output a residual life prediction value and a confidence interval thereof, and the technical problem that the prediction precision of the residual life of the transformer is not high is solved.

A system and method for predictive analysis, scenario simulation, and decision optimization is provided. The system includes a prediction management system executed on a distributed computing infrastructure, and a prediction engine configured to receive input data, including event parameters, user-defined constraints, real-time data feeds, and historical trends. The prediction engine generates predictive models using algorithms trained on historical event outcomes, assigns probability scores and confidence intervals to potential outcomes, and dynamically updates the models based on new input data. Actionable insights are generated and ranked according to predefined success criteria. A non-transitory computer-readable medium is used to store the predictive models, outcome probabilities, and actionable insights for subsequent analysis and reporting. This system facilitates enhanced decision-making by offering real-time insights and continuously refined predictions, thereby optimizing responses to complex events and scenarios.

The invention relates to a fault early warning method, device and equipment for an energy storage system. The method comprises the following steps: acquiring target data of a target parameter; the target data is generated by preprocessing real-time operation data and real-time environment data of the energy storage system; extracting a target feature corresponding to each target parameter based on the target data; calculating a feature confidence interval of each target parameter based on the historical data of the target parameters, and marking suspected abnormal features based on the feature confidence intervals; identifying at least two fault types based on the suspected abnormal features; based on the current environment data, the load power of the energy storage system and the historical operation and maintenance data of the energy storage system, carrying out fuzzy reasoning on the risk membership degree corresponding to each fault type and dynamically adjusting the basic weight coefficient corresponding to each fault type; and determining a comprehensive risk index based on the risk membership degree corresponding to each fault type and the dynamically adjusted dynamic weight coefficient, and performing fault early warning analysis processing based on the comprehensive risk index. The method can improve the accuracy of fault early warning.

The invention belongs to the technical field of wind power weather forecasting, and provides an all-region three-dimensional wind speed correction method and system, and the method comprises the steps: constructing a weather numerical forecasting model, and obtaining wind field forecasting data; fusing the preprocessed multi-source data by adopting an optimal interpolation method to obtain three-dimensional space-time continuous wind field analysis data; based on the wind field forecast data and the wind field analysis data, features are extracted and fused, then a historical forecast error sample set is constructed, a wind speed correction model is constructed, and the historical forecast error sample set is utilized to train the wind speed correction model; introducing an initial value, a physical parameter and boundary condition disturbance, calculating a mean value and a standard deviation of each set result, extracting a probability distribution feature of a wind speed, constructing a confidence interval, estimating a probability density function, and quantifying an occurrence probability of an extreme wind speed event; and the prediction result of the wind speed correction model and the multi-source wind field observation data are fused to generate final three-dimensional wind field data, so that the actual requirements of wind power prediction and power grid dispatching can be met.

The invention discloses a power grid dispatching scheme generation method and system based on load optimization, and the method comprises the steps: predicting a reference load change curve and a confidence interval of each node in a future set time period according to the load data and meteorological data of each node in a power grid topological graph in a corresponding time period; continuously determining a risk area based on the risk assessment model and determining a predicted load offset and a standard deviation so as to correspondingly optimize and broaden a reference load change curve and a confidence interval of each node in the risk area; and constructing an uncertain scene set based on the reference load change curve and the confidence interval, then constructing an objective function, and solving the objective function based on the uncertain scene set to generate an elastic scheduling scheme with the minimum power generation cost and the minimum load vacancy in the worst load scene. According to the invention, through supplementing the prediction load offset and the standard deviation, the prediction precision of the short-term load change under the condition of sudden power consumption peak or extreme weather is obviously enhanced, and the power grid dispatching level is 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 machine learning-based flight wheel block removal time prediction method, and relates to the technical field of flight prediction, and the method comprises the steps: collecting flight preorder state data, airport resource distribution data and meteorological data in real time, and generating an original data set through multi-source heterogeneous data fusion; constructing spatio-temporal features including a preorder flight delay propagation chain, a stand-vehicle conflict energy matrix and a meteorological attenuation factor, and screening and optimizing a feature set through distribution drift detection; training and verifying the Bayesian depth quantile regression model, and outputting a prediction result with a confidence interval; and combining the airport Internet of Things positioning feedback optimization feature set and parameters to generate a prediction deviation diagnosis report. According to the method, a preorder flight delay propagation chain and a stand-vehicle conflict energy matrix are constructed, flight dynamics, resource allocation and weather attenuation factors are embedded into a unified spatial-temporal feature space, and the problem of feature information loss caused by data isolation is solved.

The invention discloses a leg health state evaluation and fault tracing system and method based on a time sequence large model, and the method comprises the steps: an input layer is responsible for carrying out the synchronous collection, normalization and time alignment of various sensor signals, and constructing time sequence input data in a unified format; the feature fusion layer adopts a sliding window mechanism to carry out Patch segmentation on an original signal, and local representation is enhanced in combination with feature engineering; a channel attention mechanism is further introduced, the weight of each channel is adaptively adjusted according to the dynamic relevance between the sensors, and information fusion and feature screening are achieved; the model layer constructs a long-term dependence modeling framework based on a time sequence large model and is integrated with an LoRA low-rank adaptation module, and the prediction layer performs uncertainty quantification on a health state prediction result through dynamic confidence interval estimation; and the application layer completes fault tracing and key component positioning according to time sequence attention distribution and channel weight change, and synchronously generates a health trend curve, confidence interval distribution and a visual early warning interface.

The invention discloses a carbon quantity prediction method and device for coal-fired unit carbon emission, and a medium, and relates to the technical field of energy data intelligent analysis, and the method comprises the steps: calculating a carbon emission intensity prediction value and a confidence interval boundary value based on a real-time coal quality fusion operation parameter set and a device health index, and generating a dynamic carbon emission prediction package; comparing the dynamic carbon emission prediction packet with CEMS real-time monitoring data, calculating a prediction error rate, extracting a multi-dimensional error feature according to the prediction error rate, and generating a multi-dimensional error feature vector; and based on the multi-dimensional error feature vector, dynamically adjusting an equipment health index correlation factor and a coal quality confidence weight parameter, generating a dynamic correction instruction set, and generating a carbon emission prediction report in combination with a dynamic carbon emission prediction packet. According to the method, dynamic noise reduction, enhancement and feature quantization of the coal flow multispectral image are realized, the high-fidelity coal quality feature vector is directly generated, the problem of coal quality data lag is solved, and the coal quality sudden change response capability is improved.

The invention relates to the technical field of oil and gas field development, in particular to a fracture parameter inversion method based on a Bayesian neural network, which comprises the following steps: establishing a bottom hole net pressure conversion model based on an actual construction curve, and drawing a bottom hole net pressure curve; calculating a bottom hole net pressure index sequence and a corresponding time sequence; establishing a shaft bottom crack extension mode judgment criterion on the basis of a classic double logarithmic curve analysis method; taking the net pressure index sequence and the time sequence obtained in the previous step as input data, combining actual physical parameter constraints, and establishing an inversion fracture parameter model based on a Bayesian neural network; and inputting the pressure index sequence to be inverted into the Bayesian neural network model to obtain a specific fracture parameter inversion result. According to the technical scheme, the confidence interval of the prediction result can be given, the prediction result and uncertainty quantification capability can be synchronously provided, and the reliability and decision support value of the inversion result are greatly improved.

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 discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.

The invention discloses a super-set deterministic weather forecast method and device based on machine learning, and the method comprises the steps: obtaining multi-source meteorological data of a target region, carrying out the meshing of the multi-source meteorological data, carrying out the historical static feature analysis and dynamic feature analysis of the meshing features, obtaining the feature weight of each mode, and carrying out the recognition of the multi-source meteorological data. The method comprises the following steps: constructing a prediction sub-model according to an extreme event, obtaining enhanced numerical prediction data, obtaining posterior probability distribution of grid points through a conditional generative adversarial network and a Bayesian neural network based on the numerical prediction data, a gridding feature and a feature weight, taking a maximum probability value as a deterministic weather forecast, and calculating a confidence interval. And obtaining a joint probability product including wind speed and rainfall joint distribution and the characteristic contribution degree. According to the method, numerical forecasting set products of different mode centers are utilized to fuse probability forecasting information, deterministic weather forecasting is obtained, smoothing of extreme events is reduced, deterministic maximum value output is provided, and meanwhile good interpretability is achieved.

The invention relates to a sensor data anomaly detection method and a storage medium, and the method comprises the steps: obtaining the target time sequence data of a target sensor, and obtaining the adjacent time sequence data of an adjacent sensor; the spatial distance between the adjacent sensor and the target sensor is within a preset distance range; calculating a reference confidence interval according to the target time sequence data and the adjacent time sequence data; inputting the target time sequence data into the trained time sequence prediction model for multiple times of forward propagation, and outputting a plurality of reconstruction results; calculating a model uncertainty sequence by using the plurality of reconstruction results; adjusting the reference confidence interval based on the model uncertainty sequence to obtain a dynamic confidence interval; and detecting whether the target time sequence data falls outside the dynamic confidence interval, and generating a first anomaly detection result. According to the method and the device, the problem that detection of different types of anomalies has relatively high omission ratio and false detection rate is solved.

The invention relates to the technical field of intelligent control technology and edge computing application, in particular to an actuator real-time control method and system based on edge computing, and the method comprises the steps: carrying out the multi-mode state sensing and real-time preprocessing of an actuator at an edge node, mapping a high-dimensional feature to a low-dimensional evolution space through space-time compression projection, and carrying out the multi-mode state sensing and real-time preprocessing of the actuator; constructing a lightweight prediction model in combination with a linear skeleton and nonlinear residual reasoning, and dynamically generating a confidence interval to quantify uncertainty; further introducing an evolution updating mechanism based on a residual error, correcting a projection matrix, a dynamic operator and an inverse mapping matrix on line, and ensuring that the real states of the model and the actuator are consistent for a long time; and finally, based on the prediction result and the risk perception capability, adaptively generating an optimal control signal within the security constraint. According to the invention, high-precision, low-delay and robust edge real-time control is realized, and the robustness and adaptability of the system under complex working conditions are improved.

The invention discloses a biomass gas calorific value dynamic prediction and gas distribution optimization method and system, and relates to the technical field of biomass gas, and the method comprises the steps of data preprocessing and dynamic feature extraction, calorific value dynamic prediction, optimization target setting, gas distribution optimization calculation and output and implementation control. According to the method, the change trend of the calorific value is accurately pre-judged, uncertainty is evaluated and predicted with assistance of a confidence interval, a solid and reliable data basis is provided for optimization decision, the problem of low combustion efficiency caused by fluctuation of gas source components is effectively avoided, multi-target comprehensive optimization is realized, dynamic constraint conditions are set, and an optimal optimization target set is generated; the optimal gas distribution proportion is solved, and closed-loop control is formed through real-time feedback; according to the method, the spanning of biomass gas distribution from passive response to active predictive optimization is realized, and multiple remarkable beneficial effects are brought: firstly, the stability and controllability of a gas heat value are greatly improved;

The invention provides a power distribution equipment abnormity identification and life prediction method based on an improved EGNN network, and the method comprises the steps: taking a characteristic value of power distribution equipment and an electrical connection structure relation as an input source, carrying out the coding of multi-modal data, carrying out the geometric preprocessing, and constructing a geometric invariant representation system; feature fusion is carried out by adopting an E (n)-EGNN architecture, and geometric invariance and physical rule consistency are ensured by combining a cross-modal projection matrix, a physical constraint item and a dynamic topology updating mechanism; abnormal detection adopts a dual-path mechanism to reduce false alarms; the service life prediction is realized by integrating a grey fuzzy evaluation model and Bootstrap resampling: obtaining the service life state probability of the target power distribution equipment by using grey correlation analysis and a whitening weight function, generating a dynamic confidence interval based on Bootstrap resampling, obtaining a visual service life scoring interval, and realizing accurate prediction of the service life of the power distribution equipment. And the confidence coefficient and the engineering applicability of the prediction result are obviously improved.

The invention relates to the technical field of equipment life prediction, and provides an equipment life prediction method based on a mixed attention enhancement time sequence convolutional network, which comprises the following steps of: preprocessing original test data, extracting 10 types of time domain statistical characteristics from the preprocessed original test data, screening high-importance feature data as model input through a random forest algorithm; a life prediction model is constructed, an encoder adopts a stacked expansion causal convolutional layer and a self-attention layer, and a decoder fuses historical features and exogenous variables through cross attention; training a life prediction model by using a mixed attention enhancement time sequence convolutional network, wherein a composite loss function synchronously optimizes point prediction and multi-quantile regression loss; and inputting sensor data collected in real time into the trained model, outputting a prediction result, and generating a 95% confidence interval based on nonparametric probability prediction. The overall reliability and accuracy of equipment life prediction can be effectively improved.

The invention discloses an interpretable multi-dimensional CI index dynamic scoring and risk assessment method, and belongs to the technical field of compliance risk assessment. According to the method, firstly, multi-source heterogeneous compliance data are collected, cleaned, subjected to caliber alignment and subjected to quality evaluation, and standardized data are obtained; a hierarchical index semantic system is constructed based on the standardized data, and a core index set is obtained through multi-dimensional screening; then, constructing a multi-dimensional dynamic threshold, calculating a weighted score in combination with the core index set, quantifying a score confidence interval through a Bayesian confidence quantification model, and mapping a risk level to form complete score data; and finally, carrying out interpretability analysis on the complete scoring data, and generating hierarchical interpretation texts and visual display contents. According to the method, systematization, precision and transparency of compliance risk assessment are realized, the suitability and credibility of a scoring result are improved, and reliable support is provided for compliance decision making.

The invention provides a Bayesian network granularity prediction method and system based on physical constraints. The method comprises the following steps: collecting historical process parameters and corresponding granularity distribution indexes D10, D50 and D90 of a ternary hydroxide synthesis process; preprocessing the process data to generate an input feature matrix; a Bayesian attention neural network model is constructed, probability distribution and confidence intervals of particle size distribution parameters are output through Bayesian back propagation training, and model output layers correspond to D10, D50 and D90; and embedding physical constraint terms such as range, distribution width, proportion and dynamic process and / or PBE constraint terms in the loss function, and balancing prediction precision and physical rationality and quantifying uncertainty based on the comprehensive loss function. The system comprises a data acquisition module, a data processing module, a model construction module and a prediction module. According to the method, process data, physical constraint and data driving are fused, the accuracy, real-time performance and adaptability of particle size distribution prediction are improved, and intelligent manufacturing and process optimization of the ternary hydroxide precursor are supported.

The invention belongs to the technical field of hotel management systems, and particularly relates to a hotel storage scheduling and prediction optimization method based on artificial intelligence, and the method comprises the following steps: obtaining storage ledger data, business behavior data and external environment data, and fusing the data to generate a feature vector; constructing a semantic mapping relationship between the business behavior and inventory consumption, and outputting a mapping result; inputting the feature vector and the mapping result into a multi-model fusion prediction architecture, and outputting a demand prediction value and a prediction confidence interval of each material in a future preset period; comparing the predicted value with the actual inventory consumption, and triggering the self-learning process of the model; and regenerating a prediction result from the prediction model subjected to self-learning updating, solving an optimal replenishment and allocation scheme based on the prediction result and an inventory constraint condition, and constructing a reinforcement learning model for strategy parameter self-updating by collecting an actual execution result. According to the method, inventory ledger data and consumption behavior data are fused through AI, and a dynamic self-learning warehousing prediction system is established.

The invention relates to the technical field of computer vision and performance evaluation, and discloses a license plate recognition system performance evaluation method and device. The method comprises the steps that a scene image of a target scene is collected and preprocessed; inputting the preprocessed scene image into a scene element extraction model, and outputting an initial scene element score vector; performing anomaly detection and correction on the initial scene element score vector to obtain a standard scene element score vector; generating a disturbance score vector by applying predetermined disturbance to each dimension score of the standard scene element score vector; inputting the standard scene element score vector and the disturbance score vector into a regression prediction model to obtain a plurality of identification rate prediction values; and determining a predicted recognition rate of the external license plate recognition system in the target scene and a confidence interval of the predicted recognition rate according to the statistical distribution of the plurality of recognition rate predicted values. According to the method, reliable mapping from scene visual elements to an LPR system recognition rate is established, and uncertainty evaluation is carried out on a prediction result.

The power system inertia prediction method based on the variational Bayesian attention normalization stream comprises the following steps: constructing a system inertia data set; preprocessing the system inertia data set, and dividing the preprocessed data set into a prediction set and residual data according to a time range; respectively generating Q, K and V by adopting variational Bayes, carrying out relative position coding on the generated vectors, and carrying out weighted fusion on the Q, K and V after position coding by utilizing a multi-head attention mechanism; inputting the output of the multi-head attention mechanism into an attention normalization flow model, and capturing complex probability distribution of data through reversible transformation; carrying out model training by adopting a self-defined mixed loss function; and performing multiple Monte Carlo sampling on the trained model to obtain a sampling prediction set, calculating a prediction mean value and a standard deviation based on the sampling prediction set to obtain a significance level, and constructing an interval prediction result under a corresponding confidence interval. According to the prediction method, accurate probability prediction of the inertia of the power system is realized.

The invention discloses a new energy uncertainty interval fine modeling method based on ELM-Copula. Comprising the following steps: 1) data reading and preprocessing: obtaining a power prediction value and an actual output value by reading historical operation data of a new energy electric field, performing kernel density estimation on the per-unit power prediction value and the actual output value, and calculating an error absolute value according to the per-unit power prediction value and the actual output value; 2) constructing a dynamic Copula function model; 3) measuring goodness of fit of the model; 4) calculating a confidence interval of a prediction error, analyzing uncertainty of power generation power prediction, and giving a confidence interval of a prediction value; according to the method, a multi-type dynamic Copula model is introduced to construct a dynamic dependency structure between a prediction error and power, the ELM is applied to a post-processing stage of a dynamic Copula prediction interval, a correction coefficient is generated by learning historical deviation characteristics, and an original interval is shrunk, so that the prediction precision and practicability are improved on the premise that the coverage rate is not reduced.

The invention relates to a steam generator diagnosis and life prediction method and system, a medium and equipment. The steam generator diagnosis and life prediction method comprises the steps of collecting key signal data and preprocessing the key signal data to obtain preprocessed data; extracting a dynamic response feature vector; simulating the dynamic response feature vector to generate simulation data, and screening the simulation data to obtain key features; constructing a comprehensive diagnosis index based on the key features; predicting an evolution trajectory of the comprehensive diagnosis index and outputting probability distribution of the residual life of the steam generator; predicting according to the probability distribution to obtain a confidence interval of the residual life of the steam generator; and performing diagnosis based on the evolution trajectory of the comprehensive diagnosis index, the confidence interval and the early warning judgment condition, and outputting graded early warning signals and maintenance suggestions. The system provided by the invention solves the problems of the traditional scheme, realizes the technical span from offline and qualitative evaluation to online and quantitative-probability prediction, and provides core support for safe and economic operation and predictive maintenance of the nuclear power station.

The invention provides a multi-parameter water quality data fusion analysis method and system. The method comprises the steps that water quality parameters are collected to form a three-dimensional data cube; constructing a space-time tensor model by using Tucker decomposition and a graph convolution network, and generating a core tensor matrix; constructing a dynamic constraint library and embedding the generative adversarial network; training a generative adversarial network by using Transform and physical constraint loss, generating synthetic data and verifying the synthetic data; performing space-time fusion by using meta learning weight distribution and Bayesian deep learning to generate a weight matrix and a confidence interval; missing data are restored through physical constraint interpolation and Gaussian process regression, and SHAP and LIME interpretation and path diagrams are generated; and performing real-time analysis by using an edge-cloud collaborative architecture to generate an intelligent report. Through physical constraint modeling, dynamic weight distribution and edge-cloud collaborative architecture, the problems that synthetic data violates physical laws, weight staticization, response lag and insufficient interpretability are solved.

The embodiment of the invention provides a wheel disc fracture failure prediction method and device, computer equipment and a medium, and relates to the technical field of digital twines.The method comprises the following steps that a uniaxial quasi-static tensile test is conducted on a tensile test sample, and an engineering stress-strain curve of a wheel disc is obtained; converting the engineering stress-strain curve into a true stress-strain curve; based on the true stress-strain curve, constructing a wheel disc failure prediction model under a deterministic condition; sensitivity analysis is conducted on the uncertainty quantitative parameters, sample data under different working conditions are obtained through finite element calculation based on the key parameters, a wheel disc maximum strain proxy model is constructed based on the sample data, the key parameters are input into the wheel disc maximum strain proxy model, and the confidence interval of the rotating speed of the wheel disc during fracture is obtained. According to the scheme, by constructing the wheel disc maximum strain proxy model, high-precision global simulation of the wheel disc breaking process is realized, and the problems of limited test points and insufficient simulation precision are solved.

The invention relates to the technical field of data analysis, in particular to a wind power plant multi-dimensional operation index comprehensive evaluation system which comprises a deviation benchmark construction module, a gradient environment mapping module, a torque ripple calculation module, a dynamic interval construction module and an index generation module. In the method, a dynamic power confidence interval changing along with the environment in real time is generated by constructing a gradient vector containing a vertical height layer air density difference, finely simulating a torque pulsation waveform generated by environmental heterogeneity in a blade rotation period, quantifying a theoretical power fluctuation amplitude caused by the torque pulsation waveform, and superposing the theoretical power fluctuation amplitude to a reference error band; according to the method, a running state judgment boundary with more physical interpretation is established, meanwhile, a sensitivity weight is introduced by combining statistical morphological characteristics of wind energy flow distribution, a physical yaw angle is corrected and weighted, and multi-dimensional coupling evaluation of wind energy capturing efficiency and control precision of a unit on the basis of eliminating reasonable environmental disturbance is achieved.

The invention discloses a zero-carbon park carbon accounting method and system based on Transform, and the method comprises the steps: collecting multi-source heterogeneous carbon data of a zero-carbon park, completing the preprocessing, and generating a fusion input tensor and a corresponding data quality identifier; inputting the fusion input tensor into a multi-scale time sequence fusion coding model, and generating a time sequence feature intermediate representation through multi-branch coding processing adaptive to different data sources and sampling granularities; emission source contribution degree decomposition information is obtained through a category-constrained carbon flow tracking attention module, uncertainty analysis is completed to obtain a target carbon accounting result and confidence interval data, and a compliant carbon integral scheme and an auditable evidence packet are generated through a carbon integral generation optimization module; therefore, the problems of difficulty in aligning multi-source heterogeneous data of the zero-carbon park, insufficient consideration of period and mutation features and poor traceability of accounting results are effectively solved, the precision and compliance of carbon accounting are greatly improved, and a complete closed loop from carbon accounting to incentive management is realized.