1107 results about "Single model" patented technology

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

The invention provides a marine ranch water quality parameter multi-model fusion dynamic prediction method and system, and relates to the technical field of water quality prediction, and the method comprises the steps: obtaining data through multiple monitoring platforms, building a combined prediction model through LSTM, PSO-SVM and a random forest regression model, and dynamically adjusting the combined weight according to the prediction error of each model, and accurate prediction of the dissolved oxygen concentration is realized. Compared with single model prediction, the advantages of multiple models are fused, and the prediction precision is improved; the adaptability and robustness of the model are enhanced by a strategy of dynamically adjusting the weight; according to the method, the time sequence analysis and the early warning threshold are combined, the early warning of the oxygen deficit risk is realized, the position and range of the oxygen deficit risk area can be determined according to the spatial distribution characteristics of the prediction result, the accurate oxygenation of the oxygenation equipment is guided, the water quality safety of the marine ranch is effectively guaranteed, and the economic loss caused by oxygen deficit is avoided.

The invention belongs to the technical field of image recognition, and provides a target tracking method and system based on AI vision, and the method comprises the following steps: collecting original video frames; environment adaptive image enhancement; performing multi-target detection and multi-modal feature extraction; estimating local optical flow motion; performing multi-target trajectory association; carrying out shielding processing and re-identification; outputting a track and analyzing a result; according to the method, a physical-deep learning cascade defogging model is set, light / dense fog processing paths are dynamically switched through a dark channel mean value, atmospheric scattering physical prior and U-Net residual error correction are fused, an environment self-adaptive sensing architecture is provided, the failure bottleneck of a traditional single model under sudden change fog concentration is broken through, and the real-time performance of the system is improved. According to the method, an apparent-motion-geometry ternary coupling trajectory cognition system is constructed, a dynamic cost matrix and a feature cache pool are designed, the ID switching problem caused by similar target aggregation and long-time shielding is solved, and the accuracy of target tracking in the shielding environment is improved.

The invention provides a wind power prediction method and system based on time sequence decomposition and multi-model fusion, and the method comprises the steps: collecting the historical power generation power and meteorological data of a target wind power plant; decomposing the historical power generation power and the meteorological data to obtain a trend component, a seasonal component and a residual component; fusing with meteorological data to construct a trend feature matrix, a periodic feature matrix and a residual feature matrix; different modeling schemes are adopted to construct corresponding single models; dividing into a training set, a verification set and a test set according to a time sequence; performing training optimization on the single model by using the training set, the verification set and the test set, and constructing a wind power short-term power prediction model; and inputting the real-time meteorological data and the generated power to the wind power short-term power prediction model, and outputting the generated power prediction value of the target wind power plant, thereby effectively improving the comprehensiveness, accuracy and stability of model prediction.

The invention discloses a storm surge water increase prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seawater monitoring, and the method comprises the steps: obtaining multi-source observation data of a target sea area, the multi-source observation data comprising satellite remote sensing data, near-shore monitoring data, meteorological mode data and drainage basin data; performing space-time alignment and exception processing on the multi-source observation data to generate standardized space-time grid data; performing hybrid prediction modeling on the standardized space-time grid data through a physical data dual-drive modeling layer to obtain a target probability water increasing field; and dynamically correcting the target probability water increasing field by using real-time observation data to obtain a storm surge water increasing predicted value. A physical mechanism model and a probability generation model are combined, limitation of a single model is overcome, and the nonlinear evolution process of the storm surge is effectively captured; the time-space dual-drive architecture is adapted to complex coast terrains and changeable meteorological conditions, and the risk of missing report or false report is reduced.

The invention belongs to the field of lithium battery residual life prediction, and discloses a lithium battery residual life real-time prediction method based on dynamic uncertainty modeling, and the method comprises the steps: cooperatively collecting the full life cycle data of a lithium battery through multiple sensors, constructing a dynamic health score in combination with a convolutional neural network, and effectively capturing the non-stationary and non-linear characteristics of the data. The uncertainty of health scoring is quantified by further adopting a non-stationary random process, and related parameters are synchronously adjusted through joint optimization of an objective function to enhance the adaptability of the model to a complex degradation mode. And dynamically updating non-stationary random process parameters based on a Bayesian reasoning framework, and combining conjugate prior distribution of historical data and real-time observation values to realize parameter adaptive adjustment and dynamic prediction of the residual life of the lithium battery. According to the method, the defect that a traditional data driving method lacks physical interpretability is overcome, and the problem that precision is insufficient due to the fact that a battery degradation mechanism is complex in a single model driving method is solved.

The invention discloses an abnormal traffic detection method based on a time sequence, and the method comprises the steps: firstly mining time sequence mode features of original network traffic data streams under different time scales through integrating a plurality of basic anomaly detection models, and generating a corresponding anomaly score for each feature; secondly, performing normalization processing on abnormal scores generated by each model, and combining the abnormal scores as soft tags and time sequence feature coding vectors; further performing aggregation analysis on the fused features so as to realize efficient aggregation and deep expression of a complex sequential relationship and a potential abnormal mode; and finally, abnormal traffic detection of the original network traffic data flow is realized through the meta classifier. Through the mode, the limitation of a traditional single model or shallow statistical analysis method in the aspects of generalization ability, complex scene adaptability, real-time intelligent decision and the like is broken through, and a new generation of network abnormal flow detection solution with higher robustness is provided for an actual application scene.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention discloses an agent reasoning method based on a knowledge graph, and belongs to the technical field of knowledge graphs, and the method comprises the steps: integrating user query, the knowledge graph and multi-modal data through an NLP model and an entity linking technology, and extracting a target entity, a relation constraint and a structured feature vector. The LLM can synthesize more information to generate a more comprehensive conclusion, the knowledge embedding model maps an entity relationship into a geometric relationship in a vector space, the LLM is assisted to verify reasonability of reasoning, the system can comprehensively generate confidence through the LLM output probability, knowledge embedding similarity and data quality, and the reliability of a result can be explained through confidence score. The LLM generation capability is combined with the vector reasoning capability of knowledge embedding, and the limitation of a single model is made up.

The invention belongs to the field of combination of a large model and a knowledge graph, and particularly relates to a large model and knowledge graph combined reasoning method for false news detection, which breaks through the limitation of a traditional detection method in the aspects of knowledge instantaneity and reasoning controllability through heterogeneous architecture design. Based on multi-dimensional features such as timeliness, propagation mode and information density of news propagation, a double-engine-driven intelligent reasoning system is constructed, on one hand, important entity extraction and semantic understanding are carried out through a large language model, and on the other hand, structured fact verification is provided through a dynamic knowledge graph. And the inherent factual illusion problem of a large language model is effectively solved. Compared with a traditional knowledge iteration mode depending on single model parameter updating, the method supports an incremental updating strategy based on knowledge graph nodes.

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a system and method for estimating the charging capacity of an energy storage lithium battery, and particularly relates to the technical field of energy storage battery management, and the method comprises the following steps: collecting dynamic parameters in the charging process of the energy storage lithium battery, and forming a dynamic data set; calculating an environmental disturbance influence index and a charging response consistency factor based on time sequence analysis and feature decoupling; constructing a two-dimensional working condition mapping matrix, identifying a current working condition area and generating a scene label; determining parameter weights of the plurality of estimation models by adopting a probabilistic reasoning mode; according to the scene state, selecting a single model output result or fusing a plurality of model output results for estimation; according to the method, the environment disturbance influence index and the charging response consistency factor are constructed, so that the complex working condition is accurately identified; scene labels are automatically generated based on two-dimensional working condition mapping and a clustering algorithm, and a plurality of estimation models are dynamically selected or fused in combination with model confidence, so that the accuracy, robustness and intelligent level of estimation are improved.

The invention relates to the crossing field of hydraulic engineering hydrological monitoring technology and machine learning prediction technology, discloses a sediment concentration prediction method based on deep learning, and aims to solve the problems that in existing sediment concentration prediction, hyper-parameter manual tuning is low in efficiency, key feature attention is insufficient, local and time sequence information is difficult to consider by a single model and the like. Accurate prediction is realized through five core modules: a data preprocessing module performs missing value filling, abnormal value processing and derivative feature generation on hydrological data; the feature selection module screens key features based on mutual information; the time sequence construction module generates time sequence data through a sliding window; the hyper-parameter automatic optimization module adopts Bayesian optimization iteration to obtain an optimal hyper-parameter; the CNN-LSTM-attention prediction module fuses CNN local feature extraction, bidirectional LSTM time sequence dependence capture and multi-head self-attention mechanism key feature focusing capability, is suitable for scenes such as river channels and channels, and provides efficient decision support for hydrological regulation and control.

A QUIC encrypted traffic classification method based on multi-model fusion belongs to the field of communication, and comprises the following steps: a model training stage: dividing a data set, training each model in a high-precision model group and a high-recall-rate model group by using a training set, evaluating model performance by using a verification set, if a dynamic weight mechanism is started, calculating the weight of the model according to a model evaluation result, and if the dynamic weight mechanism is started, calculating the weight of the model; carrying out normalization processing on the weight, and searching an optimal decision threshold by using a plurality of candidate thresholds; in the model prediction stage, prediction data are input, each model generates a respective prediction probability, and if a dynamic weight mechanism is started, the prediction probability of each model is subjected to weighted averaging according to the weight obtained in the training stage so as to perform probability calibration; and combining the calibration probability of each model, judging the calibration probability through an optimal decision threshold, and generating a final classification prediction result. According to the method, the risks of overfitting and poor generalization ability of a single model are reduced, and QUIC encrypted traffic classification with high accuracy and stability is realized.

The invention discloses a memory prefetching control method and device, a storage medium and a program product, and relates to the technical field of computers. A heterogeneous logic model covering multiple access modes is constructed, a prefetch strategy candidate set is generated based on standard logic models corresponding to different access modes, the expression limitation of a single model on complex multi-mode access features is broken through, and access laws in different scenes can be comprehensively covered; through calculation, sorting and screening of the prefetch cost, targeted evaluation of the strategy is realized, bandwidth load aggravation and resource waste caused by redundant prefetch are avoided, and optimization of the target prefetch strategy in the aspects of low delay and high bandwidth utilization rate is ensured. According to the method and the device, the problems that a fixed logic model is difficult to adapt to dynamic diverse memory access behaviors and resource utilization is low in efficiency in the prior art are solved, and the technical effects that the method does not need to depend on fixed logic, can dynamically adapt to the change of the access mode through flexible combination and strategy screening of heterogeneous models, and is particularly suitable for terminal equipment with limited resources are achieved.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention belongs to the technical field of geometric pretreatment in computer aided engineering (CAE), and particularly relates to a dirty geometry restoration method for a complex three-dimensional geometric model, which comprises a dirty geometry detection and restoration module based on heuristic rules and geometric topology analysis. By means of a multi-model collaborative alignment and bonding mechanism, high-precision assembly between parts is achieved through geometric feature matching and adjacent domain self-adaptive adsorption; acquiring material attributes provided by the simulation platform, and performing accurate cutting and boundary reconstruction on an intersection domain based on an intersection domain intelligent segmentation algorithm of the material attributes to ensure correct mapping of boundary conditions of a physical field; a geometric Boolean processing engine iterative learning mechanism of a general merging algorithm is integrated, and a high-quality model for simulating a grid is generated through multi-round iterative optimization. According to the method, double challenges of internal dirty geometry of a single model and derivative dirty geometry of intersection / adjacent areas among multiple bodies are solved, and key physical attributes and topological continuity of electromagnetic signal links are ensured and are completely reserved during repair.

The invention relates to the technical field of geological disaster monitoring, and discloses a landslide dynamic simulation monitoring and early warning method based on multi-source sensing fusion, and the method comprises the steps: collecting multi-source data of a landslide body through a plurality of heterogeneous sensors, and enabling the multi-source data to be in space-time alignment after preprocessing; building a multi-parameter fusion model fusing a displacement field, a mechanical field and an environment field based on the preprocessed data, enabling the multi-parameter fusion model to output a deformation rate and a stability coefficient, and dynamically adjusting the weights of the displacement field, the mechanical field and the environment field according to a landslide evolution stage; predicting a future deformation trend of the landslide mass in combination with a geological structure and historical data, comparing a stability coefficient with a dynamic safety threshold to judge a risk level, and generating early warning information; and dynamically correcting a reference weight coefficient in the model based on the deviation between the monitoring data and the model output, so that the model is adaptively optimized. The problems of single monitoring dimension and static model solidification in the prior art are solved, and accurate and adaptive monitoring and early warning of the risk state of the landslide mass are realized.

The invention discloses a resume analysis method and system based on multiple large language models, and belongs to the technical field of large language models.The resume analysis method and system based on the multiple large language models.The resume analysis method and system based on the multiple large language models comprise the following specific steps that firstly, model parallel analysis is conducted, simultaneously inputting the data into at least two heterogeneous large language models through an application program interface; and each large language model independently performs information extraction and analysis according to the model structure and the training data of the large language model, and outputs a structured data result containing a plurality of preset fields. Through the multi-model parallel analysis and conflict re-judgment mechanism, the misjudgment risk of a single model is effectively reduced, the robustness of the whole system is improved, the resume analysis accuracy is remarkably improved, the model pool is automatically optimized and updated through the dynamic scoring mechanism, and the problem that the model is difficult to select and update is solved.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.

The invention relates to the technical field of remote sensing monitoring, solves the technical problems that an existing soil salinity inversion method is insufficient in precision and poor in generalization ability, and particularly relates to a bare soil salinity inversion method fusing partial least squares and a random forest. Comprising the following steps: acquiring spectral reflectivity data and salinity spectral index of a multiband range and spatial resolution, and preprocessing to establish a digital orthoimage; removing a water body and a vegetation coverage area in the digital orthoimage by using a normalized vegetation index to obtain bare soil data containing 24-dimensional feature variables; establishing a fusion model and carrying out training evaluation; and performing prediction by taking Sentinel-2 satellite remote sensing data as input of the fusion model to obtain the salt content of the bare soil. According to the method, the precision, reliability and generalization ability of soil salinity inversion can be effectively improved, compared with a traditional single model, the nonlinear relation and noise in remote sensing data can be better processed, and the generalization ability of salinity inversion is remarkably improved.

The embodiment of the invention discloses a collaborative reasoning method and device based on a multi-expert model and a medium, and relates to the technical field of collaborative reasoning, and the method comprises the steps: receiving a plurality of reasoning task requests, so as to obtain the task feature information of each reasoning task; based on the task feature information of each reasoning task and pre-acquired model registration information, performing task allocation to determine at least one allocation expert model corresponding to each reasoning task; predicting the model load of each distributed expert model, adjusting at least one distributed expert model corresponding to each reasoning task according to the predicted load rate, and determining at least one corresponding target expert model; and issuing each reasoning task to the corresponding target expert model, collecting single model reasoning data of each target expert model through an asynchronous message queue, and determining a collaborative reasoning result corresponding to each reasoning task through the single model reasoning data of each target expert model.

The invention relates to the technical field of rail transit detection, in particular to a machine vision detection method and system for railway sleeper supporting fastener missing, and the method comprises a data acquisition step, a detection model construction step, an environment judgment step, a model selection step and a defect detection step. And the optimal detection model is selected according to the environment type, so that the problem of long processing time of a single model in a complex environment in a traditional method is avoided, the detection efficiency is improved, potential safety hazards can be fed back in time, and the problems of long missing detection response time and low detection efficiency in the prior art are solved.

The invention relates to a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

The invention discloses a knowledge graph completion method based on combined prediction of large and small models, which comprises the following steps: 1, constructing and preprocessing a knowledge graph completion reference data set, and training by adopting a RotatE model to obtain candidate entities generated by the model and confidence scores; 2, constructing a related triad, an adjacent triad and entity long text description based on the query to form a context prompt, inputting the context prompt and the query into a large language model, and performing semantic reordering and scoring by the large language model; and 3, constructing a fine tuning data set, and performing fine tuning on the large language model to obtain the KGC task optimization-oriented large language model. And based on the KGC score, the LLM score and the dynamic weight, outputting a complementation result through joint prediction of a fusion result. According to the method, the structured reasoning ability of the small model and the deep semantic understanding of the large language are effectively combined, the prediction accuracy, robustness and specialty are remarkably improved, and the defects that a single model is weak in generalization ability and insufficient in semantic utilization are overcome.

The invention discloses a laser radar echo signal distance inversion method based on a deep learning combination model, and belongs to the technical field of laser radar signal processing. According to the method, firstly, a CNN, ResNet or DCNN architecture is adopted to construct a spatial feature extraction module, and local and global spatial features of echo signals are extracted through multi-scale convolution, residual connection and an adaptive denoising mechanism; then, a time sequence dependency relationship of the signals is captured by utilizing BiGRU bidirectional time sequence modeling and a multi-head self-attention mechanism of Transform; and integrating the spatio-temporal features through the feature fusion layer, and then outputting a distance inversion value through the full connection layer. A parallel multi-scale CNN, a shrinkage enhanced residual network and a lightweight DCNN structure are innovatively designed, and the problems of insufficient spatial-temporal feature fusion of a single model and precision degradation under low signal-to-noise ratio interference are solved in combination with dynamic gating adjustment of a bidirectional gating circulation unit and dynamic position coding of Transform. According to the method, the precision and robustness of distance inversion in a complex environment are remarkably improved, and the method can be applied to the fields of automatic driving, environment monitoring and the like.

The invention discloses a fuel cell fault diagnosis method based on a CNN (convolutional neural network)-Transform. The method comprises the following steps: collecting original data; performing data preprocessing and feature importance evaluation selection on the original data; dividing the data samples selected through feature importance evaluation into a training set and a test set; respectively inputting the training set and the test set into a CNN-Transform model for feature extraction, and outputting a new training set and a new test set; constructing double full connection layers, and outputting three operation states of fuel cell normality, membrane dryness and hydrogen leakage predicted by the corresponding CNN-Transformer model to obtain a prediction score of each group of data; the optimal parameter configuration of the CNN-Transform model is obtained, and the optimal parameter configuration of the CNN-Transform model is obtained; and substituting the new test set into the CNN-Transform model after parameter optimization, and outputting a fuel cell fault diagnosis result. Through the CNN-Transformer algorithm provided by the invention, the three operation states of the fuel cell, i.e., the normal state, the membrane drying state and the hydrogen leakage state, can be better distinguished, and the problems of insufficient robustness and poor generalization ability of a single model are solved.

The invention belongs to the technical field of gas emission concentration monitoring, and particularly discloses an Informer-based method for monitoring the emission concentration of harmful gas in a large-scale farm in real time. According to the method, aiming at the characteristics of high nonlinearity, variable redundancy, complex coupling and the like of the emission data of the farm, a fusion model based on an Informer model and an RLS model is constructed, and the perception and response capability to the harmful gas emission concentration change is improved. According to the fusion model, global trend prediction is achieved through an Informer model, then dynamic error correction is conducted through an RLS model, the real-time performance, self-adaptability and robustness of the fusion model are effectively enhanced in combination with a processing mechanism of prediction first and parameter lagging update, and the fusion model is obviously superior to a traditional static or single model prediction mode. The method provided by the invention effectively solves the problems of response lag and inaccurate prediction in the current harmful gas emission concentration detection technology.