274 results about "Parameter learning" patented technology

The invention provides a network attack link tracking and threat situation reasoning method based on a knowledge graph, and relates to the technical field of network security, and the method comprises the steps: collecting and processing original data; extracting features and constructing a network attack knowledge graph; performing representation learning by adopting a graph neural network; identifying a time sequence dependency relationship; generating and analyzing candidate attack links; constructing a dynamic Bayesian network model; performing parameter learning by adopting a variational reasoning algorithm; future attack targets and means are predicted. According to the method, the network attack link can be effectively tracked, the future threat situation is accurately predicted, and the network security defense capability is improved.

The invention discloses a hybrid model power prediction method fusing an improved snow ablation optimization algorithm and CNN + KAN + LSTM + MSA. The method comprises the following steps: constructing a snow ablation optimization algorithm according to a fusion good point set strategy and a periodic oscillation mutation strategy; and a CNN-KAN + BiLSTM-MSA hybrid model fused with the GVSAO algorithm and a hyper-parameter learning process are constructed. Through a good point set strategy, the initial solution distribution is more uniform, the coverage solution space is wider, and the global search capability is enhanced; the periodic oscillation mutation strategy prevents premature convergence of the solution, keeps diversity of the solution, and ensures exploration of the globally optimal solution; in the local development stage, candidate solutions are refined step by step, and the understanding quality and convergence efficiency are improved; the performance of a snow ablation optimization algorithm is greatly improved, and a global optimal solution can be better found; spatial feature extraction and time sequence learning are combined, so that the problem of time sequence prediction is effectively solved.

The invention discloses a VEM-Token vocal music emotion multi-mode token song sound and accompaniment deep learning method, which is different from an existing method that information is segmented into textual token lexical elements by artificial intelligence and then recognition is carried out on the textual token lexical elements. The method comprises the following steps: performing frequency spectrum processing on a vocal music file, detecting rhythm, segmenting the vocal music file subjected to frequency spectrum processing into a VEM-Token sequence according to the vocal music rhythm, establishing a VEM coordinate system, a VEM function and a VEM library according to multiple modes such as lyrics, singing sound, accompaniment, singer emotion, accompaniment emotion, video and image, performing VEM-Token identification, separating a singing sound stream and an accompaniment stream, and determining the vocal music according to a vocal music expert. And performing multi-modal emotion scoring on the vocal music sample, obtaining a VEM parameter by adopting supervised learning and deep learning algorithms, and learning to obtain the multi-modal emotion of the vocal music sample. For other vocal music works, vocal music multi-mode emotions can be recognized, and a lyric score, a VEM-Token song sound score, a VEM-Token accompaniment score and a VEM-Token music score are output. AI systems including a common large model and the like are accessed, and a vocal music agent Agent capable of listening to the singing and identifying the music score is developed.

The invention provides a karst cave environment data analysis method and system based on artificial intelligence, and the method achieves the high-precision prediction of a karst cave environment evolution mode and risk knowledge points through obtaining the karst cave environment monitoring data of a first template and a second template, and carrying out the multi-round parameter learning. By constructing the cave environment evolution mode prediction network and the cave environment risk prediction network, the evolution mode characteristics of the cave environment and the corresponding risk knowledge points can be accurately estimated and predicted. In the network parameter learning process, through continuous iterative optimization, the prediction network gradually approaches the real situation until the network convergence requirement is met, so that the cave environment risk prediction network and the cave environment evolution mode prediction network with high-precision prediction capability are generated, the intelligent level of karst cave environment monitoring is improved, and the prediction efficiency is improved. And accurate prediction of the environmental evolution mode of the karst cave and timely discovery and early warning of potential environmental risk points are realized.

The invention discloses an exoskeleton robot power auxiliary control method based on artificial intelligence, and relates to the technical field of intelligent control. The method comprises the steps that a power device of the exoskeleton robot is determined; acquiring a plurality of indexes for power output of the auxiliary power device; obtaining a first motion scene, and performing parameter learning of an index sequence on the plurality of indexes according to the correlation between the first motion scene and the plurality of indexes to obtain auxiliary learning parameters; the power device controls a power motor to adjust power parameters according to the auxiliary learning parameters. The technical problems that in the prior art, power-assisted control of an exoskeleton robot is not intelligent and accurate enough, power output is difficult to dynamically adjust according to a specific motion scene, energy efficiency is low, and user experience is poor are solved. Accurate adjustment of power parameters of the power device is achieved, and therefore the technical effects of improving energy efficiency and enhancing user experience are achieved.

The invention discloses a watershed water resource supply and demand risk prediction and evaluation method based on a multilevel Bayesian causal network, and relates to the technical field of water resource supply and demand risk management.The watershed water resource supply and demand risk prediction and evaluation method comprises the steps that a water resource supply and demand risk diagnosis knowledge graph is constructed according to key variables and interrelations input by a user; constructing a multi-level Bayesian causal network structure; estimating conditional probability distribution among the nodes, and performing parameter learning and structure training on the Bayesian causal network; carrying out risk path identification through a reverse Bayesian reasoning method; outputting a posterior probability of water resource supply and demand risk prediction; based on a preset fuzzy character string matching algorithm, typical risk events and risk features are extracted; and according to the posterior probability and the risk characteristics, comprehensively evaluating the water resource supply and demand risk level. The method can improve the systematicness and scientificity of risk identification, is suitable for multi-link and multi-scale risk assessment and scheme comparison and selection in a complex drainage basin, and has high practical value and popularization prospect.

The invention discloses a Beidou inertial navigation fusion positioning method and system for adaptive intelligent parameter adjustment, and the method comprises the steps: constructing an adaptive denoising model based on a state estimator and an actor-criticer network; training the adaptive denoising model, and inputting actual data into the trained adaptive denoising model to obtain a noise parameter correction value; performing adaptive adjustment on a noise covariance matrix of a Kalman filtering fusion positioning model according to the noise parameter correction value; and obtaining accurate position information according to the fusion positioning model after adaptive denoising. The system comprises a denoising model building module, a parameter learning module, a parameter adjusting module and a fusion positioning module. By using the method, dynamic noise which cannot be modeled in fusion positioning and self-adaptive adjustment parameters can be effectively removed, and the adaptability of a positioning system to a complex and dynamically changed urban environment is improved. The method can be widely applied to the field of satellite navigation.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention belongs to the technical field of intelligent education, and discloses a multi-modal artificial intelligence learning assistant based on an embedded platform, and the method comprises the steps: a data collection and processing module receives learning problems and student learning data, and carries out the standardization processing, and obtains a problem standardized data set; the cognitive model construction module constructs a learner cognitive model based on the problem standardized data set and student learning data, and marks knowledge gap nodes; the teaching scheme generation module determines a self-adaptive teaching scheme including knowledge depth parameters, a learning material combination strategy and an explanation expression strategy according to the structural features of the cognitive model; the learning sequence construction module constructs a customized learning sequence according to the knowledge gap nodes and the knowledge depth parameters; and the learning content generation module generates personalized learning content according to the learning sequence and the teaching scheme. Through the self-adaptive processing flow driven by the cognitive model, personalized teaching for different students is realized, and the learning efficiency and the learning effect are remarkably improved.

The invention discloses a nuclear power device fault diagnosis method based on feature vectors and Bayesian reasoning, belongs to the technical field of Bayesian reasoning, and particularly relates to nuclear power device fault diagnosis. The method solves the problem that a traditional Bayesian model only uses parameter values as a fault reasoning basis and ignores a large amount of parameter time-frequency feature information, so that the model is difficult to distinguish feature similar faults and realize accurate positioning of the faults, and the fault diagnosis accuracy and the model robustness are affected. The method comprises the following steps: a model learning step: performing structure learning and parameter learning on a Bayesian model according to fault historical operation data to obtain a Bayesian vector reasoning model; the nuclear power device fault diagnosis method based on the feature vector and Bayesian reasoning is suitable for fault diagnosis of a nuclear power device.

The invention belongs to the technical field of tunnel construction risk assessment, and provides a shield tunnel excavation face instability disaster risk quantitative assessment method and system. The evaluation method comprises the following steps: constructing a shield tunnel excavation face instability disaster risk evaluation system and converting the system into a Bayesian network topological structure; calculating the membership degree of each disaster evaluation index according to the digital feature calculation of the forward cloud generator and the to-be-evaluated data, and converting the membership degree into a Bayesian network prior probability; according to the historical data of the instability disaster of the excavation face of the shield tunnel and the Bayesian network topological structure, parameter learning is conducted on the historical data of the instability disaster of the excavation face of the shield tunnel, and the conditional probability of a Bayesian network model is calculated; according to the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topological structure, calculating a quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel; and according to the quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel, performing adjustment decision on shield tunneling control parameters.

The invention relates to a narrowband minimum mean square error echo suppression method introducing a complex neural network, and belongs to the field of adaptive echo cancellation. An existing neural network method still has the problem of inaccurate priori knowledge modeling, and phase information in complex signals is not fully utilized. According to the method, a plurality of DNNs are adopted to carry out modeling and estimation on step length, loudspeaker nonlinear distortion and error signals respectively. And the adaptive capacity to an echo channel in a complex environment is improved by utilizing the efficient parameter learning capacity of a plurality of neural networks. According to the method, the phase information of complex data is fully utilized, the nonlinear distortion problem of the loudspeaker can be effectively suppressed, meanwhile, the small model parameter quantity is kept, efficient echo suppression is achieved, high practicability and hardware adaptability are achieved, and the method is suitable for various practical application scenes.

The invention provides a sewage treatment water quality parameter real-time detection system based on deep learning, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting multi-source dynamic data in a sewage treatment process in real time through a distributed sensor network; the feature reconstruction module is used for performing feature space reconstruction on the multi-source dynamic data and generating dynamic correction parameters through time sequence correlation analysis; and the learning prediction module is used for inputting the dynamic correction parameters into a pre-trained multi-task deep learning model, analyzing and evaluating the influence degree of each feature variable through the contribution degree of the parameters, and dynamically adjusting feature importance distribution by adopting a self-adaptive weighting mechanism so as to obtain adjusted feature representation. According to the invention, real-time accurate detection of water quality parameters, timely early warning of standard exceeding risks, energy consumption optimization in a sewage treatment process and stable control of effluent quality are realized.

The invention relates to the field of image or video recognition, in particular to a local learnable query enhancement video recognition method, which comprises the following steps of: establishing a frame for adding video space-time modeling and migrating to a video task on the basis of an image large model; constructing a video behavior recognition model in the framework; constructing a training mechanism of large model main branch freezing and only new parameter learning, training a video behavior recognition model by using a server, and optimizing an objective function until network convergence to obtain a trained video behavior recognition model; and inputting a video sequence to be recognized into the trained video behavior recognition model to recognize human behaviors. The method has the beneficial effects that a video behavior recognition task can be quickly and efficiently migrated by only adding a small number of learnable parameters on the basis of an image model, human behaviors in a video can be recognized with high precision, and the method has high robustness and effectiveness in the aspect of processing action related details.

The invention relates to an Internet of Things time sequence root cause analysis method based on a dynamic causal diagram, and belongs to the technical field of Internet of Things. The method comprises the following steps: embedding a fine-tuning large language model by utilizing an Internet of Things knowledge graph, embedding, splicing and constructing a graph structure through entities and relationships, and combining text word embedding and mask prediction task optimization model; on the basis of knowledge graph subgraph construction, a triple is converted into a natural language to be input into a large model to generate a causal hypothesis; assumptions are converted into causal constraints, dynamic causal graph structure learning is carried out in combination with a Bayesian information criterion scoring function, and conditional probabilities of father nodes and historical values are modeled; performing parameter learning by adopting kernel density estimation, and quantifying time hysteresis among the features; feature probability distribution is predicted based on sliding window observation data, accumulative error contribution is calculated through asymmetric Shapley values, and causal ancestor features are preferentially sorted to determine root causes. Real-time and effective root cause analysis of the Internet of Things system is realized, and the system has the capability of intelligently solving faults.

The invention discloses a scanning radar super-resolution imaging method based on data-driven adaptive parameter learning, which is applied to the technical field of radar imaging and aims to overcome the defects that a traditional super-resolution method is high in parameter sensitivity and an existing network expansion method has a redundant structure and poor adaptability. The method comprises the following steps: firstly, converting a non-differentiable L1 regularization problem into a differentiable L2 problem based on a maximum-minimum principle; secondly, expanding an iteration process into a multi-stage cascade neural network, and performing adaptive learning on regularization parameters in a data driving mode; and finally, layered parameter optimization and super-resolution reconstruction are realized through an end-to-end network architecture. By adopting the method of the invention, the super-resolution imaging performance of the radar is effectively improved.

The invention discloses a vehicle fault diagnosis method, system and device based on a Bayesian network and a medium, and the method comprises the steps: constructing the Bayesian network according to preset vehicle data variables and fault root cause description, and initializing a conditional probability table of each node and the attention weight of each connection edge; inputting preset multi-source sensor sample data into the Bayesian network for parameter learning, and optimizing the conditional probability table of each node and the attention weight of each connection edge to obtain a vehicle fault diagnosis model; inputting the target multi-source sensor data into the vehicle fault diagnosis model to obtain target probability distribution of a plurality of target fault root causes, and determining the contribution degree of each input variable to each target fault root cause according to the attention weight; and generating a fault diagnosis thermodynamic diagram according to the target probability distribution and the contribution degree, and marking a key input variable and a corresponding causal chain in the fault diagnosis thermodynamic diagram. The method improves the intuition and interpretability of the diagnosis result, and can be applied to the technical field of fault diagnosis.

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 unmanned tracked vehicle trajectory tracking, in particular to a learning type unmanned tracked vehicle trajectory tracking prediction control method, which comprises the following steps of: establishing an interactive training virtual environment oriented to tracked vehicle trajectory tracking controller learning; receiving vehicle global expected trajectory point information output by a tracked vehicle trajectory planning module; acquiring the motion states of the crawler and the obstacle at the current moment; a controller expected parameter instruction and a crawler longitudinal acceleration and yaw angle acceleration expected instruction are determined; a tracked vehicle longitudinal lateral motion tracking controller is constructed, and a vehicle longitudinal lateral motion bottom layer control instruction is determined; motion states of the crawler and the obstacle at the next moment are acquired; data are collected while interaction between the crawler and the environment is carried out, and a data experience pool for controller parameter learning is constructed; and constructing a predictive controller parameter reinforcement learning algorithm. According to the invention, through an autonomous exploration learning technology, the trajectory tracking precision of the unmanned tracked vehicle in a dynamic complex environment is improved.

The invention discloses a blurred image NeRF modeling method, system and device based on a scattering light path model and a medium. The modeling method comprises the steps of blurred image initial pose calculation, camera pose interpolation, neural radiation field NeRF-based 3D scene modeling, scattering light parameter learning guided by an internal scattering light path model, blurred image synthesis prediction by using a scattering sensing volume rendering method, and parameter joint optimization based on blurred image luminosity loss. The system, the equipment and the medium are used for implementing the method. A light propagation phenomenon is represented as internal scattering of light at a medium or surface intersection point through an internal scattering light path model, a scattering light path direction and a sampling point distance are autonomously learned by means of a feedforward neural network, and image rendering is performed by integrating contributions of sampling points in the directions of a main light path and the scattering light path. The real imaging process of a blurred image in a complex illumination environment can be effectively simulated, the geometric ambiguity problem is effectively avoided, the convergence stability of the neural network is improved, and a finer reduction effect on geometric details of a fine object is achieved.

The invention discloses a fire safety assessment method and system based on machine learning, and relates to a machine learning and fire safety assessment technology, and the method comprises the steps: collecting image data, equipment operation data and environment monitoring data of a fire-fighting place; inputting the first image feature vector into a pre-trained deep convolutional neural network model to obtain a risk level label, and inputting the first standardized data into a long-short term memory network model to obtain a first time sequence prediction result; performing feature fusion on the first risk level label and the first time sequence prediction result, constructing a fusion feature matrix, and inputting the fusion feature matrix into a random forest model for parameter learning; and inputting newly collected fire-fighting data into the learned random forest model to carry out safety level prediction so as to obtain a fire-fighting safety prediction result and generate risk early warning information. According to the invention, risk early warning information can be generated in real time, fire-fighting potential safety hazards can be quickly responded, and the fire-fighting management efficiency is remarkably improved.

A coal slime flotation process control method based on a distributed pyramid dynamic Bayesian network comprises the steps that the coal slime flotation process is divided into three sub-processes, a global quality index variable and a local quality index variable are determined, and a global dynamic Bayesian network is established; key variables in the sub-processes are determined, correlation among the variables is analyzed, and process time delay in each process is determined; determining the number of time slices of the first layer of dynamic Bayesian network; performing structure learning, and establishing a network structure of a first layer of the model; combining the same nodes of adjacent time slices into one node, and constructing a second-layer dynamic Bayesian network structure; bayesian network parameter learning is carried out; actual working condition data information is collected, and when an abnormal working condition occurs, a sub-process causing abnormity is determined; a local pyramid dynamic Bayesian network is utilized to determine a root cause causing an abnormal working condition; making a control decision scheme according to the abnormal reasons, and eliminating abnormal working conditions; the method can eliminate abnormal working conditions in time.

The invention provides an earthquake landslide disaster chain risk assessment method, medium and equipment, and relates to the technical field of geological disaster chain risk assessment, and the method comprises the steps: obtaining earthquake-induced disaster chain data, and constructing an earthquake landslide disaster chain knowledge graph; according to the knowledge graph, acquiring disaster influence factors, determining association among the disaster influence factors, and determining key disaster influence factors with relatively high association degree; key disaster influence factors are used as Bayesian network nodes, node variable data are discretized, a maximum-minimum hill climbing algorithm is used for structural learning, and a directed acyclic graph structure of the Bayesian network is determined; performing Bayesian network parameter learning by using an expectation maximization algorithm to obtain a prior probability of Bayesian network nodes; if the prediction result of the model does not reach the expectation, the association between the nodes is adjusted, and if the prediction result reaches the expectation, the model is used for earthquake landslide disaster chain risk assessment. According to the scheme, the reliability of the prediction result can be improved.

The present invention discloses a method for collaborative simulation of multiple water quality indicators based on a physical information deep neural network, comprising the following steps: S1, determining the water quality indicators to be collaboratively simulated, and constructing a multi-source database for multi-water quality indicator system simulation; S2, selecting a deep learning model suitable for multiple output water quality indicators, and completing the construction of a physical information deep neural network for multi-indicator water quality collaborative simulation; S3, using a k-fold cross-validation method to train the water quality collaborative simulation model, and completing parameter learning of the water quality collaborative simulation model; S4, selecting evaluation indicators to evaluate the water quality collaborative simulation model, and improving the simulation effect by adjusting the network structure and optimizing hyperparameters until the water quality collaborative simulation model meets the simulation accuracy requirements; S5, based on the optimized water quality collaborative simulation model, using a deep learning model interpretation method to analyze the key driving factors of the collaborative changes of multiple water quality indicators, and complete the multi-indicator water quality collaborative simulation.

The invention provides a loudspeaker mask defect identification method and system based on visual inspection, and relates to the technical field of machine visual inspection, and the method comprises the steps: collecting a surface image of a loudspeaker mask through an industrial camera, calculating a median gray value in a pixel neighborhood to effectively filter out impulse noise, carrying out size standardization processing, and obtaining an image of the surface of the loudspeaker mask; a standardized image is obtained; according to the standardized image, a convolutional neural network is adopted, and a parameter learning rate is dynamically adjusted through an adaptive moment estimation mechanism, so that multi-scale feature mapping of mask textures is extracted; according to the multi-scale feature map, candidate defect regions are generated through a region suggestion network; and for a candidate defect area proposal, using a U-Net network and a learning rate to quickly approach an optimal solution, attenuating the learning rate according to an exponential law along with the increase of the number of iterations to stably converge to fine local optimum, and performing pixel-level fine segmentation on a defect boundary to obtain a defect mask image. According to the invention, the detection efficiency of the loudspeaker mask is improved.

The invention provides a three-dimensional target detection method based on monocular vision, and belongs to the field of three-dimensional target detection for automatic driving, and the method comprises the following steps: designing an improved backbone network Faster Net +, carrying out image feature extraction, constructing a multi-dimensional feature adaptive fusion module, and adaptively selecting and fusing high-dimensional and low-dimensional features; a feature enhancement attention module is introduced on a multi-scale feature layer extracted by the feature pyramid network, interaction between feature channels and correlation between different coordinates are considered at the same time, a target area is highlighted, and irrelevant background information is inhibited; introducing an effective target detection head network according to the three-dimensional target detection network model, and performing parameter learning on the network model by using a training data set; after training is finished, a test image is input, and the positions and categories of different types of targets in the image are determined by using the three-dimensional target detection network model.

The invention discloses a multi-water-quality-index collaborative simulation method based on a physical information deep neural network, and the method comprises the following steps: S1, determining water quality indexes needing collaborative simulation, and constructing a multi-source database for multi-water-quality-index system simulation; s2, selecting a deep learning model suitable for multi-output water quality indexes, and completing the construction of a physical information deep neural network for multi-index water quality collaborative simulation; s3, training the water quality collaborative simulation model by adopting a k-fold cross validation method, and completing parameter learning of the water quality collaborative simulation model; s4, selecting an evaluation index to evaluate the water quality collaborative simulation model, and improving a simulation effect by adjusting a network structure and optimizing hyper-parameters until the water quality collaborative simulation model meets a simulation accuracy requirement; and S5, based on the optimized water quality collaborative simulation model, analyzing key driving factors of collaborative change of multiple water quality indexes by adopting a deep learning model interpretation method, and completing multi-index water quality collaborative simulation.

The invention discloses a light-weight cabin efficient self-adaption machining method based on cutting parameter learning, solves the cutting parameter self-adaption problem in light-weight cabin machining, and belongs to the technical field of spacecraft structural part manufacturing or numerical control self-adaption machining. The method aims at improving the machining quality and the machining efficiency, minimizing the cutting force and maximizing the material removal rate, a mathematical model of two targets and cutting parameters is established, an orthogonal process test is designed to analyze the response characteristics of the two targets to the cutting parameters, the influence of the cutting parameters to the cutting force in the actual cutting process is analyzed, and the machining precision is improved. Optimized operation data and a tool database are formed; and learning of adaptive parameters is realized by applying an ACM adaptive optimization technology, and adaptive processing is carried out.

An apparatus of a vehicle comprises a memory storing at least one instruction and a processor configured to execute the at least one instruction. The at least one instruction may be configured to cause, when executed by the processor, the apparatus to: via a tuning parameter learning model for image correction, generate, based on received video data, a tuning parameter for adjusting image signal processing (ISP) for correcting the received video data; correct, based on the tuning parameter, the received video data; identify, via a video recognition model, at least one object in at least one image corresponding to the corrected video data; and control, based on the identified at least one object, autonomous driving of the vehicle.

The invention relates to a non-intrusive load decomposition method based on a hidden Markov model, and the non-intrusive load decomposition method based on the hidden Markov model is characterized in that the hidden Markov model is expanded into a factor hidden Markov model which is applied to non-intrusive load monitoring; wherein the task of non-intrusive load monitoring corresponds to a parameter learning problem and a state inference problem in the hidden Markov model; a public data set is used for experimenting a load decomposition method based on a hidden Markov model to confirm a decomposition effect, the hidden Markov model (HMM) is used for solving a non-intrusive load decomposition problem, a Viterbi algorithm is adopted for step-by-step solving, the Viterbi algorithm searches for an optimal state sequence by traversing all state transition conditions, and a non-intrusive load decomposition problem is solved. And applying dynamic programming to efficiently solve the state sequence with the maximum probability, and speculating the load state condition at the t + 1 moment according to the load condition at the t moment and the total power at the t + 1 moment in the step-by-step decomposition process.