132 results about "Madaline" patented technology

The invention discloses an intelligent auditing method and system based on a multi-modal large model, and relates to the field of artificial intelligence, and the method comprises the following steps: a field adaptive model is constructed by injecting financial and accounting field corpora into an LLaMA-3 architecture, accounting criteria, policies and regulations can be automatically analyzed, executable auditing rules can be generated, the system adopts a multi-channel structure, and the auditing efficiency is improved. According to the method, key entity extraction, logic relation identification and ambiguity resolution are synchronously completed, the accuracy and robustness of rules are ensured, multi-source heterogeneous data are integrated by constructing a finance and accounting knowledge graph, generated SPARQL query is executed to realize automatic auditing testing, block chain anchoring rule hash and a three-dimensional evidence matrix are innovatively introduced, and the auditing efficiency is improved. And the traceable and non-tampering decision traceability of the whole process is realized. According to the method, the auditing efficiency, the coverage and the automation level are remarkably improved, the auditing risk is effectively avoided, and a highly credible solution is provided for intelligent auditing of man-machine cooperation.

The invention belongs to the technical field of computer artificial intelligence, and relates to a citrus intelligent planting management-oriented large model field quantification and adaptive model deployment method, which comprises the following steps of: firstly, constructing a large model calibration data set and carrying out preprocessing, and inputting a Transform model to execute forward reasoning; then carrying out left multiplication rotation on a weight matrix of a to-be-quantized layer of the model and right multiplication rotation on an activation matrix, executing GPTQ quantization, calculating a dynamic adaptive smoothing factor of each channel of the activation matrix, executing normalization and symmetric quantization according to hidden dimension grouping, and generating a quantized Transform model; then obtaining citrus industry text data to construct a fine tuning instruction set, and performing supervised training on the quantitative model to generate a full-quantitative and full-precision model; and finally, by training a task complexity classifier, selecting a full-quantization or full-precision model as a target deployment model according to a task complexity level, so that efficient quantization and intelligent task adaptation of the model are realized.

The invention discloses a juicy peach yield prediction method based on comprehensive data analysis, and particularly relates to the technical field of agricultural intelligent perception. The method comprises the following steps: collecting multi-source data of an orchard, and constructing a data set containing meteorological, physiological, remote sensing and soil information; fruit tree physiological response parameters are extracted, and a bimodal diagram structure fusing the spatial adjacency relation and the physiological state similarity is established in combination with the dynamic climate anomaly index; inputting the graph structure into a graph neural network model, extracting spatial-temporal characteristics, dynamically adjusting an edge weight and a propagation layer number, and constructing an adaptive model; performing region division and weighted summarization according to a model output result, and finally obtaining a predicted value of the total yield of the orchard; the method improves the prediction accuracy under the conditions of complex climate and unstable data, and is suitable for refined orchard management.

The invention belongs to the technical field of network security, and provides a network threat real-time detection and defense method and system based on artificial intelligence. The method comprises the steps of multi-modal data acquisition and preprocessing, dynamic graph feature engineering and knowledge graph collaborative fusion, dual-adaptive model training and optimization, streaming real-time detection and anomaly scoring, DRL-driven hierarchical defense response and automatic disposal, and feedback-driven model adaptive updating and block chain auditing. According to the method, a mixed model of OS-ELM + dual-adaptive ridge regression + federated learning is designed, the training speed is higher than that of CNN, and over-fitting / under-fitting is avoided by dynamically adjusting a regularization coefficient; the federal learning realizes data local training and parameter uploading, and solves the problem of privacy disclosure; knowledge distillation enables the model volume to be reduced, edge equipment deployment is adapted while the accuracy is maintained, and the generalization ability is obviously superior to that of a traditional static model.

The invention provides an interpretable domain adaptive fault diagnosis method based on source domain attribution alignment. The method comprises the following steps: constructing an adversarial domain adaptive network comprising a cross-domain feature extractor, a domain discriminator and a fault classifier; calculating envelope spectrum characteristics of the input monitoring signal based on signal processing knowledge, and calculating integral gradient envelope spectrum characteristics of a source domain sample by means of a decision result in fault diagnosis model training; designing attribution alignment loss, and aligning physical envelope spectrum characteristics of a source domain sample and attribution envelope spectrum characteristics generated by model decision through maximum mean value difference; and synchronously optimizing domain discrimination loss, fault classification loss and source domain attribution alignment loss. The method fully focuses on the consistency of the model decision and the physical characteristics in the source domain training stage, introduces the physical rule constraint for the domain self-adaption process, enables the domain self-adaption model to learn cross-domain data characteristics meeting the physical rule and decision logic at the same time, and can improve the fault diagnosis precision and interpretability of the model.

The invention discloses a dynamic simulation prediction model construction method based on data driving. The dynamic simulation prediction model construction method comprises the following steps: S1, collecting multi-source heterogeneous multi-modal original data and preprocessing to obtain a standardized data set; s2, extracting a long-term dependent feature sequence by adopting a Mamba depth state space model; s3, establishing a parameter diffusion space by using the self-adaptive multi-mode diffusion model, and obtaining diffusion characteristic parameters; s4, performing reverse denoising convergence processing on the diffusion characteristic parameters to generate real-time adaptive model parameters; s5, performing dynamic weighted fusion of the feature sequence based on real-time adaptive model parameters; s6, inputting the fusion sequence into a dynamic simulation prediction model framework for training, and obtaining trained model parameters; and S7, solidifying model parameters to complete model construction. According to the invention, the dynamic prediction precision and generalization performance are improved.

The invention discloses a power system distribution robust scheduling optimization method based on optimal configuration, and relates to the field of power system robust scheduling, and the method comprises the steps: carrying out the coupling modeling of a power transmission network extension plan, a DPFC and an energy storage device, and solving a model to generate an optimal configuration scheme; constructing an uncertainty set capable of being dynamically updated, and obtaining a day-ahead scheduling scheme by adopting a dual-stage distribution robust optimization method in combination with the optimal configuration scheme; on the basis of a day-ahead scheduling scheme, a hybrid robust and adaptive model prediction control framework is utilized, and a real-time control parameter of the DPFC and a rapid power adjustment instruction of energy storage are generated through rolling optimization; according to the invention, by combining optimal configuration, dual-stage distribution robust optimization and hybrid robust adaptive model prediction control, collaborative optimization of long-term planning and short-term scheduling is realized, and through real-time model parameter updating and dynamic uncertainty processing, real-time scheduling is realized. The problems that a scheduling scheme is insufficient in robustness, and economical efficiency and real-time adaptability are difficult to consider are solved.

The invention relates to the cross technical field of artificial intelligence and geological disaster monitoring and early warning, in particular to a landslide recognition method and system based on staged feature adaptive transfer learning, and the method comprises the steps: constructing an initial landslide recognition model F1 with an encoder-decoder; a universal visual pre-training weight is migrated, and encoder parameters of the model F1 are finely adjusted by using a remote sensing image data set DS of a source domain, so that a landslide identification optimization model F2 adaptive to the field is obtained; inputting the remote sensing image data set DS of the source domain and the unmanned aerial vehicle image data set DT of the target domain into the model F2, and processing based on a covariance alignment mechanism to obtain a domain adaptive model F3; inputting a small amount of labeled target domain small sample data D 'T into the model F3, and finely adjusting specified parameters in the model F3 through a supervised learning mechanism to obtain a target domain adaptive model F4; and based on the target domain adaptation model F4, executing a landslide identification task on an input to-be-detected unmanned aerial vehicle image.

The invention discloses a federal remote sensing large model training method and system based on low-rank self-adaption. The method comprises the following steps: performing personalized heterogeneous data knowledge learning by using respective remote sensing data, a pre-trained large model and a local low-rank self-adaption model; using the trained local low-rank self-adaptive model parameters to initialize global shared low-rank self-adaptive model parameters, performing collaborative federal training based on the pre-trained large model and the global shared low-rank self-adaptive model, and learning global remote sensing task domain knowledge; after alignment of local heterogeneous data knowledge and global remote sensing task knowledge is realized in a different-rank alignment fusion mode, a federated remote sensing large model composed of parameters of a pre-trained large model and an aligned local low-rank adaptive model is output, so that the parameter quantity and communication overhead uploaded by federated training are greatly reduced, efficient training of the model is ensured, and the reliability of the model is improved. And self-adaptive alignment of local heterogeneous data and global remote sensing tasks is also realized, and the universality and robustness of the model are ensured.

The invention relates to the field of motor fault diagnosis, and particularly discloses a permanent magnet synchronous motor fault diagnosis method and system, which dynamically update motor physical model parameters through an online identification algorithm so as to reflect the characteristic change of a motor in real time. Thirdly, predicting the theoretical voltage of the healthy motor under the working condition by using the self-adaptive model and the real-time operation data, and calculating a residual sequence subjected to working condition normalization between the theoretical predicted voltage and the actual voltage; the residual signal is essentially free from the influence of working condition change, and only the abnormal characteristics caused by the fault are highlighted. And finally, inputting the high-robustness residual error sequence into a convolutional recurrent neural network, deeply fusing space-time fault features, and realizing accurate judgment on a motor fault type and confidence thereof, thereby effectively improving variable working condition adaptability and weak fault detection capability of diagnosis.

The invention relates to the technical field of household equipment intrusion detection, in particular to a mimicry intrusion detection method and system for household equipment, and the method comprises the steps: S0, starting credible verification and initialization; the method comprises the following steps: S1, carrying out multi-modal feature fusion processing; s2, disturbance type joint detection; s3, graded elastic response is carried out; s4, updating the self-adaptive model; s5, identifying a semantic exception instruction; and S6, neural morphology calculation is accelerated. According to the method, damage attack path dependence is detected through dynamic disturbance, multi-modal features are fused through a modal alignment mechanism, low-power-consumption acceleration is achieved through neuromorphic hardware, semantic anomaly instruction recognition and brain-like feedback offline optimization are combined, and the core problem that high-precision detection and high robustness cannot be considered at the same time in the prior art is solved; and particularly, real-time attacks and semantic spoofing attacks of unknown vulnerabilities are effectively resisted.

The invention provides a non-invasive brain-computer interface signal identification method and system combined with deep learning, and relates to the technical field of artificial intelligence and brain-computer interfaces. Firstly, noninvasive neurophysiological signals are collected, and time sequence fragments are obtained through impedance detection, quality scoring and time alignment; denoising the time sequence fragment to generate a time-frequency-channel three-dimensional tensor and manifold domain representation; performing self-supervised pre-training and applying mutual information alignment constraint to construct a double-branch basic model, and outputting joint representation; performing joint optimization on multi-term loss based on the tagged source domain data to obtain a double-branch recognition model; in the deployment period, manifold alignment and optimal transmission calibration are carried out on target manifold domain representation, calibration representation and time-frequency tensor are input into a model, only part of parameters are updated, and prediction entropy is minimized to obtain an adaptive model; and further outputting a final category and confidence coefficient based on dynamic weighted fusion of two paths of judgment results and consistency measurement. According to the method, the cross-session adaptability and the recognition precision are improved, and the interpretability and the real-time security are enhanced.

According to the self-adaption-based automatic control method and system for the numerical control typesetting machine, the dynamic feature tensor of the raw material is obtained, the defect that single data is prone to being interfered is overcome through the method of obtaining the multivariate data set, more comprehensive working condition input is provided for self-adaption control, a real-time machining track is obtained, and the machining efficiency is improved. The method comprises the steps of obtaining a track deviation vector through a real-time processing track, constructing a reinforcement learning model, inputting a dynamic feature tensor and the track deviation vector into the reinforcement learning model, obtaining an optimal control set, constructing a deformation self-adaptive model according to the optimal control set, and obtaining deformation quantity prediction data through the deformation self-adaptive model. According to the intelligent linkage method, deformation quantity prediction data is obtained, a composite control instruction is obtained according to the deformation quantity prediction data, the composite control instruction is input into a control module for real-time regulation and control, control is conducted through cutting data feedback typesetting paths, a reinforcement learning model and self-adaptive lifting are integrated, and typesetting precision is improved.

The invention discloses a knowledge graph-AI fusion fault diagnosis method for operation and maintenance management of a power distribution network, and relates to the technical field of artificial intelligence. The problems of low initial diagnosis accuracy, insufficient diagnosis adaptation capability in different grounding forms, long fault positioning time and the like caused by external interference of zero-sequence current signals are solved, and the conditions that the operation and maintenance efficiency is reduced and the power supply loss is increased are finally avoided. The method comprises the following steps: acquiring multi-source data and constructing a dynamically updated power distribution network knowledge graph; multi-mode fault feature extraction is carried out; using an adaptive AI model to identify a fault type and performing primary judgment; and accurately positioning a fault section and analyzing a spreading path in combination with a knowledge graph. According to the method, the single-phase earth fault preliminary diagnosis accuracy is improved to reduce invalid troubleshooting of operation and maintenance, cross-scene diagnosis errors are reduced to adapt to diversified operation and maintenance scenes, average positioning time consumption is shortened, and the supporting capacity of fault diagnosis on operation and maintenance management of the power distribution network is remarkably improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and particularly relates to a hypersonic flight vehicle protection control method considering non-starting of an air inlet channel. The method specifically comprises the steps of establishing a quasi-one-dimensional nominal mechanism model to describe flow field changes of an isolation section and a combustion chamber; adopting a physical information neural network to construct a reduced-order model as a real-time agent of the quasi-one-dimensional model; designing a dual extended Kalman filtering algorithm to realize joint online estimation of model parameters and blind area states, and introducing shock wave position pseudo measurement to form multi-loop feedback; a dual-channel fusion early warning system of a model channel and an intelligent sensor channel is constructed, and robust early warning is realized through adaptive weight; and designing a continuous adaptive model prediction controller, and dynamically adjusting a control strategy according to risk indexes and parameter uncertainty to realize active protection control.

The invention relates to a self-adaptive model updating method and device, electronic equipment and a storage medium. The self-adaptive model updating method comprises the following steps: acquiring distributed external sample data newly generated by a large language model; clustering processing is carried out on the out-of-distribution sample data to obtain an out-of-distribution sample cluster, and the out-of-distribution sample cluster at least comprises out-of-distribution sample data of the same structure label; and performing search training on the out-of-distribution sample cluster, determining a target adapter, and sending the target adapter to a database of the large language model to update the large language model. Static model updating is converted into a time-driven learning process by setting a self-adaptive updating mode, so that a large language model can be subjected to self-adaptive updating operation according to failure result feedback during reasoning in a training process, the model is easier to adapt, error accumulation is reduced, stable updating is ensured, accuracy is improved, and the training efficiency is improved. And the precision and robustness of the model are enhanced.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion method and system for an AI application all-in-one machine, and the method comprises the steps: receiving an original data set from a private cloud, a public cloud and an edge cloud, and carrying out the cross-modal feature fusion of the original data set, and obtaining a feature data set; executing cloud distribution on the received AI processing task and the feature data set to obtain a task execution strategy; performing resource state monitoring and access control on each cloud platform according to the task execution strategy to obtain a resource scheduling scheme; and performing federal learning coordination processing on the distributed AI model based on the resource scheduling scheme to obtain an adaptive AI model, the method realizes cross-cloud AI model collaborative optimization, enhances the generalization ability of the model in a heterogeneous cloud environment, and provides a technical solution for efficient operation of an AI application all-in-one machine in a hybrid cloud environment.

The invention discloses a three-stage cooperative lane changing method for a vehicle queue in a ramp diversion area in an intelligent network connection environment, and the method comprises the steps: firstly, determining a safety gap of a target lane based on a minimum safety distance and a maximum deceleration, and selecting a vehicle which can change the lane to the safety gap from a CAV queue for preferential lane changing; secondly, establishing a longitudinal trajectory planning model based on self-adaptive model predictive control, performing longitudinal distance adjustment on a lane-changed vehicle by using the model, and reversely calculating an initial position required by the vehicle to smoothly drive into a ramp based on a quintic polynomial trajectory planning method; embedding into adaptive model predictive control as a ramp tail end constraint to form a new safety gap of the target lane; and finally, the vehicles which do not change lanes are controlled to sequentially change lanes to the corresponding safety gaps, and lane changing of the CAV queue is achieved. According to the method, the CAV queue can efficiently and orderly drive into the exit ramp, the vehicle speed fluctuation of a shunting area can be reduced, and the overall passing efficiency of a road section is improved.

An adaptive model partitioning method and system applied to distributed training, which method and system belong to the technical field of deep learning, and aim at solving the technical problem of how to implement, in respect of distributed training, distributed model training by means of combining deep reinforcement learning and a Bayesian optimization algorithm. The method comprises the following steps: constructing a Q network on the basis of a deep neural network, and defining state information, actions and a reward function, wherein the state information comprises feature vectors of partitioned models, and training times, resource utilization rates and inter-node communication overheads of when the partitioned models are subjected to distributed training by means of distributed computing nodes, each action is a partitioning strategy used by an agent under the current state information, the reward function R is used for evaluating the effect of the current partitioning strategy, and the Q network uses the state information as input to predict and output a Q value of each action that the agent may take; and performing multiple iterative training on a deep reinforcement adaptive model, so as to obtain a final partitioning strategy and parameters of the Q network.

The invention discloses a fan nonlinear load self-adaptive control method and device and a storage medium. The fan nonlinear load self-adaptive control method and device are used for efficiently achieving dynamic tracking control over fan nonlinear loads. The method comprises the steps of collecting operation state data of a fan system; constructing a dynamic neural network model containing a long short-term memory network-attention mechanism mixed structure, inputting the operation state data into the dynamic neural network model, and outputting a dynamic predicted value of the fan nonlinear load; constructing an adaptive model prediction controller based on the dynamic prediction value, and generating a control sequence; calculating a prediction residual error of the dynamic neural network model, and when the prediction residual error exceeds a standard threshold value, triggering a model updating mechanism; performing parameter updating on the dynamic neural network model by adopting an incremental learning algorithm with gradient constraint to obtain updated parameters; and the safety control quantity is output to a fan execution mechanism, and dynamic tracking control over the nonlinear load of the fan is achieved.

The invention discloses a laser radar point cloud semantic segmentation method based on passive domain self-adaption, and relates to laser radar point cloud processing. Under the condition that source domain data cannot be obtained in a data privacy scene, a passive domain adaptive model is invented, two-way pseudo tag selection and multi-level consistency learning are included, and a domain adaptive point cloud segmentation task is solved. The model adopts a knowledge distillation method to carry out domain transfer learning, and comprises a teacher model and a student model which are initialized by using a source domain model. And sampling the target domain point cloud data set, inputting the sampled target domain point cloud data into the teacher model, performing disturbance enhancement on the sampled target domain point cloud data, and inputting the enhanced target domain point cloud data into the student model. The method comprises the following steps: generating a bidirectional pseudo tag, dividing a target domain point cloud into two types of data similar to a source domain and data dissimilar to the source domain according to the pseudo tag, carrying out multi-level target domain consistency learning, carrying out model optimization by adopting global and category prototype consistency constraints, and generating a reliable pseudo tag for predicting the category of the point cloud. The method can be used in the fields of automatic driving systems, unmanned aerial vehicle equipment, virtual reality equipment and the like.

The application provides an asynchronous federated learning system and a neural network framework, and relates to the technical field of picking robots. The asynchronous federated learning system solves the ''short board effect'' of synchronous federated learning, and improves the performance of the global model on non-independent and identically distributed data through dynamic clustering and adaptive model aggregation. At the same time, the neural network framework enhances the perception ability and decision accuracy of the picking robot in a complex agricultural scene, thereby improving the collaborative training efficiency of the asynchronous federated learning system in the picking robot in the smart farm.

The invention relates to the technical field of digital twinning, in particular to a construction process digital twinning online deduction method and device.The method comprises the steps that a structured knowledge base covering safety risks, potential quality hazards and a process dynamic interaction mechanism is constructed, and a core knowledge basis is provided for generation of scheduling responses; aiming at the uniqueness of construction process scheduling, a reinforcement learning network special for dynamic scheduling of the construction process is designed, and intelligent and near-real-time construction scheduling decision is realized; aiming at the problem that a reinforcement learning model is difficult to effectively learn and respond to sudden construction disturbance, an adaptive model training method is researched, so that the model can learn how to dynamically adjust a process network when disturbance occurs, and the influence caused by process adjustment is reduced as much as possible while an engineering construction target is ensured. Therefore, the problems that most of dynamic change elements of the construction site considered in the prior art are resource change, weather change and the like, and response to events such as safety problems or potential quality hazards of the construction site is not achieved are solved.

The invention discloses a T ferromagnetic annealing performance consistency optimization method based on reinforcement learning, and the method comprises the steps: collecting the annealing temperature, heat preservation time, magnetic field intensity, cooling rate and other key process parameters in real time through an industrial Internet of Things, and improving the data quality through normalization and multi-stage data cleaning processing; intelligent evaluation and label classification of the process state are realized by using a multi-layer neural network; a dynamic reward function weight is introduced in combination with a process state label, and a deep Q network reinforcement learning model is driven to perform strategy optimization; the method supports self-adaptive model updating of periodic data acquisition and strategy rapid switching under significant change of a process state, realizes intelligent cooperative closed-loop control of multiple parameters in the annealing process, and improves the operation stability of the annealing process and the consistency of material performance.

The invention provides an asynchronous federal learning system and a neural network framework, and relates to the technical field of picking robots. The'short plate effect 'of synchronous federated learning is solved through an asynchronous federated learning system, the performance of a global model on non-independent identically distributed data is improved through dynamic clustering and adaptive model aggregation, meanwhile, the sensing ability and decision precision of the picking robot in a complex agricultural scene are enhanced through a neural network framework, and the picking robot can be applied to the agricultural field. Therefore, the cooperative training efficiency of the picking robot of the asynchronous federal learning system in the smart farm is improved.

The application discloses a double-layer adaptive RVM reliability analysis method for small failure probability, first, a first-layer adaptive RVM model is constructed by combining a Harris Hawks optimization algorithm and an adaptive RVM, and an important sampling sample is generated at a design point according to an iterative updating strategy to approximate the design point; then, the idea of active learning is utilized, a learning function is adopted to continuously update the RVM model, and a failure probability is solved after convergence. The application greatly improves the accuracy of reliability failure probability calculation results, reduces the calculation times, saves the calculation cost, and improves the ability of calculating small failure probability reliability.

The present disclosure proposes an actuator failure compensation and safety control method based on a double-layer evaluation architecture, a storage medium and a system. The bottom layer uses an adaptive model predictive control to output a nominal instruction, a reinforcement learning model is built thereon to output a high-frequency residual compensation instruction to eliminate model mismatch, and a second evaluation network is independently built to evaluate the future global loss-of-control risk of the vehicle. The Lagrange dual optimization is introduced to convert the macro risk evaluation into a mathematical constraint to guide the first strategy network correction; the global risk index is used to feed forward the target cost weight of the predictive control to realize continuous flexible degradation, and the control barrier function is triggered at the limit boundary to realize discrete quadratic programming instruction projection. The present application overcomes the conservative strategy degradation defect caused by the traditional mixed reward, establishes an absolute safety line, and improves the control accuracy and safety robustness of the vehicle in complex nonlinear working conditions.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based multi-model combinations in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling of model information exchange can be heavily congested. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be set up between network and UE by configuring multi-model combinations in association with UE RRC states.

The application belongs to the technical field of computer artificial intelligence, and relates to a large model field quantization and adaptive model deployment method for citrus intelligent planting management, comprising the following steps: firstly, a large model calibration data set is constructed and preprocessed, and a Transform model is input to perform forward inference; then, the weight matrix of the model to be quantized is left-multiplied and the activation matrix is right-multiplied and rotated, GPTQ quantization is performed, and the dynamic adaptive smoothing factor of each channel of the activation matrix is calculated, normalization and symmetric quantization are performed according to the hidden dimension grouping, and a quantized Transform model is generated; then, citrus industry text data is acquired to construct a fine-tuning instruction set, and the quantized model is supervised and trained to generate a full-quantization and full-precision model; finally, a training task complexity classifier is used to select the full-quantization or full-precision model as a target deployment model according to the task complexity level, and the application realizes efficient model quantization and intelligent task adaptation.

This invention discloses an adaptive voltage control method for a wireless power transfer system in an electric vehicle. The method includes: constructing an adaptive model predictive controller (EMC) using an RBF neural network; the EMC outputs a control quantity at time k and applies it to the wireless power transfer system; the EMC adaptively adjusts the state deviation between the wireless power transfer system and the state quantity at time k+1 of a fixed model reference system, then outputs the next control quantity and applies it to the wireless power transfer system; and the method continuously collects the system's state quantity and adjusts the control quantity to achieve online adaptive control of the wireless power transfer system. This invention achieves precise control of the dynamic wireless power transfer system in an electric vehicle, significantly improving the system's dynamic response performance and anti-interference performance. In particular, it can dynamically adjust the model in the face of various external disturbances, enhancing the stability of the control system.