2542 results about "Model prediction" patented technology

The present invention relates to the technical field of dynamic carbon sink accounting, and specifically relates to a dynamic carbon sink accounting system based on multi-modal AI remote sensing monitoring and blockchain-based evidence storage. The system collects optical remote sensing, radar, photosynthetically active radiation and meteorological data, performs unified spatiotemporal calibration on the data, and then fuses the calibrated data by means of a cross-modal attention mechanism, so as to generate multi-modal feature vectors, and inputs same into a TCN model for carbon stock and trend prediction. A prediction result and metadata are uploaded to a blockchain by means of smart contracts, so as to generate a carbon sink NFT including a geographic fence and a confidence level, thereby realizing trusted evidence storage. A residual mapping function is established in view of on-chain historical data, so as to dynamically optimize the model, and improve the accounting accuracy. The system improves the fusion capability and prediction accuracy, and enhances the credibility and transparency of carbon asset management and transactions.

The invention discloses a digital twin-driven bridge full life cycle damage prediction and evaluation method and system, and belongs to the field of bridge structure health monitoring, and the method comprises the steps: obtaining a monitoring data set of a bridge structure; an initial digital twinborn model embedded with a micro physical layer is constructed and trained, the micro physical layer constructs a damage evolution model applied with monotonic physical constraint based on multi-source monitoring data, and a damage evolution trajectory of the bridge in a future time period is predicted through the damage evolution model based on the physical parameter vector; in combination with an uncertainty quantification method, generating a time-varying reliability index of the bridge in a future time period; and based on the time-varying reliability index, constructing and solving a maintenance decision optimization model to generate a maintenance decision of the bridge. According to the invention, the physical authenticity and reliability of the long-term prediction result are ensured.

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 provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention relates to the technical field of industrial automation and intelligent control, in particular to a general control system strategy optimization method based on reinforcement learning, and the method comprises the steps: firstly collecting the operation data of a system, constructing a state vector of reinforcement learning, and enabling a reinforcement learning agent to fully understand the current operation condition of the system; then, the state vector is input into a reinforcement learning strategy network, an action for adjusting a control strategy is generated by the network, and the action can be used for modifying the proportion, integral or differential coefficient of PID and can also be used for adjusting the prediction step length, weight coefficient or constraint strength of model prediction control, so that the adaptive capacity of a controller to external changes is enhanced; then, a reward signal is constructed according to a response result of the reference controller; the reward function comprehensively considers the error size, the steady-state characteristic, the system energy consumption, the control smoothness and the stability requirement, so that the reinforcement learning not only pays attention to the error minimization when optimizing the strategy, but also considers the low energy consumption, the smooth action and the anti-interference performance at the same time.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

The invention discloses a flood type landslide disaster monitoring and early warning method, and belongs to the technical field of geological disaster prediction. The method comprises the following steps: step 1, acquiring a flood type landslide disaster case in which a rainstorm event and a landslide event coincide in time and space, collecting landslide factor data, environment data, monitoring data and historical landslide data, and constructing a historical database; step 2, constructing and training a Bayesian network model based on a historical database; step 3, acquiring landslide monitoring data and preprocessing the landslide monitoring data; step 4, training an LSTM time sequence prediction model; 5, inputting the real-time monitoring data into the LSTM model, and predicting to obtain future landslide monitoring data; and step 6, inputting future monitoring data into the Bayesian network to obtain a slope instability probability based on a future trend. According to the invention, through organic fusion of the Bayesian network and the LSTM, dynamic prediction and real-time response of the landslide instability probability are realized, and timeliness, accuracy and robustness of early warning are significantly improved.

The invention provides a ship unloader grab bucket system and method based on multi-modal perception and artificial intelligence semantic segmentation. According to the system, point cloud data and image data are synchronously collected through a laser radar and an RGB camera, feature level alignment and fusion are conducted through an image fusion module, an artificial intelligence semantic segmentation network is combined with attention gating to restrain dust interference, grab bucket boundary information is output, and a position and posture estimation module solves the grab bucket position and posture based on the boundary information and geometric constraints. The path planning module combines reinforcement learning and model prediction control to generate a trajectory instruction, and the compensation module implements hierarchical correction according to the pose deviation and drives an execution device to realize high-precision positioning and stable control under severe working conditions.

The invention discloses a storage and calculation separation key value storage delay optimization method based on prediction driving, and aims to solve the problems of performance bottleneck and high tail delay caused by passive management and high network delay in a key value storage system in a storage and calculation separation scene. The method comprises the following steps: time load prediction: deploying a time sequence prediction model at a client, and predicting a future read-write request based on a historical access sequence; active cache prefetching: according to the predicted read request, actively preloading hotspot data from a server side to a client side for caching so as to improve the cache hit rate and hide network delay; active write-in optimization: according to the predicted write request, executing maintenance at a server side through predictive pre-insertion and active node splitting, and moving high index structure adjustment overhead out of a key request path to eliminate a write delay peak; and structure sensing batch synchronization: pre-fragmenting a local write buffer by using a server index top layer model of a client cache, and combining multiple independent remote insertion operations into one efficient batch update to reduce data synchronization overhead. Compared with an existing passive management system, the characteristics of model prediction and active cooperation are fully utilized, and the average delay and the tail delay of the system are reduced.

The invention discloses a pressure sensor zero drift compensation method and system for a high-voltage direct-current converter valve cooling system, and relates to the technical field of monitoring and calibration of high-voltage direct-current transmission equipment. The method comprises the following steps: constructing a multi-factor coupling drift model taking temperature, vibration frequency and electromagnetic interference intensity as inputs; environment parameters and pressure signals are collected in real time, and reference pressure signals are collected regularly; short-term dynamic compensation is realized based on model prediction and reference deviation correction; updating the model through incremental learning according to the drift trend; and real-time monitoring and abnormal early warning are carried out. The system comprises a multi-parameter acquisition unit, a data processing unit, a reference pressure generation unit and a communication unit. According to the method, shutdown calibration is not needed, the compensation precision is improved by 75% or above, the calibration period is prolonged to 12 months, meanwhile, the method has the abnormal diagnosis and standby compensation functions, measurement continuity is guaranteed, compatibility is high, industrial popularization is easy, and the method is particularly suitable for being used in a complex environment.

The invention provides a rice irrigation online learning forecasting method and system, the method is realized according to a pre-constructed physical mechanism-neural network hybrid model based on physical mechanism model prediction and neural network error correction fusion, and the method comprises the following steps: S1, obtaining real-time environment data of a current decision period of a target rice field; s2, on the basis of the real-time environment data of the current decision period, forecasting a paddy field water layer depth predicted value of the next decision period through a physical mechanism-neural network hybrid model, and generating an irrigation drainage forecast in combination with a crop irrigation drainage mode; s3, real-time environment data of the target rice field in the next decision period after irrigation drainage forecast is executed are obtained, training samples are constructed and put into an experience playback pool, and the physical mechanism-neural network hybrid model executes online learning based on the experience playback pool; and S4, when the next decision cycle starts, returning to S2 until a preset stop condition is met.

The invention relates to the technical field of digital twinning, physical modeling and multi-source data fusion, and provides a digital twinning dynamic construction method based on multi-source data fusion and physical simulation. The method comprises the following steps: acquiring a multi-source heterogeneous data stream from a preset sensor array, a numerical simulation result and a historical database, identifying a key feature mode of a dominant physical process in the multi-source heterogeneous data stream, acquiring a key feature mode time-varying physical field evolution rule corresponding to the key feature mode by using a time sliding window and a forgetting mechanism, extracting a low-dimensional sparse characteristic parameter set reflecting dynamic behaviors from a high-dimensional observation space, and constructing a reduced-order proxy model by adopting Gaussian process regression, a neural network proxy model or an intrinsic orthogonal decomposition combined interpolation technology; and receiving a corresponding real-time observation data stream to establish a full-closed-loop feedback link from model prediction, high-fidelity solution verification to observation data correction in combination with the reduced-order proxy model so as to complete the construction of the digital twin.

The invention relates to an ORC heat exchanger optimization design method and system based on deep learning. The method comprises the steps that S1, original data samples are expanded based on a data enhancement method; s2, screening key input features of the data samples; s3, synthesizing minority class samples to balance a sample data set; s4, a physical information neural network model is constructed and trained, and heat exchanger performance indexes under different structure parameter combinations are predicted based on the trained model; s5, optimizing the structural parameters of the heat exchanger by adopting a multi-objective coevolution optimization algorithm; and S6, simulation verification is conducted, and the optimal plate heat exchanger design scheme of the target scene is obtained. Through three technical breakthroughs of data enhancement driven by physical constraints, neural network architecture embedded in thermotechnical physics and multi-target collaborative optimization guided by forward distance, systematic technical obstacles in design of the ORC heat exchanger are solved, and an unexpected synergistic effect is generated.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention relates to the cross technical field of artificial intelligence and traffic infrastructure monitoring, discloses an intelligent health monitoring and prediction method and system for a road roadbed, and aims to solve the problems of insufficient monitoring coverage, shallow data mining, low prediction model precision and disjunction of operation and maintenance decisions in the prior art. The method comprises the following steps: collecting roadbed multi-dimensional physical field data through a multi-source sensor network; denoising, abnormity correction, time alignment and feature compression are carried out at the edge end; fusing multi-scale time sequence modeling and spatial correlation analysis to extract health features; predicting a future health state and a risk probability by using an LSTM-Attention model in combination with historical data and environment variables; and triggering graded early warning based on the dynamic threshold and generating a maintenance strategy. According to the technical scheme, high-precision and high-timeliness roadbed health perception and prediction can be realized, and the early warning response speed and the maintenance decision intelligent level are improved.

The invention discloses an underground water pollution diffusion model construction method based on multi-modal data fusion, and the method comprises the steps: fusing the dynamic time sequence characteristics of a monitoring well and the static space attributes of a geological field, and generating an initial state vector with rich information for a graph node; based on the instantaneous water level difference between the nodes and the equivalent permeability coefficient, a dynamic graph topological structure which evolves along with time and represents the hydraulic connection relation is constructed; deducing a future state through numerical integration by using a graph neural network model with a node state derivative as output; and constructing a composite loss function containing a data fitting item and a physical law residual item, and training the model through back propagation. According to the method, the dynamic graph of physical driving is constructed and the physical equation constraint is introduced, so that the model prediction has the data driving precision and the reliability of the physical mechanism, and the generalization ability and the prediction precision of the model under the complex hydrogeological condition are improved.

The invention relates to an anti-interference speed control method for a permanent magnet synchronous motor, in particular to the technical field of electrical engineering, and effectively solves the problem that the control performance of the permanent magnet synchronous motor is degraded due to model parameter drift and external load sudden change under complex working conditions. According to the method, the high robustness of sliding mode control and the rolling optimization characteristic of model prediction control are creatively fused, and an adaptive observer is introduced to estimate the system state and lumped disturbance online, so that the real-time feedforward compensation and active suppression of parameter uncertainty and disturbance are realized; according to the method, the dynamic response speed and the steady-state precision of the system are remarkably improved, the rotating speed fluctuation and the torque ripple are effectively inhibited, meanwhile, controller parameters are adaptively adjusted through an intelligent learning mechanism, the stability and the adaptability in long-term operation are ensured, and the method is suitable for large-scale popularization and application. The limitation that a traditional control strategy depends on an accurate model and the disturbance boundary is unknown is overcome fundamentally, and high-performance and high-robustness speed control is achieved.

The invention relates to the technical field of computer storage, and particularly provides a large model training storage resource dynamic allocation method, device and system, and the method comprises the steps: collecting a workload index and a storage system state index of a large model training task; inputting the real-time monitoring data into a convolutional neural network (CNN) model, and outputting a feature identifier of a current training stage; inputting the real-time data and the stage identifier into a long short-term memory network LSTM model, and predicting future bandwidth, IOPS and storage space requirements; and generating a resource allocation scheme through multi-objective optimization according to a prediction result in combination with a system state, and executing load allocation, data layering and bandwidth reservation operations. According to the method, training stage perception and resource demand prediction are realized through cooperation of the CNN and the LSTM, and dynamic allocation and advanced scheduling of storage resources are realized, so that the resource utilization rate, the training efficiency and the system stability are improved.

The invention discloses a greenhouse gas concentration time sequence prediction method based on a sudden change perception attention mechanism. The method comprises the steps of data preprocessing, sudden change intensity sequence construction with boundary processing, time sequence feature coding, sudden change perception attention weight calculation, context vector generation and concentration prediction, model training and optimization and model prediction. The method aims to solve the problem that a standard deep learning model is slow in sensing and lagged in prediction for a sudden change event in a concentration sequence, and finally realizes high-precision prediction for future concentration change, especially a sudden concentration peak value by endowing the model with the capability of actively identifying and reinforcing the learning of a historical sudden change mode. The urgent demand for early warning of abnormal emission in practical application is met. The method is particularly suitable for processing foundation observation data with small resolution and even higher resolution, has the core value of improving the prediction capability of concentration dramatic change driven by sudden emission events, and can be widely applied to key scenes such as accurate carbon emission monitoring, environmental pollution early warning and climate model simulation.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention belongs to the technical field of electric power energy storage systems, and discloses a multi-element energy storage cooperative adjustment method, system, device and medium, and the method comprises the steps: constructing a discrete time dynamic equation based on the power system frequency response characteristics of a multi-element energy storage system; constructing a cost function of model prediction control according to a state matrix output by the discrete time dynamic equation, and solving the cost function to obtain a model prediction controller; outputting a target value through a deep learning model, substituting the target value into the model prediction controller, and generating a learnable model prediction controller; and calculating a prediction trajectory by using the learnable model prediction controller, comparing the prediction trajectory with a reference prediction trajectory obtained by pre-calculation, and updating deep learning model parameters according to a prediction trajectory comparison result. According to the learnable model prediction controller provided by the invention, the reference target of the model prediction controller is adaptively adjusted based on the deep learning model, so that the control effect of the model prediction controller is improved.

The invention discloses an operation control method for a wind power generation system, and relates to the technical field of operation control, and the method comprises the steps: obtaining the historical operation data of a wind power generation system in a target sea area, taking an offshore environment factor as an interference factor, and coupling the time-space parameters of ocean turbulence intensity, wave load and salt spray corrosion rate; establishing a fan-marine environment digital twinborn body; operation data is acquired in real time, synchronous mapping and dynamic updating of the digital twin are realized, and a state prediction model is established in combination with historical operation characteristics; taking the maximum power generation efficiency and the minimum mechanical load as a multi-objective optimization function, and introducing a model prediction controller to obtain an optimal control sequence of the wind power generation system in a future time domain of each control period; and selecting a first control instruction in the optimal control sequence, issuing the first control instruction to a physical fan torque controller and a variable pitch system in real time, realizing rolling optimization of the wind power generation system, and obtaining a self-adaptive operation control scheme of the wind power generation system. The power generation benefit of the whole life cycle is improved.

The invention discloses an electric vehicle lifting torque control method and system. The method comprises the steps that driving operation, vehicle state and environment data are collected in real time and preprocessed into multi-dimensional real-time feature vectors; based on the vector, extracting and updating a driving style feature vector by using an incremental clustering and lightweight time sequence convolutional network, and further mapping to generate a personalized cost function weight coefficient in model prediction control; synchronously operating the vehicle dynamics and thermodynamics simplified model, predicting a dynamic physical constraint boundary of a driving system in a future time domain, and constructing a dynamic feasible domain curve; taking a driver instant demand as a tracking target, fusing a personalized weight and a dynamic feasible region curve, constructing and solving a finite time domain constraint optimization problem, and generating a current period target torque instruction; and filter parameters are adaptively adjusted through inverse dynamic model feedforward compensation in combination with a dynamic feasible region, a final torque control instruction is generated, and unification of safety, smoothness and individuation of torque control is achieved.

The invention relates to the technical field of environmental protection, in particular to an intercepting well model prediction control method and system. Collecting multi-source information data, and performing weighting after data alignment to obtain a multi-source information set; based on the multi-source information set, constructing a hybrid model comprising a hydraulic model and a water quality model; automatically updating the hybrid model based on the real-time multi-source information set, and outputting state data; establishing a multi-objective optimization function for the water quality model, and solving to obtain a control parameter optimal solution set by taking the sewage overflow pollution load, the waterlogging risk data, the inflow water quality fluctuation range of the sewage treatment plant and the weighted minimum value of the system energy consumption as objective functions; and on the basis of the state data and the control parameter optimal solution set, rolling optimization control is implemented. Through fusion of multi-source prediction, dynamic adaptive modeling, multi-target collaborative optimization and closed-loop rolling control, the overall efficiency of the urban drainage system is significantly improved, and the operation level of the system is comprehensively improved.

The invention discloses a disaster recovery resource scheduling method and device based on a heterogeneous cloud environment, and the method comprises the steps: constructing a multi-dimensional resource portrait model and a cross-cloud network topological graph in the heterogeneous cloud environment, and generating a standardized resource vector set and a topological affinity scoring matrix; aggregating the local SLA default risk prediction model of each cloud node through federated learning, and performing fine tuning through transfer learning to obtain a global prediction model; calculating load fluctuation entropy based on the SLA constraint template and real-time load data, and inputting the load fluctuation entropy into a global model to predict and obtain an SLA default probability set; constructing and solving a dynamic weight multi-objective optimization function to obtain an optimal takeover cloud node list and a migration strategy identifier set; a target cloud node and a migration strategy are determined through SLA simulation verification, and an adaptive execution adapter is called to complete virtual machine incremental snapshot migration or cross-cloud arrangement deployment of containerized services. According to the invention, intelligent and automatic scheduling of disaster recovery resources is realized, and the fault takeover speed and the cross-cloud resource utilization rate are improved.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention discloses a method and system for accurately controlling the concentration of dissolved oxygen (DO) of a sewage treatment aeration unit, and the method comprises the steps: constructing a prediction model, and obtaining a model prediction value of the DO concentration at a current moment or a future moment according to the prediction model; calculating a current confidence coefficient; taking the current confidence coefficient as a weight coefficient, and carrying out weighted fusion calculation on a DO concentration measured value at the current moment and a DO concentration model prediction value to obtain a DO concentration fusion variable; the aeration air volume is adjusted or controlled with the purpose of minimizing the error between the DO concentration fusion variable and the DO concentration target value; the problem of overshoot oscillation caused by DO response lag in traditional control is solved, and a dynamic and accurate control solution is provided for the DO concentration of the aeration unit of the sewage plant.

The invention discloses a multi-parameter self-adaptive optimization intelligent vehicle trajectory tracking control method in the technical field of intelligent vehicle control. The method comprises the following steps: estimating a vehicle motion state in real time according to a three-degree-of-freedom dynamic model and a sensor measurement value by adopting a self-adaptive extended Kalman filtering algorithm; based on the estimated vehicle motion state, designing and implementing a model prediction controller to perform trajectory tracking; calculating to obtain an optimal control increment sequence in a future control time domain; taking a first element of the optimal control increment sequence as an actual control instruction increment at the current moment to act on a vehicle actuator, and entering a next control period, so as to adaptively balance closed-loop trajectory tracking control of tracking precision and driving stability; according to the method, adaptive state estimation and fuzzy dynamic predictive control are fused, deep coupling of high-precision perception and intelligent decision making is achieved, and the trajectory tracking precision, the driving stability and the system robustness are remarkably improved under the complex working condition.

The invention discloses a line-imitating flight control method and system of an unmanned aerial vehicle, a medium and equipment, and belongs to the field of unmanned aerial vehicle flight control, and the method comprises the steps: obtaining a three-dimensional point cloud based on an unmanned aerial vehicle laser radar, calculating local geometric features, and marking power line points and tower points according to preset rule semantics; fitting a power line reference track through the mark points, and taking the area occupied by the tower as an obstacle; in combination with the reference trajectory and obstacle information, generating an initial feasible path under the condition of meeting the dynamic constraint of the unmanned aerial vehicle; with a weighted cost function of path smoothness and trajectory closeness as a target, iteratively optimizing path point positions to obtain an optimized flight path; in combination with the real-time state, a flight instruction is generated through model prediction control, and the unmanned aerial vehicle is driven to fly along the optimized path. By implementing the invention, the problem that the unmanned aerial vehicle in the prior art cannot sense the line structure in real time and cannot autonomously generate the safe line-sticking flight path in a complex electric power inspection environment can be solved.