169 results about "Bayesian estimator" patented technology

The invention relates to a campus Internet of Things terminal scheduling method based on digital twinning. The method comprises the following steps: defining a general twinning state representation structure; introducing a state mapping relationship, and establishing a one-to-many dynamic mapping relationship between a physical state and a virtual twinning state by using a finite state machine; when the state of the physical terminal changes, triggering multi-level response mapping of the twin in the virtual space; semantic fusion is carried out on twinborn real-time data, a scheduling sensitivity index is introduced, disturbance simulation is carried out based on past scheduling behaviors, and the potential influence of a certain scheduling behavior on the whole system is analyzed; carrying out local division on the situation map; using Bayesian estimation and Monte Carlo combination to sample and evaluate the feasibility and income of each solution set in a period of time in the future; simulating the expected influence of each scheduling strategy through a twin body, and selecting a scheduling path; unified modeling and semantic fusion of multiple types of terminals are realized, the intelligent level and predictive ability of scheduling decision are improved, the collaborative scheduling ability of the terminals is enhanced, and coupling unit level regulation is realized.

The invention provides an automatic driving decision-making system based on information entropy-Bayesian estimation. The automatic driving decision-making system comprises an environment sensing module, a hybrid quantification module and a decision-making optimization module. The environment sensing module is used for collecting and processing sensor data and completing target detection and classification; the hybrid quantization module performs reliability quantization on the recognition result based on information entropy and Bayesian uncertainty estimation, generates uncertainty evaluation results of different targets and scenes, and transmits the uncertainty evaluation results to the decision and control module; and the decision and control module dynamically adjusts the strategy of the automatic driving system according to the uncertainty measurement of each target. The automatic driving decision-making system based on information entropy-Bayesian estimation is used for quantifying the target recognition reliability in an automatic driving scene. The output confidence distribution of the target recognition model is evaluated by calculating the information entropy, and the uncertainty of the recognition model is estimated by using a Bayesian uncertainty estimation (such as Monte Carlo Dropout) method.

The invention provides an intelligent power distribution comprehensive monitoring system integrating the Internet of Things and cloud computing. The system is applied to the technical field of power distribution monitoring and comprises a communication network and a cloud computing platform module, the data acquisition and transmission module is used for acquiring equipment operation parameters, environment parameters and the like of key point locations of a transformer substation or a power distribution substation in real time, and uploading the data to the cloud computing platform after data protocol conversion; the data analysis and processing module is used for carrying out time sequence modeling and fusion by adopting intrinsic mode modeling and warp dynamic linear modeling respectively to obtain a target parameter comprehensive predicted value; the fault prediction module is used for performing fault prediction based on the closed continuous neural network and a Bayesian estimator, fusing prediction results and outputting a final fault prediction result; and the intelligent control module is used for optimizing the equipment operation environment and early warning, and realizing integrated supervision of the transformer substation or the power distribution substation. According to the invention, the operation monitoring and fault prediction capabilities of the substation (distribution substation) equipment are improved.

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention discloses a degradation track-based cylinder residual life dynamic prediction method and system. The method comprises the following steps of: preprocessing multi-source degradation original data to obtain a standardized training sample set; quantizing a cylinder performance degradation rate trend by adopting a drift coefficient, quantizing cylinder performance degradation random fluctuation intensity by adopting a diffusion coefficient, and establishing a Wiener process degradation model through the drift coefficient and the diffusion coefficient; substituting the standardized training sample set into the Wiener process degradation model, carrying out parameter self-learning by fusing Bayesian estimation and an EM algorithm, and outputting a final Wiener degradation model; inputting the real-time recorded data into a preset Wiener degradation model to obtain a prediction result of the residual life of the cylinder; dynamically updating the Wiener degradation model according to the prediction result of the residual life of the air cylinder and the real-time recorded data of the air cylinder at the next time step; the online dynamic accurate prediction of the residual life of the cylinder is realized, and the prediction precision and the model environment adaptability are effectively improved.

The invention discloses a comprehensive monitoring and early warning method and device for roadway surrounding rock, and relates to the technical field of underground engineering geotechnical engineering. Collecting physical parameter data of the surrounding rock in real time through a sensor network; transmitting the collected physical parameter data to a monitoring center through a network, and carrying out filtering, denoising and abnormal value elimination to obtain standard multi-source data; comprehensively analyzing the multi-source data by using a method of combining Bayesian estimation and fuzzy logic, and extracting a stability score of the surrounding rock stability; and training the relationship between the physical parameter data and the stability score, establishing a prediction model of the surrounding rock stability, and performing early warning when the predicted real-time stability score exceeds an early warning threshold. Therefore, through the constructed surrounding rock stability prediction model, comprehensive and real-time monitoring and high-precision early warning of the surrounding rock of the roadway are realized, the unstable condition of the surrounding rock is found and processed in time, and safety accidents are avoided.

The invention provides an end-to-end automatic driving behavior decision-making method and system considering uncertainty estimation, and the method comprises the steps: inputting image information into a convolutional neural network, carrying out the feature extraction, obtaining the state information of surrounding traffic participants, carrying out the feature fusion of the state information of a vehicle, and inputting the fusion features into a full connection layer; constructing an integrated quantile network to obtain complete uncertainty estimation of an agent decision result, wherein the complete uncertainty estimation comprises data uncertainty estimation based on an implicit quantile network and model uncertainty estimation based on a multi-model integrated network considering prior Bayesian estimation; evaluating the confidence coefficient of the agent decision result, if the confidence coefficient is high, outputting the optimal driving action of the vehicle predicted by the integrated quantile network, and if the confidence coefficient is low, switching to the braking strategy output; and a control module of the end-to-end automatic driving decision model agent is further input, and a control signal is output to enable the vehicle to execute different control actions. According to the invention, the reliability and credibility of end-to-end automatic driving behavior decision are improved.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel laser radar three-dimensional dynamic monitoring method. Aiming at the problems that in the prior art, data of a laser radar is easy to interrupt or unreliable under a severe working condition, and monitoring continuity is poor due to insufficient multi-source data fusion, the method is characterized in that laser radar surface data and optical strain gauge point data are synchronously acquired; constructing a roadway digital twinborn model; carrying out fusion and assimilation on the multi-source data by utilizing a Transform mechanism to obtain initial roadway deformation data; judging the credibility of the laser radar data based on Bayesian estimation; when the data are missing or uncredible, deducing full-field deformation by utilizing a gating mechanism in combination with optical strain gauge point data and a digital twin model; and finally, generating and outputting a three-dimensional dynamic deformation field. The system is mainly used for real-time, continuous and high-reliability deformation monitoring and safety early warning of the coal mine tunnel.

The invention relates to a brain-computer interface online model updating method and device based on Bayesian estimation, electronic equipment and a storage medium. The method comprises the steps of establishing a classification model based on an online updating framework, performing training, and generating a verification set decision value corresponding to a verification set sample based on the classification model; based on the real label of the verification set sample, performing Gaussian fitting on the verification set decision value of each category, and calculating and generating the category conditional probability of the verification set sample; performing posterior probability calculation on the test decision value, and generating a test set sample to predict the posterior probability of each category; and predicting a test set sample into a posterior probability of each category as a sample weight, combining the sample weight with an existing training sample, and retraining the classification model to complete brain-computer interface online model updating based on Bayesian estimation. The problem that the performance of the brain-computer system becomes poor due to data distribution changes in the long-time use process can be solved, and the practicability of the brain-computer system is improved.

The invention provides a carbonate pore structure inversion method and related equipment, and relates to the technical field of geological prospecting, and the method comprises the steps: obtaining test data, employing a trained deep learning prediction model to carry out pore structure inversion through employing a Bayesian estimation algorithm for the test data, so as to obtain a pore structure prediction result, the deep learning prediction model comprises a Bayesian convolutional neural network layer. According to the technical scheme, the test data are input into the trained deep learning prediction model, and the Bayesian estimation algorithm is adopted to perform pore structure inversion so as to obtain the pore structure prediction result including the confidence interval of the pore structure parameters. Therefore, the uncertainty quantification of the prediction result can be realized, the uncertainty is further expressed, the confidence interval is obtained, and the prediction precision can be effectively improved, namely the accuracy of the pore structure prediction result is improved. The method has great application potential in the field of data-driven pore structure prediction, and better decision support is facilitated.

The invention discloses a privacy perception graph prompt learning method based on adaptive topology recovery, which belongs to the technical field of computer network security and artificial intelligence crossing, and adopts an adaptive structure Bayesian estimation method to reconstruct privacy graph data into graph data closer to an original graph sample, and extracts an internal privacy mode from node embedding, so as to improve the privacy perception graph prompt learning efficiency. A learnable prompt of a noise mode introduced based on dynamic perception and responding to privacy disturbance is constructed, progressive privacy consistency is introduced in the optimization process, semantic alignment between multiple noise views is promoted, two loss functions are jointly optimized, multiple rounds of iterative training are performed, and the optimization efficiency is improved. According to the method, the final generalization ability of the graph prompt learning model and the balance of task adaptation can be realized, and a load balance topology is generated based on an adaptive disturbance estimation topology recovery technology so as to establish disturbance distribution; a noise suppressed reliable cue and multi-view cue optimization is designed to mitigate the effects of residual perturbations, generating robust and semantically aligned cues.

The invention discloses a Bayesian estimation method for longitudinal multistage and multilevel item reaction model parameters, and belongs to the technical field of model parameter estimation. The method comprises the following steps: S1, establishing longitudinal multi-level and multi-level project reaction models, including a level 1-level reaction model, a level 2 longitudinal individual growth model and a level 3 multi-level model; s2, based on an improved Bayesian parameter estimation method, estimating all parameters of the project reaction model proposed in the step S1 by introducing auxiliary variables; and S3, verifying the effect of the improved Bayesian parameter estimation method, and comparing the improved Bayesian parameter estimation method with the traditional Bayesian parameter estimation method. According to the Bayesian estimation method for the longitudinal multi-level and multi-level item reaction model parameters, on the basis that estimation precision is guaranteed, the operation time of the QMCMC is better than that of a traditional MCMC, and the operation speed and the estimation efficiency of a longitudinal multi-level and multi-level scoring item reaction theoretical model are improved.

The invention discloses a method for positioning and predicting a wireless signal equal-strength boundary value based on a motion vector set. The method comprises the following steps: constructing an offline map of an indoor scene in a server; performing grid division on the scene, acquiring a signal intensity value of the Bluetooth beacon at each grid center point, performing intensity value region division on the signal intensity values, and performing numerical fitting to obtain an intensity boundary line; when a user carries wireless equipment to move in a scene, cutting an intensity boundary line to obtain a dynamically updated movement vector set; at each moment, a position estimation area is constructed according to the position estimation results of the multiple motion vectors; and fusing a plurality of position estimation results through Bayesian estimation, namely predicting the position at the current moment in combination with historical data before the current moment. According to the invention, a strategy of first mapping and then positioning is adopted, a user downloads a scene map through a server, information fusion calculation is realized through a positioning algorithm, and accurate positioning and prediction of the user are realized.

The invention relates to the technical field of earthquake disaster emergency monitoring, in particular to an intelligent recognition system and method for an earthquake ground surface broken zone, and the system comprises a heterogeneous data registration module, a feature extraction and fusion module, a broken zone segmentation module, an active learning module, a morphological optimization module and the like, and achieves the precise processing of remote sensing data and the intelligent recognition of the broken zone. The system adopts multi-scale feature point matching and self-adaptive weight registration, so that the data registration precision is improved by more than 30%, and the dependence of a traditional method on artificial experience is overcome; through feature fusion of multi-modal feature decoupling and attention guidance, and in combination with multi-scale spatial pyramid pooling and deformation sensitive segmentation, the system effectively identifies a fractured zone and evaluates and predicts uncertainty; the active learning module optimizes training data through Bayesian estimation and a hybrid sampling strategy, and the form optimization module finally outputs accurate broken zone distribution and attribute information by utilizing geological constraint and fault topology reasoning, so that the intelligent level and efficiency of earthquake disaster emergency monitoring are improved.

The invention provides a pipettor automatic calibration positioning and accuracy evaluation method based on photoelectric sensing, and relates to the technical field of pipettor calibration, and the method comprises the following steps: arranging an annular photoelectric sensor array to collect interference fringe phase difference, liquid level reflection light signals and surface acoustic wave signals, and adopting a five-step phase shift method to obtain a three-dimensional displacement vector; dynamic calibration is achieved through liquid level morphological parameter analysis in combination with adaptive Kalman filtering, numerical simulation is conducted on the liquid drop forming process through time-frequency analysis and a fluid mechanics model, and finally the accuracy is evaluated through adaptive fuzzy reasoning and Bayesian estimation. According to the invention, high-accuracy automatic calibration and evaluation of the pipettor are realized.

The invention discloses a point cloud intersection-based space target optical segmental arc rapid association method. The method comprises the following steps: 1, acquiring optical segmental arc data and constructing a constraint admissible domain; 2, generating a track point cloud; 3, forecasting parallel orbit point cloud; 4, fast point cloud intersection is carried out; and 5, determining an initial orbit based on point cloud intersection. Through the above process, the space target rapid segmental arc correlation method based on point cloud intersection is provided, an admissible domain is constructed by two optical observation segmental arcs and orbit point clouds are generated, and then the point clouds of the two segmental arcs are forecasted to the same moment through a parallel orbit point cloud forecasting technology. Quickly locking an overlapping region of the two point clouds by adopting a minimum bounding box method, and judging the relevance between the point clouds through a mahalanobis distance; and finally, based on the Bayesian estimation principle, carrying out initial orbit determination on the successfully associated arc segments.

The invention discloses a dynamic master clock selection method based on Kalman filtering and Bayesian estimation, which is applied to the technical field of master clock selection, and comprises the following steps: constructing a virtual clock by calculating observation equations of a plurality of atomic clocks; performing state estimation on the atomic clocks by using a Kalman filtering method to obtain state estimation values of the atomic clocks; the state estimation value of each atomic clock comprises the phase and frequency estimation value of the atomic clock; the phase of each atomic clock is the phase deviation of the atomic clock relative to the virtual clock, and the frequency estimation value is the frequency estimation value of the atomic clock relative to the virtual clock; calculating the short-term frequency stability and the long-term frequency drift rate of the atomic clock based on the state estimation value of each atomic clock, and defining the comprehensive performance index of the atomic clock based on the short-term frequency stability and the long-term frequency drift rate; and calculating the posterior probability of each atomic clock based on the comprehensive performance index of each atomic clock and the current observation data by using a Bayesian estimation method so as to perform high-flexibility and high-reliability main clock selection.

The invention discloses a vehicle driving electronic information system, which comprises an edge computing node, a central control node, a path planning module, a navigation instruction module and a feedback module, and realizes timely response and dynamic strategy adjustment of sudden abnormal events through a real-time data stream processing technology and a reinforcement learning technology. The response speed and flexibility of the system are improved, the data cleaning algorithm and the multi-source data fusion technology are adopted, the multi-source data are processed through the Kalman filtering algorithm and the Bayesian estimation algorithm, and the accuracy and reliability of the data are improved. Through the deep learning technology and the transfer learning technology, the recognition effect under new vehicle models and special conditions is remarkably improved, and the phenomena of misrecognition and missed recognition are reduced. The automatic and intelligent data cleaning and preprocessing reduces manual intervention, improves the efficiency and accuracy of data processing, updates the global model through the federated learning technology, and adapts to data processing requirements in different scenes.

The invention belongs to the technical field of ship formation, and particularly relates to a marine ship formation attack and defense deduction method based on multi-agent game and expert knowledge and a computer device. The method comprises the following steps: S1, acquiring environment data information; the environment data information comprises formation situation information used for representing ship states of the friend and the foe; s2, obtaining a likelihood function value for Bayesian estimation according to the formation situation information in combination with pre-designed expert knowledge, and then obtaining an initial task overall strategy of the marine ship formation through Bayesian estimation; and S3, inputting the environment data information into a pre-trained strategy decision model corresponding to the initial task overall strategy to obtain an action instruction of each agent in the marine ship formation. According to the invention, the technical problem of poor interpretation of formation overall task decision in the prior art is solved.

The invention discloses a power distribution system generalized comprehensive load modeling method based on a Bayesian estimation theory, and relates to the technical field of power distribution system modeling, and the method comprises the following steps: obtaining a photovoltaic grid-connected sub-model, an energy storage grid-connected sub-model, an induction motor sub-model and a static load sub-model of a power distribution system generalized comprehensive load; modeling the photovoltaic grid-connected sub-model and the energy storage grid-connected sub-model by adopting an RBF (Radial Basis Function) neural network, and modeling the induction motor sub-model and the static load sub-model by adopting a parameter identification method; and based on the modeling result, calculating the probability of occurrence of each sub-model in the generalized comprehensive load by using the Bayesian estimation theory, obtaining the output result of the generalized comprehensive load model of the power distribution system, and completing the modeling of the generalized comprehensive load of the power distribution system based on the Bayesian estimation theory. According to the method, the problem that complex dynamic response caused by photovoltaic and energy storage systems in a power distribution system cannot be processed by a traditional method is solved.

A method for vehicle user prediction includes receiving, in real time, Ultra-wideband (UWB) sensor data from UWB sensors. The UWB sensors include UWB and a UWB tag. The method further includes tracking, in real time, a motion of the UWB tag using the UWB sensor data to determine whether the vehicle user is approaching the vehicle and determining a real-time location of the UWB tag relative to the vehicle using a Bayesian estimation, UWB sensor data, and the motion of the UWB tag. The method also includes predicting, using a machine learning model, an intention of the vehicle user using the motion of the UWB tag and the real-time location of the UWB tag relative to the vehicle and commanding an actuator of the vehicle, using a controller of the vehicle, to actuate in response to predicting the intention of the vehicle user.

Certain aspects of the present disclosure are directed towards a method for circuit testing. The method generally includes: determining a probability distribution indicating prior failure probabilities associated with a circuit design; determining a first likelihood associated with occurrence of at least one failure for the circuit design; determining a quantity of test instances to be performed using simulation to detect the at least one failure based on the probability distribution and the first likelihood; and outputting the quantity of test instances.

The invention provides a low-altitude take-off and landing facility demand scale space distribution prediction method for a target area, and relates to the technical field of infrastructure planning, and the method comprises the steps: S1, analyzing and predicting the historical demand data of four scene types according to the historical demand data of the target area; s2, performing parameter calibration by adopting Monte Carlo simulation, and improving the prediction precision of the corrected exponential growth model and the logistic growth model through Bayesian estimation; s3, determining the demand quantity of low-altitude passenger transport take-off and landing facilities and the demand quantity of low-altitude loading take-off and landing facilities, and optimizing to obtain the total demand quantity of low-altitude take-off and landing stations in the target And S4, planning the low-altitude infrastructure of the target area, and enabling the nodes of the low-altitude take-off and landing facility to be matched with the space of the demand hotspot. According to the method, a plurality of dynamic technology progress models and a multi-scenario analysis mechanism are integrated, so that the accuracy and scientificity of data prediction are improved; and a station reuse coefficient is introduced, efficient sharing of passenger transport and freight transport facilities is realized, and the planning layout of low-altitude take-off and landing facilities is optimized.

A system and method for localizing an autonomous vehicle using mapping and real-time data. And scanning and matching the mapping data and the real-time data. Characteristics of the autonomous vehicle, such as, but not limited to, linear and angular velocities, heading, and motion prediction, are provided to a Bayesian estimation algorithm, and a final pose is calculated.

The invention provides a wide-field magnetic field imaging method based on sparse sampling and Bayesian estimation, and belongs to the field of magnetic field imaging. The method comprises the following steps: constructing an NV color center system, measuring and recording information of a magnetic field to be measured; selecting a small number of discrete sampling points to measure magnetic field intensity data in an imaging field of view of a measured area; the method comprises the following steps of: constructing an error correlation function among pixel points in a measured area, establishing a Bayesian estimation model, and predicting the magnetic field intensity of unmeasured pixel points in a full field of view by taking magnetic field intensity data of a small number of discrete sampling points as observed quantity to obtain a preliminarily reconstructed magnetic field distribution image; selecting a plurality of reference points in the measured area, acquiring nominal magnetic field values and actually measured magnetic field values of the reference points, and calibrating the preliminarily reconstructed magnetic field distribution image by adopting a mean value proportion correction mode; on the premise that the number of sampling points is greatly reduced, magnetic field distribution reconstruction with high spatial resolution and high structural similarity is achieved, and therefore imaging efficiency and reliability are improved.

This invention provides a method and related equipment for inverting the pore structure of carbonate rocks, relating to the field of geological exploration technology. The method includes acquiring test data, and using a trained deep learning prediction model with a Bayesian estimation algorithm to invert the pore structure based on the test data, thereby obtaining pore structure prediction results. The deep learning prediction model includes a Bayesian convolutional neural network layer. According to the above technical solution, test data is input into the trained deep learning prediction model, and the Bayesian estimation algorithm is used to invert the pore structure, obtaining pore structure prediction results, including confidence intervals for pore structure parameters. This not only quantifies the uncertainty of the prediction results, thereby expressing the uncertainty and obtaining confidence intervals, but also effectively improves prediction accuracy, i.e., improves the accuracy of the pore structure prediction results. It has great application potential in the field of data-driven pore structure prediction, facilitating better decision support.

The invention relates to the field of ship engineering technology, and specifically discloses a method for measuring the spatial angle of a dredger rake pipe. The method collects motion data of a hull and a rake pipe in real time, uses median filtering to remove high-frequency noise, extracts features and constructs a comprehensive feature vector, uses a fuzzy inference system to separate the nonlinear influence of hull sway and rake pipe posture, and corrects the posture estimation result in combination with real-time error feedback. The estimation result is dynamically optimized through Bayesian estimation and particle filtering algorithms, and the system deviation is corrected, thereby improving the measurement accuracy. The system also evaluates the overall measurement accuracy in combination with a machine learning model, automatically adjusts measurement parameters and processes, and improves the system adaptability and robustness. For inaccurate measurement results, the system can analyze the causes and re-measure the spatial angle to ensure long-term stable operation.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Bayesian framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the signal passes through a nonlinear system. And secondly, under the Bayesian criterion, judging which hypothesis in the binary hypotheses is established by utilizing the output of a nonlinear system of noise correction, and estimating unknown parameters in the signal of which the judgment result is H1. On the premise that the detection performance is not reduced, a noise enhancement nonlinear system joint detection and estimation model which minimizes the estimation risk is constructed. The additive noise is the optimal solution of the model and is random distribution formed by not more than two constant vectors. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Bayesian framework are combined, and the Bayesian estimation risk is further reduced under the condition that the Bayesian detection cost is not increased.

The invention relates to unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system for transmission and distribution line inspection, and the method comprises the steps: carrying out the modeling of a tower and a surrounding environment of a transmission and distribution line, and determining obstacles and detection points in the transmission and distribution line; planning a first unmanned aerial vehicle inspection path according to obstacles and detection points in the transmission and distribution line; acquiring three-dimensional point cloud data of the transmission and distribution line, and establishing a three-dimensional model; filtering the three-dimensional point cloud data by adopting a point cloud filtering algorithm to obtain obstacles in the three-dimensional model; updating the confidence coefficient of the obstacle in the three-dimensional model based on Bayesian estimation to obtain an obstacle map; calculating Euclidean distances among obstacles in the obstacle map by adopting a fast marching algorithm, and planning a second unmanned aerial vehicle inspection path according to the obstacle map and the Euclidean distances; the technical scheme provided by the invention can effectively overcome the defects that the method is difficult to adapt to dynamically changing obstacle distribution, obstacles in a complex environment cannot be accurately recognized, and potential safety hazards of path planning are large.