490 results about "Radial basis function neural" patented technology

The invention provides a lithium battery health state prediction method and system based on fusion health indicators, and the method comprises the steps: obtaining lithium battery charging and discharging parameters and a data set, extracting indirect health indicators for reflecting the degradation characteristics of a battery, and verifying the reasonability of the indirect health indicators through a correlation analysis method; performing dimension reduction processing on the indirect health index by using a KPCA method to obtain first principal component data, constructing a lithium battery health state comprehensive estimation model, and further obtaining a battery health state; and decomposing an original battery capacity sequence into a local fluctuation sequence and a global degradation sequence, performing prediction by using a radial basis function neural network and Gaussian process regression, and performing superposition to obtain a battery residual life prediction value. Aiming at the problems of low accuracy, unstable model and the like in the prior art, the battery health state is measured and calculated by adopting a mode based on the fusion health indexes, the method has the advantages of high estimation precision, good application prospect and the like, and the prediction accuracy is effectively improved.

The invention discloses a rapid simulation method for infrared radiation characteristics of an aero-engine exhaust system based on a radial basis function neural network, and belongs to the technical field of aero-engines. The method comprises the steps of establishing an aero-engine exhaust system geometric model; flow field calculation is carried out, and aero-engine exhaust system infrared radiation characteristic simulation flow field parameters are obtained; calculating intrinsic infrared radiation characteristics of the aero-engine exhaust system; calculating the infrared radiation characteristics of the coupling background environment infrared radiation and the atmospheric effect of the aero-engine exhaust system; constructing a nonlinear association database; and training the RBF neural network model to obtain the RBF neural network-based aero-engine exhaust system infrared radiation characteristic rapid simulation model. According to the method, the remarkably improved calculation precision and the abundant and clear distribution details of the infrared radiation brightness are replaced by the acceptable additional calculation amount.

The invention provides a power cable joint defect detection method and system based on a digital twin model. The method comprises the following steps: acquiring performance parameters, temperature data and ultrasonic parameters of a cable joint; constructing a cable joint multi-field coupling simulation model according to the physical size material parameters of the cable joint; constructing an objective function according to the performance parameters and the temperature data, interacting data of the multi-field coupling simulation model with data under actual operation conditions through the objective function, and updating a pre-constructed cable joint digital twinborn simulation model to obtain a real-time updated cable joint digital twinborn simulation model; and training and testing the radial basis function neural network based on the output data of the cable joint digital twin simulation model updated in real time to obtain a fault diagnosis model of cable joint temperature and ultrasonic data based on deep learning, and detecting the real-time operation state of the cable joint according to the fault diagnosis model. And predicting a potential fault of the cable joint.

The invention provides an earth and rockfill dam seepage pressure prediction method based on mechanism and data dual drive, and the method comprises the following steps: generating multi-working-condition seepage pressure data, optimizing a radial basis function neural network through an aurora optimization algorithm, and constructing an efficient proxy model; on the basis of a proxy model prediction result, fitting is carried out by combining measured data, and a seepage pressure prediction mechanism model based on a hysteresis effect function is established; with the predicted value of the mechanism model as a label, supervising the training model to learn the physical law of the seepage field; and freezing the physical feature extraction layer in the trained model, and finely adjusting the time sequence modeling layer by using actually measured data to realize approximation of an actually measured value. According to the method, mechanism driving and data driving are organically combined, the interpretability of a physical rule is reserved, complex factors which are not considered by a mechanism model are complemented by utilizing actually measured data, and the generalization ability of a deep learning model under extreme working conditions is remarkably improved while the prediction precision of the model is ensured.

The invention provides a stabilization control method and system for a fuel filling ship at a zero navigational speed. The method provided by the invention comprises the following steps: acquiring rolling data and wind wave data of a refueling ship under a target sea condition in a fuel refueling process in real time; the rolling data and the wind wave data are processed based on a radial basis function neural network, and the precession angular velocity of the stabilization gyro device under the target sea condition is obtained through calculation; the stabilization gyro device is controlled to rotate at the precession angular speed, the stabilization gyro device generates a gyroscopic effect and generates rolling moment, the rolling moment is converted into stabilizing moment for resisting rolling through precession motion, and the rolling angle of the filling ship is reduced; and the stabilizing moment is opposite to the vibration direction of the rolling. According to the method and system, rolling control of the filling ship at the low speed or the zero navigational speed is achieved, the stabilization torque can be adjusted in real time according to sea wave changes, and the stability and safety of the filling ship in the fuel filling operation process are improved.

The invention discloses a fault prediction model training method and a photovoltaic equipment inspection method, which are applied to the field of artificial intelligence, and comprise the following steps: obtaining historical data and an operation state of photovoltaic equipment in a distributed photovoltaic field station; training the self-organizing mapping model by using historical data to obtain a trained self-organizing mapping model and output data; the output data is a feature identification vector corresponding to each piece of historical data; training a radial basis function neural network model by using the output data, the historical data and the operation state to obtain a trained radial basis function neural network model; the trained self-organizing mapping model and the trained radial basis function neural network model jointly construct a fault prediction model, and the output of the trained self-organizing mapping model serves as the input of the trained radial basis function neural network. According to the method, the complexity and the calculation amount are reduced, the accuracy of fault identification can be improved, the inspection efficiency and accuracy are further improved, the operation and maintenance cost is reduced, and the safety is enhanced.

A method includes: calculating a first difference between a current actual attitude of a fuselage and a desired attitude and a second difference between an actual depth and a desired depth; inputting the first difference and the second difference into a set terminal sliding mode surface to obtain an output value of the terminal sliding mode surface; using the output value as an input of a preset high-order observer, a radial basis function neural network and a terminal sliding mode control law, respectively, and using an output of the high-order observer and an output of the radial basis function neural network as a compensation input of the terminal sliding mode control law; performing power distribution for each propeller of a propeller assembly on the basis of the virtual force to obtain a propelling force of each propeller; and controlling the propellers of the underwater submersible robot.

The present invention belongs to the field of municipal solid waste management, and also belongs to the field of intelligent optimization. Provided is an intelligent operation optimization method for a municipal solid waste incineration process. The method comprises: collecting historical data in a municipal solid waste incineration process to construct a sample data set; using nitrogen oxide emissions and combustion efficiency as optimization objectives to establish an index model based on a self-organizing radial basis function neural network, so as to characterize a mapping relationship between operation variables and the optimization objectives in the municipal solid waste incineration process; and using the established index model as an evaluation function of a multi-objective optimization algorithm, and using a multi-objective particle swarm optimization algorithm to obtain optimal set values of the operation variables, so as to improve the combustion efficiency while reducing nitrogen oxide emission concentration. In the present invention, an intelligent operation optimization method for a municipal solid waste incineration process is established, such that the combustion efficiency and denitrification efficiency can be synergistically improved, and technical support can be provided for efficient and environment-friendly operation of the municipal solid waste incineration process.

Provided are an underwater submersible robot capable of improving its ability to resist interference with underwater turbulence, and a control method and control device for the same. [Solution] A first difference value between the current actual posture and desired posture of the robot body and a second difference value between the actual depth and desired depth are calculated, the first difference value and the second difference value are input into a terminal sliding mode surface to be set, an output value of the terminal sliding mode surface is obtained, the output value is input into a pre-set high-order observer, radial basis function neural network and terminal sliding mode control law, and the output of the high-order observer and the output of the radial basis function neural network are input into the terminal sliding mode control law to obtain a virtual force of the terminal sliding mode control law output, power allocation is performed for each propeller of the propeller component based on the virtual force, thrust of each propeller is obtained, and the propeller of the underwater submersible robot is operated and controlled based on the thrust.

The invention provides an intelligent fault-tolerant formation method for triggering a sea-air heterogeneous unmanned system by a preset time event. According to the technical scheme, the method comprises the following steps: step 1, converting an under-actuated heterogeneous USV-UAV system into a full-driven second-order dynamic system by using coordinate transformation; 2, designing a self-adaptive preset time dynamic event triggering mechanism; 3, designing a preset time sliding mode intelligent fault-tolerant formation control algorithm based on distributed errors; 4, designing an event triggering preset time intelligent fault-tolerant formation control algorithm based on a radial basis function neural network minimum learning parameter method; and step 5, carrying out stability proving on the designed intelligent fault-tolerant formation control algorithm, and eliminating a sesame phenomenon. The method has the beneficial effect that the communication efficiency is greatly improved through a self-adaptive dynamic event triggering mechanism.

A robot kinetic parameter identification method based on double-layer iteration and friction compensation comprises the following steps: S1, establishing a kinetic model of a robot, and performing linearization processing on the kinetic model to obtain a linearization model represented by an observation matrix and an inertial parameter vector; s2, designing an excitation trajectory for the linearized model by adopting improved Fourier series of a quintic polynomial, and setting constraint conditions of joint positions, speeds and accelerated speeds; s3, the robot is controlled to move according to the excitation track, and joint state data of the robot are collected and subjected to noise reduction processing; and defining the noise-reduced driving torque as a measurement torque. According to the robot kinetic parameter identification method based on double-layer iteration and friction compensation, the physical feasibility of the robot kinetic parameters can be ensured, the friction model is improved to identify the friction parameters, the friction model is fitted by adopting the radial basis function neural network, and the precision of subsequent robot control is ensured.

The invention relates to the technical field of PCBA board anti-interference, and discloses a vehicle-mounted display PCBA board anti-interference method, device and equipment. The method comprises the following steps: carrying out electromagnetic interference data acquisition on a PCBA (Printed Circuit Board Assembly) of the vehicle-mounted display to obtain an electromagnetic interference data set, and constructing a PCBA structure matrix; performing feature extraction to obtain interference feature data; constructing a probability distribution function, generating a feature transformation matrix and performing joint operation to obtain an interference suppression matrix; constructing a multivariable constraint condition and solving an anti-interference optimization parameter; inputting the anti-interference optimization parameters and the interference signal matrix into a particle swarm-radial basis function neural network for training to obtain an anti-interference neural network model; and performing filtering processing and compensation correction on the original input signal of the PCBA board through the anti-interference neural network model to obtain a target output signal. Compensation correction can be dynamically carried out, the compensation coefficient is adjusted in real time according to the signal error, and the stability and accuracy of the output signal are ensured.

The embodiment of the invention provides an MEMS pressure sensor temperature drift compensation method, system and device and a medium. The method comprises the following steps: acquiring a pressure value and an environment temperature value of the MEMS pressure sensor; inputting the pressure value and the environment temperature value into a pre-established temperature drift compensation model, and outputting to obtain a compensation value of the MEMS pressure sensor; wherein the temperature drift compensation model is pre-established through a radial basis function neural network comprising a particle swarm optimization algorithm and a Sudoku algorithm. According to the embodiment of the invention, by dynamically adjusting the output of the sensor, the negative influence of the temperature effect can be reduced to the minimum, the measurement result of the sensor is ensured to be more accurate and reliable, and the measurement precision of the MEMS sensor can be remarkably improved; automatic temperature compensation of the sensor is allowed, manual intervention or calibration is not needed, the burden of maintenance and operation is reduced, and the usability of the sensor is improved; through software implementation, the cost can be remarkably reduced, and the competitiveness of the sensor is improved.

The invention discloses a ground surface settlement measurement data real-time fitting method based on an evolutionary algorithm. The method comprises the following steps: S1, acquiring a ground surface settlement measurement data set; s2, preprocessing the ground surface settlement measurement data set; s3, generating a ground surface settlement measurement data feature matrix; s4, constructing a radial basis function neural network model for ground surface settlement measurement data fitting; s5, optimizing parameters of the initial ground surface settlement measurement data fitting function by adopting an alpha evolutionary algorithm; and S6, adopting the optimized ground surface settlement measurement data fitting function to predict the input ground surface settlement measurement data. According to the method, the calculation efficiency, the nonlinear expression capability and the global optimization capability of ground surface settlement measurement data fitting are effectively improved, and the method can be widely applied to multiple scenes of ground surface settlement monitoring, infrastructure safety evaluation and mining area collapse prediction.

A system, computer readable storage medium and method for controlling a trajectory of coordinated time-varying maneuvers of a geometric formation of unmanned vehicles is disclosed. The system includes unmanned vehicles, each configured with communication circuitry to communicate between the vehicles. A subset of the unmanned vehicles function as leader vehicles, with the remaining vehicles functioning as follower vehicles for leader-follower maneuvering. The system further includes an actuator suite configured to adjust the direction and orientation of each vehicle, a sensor suite for stabilization and navigation, and a flight controller for maintaining stable maneuvering, even in the presence of actuator faults and sensor deception attacks. Processing circuitry is configured with a reinforcement learning neural network that includes identifier, actor, and critic radial basis function neural networks to estimate movement, adjust control actions, and assess vehicle performance based on feedback signals, including corrupted signals from the sensor suite due to deception attacks.

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention discloses a wireless power transmission electromagnetic environment prediction method based on a PSO-RBF neural network, and the method comprises the steps: arranging a plurality of sensors in a target region of a wireless power transmission system, collecting space coordinate data (xi, yi, zi) and electromagnetic field intensity data Bi, and carrying out the preprocessing of the collected data, including data cleaning and normalization; constructing a radial basis function neural network model for predicting the electromagnetic field intensity by taking the space coordinates as input and the electromagnetic field intensity as output; the method comprises the following steps: constructing a PSO-RBF neural network wireless power transmission electromagnetic environment prediction model, optimizing RBF neural network parameters by using a PSO algorithm, training the PSO-optimized RBF neural network, and performing multiple iterations and cross validation to obtain a trained RBF neural network model to predict the electromagnetic field intensity; in the to-be-measured area, the electromagnetic field intensity of the corresponding position is predicted according to the space coordinates of the to-be-measured position. According to the invention, the electromagnetic environment of the wireless power transmission system can be monitored and evaluated in real time, and potential electromagnetic environment risks can be found in time.

The invention discloses a grain condition data prediction method and device, and relates to the technical field of grain, and the method comprises the steps: carrying out the data cleaning processing of original grain condition data, so as to generate a grain condition data optimal attribute set; training the grain condition data optimal attribute set through a radial basis function (RBF) neural network to generate a grain condition data prediction model; and outputting predicted grain condition data within a specified time step length through the grain condition prediction optimal attribute set and the grain condition data prediction model, and performing early warning according to a comparison result of the predicted grain condition data and a preset safe grain storage critical value. According to the invention, the future development or change trend of the grain condition is predicted through the multi-type grain condition data, so that early warning is carried out on the possible dangerous grain condition to ensure the grain storage safety.

The invention belongs to the technical field of photoelectric stabilized platform intelligent control, and particularly relates to a photoelectric stabilized platform composite anti-interference method based on improved active-disturbance-rejection control, which realizes multi-band disturbance cooperative suppression through an improved extended state observer (ESO) and non-linear state error feedback (NLSEF) core architecture. For the problems of phase distortion and parameter sensitivity under traditional ADRC high-frequency disturbance, an ALMRBF-ESO observer is constructed by adopting a dynamic regularization constrained radial basis function neural network and an adaptive damping adjustment mechanism, gradient dispersion limitation of a traditional RBF in a high-frequency domain is broken through, and disturbance root-mean-square errors are reduced; by reconstructing a sliding mode gain equation of nonlinear tracking error feedback and implanting a parameter self-correction mechanism, dynamic response of a system is accelerated, overshoot is reduced at the same time, and the parameter drift rate of a controller is stabilized at 0.12. According to the method, on the basis of completely reserving ADRC robustness, the problem of collaborative optimization of ESO broadband disturbance observation precision and NLSEF dynamic tracking performance is solved.

The invention discloses a microcomputer host aging test method and system, and relates to the technical field of computer performance tests.The microcomputer host aging test method comprises the steps that to-be-tested equipment is connected to a test system, system standby test is conducted, and initial reference data are collected; constructing an aging prediction model and optimizing a model training strategy in combination with course learning and transfer learning; and performing a host preheating test and a high-load test by using AIDA64 based on a prediction result, synchronously operating a Heven Benchmark tool, and testing the graphic processing capability of the computer. The method combines a radial basis function neural network aging prediction model, carries out model training through curriculum learning and transfer learning, intelligently analyzes the CPU / GPU degradation trend, combines a quantum random number generator to generate a random load mode, simulates load fluctuation in an actual application scene, and improves the test trueness. In addition, a BIOS optimization strategy is provided to realize intelligent aging prediction and optimization regulation and control, so that the long-term stability and the service life of the microcomputer host are improved.

The invention relates to the technical field of inverter control, and discloses a micro inverter control method and system in a photovoltaic system and a storage medium. The method comprises the following steps: carrying out parameter acquisition and processing on the photovoltaic micro inverter to obtain an initial parameter set; designing a radial basis function neural network compensation uncertainty parameter based on the initial parameter set; inputting the compensation parameter set into a particle swarm optimization algorithm to optimize control parameters; and training a back propagation neural network based on the optimized parameter set to realize maximum power point tracking control. According to the method, the uncertainty of the system is compensated by introducing the radial basis function neural network, the control parameters are optimized in combination with the particle swarm optimization algorithm, and the real-time prediction and adjustment of the parameters are realized by using the back propagation neural network; the technical problems that a traditional control method is low in control precision and poor in adaptability when facing nonlinear system characteristics, environmental condition changes and parameter uncertainty are effectively solved.

The invention discloses a mechanical arm iterative learning control method based on a radial basis function network. The method solves the limitation that a traditional mechanical arm control method deals with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method provided by the invention comprises the following steps: firstly, constructing a mechanical arm system dynamic model, and dynamically correcting and optimizing a reference track by adopting a dynamic correction strategy; then designing a radial basis function neural network to construct a nonlinear compensation item, and designing a dynamic weight parameter and a dynamic learning gain for optimizing the performance of the controller; and finally, designing an iterative learning controller, and verifying the stability and error convergence of a control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, the adaptive capacity and the error convergence speed of the mechanical arm system are improved.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention relates to the technical field of robot control, in particular to a cross-modal perceptual driving body robot compliance control system which comprises the steps that a sensor is adopted to synchronously collect environment information, multi-source data bias is eliminated through a space-time alignment algorithm, a radial basis function neural network is adopted to analyze multi-modal fusion features, and a multi-modal model is obtained; human operation intention probability distribution is extracted to decompose a task into a path planning layer and a motion control layer, a collision-free trajectory is generated through an RRT algorithm, a high-fidelity physical engine is adopted to construct a virtual interaction scene, and robot learning results are shared through federal learning. According to the method, the problems of inaccurate perception, incoordination between intention recognition and interaction control, difficulty in control strategy verification, slow new task adaptation and difficulty in multi-robot learning result sharing caused by multi-source data deviation and large multi-modal semantic difference of the body robot in a complex environment are solved.

The invention discloses a self-adaptive brushless motor control method and system, and relates to the field of intelligent control, and the method comprises the steps: collecting the original data of the operation state of a motor through a sensor group, and carrying out the preprocessing; time-varying parameter identification is completed through combination of an extended Kalman filtering algorithm and a radial basis function neural network, an evaluation index system is constructed based on an analytic hierarchy process to obtain a comprehensive evaluation value, and a related trend is predicted through a long and short-term memory neural network; constructing a multi-modal control strategy library, determining an adaptive strategy, optimizing core parameters by using an improved particle swarm optimization algorithm, generating a control instruction, and outputting a corresponding current through a power driving module; and monitoring motor parameters in real time, comparing with a control target value, calculating deviation, correcting an identification result, adjusting a strategy threshold value, and updating and optimizing an objective function. The method has the advantages that by accurately sensing the state of the motor, dynamically adapting the control strategy and optimizing parameters in real time, it is ensured that the motor stably and efficiently operates under the complex working condition, and the characteristics of energy conservation and long service life are achieved.

The invention relates to the technical field of thermal protection of hypersonic aircrafts, in particular to a multi-scale prediction method for an ablation behavior of a thermal protection structure based on data driving, which comprises the following steps: performing molecular dynamics simulation on a gas-solid interface of an ablation type thermal protection material to obtain simulation calculation activation energy; obtaining the experimental activation energy of the material; performing Bayesian optimization on the simulation calculation activation energy and the experiment measured activation energy to obtain optimized activation energy; performing macroscopic wall surface ablation simulation by taking the optimized activation energy as a boundary condition to obtain an ablation parameter database recording ablation wall surface temperatures, ablation rates and ablation depths corresponding to different flow rates, altitudes and time; training a radial basis function neural network based on the ablation parameter database, and establishing an ablation prediction model; obtaining a prediction result; the calculation efficiency and accuracy of the thermal protection structure ablation behavior multi-scale prediction method can be improved.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

The invention discloses a data-driven risk assessment method for salt deposition characteristics of easily-crystallized components of a multi-fluid. The method comprises the following steps: firstly, collecting salt deposition related characteristic data of easily-crystallized components of the multi-fluid to construct a sample database, preprocessing the data to form a preprocessing database, and dividing the preprocessing database into a test set and a verification set according to a certain proportion; then constructing a particle swarm optimization algorithm to optimize the radial basis function neural network, and carrying out prediction analysis based on the particle swarm optimization algorithm to optimize the radial basis function neural network; comparing and analyzing a result obtained by prediction and analysis with actual data, and verifying the prediction precision and reliability of the established radial basis function neural network model through indexes such as a root mean square error and the like; and finally, evaluating the salt deposition risk of the easily-crystallized component of the multi-element fluid by using the radial basis function neural network model. According to the method, the salt deposition risk in the complex multi-fluid environment can be rapidly and quantitatively predicted, the equipment failure detection rate is increased, and safe, stable and long-period operation of a multi-fluid salt deposition high-risk equipment system is promoted.

The invention discloses an efficient autonomous three-dimensional path planning method and system for a heavy-load low-altitude aircraft, path planning is realized through an improved A * algorithm, firstly, a planning area is rasterized, an obstacle avoidance area is defined, and node search and cost value calculation are only performed in the obstacle avoidance area, so that the calculation amount is reduced. The node cost value is jointly determined by heuristic estimation of a known operation distance and an unknown operation distance and motion energy consumption cost, and the path searching efficiency and quality of the aircraft are optimized. In the path post-processing stage, redundant steering nodes are removed through a key path node screening mechanism, and a more efficient path is obtained. In addition, an efficient learning quantification method of non-structural pruning optimization is introduced, and accurate prediction of the energy consumption of the aircraft is realized through an improved radial basis function neural network. The method has the advantages that the method is not only suitable for autonomous navigation of low-altitude aircrafts, but also can be widely applied to path planning and energy consumption optimization in various fields of unmanned aerial vehicles, automatic piloting aircrafts and the like.

The invention discloses a low-coherence interference demodulation method based on modal decomposition and a radial basis function neural network, and the method comprises the steps: carrying out the empirical mode decomposition of a filtered low-coherence interference signal, extracting the effective time-frequency domain features of each IMF and the mathematical statistics time domain features of the low-coherence interference signal, and forming a low-coherence interference signal feature data set; constructing and training a radial basis neural network, establishing a nonlinear model between low-coherence interference signal features and pressure, setting an input layer to correspond to a fusion feature vector, setting a hidden layer to adopt a radial basis function as an activation function, realizing nonlinear mapping by using a Gaussian kernel function, and outputting a layer to correspond to a pressure value; the network is trained through the feature data set, a mean square error is used as a loss function, a gradient descent method is adopted to optimize the weight and threshold of the network, and iterative training is carried out until the error converges; and inputting a low-coherence interference signal acquired by an optical fiber Fabry-Perot pressure demodulation device, and demodulating the signal through the trained radial basis function neural network model to obtain a corresponding pressure value.