40 results about "Neural network identification" patented technology

The invention discloses an intelligent regulation and control method for a power battery positive electrode binder production process, and the method comprises the steps: synchronously collecting multi-dimensional process parameters such as temperature, viscosity and the like and performance indexes such as particle size distribution, bonding strength and the like, carrying out the normalization, denoising and time sequence alignment processing, and fusing hydrodynamic simulation and historical data to construct an initial parameter coupling model; identifying a parameter influence weight through an attention mechanism neural network, generating a decoupling factor matrix to reconstruct a parameter space, and establishing a virtual control channel; executing constrained gradient descent multi-objective optimization in the channel, generating a regulation and control instruction, and reversely mapping the regulation and control instruction into an equipment executable parameter; according to the method, performance indexes and optimization targets after regulation and control are continuously compared, model parameters are updated, retraining is triggered when deviation exceeds a threshold value, regulation and control precision and process stability are guaranteed, and the real-time performance of parameter regulation and control, the collaborative optimization capability and the quality consistency of the production process are remarkably improved.

The invention discloses a GIS combined electric appliance operation on-line detection method, and relates to the technical field, and the method comprises the following steps: obtaining a partial discharge original time domain signal, carrying out the signal enhancement processing of the original time domain signal, extracting a weak discharge pulse signal, and generating an enhanced discharge pulse signal; constructing a holographic time-frequency matrix atlas according to the enhanced discharge pulse signals; the holographic time-frequency matrix atlas is used as an input item, and a weak discharge type is identified based on a weak discharge neural network identification model; based on the identification result and the sensor array data, acquiring space coordinates of a discharge source, and outputting a discharge type and a positioning result to the monitoring terminal; according to the method, the holographic time-frequency multi-scale analysis algorithm and the weak discharge neural network recognition mechanism are fused, so that the recognition capability, the anti-noise robustness and the positioning precision of the GIS combined electric appliance online detection system on weak discharge are remarkably improved.

The embodiment of the invention discloses an insulation aging early warning method and device for a medium-high frequency transformer and a medium, and relates to the technical field of transformers, and the method comprises the steps: collecting a multiband light intensity distribution signal of a target resin region in the transformer according to a preset collection frequency through a preset multispectral distributed optical fiber sensing array; the insulation state of the target resin area is predicted through a pre-constructed neural network identification model and a multi-spectral distributed optical fiber sensing array, and the predicted insulation state health degree of the target resin area is determined; acquiring real-time environment parameters of the target resin area, dynamically correcting a preset early-warning discharge threshold according to the real-time environment parameters, and determining a current early-warning dynamic discharge threshold; and extracting a plurality of current light intensity parameters in the multiband light intensity distribution signal, predicting an insulation state health degree and a current early warning dynamic discharge threshold through the plurality of current light intensity parameters, and performing insulation defect early warning on the insulation state of the target resin area.

The invention discloses a laser-based surface treatment method and equipment, and the method comprises the steps: obtaining drawing document data of a to-be-treated metal material, carrying out the graphic layering processing of the drawing document data, and generating a multi-layer drawing document model; generating a first laser processing parameter set by combining the type, thickness and surface characteristics of the metal material; the metal material is placed on a jig provided with an automatic adjusting device, and the position of the jig is sequentially adjusted; the metal material is marked through a first laser according to the multi-layer drawing and document model, and machining feedback information of the metal material is collected through a sensor in the marking process; according to the difference between the processing response data and the drawing file data, generating a single-layer drawing file for correcting the processing deviation through an image processing and neural network identification method; the laser marking process and data feedback analysis are deeply fused, so that the precision and consistency of pattern forming are effectively improved; and meanwhile, the machining deviation can be remarkably reduced, so that the overall surface treatment quality and the product yield are improved.

The invention provides a geological disaster prediction and early warning method and system combining monitoring data and numerical simulation, and relates to the technical field of geological disaster early warning, and the method comprises the steps: arranging a plurality of sensors in a geological disaster prone region to continuously collect data, carrying out the preprocessing, forming a standardized data matrix, and constructing a catastrophe structure map; each variable is regarded as a map node to calculate an edge weight, threshold screening is carried out, a sparse weighted adjacent matrix is generated, node features are updated through a neural network, probability distribution of a catastrophe stage is output through a full connection layer to identify a current catastrophe state, a coupling strength index is calculated, and similarity matching is carried out; coupling strength and structural similarity are comprehensively evaluated, map migration weight is obtained, then critical variable threshold and time are predicted, critical parameters and neural network recognition results are fused to construct a unified risk index model, early warning information and corresponding measures are issued according to risk grade division, and risk assessment is performed. Structured fusion and dynamic intelligent identification of multi-source data are realized.

The invention discloses a modular mechanical arm actuator additive fault optimal fault-tolerant control method and device of a zero sum differential game, and relates to the field of robot control algorithms, and the method comprises the steps: representing a nonlinear damping characteristic through a joint friction torque, describing the dynamic interaction of multiple joints through a cross-linking coupling item, and determining the optimal fault-tolerant control of the additive fault of the modular mechanical arm actuator; a dynamical model containing faults is constructed. And uncertain items in the model are updated online by adopting a radial basis function neural network identifier, so that the model precision is improved. A performance index function is constructed based on position errors, actuator faults and controller input are regarded as two opposite parties of a zero and differential game, the performance index function is approximated through a single evaluation neural network, a Hamiltonian-Jacobi-Axaxi equation is approximately solved, and an optimal fault-tolerant control strategy is obtained. According to the method, the game theory is combined with the neural network, the dynamic unknown fault problem of the modular mechanical arm is effectively solved while the system energy consumption integration is reduced, and real-time optimal control over the modular mechanical arm is achieved.

The invention discloses an unmanned aerial vehicle route automatic planning system based on AI identification, and relates to the technical field of unmanned aerial vehicle route planning and obstacle avoidance. The unmanned aerial vehicle route automatic planning system based on neural network identification processes camera and laser radar data in real time through a lightweight convolutional neural network of an environment sensing module; accurate identification and classification of dynamic obstacles are realized, the perception ability in a dense city environment is effectively improved, and the risk of obstacle avoidance failure caused by sensor data updating delay is reduced. The fusion and tracking module adopts a space-time alignment and multi-source data fusion technology to generate uniform occupation representation and motion trail, so that the system can adapt to sudden obstacle change, the dependence on a preloaded map is reduced, and the navigation reliability in an unknown or dynamic scene is enhanced; the path planning module integrates a reinforcement learning algorithm, takes a dynamic obstacle state as input, and optimizes path generation through a multi-target reward function.

The invention discloses a CPE anti-recognition privacy protection system based on a lightweight homomorphic encryption security large model. Relates to the cross technical field of network security and privacy calculation. Comprising a lightweight homomorphic encryption module, a scattering invariance neural network identification module and a quantitative perception training module. The lightweight homomorphic encryption module processes the obtained original CPE data into a ciphertext, and inputs the ciphertext into the scattering invariance neural network identification module; the scattering invariance neural network identification module extracts and identifies the ciphertext and inputs the ciphertext as feature data to the quantitative perception training module; and the quantitative perception training module performs quantitative processing on the feature data, performs analog quantization and training optimization according to the ciphertext in the lightweight homomorphic encryption module, and adjusts parameters and a quantization strategy of the model. According to the method, organic unification of privacy security, anti-attack capability and operation efficiency can be realized, and a systematic solution is provided for security deployment of CPE identification.

The application discloses a kind of metal weld point shape detection methods, it is related to the field of welding shape identification, including the following steps: the sampling system of multi-angle progression and multi-angle collection weld point point cloud of weld point is constructed;First, establish main point cloud in main visual angle, and design algorithm to judge the quality of main point cloud, if main point cloud data is insufficient, continue to collect auxiliary point cloud from other angles, and then carry out secondary discrimination;According to the set characteristics of the sample to be detected, design a fast 3D point cloud fusion scheme;Finally, the morphology of the weld point is output by machine learning description.The application also discloses a kind of weld point post-weld quality detection and identification system, based on the above method, and the output weld point shape information is matched with standard qualified weld point, and the quality evaluation result of weld point is given in real time.The collection efficiency of the application is high, the interference of environmental influence and sensor noise can be effectively overcome, the accuracy and integrity of point cloud file are improved, and then the accuracy of neural network identification data is improved.

Disclosed in the present invention is a disturbance observation method for a six-axis vibration table. The method comprises: acquiring data information of a target six-axis vibration table; constructing an extended state observer for the target six-axis vibration table; constructing a fuzzy neural network identifier and a fuzzy neural disturbance observer for the target six-axis vibration table, setting start-stop rules, and performing training; and using a trained fuzzy neural disturbance observer to complete disturbance observation for the target six-axis vibration table. Further disclosed in the present invention are a system implementing the disturbance observation method for a six-axis vibration table, and a disturbance rejection control method comprising the disturbance observation method for a six-axis vibration table. In the present invention, by means of the design and implementation of an extended state observer, a fuzzy neural network identifier and a fuzzy neural disturbance observer, not only disturbance observation and disturbance control of a system are realized, thus improving the tracking performance of the system, but also higher reliability and higher accuracy are realized.

The invention discloses a damage identification method and device for an in-service steel wire rope type horizontal lifeline, and belongs to the technical field of high-altitude operation safety facilities. The method comprises the following steps: synchronously acquiring a magnetic flux leakage signal and a surface image of an in-service steel wire rope through magnetic flux leakage detection equipment and a high-definition camera which are carried on a steel wire rope inspection robot; preprocessing the acquired magnetic flux leakage signal, wherein the preprocessing comprises singular value elimination and trend term removal processing; carrying out de-noising processing on the pre-processed signal by adopting an improved wavelet threshold de-noising algorithm fused with a Sigmoid function; extracting a characteristic value for representing the damage of the steel wire rope, and performing normalization processing to form a characteristic vector; and inputting into a BP neural network identification model optimized by a genetic algorithm for identification, and outputting an assessment result of the damage type and positioning of the steel wire rope. The method can realize automatic and quantitative detection and accurate identification of internal and external damages of the steel wire rope.

This invention discloses a lightweight neural network identification method for high-speed dynamic impact signals. In the process of penetrating multi-layered hard targets, the presence of signal adhesion in the overload signals from multiple dynamic impacts makes identification exceptionally difficult. To address this problem, a lightweight network identification method based on an attention mechanism is proposed. First, time-frequency feature analysis is performed on the overload signal equivalent to that from multiple impact test benches, and continuous wavelet transform is used to extract the time-frequency features as input to the neural network. A lightweight network architecture based on an attention mechanism is designed, eliminating redundant layers and adding residual connection structures and a lightweight attention mechanism, thus ensuring recognition accuracy while significantly reducing parameters.

The application discloses a kind of numerical control processing arbitrary tooth number cutter vibration signal adaptive identification analysis method, belong to aviation manufacturing field, including the following steps: based on vibration sensor obtains cutter vibration data, parses vibration data and draws vibration data image v_image, design vibration image feature descriptor realizes feature extraction, based on the feature difference evaluation function designed in two-dimensional space feature distribution map obtains feature difference key point, according to adaptive regional threshold value judging mode and neural network identification mode obtains identification result, fusion result and comparison obtain signal output.The application is suitable for analyzing and identifying the wear amount, broken tooth, missing tooth, missing and other abnormalities or defects of cutter tooth, based on adaptive identification and result fusion analysis, can further improve the accuracy of tool abnormality identification, reduce false alarm rate and other technical indicators, reduce the possibility of workpiece scrap caused by tool abnormality, thereby avoiding or reducing economic loss.

This disclosure provides a method and system for optimal backstepping control of unmanned surface vessels (USVs) in a specified performance context, relating to the field of optimal backstepping control technology for USVs. The method includes: establishing a dynamic model of multiple USVs; using a disturbance observer and a neural network identifier to compensate for external disturbances and unknown nonlinear terms in the dynamic model; constructing an optimal virtual controller by introducing a first-order integral filter to calculate the derivative of the optimal virtual control, thereby constructing an optimal actual controller; designing a monotonic tube boundary function using the characteristics of a hyperbolic cosecant function; and performing formation control using the virtual controller and the actual controller, employing a continuous control law and the monotonic tube boundary function to maintain synchronization between the position of the following USVs and the desired trajectory of the leader USV under external disturbance conditions. This invention utilizes a disturbance observer estimator to detect time-varying external disturbances and uses a neural network to approximate the unknown parameters of the dynamic model, employing the monotonic tube boundary function to allow the formation error to evolve within a specified region.

The present invention discloses a neural network modeling and control method for a dual-flexible nozzle of an electric servo mechanism, relating to the field of simulation technology. The method comprises: a neural network identifier employing a BP neural network algorithm identifies model parameters of a multi-loop system based on the swing angle to obtain a dual-flexible nozzle neural network model; a Gray Wolf optimized PID controller uses the deviation between the multi-loop system's command swing angle and the dual-flexible nozzle neural network model as an error signal based on parameter changes of the dual-flexible nozzle neural network model and the disturbance experienced by the electric servo mechanism, thereby determining the neural network system learning algorithm. The output of the Gray Wolf optimized PID controller is the control voltage of the electric servo mechanism system. The present invention can establish an accurate load model for the dual-flexible nozzle under different conditions and disturbances, addressing the problems of time-varying parameters, severe interference from nonlinear factors, and structural defects of conventional models.

The method is suitable for PMSM speed regulation control in the field of artillery follow-up systems. The invention provides a motor dead-beat prediction control method and system based on a real-time neural network identifier aiming at the requirements of an artillery follow-up system on quick response, high steady-state precision and good anti-interference capability of a permanent magnet synchronous motor. Dead-beat prediction control and neural network control are combined, a dead-beat prediction controller can ensure that a motor tracks an expected target with the maximum output capacity, and an improved neural network identifier can compensate lumped disturbance of a rotating speed ring of the permanent magnet synchronous motor in real time through an adaptive gradient descent algorithm. The disturbance resistance performance of the dead-beat prediction controller is improved, the steady-state control precision of the motor and the rotating speed is improved, and meanwhile the complex global parameter identification process is avoided. According to the motor dead-beat prediction control method based on the real-time neural network identifier, the rotating speed tracking performance and the robust performance of the permanent magnet synchronous motor can be effectively improved.

The present invention relates to the field of aircraft control technology. The present invention provides a method, system, and storage medium for adaptive attitude control of a cross-domain, variable-configuration, high-speed aircraft based on neural network identification. The method addresses the problem that existing methods require unified parameterized modeling of the aircraft's aerodynamics and identification of the parameters in advance. This method fails to address the attitude control challenges posed by aerodynamic uncertainty when the aerodynamic parameter model of the cross-domain, variable-configuration, high-speed aircraft is completely unknown. The present invention constructs a three-channel neural network model, including a pitch channel neural network model, a yaw channel neural network model, and a roll channel neural network model. A loss function is constructed based on the weighted sensitivity of the influence of rudder error on attitude changes, and a three-channel rudder error solver neural network model is trained. An online transfer learning sample library is constructed for online transfer learning, and a backstepping controller is used to solve the aerodynamic torque, ultimately solving for the yaw command.

The application discloses a mobile terminal nasopharyngeal carcinoma identification method and belongs to the technical field of intelligent identification systems. The system can identify the nature of nasopharyngeal position lesions after training image information of confirmed nasopharyngeal carcinoma patient lesion sites, and has the characteristics of fast identification speed and high accuracy. The system comprises an image preprocessing module, an identification module, and a training identification module. The image preprocessing module is used for preprocessing images of nasopharyngeal sites. The identification module is used for storing a trained identification model. The training identification module is used for training an identification model through lesion images of known nasopharyngeal carcinoma patients. The image preprocessing module is connected with the identification module and the training identification module. The training identification module comprises a case library, an identification model unit, and a training unit. The case library is used for storing identification preprocessing data. The identification model unit is used for storing a trained identification model. The training unit is used for training a neural network identification model in the identification model unit through the identification preprocessing data in the case library.

The invention relates to the technical field of medical electronic equipment, in particular to an intelligent electrocardio lead system based on adaptive impedance matching and neural network recognition, which is characterized in that an impedance feature extraction module is used for collecting multi-lead multi-band impedance data of a human body, constructing a differential feature vector and generating parameterized impedance representation; the neural network identification module carries out hierarchical lead position judgment and outputs a position result and confidence; the self-adaptive optimization module is combined with operator feedback update parameters to execute multi-level learning so as to optimize the performance; and the interaction feedback module generates a voice prompt to guide an operator in real time, so that intelligent recognition and self-adaptive optimization of a lead position are realized, the electrocardiogram detection accuracy and the operation convenience are improved, the electrocardiogram examination accuracy is remarkably improved, and misdiagnosis and missed diagnosis caused by lead errors are effectively avoided.

The invention relates to a robust adaptive dynamic planning method for spacecraft attitude control, and belongs to the technical field of spacecraft control. According to the method, the neural network identifier is designed, online identification and reconstruction are carried out on unknown dynamic items caused by inertia parameter uncertainty in spacecraft attitude dynamics, the attitude tracking optimal control law based on adaptive dynamics is constructed on the basis, and under the condition that inertia parameter uncertainty exists, the attitude tracking optimal control law is optimized. The balance between tracking precision and control energy consumption is realized, and the applicability of the attitude control method in complex space tasks is improved.

The invention discloses a large-aperture space telescope fixed time sliding mode anti-interference control method and related equipment. The method comprises the following steps: establishing a double-inertia system dynamic model containing motor end and load end dynamic parameters and total disturbance; constructing a fixed time expansion state observer based on the model, and estimating the total disturbance of the motor end; converting the model into a generalized all-wheel-drive system model through an all-wheel-drive system theory, designing an expected closed-loop system model, and identifying the expected closed-loop system model by using a neural network to obtain a preset performance function; a motor end angle position tracking error is obtained, and a speed ring reference speed instruction is generated after preset performance function constraint transformation; defining an angular velocity tracking error based on the instruction, constructing a fixed time terminal integral sliding mode control surface and designing a sliding mode reaching law; and finally, synthesizing a motor control law and applying the motor control law to a driving motor to complete anti-interference control. The problems that a traditional preset performance function is single in form, an observer cannot converge at fixed time, and a traditional control mode is difficult to suppress vibration, high in cost and limited are solved.

The application discloses a low signal-to-noise ratio condition modulation identification method based on wavelet transform and channel attention, comprising: acquiring a received signal; inputting the received signal into a pre-trained neural network identification model to output a classification identification result and a classification accuracy rate. Based on the signal reconstruction and the identification model of the neural network, the wavelet threshold estimation module and the wavelet reconstruction module constitute a signal reconstruction path, the multi-scale feature extraction module and the prediction classification module constitute an identification path, the two paths are mutually enhanced, the advantages of the digital signal processing technology and the neural network are combined, the wavelet threshold estimation module is introduced to predict a denoising threshold through the neural network, and the network parameters of the wavelet threshold estimation module are updated according to the back propagation; the signal reconstruction path and the identification path are combined, the network interpretability is enhanced, the multi-scale modulation features are utilized, and the modulation identification accuracy rate in the low signal-to-noise ratio scene is significantly improved.

This invention discloses a neural network-based, disturbance-resistant, high-maneuverability attitude control method for variable-configuration unmanned aerial vehicles (UAVs). The method includes the following steps: Step 1: Establishing a multi-rigid-body kinematic model and a dynamic model of the variable-configuration UAV; Step 2: Constructing the input vector and sensitive region of the neural network identifier, and building an adaptive learning law for the weights of the neural network identifier; Step 3: Designing a baseline attitude controller for the variable-configuration UAV based on an offline-modeled nonlinear dynamic model; Step 4: Solving the control signal within a single control cycle and driving the servo motor to complete the actuation command; Step 5: In the next control cycle, using online measurement data from sensors to complete the adaptive learning and updating of the neural network identifier's weights. This method can comprehensively identify nonlinear unknown dynamics and external disturbances based on the current state of the variable-configuration UAV, and perform high-maneuverability attitude control based on the identification results, thereby improving the flight quality of the variable-configuration UAV.

The invention discloses a tactile data processing method and system based on an event-driven pulse neural network, and belongs to the technical field of robot perception. The method aims at solving the problems that in the prior art, topological representation of a tactile graph is insufficient, the generalization ability of a model on sparse data is weak, and training is unstable. According to the technical scheme, the method comprises the steps that firstly, according to three-dimensional space coordinates of a sensor unit, a model is built through a multi-stage graph combining a KD tree and Z-sequence coding, and an optimized tactile graph capable of representing local and non-local space relations at the same time is generated; secondly, preprocessing a real-time event driving pulse signal through a self-adaptive normalization model so as to enhance fine features; and finally, on a pulse neural network identification model which introduces Gaussian prior regularization for training, processing the preprocessed signal in combination with the optimized touch image. According to the method, the precision, the robustness and the generalization ability of the tactile recognition system on sparse data can be remarkably improved.

The application relates to the technical field of machine vision, in particular to a glass product real-time vision detection system based on a CNN neural network, which comprises an image acquisition subsystem and a neural network identification subsystem, and the specific functions are as follows: the image acquisition subsystem acquires a glass plate original image and an enhanced image based on a dynamic background, the dynamic background comprises a two-color grid image and a two-color stripe image; the neural network identification subsystem performs double-mode enhancement on the image of the glass product based on linear defect and mottle defect identification, uses an improved CNN to extract defect features, and outputs defect types and defect positions. The application enhances the features of the image of the glass product through the dynamic background, strengthens the contrast difference between the defect position and the normal position, effectively reduces the identification difficulty, meanwhile, the CNN parallel acquisition of the original image and the enhanced image avoids missing defect features, and strengthens the identification precision and speed of the neural network.

The present application relates to the technical field of medical electronic equipment, in particular to an intelligent electrocardio lead system based on adaptive impedance matching and neural network identification, which collects multi-lead multi-frequency band impedance data of human body through an impedance feature extraction module, constructs a differential feature vector and generates a parameterized impedance representation; a neural network identification module makes hierarchical lead position judgment accordingly, outputs a position result and a confidence level; an adaptive optimization module updates parameters in combination with operator feedback, performs multi-level learning to optimize performance; an interactive feedback module generates a voice prompt to guide the operator in real time, realizes intelligent recognition and adaptive optimization of lead position, improves electrocardio detection accuracy and operation convenience, significantly improves electrocardiogram examination accuracy, and effectively avoids misdiagnosis and missed diagnosis caused by lead errors.

The invention discloses an improved active-disturbance-rejection control method for an electromagnetic MEMS micromirror based on DNN model identification, and belongs to the technical field of automatic control. The method comprises the following steps: establishing a micro-mirror dynamic model; building a micro-mirror hardware control system, carrying out a wide-spectrum open-loop frequency sweeping experiment, identifying a micro-mirror model through a deep neural network based on a frequency sweeping experiment result, and designing a DNN feedforward control compensation unmodeled dynamic state; a self-adaptive extended state observer is designed, and lumped disturbance in a compensation micromirror control system is estimated; and designing terminal sliding mode control, and constructing an improved active-disturbance-rejection control algorithm in combination with inverse model feedforward control and a self-adaptive expansion state observer. According to the invention, the control performance and anti-interference capability of the electromagnetic MEMS micromirror are improved through the fitting capability of the deep neural network to unmodeled dynamics and the disturbance estimation capability of the adaptive expansion state observer in combination with quick-response terminal sliding mode control.

The invention discloses a plateau yak oestrus sound recognition method based on a multi-branch fusion model. The method comprises the following steps: step 1, collecting audio data of a plateau yak; step 2, converting the audio data of the yak into a logarithm Mel spectrogram, performing time mask and frequency mask operation on the logarithm Mel spectrogram, and constructing a feature vector; step 3, performing enhancement processing on the audio data and the logarithm Mel spectrogram of the plateau yak; step 4, training a multi-branch fusion neural network identification model by using the enhanced yak sound data and the logarithm Mel spectrogram; a MobileNetV3-CBAM module, a bidirectional LSTM module and a visual Transform module of the multi-branch fusion neural network recognition model extract local time-frequency features, time sequence dynamic features and global dependency features respectively and input the local time-frequency features, the time sequence dynamic features and the global dependency features into a gating fusion unit to obtain fused features, the fused features are input into a full connection layer, and the full connection layer outputs the prediction probability of the yak sound category. And identifying the yak oestrus by using the trained multi-branch fusion neural network identification model.

The application provides a method for eliminating burrs and improving precision of a temperature detection device, and comprises the following steps: S1, acquiring current measured temperature data within a preset time, calculating a feature vector according to the temperature data, and presetting a standard deviation in a constant temperature environment; S2, performing physical constraint checking and statistical anomaly checking according to the feature vector; S3, performing neural network identification and correction; and S4, judging a confidence C and outputting a corresponding result. The method can effectively eliminate burrs generated during temperature measurement, improve measurement precision, and has high temperature measurement reliability.

The invention discloses a collaborative attack falling angle identification method based on a neural network. The method comprises the following steps: establishing an impact angle identification model; establishing a neural network identification model based on the impact angle identification model; and acquiring the impact angle of the incoming aircraft at the current moment by adopting a neural network identification model. The method disclosed by the invention is high in recognition precision and strong in robustness.