48results about How to "Reduce computing resource consumption" patented technology

The invention discloses a large model question answering method and device based on a cache mechanism, a storage medium and equipment, and the method comprises the steps: receiving question description information inputted by a target question initiator, and querying whether first answer information matched with the question description information exists in a corresponding trust domain cache of the target question initiator; if the first answer information matched with the question description information exists in the trusted domain cache, performing answer feedback to the target question initiator based on the first answer information; and if the first answer information matched with the question description information does not exist in the trusted domain cache, calling a large model to generate second answer information corresponding to the question description information, and performing answer feedback to the target question initiator based on the second answer information, and associating the question description information with the second answer information and then storing the question description information and the second answer information in the trusted domain cache. And the response efficiency can be improved.

The invention provides a highway unmanned aerial vehicle autonomous inspection method and system, and relates to the technical field of data processing, and the method comprises the steps: receiving a preset inspection path from an inspection starting point to an inspection terminal point; unmanned aerial vehicle sensor information and unmanned aerial vehicle angular velocity information in the inspection process are collected; when an obstacle is detected by the unmanned aerial vehicle sensor information, obstacle avoidance is carried out, and otherwise, whether yawing is carried out or not is detected; generating an obstacle avoidance direction based on a potential field principle, and performing obstacle avoidance according to the obstacle avoidance direction; calculating a yaw angle of the unmanned aerial vehicle according to the angular velocity information of the unmanned aerial vehicle, and judging whether to yaw based on the yaw angle of the unmanned aerial vehicle; correcting the course of the unmanned aerial vehicle according to the yaw angle, and performing inspection along the adjusted course of the unmanned aerial vehicle; collecting real-time video frames of the expressway; constructing a highway abnormal scene recognition model, and outputting an abnormal scene; and uploading the abnormal scene to the holder, and sending out an abnormal prompt. The autonomous inspection efficiency and inspection precision are improved, and the manual intervention frequency and deployment cost are reduced.

The invention relates to a voice interaction method and a storage medium, in particular to a voice interaction method based on a decision of a hybrid training strategy and a large language model and a storage medium. The method aims at solving the problems that an existing large language model cannot provide accurate answers due to lack of sufficient domain knowledge, and the calculation cost is high and the response time is long due to the fact that two large language models are adopted. The method comprises the following steps: setting a specific answer format; constructing a decision data set; constructing a dialogue data set for specific scene dialogue questions and answers; performing first fine tuning on the large language model by adopting full-parameter fine tuning based on the dialogue data set to obtain a large language model after the first fine tuning; performing second fine tuning on the large language model after the first fine tuning by adopting LoRA based on the decision data set to obtain a large language model after the second fine tuning; and accessing the voice recognition module and the voice synthesis module into the large language model after secondary fine tuning, processing the to-be-tested voice problem of the user, and generating voice to interact with the user.

The invention discloses a time series prediction method of a multi-time scale channel mixed attention mechanism, and relates to the technical field of time series prediction and deep learning. Specifically, weighted processing is carried out on extracted features of time sequence data through a channel mixed attention mechanism. The mechanism can dynamically adjust the weight of the feature according to the importance of the feature, and ensures that the key feature plays a greater role in prediction. Inputting the time sequence data into a multi-time scale convolutional neural network to obtain feature vectors under different time scales; and fusing the weighted features to obtain comprehensive multi-time scale features, and completing the training process of the time sequence prediction model. According to the method, the accuracy and adaptability of time sequence prediction are improved, and complex modes and trends in time sequence data can be captured more accurately. Through the method, the method can play a role in various application scenes needing high-precision prediction.

The invention discloses a data fusion method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: carrying out the classification of various types of sensor data in an unmanned system according to a set classification rule, and obtaining the classification data; performing preprocessing based on the basic attributes of the classified data to obtain preprocessed classified data; performing data-level fusion on the preprocessed classification data to obtain a plurality of classification fusion data; based on a deep recurrent neural network DRNN, in combination with a bidirectional long-short term memory unit Bi-LSTM and a sparse attention mechanism, constructing a data fusion model, and optimizing the data fusion model through model pruning to obtain an optimized data fusion model; and inputting the plurality of classified fusion data into the optimized data fusion model for secondary fusion to obtain a final fusion feature. The potential of multi-source information is fully mined, and the accuracy and effectiveness of decision making in a complex environment are improved.

The invention provides a tumor medical image classification method of zero sample evolution NAS based on double-index collaborative evaluation, which comprises the following steps: collecting tumor medical image data, and constructing a data set containing a tumor focus image and a normal tissue image; constructing an extended search space based on a cell structure, wherein the search space comprises a basic operation set, the cell structure and a network overall structure; steady-state evolution algorithm parameters are set, and steady-state evolution initialization is carried out to obtain an initial candidate architecture; taking a double-index comprehensive score calculated by the information rate and the FIM stability as a comprehensive index for evaluating the current candidate architecture, updating the candidate architecture through a steady-state evolutionary algorithm based on a dynamic optimization mechanism until the maximum evolutionary algebra is reached, and obtaining an optimal architecture; and training the optimal architecture by using the data set, and carrying out tumor medical image classification by using the trained optimal architecture. According to the method, the dependence on the annotated data is reduced, the consumption of computing resources is reduced, and meanwhile, the diagnosis efficiency and accuracy are improved.

The invention discloses a model-free robust control method and system for a pantograph with external disturbance. Collecting dynamic data in the running process of the train pantograph; establishing a train pantograph data model with external disturbance; estimating external disturbance; optimizing and updating pseudo partial derivative information in the train pantograph data model; designing a model-free robust control scheme for the train pantograph with external disturbance; optimizing and updating an error control gain and a disturbance compensation control gain in the control scheme; a model-free robust control scheme is adopted to calculate and obtain a control input signal, and the train pantograph is controlled until a control task is finished; the control method and system provided by the invention are not only suitable for the train pantograph with external disturbance, solve the disturbance compensation problem, reduce the modeling difficulty and reduce the computing resource consumption, but also have the application prospect of being popularized to other single-input single-output systems.

The invention belongs to the field of computer vision, and discloses a multi-target detection method, a multi-target detection model training method, an edge computing device and a medium, and the multi-target detection model training method comprises the steps: extracting a first-class feature map; respectively inputting the first type of feature map into a first target detection module, a multi-layer feature fusion module and a second target detection module; calculating a target loss function value based on the obtained target detection result and the target labeling result; and adjusting network parameters of the to-be-trained model until a preset model training convergence condition is met, and obtaining a multi-target detection model. According to the method, the generalization ability of the model is improved, and the driving safety is enhanced.

This disclosure provides a method for determining a retrieval model and a retrieval method. The method includes: acquiring training samples; acquiring video features of video segments based on a video encoding module, and acquiring text features of first descriptive information based on a text extraction module; obtaining a first loss based on the video features, text features, and ground truth information of the training samples, and correcting first model parameters of a machine learning model based on the first loss; and obtaining a retrieval model based on the machine learning model in response to the machine learning model reaching a convergence condition. The technical solution of this disclosure, by correcting the machine learning model based on video features, text features, and ground truth information of the training samples, and obtaining a retrieval model based on the machine learning model, achieves efficient parameter fine-tuning by freezing the pre-trained model and introducing lightweight adaptation units, reducing training computational resource consumption and improving the adaptability flexibility of downstream retrieval tasks.

The invention discloses a navigation mark visual identification and collision early warning method and system based on deep learning, and relates to the technical field of intelligent shipping, and the method comprises the steps: collecting a navigation channel monitoring video stream in real time, and carrying out the preprocessing of a key image frame; constructing a navigation mark detection model based on an improved Officient Det network, and outputting the position and category of the navigation mark in the key image frame; the method comprises the following steps: constructing a ship track prediction model through ship historical track data, predicting a short-time track of a ship, calculating the relative position, speed and course angle of the ship and a navigation mark, and constructing a dynamic collision risk field; and fusing the visual detection result, the radar ranging data and the AIS information to obtain a ship collision risk, and triggering graded early warning based on a preset risk threshold. According to the method, the improved deep learning model and the real-time calculation framework are combined, the robustness of navigation mark identification is improved, an efficient collision early warning mechanism is established, and technical support is provided for intelligent shipping.

The invention provides a vehicle-mounted camera dynamic external parameter estimation method, system and device and a medium, and the method comprises the steps: extracting angular points of a previous-frame downsampling image to obtain an initial angular point set, and filtering the initial angular point set according to a previous-frame target detection result to obtain a previous-frame angular point set; performing angular point tracking on the angular point set of the previous frame by adopting an angular point tracking algorithm through various of the vanishing point of the previous frame, the vanishing point of the lane line of the previous frame, the vanishing point of the lane line of the current frame, the target detection result of the previous frame and the target detection result of the current frame to obtain an angular point set of the current frame; and acquiring a four-degree-of-freedom affine transformation matrix of the current frame, a vanishing point of the current frame and a yaw angle rate through the angular point set of the previous frame and the angular point set of the current frame. According to the invention, external parameter estimation is carried out by combining the vanishing point of the lane line and the target detection result, so that the stability of pixel tracking is enhanced, the efficiency of a tracking algorithm is improved, and real-time, continuous and accurate external parameters of the camera are realized.

The present application belongs to the field of big data processing, and specifically relates to a multi-supplier cooperative store digitized management system and method. Multi-supplier historical delivery time sequence data and store warehouse space time-varying occupation data are collected; a dynamic space-time graph is constructed with supplier nodes and store physical storage location nodes as vertices and logistics time delay and supply-demand dependency as edges; a time sequence attention mechanism is embedded into a graph convolution network to extract space-time coupling features in a concurrent performance state, representing the dynamic mapping correlation between supplier delivery time sequence and store storage location release time sequence; a storage location allocation and delivery time sequence joint optimization model is constructed based on the space-time coupling features, with physical storage location capacity constraints and logistics distribution time sequence constraints as boundary conditions. Dynamic adaptation of cross-supplier logistics time sequence and store physical space is achieved, physical warehouse space occupation and blockage defects caused by concurrent warehousing are overcome, and inventory accumulation and shortage phenomena caused by supply-demand space-time mismatch are eliminated.

The invention provides a power grid dispatching method, a control device and a storage medium, and belongs to the technical field of power grids. The method comprises the following steps: inputting acquired operation data into a multi-objective optimization model to output an optimization scheduling variable; performing constraint condition verification on the optimal scheduling variable, when the optimal scheduling variable meets a preset constraint condition, generating a scheduling scheme of the target power grid, and issuing a scheduling scheme instruction; in the process that the target power grid executes the scheduling scheme instruction, monitoring an operation index of the target power grid; and calculating the deviation between the monitored operation index and the optimization target, and when the deviation exceeds a preset threshold value, adjusting the scheduling scheme of the target power grid. Through a technical path of offline training and online reasoning of the neural network, the generation time of the scheduling scheme is greatly shortened, and real-time response to dynamic change of the power grid is realized.

The invention belongs to the technical field of computer vision, and particularly provides a quick image recognition method and system based on a yolov8 architecture, and the method comprises the steps: modifying a yolov8 serial network architecture into a parallel network architecture, and enabling image data to pass through a network of a teacher model and a student model during forward propagation; combining the positive sample intervals of the student model and the teacher model into a unified positive sample interval based on a yov8 positive sample distribution method; designing a loss function to obtain an optimized knowledge distillation model; and quickly outputting an image recognition result based on the optimized knowledge distillation model. Aiming at a current leading target detection model, namely, the yolov8, an innovative knowledge distillation system is provided, and aims at further optimizing the performance of the yolov8 through the system and expanding the application potential of the yolov8 in a resource-constrained environment. The yolov8 not only maintains the rapid and accurate target detection capability, but also realizes better balance between computing resource consumption and model performance.

The application discloses a kind of based on cross-modal data class increment learning's unmanned plane inspection method and system, comprising: by cross-modal feature mixed fusion module, the features of each mode are spliced and reorganized;Each group of processed input feature map is assigned a set of independent convolution kernel, and grouping convolution is carried out, and finally complete feature map is generated;For each pooling window in complete feature map, the average value of all elements in the window is calculated according to adaptive average pooling, the average value is used as the element of the corresponding position of the output feature map, the process is repeated for all regions, and the final output feature map is obtained;The output feature map obtained after being processed by the adaptive average pooling module is flattened and input into the fully connected layer to extract and combine features, generate the final classification output, then update the model parameters according to the gradient of the loss function;The trained model is deployed on the unmanned plane to collect visible light and thermal imaging images in real time, extract features and identify and predict through the model.

The invention discloses an unmanned aerial vehicle inspection method and system based on cross-modal data class incremental learning. The method comprises the steps that features of all modals are spliced and recombined through a cross-modal feature mixing and fusing module; a set of independent convolution kernels is distributed to each group of processed input feature maps, grouping convolution is carried out, and finally a complete feature map is generated; for each pooling window in the complete feature map, calculating an average value of all elements in the window according to adaptive average pooling, taking the average value as an element of a corresponding position of the output feature map, and repeating the process on all regions to obtain a final output feature map; flattening an output feature map obtained after processing of the self-adaptive average pooling module, inputting the output feature map into a full-connection layer to extract and combine features, generating final classification output, and updating model parameters according to the gradient of a loss function; and deploying the trained model to an unmanned aerial vehicle to collect visible light and thermal imaging images in real time, extracting features, and performing identification and prediction through the model.

This invention provides a task execution method, apparatus, electronic device, and storage medium, relating to the field of embodied intelligence technology. The method includes: determining redundant feature components from the first task reference information based on the feature sources of feature components and the importance values ​​of each feature source, wherein the importance value of each feature source represents the magnitude of the influence of the feature component of that feature source on the successful output of the robot's expected action by the first VLA model; performing redundancy removal processing on the redundant feature components in the first task reference information to obtain processed first task reference information; inputting the processed first task reference information into the first VLA model to obtain the predicted action of the robot output by the first VLA model; and controlling the robot to execute the task based on the predicted action. Applying the solution provided by this invention can improve the robot's motion control frequency, thereby improving task execution performance.

The invention discloses a method for efficient simulation of TACAN navigation, comprising the following steps: S1, defining coarse angular resolution and fine angular resolution; S2, calculating the signal propagation result by the initial first simulation sampling and recording it; S3, simulating the rotation antenna according to the fine resolution; S4, if the angle variation exceeds the coarse angular resolution, then performing electromagnetic propagation simulation calculation of the signal; S5, looping the intermediate sampling points according to the simulation result of the sampling point of the last simulation calculation and the simulation result of the sampling of the current simulation calculation, and obtaining the simulation result by interpolation calculation; S6, summarizing all the radio wave propagation results to obtain the simulated angle measurement result. The angle resolution of TACAN angle measurement is processed by the interpolation method, and the simulation analysis is performed using a larger coarse angular resolution. The angle resolution between the results of the two analyses is quickly obtained using the linear interpolation method, thereby reducing the calculation amount of the simulation processing to one tenth of the original amount, and greatly improving the processing efficiency of the simulation.

The invention discloses a coal-fired power plant boiler heating surface soot blowing prediction method, and relates to the field of coal-fired power plant boiler heating surface soot blowing prediction. Comprising the following steps: acquiring a coal-fired power plant boiler heating surface all-day cleaning factor data set, and intercepting ash deposition section cleaning factor time sequence data as original cleaning factor time sequence data; injecting white noise into the original cleaning factor time sequence data by adopting improved self-adaptive noise complete set empirical mode decomposition, constructing a to-be-decomposed sequence, and decomposing the to-be-decomposed sequence to obtain an intrinsic mode function component and a residual component; predicting each component by using a bidirectional recurrent neural network prediction model to obtain a predicted value of each component; and superposing the predicted values of the components to obtain a final predicted value for representing the ash condition of the heating surface of the boiler. The improved self-adaptive noise complete set empirical mode decomposition is combined with the bidirectional recurrent neural network, the soot condition of the boiler heating surface can be accurately predicted, and guidance is provided for soot blowing operation of a coal-fired power plant.

The invention relates to the field of turbine fluid dynamics, and discloses a gas compressor numerical simulation method based on a blade geometric volume force model, which comprises the following steps: S1, constructing a volume force source item based on normal volume force, parallel volume force and a correction function; s2, introducing a blade blockage factor, and constructing a volume force theoretical model; s3, geometric information of the blade surface is obtained, a mid-plane normal vector and a blocking factor are extracted, and a volume force model suitable for the adjustable guide blade is constructed; s4, extracting model parameters; s5, substituting the extracted model parameters into the volume force model, and carrying out iterative calculation until a convergence condition is met; in the iterative calculation process, the volume force source item is updated. According to the invention, the influence of the change of the adjustable stationary blade mounting angle on the performance of the gas compressor can be quickly and accurately simulated.

The invention relates to the technical field of damage detection of oil and gas transportation pipelines, in particular to a marine pipeline damage degree intelligent identification method based on multi-source feature deep fusion. The method comprises the following steps: S1, acquiring damage data of a damaged pipeline; s2, performing noise reduction on the damage data by adopting an improved wavelet threshold noise reduction method; s3, performing normalization processing on the data after noise reduction; s4, inputting the normalized data into a preset multi-feature deep fusion white box model for training; and S5, calculating the accuracy to reflect the accuracy of model identification. The method overcomes the defects of a hard threshold function and a soft threshold function, flexibly switches between the hard threshold function and the soft threshold function by adjusting the factor alpha, reduces discreteness, avoids the problem of constancy, is high in adaptability, can be automatically adjusted according to different signal characteristics, effectively removes the noise of experimental acquisition data, improves the signal quality, and greatly improves the identification accuracy of the damage degree.

The invention discloses a steel material temperature field time sequence prediction method and system based on multi-objective evolution, and relates to the field of steel material laser cladding temperature field analysis, and the scheme comprises the steps: obtaining historical temperature field data determined through a finite element simulation strategy; processing the historical temperature field data according to a data set division strategy to obtain a training set and a verification set; carrying out training and parameter adjustment on the deep learning model so as to determine a basic value interval corresponding to each parameter of the model; and determining a target parameter value corresponding to each parameter in combination with a preset multi-target evolutionary optimization strategy so as to retrain the deep learning model to obtain a target deep learning model, and determining predicted temperature field data corresponding to the current process parameter in a future time step based on the target deep learning model. According to the scheme, the deep learning model is introduced into base material laser cladding temperature field data prediction, the prediction precision is kept, meanwhile, the calculation time is greatly shortened, and the overall calculation efficiency is remarkably improved.

The application discloses a kind of force partition weighted railway bed dirt visual detection method and system, belong to railway engineering intelligent detection technical field.The application constructs the dynamic mapping model of interframe displacement and field of view overlap rate by real-time acquisition train motion parameter, and effectively frame is adaptively filtered;Adopt coarse cutting-fine identification hierarchical parallel vision architecture to identify dirty area;Based on the stress characteristics of bed, high stress area and low stress area are divided, and a mechanical weighting model is introduced to calculate the comprehensive risk index;The risk index is associated with the geographic location to generate a risk space distribution map, triggering hierarchical early warning and maintenance decision.The application solves the problems of data redundancy, heavy computing burden, and the disconnection between evaluation indicators and stress state in the prior art, achieving efficient and accurate detection of bed dirt and risk quantification, providing a scientific basis for railway maintenance decisions, and being suitable for heavy haul railways, coal lines and other scenarios.

The present disclosure provides a game processing method, a game processing device, a program product and an electronic device, and relates to the technical field of games. The method comprises the following steps: in the action link of a first virtual character, in response to a first attack event, if it is determined that the attack is successful according to attack resources associated with the first attack event and defense resources associated with an attack object, a target game area is triggered for the first virtual character to obtain; in the action link of a second virtual character, in response to a second attack event of the second virtual character triggering a first defense event of the first virtual character, if it is determined that the attack is not successful according to attack resources associated with the second attack event and defense resources associated with the first defense event, the game area already possessed by the first virtual character is maintained; and in response to the end of a game match, the game area possessed by each virtual character is settled. The present disclosure reduces the consumption of computing resources of a terminal or a server, and improves the game strategy.

The application relates to the technical field of elevator intelligent monitoring, and discloses an elevator abnormal behavior recognition and early warning method and system based on AI vision analysis, which comprises the following steps: receiving a door machine state signal, waking up a static target analysis process when the door is opened, calculating a target physical size based on perspective transformation and outputting a static early warning, reading a high-frequency load sampling sequence to generate a physical dynamics sequence when the door is closed, triggering a hardware interrupt when a first-order difference of the physical dynamics sequence exceeds a threshold value, waking up a dynamic behavior analysis process in response to the interrupt, intercepting a video stream to extract moving foreground pixels and projecting the moving foreground pixels to a two-dimensional grid of a car bottom plane, calculating a visual kinematics acceleration sequence and a spatial distribution variance, using a dynamic time warping algorithm to obtain a time domain matching distance parameter of the physical dynamics sequence and the visual sequence, and constructing a joint confidence based on the spatial distribution variance and the time domain matching distance parameter to output an early warning. Through multi-modal data fusion and dynamic time alignment, the application reduces the consumption of computing resources and improves the recognition accuracy.

The invention provides an automatic obstacle avoidance method based on a mobile device, an electronic device and a storage medium. The mobile device comprises a central processing unit and data processing hardware. The automatic obstacle avoidance method comprises the following steps: acquiring an environment image of the movable equipment in a movement process; the environment image is input into data processing hardware, so that the data processing hardware extracts feature points used for representing obstacles in the environment image according to the environment image, and the central processing unit receives the feature points extracted by the data processing hardware; obtaining depth information of the environment image according to the feature points, wherein the depth information is used for representing the distance between the obstacle and the mobile device; and performing motion path planning on the mobile equipment according to the depth information. According to the invention, the method can reduce the calculation resource consumption of the mobile device for obstacle avoidance, and achieves the precise obstacle avoidance of the mobile device.

The invention discloses an electric power operator behavior edge identification and AR correction method and system based on a double-flow heterogeneous lightweight network. The method comprises the following steps: acquiring a first visual angle video stream and sensor data of a six-axis inertial measurement unit through intelligent equipment; performing normalization and de-noising processing on the video frame, and segmenting the video frame into short-time video clips according to a time window; running the pre-trained double-flow heterogeneous lightweight network, performing real-time reasoning on the video clip, and outputting a current behavior category label; the behavior confidence degree output by the double-flow heterogeneous lightweight network model is compared with a preset threshold value to judge whether the event is a violation event or not; if the event is identified as a violation event, a deviation correction mechanism is triggered immediately; and encrypting and uploading the violation event log to an enterprise security management platform or other third-party platforms for users to check, count and trace. The method can effectively solve the problems of high behavior identification delay, dependence on cloud, low edge equipment operation efficiency, insufficient multi-behavior identification precision and the like in the prior art.

The invention discloses a project question answering method and device, electronic equipment and a computer storage medium. The method comprises the following steps: determining a target problem vector according to acquired target problem information; according to the target problem vector, a target related vector is retrieved in a target vector database, the target vector database comprises target code block vectors corresponding to a plurality of target code blocks in the target item, and the target code blocks are code snippets with complete semantics obtained after a target file is segmented according to a preset rule; querying in a target text database according to the target correlation vector to obtain target correlation metadata; and inputting the target question information and the target related metadata into the target model, and outputting target answer information. According to the method, the semantic integrity input by the target model is ensured by segmenting the target code blocks with complete semantics, the input of irrelevant contents is reduced, the target question information is answered in a targeted manner, and the question answering efficiency and accuracy are improved.

The invention discloses a code abstract generation method and device supporting a low-resource programming language. And translating a source code of a low-resource programming language into candidate codes of a target high-resource programming language by utilizing a large language model, and selecting a code which is closest to the input source code semantically from the candidate codes passing analysis verification as an optimal translation code. And retrieving a plurality of codes similar to the optimal translation code and abstracts thereof from a preset knowledge base as reference examples. And segmenting the optimal translation code and the reference example code into code statements, and screening out core statements. And taking the code, the core statement and the abstract of the reference example and the optimal translation code and the core statement as prompt contexts, inputting into a large language model, and outputting a code abstract of a source code of a low-resource programming language. Through cross-language translation and multi-source information fusion, under the condition that low-resource language training data and zero fine tuning are not needed, the problem that low-resource language corpora are deficient is effectively solved, and high-quality code abstract generation is achieved.

The invention relates to the technical field of aircrafts, and provides an environment sensing method and device and an aircraft. The method comprises the following steps: acquiring a current environment image of an aircraft, and performing feature extraction on the current environment image to obtain image features of the current environment image; obtaining the prior distribution of the obstacle, wherein the prior distribution of the obstacle is generated in advance according to the three-dimensional occupied grid sample set; performing aggregation processing on the image features according to prior distribution of obstacles to obtain aggregation features; based on the aggregated feature, a three-dimensional occupancy grid corresponding to the aircraft is predicted, the three-dimensional occupancy grid being used to indicate an obstacle present in a flight environment of the aircraft. The sensing precision and real-time performance of the aircraft in a complex flight environment can be remarkably improved, and the flight stability and safety of the aircraft are effectively guaranteed.