98results about How to "Accurate representation" patented technology

The invention belongs to the field of engineering technology risk management, and provides a tunnel and underground engineering safety risk multi-identification intelligent sensing method. The implementation of the method comprises the following steps: firstly, acquiring multi-modal data on a tunnel and an underground engineering structure by using a multi-identification method; constructing and training an engineering safety risk prediction model; and performing dynamic quantitative evaluation on the tunnel and underground engineering safety risk by using the trained engineering safety risk prediction model. According to the method, the multi-recognition perception technology and the artificial intelligence technology are combined, safety risk information in the tunnel and underground engineering can be observed, the method can be applied to safety risk management in the tunnel and underground engineering, the safety risk level of the tunnel in the construction period and the operation period is evaluated, and the safety risk management efficiency is improved. And the safe state of the tunnel and the underground engineering can be expressed quickly and accurately.

The invention belongs to the technical field of deep learning, and discloses a drug and protein interaction prediction model construction method based on multi-modal fusion. Due to the lack of protein three-dimensional data, the three-dimensional data of the protein is subjected to high-accuracy complementation by using a prediction model AlphaFold 2, and for the modal fusion problem, the drug molecules are fused at the atomic level with finer granularity, so that the multi-modal fusion effect is further enhanced, and the multi-modal fusion effect is improved. Protein 1D data and protein 3D data are processed by using Cross-Attention, the processed protein 1D data and protein 3D data are subsequently fused by using Cross-Attention, multi-modal fusion is performed by using Cross-Attention, so that the interaction relationship between modals can be carefully captured, and in a word, the DTI task effect is improved by designing a multi-modal fusion mode.

The invention belongs to the technical field of fuel cells, and particularly relates to a fuel cell centrifugal air compressor irreversible loss prediction method and system, and the method comprises the steps: collecting performance data based on a fuel cell centrifugal air compressor comprehensive performance test platform, and building and verifying a multi-physics coupling CFD simulation model; defining an irreversible loss type and related structure parameters, constructing an integrated CFD simulation framework, generating a parameter combination table through an orthogonal test, and performing simulation calculation; an RL-GHEM method is provided, the weight and combination strategy of a base model are optimized, linear and nonlinear combination modes are dynamically switched, and irreversible loss prediction models under different working conditions are constructed. The hybrid integrated model based on reinforcement learning guidance is provided, the model combination strategy is dynamically optimized through the reward mechanism, the base model with the optimal performance is optimized, the prediction precision, stability and robustness are improved, precise prediction of the irreversible loss of the air compressor is achieved, and the problem that in the prior art, the prediction precision is insufficient under variable working conditions is solved.

The invention relates to a point cloud quality assessment method based on depth ordinal regression. The method comprises a feature fusion step based on a symmetric cross-modal attention mechanism and an ordered regression step of point cloud quality assessment. In the feature fusion step, point cloud features are extracted through farthest point sampling and a K nearest neighbor algorithm, point cloud projection is performed to generate an image, image features are extracted, the two features are fused by using a cross-modal symmetric attention mechanism, and global feature representation is generated; in the step of ordered regression, a depth ordinal number loss function is designed to optimize network parameters, the prediction probability is converted into a continuous point cloud quality score by using ordered information of the quality score, a step effect is prevented, and an evaluation result is closer to a true value. Through feature fusion and ordered regression, the accuracy and stability of point cloud quality evaluation are effectively improved, and the evaluation result is closer to a true value.

The invention relates to the field of image clustering analysis, in particular to an image clustering method and device based on a particle multi-kernel k-mean value and electronic equipment. The method comprises the following steps: acquiring a discrete data point set of a plurality of to-be-clustered images; performing granular ball division on the image data to obtain an image granular ball set; calculating to obtain an image pellet center, an image pellet distance matrix and an average distance between image pellets; calculating to obtain an image pellet kernel matrix; according to the image pellet kernel matrix and a preset clustering division matrix, constructing a multi-kernel k-means clustering objective function based on pellets; calculating to obtain a corresponding target clustering division matrix when the target function value is minimum; discretizing the target clustering division matrix according to a multi-kernel k-mean algorithm to obtain a clustering result of image pellets; and distributing the clustering result of the image pellets to all discrete data points contained in the image pellets to obtain the clustering result of the image data. According to the invention, the efficiency and accuracy of image clustering are improved.

The invention discloses an unmanned aerial vehicle navigation method, device and equipment and a storage medium, and the method constructs an LIEKF algorithm on a world coordinate system (local coordinate system), enables the value to be small, and guarantees the stability of a linear error kinematics matrix and an observation matrix. A left invariant error on a matrix Lie group is defined under a world coordinate system, and the error is kept unchanged on a left group. According to the method, state quantities such as attitude, position and speed and errors are defined in a group space, and a linear state space model independent of the state is deduced through multiplication closure of the Lie group and affine characteristics between Lie algebra and corresponding nonlinear error quantities constructed by the multiplication closure and the Lie algebra. Because the redefined speed error and position error both comprise attitude items, the real error can be represented more accurately.

The invention discloses a structural plane network sequence modeling method and system suitable for layered rock mass, and relates to the technical field of rock mass engineering, and the method comprises the steps: obtaining layered rock mass outcrop data as basic data, and obtaining basic data interpretation representation after processing; determining a rock mass outcrop homogeneous region division result according to the type attribute of the basic data; according to the interpreted occurrence data in combination with a rock mass outcrop homogeneous region division result, correcting the occurrence data in each homogeneous region by using a weighting function, and grouping the structural surfaces by using a K-mean particle swarm algorithm to obtain grouped structural surfaces; determining the density of the distribution space of each group of structural surfaces according to the spacing, occurrence, size and survey line trend data of each group of structural surfaces; determining the position and size of a layer in the layer structure space according to the determined distribution position of the structural surface; and randomly combining parameters of each group of structural surfaces, and constructing a stratified rock mass structure spatial network model in combination with sequence modeling and Monte Carlo simulation methods.

The invention discloses a time-frequency analysis method for processing a multi-component fast-changing time-frequency cross signal, relates to the technical field of signal processing, is suitable for processing and analyzing the fast-changing time-frequency cross signal, and can realize accurate characterization and processing of the fast-changing time-frequency cross signal under a strong noise condition. According to the technical scheme, the method comprises the steps that S1, an EMCT is used for processing collected time-frequency cross signals, and an EMCT result of the signals is obtained; and S2, establishing a new frequency redistribution operator, processing an EMCT result of the signal, obtaining an intermediate frequency track of the signal, and carrying out adaptive time-frequency cross ridge extraction. And S3, establishing a time-frequency coefficient compression method for a time-frequency cross signal, and processing an EMCT result of the signal in combination with the obtained intermediate-frequency trajectory to obtain a time-frequency result. And S4, in the generated time-frequency result, extracting a target component of the signal by using an intermediate-frequency track, and representing and reconstructing a time-frequency cross signal.

The embodiment of the invention discloses a business type determination method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence, such as the field of machine learning. According to the embodiment of the invention, subject business data of an operation subject is acquired, the subject business data comprises subject attribute data and transaction data, and the transaction data comprises behavior data and position data; according to an association relationship between the behavior data and the position data, classifying the behavior data and the position data to obtain target behavior data corresponding to the specified transaction mode and transaction position distribution; determining a transaction distribution rule according to the target behavior data and the transaction position distribution; and determining the business type of the operation subject according to the transaction distribution rule and the subject attribute data. According to the scheme, the real business type of the business subject can be determined based on the multi-dimensional business data, so that a business platform can conveniently manage and supervise the business subject.

The invention discloses a path planning method, system and device for a complex terrain and a storage medium. The method comprises the following steps: acquiring topographic data of a complex terrain through a laser radar, and constructing a map model based on an irregular triangular network; calculating the topographic cost of each triangle according to the topographic features of the map model, and converting the topographic cost of each triangle into the total topographic cost; and inputting the total terrain cost of each triangle into a Double DQN algorithm, and planning an optimal path. By combining the three-dimensional terrain modeling technology and the reinforcement learning algorithm, efficient, accurate and real-time path planning can be realized in a complex three-dimensional environment, the limitation of the prior art is overcome, and the development of the autonomous navigation technology of the robot is promoted.

The invention relates to the technical field of lithium metal battery health monitoring, and particularly discloses a lithium metal battery pack health monitoring system and method based on thin film sensors, the system comprises a plurality of thin film sensors distributed among lithium metal battery pack cells, and each thin film sensor comprises a temperature measuring unit, a pressure measuring unit, a flexible circuit board and silica gel foam; the adapter plate is electrically connected with the plurality of thin film sensors; the signal acquisition unit is electrically connected with the adapter plate and comprises a constant current source circuit, a signal conditioning circuit, a filter circuit and an analog-to-digital conversion circuit; the input end of the data transmission unit is electrically connected with the output of the analog-to-digital conversion circuit; the input end of the digital signal processor is electrically connected with the output end of the data transmission unit; and the input end of the battery management unit is electrically connected with the output end of the digital signal processor. The mechanical information and the temperature information between the battery monomers can be monitored at the same time, the battery management can evaluate the health state of the battery more comprehensively based on the information, and the abnormal problem can be warned in advance.

The invention relates to a thin-wall ceramic tube for static pressure strength testing and a preparation method thereof. The ceramic tube comprises a prefabricated body, and grinding sections are arranged at the two ends of the prefabricated body; and densifying the formed preform to obtain a ceramic tube, and grinding the surfaces of the two ends of the ceramic tube to obtain the thin-wall ceramic tube for static pressure strength test. The coaxiality of the two ground ends is not larger than 0.2 mm, and the surface smoothness is smaller than 0.2. Compared with the prior art, accurate characterization of the circumferential strength of the ceramic tube is realized.

The invention relates to an object point cloud reconstruction method based on a guided cross-modal robot, and the method comprises the steps: obtaining a depth image and a corresponding RGB image of an object under a single visual angle of the robot, carrying out the preprocessing, and obtaining the incomplete data of the point cloud of the object; a point cloud classification encoder is adopted to encode the point cloud classification task, a point cloud classification decoder is adopted to decode the point cloud classification task, and a classification result of the object is obtained; based on the object point cloud incomplete data, a point cloud data encoder is adopted to carry out position embedded coding, and point cloud features containing position codes are obtained; acquiring tactile data of an object at a single-view blind area position of the robot, and encoding the tactile data by a tactile space encoder to obtain tactile features; a visual touch cross-modal fusion module is adopted to fuse the point cloud features and the touch features, and a classification result is utilized to guide in the fusion process; and decoding the fused features by adopting a pyramid decoder to obtain a complete point cloud of the object. Compared with the prior art, the method has the advantages of high precision and the like.

The invention provides a geological disaster risk evaluation method and device fusing ascending and descending orbit time sequence InSAR deformation, and the method comprises the steps: carrying out the deformation inversion based on ascending and descending orbit time sequence InSAR data, obtaining an ascending orbit surface deformation quantity and a descending orbit surface deformation quantity, and obtaining a dynamic deformation factor corresponding to each grid in a research region after fusion; determining a multi-dimensional static environment factor corresponding to each grid in the research area based on the optical remote sensing data and the environment data; traversing each grid in the research area, and screening the dynamic deformation factors and the multi-dimensional static environment factors corresponding to the grids to determine a plurality of target evaluation factors corresponding to the grids; and based on the contribution degree of the target evaluation factor corresponding to each grid to the geological disaster event occurring in the research area, generating a geological disaster risk evaluation result corresponding to the research area. According to the method, geological disaster deformation can be represented more comprehensively and accurately, so that the geological disaster risk evaluation precision is improved.

The application discloses a kind of multi-element fusion carotid vascular monitoring method, system, electronic equipment and storage medium, it is related to medical monitoring field.The method comprises: obtaining the time-domain acoustic signal of carotid artery of the individual to be measured, and the time-domain acoustic signal is carried out frequency domain transformation, and frequency-domain acoustic signal is obtained;Nonlinear flow state feature and topological form feature are extracted based on the frequency-domain acoustic signal;The nonlinear flow state feature and the topological form feature are compared with the corresponding baseline feature in the historical acoustic baseline of the individual to be measured, and feature deviation result is obtained;Risk assessment is carried out based on the feature deviation result, when the feature deviation result exceeds preset range, risk early warning is triggered.The application suppresses the interference such as power frequency, baseline drift, respiratory body movement and environmental noise mixed in time-domain waveform, thereby reducing the false positive rate of carotid vascular acoustic monitoring, and realizing early risk early warning of vascular lesion.

This invention relates to a method, system, and medium for measuring the spatial parameters of a monocular target object based on a 2D camera. The method includes: acquiring a calibration board image and an original distorted image captured by the 2D camera; calibrating the calibration board image using a calibration algorithm to obtain calibration parameters; correcting the original distorted image using the calibration parameters to obtain a corrected image; performing target detection; and outputting the external contour and target contour feature points of the target object; classifying the target contour feature points into upper boundary feature points and lower boundary feature points; calculating the relative distance between the target object and the 2D camera based on the lower boundary feature points and the calibration parameters; and performing curve fitting on the upper and lower boundary feature points and the lower boundary feature points, respectively, and calculating the actual height of the target object based on the calibration parameters. Compared with existing technologies, this invention has advantages such as improved measurement accuracy and adaptability to complex boundary targets.

The present invention provides a method and system for evaluating the heating demand load based on room temperature and IBA-LSTM, including: setting multiple indoor temperature measurement points for different heat users in a heating system, obtaining indoor temperature measurement values ​​at multiple times within a preset period; assigning different weight coefficients to multiple indoor temperature measurement points of different heat users, and calculating the indoor temperature fusion value of each indoor temperature measurement point; using a sensitivity analysis method to analyze the various influencing factors affecting the heating demand load evaluation, and screening out important influencing factors; using an improved bat algorithm IBA to optimize the parameters of the LSTM network algorithm; constructing a heating demand load evaluation model based on the optimized LSTM network algorithm, and evaluating the load demand of the heating system. The present invention determines the indoor temperature fusion value by determining the weight coefficient for each room temperature measurement point, and uses IBA-LSTM to construct a heating demand load evaluation model, which can improve the model training speed and has high model evaluation accuracy.

This application relates to the field of soil remediation technology, and discloses a method, system, and storage medium for real-time monitoring of soil remediation effects. The method includes: collecting multi-depth data samples of soil in the vertical direction to construct a stratified soil characteristic dataset; identifying locations of highly heterogeneous sections based on the dataset; injecting a tracer at these locations and imaging in real time to obtain a sequence of infiltration path images; using a random forest model to predict diffusion velocity and blockage point locations, dynamically adjusting injection parameters to eliminate blockage, and obtaining infiltration path data; tracking and quantifying the effects of the soil amendment on each soil layer through time-series analysis to obtain quantitative indicators of the effect; comparing these indicators with the initial dataset to optimize the stratification model and construct a stratified monitoring framework; integrating multi-depth data samples to validate the framework and generating an evaluation report on the soil remediation effect. This invention achieves accurate characterization of soil vertical stratification characteristics and scientific, quantitative evaluation of remediation effects, improving the efficiency of soil amendment use.

The invention discloses a building three-dimensional damage detection method, which comprises the following steps of: 1, carrying out image acquisition on a target building by using an unmanned aerial vehicle carried with a visible light imager to obtain a plurality of building images covering the whole target building, and generating a building DSM (Digital Subscriber Model); 2, acquiring a top elevation image of the target building; 3, detecting the elevation change of the target building; 4, detecting the top damage of the target building; 5, detecting side damage of the target building; and 6, visualizing the damage of the target building. The method is simple in steps and reasonable in design, the unmanned aerial vehicle is used for shooting the images of the target building at multiple angles, detection of elevation change, top damage and side damage of the target building is achieved, and overall visualization of damage of the target building is achieved.

The present invention provides a method for evaluating the diffusion behavior of a regeneration agent when mixing and regenerating old asphalt based on non-equilibrium molecular dynamics simulation. Through stretching dynamics simulation, the dynamic process before the system reaches equilibrium during mixing and a period of time thereafter is more realistically simulated, and the non-equilibrium dynamic characteristics of the system are obtained. The gyration radius and diffusion coefficient of the regeneration agent calculated based on the obtained non-equilibrium dynamics trajectory can more objectively evaluate the diffusion behavior of the regeneration agent. The advantage of the present invention is that the mixing process is simulated more realistically by using non-equilibrium dynamics. In addition, after the mechanical mixing is completed, the mixed system is still in a non-equilibrium state, and the further diffusion of the regeneration agent is also described by NVT ensemble dynamics simulation. Therefore, these two steps of non-equilibrium dynamics simulation better achieve the reproduction of the fusion process of old asphalt and regeneration agent at the nanoscale on a macro scale, and can provide a more accurate and realistic characterization of the diffusion properties and behavior of the regeneration agent.

The invention discloses a target positioning method based on a photoelectric pod, and belongs to the technical field of image processing, and the method comprises the following steps: S1, collecting image data through a photoelectric pod installed on an unmanned plane; s2, generating a color array value according to the standard color matrix of the photoelectric pod and each structure matrix of the image data; and S3, determining target positioning according to the contour of the image data and the color array value. According to the method, the color array value is generated according to the standard color matrix of the photoelectric pod and each structure matrix of the image data, and the color array value can reflect the color characteristics of the target object, so that the target can be accurately positioned in a complex background; according to the invention, the contour of the image data is analyzed by using the color array value, so that the target can be further accurately positioned.

This invention discloses a method and system for processing personnel information for business administration based on big data, relating to the field of personnel information data processing technology. The method includes the following steps: acquiring personnel information; preprocessing the personnel information to obtain preprocessed text information; processing the preprocessed text information through an embedding layer based on a CNN-BERT model to obtain a word vector matrix; processing the word vector matrix sequentially through a CNN convolutional layer, pooling layer, and fully connected layer to obtain structured data; using an improved ant colony optimization algorithm to perform path optimization for multi-condition combined queries on the structured data to obtain the globally optimal retrieval path; retrieving personnel information that satisfies the multi-condition combined query from the structured data based on the globally optimal retrieval path; and adaptively updating the global pheromone of the improved ant colony optimization algorithm after each round of multi-ant colony collaborative search to accurately locate the target personnel information.

This invention provides a non-uniform discrete equivalent circuit modeling method and system for electromigration reliability. It employs an equivalent circuit-based approach to analyze the electromigration stress evolution of general complex interconnect structures, fully considering thermal field effects in the model. This achieves equivalent circuit modeling for stress analysis under multi-physics coupling. Furthermore, by using an adaptive mesh method with non-uniform spatial discretization and a steady-state / quasi-steady-state filtering mechanism, it solves the problem of wasted computational resources in traditional uniform discretization methods when dealing with large-span interconnect structures, and avoids unnecessary transient simulations of immortal interconnect trees, reducing the computational load of full-chip-level analysis. This enables efficient and high-precision transient analysis of the electromigration reliability of large-scale interconnect networks under multi-physics coupling effects.

The invention discloses an ultrafast electronic pulse width measuring system and method based on time amplification fringe scanning. The problems that when the pulse width of an ultra-short electronic pulse is measured through an existing pulse width measurement technology, the measurement requirement for the length of an ultra-fast electronic pulse bunch with the electronic pulse width smaller than 100 femtoseconds is difficult to meet, the measurement precision is low, a measurement system is complex, and the cost is high are solved. According to the method, a time broadening effect is generated by utilizing a space charge effect and a vacuum dispersion effect of femtosecond-order ultrafast electronic pulse, the ultrafast electronic pulse is broadened to a picosecond order which can be detected by a traditional stripe scanning method, and the time broadening amount of the electronic pulse can be obtained by utilizing a space-time broadening theory; the pulse width of the broadened ultrafast electronic pulse is obtained through a stripe scanning deflection method, and the pulse width of the ultrafast electronic pulse can be obtained through inversion; the method provided by the invention can be used for accurately characterizing the femtosecond-order ultrafast electronic pulse ground pulse, and is high in time resolution, simple in system principle and easy to implement.

The invention relates to a raw material conveying optimization control method and system for an automobile interior trim part production line, and the method comprises the steps: U1, enabling a conveying robot to convey raw materials to a next process on the automobile interior trim part production line, obtaining the image data information of a conveying robot region in real time based on an online camera, the rotation angle data information of the material conveying robot is obtained in real time based on an airborne tilt angle sensor; and U2, based on the image data information of the material conveying robot area, carrying out feature point extraction on the image by adopting an improved SURF algorithm to obtain image feature point matrix data information types of the material conveying robot and the raw materials. According to the method, the raw material conveying process of the material conveying robot can be predicted in advance, so that dynamic adjustment and control are conducted, the stability of raw material conveying is guaranteed, the operation accuracy of the material conveying robot is improved, the efficiency of raw material conveying is guaranteed, and the production efficiency of products is improved.

The invention belongs to the technical field of power switch equipment monitoring, and particularly relates to a circuit breaker fault diagnosis method and system and a computer device. The method comprises the following steps: firstly, obtaining characteristic quantity data of the circuit breaker, wherein the characteristic quantity is a characteristic quantity which is obtained by carrying out LDA dimension reduction processing on an obtained multi-source characteristic signal reflecting the operation condition of the circuit breaker and is required for fault diagnosis of the circuit breaker; inputting the obtained characteristic quantity data into a fault diagnosis model to obtain a circuit breaker fault classification result; the fault diagnosis model is obtained by training a machine learning model by using a training set, and the training set is a data set containing multiple groups of historical characteristic quantity data of fault classification labels. According to the method, some redundant and interference diagnosis information can be removed through LDA dimension reduction processing, enough important features are reserved, and category information of original multi-source feature signals can be accurately represented, so that the efficiency and accuracy of a fault diagnosis model are relatively high.

The invention provides a target object segmentation method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining a detection image containing a target object, and carrying out the feature extraction of the detection image, and obtaining a bottom feature and an up-sampling feature; fusing the underlying features and the up-sampling features through a cascade aggregation module to obtain a cascade feature map; converting the underlying features into a boundary feature map through a boundary extraction module, and respectively performing boundary extension and semantic extension on the boundary feature map and the cascade feature map to obtain a final boundary feature map and a final cascade feature map; and predicting the target object according to the final boundary feature map and the final cascade feature map to obtain a segmentation result of the target object. According to the method, the boundary details and the overall semantic information of the target object are comprehensively considered, so that the segmentation accuracy is effectively improved.

The invention relates to a high-order interface capturing method for a coupling level set and a fluid volume based on mixing precision, which comprises the following steps of: firstly, dividing a calculation area into a grid comprising a plurality of control body units, initializing, then identifying interface units, and reconstructing a THINC interface function by adopting a high-order polynomial based on an LS value; thirdly, introducing a mixed precision iterative algorithm to solve an interface position nonlinear equation, and adaptively switching to multi-precision operation to obtain a robust solution when double precision cannot converge; then, flux is calculated through an accurate interface, a VOF transport equation is solved, and mass conservation is guaranteed; and finally, on the basis of the predicted interface geometry, an LS field which does not need to be reinitialized is directly reconstructed through pure geometric methods such as interface point cloud generation through projection and nearest point searching in a narrow band. According to the method, the capture precision and geometric fidelity of complex interfaces with high curvature, sharp corners and the like on the unstructured grids are remarkably improved, and the method has high precision and strong robustness.

The invention belongs to the technical field of wear detection, and discloses a non-linear Rayleigh wave-based metal friction wear detection method and system, and the method comprises the steps: carrying out the non-linear Rayleigh wave detection of wear damage obtained after a wear test of a metal test piece, and obtaining a corresponding non-linear response signal, performing multiple groups of wear tests under different wear parameters to obtain multiple groups of wear damages; extracting a fundamental wave, a second harmonic wave and a third harmonic wave in the nonlinear response signal, obtaining a second-order ultrasonic nonlinear coefficient based on the fundamental wave and the second harmonic wave, and obtaining a third-order ultrasonic nonlinear coefficient based on the fundamental wave and the third harmonic wave; and constructing mapping relationships between the wear characteristic parameters of the metal test piece after the wear test and the second-order ultrasonic nonlinear coefficient and between the wear characteristic parameters of the metal test piece and the third-order ultrasonic nonlinear coefficient, and comprehensively characterizing and evaluating frictional wear based on the mapping relationships. According to the invention, nondestructive in-situ detection of metal wear damage of a mechanical system can be realized.

The present invention relates to the field of radar imaging technology, and discloses a synthetic aperture radar forward oblique imaging polar coordinate imaging algorithm, including: obtaining electromagnetic signals when radar waves are reflected when they arrive at a detection target and flight path data of the radar waves, performing data preprocessing, and establishing echo data. Convert the rectangular coordinate system of the echo data into a polar coordinate system, and perform grid processing. Remove the Doppler effect in the echo data after grid processing based on the range Doppler algorithm, and determine the azimuth of the echo data based on the polar coordinate fast Fourier transform. Divide the echo data after the Doppler effect is removed into several sub-areas based on a pre-configured viewing angle for block processing, and reconstruct into an image to be generated. Perform image filtering enhancement and eliminate artifacts on the image to be generated, and convert the image to be generated into a standard image format for output. The present invention can effectively improve the accuracy and reliability of radar imaging through signal processing and image reconstruction technology.