681results about How to "Accurate tracking" patented technology

The invention relates to the technical field of law enforcement intelligent alarm, in particular to a multi-mode intelligent analysis method and system based on law enforcement supervision. The method comprises the following steps: acquiring a law enforcement scene video file from a law enforcement supervision recorder platform, and performing audio and video segmentation and law enforcement behavior information identification analysis to obtain law enforcement scene audio behavior information data and law enforcement scene image behavior information data; performing timestamp and spatial position extraction processing on the law enforcement scene video file, performing multi-modal time-space synchronization association analysis, and performing law enforcement behavior sequence connection processing and event behavior intention recognition analysis at the same time to obtain law enforcement scene event behavior intention information data; and obtaining a preset law enforcement event behavior knowledge graph, and carrying out behavior normative score calculation and nonstandard behavior intelligent alarm optimization analysis to generate a law enforcement scene event nonstandard behavior optimization suggestion report. According to the invention, the accuracy of law enforcement nonstandard behavior supervision alarm can be improved.

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention discloses a data format conversion method and system, a storage medium and a program product, and relates to the field of electric digital data processing, and the method comprises the steps: receiving first format data, and determining a data format of the first format data and a target format of second format data as a conversion target; the data format is a DWG format or an SHP format; extracting feature data corresponding to the data format and mapping data corresponding to the target format in the first format data; inputting the mapping data and the target format into a data conversion model to obtain supplementary data; generating second format data based on the mapping data and the supplementary data; and extracting data different from the mapping data in the feature data as loss data, and storing the loss data to a conversion database corresponding to the second format data. By implementing the method, lossless conversion of the DWG format and the SHP format can be realized, and the utilization rate of heterogeneous data is improved.

The invention is suitable for the technical field of flying dust monitoring, and provides a road construction site all-weather flying dust online monitoring method and system, and the method comprises the following steps: collecting flying dust data and wind power information through a sensor; the particulate matter concentration is judged, and the particulate matter concentration exceeding the standard and the corresponding coordinate position, wind speed data and wind direction data are screened out; determining a flying dust source area according to the coordinate position, the wind speed data and the wind direction data; a road transportation monitoring video is called, and the area where the transportation vehicle is located is determined according to the time information and the road transportation monitoring video; a road construction map is called, and the area where the transport vehicle is located is marked in the road construction map; and comparing and analyzing the flying dust source area with a road construction map, determining a potential flying dust source position, and sending the potential flying dust source position to an inspection terminal. According to the invention, the flying dust pollution source can be accurately tracked, and the inspection personnel can quickly position the potential flying dust pollution source according to the information.

The invention discloses an unmanned aerial vehicle antenna dynamic adjustment method and device under single Beidou support, and relates to the technical field of antenna control, and the method comprises the steps: carrying out the positioning of a target unmanned aerial vehicle, and obtaining a spatial positioning data signal; transmitting and receiving signals to obtain a signal transmitting and receiving record log; analyzing the flight area of the target unmanned aerial vehicle to obtain terrain environment parameters, performing flight analysis, and generating flight attitude change information; performing operation calculation on an antenna module of the target unmanned aerial vehicle, generating an antenna dynamic operation coefficient, and formulating an antenna adjustment strategy; and performing optimization, generating an antenna adjustment optimization strategy, and performing dynamic adjustment. The technical problem of poor communication and positioning accuracy and stability caused by insufficient antenna adjustment precision of the unmanned aerial vehicle in a complex environment in the prior art is solved, and the technical effects of realizing accurate tracking of the antenna of the unmanned aerial vehicle on the target signal source in the complex environment and improving the accuracy and stability of communication and positioning of the unmanned aerial vehicle are achieved.

The invention relates to a multi-modal target identification and tracking prediction method based on a laser radar and a binocular camera, and belongs to the field of robot / unmanned system perception identification. Comprising the following steps: preprocessing a target two-dimensional image acquired by a binocular camera and target point cloud data acquired by a laser radar to obtain a preprocessed two-dimensional image and point cloud data; s2, performing two-dimensional target detection and tracking detection based on the preprocessed two-dimensional image to obtain a two-dimensional target detection and tracking result; s3, performing three-dimensional target detection based on the preprocessed point cloud data and the two-dimensional target detection and tracking result to obtain a three-dimensional detection result; and S4, based on the pixel coordinates in the two-dimensional target detection and tracking result and the pixel coordinates of the vertexes of the target point cloud boundary in the three-dimensional detection result, target fusion tracking prediction is carried out, and the target ID, the position, the category and the target point cloud boundary are obtained. And accurate identification and tracking of the target are realized.

The invention relates to the technical field of heart data analysis, in particular to an AI (artificial intelligence)-based heart disease prediction system, which comprises a feature relationship modeling module for mapping each type of information into a multi-dimensional feature space based on collected heart data, analyzing the condition correlation degree between feature nodes, connecting the feature nodes through correlation information, and establishing a model; and quantifying a linkage effect between the features to obtain a feature condition dependency relationship analysis result. According to the method, various types of heart health data are noded by constructing a multi-dimensional feature space, and a correlation structure is established according to condition correlation among data features. And a synergistic effect and an interdependence relationship among the features are disclosed. By analyzing the joint state probability of the feature nodes, the linkage effect of the feature combination on the overall risk is quantified, and the analysis depth of the disease risk is enhanced. In addition, by comparing the independent state probability of a single feature with the joint state probability of the feature combination, a feature combination that makes a significant contribution to the risk can be identified.

The invention discloses a high-robustness target loss detection and rapid tracking method, and belongs to the technical field of calculation, reckoning or counting. By combining the YOLO algorithm and the MOSSE algorithm, the method can actively search the target and realize re-detection and rapid recovery tracking of the target after the space target is lost for a long time. And when the target moves out of the visual field, the MOSSE algorithm cannot continue tracking, and at the moment, the YOLO algorithm is automatically started to carry out target detection so as to confirm that the target appears again. Once a target is detected, the features of the target are immediately transmitted to the MOSSE algorithm so as to quickly switch back to the tracking mode, thereby ensuring the continuity and stability of tracking. The key point of the method is that a switching mechanism can quickly respond between target loss and reoccurrence, and time delay in the detection and tracking process is remarkably reduced.

The invention discloses a harmonic traceability method based on a harmonic power direction of an electric power system, which belongs to the technical field of electric power quality of the electric power system and comprises the following steps of: constructing matrixes of fundamental waves and harmonic signals, initializing active and reactive power deviation values of nodes, and adopting a decoupling algorithm combined with a Jacobian matrix iteration method to carry out harmonic traceability. And sequentially calculating the fundamental wave voltage and each harmonic current, and finally obtaining the power flow direction and judging the harmonic source attribute. When harmonic power flows from a load to a power grid, the node is identified as a harmonic source; and when the harmonic power flows to the load from the power grid, identifying the node as a harmonic receiving point. An equivalent circuit model is used for separating harmonic influences of a linear load and a non-linear load, and a real-time monitoring technology is combined to realize dynamic tracking and accurate positioning of a harmonic source. The method is low in calculation complexity, excellent in convergence performance and suitable for a modern power distribution network scene where distributed energy and power electronic equipment are widely connected, the harmonic suppression efficiency is remarkably improved, and the safety and stability of power grid operation are ensured.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

The invention discloses an energy regulation and control method of a photovoltaic system, and relates to the field of energy regulation and control of the photovoltaic system, and the method comprises the steps: collecting comprehensive state data in a time period T, processing the comprehensive state data, and obtaining time sequence state data; performing primary processing on the time sequence state data by using moving average filtering to obtain a smoothed initial data set, taking the initial data set as observation input of Kalman filtering, further smoothing the data by using the Kalman filtering to obtain a high-precision gain data set, and using improved Pamp; performing disturbance judgment on the gain data set through an O algorithm; according to the method, power oscillation of voltage in a prediction interval is eliminated by using an IncCond method, subdivision demand judgment is performed, a maximum power point is accurately positioned by using a subdivision search method, and the maximum power point is output as a working point of the system, so that energy regulation and control of the photovoltaic system at the maximum power point are realized, and the energy efficiency of the photovoltaic system is improved.

The invention discloses a Pick ball motion trail tracking and tactical decision-making auxiliary system which adopts a high-frame-rate camera, a micro sensor installed on a ball and an athlete motion sensor worn on an athlete to respectively collect video image data, ball motion data and athlete motion data in the Pick ball motion process. Precise positioning and trajectory tracking are carried out on a ball through a target detection and tracking algorithm based on deep learning, a tactical knowledge model is constructed by combining site information and athlete action data and applying a decision tree and a neural network algorithm, real-time tactical decision suggestions are provided for athletes and coaches, and the training efficiency is improved. And the competitive level and the training effect of the Pick ball sports can be improved.

The invention discloses a multi-source data fusion low-altitude unmanned aerial vehicle trajectory tracking method and system. The method comprises the steps of flight path data collection, wherein the flight path data collection is divided into cooperative flight path data collection and non-cooperative data collection; flight path data and flight path fusion: performing cooperative flight path data and cooperative flight path fusion processing and radar flight path data and radar flight path fusion processing; the flight path data and flight path fusion comprises data preprocessing, flight path data filtering updating and flight path data and flight path association; the cooperation track and radar track fusion is used for carrying out association fusion on the cooperation track and a radar mark track, and comprises a track filtering sub-process and track-to-track association; the flight path management is used for maintaining and managing all current flight paths, including temporary flight path maintenance and management and stable flight path maintenance and management, the temporary flight path is mainly a radar temporary flight path, and the stable flight path comprises a stable cooperative target flight path and a radar stable flight path. Fusion tracking of the flight path of the unmanned aerial vehicle detected by various heterogeneous sensing devices can be realized.

The invention relates to the technical field of sewage traceability positioning, and discloses a sewage traceability positioning method and device for carbon nitrogen ratio imbalance. The method comprises the following steps: performing carbon-nitrogen ratio standard curve deviation calculation and data dimension reduction processing on carbon-nitrogen space-time attenuation data to obtain carbon-nitrogen imbalance feature vector data; performing pipe network connectivity calculation and transmission path analysis on the coordinate data of the multi-level sewage system to obtain migration trajectory data of carbon and nitrogen pollutants; performing multi-dimensional data fusion on the carbon-nitrogen imbalance feature vector data and the carbon-nitrogen pollutant migration trajectory data to obtain spatial distribution data of the carbon-nitrogen imbalance pipe section; and carrying out pollution contribution calculation and region boundary identification on the spatial distribution data to obtain position region data of a carbon-nitrogen ratio abnormal source. According to the method, correlation analysis of the carbon and nitrogen space-time attenuation characteristics and the pipe network connectivity is established, and data dimension reduction and space analysis technologies are combined, so that the problem region range can be quickly reduced, and a more accurate tracing positioning result can be provided.

The invention provides a visual tracking system and method with multi-scale attention, and the system employs a Siamese framework, and comprises a linear projection layer which generates corresponding image blocks based on an image block sequence of a template image and a search region image; the feature extraction module is used for extracting feature information of different scales from the template image and the search area image; the information fusion block is used for fusing the template image block and the search image block on different scale features through an attention mechanism to generate an image block containing key target position information; and the prediction head is used for predicting the position of the target according to the image block containing the key target position information. The invention provides a novel multi-scale attention mechanism, the mechanism captures global dependence between a template and a search area from feature maps of various sizes, so that the sensitivity of a tracker to scale change is improved, attention guidance is adopted before modeling of small-size feature dependence, and main target information is effectively and preferentially concerned.

The invention discloses a phased array radar for unmanned aerial vehicle detection based on multi-target tracking, which comprises a radar antenna array, a signal processing unit, a control unit and a display unit, and is characterized in that the control unit comprises a motion state judgment module; a filtering switching module; a Kalman filtering module; a particle filtering module; and a data association module. The method can achieve the high-precision detection and positioning of an unmanned plane target, has a multi-target tracking capability, and improves the detection efficiency and accuracy of the unmanned plane.

The invention discloses a variable gain inversion maneuvering target interception guidance method considering attack angle constraint, and belongs to the technical field of aircraft guidance control. The implementation method comprises the following steps: considering necessary conditions for interception success and end attack angle constraints, based on trajectory shaping, representing a sight angle reference profile by constructing a polynomial function taking a flied distance as an independent variable, and realizing reduction of initial and end constraints; the profile only utilizes missile-target relative information and does not depend on motion information of the target, so that the profile is suitable for intercepting targets in various motion forms. In order to accurately track a line-of-sight reference profile, adverse effects of aircraft control input saturation constraint and target maneuvering interference on a guidance system are considered, an input saturation compensator and a finite time interference estimator are introduced, and a variable gain interception guidance law is designed based on an inversion control architecture. And the aircraft can accurately track the profile and can intercept the maneuvering target at an expected angle at the same time. The method has the advantages of low required overload, high interception precision and good robustness.

The invention provides an AIGC video creation method for cultural relic explanation, and belongs to the technical field of AIGC video creation. S2, cultural relic video processing; s3, audio data processing; and S4, carrying out fusion synthesis treatment. According to the method, the high-precision video processing effect and the audio authenticity processing effect can be increased, meanwhile, the interestingness can be increased by adding 3D personnel, and cultural relics can be known from multiple angles through cultural relic 3D restoration.

The invention relates to a constructor positioning and tracking method, which comprises the following steps of performing continuous video acquisition on site constructors to obtain real-time images of the constructors, performing real-time distinguishing on the real-time images to obtain constructors and non-constructors in the images, and removing the non-constructors in the real-time images; drawing a rectangular frame image based on the target contour of the constructor to obtain a rectangular frame image corresponding to the constructor; comparing the position change of the rectangular frame image corresponding to the constructor to obtain a movement track corresponding to the constructor; performing feature extraction on the target contour of the constructor by using a preset target contour algorithm to obtain features of the corresponding constructor; acquiring coding information of each constructor, and obtaining real-time positioning coordinates of the corresponding constructor based on the coding information of each constructor, the motion trail and the characteristics of the constructor; accurate tracking and management of constructors are realized.

The invention discloses a non-coaxial two-wheeled unmanned platform trajectory tracking hierarchical control method and system, and relates to the technical field of intelligent robots, and the method comprises the steps: obtaining the real-time pose information and coordinate information of an unmanned motorcycle in the driving process; inputting the pose data and the coordinate information into a vehicle-mounted calculation unit, and correcting the driving deviation of the non-coaxial double-wheel system on the premise of ensuring the stability of the vehicle based on a double-closed-loop trajectory tracking control strategy so as to track an expected trajectory; an off-line expected trajectory, a trajectory tracking controller, a self-balancing controller, an unmanned motorcycle system kinematics model and an unmanned motorcycle system dynamics model are preset in the vehicle-mounted calculation unit; the double-closed-loop trajectory tracking control strategy comprises an outer-loop pose planning layer and an inner-loop balance control layer. According to the invention, a double-closed-loop control structure is adopted, so that the trajectory tracking precision and stability of the unmanned motorcycle can be effectively improved.

The invention provides a link tracking method and system for a service cluster based on log burying points, and belongs to the technical field of distributed monitoring. According to the method, a link tracking ID is generated through a snowflake algorithm at a starting point of service calling, and service IDs are distributed to micro-service nodes participating in service calling; setting log burying points at the entrance, the exit and the key logic processing points of each service according to preset burying point logic; asynchronously recording link tracking information contained in the service node through the log burying point, and uploading the information to a storage system for storage; according to the link tracking ID and the service ID in the link tracking information, the link tracking information is connected in series, a complete calling link called by the service is restored, and meanwhile a service calling topological graph is generated. According to the method, real link information can be quickly and accurately tracked, and quick retrieval and analysis of a large amount of real-time link data can be supported.

The invention discloses a whole-tray warehouse-out system and method, and belongs to the technical field of cigarette manufacturing, and the system comprises a stacking robot, a cigarette carton temporary storage channel, a cigarette carton backflow channel, a latent AGV, an elevator, a tray on-line device and a whole-tray warehouse-out line; wherein the latent AGV is used for moving to a designated goods location of an ex-warehouse cache region to carry finished cigarette trays and carrying the finished cigarette trays to the tray online device, moving to a designated goods location of a target floor to carry the finished cigarette trays, descending to an ex-warehouse floor through the elevator and then carrying the finished cigarette trays to a connection platform on the same floor; then the tray is carried from the connection platform to the tray on-line device; and the whole-tray delivery line provides a conveying channel for the finished cigarette trays, so that the finished cigarette trays can be conveyed to a delivery area for whole-tray loading and delivery. According to the invention, accurate tracking and management of goods are facilitated, and the accuracy and efficiency of delivery of the whole pallet are improved.

The invention discloses a fault-tolerant controller design method for a quad-rotor unmanned aerial vehicle, and relates to the technical field of quad-rotor unmanned aerial vehicle control, and the method comprises the steps: building a state-space equation; designing a state observer based on an interval type-2 fuzzy logic system, and calculating an observation error between an unmeasurable state and a state observation value; the attitude subsystem and the position subsystem of the four-rotor unmanned aerial vehicle are decomposed into two-order subsystems; aiming at the first-order subsystem, designing a virtual controller alpha i, 1; and aiming at a second-order subsystem, designing a preset time filter, a self-adaptive preset time fault-tolerant controller taui (t) triggered based on a mixed event, a parameter updating law # imgabs0 # and a parameter updating law # imgabs1 #. The fault-tolerant controller designed by the invention can ensure that the actual preset time of a closed-loop system of the four-rotor unmanned aerial vehicle is stable; and the minimum upper bound of the system stabilization time can be determined through a controller gain, so that the quad-rotor unmanned aerial vehicle can accurately track a reference trajectory under the conditions of only measurable output signals, saturated input and actuator faults.

According to the character expression driving method and device, the storage medium and the computer equipment provided by the invention, during expression driving, the continuous video frame images and the expression driving target in the character video are determined firstly, so that expression driving is realized on the video frame images, and the naturalness of an expression driving effect is ensured; and then face alignment is carried out based on the face key points in the video frame image to obtain a standard face image, expression driving is carried out on the standard face image based on the expression driving target through the target expression driving model, an expression driving video is generated, and thus the quality of the expression driving effect is improved. The target expression driving model is formed by the three-dimensional convolutional neural network and is used for mapping the two-dimensional image to the three-dimensional hidden space for feature coding and expression driving, so that the model can accurately track human faces at various angles in the video frame image and perform expression editing and driving; and expression natural connection of video frame images between frames is realized under the condition that the quality of a figure video is not influenced.

The invention discloses a laser welding device for automobile part production. The laser welding device comprises a welding machine base, a multi-shaft combination mechanism, a bearing platform base and a navigation system. A welding mechanical arm and a speed reduction steering engine are arranged on the surface of the welding machine base, and a laser welding head is arranged at the execution tail end of the welding mechanical arm and used for welding. The output end of the speed reduction steering engine is connected with the bottom face of the deflection bearing platform through a ball coupling, and the deflection bearing platform is driven to achieve deflection motion. The bottom end of the multi-shaft combined mechanism is fixedly connected with the welding machine base. Multi-axis movement inclination is achieved through a multi-axis combined mechanism, deflection movement is achieved through a speed reduction steering engine, a welding path is dynamically adjusted in combination with a navigation system, and it is ensured that a welding head runs along the optimal path; and through linkage of the vibration sensor and the control system, vibration in multi-axis movement is effectively restrained, and the welding precision is improved. The method has the characteristics of high degree of freedom, high precision and high reliability, and is suitable for welding production of automobile parts in complex shapes.

The invention discloses a signal data real-time acquisition, storage and transmission device applied to a hydrophone, and the device comprises a plurality of sound wave receivers disposed on a measurement platform to form a matrix layout; receiving sound wave signals of different underwater paths, and performing electric signal conversion; analyzing the received sound wave signal, extracting signal data of the sound wave, and calculating the arrival angle of the sound wave signal; calculating the position of a target signal source, determining main signals in different paths, and correcting the main signals; the corrected signal data is transmitted to a storage device for segmented storage; according to the invention, the plurality of receivers are deployed on the measurement platform to form the matrix layout, and the position of the target signal source is calculated, so that the main signals received by the receivers are determined, the multipath effect caused by water body boundary reflection is eliminated, the main signals are corrected, the quality and accuracy of the signals are improved, and the measurement accuracy is improved. And accurate transmission and storage of underwater signals are ensured, so that the performance of the hydrophone system in a complex underwater environment is ensured.

The invention relates to the technical field of wild animal monitoring, in particular to an animal sensing method and device based on infrared and radar triggering, a medium and a product, and the method comprises the steps: controlling an image collection device, and carrying out the image shooting of a wild animal according to a zooming instruction, and obtaining an initial image; then, performing wild animal state analysis based on the initial image, determining shooting adjustment information, and generating a shooting adjustment instruction based on the shooting adjustment information; and further, the image acquisition device is controlled to perform image shooting on the wild animal according to the shooting adjustment instruction to obtain a wild animal image. Through evaluation of the initial image definition degree, it can be ensured that the shot wild animal image meets a certain quality requirement, the focal length of the image acquisition device is adjusted in time, and the resolution and definition of the image can be remarkably improved. Meanwhile, a dynamic tracking mechanism is introduced, so that the behavior mode of the wild animals is tracked and recorded more accurately, and the accuracy and comprehensiveness of image shooting are improved.

The invention discloses a handheld security check imaging method based on MEMS assistance, and aims to combine an MEMS sensor with an MIMO radar plate to form a handheld convenient security check imaging method with high precision and low cost according to the rapid and fine security check requirement of people in a security check place with dense people. Firstly, the radar board transmits a linear frequency modulation signal and receives an echo signal reflected by a target, and during the period, the MEMS sensor feeds back the motion track and position information of the handheld security check system in real time, and provides accurate attitude and position information. And secondly, position calibration is carried out on the track information of the MIMO radar antenna through the data of the MEMS sensor, and the zero drift of the accelerometer is compensated based on the wavelet packet change, so that the error caused by the system motion and the position change is reduced. And finally, residual phase errors are estimated and eliminated based on a particle swarm algorithm, non-contact three-dimensional imaging of the target is realized in combination with a wavenumber domain imaging algorithm on this basis, and hazardous article inspection is realized in combination with technologies such as deep learning and image recognition.