2966 results about "Wave radar" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a human body posture estimation method based on millimeter wave radar point clouds, which comprises the following steps of: processing each frame of input millimeter wave radar sparse point clouds by utilizing a built posture estimation model, and finally predicting and outputting a three-dimensional coordinate sequence of corresponding human body key joint points; wherein the attitude estimation model is composed of a point cloud completion network based on an encoder-decoder architecture, a global-local double-branch network and a feature fusion and prediction mechanism, and the method comprises the following steps: firstly, enhancing the density and integrity of an original sparse point cloud by using the point cloud completion network; the method comprises the following steps: complementing point clouds and original point clouds, respectively processing the complemented point clouds and original point clouds through a global-local double-branch network, extracting global structure features and local detail features, finally fusing the two features through a feature fusion and prediction mechanism, predicting three-dimensional coordinates of human body joint points through a regression layer, and realizing accurate attitude estimation. The method improves the estimation precision and robustness, maintains the privacy protection advantage, and is suitable for complex application scenes.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention relates to a robot path planning method based on multi-sensor fusion and free space topology composition, and belongs to the technical field of robot autonomous navigation. The method comprises the following steps: firstly, fusing laser radar and millimeter wave radar data, constructing multi-modal point cloud data, extracting a three-dimensional obstacle boundary by combining depth and normal vector mutation features, and generating a three-dimensional obstacle map and a free region tree; according to the constructed space model, factors such as energy consumption, dynamic obstacles, path smoothness and the like are integrated, target selection weights are dynamically regulated and controlled, and self-adaptive screening of intermediate navigation targets is achieved. And based on the intermediate navigation target, generating a safe trajectory satisfying dynamic constraints by adopting geometric-dynamic dual-mode fusion modeling, and enhancing the feasibility and robustness of the trajectory through trajectory envelope optimization and pre-execution fault-tolerant control. The method can be widely applied to autonomous navigation systems such as mobile robots and unmanned vehicles, and has good environment adaptability and path execution stability.

The invention provides a reservoir bank slope deformation body instability volume prediction method, which comprises the following steps: respectively acquiring earth surface displacement and rock mass internal deformation data through a millimeter wave radar and a tilt angle sensor, and after processing through an adaptive noise decomposition algorithm, identifying a key deformation area and generating a data set. And performing space-time alignment on the data by using the engineering coordinate system and the topological relation to generate a fusion matrix. And reconstructing a potential slip crack surface geometric model in combination with slip crack surface features of historical cases, and calculating instability volume probability distribution by adopting Monte Carlo simulation. And finally, inputting the multi-dimensional data into the space-time prediction model, and outputting an instability volume prediction result with probability distribution. According to the invention, the accuracy and reliability of the prediction result can be improved, and scientific basis and technical support are provided for safety monitoring and disaster early warning of the reservoir bank slope.

The invention discloses a target detection tracking method and device based on radar visual fusion perception, and a medium, and the method employs visual detection as a leading part to establish a radar visual fusion tracking module, and solves a problem that the detection precision, tracking stability and environment robustness are difficult to give consideration to the existing single-mode perception in a complex traffic environment at the same time. Through unified multi-modal fusion of distance and speed information of visual detection, visual tracking and millimeter wave radar, stable, continuous and reliable target identification and track output of targets such as pedestrians and vehicles under a low-computing-power platform are realized.

The invention discloses a target identification method for multi-sensor data fusion, particularly relates to the technical field of data fusion, and comprises a dynamic weight fusion module, a double-branch neural network and an abnormal sensing compensation system. Data are synchronously collected through a visible light camera, a thermal infrared imager and a millimeter wave radar, and a standardized feature map is generated through sensor specificity preprocessing; the dynamic weight fusion module generates an adaptive weight matrix based on the real-time confidence score and the environmental parameters, emphasizes infrared data when illumination suddenly changes, and improves intelligent distribution of radar weights in a rain and fog environment; the combined feature map after weight fusion is input into a double-branch neural network, a channel self-calibration branch suppresses interference noise, and a target category and coordinates are output after residual connection optimization; when the recognition confidence is insufficient, the abnormal compensation system starts visible light Wiener filtering restoration, generative adversarial network infrared compensation and radar multi-frame accumulation algorithms, and the system reliability is ensured when a single sensor fails.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention provides a falling detection method and system based on millimeter wave radar fusion human body postures, and relates to the technical field of falling detection, and the method comprises the steps: collecting human body echo signals, and constructing three-dimensional point cloud data through distance, angle and speed estimation; the point cloud is processed to generate a dynamic sequence, and skeleton features and key point coordinates are extracted in combination with a graph convolutional network. A skeleton connection relation is constructed based on the key points, attitude features are calculated, key change features are extracted through a self-attention mechanism, and the key change features are fused with a point cloud sequence to construct multi-modal features. And the double-branch state recognition network processes the fusion features, and when abnormality is detected, further analysis is carried out through residual attention and space-time diagram convolution, and finally, the falling state is recognized and the risk level is evaluated.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The invention discloses a millimeter wave radar and range finder golf sports monitoring system and method and a medium, and relates to the field of sports monitoring, the system comprises a handheld integrated terminal, a laser ranging module, a millimeter wave radar module, a processing unit and a display unit are integrated in the handheld integrated terminal, and a first communication module is connected with the processing unit; the data interaction module is used for performing data interaction with an external mobile terminal; the mobile terminal comprises a second communication module, a trajectory prediction module and a recommendation decision module, and is used for comparing the predicted flight trajectory with a target trajectory preset by a user and generating a swing suggestion in a training mode; and in the motion mode, the central processing unit is used for planning and recommending a ball hitting strategy based on the expected drop point distance input by the user and the real-time environment data measured by the handheld integrated terminal in combination with historical training data of the user. According to the invention, accurate monitoring and dynamic analysis of the golf ball flight path are realized.

The invention relates to the technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

The invention discloses a millimeter-wave radar sparse point cloud-based human body posture recognition method, which belongs to the technical field of human body posture recognition, and comprises the following steps of: acquiring three-dimensional point cloud data of human body actions through a millimeter-wave radar, and preprocessing the three-dimensional point cloud data; and training a lightweight neural network model by using the preprocessed point cloud data, wherein the model comprises an edge convolution module and a grouping sparse Transform encoder module. The edge convolution module extracts spatial geometric features and detects dynamics, static postures are directly classified, dynamic postures capture a time sequence dependency relationship through a grouping sparse Transform module, attention calculation is only carried out on frames with feature changes exceeding a threshold value, and mean pooling aggregation is carried out on other frames. And finally, classifying the human body postures based on the spatial geometric features and the time sequence dependency relationship to obtain a classification result. The device is simple in structure, accurate in recognition and suitable for efficient deployment of edge equipment.

A computing device for monitoring a sleep stage of a user in a contactless manner includes a processor, a radar unit, and a memory. The processor executes instructions from memory to cause the computing device to perform a series of signal processing methodologies on the waveforms received from the radar unit to accurately determine the sleep stage of the user in a contactless manner.

The invention discloses a camera and millimeter wave radar fusion three-dimensional target detection method based on time sequence fusion, and belongs to the technical field of three-dimensional target detection of computer vision. The invention provides a time sequence modeling framework integrating a camera and a millimeter-wave radar for solving the problems that pure visual perception lacks space measurement capability and a traditional time sequence fusion strategy has limitation in the aspects of dynamic target alignment and fusion efficiency. According to the framework, firstly, clustering processing is carried out on radar point clouds, instance features are extracted, and the instance features are used as initialization input of self-adaptive query, so that the number of iterations of a decoder is effectively reduced. By introducing a time sequence transmission mechanism based on a query instance, high calculation overhead caused by global feature alignment is avoided, and the motion state and time-space characteristics of a dynamic target are captured more accurately. A local self-attention mechanism is constructed by introducing a distance penalty term, close-range matching between query instances is realized, and the spatial alignment precision and fusion effect of multi-source data are further improved.

The invention relates to the technical field of massage, in particular to an intelligent abdominal acupoint massage system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module which comprises a 3D depth camera, an infrared thermal imager, a myoelectricity sensor and a millimeter wave radar; the intelligent decision-making module is used for updating a massage strategy in real time by applying a Poisson reconstruction algorithm, a multi-modal fusion positioning algorithm and a dynamic coordinate mapping method; the execution device comprises an end effector and is composed of a bionic massage head for executing massage, a six-dimensional force sensor for feeding back a pressure value in real time and an infrared temperature control module; the three-degree-of-freedom mechanical arm is composed of xyz translation compensation and a rotating joint and is used for controlling the end effector to move; the power module is used for supplying power to the system. The device has the advantages that accurate acupoint tracking and positioning can be provided for a user, safe, controllable, autonomously adjustable massage force is provided, and different massage schemes are set according to individuation of the physique of the user.

The invention relates to a tunnel surrounding rock three-dimensional deformation monitoring system integrating monocular vision and millimeter wave radar. The tunnel surrounding rock three-dimensional deformation monitoring system comprises in-tunnel sensing equipment, a tunnel opening terminal, a database and application server and a remote monitoring end. The invention also relates to a tunnel surrounding rock three-dimensional deformation monitoring method fusing monocular vision and millimeter wave radar. The method comprises the following steps: S1, setting a monitoring target; s2, multi-modal data acquisition is carried out; s3, visual and radar target detection; s4, performing visual and radar heterogeneous data matching; s5, solving pixel displacement and radial displacement; s6, calculating the three-dimensional deformation of the surrounding rock; and S7, alarm judgment and result output. According to the invention, the precision, the real-time performance and the automation level of tunnel surrounding rock deformation monitoring can be obviously improved, and the method is especially suitable for scenes with complex environment and high safety risk in the early stage of tunnel excavation; the invention aims to provide an efficient, accurate and automatic monitoring means capable of adapting to the early stage of excavation for tunnel surrounding rock deformation.

The invention relates to the technical field of computer vision and multi-sensor data fusion, in particular to a multi-sensor image fusion obstacle real-time detection and tracking system, which comprises the following steps of: firstly, fusing data of a camera, a millimeter wave radar and a laser radar, and extracting and fusing multi-modal features; performing multi-target tracking based on a recurrent neural network: generating a target state through space-time modeling, associating a target with a historical track by using an attention mechanism, and maintaining track consistency; and the system performs semantic classification and interaction analysis on the obstacle, predicts the movement track of the obstacle, realizes deep semantic understanding, and finally outputs the identity label and the complete historical track of the obstacle.

The invention discloses a human body posture estimation method based on radar point cloud imaging and multi-dimensional feature fusion, and relates to the cross technical field of computer vision and radar signal processing, and the method comprises the following three key technical links: firstly, improving the target resolution through spatial energy distribution estimation; reconstructing target three-dimensional space distribution by using the positive correlation between radar signal energy and a target reflection area and adopting a least square estimation algorithm; secondly, constructing a structured multi-dimensional point cloud matrix, and converting sparse radar point cloud into high-information-density imaging representation through a distance-speed hierarchical sorting strategy; and finally, designing a multi-dimensional feature fusion attitude estimation network, integrating three-dimensional convolution, a multi-head attention mechanism and a gating circulation unit, and realizing collaborative extraction of spatio-temporal features. According to the method, the problems of sparse target features, noise sensitivity and poor universality in traditional millimeter wave radar attitude estimation are solved.

The invention discloses an obstacle avoidance method and system for an unmanned aerial vehicle, and the method comprises the steps: collecting the three-dimensional point cloud and image information of a laser radar, binocular vision and a millimeter-wave radar, and obtaining an obstacle comprehensive parameter set; carrying out dimensionless processing on the obstacle avoidance feature vector to obtain an obstacle avoidance feature vector; inputting the obstacle avoidance feature vector into a discrimination model constructed based on a double-membership adaptive fuzzy decision tree, and outputting an obstacle avoidance action type according to an obstacle avoidance rule; and calling a corresponding trajectory generator to calculate the vertical speed, the lateral speed and the course instruction of the target in real time, driving a propulsion motor to complete maneuvering, and when the vertical clearance coefficient, the horizontal clearance coefficient and the distance safety margin all exceed preset exit thresholds again, automatically terminating the obstacle avoidance mode and restoring to the original planned route. According to the invention, the problems of low precision and insufficient stability of real-time obstacle avoidance decision making of the unmanned aerial vehicle in a complex environment are effectively solved.

The invention discloses an AI-based illumination control system self-adaptive dimming method, which comprises the following steps of: acquiring a two-dimensional coordinate, a motion track curve and voice communication data of a personnel position, and identifying social interaction scene categories of different time slices; determining a social interaction strength score according to the voice communication data, and predicting the most adaptive spectrum configuration data in the current social interaction scene in combination with the social influence factor matrix and the social interaction scene type; according to a historical dimming log and an energy consumption metering curve, identifying a short-term high-frequency dimming segment, and adjusting a real-time response strategy and a disturbance classification processing mechanism of the spectrum configuration data; and adjusting the illumination power distribution values of the high-frequency use area and the low-frequency use area according to the occupation raster data generated by the millimeter-wave radar and the infrared array in combination with the lamp area mapping relation and the power budget constraint. Through multi-source data fusion and a self-adaptive regulation and control mechanism, unification of real-time performance, pertinence and energy-saving performance of spectrum regulation is realized, and the dynamic response capability and the user perception experience of the intelligent illumination system are improved.

The invention relates to the technical field of multi-unmanned-aerial-vehicle cooperative operation and three-dimensional modeling, in particular to a multi-unmanned-aerial-vehicle cooperative three-dimensional rapid modeling method for a highway accident scene, and the method comprises the steps: generating a three-dimensional grid map of an accident area through the scanning of a millimeter-wave radar by a main control unmanned aerial vehicle; subareas are divided according to a load balancing strategy and are distributed to slave unmanned aerial vehicles, the slave unmanned aerial vehicles traverse grids along a snake-shaped track, laser radar point clouds and five-view-angle images are synchronously collected, data are bound through double time stamps and space coordinates, the point clouds are preprocessed through edge computing nodes, and the point clouds are stored in a database; the master control unmanned aerial vehicle evaluates quality based on density standard deviation and overlapping matching degree and instructs to reacquire, performs high-precision Poisson reconstruction on an accident core area, performs voxelization processing on a peripheral area, maps image textures, optimizes vehicle deformation details, simplifies a model and retains key element precision, and finally performs data processing. License plate coordinates, a scattered object thermodynamic diagram and an emergency lane occupation state are automatically marked, a visual model is generated, and rapid and accurate restoration of an accident scene is realized.

The invention relates to an unmanned aerial vehicle autonomous path navigation system based on multi-modal fusion and adaptive optimization and an algorithm thereof. The system comprises a multi-modal sensor module, a data fusion processing module, an adaptive algorithm processing module and a flight control module. The method comprises the following steps: step 1, data acquisition; step 2, data fusion; step 3, algorithm processing; and 4, controlling the flight of the unmanned aerial vehicle. The method has the advantages that multiple sensors such as a visual camera, a laser radar and a millimeter wave radar are integrated through deep fusion of multi-modal sensors, a fusion algorithm based on deep learning and a Transform architecture is adopted, weights are automatically distributed through an attention mechanism, deep data fusion is achieved, and the accuracy and accuracy of data fusion are improved. Compared with a single sensor or a simple data fusion mode, the problem that the single sensor is easily interfered by the environment is effectively solved, and the obstacle recognition accuracy and the environment modeling precision are greatly improved under the complex environments of low illumination, strong smoke, electromagnetic interference and the like.

The invention relates to a cooperative control method and system for realizing self-organizing protection of a multi-unmanned aerial vehicle cluster, and the method comprises the steps: M1, enabling the unmanned aerial vehicle cluster to execute a task, obtaining the data information of the remaining power, RSSI signal intensity and IMU attitude of each unmanned aerial vehicle in real time based on an airborne built-in sensor, and acquiring data information of distances and relative speeds of obstacles around the unmanned aerial vehicles in real time based on an airborne external millimeter-wave radar, constructing a dynamic threshold function stop, and if the RSSI signal strength of each unmanned aerial vehicle is greater than the stop, outputting data information of the residual electric quantity and IMU attitude of each unmanned aerial vehicle. According to the method, misjudgment (such as transient packet loss due to electromagnetic shielding) caused by temporary signal interference is effectively avoided, the misjudgment rate is reduced by 40%, unnecessary return or cluster recombination is reduced, continuous task execution is guaranteed, it is ensured that leader handover is completed quickly (at the millisecond level) when a host fails, and the situation that a cluster falls into disorder due to single-point failure is avoided.

The invention provides a target frame detection optimization method based on a millimeter wave radar, a medium and electronic equipment. The method comprises the following steps: acquiring an initial target frame set generated based on the millimeter wave radar; calculating an overlapping area ratio of any two target frames in the initial target frame set; and determining an output target frame according to the overlapping area proportion and updating the initial target frame set. The method effectively solves the problem of frame size estimation deviation caused by point cloud sparsity in a traditional method, improves the precision and reliability of target detection, achieves the adaptive processing of a complex road scene, effectively reduces the false detection rate, improves the accuracy and stability of a detection result, and improves the detection efficiency. And the perception performance of the automatic driving system can be effectively improved.

The invention discloses an AI scene conversion method and system based on visitor behaviors. According to the method, visitor behavior data are collected in real time through a multi-mode sensor network, and a UWB positioning base station, a millimeter wave radar module, a high-definition camera, a microphone array and a pressure sensor matrix are included. The system adopts an edge computing architecture, is equipped with an NVIDIAJetsonAGXXavier processor and a 512-core VoltaGPU (Graphics Processing Unit), and operates three core AI models to realize intelligent analysis. The LSTM residence time prediction model adopts a three-layer bidirectional architecture, each layer is configured with 256 hidden units, and visitor behavior sequences within 30 minutes are analyzed to predict the residence time. The Transform interaction intention recognition model comprises six layers of encoders and eight multi-head attention modules, and visual, voice and behavior three-mode characteristics are fused to judge visitor interaction requirements. According to the technical scheme, self-adaptive adjustment of the exhibition hall environment is achieved, the visitor satisfaction degree is improved by 28%, the operation cost is reduced by 15%, and important technical support is provided for intelligent transformation of modern exhibition places.

The invention discloses a millimeter-wave radar fusion system and method for electrocardiograph monitoring false alarm recognition, and the method comprises the steps: carrying out the multi-scale time-frequency feature extraction of a millimeter-wave radar signal and an electrocardiograph signal, obtaining a radar feature sequence and an electrocardiograph feature sequence, carrying out the time alignment of the radar feature sequence and the electrocardiograph feature sequence, and inputting a multi-modal fusion architecture; outputting cross-modal joint embedding features based on the multi-modal fusion architecture; a cross-modal consistency judgment model obtained based on self-supervised contrast learning optimization training is constructed, whether a cross-modal feature inconsistent state exists in the joint embedded features or not is judged, and if yes, it is judged that a potential false alarm event exists; and based on the potential false alarm event, extracting sequence feature fragments before and after alarm triggering in the joint embedded feature, inputting the sequence feature fragments into an anomaly classification network, outputting a final judgment result about whether false alarm is formed, and when the final judgment result is false alarm, generating an alarm adjustment instruction and sending the alarm adjustment instruction to a monitoring terminal.