462 results about "Active detection" patented technology

The invention relates to the technical field of wireless signal detection, and discloses a wireless anti-interference signal detection method, device and equipment and a storage medium, and the method comprises the steps: collecting a standardized interference feature vector through a multi-scene wireless signal receiving device; performing interference feature enhancement processing on the standardized interference feature vector to obtain an interference feature sample set; constructing a dual-array cooperative detection system comprising a passive detection array and an active detection array, and performing real-time monitoring to obtain dual-path interference signal monitoring data; performing spatial domain and time-frequency domain dual feature extraction and fusion processing on the dual-path interference signal monitoring data to obtain a comprehensive interference feature; the receiver filtering parameter matrix and the detection threshold vector of the double-array cooperative detection system are dynamically adjusted according to the comprehensive interference characteristics, the method can adapt to changes of different interference environments in a self-adaptive mode, comprehensive coverage of communication spectrums is achieved, and the wireless anti-interference monitoring range and the spectrum sensing capacity are greatly expanded.

The invention provides a cross-platform network speed measurement and adaptive path switching method and device and a storage medium. The method comprises the following steps: receiving a target domain name and a multi-source DNS server list; extracting historical network quality data corresponding to the environment identifier and each candidate IP address, inputting the historical network quality data into a link quality prediction model to obtain a prediction score of each candidate IP address, and screening according to a preset threshold to obtain a target IP subset; performing active detection of limited times on each IP address in the target IP subset to obtain a detection index; calculating the comprehensive score of each IP address based on the prediction score and the detection index, determining the optimal IP address with the highest comprehensive score, and recording the DNS server address of the optimal IP address; and establishing a network connection path according to the optimal IP address, and providing a DNS server address of the optimal IP address for a service layer. According to the method and the device, the cross-platform maintenance cost can be reduced, the real-time performance and the performance overhead are considered, the path is dynamically optimized, and rapid connection switching is realized.

The invention provides a signal exception processing method and system, a server and a storage medium, and relates to the field of servers, the method is applied to the signal exception processing system, the signal exception processing system comprises a substrate management controller and a hardware extension module, and the hardware extension module is used for performing function extension on a central processing unit and a universal input and output interface. Wherein the substrate management controller does not immediately respond to the signal sent by the hardware extension module, but can firstly take the signal as a to-be-detected signal, and detect the signal according to a preset signal parameter to determine whether the signal form is abnormal or not. And if the to-be-detected signal is determined to be abnormal, in order to distinguish whether the to-be-detected signal is a normal signal, the substrate management controller can also actively acquire a register value corresponding to the signal from the hardware extension module, and determines whether to respond to the to-be-detected signal according to whether the logic meaning of the to-be-detected signal is consistent with the logic meaning of the register value. In this way, signal abnormity can be actively detected, and error response can be avoided.

The invention discloses a micro-grid load distribution control method, device and equipment based on entropy increase disturbance, and the method comprises the steps: analyzing the operation state of a micro-grid through chaos measurement, and constructing an entropy increase disturbance excitation mechanism to actively excite the vitality of a system; after random disturbance is applied, inherent frequency characteristics of each power supply are extracted, and a frequency synchronization coordination mechanism is established to realize multi-source coordination; adopting counter-example search to actively identify distribution scheme vulnerabilities, and generating an enhanced distribution strategy to improve robustness; evaluating the load bearing capacity of each power supply, constructing a load conduction topology and analyzing pressure distribution; each power supply bearing limit parameter is obtained through boundary active detection, and the equipment potential is fully excavated; and a final control instruction is generated based on a multi-layer time sequence synchronization mechanism, and multi-power-supply coordinated output is realized. A chaos theory is introduced into micro-grid control, local optimum is avoided through active disturbance, a multi-layer coordination mechanism is adopted to adapt to new energy volatility, and a self-adaptive load distribution control scheme is provided for a micro-grid with high-proportion renewable energy access.

A system and method for agentless detection of network exposure of a software appliance is presented. The method includes detecting a software appliance in a cloud computing environment, wherein the software appliance includes an immutable preconfigured, self-contained software application; inspecting the software appliance for a proxy-type application; detecting network traffic associated with the software appliance; generating a network path based on the network traffic and a network identifier of a component of the cloud computing environment based on detecting the proxy-type application and an identifier of the component in the detected network traffic; actively inspecting the generated network path through an external network, wherein the external network is external to the cloud computing environment; determining that the network path exposes the component based on a result of actively inspecting the generated network path; and initiating a remediation action based on the result of the active inspection.

The invention provides an oil field information asset automatic discovery and management method and system, and the method comprises the steps: carrying out the flow reduction, carrying out the active detection, carrying out the asset identification label marking of all types of attribute information and components of assets according to the asset information and feature information obtained through the active detection and flow reduction, and carrying out the asset marking. And for unidentified assets found by active detection and flow reduction scanning, inputting corresponding information of the unidentified assets according to the asset identification tag, and carrying out security monitoring on the identified asset information. And the asset information is fed back to the security platform, and the found asset information is compared with alarm information in the security platform, so that non-asset alarm information and alarm information inconsistent with the asset category are removed, and the security platform is assisted to remove invalid information. Through flow reduction and active detection, docking of various network products and automatic identification of asset information in the existing network, asset management ecology of part of third-party products can be integrated, information islands can be broken through, and a clear asset ledger can be established.

The invention discloses a temperature drift compensation method for a miniature crystal oscillator chip, and particularly relates to the field of temperature drift compensation for electromagnetic interference signal measurement, which comprises the following steps of: arranging a double-shaft Hall sensor array around a temperature sampling channel of the miniature crystal oscillator chip, and carrying out spatial distribution acquisition on a low-frequency electromagnetic field in a chip operation environment; generating an electromagnetic disturbance signal for representing a parasitic electromagnetic interference behavior; and pilot frequency convolution processing is carried out on the electromagnetic disturbance signal and a thermal response signal obtained in a temperature sampling channel, coupling response characteristics related to an electromagnetic interference behavior are extracted, and a spectrum fitting template of an electromagnetic parasitic injection behavior is constructed. A low-frequency electromagnetic disturbance sensing channel is synchronously introduced outside a temperature sampling path, and an interference identification, stripping and compensation dynamic linkage mechanism is constructed to realize active detection and correction of latent electromagnetic interference components in a temperature response signal, so that the problems of frequency compensation error and long-term offset accumulation in the prior art are solved.

The invention discloses an oil and gas storage and transportation oil leakage detection system based on multi-source data analysis, and relates to the technical field of oil leakage detection. An original sensing data set is constructed, the original sensing data set is preprocessed, an alignment data set is generated, a pipeline digital twin baseline model in a leakage-free state is established, and a baseline feature set is generated; according to the method, micro-disturbance active detection and dynamic compliance inversion are implemented, an active feature set is generated, the diffusion trend of potential leakage liquid and an earth surface exposure time window are predicted, earth surface evidence features are generated, multi-modal features are fused, and a leakage event and a response scheme are output. A diffusion closed loop from the underground to the earth surface is realized through multiphase seepage and remote sensing verification, the detection precision is improved by utilizing adaptive fusion and dynamic threshold control, self-learning updating is realized through model recharge, and the method has the advantages of high real-time performance, strong anti-interference and adaptive optimization.

The invention relates to the technical field of artificial intelligence and equipment control, and discloses an AI-driven commercial refrigerator dynamic energy-saving and load prediction system, which comprises the following steps: circularly multiplexing the working and shutdown of a compressor into an active detection process for the thermodynamic state of a refrigerator, and calculating the temperature of the refrigerator according to the temperature time sequence data generated in the process; according to the method, multi-scale dynamic characteristics capable of representing the total heat load and the heat exchange efficiency at the same time are extracted, a machine learning model is input for working condition classification, and then self-adaptive adjustment of a control strategy is achieved. The insight from the passive response to the single temperature to the core hidden variable of the dynamic thermal load is realized, so that the control system can identify the real working condition and make the optimal energy-saving decision, and the energy efficiency and the operation reliability of the equipment in the complex dynamic environment are improved.

The invention discloses an electromagnetic interference suppression method and system for ultra-low field magnetic resonance imaging, and belongs to the technical field of nuclear magnetic resonance, and the system comprises an electromagnetic interference signal collection module which is composed of a plurality of EMI coils and corresponding signal collectors and actively detects EMI signals from the environment; the electromagnetic interference suppression module is used for performing model training by using the data, performing electromagnetic interference suppression through a model, predicting and eliminating an EMI component based on the electromagnetic interference suppression model, and outputting k space data without EMI; and the image reconstruction module is used for receiving the EMI-free k-space data and carrying out averaging and image reconstruction on the data so as to generate a high-quality MRI image. According to the invention, EMI noise is effectively suppressed, image quality is improved, and mobile and portable development of MRI equipment is promoted.

The invention relates to the technical field of electronics, in particular to a dynamic threat spectrum library construction method based on a gallium oxide solar blind antenna detection module. According to the method, through cooperative acquisition and analysis of a gallium oxide solar blind antenna detection module and a radio frequency signal, a low-altitude target is accurately positioned by utilizing the low-noise characteristic of solar blind ultraviolet detection, the signal response speed is effectively improved, the target positioning capability and speed in a complex environment are improved, and the target positioning accuracy is improved through protocol feature extraction and dynamic fingerprint construction. Identity recognition and behavior tracking of a threat source are achieved, a closed-loop optimization mechanism is formed, the system has the continuous self-adaptive active detection capability, and the protection level of an existing low-altitude defense system from passive defense to active early warning is effectively upgraded.

The embodiment of the invention discloses an asset management method and system integrating NMAP active detection and AI. The method comprises the following steps: detecting a target network by taking an NMAP as a core detection engine, and collecting original asset information; performing asset identification and portraying on the original asset information by using a machine learning classification model to generate asset portraying information; continuously monitoring the asset state change, and obtaining an abnormal event by using an abnormal detection algorithm; asset portrait information, external CVE vulnerability library information, threat intelligence information, asset importance and abnormal events are collected, and risk assessment and priority ranking are performed on each asset by using a machine learning model to obtain risk assessment information. Compared with the prior art, the method can solve the problems that asset identification accuracy is insufficient, a risk assessment model is simple and lacks dynamic context sensing, mass data cannot be effectively processed, deep association and abnormity cannot be mined from the data, and the intelligent degree is low.

A computer-implemented method for classifying points in a point cloud obtained by an active detection and ranging system is provided. The method comprises: computing a latent representation of the point cloud, determining from the latent representation, for each point in the point cloud, a probability that the point represents an object belonging to one or more object classes, and determining from the latent representation, for each point in the point cloud, a probability the point represents a ghost object. An object classification network is used for determining the probability that the point represents an object, and a ghost classification network is used for determining the probability the point represents a ghost object.

The invention discloses a method and a system for verifying the reliability of remotely acquired data of an electric meter box, particularly relates to the technical field of communication and data transmission, and aims to solve the problem that the reliability of the remotely acquired data of the electric meter box is verified without changing an existing netmeter and a communication protocol. The invention aims to solve the problem of how to execute cross-layer reviewable reliability verification and quantitative credibility evaluation on a single reading result remotely acquired by an electric meter box so as to identify and eliminate the abnormity that communication is successful but data is not credible in real time, and support source end positioning. By introducing fusion of a cross-layer evidence set and an active detection mechanism, the credibility of each copying and reading result can be comprehensively evaluated. Multi-level data of a physical layer, a link layer, an application layer and the like are verified, and data exception is identified through active detection. Through the mechanisms, the method can effectively eliminate reading results which succeed in communication but have errors in actual data, thereby ensuring that the collected data are more reliable and accurately reflect the actual state of the electric meter.

The invention relates to the field of automobile part welding processing, in particular to a tool clamp for integral welding of multiple accessories, the tool clamp comprises a jig frame, a clamp and a detection device, the jig frame is used for placing and positioning the accessories, the clamp is rotatably connected with the jig frame, the clamp is used for fixing the accessories in the jig frame, and the detection device is arranged on the jig frame. The detection device is used for detecting the position of the accessory and controlling the welding robot to conduct welding. The detection device comprises a direct detection device and an indirect detection device, the direct detection device is used for directly detecting the position of the part, the indirect detection device comprises an active detection device and a passive detection device, the passive detection device is connected with the clamp, and the passive detection device is used for detecting the position of the part along with rotation of the clamp; the active detection device is fixedly connected with the jig frame and is driven by the power source to detect the positions of the parts, and the effect of reducing the reject ratio of automobile part welding finished products is achieved.

The invention provides a heterogeneous database structure dynamic synchronization and fault-tolerant migration method, system and device, and belongs to the technical field of database migration. The method comprises the following steps: detecting a structural difference through combination of active detection and passive triggering; realizing DDL statement conversion of different databases by utilizing AST analysis through a cross-platform DDL converter; defining a migration process by adopting a migration state machine model so as to ensure orderly migration; and a breakpoint resume and consistency guarantee mechanism is utilized to ensure that migration fault tolerance is consistent with data by means of a vernier triple, an intelligent recovery strategy and a bidirectional check protocol. According to the method, the problems of structure synchronization and migration fault tolerance of the heterogeneous database in an environment without native change log support are effectively solved, the automation, high efficiency, stability and reliability of database migration are improved, and the method has wide application value.

The invention discloses a tiny unmanned aerial vehicle detection method and system based on target reflectivity, solves the problem that a low-reflectivity target is difficult to detect in a complex environment in the prior art, and realizes recognition of a tiny unmanned aerial vehicle by combining laser active detection and target reflectivity characteristic analysis. The method comprises the following steps: transmitting pulse laser to a target area, and synchronously acquiring a target signal through double detectors with a common-path design to obtain a target geometric feature and a reflectivity intensity signal; calculating an original reflectivity based on the reflectivity intensity signal, obtaining target distance information, and correcting the original reflectivity based on the target distance information to obtain a corrected reflectivity value; target motion features are generated through multi-frame time sequence analysis, the corrected reflectivity value, the target geometric features and the target motion features are fused based on a fusion strategy, and a target comprehensive feature vector is constructed; and inputting the target comprehensive feature vector into a classifier, and outputting an identification result of the tiny unmanned aerial vehicle.

The invention discloses a walking dysfunction intelligent evaluation system and method based on a flexible electronic technology, and the method comprises the following steps: S1, setting a multi-modal flexible sensor array, and synchronously collecting multi-modal data; s2, synchronously aligning the multi-channel signals by using an edge calculation unit to obtain a standardized gait feature tensor; s3, based on the dynamic Bayesian network, forming a high-dimensional hidden state probability distribution sequence of a complete gait cycle; s4, outputting a gait anomaly typing result and a walking dysfunction risk level based on the echo state network; s5, in combination with a knowledge rule engine and historical gait data, performing multiple verification and dynamic Bayesian network model parameter adaptive updating on a gait anomaly typing result; and S6, generating a rehabilitation evaluation report according to the gait anomaly typing result and the walking dysfunction risk level. According to the method, dynamic Bayesian network time sequence modeling and an echo state network intelligent discrimination algorithm are combined, and active detection and grading evaluation of walking dysfunction are achieved.

The invention relates to the technical field of intelligent building safety and artificial intelligence, and discloses an intelligent building dangerous case identification and grading early warning system based on multi-source data fusion, and the system comprises a data collection and management module which is used for obtaining and outputting a standardized multi-dimensional time sequence data flow and a building logic relation; the dynamic state modeling module is used for establishing a normal state model library for different space-time scenes; the event processing and analysis core module is used for calculating a risk entropy value representing an abnormal degree and a risk entropy gradient vector indicating an abnormal source; the adaptive reasoning and detecting module is used for performing source reasoning and risk diffusion prediction according to the risk entropy gradient vector; and the early warning and decision output module is used for judging the risk evolution situation and outputting a graded early warning signal. According to the method, the source of the dangerous case can be accurately positioned, the diffusion can be predicted, intelligent conversion from passive alarm to active detection is realized, and the accuracy and timeliness of early warning and the scientificity of decision making are remarkably improved.

The invention discloses an on-line insulation monitoring system and method for a national railway vehicle, and relates to the technical field of on-line insulation monitoring, and the system comprises a detection signal injection module which is used for injecting a preset broadband active detection signal, and synchronously collecting a response signal set transmitted through a vehicle body grounding network; the fingerprint feature construction module is used for constructing network fingerprint features in a current insulation state; the spatial distribution atlas acquisition module is used for carrying out inversion calculation to obtain an admittance spatial distribution atlas of the whole vehicle; and the insulation abnormal area identification module is used for identifying an insulation abnormal area. According to the invention, the technical problems of long troubleshooting time and poor accuracy caused by incapability of online real-time monitoring of the insulation state of the vehicle body grounding network of the national railway vehicle and low abnormal area positioning precision in the prior art are solved, and online monitoring of the insulation state of the vehicle body grounding network of the national railway vehicle and accurate positioning of the abnormal area are realized. And the efficiency and the accuracy of insulation fault checking are improved.

The invention belongs to the technical field of environment monitoring, and discloses an underground water environment monitoring system, which comprises a monitoring well body; the packers can be remotely controlled and are arranged in the depth direction of the monitoring well body; the optical fiber array can invert the seepage velocity field of the stratum around the well by applying controlled thermal pulse and monitoring temperature response; the robot flying shuttle can move in the well, and an in-situ chemical analysis module or a geophysical imaging module can be carried on the robot flying shuttle; the artificial intelligence model runs on the remote cloud platform; the artificial intelligence model can fuse multi-source data, takes a seepage field obtained by active detection and a stratum structure obtained by imaging as physical constraints, and can autonomously dispatch a robot shuttle to execute minute-level in-situ chemical diagnosis or scan inter-well strata. According to the invention, the leap from passive observation to active intelligent exploration is realized, and the monitoring precision and timeliness are significantly improved.

The invention provides a spinach cleaning quality detection method based on image recognition, and relates to the technical field of image or video recognition. A closed-loop control framework integrating active detection, cross-modal sensing, physical modeling decoupling and intelligent decision optimization is constructed; cleaning efficiency and quality bottlenecks caused by single information dimension and passive control mode in the prior art are fundamentally solved; the self-adaptive cleaning device has the final beneficial effects that the energy consumption and the water consumption of a unit product are greatly reduced by precisely putting water flow energy according to requirements while the cleaning cleanliness stability under complex working conditions is remarkably improved, and self-adaption and self-optimization of the whole cleaning process are realized.

The invention relates to the technical field of underwater communication, in particular to a collision avoidance method suitable for digital underwater acoustic communication, which is characterized in that a neighbor propagation delay mapping database is maintained at each communication node, and inter-node propagation delay information is acquired through passive monitoring and active detection. When a node wants to send data, combining delay mapping to predict a channel occupation time slot, and judging whether the sending is conflicted with an RTS / CTS handshake stage, a data transmission stage and an ACK (Acknowledgement Character) confirmation stage which are carried out by a neighbor; if a conflict risk exists, the node delays to send the RTS and then initiates handshake until the security window is finished, so that collision caused by hidden nodes and exposed nodes is avoided, meanwhile, parallel transmission under the conflict-free condition is allowed, the channel space-time multiplexing efficiency is improved, the network throughput can be remarkably improved, the data packet collision rate is reduced, and the network performance is improved. And the node access fairness is improved.

The invention provides an intelligent crust breaking and blanking system based on machine vision. The intelligent crust breaking and blanking system comprises an active detection module, an image acquisition module, an image processing and calculating module, a data communication module, a cell control machine, a high-temperature-resistant insulation structure and a cooling device, the active detection module forms a reference line in an aluminum electrolysis cell monitoring area; the image acquisition module is used for respectively acquiring image data of a motion state of a crust breaking hammer and a material falling process of a blanking port; the image processing and calculating module is used for carrying out preprocessing, feature extraction and calculation on the image to obtain a crust breaking depth parameter and blanking flow data; the data communication module transmits a processing result to the cell control machine, and the cell control machine adjusts the crust breaking set depth and optimizes the blanking interval according to the processing result; the high-temperature-resistant insulation structure carries out physical protection and electrical isolation on key modules, and the cooling device keeps the internal temperature stable and prevents the lens from being polluted. Therefore, the crust breaking depth and the blanking quantity control precision can be improved, the maintenance cost is reduced, and the adaptability to a high-temperature strong electric field environment is improved.

The invention discloses a stiff steel rib column welding seam detection device, and relates to the technical field of welding seam detection devices. A stiff steel rib column welding seam detection device comprises fixing rods, two ends of each fixing rod are respectively connected with a mounting rack, the fixing rods are symmetrically arranged on the mounting racks, and the mounting racks are connected to H-shaped steel; the sliding frame is connected to the fixing rod in a sliding mode, and a weld joint movable detection area is formed between the two mounting frames; by arranging the wave guide rail, the ultrasonic probe can automatically and transversely swing along a wave path, the welding seam detection effect can be improved for H-shaped steel, automatic spraying of a coupling agent can be achieved by means of design and linkage of the wave guide rail, and even by means of combination of the wave guide rail and the L-shaped scraping plate, the welding seam detection efficiency is improved. The effect of uniformly smearing the coupling agent in a wave path is achieved; and meanwhile, through the arrangement of the wave guide rail, whether the welded wing plate is bent and deformed or not can be automatically judged in the fillet weld detection process, and then the welding quality of the H-shaped steel is improved.

The invention discloses an unmanned aerial vehicle target active detection method and system based on a millimeter wave radar, and relates to the technical field of unmanned aerial vehicle detection, and the method comprises the steps: carrying out the frequency mixing of an obtained emission frequency modulation continuous wave signal and an echo signal, and obtaining an intermediate frequency signal; performing fast Fourier transform on the intermediate frequency signal to obtain distance information of the target unmanned aerial vehicle; acquiring a plurality of Chirp signals based on the echo signals; based on the phase difference among the plurality of Chirp signals, calculating to obtain the speed information of the target unmanned aerial vehicle; performing angle estimation on the echo signal to obtain angle information of the target unmanned aerial vehicle; integrating the distance information, the speed information and the angle information of the target unmanned aerial vehicle to obtain motion parameters of the target unmanned aerial vehicle; and transmitting the motion parameters of the target unmanned aerial vehicle to a flight control system or other data receiving equipment of the unmanned aerial vehicle. The detection capability and the tracking effect of the unmanned aerial vehicle target can be improved, and the operation safety of the unmanned aerial vehicle is effectively guaranteed.

The invention belongs to the technical field of data processing, and discloses an active intelligent operation and maintenance monitoring method for a data center, which comprises the following steps: step 1, component modeling and topology configuration; 2, carrying out distributed health detection and data acquisition; 3, performing real-time health assessment and anomaly detection; 4, performing intelligent alarm and root cause analysis; 5, unified operation and maintenance and closed-loop control are carried out; and step 6, dynamically optimizing the intelligent operation and maintenance strategy. According to the method, a monitoring object and a dependency relationship are clarified through component modeling and topological configuration, and specific scenes, such as multi-mode acquisition, active detection, index pulling, log analysis, coverage message queue theme accumulation and database connection pool exhaustion, of distributed health detection are combined. The quantitative health score is calculated based on the preset scoring model, and by combining with the dynamic baseline learned by the ARIMA or LSTM algorithm, the module abnormity can be actively detected in different periods and weekly updating, and the problems of fault discovery lagging and incomplete monitoring coverage are solved.

The invention discloses a 3D (three-dimensional) perimeter security system and an operation method thereof, the system comprises a server terminal, a front-end sensing system, a data transmission system and a central control system, the front-end sensing system is networked in multiple groups of partitions, and each group of partitions of the front-end sensing system is provided with a nine-axis sensor, an edge computing all-in-one machine and a switch; and lighting, wind and rain sensor, broadcasting, microwave radar and camera equipment. The data transmission system transmits data; the central control system comprises an application server and a data server. The system forms a first defense line for active detection of the microwave radar and the camera and a second defense line for vibration sensing of the nine-axis sensor, and a monitoring blind area is eliminated through three-dimensional data fusion. The operation method comprises four groups of subsystem modules. According to the method, 'sky-ground-fence 'dead-corner-free monitoring is realized, the false alarm rate is reduced by combining multi-modal data fusion with an intelligent algorithm, self-evolution is realized by edge-cloud federal learning, and threat disposal efficiency is improved by automatic linkage response.

The invention relates to the technical field of robot attitude balance control, and provides a robot attitude balance control method and system. Before the feet of the robot make contact with the supporting face or at the initial stage of contact, preset micro-force excitation is actively applied to the supporting face for detection, and mechanical response information and foot displacement information between the feet and the supporting face are collected in real time; based on the information, the system can estimate the local physical characteristics of the supporting surface in real time, and dynamically control and adjust the balance control strategy of the robot according to the estimated local physical characteristics. Through active detection and foresight adjustment, the characteristic change of the supporting surface can be identified and adapted before or at the initial stage of instability, so that the control conflict and vicious circle possibly brought by passive compensation are avoided.

The invention discloses a bridge dynamic deflection monitoring method and system based on a 4D millimeter wave radar, and the method comprises the steps: actively detecting the precise distance between a target object at a specific position of a bridge and the device through 4D millimeter wave radar equipment, and building a coordinate system through the combination of a spatial position and the positions of a plurality of radar equipment in a radar system; according to the method, the spatial position coordinates of a target object are measured and solved, the spatial position coordinates of the target object in a set time period are measured, so that offset degree data of a specific position in the set time period are calculated, and continuous and stable dynamic deflection data recording of the specific position in the set time period is completed through fitting of a data curve. The defects of high construction complexity, high difficulty in long-time monitoring and difficulty in long-distance monitoring in a traditional bridge detection mode are overcome.