2081 results about "Radar detection" patented technology

The invention relates to the technical field of airport safety protection, and discloses an airport bird strike prevention method and system based on multi-sensor fusion. The method comprises the steps of collecting airport peripheral airspace radar detection, radio detection, laser monitoring, weather and other multi-mode sensor data, and performing time synchronization and space registration to generate a fusion data stream; features are extracted based on a dynamic weight distribution algorithm, target types are identified by using a multi-modal classification model, and birds and unmanned aerial vehicles are distinguished; constructing a dynamic risk assessment model based on a Markov chain to generate an intrusion risk level; and when the risk level exceeds a preset threshold value, triggering a multi-level alarm strategy and linking the expelling equipment to execute a self-adaptive expelling instruction. According to the method and system, through multi-sensor fusion, the monitoring precision is improved, the target is accurately recognized, the risk is evaluated in real time, efficient alarming and expelling are achieved, the airport bird strike prevention capacity is effectively improved, and aviation safety is guaranteed.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses an unmanned aerial vehicle millimeter wave radar detection system and method and an unmanned aerial vehicle, and the system comprises a millimeter wave radar module which is used for executing multiple detection tasks and detecting the distance, speed and angle information of a target object, and has the functions of beam scanning and signal transmitting and receiving; the signal processing module is used for processing the received radar echo signal, performing target object detection and positioning through signal analysis, and generating a target positioning result; the target tracking module is used for tracking the target according to the target positioning result and the sensor data of the unmanned aerial vehicle; the method improves the signal intensity and detection distance in different detection environments by optimizing the direction and focusing of radar beams in real time, effectively eliminates noise interference through the Kalman filtering and particle filtering technology, accurately estimates the distance, speed and angle information of a target, and improves the detection accuracy through combining a deep learning algorithm and an adaptive filtering strategy. And processing details of the radar echo signals are dynamically optimized.

The invention discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data missing of a key area is caused by a blocking effect, and detection nodes are not uniform. The method specifically comprises the following steps: S1, executing a multi-elevation-angle body scanning detection mode and a wind profile detection mode by using a wind measurement laser radar, and obtaining radial wind speed data of a target area; s2, quality control is carried out on the radial wind speed, isolated abnormal points are eliminated, and isolated data missing points are filled; s3, carrying out blocking correction on a single scanning surface by using a smooth spline interpolation method; s4, after the radial wind speed is corrected based on the wind profile, an initial background three-dimensional wind field is obtained; and high-precision wind field inversion of a wind field in a three-dimensional space can be realized by using the radial wind speed of multi-layer body scanning of a single wind measurement laser radar.

The invention relates to the technical field of engineering, in particular to a geomembrane defect detection system and method under the earth and stone medium coverage condition, and the geomembrane defect detection system comprises a multi-mode adaptive sensing module, an edge calculation module, a data fusion module, a digital twinning module and a decision and application module; compared with the defects that single resistivity or geological radar detection is adopted in the prior art, interference of medium conductivity and metal impurities is likely to happen, and the deep resolution is insufficient, the scheme adopts multi-mode sensor network cooperative work, and a dynamic density layout and time-phased acquisition strategy is combined; through multi-mode complementation of an electrode array, an ultra-wideband radar, a distributed optical fiber and an MEMS vibration sensor, a dominant sensing mode is automatically switched in a conductive clay area, temperature gradient interference is avoided through night acquisition, the penetration depth is increased to 15 meters through multi-band radar fusion, and metal interference is inhibited; the detection precision and the anti-interference capability in a complex medium environment are remarkably improved, and the problems of high false alarm rate and high omission ratio of a traditional method are effectively solved.

The invention relates to a target person detection method and device based on millimeter wave radar, equipment and a medium. According to the method, radar detection configuration is adjusted, a gridding coordinate system is established in a target treadmill monitoring area, millimeter wave radar is used for collecting feature data related to movement of a target person, and movement track changes of the target person are extracted to generate person position information; acquiring breathing and heartbeat signals of a target person, and performing signal filtering and feature extraction to obtain vital sign information; comprehensively evaluating the existence state of the target person according to a preset threshold value and a discrimination standard in combination with the person position information and the vital sign information; and when the evaluation result shows that the target person leaves, the vital signs are abnormal or a cheating behavior exists, judging that the target person is in an abnormal state, and generating a corresponding early warning instruction. By adopting the method, abnormal conditions can be found in time, early warning can be generated, and the monitoring accuracy and response speed are improved.

The invention relates to the technical field of target detection, in particular to a composite radar photoelectric detection system, which comprises a radar detection module, a photoelectric detection module, a data processing module, a control module and a dynamic resource scheduling module, and is characterized in that the radar detection module and the photoelectric detection module realize physical layer fusion through a common-aperture integrated design; the data processing module realizes data layer depth fusion based on a multi-modal algorithm; the dynamic resource scheduling module is integrated with an environment sensing unit and is used for acquiring environment data in real time and transmitting the environment data to the control module; the control module is internally provided with a priority decision tree and is used for dynamically distributing the main / auxiliary sensors according to environmental conditions. Therefore, the problems of low integration level, insufficient data fusion precision, poor environment adaptive ability, heat dissipation bottleneck and the like of aperture division design in the prior art are solved.

The invention relates to an active train obstacle detection method and apparatus based on a positioning technique. The active train obstacle detection method comprises the following steps: S1, acquiring an electronic map; S2, correcting initial parameters; S3, performing parameter calibration of a video camera; S4, detecting a train obstacle; and S5, outputting an obstacle recognition result. The apparatus comprises a positioning module, a laser radar detection module, a video recognition module, an operational host and an interface module, wherein the operational host is connected to the positioning module, the laser radar detection module, the video recognition module and the interface module respectively. Compared with the prior art, the invention can increase the obstacle detection rate and reduce report failures and errors.

The invention provides a deep high-precision underground disease detection method based on GPR, and relates to the technical field of geophysical exploration. Establishing a road disease forward model, and initializing the road disease forward model; obtaining an initialized road disease forward model and adding preset noise information into a result to generate fused echo data; preprocessing the fused echo data to generate target echo data; constructing a road disease data set according to the target echo data, and performing multi-frequency data fusion in the road disease data set to generate multi-frequency data; constructing a super-resolution convolutional neural network according to the multi-frequency data, and training the super-resolution convolutional neural network; inputting detection data into the trained super-resolution convolutional neural network model, and obtaining a detection report; and the road disease radar detection performance is improved.

The invention relates to a single-channel moving target refocusing method and device based on a bunching mode, and the method comprises the steps: detecting a ground moving scene through a terahertz video radar, obtaining a single-channel echo signal, generating a two-dimensional image of the ground moving scene, and extracting a target image of a moving target from the two-dimensional image; the method comprises the following steps of: performing azimuth inverse Fourier transform on a target image to obtain a one-dimensional range image, performing secondary phase compensation by using an image displacement algorithm, and performing range envelope alignment on a compensated signal by using an adjacent cross-correlation algorithm of linear interpolation to correct a nonlinear error term so as to obtain a moving target refocusing image after the nonlinear error term is corrected. And the two-dimensional image is filled with the image to obtain a complete image. By adopting the method, the problem of image defocusing caused by micro-distance migration can be effectively solved.

The invention belongs to the technical field of data acquisition, and provides an underground pipeline detection data acquisition method and system, and the method comprises the following steps: collecting underground pipeline drawing data of a target region, and determining the information parameters of underground pipelines; performing field exploration, marking a ground outcrop point of the underground pipeline, and preliminarily judging the trend of the pipeline; detecting the underground pipeline through an electromagnetic method or a ground penetrating radar, and determining the position and the burial depth of the underground pipeline; reflected and scattered sound wave signals propagated through the internal fluid of the underground pipeline are collected through sound wave detection, and the state of the internal fluid is evaluated; fusing different detection data to generate an underground pipeline space model; by comprehensively using the electromagnetic method, the ground penetrating radar and the sound wave detection technology, the position, the burial depth and the internal fluid state information of the underground pipeline can be comprehensively collected, the physical position of the pipeline is determined, the state of the internal fluid of the pipeline is evaluated, and the accuracy and the reliability of the detection result are effectively improved.

The invention provides an animal target detection system and method based on airborne radar and intelligent identification, and belongs to the technical field of radar detection. The system comprises an unmanned aerial vehicle, an antenna array fused with a fuselage of the unmanned aerial vehicle, and a radar host. The radar host comprises a signal processing unit; the antenna array is designed to be of a deformable structure and can be attached to the surface of a fuselage of the unmanned aerial vehicle. The antenna array is used for sending a radio frequency signal of a terahertz frequency band to a target area under the control of the system and receiving an echo signal reflected by an obstacle; and the signal processing unit is used for receiving the echo signals, extracting multi-dimensional features from the echo signals by using a spatio-temporal feature extraction network based on an attention mechanism, and classifying the multi-dimensional features through a deep reinforcement learning algorithm to obtain animal categories. The airborne radar detection method is light in weight and low in cost, the efficiency and precision of biodiversity observation can be improved, and the monitoring cost is greatly reduced.

The invention discloses a radar detection blind area wind field fine reconstruction method based on anemograph collaborative networking, and the method comprises the specific steps: firstly, separating wind speed measurement deviation caused by vibration displacement in real time, eliminating vibration interference, carrying out the automatic diagnosis and restoration of data based on machine learning, and optimizing the integrity of wind speed data and the measurement continuity; secondly, an ultrasonic anemograph array optimization arrangement method oriented to global wind field sensing is established, time-space consistency calibration of multi-sensor data is achieved, and the precision and reliability of wind speed measurement are improved; and finally, carrying out data-physical fusion bridge flow field high-resolution intelligent reconstruction, and improving the data integrity of a complex flow field area. The reconstruction method effectively cooperates with the wind measurement laser radar and the anemograph, the advantages of high-frequency single-point measurement and wide-area space scanning of the wind measurement laser radar and the anemograph can be exerted, high-precision reconstruction of the global flow field of the bridge is expected to be achieved, and reliable support is provided for guaranteeing wind resistance safety of a large-span bridge.

The invention discloses a vehicle detection and identification method and system based on laser sensing, and relates to the field of vehicle detection.The method comprises the steps that firstly, obstacle reflection signals are obtained through a multi-band laser array, preliminary classification is conducted through wavelength, time difference and intensity difference, and point cloud features are constructed; a lightweight network is adopted to filter noise points, and point cloud holes and shielding areas are intelligently complemented; then, performing hierarchical voxelization processing according to the obstacle distance, performing coarse-grained downsampling on a long-distance target, and keeping details on a short-distance target fine-grained; and finally, through a double-stage verification mechanism of a lightweight PointPill network and a three-dimensional convolutional network, precise grading detection and target reliability rechecking of far and near targets are realized, real-time performance and accuracy are considered, the problems of poor adaptability, low precision and insufficient dynamic scene adaptability of an existing laser radar detection technology are solved, and the method has the advantages of high detection precision and high reliability. According to the invention, high-precision and high-real-time vehicle detection and identification can be realized.

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

The invention relates to the technical field of vehicle-mounted radar detection, and discloses a vehicle-mounted laser radar point cloud real-time target detection method and system, and the method comprises the steps: collecting an image sequence through a vehicle-mounted polarization camera, obtaining a light entropy gradient spectrum of a tunnel entrance and exit region, recognizing a gradient abrupt change point, and predicting a light entropy abrupt change interval; generating a dual-channel gain weight matrix to receive an original point cloud; then, a dispersion area is identified, dispersion point clouds are marked, a space-time motion equation is constructed to predict ossification point clouds to obtain complete point clouds, finally, artifacts in an artifact vortex area are identified and removed, and a final detection result is obtained; according to the method, the problems of laser radar point cloud local saturation distortion and vehicle false detection caused by sudden change of strong light at the tunnel entrance and exit are effectively solved, and safe and reliable operation of applications such as automatic driving is guaranteed.

The invention relates to the technical field of radar detection, and provides a multi-model filtering target positioning method, system, device, product and medium, and the method comprises the steps: determining a model matching parameter, carrying out the initialization assignment, obtaining an initialization parameter, and obtaining a sampling point set and a weight coefficient through the initialization parameter; performing state one-step prediction to obtain a state one-step prediction value, and performing secondary sampling and measurement one-step prediction to obtain an autocorrelation covariance and a mixed covariance; carrying out SVD operation to obtain a decomposition value set, detecting a target by a target radar to obtain a radar detection value, obtaining a Kalman gain matrix according to the mixed covariance, and calculating state estimation and an initial covariance; updating the model probability to obtain a Markov transition probability, and normalizing the Markov transition probability to obtain a normalized probability; and state estimation and target covariance are obtained through state fusion, so that the target orientation is determined. According to the invention, accurate tracking of the high maneuvering target is realized.

The invention discloses a radar and WiFi channel state information fused dual-mode household respiration monitoring device which comprises a radar signal collecting and processing module used for detecting a human body respiration signal through a millimeter wave FMCW radar and processing an I / Q orthogonal echo signal obtained by the FMCW radar to obtain a respiration signal; the WiFi signal acquisition and processing module is used for acquiring a CSI signal containing respiratory activity information through a WiFi wireless network card and calculating a CSI ratio of a receiving channel so as to remove time-varying noise and further extract a respiratory signal; the data communication module is used for constructing a networking system based on a millimeter wave FMCW radar and WiFi through different network cable transmission modes; and the breathing information fusion module is used for dynamically fusing the two breathing signals. Starting from multi-sensor respiration signal extraction, accurate detection of personnel respiration signals in a home indoor environment is realized through a fusion algorithm, and the detection method is effective, feasible and reliable in performance.

The invention discloses an offshore small target radar detection method and system, and relates to the technical field of radio detection. The method comprises the following steps: acquiring radar echo data, a visible light image, an infrared image, an AIS signal and navigation attitude data in real time; performing sea clutter suppression and pre-filtering on the radar echo data, and correcting target plots in combination with a historical radar plot set to obtain a radar single-source target track set; performing target detection on the visible light image and the infrared image to obtain an optical target detection set and an infrared target detection set; the radar single-source target track set, the optical target detection set, the infrared target detection set, the AIS signals and the navigation attitude data are associated and fused, and finally deception feature recognition and consistency verification are performed to obtain an updated fused target track set; according to the method, man-made confrontation and cheating behaviors on the sea can be accurately identified, and reliable support is provided for safety monitoring on the sea, maritime affair supervision and emergency response.

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three-dimensional (3D) space, in both highway and urban scenarios. RADAR detections may be accumulated, ego-motion-compensated, orthographically projected, and fed into a neural network(s). The neural network(s) may include a common trunk with a feature extractor and several heads that predict different outputs such as a class confidence head that predicts a confidence map and an instance regression head that predicts object instance data for detected objects. The outputs may be decoded, filtered, and / or clustered to form bounding shapes identifying the location, size, and / or orientation of detected object instances. The detected object instances may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

The invention relates to an airport intelligent bird repelling system based on multi-target detection optimization, and belongs to the technical field of image recognition. The system comprises a detection unit, an identification unit, a repelling unit and an intelligent optimization unit, the detection unit obtains the three-dimensional coordinate information of a whole airspace target, regularly scans the airspace around an airport, continuously tracks and updates the position information of the airport in real time, and then transmits the information to the visual confirmation module. And the visual confirmation module performs visual verification on the bird target found by the radar detection module. And the identification unit performs dynamic identification by using the target tracking model, and performs classification and threat level evaluation on the identified birds according to a preset bird threat level. According to the evaluation result, the repelling unit takes corresponding bird repelling measures; and the intelligent optimization unit optimizes a bird repelling strategy according to the historical bird repelling data and the real-time environment information. The system improves the accuracy and real-time performance of bird detection through a multi-target detection technology.

The invention relates to the technical field of cable monitoring, in particular to a power cable damage monitoring system and method based on a digital twin model, and the system comprises a data collection module, an optical calculation processing module, a terahertz radar detection module, a digital twin modeling module and an underwater communication module. The optical calculation processing module performs high-frequency parallel processing on the acquired environmental data to generate a three-dimensional damage map of the submarine cable; the terahertz radar detection module obtains echo signals of a submarine cable insulation layer and extracts insulation aging state information. The digital twinborn modeling module constructs a cable digital twinborn model based on the three-dimensional damage map and the aging information, and predicts a fault diffusion path; and the underwater communication module transmits the model data to a target receiving end. According to the system, the insulation aging detection precision and the real-time prediction capability of the submarine cable are improved, and the intelligence and the engineering applicability of the system are enhanced.

The invention provides a three-dimensional tracking method applied to a single-photon laser radar to detect a far-field dynamic unmanned aerial vehicle point target, and belongs to the technical field of laser radar detection and target tracking. The problems that in the prior art, under the long-distance condition, the target imaging size is smaller than one pixel, and a detection and tracking algorithm based on shape, texture or edge features fails are solved. The method comprises the following steps: constructing a six-dimensional state vector containing a three-dimensional position and a three-dimensional speed of a target; predicting a state vector according to a state transfer function containing air resistance and turning acceleration; and an extended Kalman filtering framework is utilized to update the predicted state and obtain state estimation of the target, and the extended Kalman filtering framework comprises a self-adaptive process noise adjustment mechanism based on innovation feedback and is used for adjusting a process noise covariance matrix on line. The method is mainly used in the field of detection and multi-dimensional tracking of airspace moving targets.

The invention discloses a phased array weather radar calibration method based on a generalized metal ball calibration theory. Comprising the following steps: in a microwave anechoic chamber, collecting two-dimensional antenna pattern data of a phased array weather radar in at least one working mode and pointing to a plurality of different beams; performing numerical integration on the two-dimensional antenna pattern data, calculating a combined beam main lobe solid angle of the transmitting antenna and the receiving antenna, and calculating an equivalent beam width product; calculating a calibration theoretical reflectivity factor of the metal ball based on the product of the theoretical radar scattering sectional area and the equivalent beam width of the metal ball; actual detection echo data of the phased array radar on the metal ball under different working modes and beam pointing is acquired, and an actual reflectivity factor is calculated; and comparing the actual reflectivity factor with the calibration theoretical reflectivity factor to complete calibration of the phased array weather radar. Aiming at the non-Gaussian working mode of the phased array weather radar, the reflectivity factor of the phased array weather radar can be quantitatively evaluated, and the radar detection performance is improved.

The invention relates to the technical field of laser radar application, and discloses a boundary layer detection system based on laser radar, which comprises a data acquisition module, a laser radar modeling module, a feature extraction module, a comprehensive evaluation module and a grading early warning module. The data acquisition module is used for acquiring atmospheric parameters and equipment operation data in a monitoring area and around equipment and pre-processing the atmospheric parameters and the equipment operation data; the modeling module constructs a three-dimensional point cloud model, simulates atmospheric flow and radar detection characteristics, and simulates parameters in combination with real-time data; the feature extraction module extracts boundary layer height, temperature and humidity gradient and turbulence intensity through an algorithm; the comprehensive evaluation module performs correlation calculation after normalization processing, and compares with a reference value to evaluate the stability; and the grading early warning module calculates an evaluation value in combination with environmental factors when being unstable, and generates third-level early warning after secondary comparison. The system realizes high-precision and dynamic monitoring and graded early warning of the atmospheric boundary layer, and improves the detection accuracy and reliability.

The invention relates to the technical field of unmanned aerial vehicle signal processing, and discloses an unmanned aerial vehicle countering multiband radar early warning system, which comprises a multiband radar detection module, an echo data processing module, a target identification and positioning module and an early warning information generation module. The multi-band radar detection module emits radar waves of different frequency bands to obtain echo data, the echo data processing module calculates the difference degree and the comprehensive difference coefficient for target identification, the target identification and positioning module determines the position of the unmanned aerial vehicle, and the early warning information generation module issues early warning. The method has the advantages that accuracy and reliability are improved through multi-frequency-band detection, targets are accurately recognized and positioned, threat levels can be evaluated, interference resistance, clutter suppression and environment self-adaption capacity are achieved, multiple targets can be distinguished and tracked, the defects of an existing unmanned aerial vehicle monitoring technology are effectively overcome, powerful support is provided for unmanned aerial vehicle countering, and the method is worthy of popularization and application. And public safety and key area stability are ensured.

The invention discloses a self-adaptive human body posture detection method based on a privacy protection camera and a radar, and the method comprises the steps: collecting spatial radar three-dimensional data through the radar, and carrying out the calculation of the spatial radar three-dimensional data, and obtaining a radar human body posture feature; whether the current human body posture is abnormal or not is judged through the radar human body posture features, if yes, a privacy protection camera is started to collect a space blurred image, and image human body posture features are recognized through the space blurred image; judging whether the current human body posture is abnormal or not through the image human body posture features, and if yes, adjusting the radar detection resolution; acquiring radar target human body postures at corresponding positions by detecting the radar with the adjusted resolution, judging whether the radar target human body postures are abnormal or not, and if yes, acquiring image human body posture features at the same time point and at the same position; and judging whether the abnormal type of the image human body posture features at the same time point and the same position is the same as the abnormal type of the radar target human body posture or not, and if yes, performing abnormal type alarm.

The invention provides a passive perception radar target classification method and system based on electromagnetic detection feature extraction, and the method comprises the steps: carrying out the multi-dimensional signal decomposition of an original scattered signal sequence through obtaining an electromagnetic radiation signal set of a target region; obtaining a time-frequency distribution feature set and a polarization response feature set corresponding to each signal acquisition period, extracting a target electromagnetic feature combination from the time-frequency distribution feature set and the polarization response feature set according to a preset electromagnetic feature association rule, generating a weight distribution parameter, and performing weighted fusion processing on the target electromagnetic feature combination to obtain a target electromagnetic feature combination; and the fusion feature vector set is input into an adaptive classification network, hierarchical classification operation is performed on the fusion feature vector set, the category identifier of the target scatterer and the corresponding classification confidence are output, and a radar detection optimization instruction is generated based on the category identifier and the corresponding classification confidence. According to the method, high-precision distinguishing of multiple types of scatterers is achieved under the passive sensing condition, and meanwhile the environmental adaptability and the calculation efficiency of the classification process are guaranteed.

The invention provides an all-solid-state high-power single-frequency laser based on an optical fiber-slab mixed structure, and relates to the technical field of solid lasers, and the all-solid-state high-power single-frequency laser comprises an all-solid-state continuous single-frequency seed module, an optical fiber pulse modulation module, an optical fiber pre-amplification module, a slab laser pre-amplification module, a slab laser main amplification module and a frequency multiplication module which are sequentially arranged; the lath laser pre-amplifier and the lath laser main amplifier are used for compensating thermally induced aberration generated by lath laser through multi-pass amplification designed according to the spherical aberration self-compensation principle, and then the quality of light beams is improved. In addition, a discrete mirror group is adopted to replace a complete amplifier cavity mirror, spontaneous radiation and parasitic oscillation in the working process of the amplifier are effectively restrained, nearly hundred mJ-magnitude single-frequency pulse laser with excellent light beam quality can be obtained, and application to laser radar detection or industry can be met.

The invention belongs to the technical field of road engineering detection, and discloses a geological radar and distributed optical fiber roadbed compaction detection method and device, and the method comprises the steps: obtaining a dielectric constant profile detected by a radar and the initial Brillouin scattering light frequency offset of optical fiber strain; establishing an initial reference state including optical fiber strain distribution and a radar dielectric constant profile; comparing the Brillouin scattering light frequency offset and the radar dielectric constant profile image which are acquired in real time with an initial reference state, determining a strain gradient abrupt change region, and correcting a preset optical fiber temperature drift compensation model by using a radar scanning result; and performing temperature drift component removal on the Brillouin scattering light frequency offset obtained in real time to obtain pure strain frequency shift data, calculating according to the frequency shift data to obtain accumulative settlement, and generating a roadbed compaction effect detection result. Continuous longitudinal strain monitoring is provided through the geological radar and the sensing optical fiber, and an abnormal section is accurately positioned.

The invention provides a road detection method and system based on a ground penetrating radar and electronic equipment, and relates to the technical field of radar detection, and the method comprises the steps: scanning a to-be-detected road through the ground penetrating radar, and obtaining radar image data; preprocessing the radar image data to obtain target image data; performing feature extraction on the target image data to obtain a group of features, the group of features including amplitude, frequency, phase, event continuity and multiple reflection features; identifying the group of features to obtain an identification result, the identification result being used for representing whether an underground disease body exists in the to-be-detected road, and determining a disease body type of the underground disease body under the condition of determining that the underground disease body exists in the to-be-detected road; and determining a target risk level of the to-be-detected road by using a road collapse risk assessment model in combination with the identification result and the surrounding environment factors. By implementing the technical scheme provided by the invention, the effect of improving the detection efficiency and accuracy is achieved.