1245 results about "Recognition algorithm" patented technology

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention discloses a dark light enhancement method under the view angle of an unmanned aerial vehicle, and relates to the technical field of image processing and enhancement, and the method comprises the steps: collecting a continuous frame dark light image sequence of a target when the unmanned aerial vehicle flies, carrying out the preprocessing operation including denoising and normalization, and forming a dark light image set; and estimating the motion between adjacent frames by using an image recognition algorithm. According to the invention, through dynamic range compression and detail enhancement processing, the image definition and visibility in a dark light environment are improved, the brightness difference of the image is balanced by adopting a dynamic range compression algorithm, local overexposure or underexposure is avoided, the image can keep a good visual effect under different illumination conditions, and the image quality is improved. And the detail enhancement processing highlights texture and edge information in the image through multi-scale gradient fusion and adaptive sharpening, and improves the detail definition in dark light, so that the unmanned aerial vehicle can recognize a target more clearly when executing a task at night or in a low-light environment, and the task execution efficiency and accuracy are improved.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

The invention discloses a tank equipment sealing micro-leakage intelligent early warning and positioning method, which comprises the following steps of: deploying acoustic sensors, vibration sensors, gas concentration sensors, temperature and humidity sensors and the like aiming at different positions of a tank to realize multi-mode original signal acquisition; various signals are standardized and corrected through denoising, normalization and structural difference mapping, and the influence of the environment and the material structure on the signals is compensated; extracting multi-dimensional feature parameters, and carrying out space-time weighted fusion by adopting an attention mechanism to generate feature vectors marked by confidence; according to the method, a self-learning anomaly recognition algorithm is combined, a judgment model is continuously optimized through transfer learning and online sample accumulation, micro-leakage anomaly judgment is achieved, accurate positioning of a tank leakage source point is executed based on a physical propagation model, and the sensitivity and the self-adaptive capacity of micro-leakage detection are improved; and leakage identification and positioning requirements in a complex environment and a dynamic working condition can be effectively met.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention relates to the technical field of intelligent sensing and data fusion, in particular to a real-time motion guidance and error correction method and system based on multi-source data analysis, and the method comprises the following steps: S1, synchronously collecting the motion data of a user through a hardware-synchronized multi-source sensing network, completing the timestamp alignment and standardization processing of the multi-source data, and obtaining the motion data of the user; outputting standardized motion data with a unified time sequence; and S2, performing multi-sensor data fusion processing on the standardized motion data, and reconstructing three-dimensional human body joint posture data based on a state estimation algorithm. Through a time sequence mode recognition algorithm, in combination with various motion data such as joint Euler angles and angular velocities, accurate recognition is performed on different motion stages, through recognized stage labels, a user personalized model is called to dynamically generate an evaluation threshold value of the current stage, then motion deviation diagnosis is performed, and the accuracy of motion deviation diagnosis is improved. The problem caused by the fact that the motion stages cannot be distinguished due to the fact that a fixed threshold value is used is effectively solved, and adaptability and accuracy of posture deviation diagnosis are remarkably improved.

The invention relates to the technical field of flight simulation control and precision measurement, provides a precise positioning method, system and device for a three-dimensional adjustable connecting piece of a flight simulator and a storage medium, and solves the problems of flight simulation control and precision measurement. The method comprises the following steps: acquiring the real-time stroke length of a motion control rod, rotation angle change data and a connecting piece vibration image, outputting a displacement prediction parameter through a platform kinematics model, and generating three-dimensional point cloud data; performing sub-pixel identification and distortion compensation on the image through a mark point identification algorithm to generate a three-dimensional visual coordinate; fusing the point cloud and the visual data, and processing through a Kalman filter to obtain a space pose offset; finally, a displacement compensation instruction is generated, the pose of the connecting piece is adjusted through a closed-loop controller, and accurate positioning of the connecting piece relative to a flight target reference coordinate system is achieved. According to the invention, the accuracy and stability of the pose control of the connecting piece in the dynamic vibration environment are improved.

The invention discloses a time sequence streetscape image-based building update type identification method and device, and relates to the technical field of civil engineering and computer vision. The method comprises the following steps: acquiring a plurality of streetscape images; outputting a target detection result of the building through a YOLO target detection model; according to the metadata information of the streetscape image and the target detection result, determining geographic coordinates of the building; constructing an initial building group geographic information database by combining a target re-recognition algorithm according to the geographic coordinates; constructing a building update type identification data set according to the initial building group geographic information database; building a building update type identification model; using the building update type identification data set to train a building update type identification model; and outputting an update type identification result of the building through the trained building update type identification model. And storing the update type identification result to the initial building group geographic information database to obtain a target building group geographic information database.

The invention belongs to the technical field of track detection, and more specifically relates to a track scene thunder-vision fusion anti-intrusion monitoring method and system based on sparse feature fusion. The method comprises the steps that a laser radar obtains three-dimensional point cloud data, a high-definition camera obtains visible light image data, and data preprocessing is carried out; performing external parameter automatic calibration optimization and time alignment compensation on the laser radar and the high-definition camera to obtain time-space aligned multi-modal data; fusing the camera semantic features and the laser radar geometric features based on multi-modal data of space-time alignment to obtain fused features; and after multi-modal feature fusion is completed, a target heat map is generated through a sparse detection head, and graded early warning is realized in combination with a dynamic safety distance model. The method solves the problems that an existing recognition algorithm is high in false alarm rate, and the false alarm rate is higher under low-light conditions such as rainy days or dusk; the data dependence is strong, and the ability to detect unlabeled novel foreign matters is almost zero.

The invention discloses a glass bottle grabbing method and system based on visual inspection, and the method comprises the steps: carrying out the real-time recognition of a bottle opening, a bottle body contour and defect features through an image recognition algorithm, and outputting a detection data set containing the precise three-dimensional pose and feature identification of a glass bottle; based on the detection data set, generating an action parameter set containing the expansion and contraction amount, the rotation angle and the grabbing time sequence; according to the action parameter set, the movable guide rail is controlled to slide to a target area along the fixed guide rail, and self-adaptive adjustment of grabbing force is achieved through an impedance control algorithm; and after grabbing is completed, the conveying path is dynamically corrected through a model prediction control algorithm in combination with the real-time movement speed of the conveying belt, the conveying belt is accurately placed on a preset detection station, and a closed-loop grabbing-conveying process is formed. By utilizing the embodiment of the invention, high-precision identification, dynamic task allocation and self-adaptive flexible grabbing of the glass bottle can be realized, and the grabbing success rate and the production efficiency are improved.

The invention discloses a computer file protection method, which relates to the technical field of computer information security, and comprises the following steps of: S1, classifying files and marking security levels based on content characteristics and sensitivity; s2, dividing system user permission levels and configuring a multi-factor authentication mechanism; s3, constructing a file access control matrix and setting a fine-grained operation authority rule; s4, deploying an access behavior monitoring module, and recording a file operation behavior log in real time; s5, performing dynamic risk analysis based on behavior modeling and an anomaly recognition algorithm; s6, performing hierarchical response according to the risk level, wherein the hierarchical response comprises measures such as warning, limiting, network disconnection and locking; and S7, encrypting and storing the log information, and providing an auditing interface for behavior traceability. According to the method, static permission control and a dynamic behavior recognition mechanism are fused, full-process intelligent protection of files from creation, access, transmission to storage is achieved, and the method has the advantages of being high in safety, timely in response, capable of achieving self-adaptive evolution of strategies and the like.

The invention discloses a method for realizing holographic traffic at an intersection. The method comprises the following steps: acquiring a real-time video stream at the intersection; motor vehicles, non-motor vehicles and pedestrians in the video are identified based on a deep learning target detection algorithm, a continuous motion track of each target is generated through a multi-target tracking algorithm, identity association is carried out on cross-camera targets in combination with a re-identification algorithm, and structured traffic data including target types, real-time speeds and motion directions are output; obtaining coordinate points of each lane of the intersection based on a geographic information system, and constructing a lane network topological relation; combining with the structured traffic data to generate target trajectory data; and constructing an intersection three-dimensional model based on an unreal engine, generating a virtual target in the three-dimensional model according to the target trajectory data, and driving the virtual target to move in real time. According to the method, a holographic traffic implementation scheme is provided for a business scene which is free of radar and only monitored by a video.

The invention discloses a construction operation safety intelligent supervision system and method based on an AI identification algorithm, and relates to the technical field of operation supervision. The method comprises the following steps: acquiring a defect image sample and a non-defect image sample, performing defect classification and target feature library training, acquiring an original image, performing image pre-processing, feature library comparison and processed image output, framing a detected target by using an outer line rectangle, and obtaining a non-defect image sample; the verification terminal and a work card of an operator are networked and identity information is verified, the camera analyzes the identity information of the operator based on AI identification, and the verification terminal and the camera perform cross authentication to confirm the operator. Multi-target real-time intelligent analysis and identification of a non-digital display vernier caliper, a grounding resistance megger, a test pen, an anti-falling device and a safety cone barrel are realized, the requirements of three-dimensional supervision, operation and service in the industrial field are met, and the intelligent identification capability of the system is improved.

The invention belongs to the field of edible mushroom planting control, and particularly relates to a robot collaborative operation control method and system in the edible mushroom planting process, and the method comprises the steps: an inspection robot scans a mushroom bed, judges the maturity of edible mushrooms in combination with an image recognition algorithm, calculates the regional to-be-picked density, and generates a to-be-picked task list; according to the list, a preset parameter table and a curve, dynamically generating a coordinated picking task control instruction including the target coordinate, the number of the robots and the specific clamping posture, force and lifting speed of each robot; after the picking robot responds to the instruction to execute operation, a signal is fed back to the nearest inspection robot, secondary imaging confirmation is triggered, if residues exist, a supplementary picking list is generated according to the residue density, execution is repeated, and therefore efficient, accurate and lossless automatic cooperative harvesting of the edible mushrooms is achieved.

The invention provides a multi-modal data desensitization method, system and device and a medium, and belongs to the technical field of information security. The method comprises the following steps: firstly, acquiring multi-modal original data based on preset access information and acquisition frequency, and after standardized preprocessing, positioning sensitive information by using a classification recognition algorithm and generating an analysis report. Then, according to a report, matching a strategy from a desensitization strategy library bound with the sensitivity level, adjusting a dynamic confusion algorithm parameter to desensitize, integrating and generating desensitized multi-modal data, and recording and storing a mapping relationship between the original data and the desensitized data; and if a restoration request is received, after authorization verification is passed, reverse desensitization restoration data is carried out by using the mapping relation. Besides, full-process feedback information is collected, the desensitization effect is quantitatively evaluated according to a preset index, the desensitization strategy and algorithm are iteratively optimized accordingly, and effective desensitization and safety management of multi-modal data are achieved.

The invention discloses a manipulator grabbing control method and equipment based on position and posture recognition. The manipulator grabbing control method comprises the following steps that S1, initial image information of a target object is acquired through image acquisition equipment; s2, performing preprocessing and feature extraction on the initial image information, and obtaining three-dimensional position coordinates and attitude parameters of the target object through a preset position and attitude recognition algorithm; and S3, according to the three-dimensional position coordinates and the posture parameters, a grabbing path of the manipulator is planned by combining a kinematic model of the manipulator. By introducing technical means such as a deep learning recognition algorithm, forward / inverse kinematics model collaborative planning, real-time posture dynamic adjustment and force sensing feedback control, the problems that a traditional mechanical arm is low in grabbing precision, poor in adaptability and insufficient in operation stability are solved, intelligent and high-precision grabbing control in a complex scene is achieved, and the grabbing precision of the mechanical arm is improved. And the requirements of the modern industry on high efficiency, reliability and flexibility of automatic equipment are met.

The invention discloses a tunnel peripheral hole smooth blasting parameter intelligent optimization system and method, and relates to the technical field of blasting, and the system comprises a data management module, a factor identification module and a parameter optimization and scheduling module. Dynamic adjustment and scheduling optimization of the blasting parameters are achieved by collecting blasting data in real time, extracting key parameters, screening influence factors in combination with a factor recognition algorithm and constructing a parameter scheduling optimization model, and the system can effectively improve scientificity and accuracy of blasting design, so that efficient, accurate and self-adaptive optimization of the blasting parameters can be achieved; and the requirements of high quality and high efficiency of modern tunnel engineering construction are met.

The invention relates to the technical field of medical image processing, in particular to a tumor development analysis method and system based on image recognition. The method comprises the following steps: acquiring a multi-temporal tumor image data set, analyzing the multi-temporal tumor image data set, and determining spatial probabilistic boundary representation information; according to the spatial probabilistic boundary representation information, performing multi-dimensional feature fusion analysis on the tumor boundary, and determining tumor development situation feature information; performing analogue simulation on the tumor development situation according to the tumor development situation feature information, and determining tumor development risk information; and analyzing the tumor development risk information, and determining and outputting dynamic follow-up visit suggestion information. According to the method and the device, the spatial probabilistic boundary representation information is constructed, so that the misjudgment on the tumor boundary caused by binarization decision due to the forced closed contour tendency of an image recognition algorithm is avoided, the boundary illusion generated when the tumor boundary is analyzed in the prior art is effectively eliminated, and the evaluation accuracy of the tumor development situation is improved.

The invention provides a bridge stay cable video multi-target identification tracking and vibration extraction method based on an unmanned aerial vehicle. The method comprises the following steps: 1, constructing a bridge stay cable refined inclined slender target detection model based on a YOLOv11 model; 2, providing a multi-target tracking algorithm fusing the inclined slender target detection model and a StrongSORT algorithm; step 3, improving a displacement extraction method combining SIFT / ORB feature point matching and a sub-pixel refinement technology; 4, designing an unmanned aerial vehicle motion correction algorithm based on variational mode decomposition and time-frequency domain combined screening; and 5, constructing a joint working modal analysis algorithm for realizing combination of a natural excitation technology and a random subspace recognition algorithm. According to the method, high-precision extraction of the vibration signals of the stay cable and identification of modal parameters of the stay cable are realized, and technical support is provided for health monitoring of the large-span cable-stayed bridge.

The invention discloses a 3D printing extrusion monitoring method and system based on continuous image analysis. The method comprises the following steps: acquiring image data of a printing head of a 3D printing platform at a plurality of continuous time points; according to the image data, a printing model part of the printing head is determined based on an image recognition algorithm; according to the image data, the discharging flow corresponding to the printing head is calculated based on a continuous image recognition algorithm; and according to the printing model part and the discharging flow, the flow abnormal condition corresponding to the printing head is analyzed. Therefore, accurate flow anomaly detection based on image analysis can be realized, the stability and the printing quality of the 3D printing process are improved, and the printing defect risk caused by flow anomaly is reduced.

The invention discloses a neck-hung sign language interpretation device and a sign language semantic recognition interpretation method thereof.The neck-hung sign language interpretation device comprises a wearing support which defines a neck surrounding space and comprises a middle connecting arm and two shells, and the two shells are connected to the two sides of the middle connecting arm respectively; the image acquisition unit is arranged on the front side of the shell, comprises a three-dimensional depth camera and an adjusting mechanism for driving the three-dimensional depth camera to rotate, and is used for dynamically tracking and acquiring continuous sign language videos; the processing module is integrated in the shell, comprises a main control processor and a memory, and is used for operating a sign language recognition algorithm; the voice output unit is electrically connected with the main control processor and is used for playing the translation result; and the power supply unit supplies power to the image acquisition unit, the processing module and the voice output unit. Gesture languages used by the deaf-mute for communication are converted into natural languages which can be easily understood by normal people, the deaf-mute group is helped to speak, and the problem of information output when the deaf-mute communicates with the normal people is solved.

The invention provides a driving authority management method and device based on biological feature verification and a medium, and belongs to the technical field of vehicles. The method comprises the steps of collecting facial features, voiceprint data and driving behavior data by detecting a starting operation of a current driver; the identity of the current driver is recognized by using a multi-modal biological recognition algorithm, and the accuracy and anti-counterfeiting capability of identity verification are improved by adopting a bimodal fusion scheme of face recognition and voiceprint recognition; when it is determined that identity recognition of the current driver succeeds, a driving account is determined to judge whether the driver has the use permission or not, a binding relation of driver identity-account-function permission is established, and accurate matching of the intelligent driving permission and the driver qualification is achieved; the open state of the intelligent driving function is dynamically controlled based on a permission verification result, if the permission exists, the intelligent driving function is automatically prepared, if the permission does not exist, the function is locked, a clear prompt is given, and the safety risk that the intelligent driving function is used before being learned is avoided from the source.

The invention discloses a low-carbon asphalt road periodic maintenance method based on an edge computing gateway, and the method comprises the steps: accessing a multi-source sensing device through the edge computing gateway, collecting road surface images, structural stress, environment and maintenance material carbon emission associated data, and carrying out the shunting conversion; calling a multi-modal feature fusion crack recognition algorithm to extract fusion crack features, and synchronously obtaining historical maintenance carbon footprint data; performing association mapping on the multi-dimensional data and then transmitting the data to a carbon-effect double-target maintenance period prediction model; a period prediction result is output through model feature weighting and dimension recombination, and dynamic correction is carried out in combination with material consumption rate data; calling a performance attenuation early warning algorithm driven by time sequence data to analyze the time sequence relevance, and generating early warning information; the edge cloud end cooperates with the carbon footprint monitoring platform to synchronize information to the terminal, and a maintenance execution scheme is generated in combination with different parameters. According to the method, the dynamic adjustment of the maintenance decision is realized, the road performance requirement is ensured, and the carbon emission minimization is realized.

The invention discloses a remote sensing satellite network security situation awareness method and system with dual prevention mechanisms, and the method comprises the steps: obtaining multi-source heterogeneous data from a remote sensing satellite ground system network, and generating a structured data set through cleaning, denoising and standardization processing; and according to the structured data set, recording network assets by adopting an asset identification algorithm, and generating a risk thermodynamic diagram of the network assets through a Bayesian network model and an entropy weight method. And according to the risk thermodynamic diagram and the structured data set, adopting a deep learning model to detect abnormal traffic and behaviors, and generating a hidden danger list marked with priorities through a general vulnerability scoring standard and a threat intelligence library. And according to the risk thermodynamic diagram and the hidden danger list, constructing a dynamic security situation map by adopting a graph database and a community discovery algorithm. Therefore, the real-time fusion capability of multi-source heterogeneous data is improved, a dynamic attack chain reasoning mechanism is enhanced, and collaborative decision-making of risk early warning and hidden danger treatment is realized.

The invention discloses a frozen soil temperature feature recognition algorithm and a moisture and electric field state prediction model, and the method comprises the steps: synchronously collecting temperature, moisture, resistivity and meteorological data through arranging a multi-depth sensor array, and constructing a multi-source data set with time-space alignment; utilizing continuous wavelet transform and cross-modal coherence analysis, and combining a hidden semi-Markov model to extract a depth-periodic characteristic profile; a time domain fusion Transform model embedded with physical constraints is adopted to realize frozen soil multi-parameter physical consistency prediction and uncertainty quantification; and finally, outputting a prediction result through a visual interface and setting an automatic alarm. The problem of cycle identification caused by weak and non-stable deep frozen soil signals is solved, the prediction accuracy and the physical rationality are remarkably improved, an end-to-end solution from data quality control to early warning is formed, and the reliability and the practicability of frozen soil engineering monitoring are greatly enhanced.

The invention discloses a BIM-based building construction monitoring method, and the method comprises the steps: arranging a data collection device at a construction site, and obtaining multi-source data, such as structural members, construction machinery, spatial positions of operators, construction states, safety indexes and environmental parameters; automatically mapping multi-source data to a BIM three-dimensional model through an adaptive coordinate transformation and semantic recognition algorithm, matching the multi-source data with construction plan nodes, and generating a BIM construction state model reflecting a field state in real time; the system automatically compares the model with a construction plan model, identifies a state and calculates deviations such as progress, position, size, quality and safety; the method comprises the following steps: displaying deviation parts and potential safety hazards in a BIM interface in a highlight manner, superposing rectification suggestions, issuing a construction adjustment instruction confirmed by a manager to a field terminal, and storing monitoring, comparison, early warning and processing records in a database according to a time sequence to form a digital file; and real-time intelligent monitoring and visual management of construction progress, quality and safety are realized.

The invention discloses a rain and sewage pipeline defect detection method and system based on distributed optical fibers. The method comprises the following steps: 1, laying sensing optical fibers in a pipeline; 2, calibrating an excitation position; 3, collecting and demodulating optical fiber vibration data; 4, performing defect identification and defect positioning on data processed by an intelligent algorithm; vibration signals are sent out through the excitation device, signal collection is carried out in combination with the distributed optical fiber sensing module, efficient recognition and accurate positioning of pipeline defects are achieved in cooperation with an intelligent detection algorithm, and hidden diseases which cannot be perceived by naked eyes can be recognized. By means of an intelligent defect recognition algorithm, signals collected by the optical fiber sensing module are deeply analyzed, the positions and the disease degrees of the defects in the pipeline can be automatically judged, dependence on manual image interpretation is avoided, and automation and intelligentization of pipeline defect detection are achieved. The system has the advantages of being high in adaptability, wide in coverage range, efficient, accurate, high in intelligent processing capacity, low in cost, high in efficiency and the like.