9533results about How to "Precise positioning" patented technology

The invention relates to the field of vehicle data analysis, in particular to an automobile data analysis method and system based on an intelligent diagnostic instrument. The method comprises the following steps: acquiring vehicle multi-modal monitoring data based on an intelligent diagnostic instrument; operation power change calculation is conducted on the vehicle multi-mode monitoring data, time sequence state feature mining is conducted, and vehicle time sequence state features are obtained; carrying out multi-frequency noise reduction optimization on the vehicle multi-modal monitoring data, and carrying out normalized periodic evolution mining so as to generate normalized vibration periodic evolution characteristics; potential feature association deep mining is carried out on the vehicle time sequence state features and the normalized vibration period evolution features, then association graph modeling is carried out, and a vehicle multi-modal feature knowledge graph is constructed; and abnormal parameter deviation calculation and fault positioning traceability are carried out on the real-time operation parameters of the vehicle based on the vehicle multi-modal feature knowledge graph, so that a potential abnormal fault positioning point is obtained. According to the invention, efficient and accurate vehicle fault risk prediction analysis is realized.

The invention belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention relates to a feature enhancement-based multi-modal image-text tampering detection and positioning method, which comprises the following steps of: obtaining a tampered image-text pair, respectively preprocessing an input image and an input text, and constructing a data set; constructing a multi-modal image-text tampering model, wherein the multi-modal image-text tampering model comprises a text encoder, an image encoder, an image frequency domain enhancement module, a text enhancement module, a multi-cross attention fusion module and a multi-task tampering detection and positioning head; the image frequency domain enhancement module is used for selecting an optimal frequency band and sending the optimal frequency band out of the module; performing feature fusion on the frequency domain feature output by the image frequency domain enhancement module and the image domain feature output by the image encoder to obtain an image enhancement feature; fusing the aligned image enhancement features and text enhancement features by using a multi-cross attention fusion module; and training a multi-modal image-text tampering model by using the data set, and detecting and positioning multi-modal image-text tampering. According to the invention, the feature extraction capability of the network is enhanced, and the tampering detection and positioning capability is improved.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention relates to the technical field of vehicle-mounted 5G communication, in particular to a cooperative self-adaptive anti-interference 5G communication system and method based on spectrum sensing, and the system comprises a spectrum sensing module which is used for carrying out spectrum sensing, automatically detecting the interference condition of each frequency band in a wireless channel, and generating sub-band interference information; the interference avoidance module is used for selecting a frequency band with the minimum interference according to the sub-band interference information; the dynamic bandwidth adjusting module is used for dynamically adjusting the working frequency band to the frequency band with the minimum interference according to the real-time channel quality condition; the subcarrier intelligent distribution module is used for identifying and eliminating subcarriers influenced by interference; and the reinforcement learning modulation and coding module is used for dynamically adjusting modulation and coding strategies through real-time channel quality state feedback. According to the scheme, the anti-interference capability of the vehicle-mounted 5G communication system can be improved when the vehicle-mounted 5G communication system faces various interference sources, and the stability and reliability of communication are enhanced.

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

The invention discloses a poultry breeding monitoring management system based on big data, and relates to the technical field of poultry breeding supervision, the poultry breeding monitoring management system monitors the environment in a poultry house in real time, immediately triggers a behavior monitoring module when abnormality is detected, generates a specific environmental abnormality score, and combines visual monitoring and infrared thermal imaging to realize the monitoring and management of poultry breeding. According to the method, individual behavior characteristics of each poultry are effectively extracted, an individual behavior data stream is generated, individual potential health risks are identified, a health archive is adopted to accumulate long-term behaviors and environmental data of each poultry, and a time sequence analysis model is utilized to identify individual behavior modes and health change trends. Possible health risks are predicted based on historical data and models, management perspectiveness and disease prevention and control efficiency are effectively improved, meanwhile, abnormal features with the most influence are automatically recognized through comprehensive risk scoring and contribution degree analysis and are pushed to management personnel, the health archive of each poultry is updated in real time, and the management efficiency is improved. And the overall operation efficiency of the farm is improved.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention discloses a magnetic wave ring which comprises a base, supporting legs are arranged at the bottom of the base, a machine body is installed on the base, a displayer support is installed on the top of the machine body, a displayer is installed on the displayer support, an operation interface is integrated in the displayer, a handle connector is arranged at the end, close to the displayer support, of the machine body, and a magnetic therapy handle is installed in the handle connector. A handle support is arranged on one side of the machine body, a plurality of radiators are arranged on the back of the machine body, power sockets are arranged at the bottoms of the radiators, and power switches are arranged on one sides of Compared with the prior art, the device has the advantages that painless and noninvasive electromagnetic stimulation treatment is achieved through the magnetic induction principle, the curative effect is definite, the side effect is small, and addiction is not prone to occurring. The equipment is reasonable in design, simple and convenient to operate, easy to master and suitable for medical institutions and families. Personalized treatment parameter setting is provided, and the requirements of different patients and different treatment parts are met. The device has multiple safety protection functions, and safety and effectiveness of the treatment process are ensured.

The invention relates to the technical field of electric power operation and maintenance, and particularly discloses a power distribution network fault line selection method considering topological structure change, and the method comprises the steps: collecting zero-sequence current data, converting the zero-sequence current data into graph structure data, and providing input information representing the topology and current characteristics of a power distribution network for a model; learning the adjacency matrix and the node features by using a graph attention network until the model can accurately predict the probability of the single-phase earth fault of the power distribution network; based on the node fault probability output by the model, a threshold value is set to identify and position a specific fault line; and inputting field zero-sequence current data into the trained model so as to quickly and accurately judge the fault position of the power distribution network. According to the power distribution network fault line selection method considering topological structure changes, the efficiency and accuracy of fault line selection can be improved, and therefore effective fault monitoring can be achieved in a wider power distribution network environment.

The invention relates to a power station screen cabinet equipment temperature early warning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a dual-light image of power station screen cabinet equipment to be analyzed according to acquisition parameters corresponding to current acquisition; the dual-light image comprises an infrared thermal imaging image and a visible light image; performing preprocessing, feature extraction processing, spatial registration processing and information fusion processing on the dual-light image to generate a fused image; performing temperature field reconstruction processing and hot spot detection processing on the fused image to obtain temperature field distribution information and hot spot area information; and according to the temperature field distribution information and the hot spot area information, performing temperature analysis on the power station screen cabinet equipment to obtain temperature early warning information of the power station screen cabinet equipment. By adopting the method, the temperature early warning accuracy of the power station screen cabinet equipment can be improved.

The invention relates to the technical field of image recognition, and discloses an automatic image recognition garbage classification whole process implementation method based on AI calculation. The method comprises the following steps: carrying out multi-modal data acquisition on to-be-identified garbage to obtain an image data matrix, a weight data vector and a spectral data matrix; performing semantic feature and detail feature extraction on the image data matrix to obtain a fusion feature map; performing channel attention and space attention mechanism enhancement to obtain an enhanced feature map; overlapping degree calculation and region merging are carried out to obtain garbage positioning data; calculating probability density distribution data of garbage category nodes based on the garbage positioning data, the weight data vector and the spectral data matrix; according to the method, the problems that the target size is small and the features are not obvious are solved, and the reliability of the classification result is ensured.

The invention provides a method for creating a structured document based on a deep learning model, and relates to the technical field of data processing, and the method comprises the steps: obtaining basic text information collected by a user, obtaining text data, fragmenting the text data, obtaining text fragmented data, and creating a structured document according to the text fragmented data. Analyzing the structures of the sentences in the paragraphs, determining keywords in the sentences, analyzing the logical relationship between the sentences in the paragraphs, associating the sentence keywords in the paragraphs, analyzing the association degree between the paragraphs in the chapters, identifying core keywords of the chapters, obtaining a chapter keyword data set, and analyzing the field involved in the text. The method comprises the steps of extracting field information of a text to obtain a text keyword data set, constructing a structured document model based on a graph neural network model, and inputting the text keyword data set and text data into the structured document model to obtain a structured document. According to the method, the structured document can be constructed through the deep learning model.

The invention relates to the technical field of laser welding, in particular to a laser welding system control method for metal accessories. The method comprises the following steps that automobile industry metal fitting data are obtained, welding demand analysis is conducted according to the automobile industry metal fitting data, and therefore welding demand data are obtained; laser welding feature extraction and resistance spot welding feature extraction are carried out according to the welding demand data, so that laser welding data and resistance spot welding data are obtained; carrying out welding simulation according to the laser welding data and the resistance spot welding data so as to obtain welding simulation data; welding form feature extraction and welding strength feature extraction are carried out according to the welding simulation data, so that welding form data and welding strength data are obtained; and carrying out welding defect detection according to the welding form data and the welding strength data so as to obtain welding defect data. Automatic adjustment and accurate control of the laser welding process are achieved based on the laser welding technology.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention discloses a humanoid robot grabbing method based on three-dimensional vision, and the method comprises the following specific steps: S1, environment modeling and perception: carrying out the three-dimensional scanning of a working environment, and constructing a three-dimensional map of the environment; s2, object identification and 6D attitude estimation: identifying a target object, and estimating the position and attitude of the object in a camera coordinate system; s3, coordinate conversion and target positioning, wherein the posture of the object is converted into a robot base coordinate system from a camera coordinate system through TF conversion; and S4, path planning and motion control are conducted, specifically, path planning is conducted through a cumotion algorithm, a grabbing path is generated, and the two arms are driven to move according to the planned track to achieve grabbing. By means of environment modeling and sensing, the robot can update environment information in real time, conduct three-dimensional scanning on the working environment and generate the three-dimensional grid model of the environment in real time, the model is used for obstacle detection and avoidance planning, and the dynamic environment adaptability of the robot to object grabbing is improved.

The invention relates to the technical field of welding equipment, and particularly discloses steel reinforcement framework welding equipment which comprises a machine body, a positioning mechanism, a welding mechanism and two clamping mechanisms are arranged on the machine body, and the two clamping mechanisms are used for clamping two steel reinforcements and driving the steel reinforcements to ascend and descend relative to the machine body; the positioning mechanism comprises a driving assembly and two positioning pieces, the two positioning pieces are arranged in parallel, the driving assembly drives the two positioning pieces to synchronously get close to or get away from each other, fixing parts are arranged on the positioning pieces and used for movably abutting against the lap joint sections of the reinforcing steel bars, and the driving assembly is rotationally matched with the machine body through elastic pieces; and the welding mechanism is installed on any positioning piece, the welding mechanism comprises a support, a moving assembly and two welding pieces, the two welding pieces are installed on the support, the moving assembly drives the support to be in sliding fit with the positioning pieces, and the welding equipment improves the efficiency and the welding effect of lap welding of the reinforcing steel bars.

The invention belongs to the technical field of safety monitoring of subway tunnels, and particularly relates to a subway tunnel water leakage monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-source sensor time sequence data in a subway tunnel, carrying out the feature extraction, and extracting feature parameters related to water leakage; carrying out fusion analysis on the characteristic parameters through a data fusion algorithm, and evaluating the position and severity of water leakage, including extracting time-space correlation characteristics of data by using a dynamic time warping method; after feature extraction is performed on the data set and the space-time correlation features by using a convolutional neural network and a recurrent neural network, feature fusion is performed; and performing anomaly detection and classification on the fused high-level features by using a neural network method to obtain the position and severity of the leakage water. The advanced data fusion technology and the feature extraction algorithm are adopted, various collected data can be analyzed in real time, abnormal conditions can be found in time, and early warning is carried out.

The invention relates to the technical field of space positioning and path planning, in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. The method comprises the following steps of performing accurate longitude and latitude height data acquisition based on a laser range finder to generate accurate longitude and latitude height data; obtaining tower equipment parameters; performing inspection point three-dimensional coordinate calculation according to the tower equipment parameters and the tower three-dimensional coordinate system to generate inspection point three-dimensional coordinate data; cruise point comprehensive constraint range data are obtained according to the unmanned aerial vehicle flight association data and the polling area terrain data, and cruise point comprehensive constraint range data are generated; performing cruise node three-dimensional coordinate design on the three-dimensional coordinate data of the inspection point according to the cruise point comprehensive constraint range data to generate cruise point three-dimensional coordinate data; and performing optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data. According to the invention, more accurate unmanned aerial vehicle tower space positioning and path planning are realized.

The invention belongs to the field of artificial intelligence and finance, and relates to a personalized copywriting generation method, which comprises the following steps: classifying all customers according to historical customer feature data to obtain different customer groups, obtaining copywriting keyword combinations of the customer groups according to historical copywriting of each customer group, and generating personalized copywriting keywords of the customer groups according to the copywriting keyword combinations; and constructing a customer group portrait according to the customer group characteristics and the customer group document keyword combination, matching a target customer group portrait according to the customer characteristics of the target customer, and generating a product recommendation scheme according to the customer characteristics and the target customer group portrait through a product recommendation model. And generating a personalized copywriting according to the customer features, the customer group portrait features and the product recommendation scheme through the copywriting generation large model. The invention further provides a personalized copywriting generation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and historical customer feature data can be stored in a block chain. According to the invention, the client can be accurately positioned, and the generated copywriting can be accurately put.

The invention relates to the field of medicine, and discloses a thyroid nodule surgical operation planning and navigation system based on artificial intelligence, which comprises a multi-modal medical image acquisition module, an image preprocessing and enhancing module, an intelligent anatomical structure analysis module, an operation planning decision module and an intraoperative real-time navigation module, the multi-modal medical image acquisition module is used for integrating data of various image equipment such as ultrasonic equipment, CT equipment and MRI equipment, and space-time consistency of multi-modal images is ensured. According to the thyroid nodule surgical operation planning and navigation system based on artificial intelligence, the real-time calculation problem is solved, the system delay is reduced, which is crucial to real-time navigation in an operation, the real-time performance and the operation efficiency are improved, the operation time is shortened, and complications are reduced; by integrating the advantages of different images, the system can provide more comprehensive dissection views, help doctors to more accurately position nodules and important structures and avoid injury to nerves and blood vessels, so that the accuracy is improved, and the surgical risk is further reduced.

The invention discloses a full-automatic laser welding forming system, which relates to the technical field of laser welding, and comprises a laser generation and light beam modulation module, a material conveying and positioning module, a welding execution module, a visual inspection and monitoring module, an intelligent self-adaptive control module and an energy recovery and reutilization module, the material module accurately conveys and positions materials; the welding module robot is accurate in operation; the visual module detects welding seams; the intelligent module performs self-adaptive control; the energy module recycles energy. The welding quality is improved, and defects are reduced; the production efficiency is improved and the cost is reduced; operation is simple, and manpower requirements are reduced; energy is saved, environment is protected, and green concept is met; real-time monitoring is achieved, and the product quality and the management level are guaranteed.

The invention discloses a transformer substation inspection robot positioning and mapping method based on point-line feature fusion. According to the method, a binocular camera is used for collecting point-line features in an image frame, and high-quality long-line features are quickly obtained based on an adaptive criterion optimized EDLines algorithm; the spatial depth of point-line features is obtained through triangularization of a binocular camera, and redundant line features are filtered based on a length response threshold strategy of the spatial depth of a transformer substation. Based on a point-line pole feature pole plane constraint method, IMU information and visual information are combined, gyroscope zero offset is independently estimated and optimized, IMU pre-integration information is updated, and a more accurate initial posture of the inspection robot is obtained. A point-line feature dictionary based on a transformer substation scene and a large-scale equipment target detection model in a transformer substation are made, closed-loop detection is performed through transformer substation equipment road signs and point-line features, and rapid relocation correction is performed on sliding windows of a current key frame and a previous fixed key frame number. And finally, the pose of the substation inspection robot and a substation scene map are optimized through global BA, and the problem of large-scale scene error drift accumulation is solved.

The invention discloses a water quality comprehensive regulation and control intelligent model based on water quality hydrodynamic force, and relates to the technical field of water quality comprehensive regulation and control, which comprises the following steps: constructing a comprehensive meteorological monitoring network, capturing key meteorological parameters of air temperature, rainfall, humidity and wind speed in real time, and formulating water area monitoring paths of an unmanned aerial vehicle and an unmanned ship according to detected environment data; the method comprises the following steps: setting an unmanned aerial vehicle monitoring team to monitor the water surface condition in real time, setting an unmanned ship formation to run according to a formulated route, carrying out key monitoring and sampling on a specific polluted area, constructing a water quality standard-reaching early warning system, and carrying out prediction and early warning simulation calculation and correction, and analyzing and evaluating the water quality condition. According to the water quality comprehensive regulation and control intelligent model based on the water quality hydrodynamic force, water area pollution can be quickly found, pollutants can be traced and researched in time, pollution expansion is prevented, meanwhile, pollution early warning can be conducted according to data, potential environmental risks can be found, and powerful technical support is provided for environmental protection work.

The invention discloses a multi-mode adaptive fusion navigation positioning method and system in a satellite navigation denial environment, the system adopts GNSS / IMU / visual sensors, and the method comprises the following steps: receiving data of the three sensors and completing modeling; initializing the three sensors; judging the strength of the GNSS signal by utilizing chi-square test; optimization variables are set, a residual matrix is constructed, and nonlinear optimization is carried out; setting a GNSS trust factor when the GNSS signal is weak or denial, and calculating a weight; and weighting and fusing sensor data, outputting optimal state estimation of the system, and realizing continuous navigation and positioning. According to the method, a self-adaptive weight adjustment mechanism is established, GNSS trust factors are introduced, and the weights of multi-sensor data are adjusted in a self-adaptive manner, so that the positioning difficulty under the conditions that the GNSS is subjected to deception interference, suppression interference, building blocking and the like can be overcome, and continuous navigation positioning of an unmanned system (such as a robot, an unmanned vehicle, an unmanned aerial vehicle, an unmanned ship, an unmanned underwater vehicle and the like) is realized.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention relates to the technical field of fan blade detection, and provides a wind power plant fan blade detection method and system based on three-dimensional point clouds, and the method comprises the following steps: carrying out the multi-angle scanning of a fan blade through a laser scanner, and obtaining the three-dimensional point cloud data of the fan blade; performing registration processing on the three-dimensional point cloud data, and unifying the three-dimensional point cloud data into the same coordinate system to obtain registered point cloud data; performing block acceleration processing on the registered point cloud data by adopting an octree partitioning algorithm to obtain block acceleration point cloud data; performing feature extraction and feature segmentation on the block acceleration point cloud data to obtain a key feature region of the fan blade; performing defect identification analysis on the key feature region based on a point cloud network enhancement model to obtain a defect detection result, the defect detection result comprising a defect position and a defect type; and generating a detection report containing the defect position and the defect type, and visually displaying the detection report. According to the invention, the integrity of data acquisition and the precision of defect identification are improved, and the efficiency of fan blade detection is improved.

A self-adaptive multi-sensor fusion method using radar, laser radar, IMU and GPS in a complex environment comprises the following steps: receiving radar point clouds, and preliminarily distinguishing dynamic point clouds and static point clouds in a single frame; the dynamic point cloud and the static point cloud are processed respectively, and the absolute speed of the robot is obtained; receiving point cloud data through the laser radar, and performing time synchronization and space calibration on each frame of data of the millimeter wave radar and the laser radar; dynamic points in each frame are removed in real time, and laser radar point cloud data only containing static points and static millimeter wave radar point cloud data are obtained; geometric feature extraction is carried out on the static laser radar point cloud data, and whether the robot enters a degradation area or not is judged; laser radar or millimeter-wave radar point cloud data are selected according to the number of feature points of the laser radar point cloud for matching of scanned sub-maps; the pose and the speed of the robot are obtained through a GPS, the reliability of GPS data is judged through the speed estimated by the millimeter wave radar and the pose obtained by the front-end odometer, the weight of the GPS data in rear-end optimization is determined by calculating the residual error of the speed estimated by the millimeter wave radar and the pose obtained by the front-end odometer, and the weight is added into a factor graph to be jointly optimized with the front-end odometer.