324 results about "Industrial inspection" patented technology

The invention discloses a multi-modal analysis method, system and device for an industrial inspection scene and a medium. The method comprises the following steps: acquiring multi-modal data of an industrial inspection area in real time, wherein the multi-modal data comprises a video image and voice data; inputting the currently collected multi-modal data into a scene type identification model, and identifying a scene type; matching a corresponding multi-modal fusion mode based on the determined scene type; according to the matched multi-modal fusion mode, performing feature extraction on the video image by using a deep learning algorithm, performing feature extraction on the text converted from the voice data by using a natural language processing technology, and performing multi-modal fusion on the extracted features; performing joint reasoning on the multi-modal fusion features by using a deep learning algorithm, and identifying potential risks or equipment faults in the industrial inspection area; and generating an inspection report and decision based on the identified potential risk or equipment fault. According to the invention, intelligent analysis of the industrial inspection scene can be realized, and the inspection efficiency and accuracy are improved.

The invention discloses an industrial inspection intelligent decision-making method and system based on large and small model collaboration. The industrial inspection intelligent decision-making method comprises the following steps: dividing an inspection task into a plurality of sub-tasks by using a cloud large model, and performing dynamic task planning based on the sub-tasks; calling an edge small model to execute the subtask to obtain multi-modal original data; performing cross-modal feature fusion on the multi-modal original data by using a cloud large model to obtain cross-modal fusion features; and reasoning based on the cross-modal fusion features by using a cloud large model, extracting abnormal factors, and formulating a new inspection task based on analysis of the abnormal factors. Through the comprehensive method of comprehensive multi-mode perception, conditional diffusion model enhancement and cloud-edge collaborative decision, the problems of fault sample scarcity and perception deviation in an extreme environment are effectively solved, high precision and low time delay are considered, and a new breakthrough of intelligent inspection is brought.

The invention discloses a visual algorithm self-training method based on multi-agent collaborative optimization, and the method comprises the following steps: constructing a multi-agent system architecture which comprises a user interaction layer, an intelligent scheduling layer, an A2A protocol communication layer and a professional agent cluster layer; the user interaction layer analyzes a user task intention and generates an execution plan; the scheduling agent calls the professional agent to complete data processing, model construction, training, testing and deployment; a task process is coordinated through a standardized communication mechanism, and task execution is supported by combining an MCP tool set, a knowledge base module and a memory system; and when the task fails, automatically executing rescheduling operation, and finally outputting a self-training result. According to the method, the development efficiency, the self-adaptability and the intelligent level are remarkably improved, and the method is suitable for computer vision tasks such as industrial detection, intelligent security and protection and automatic driving.

The invention discloses a fan metal surface defect detection method and device based on lightweight YOLO11 and a medium, and relates to the technical field of computer vision and industrial detection. The method comprises the following steps: acquiring fan metal surface defect image data, and preprocessing to obtain a training data set; yOLO11n is used as a basic model, and a lightweight StarNet adopting a four-level layered architecture and a star operation feature fusion mechanism is used as a model backbone network; combining a bottleneck module with a multi-scale convolution block, and constructing a neck network by applying a global heterogeneous kernel selection mechanism and an efficient up-sampling module; a heavy parameterized detail enhanced convolution and group normalization GN strategy is used to construct a detail enhanced lightweight shared convolution detection head; and sequentially connecting the backbone network, the neck network and the output layer of the detection head to form the lightweight metal surface defect detection model. On the premise that the detection efficiency is guaranteed, high-precision identification of metal weld defects can be achieved.

The invention relates to an automatic image segmentation technology based on a convolutional neural network, and is suitable for the field of medical image and industrial detection. In order to solve the problems of rigid feature fusion, insufficient context capture, low efficiency of boundary optimization and poor small target segmentation precision in the existing method, an adaptive multi-scale feature fusion network is constructed: an encoder adopts a progressive expansion strategy and gated attention to intensify cross-scale features; the decoder optimizes hierarchical feature contribution through a dynamic weighted fusion module; the end-to-end boundary optimization is realized by integrating the lightweight differentiable CRF; and designing a composite loss function balance category weight. The segmentation recall rate of the fine structure is obviously improved by more than 18%, the boundary sawtooth rate is reduced by 41%, and the calculation efficiency is improved by 76%.

The invention discloses an aluminum profile surface defect detection method based on a DMSA-Swindow-Unet network model, and relates to the technical field of industrial detection, and the method comprises the steps: carrying out the preprocessing of a collected aluminum profile surface image; dividing the image into image blocks by using a convolution operation with a predetermined step length, and performing linear projection to generate an embedded feature sequence; the features are input into a Swin Unet encoder module to extract multi-layer local and global features, and an alternate window attention and shift window attention mechanism is adopted by a multi-layer nested Swin Transform module; in the decoding process, coding features of corresponding scales are fused through jump connection; a DMSA module is configured after the jump connection and is used for enhancing the context information sensing capability after fusion; and step-by-step up-sampling is carried out through image block expansion, and a pixel-level defect segmentation image consistent with an original image in size is output. The method is high in detection precision, strong in robustness, good in adaptability and strong in deployability, and is suitable for automatic identification and intelligent quality inspection scenes of surface defects of the aluminum profile on an industrial site.

The invention belongs to the technical field of industrial robot inspection, and discloses an instrument identification method for industrial robot inspection, an instrument identification system comprises a pose adjustment module, a lightweight detection module, an image double enhancement processing module and a double-branch instrument reading identification module, and the instrument identification method comprises the following steps: obtaining an instrument image, inputting the instrument image into an image dual-enhancement processing module to obtain a preprocessed instrument image; the preprocessed instrument image is input into a lightweight detection module, an instrument target detection result is output, the instrument type is judged according to the result, and an instrument reading result is output and recorded. According to the method, the improved lightweight target detection model and the adaptive image optimization processing algorithm are combined, the real-time performance and the high efficiency of instrument reading in an industrial scene are effectively improved, more intelligent and accurate instrument reading and industrial inspection can be provided, and powerful support is provided for information acquisition in a complex industrial environment.

The invention discloses a point cloud registration method based on a fast point pair feature attention module, and the method comprises the steps: firstly collecting point cloud data, and then carrying out the matching of point clouds through a point cloud registration model. The point cloud registration model is based on an encoder-decoder architecture, a fast point pair feature attention module FPFA, an Inception deep convolution module IDCM, a lightweight cross-frame attention aggregation module LCAAM and a matching matrix calculation module TSMM based on two stages are fused, and a final point cloud registration result is obtained. According to verification of a contrast experiment and an ablation experiment on a public data set and a self-built workpiece data set, the method is excellent in accuracy, speed and robustness, and especially has remarkable advantages in low-overlapping-rate scenes and industrial workpiece registration; an efficient solution is provided for factory assembly line automation and mechanical arm accurate grabbing, and the method has important application value for industrial detection scenes with limited computing resources.

The invention discloses a time stretching sweep frequency interference high-precision distance measurement method and device based on double auxiliary interferometers, and relates to the technical field of non-cooperative target absolute distance measurement. In order to solve the technical defects that in the prior art, due to initial wavelength fluctuation of a light source and frequency sweeping nonlinearity, drift of a ranging result is large, and interference signals are interfered by zero-frequency noise, the technical scheme provided by the invention comprises the following steps: acquiring linear frequency modulation optical signals output by a common-source frequency sweeping laser; the signals are respectively transmitted to a measurement interferometer, an auxiliary interferometer and a resampling auxiliary interferometer to form a homologous three-interference structure; a dispersive medium is introduced through the reference arm, so that the reference light generates time stretching and interferes with the measurement light to form a chirp beat frequency signal; k-domain resampling and cross-correlation processing are carried out on the beat frequency signals, and target distance information is extracted. The method is suitable for high-precision and wide-range non-cooperative target absolute distance measurement work, and is especially suitable for the fields of laser radar three-dimensional modeling, precision industrial detection, robot calibration and the like.

The invention relates to the technical field of machine vision industrial detection, and discloses an all-welded heat exchanger industrial detection method and system based on machine vision, and the method comprises the steps: obtaining a welding line region image of a to-be-detected heat exchanger, and extracting a boundary contour of a welding line region through an image preprocessing algorithm; calculating a welding line center line according to the boundary contour of the welding line area, and dividing the welding line center line into a plurality of detection units with equal length; in each detection unit, linear light scanning data of the surface of the welding seam and ultrasonic echo signals of a welding seam area are collected; generating a three-dimensional shape model of the surface of the welding seam according to the linear light scanning data, and extracting acoustic characteristic parameters in the welding seam; preprocessing the three-dimensional shape model and the acoustic characteristic parameters, and inputting the preprocessed three-dimensional shape model and acoustic characteristic parameters into a pre-trained defect evaluation model to obtain a comprehensive defect index of the welding seam area; and according to a comparison result of the comprehensive defect index and a specified threshold group, judging whether obvious defects exist in the welding seam area, and completing comprehensive evaluation of the welding seam quality.

The invention discloses a generative MR industrial inspection method based on a large model and natural man-machine interaction, and belongs to the technical field of intelligent inspection. In order to solve the technical problems of long development period and high cost of MR inspection, the method comprises the following steps: acquiring a scene image sequence to be inspected by using MR head display equipment, constructing an inspection scene image data set, and training a multi-modal large model by using the inspection scene image data set to obtain category sequence information data to be inspected; acquiring corresponding spatial positioning information and interaction information, constructing an inspection module data set based on the acquired spatial positioning information and interaction information, and then training an inspection module by using the inspection module data set to obtain to-be-detected information of an inspection sequence of the inspection module; fitting the category sequence information to be inspected and to-be-detected information of the inspection sequence of each inspection module to obtain an inspection execution model; and executing a generative MR industrial inspection process based on the large model and natural man-machine interaction by using the inspection execution model.

The invention relates to the technical field of mixed reality and artificial intelligence fusion application, in particular to a mixed reality intelligent inspection method and system based on multi-modal fusion. According to the technical scheme, the method comprises the following steps: synchronously acquiring a user view image and voice stream data through mixed reality equipment; performing target detection on the view image by adopting an improved YOLOv10 model, and outputting a candidate target area and a confidence score # imgabs0 # thereof; voice input is transferred through a Whisper voice recognition model, and a semantic vector # imgabs1 # is generated by using a BERT semantic embedding model. According to the invention, through a multi-mode cooperative sensing mechanism of fusing image, voice and space gazing information, in combination with context semantic optimization and lightweight terminal deployment, intelligent target identification and natural interaction with high precision, real-time performance and strong robustness are realized in a complex industrial inspection scene.

The invention relates to the technical field of three-dimensional scanning, in particular to a method for determining a three-dimensional scanning distance and range under a given precision condition, which determines the precision of a three-dimensional scanner within a specified range under different distances through a calibration test and detailed data analysis of a system, provides reliable scanning parameter selection, and improves the accuracy of the three-dimensional scanning distance and range. The method is widely applied to the fields of building measurement, industrial detection, cultural heritage protection and the like; according to the method, subjectivity and blindness of scanning parameter selection are avoided through a scientific parameter selection method, meanwhile, the working efficiency of scanning operation is improved, accidental errors are reduced through multiple tests and data averaging processing, comprehensive data are provided, influence factors and rules of scanning precision are clarified, the scanning process is optimized, and the scanning accuracy is improved. The efficiency and the result precision are improved, and the problem that the precision reliability of an existing three-dimensional scanning system in practical application is limited is solved.

The invention discloses a steel surface defect detection method based on a YOLOv8 multi-scale convolution attention mechanism, and relates to a steel surface defect detection method. Firstly, a multi-scale convolution attention mechanism MSCA module is introduced into a backbone network, accurate expression of small target features is enhanced, and the detection precision is improved; besides, a brand-new deformable convolution C2fDCNv2 module is provided to replace an original C2f module, so that the capability of capturing complex shape and irregular target features by the model is enhanced, and the efficiency of feature extraction is further improved; and finally, replacing the original loss function with a Wise-IoU (WIoU) loss function, accurately measuring the similarity between the target frames, and improving the defect detection performance of the model. According to the model, an experiment is carried out on an NEU-DET data set, an experiment result is compared with different-scale models of YOLOv8, the steel defect detection precision is effectively improved, and the industrial detection requirement is better met.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention discloses an industrial inspection robot, and relates to the technical field of inspection robots, the industrial inspection robot comprises an inspection track, the inspection track comprises a track main body, the track main body comprises a high-altitude inspection section, an inclined section and a landing section, the two sides of the track main body are respectively provided with a side guide groove, and the top of the track main body is provided with a raised line; the robot comprises a base plate, a side shell is fixedly connected to the edge of the top of the base plate, a shell cover is detachably installed on the top of the side shell, a side notch is formed in one side of the side shell, and the side notch is formed in the other side of the side shell. A top notch corresponding to the side notch is formed in one side of the shell cover, and a round hole is formed in the rear side of the shell cover; the industrial inspection robot can inspect factory workshops in the air along the inspection track and can also move freely from the lower portion of the inspection track to the ground, and the inspection track can be conveniently adjusted according to needs.

The invention relates to an industrial production intelligent inspection method and system, electronic equipment and a medium, which are applied to the technical field of industrial inspection, and the method comprises the following steps: obtaining equipment monitoring data and inspector information; performing fault prediction based on the fault prediction model and the equipment monitoring data to obtain a fault type and a fault occurrence rate of the equipment; correcting the fault occurrence rate of each device based on the fault occurrence rate and the first device association; dividing the equipment into first equipment, second equipment and third equipment based on the fault occurrence rate; determining the inspection time of the second equipment based on the inspection period and the historical inspection record; determining the inspection time of the third equipment based on the inspection period, the historical inspection record and the first frequency; determining the inspection grade of each device based on the to-be-processed task corresponding to the device, the fault type and the second device association; and determining an inspection plan based on the inspector information, the inspection grade and the inspection time. The method has the effect of improving the inspection efficiency.

The invention discloses a target tracking method and device based on a rolling and pitching type holder, and the method comprises the steps: building a polynomial response model through adaptive color calibration, and achieving the dynamic color correction; an improved YOLOv8 model is combined with a bidirectional feature pyramid network to carry out multi-scale target detection, and template matching is assisted to improve a low-confidence-coefficient scene recognition rate; predicting the position and the speed of the target by using Kalman filtering to cope with a shielding condition; and high-precision smooth rotation of the holder is realized through quaternion attitude solution in combination with PID (Proportion Integration Differentiation) and FOC (Fiber Operating Control) double-loop cooperative control. The device adopts a rolling and pitching type holder structure and comprises a main controller, an angle sensor, a visual sensor and a magnetic encoder, the mechanical structure is simplified, the weight is reduced, and higher response speed and higher control precision are achieved. The target tracking accuracy, stability and anti-interference capability of the intelligent mobile platform in a complex environment are remarkably improved, and the method is suitable for various dynamic scenes such as aerial photography and industrial inspection.

The invention discloses an inspection method of an industrial inspection system based on multi-modal fusion, which belongs to the technical field of industrial inspection, and comprises the following steps: S1, obtaining positioning data of a node to be positioned by using an HDS-TWR distance measurement algorithm and a Taylor full centroid algorithm, and outputting a positioning coordinate; s2, using a sensor to measure the acceleration and angular velocity of the inspection system so as to align the motion and posture for evaluation; s3, improving a Kalman filtering algorithm to enable an output prediction result to be close to a true value; s4, infrared-vision dual-mode cooperative tracking is adopted, and meanwhile, a time synchronization displacement compensation mechanism is utilized to calculate a relative displacement difference between two tracking modes, and dynamic calibration is carried out; s5, a YOLOv8 model is adopted to process video frames or pictures collected in the inspection process, a target box and category probability are output, and finally an inspection result is obtained; according to the inspection method of the industrial inspection system based on multi-modal fusion, a high-precision and low-delay automatic inspection solution is provided for severe environments such as mines and nuclear power stations.

The invention discloses a rapid quantitative differential interference microscopy system and method based on holographic recording. The system comprises a partially coherent illumination module, and a microscope objective, a tube lens, a transmission grating, a first linear polarizer, a first thin lens, a half-wave plate, a shading plate, a phase-type spatial light modulator, a second thin lens, a second linear polarizer and an image acquisition module which are sequentially arranged along the optical axis direction of the partially coherent illumination module. According to the invention, the phase-type spatial light modulator is utilized to realize shearing offset in any direction and at any degree, so that the device is suitable for different types of application occasions; the system realizes holographic recording of differential interference by utilizing grating carrier frequency, realizes quantitative differential interference detection on a to-be-detected sample only by utilizing two acquired images, has very high time resolution, spatial resolution and imaging precision, and has a wide application prospect in biological imaging and industrial detection.

The invention provides an imaging optical system for industrial visual inspection, which is characterized in that a first lens is mainly composed of nine lenses and has focal power, the object side surface of the first lens is a convex surface, and the image side surface of the first lens is a concave surface; the second lens has focal power; the third lens has focal power; the fourth lens has focal power; the fifth lens has positive focal power; the sixth lens has focal power, and the object side surface of the sixth lens is a convex surface; the seventh lens has focal power, and the object side surface of the seventh lens is a concave surface; the eighth lens has focal power; and the ninth lens has positive focal power, the image side surface of the ninth lens is a convex surface, and through the combination design of nine lenses and the reasonable configuration of the refractive power and the surface type of each lens, high-resolution imaging under a compact structure and a large aperture is realized, the aberration correction capability is particularly enhanced, and a relatively high MTF value is ensured in the whole zoom range. Accuracy and reliability of industrial detection are remarkably improved, and the system has the advantages of zooming, low distortion and low cost.

The invention discloses a mobile robot autonomous search navigation method based on a visual language large model. The method comprises the steps of receiving a natural language instruction of a user, and synchronously collecting an RGB image and a laser point cloud; two-stage visual language reasoning is adopted, and target semantic analysis and existence judgment are completed in sequence; when the target does not exist, autonomous exploration is executed based on the point cloud data and is parallel to target judgment, and an exploration-judgment closed loop is formed; after determining that the target exists, switching to a visual language navigation model, generating an incremental motion instruction, and guiding the robot to approach the target; target state monitoring is continuously carried out during navigation, if the target is lost, a dynamic backtracking mechanism is triggered, the robot is driven to return to the historical state of the recently confirmed target, and navigation is restarted. The method does not need to depend on a prior map, can execute efficient and robust autonomous search and navigation tasks on edge equipment through task collaboration, model quantification and state backtracking, and is suitable for complex scenes such as industrial inspection and emergency rescue.

The invention provides an industrial inspection-oriented defect autonomous decision-making method, device and equipment and a medium, and belongs to the technical field of intelligent manufacturing. The equipment defect autonomous decision-making method comprises the following steps: acquiring equipment operation state information of industrial equipment by using a multispectral fusion terminal, and uploading the equipment operation state information to a cloud server; a built-in cross-modal deep analysis model of the cloud server is used to carry out cross-modal learning on the equipment operation state information according to the double-flow heterogeneous deep network architecture, and joint distribution of visual-text features of the industrial equipment is learned to obtain defect feature vectors of the industrial equipment; performing multi-dimensional matching on the defect feature vectors and similar cases in a maintenance knowledge base, and screening treatment schemes corresponding to the defect feature vectors to obtain a maintenance strategy; and issuing the defect information corresponding to the defect feature vector and the maintenance strategy to a communication terminal of a maintainer. According to the method and the device, the problem of low cross-modal information utilization rate caused by multi-modal data splitting and no feature level fusion in the prior art can be solved.

The invention relates to the field of AI visual detection, in particular to an AI visual detection method based on supervised and unsupervised deep learning, which comprises the following steps: establishing a sampling image visual feature by using real-time sampling image data; establishing an AI visual feature analysis model based on supervised and unsupervised deep learning by using the visual features of the sampled image; according to the AI visual feature analysis model, an AI visual detection result is obtained, high-precision and high-robustness visual detection is achieved under a small amount of annotation data through a supervised and unsupervised mixed learning architecture in combination with the discrimination ability of deep learning and the anomaly detection advantage of unsupervised learning, a supervised main model is trained based on the annotation data, and a supervised auxiliary model is trained based on the unsupervised learning. Precise recognition of known defects is ensured, limitation of a traditional supervised model is broken through, generalization ability is improved while high precision is ensured, and the method is suitable for complex and changeable industrial detection scenes.

The invention provides a weld defect intelligent detection method based on high-definition image deep learning, which comprises the following steps: S1, collecting a high-definition weld image, manually marking the selected image, and constructing a weld defect image database; s2, performing first fine tuning on the pre-training weight GLIP-L by adopting a GLIP model; s3, a self-made welding test piece is made, a high-definition camera and ultrasonic detection equipment are used for collecting a weld joint surface high-definition image of the self-made welding test piece and ultrasonic echo characteristic data of internal defects, and a high-definition weld joint image and ultrasonic multi-mode database is established; s4, introducing a multi-layer perceptron module into the GLIP model, constructing an image and ultrasonic multi-modal feature alignment mechanism, and performing second fine tuning to obtain a trained GLIP model; and S5, performing defect detection on the welding seam by using the trained GLIP model, and outputting a detection result. According to the invention, through multi-modal data fusion, the accuracy and robustness of weld defect detection are improved, and the method is suitable for automatic and accurate industrial detection scenes.

The invention relates to the technical field of industrial detection maintenance and deep learning, in particular to a steel wire rope defect detection method with multi-dimensional interpretability, and the technical scheme comprises the steps of steel wire rope broken wire data set manufacturing, YOLOv8 model training and prediction and model evaluation and optimization. According to the method, the electromagnetic signal is converted into the image, the YOLOv8 deep learning model is used for training and predicting the broken wire of the steel wire rope, meanwhile, the multi-dimensional interpretability technology is used for carrying out interpretability analysis on the model from different angles, and indexes for quantifying the interpretability of the model are provided, so that the broken wire is more concrete, the identification degree is higher, and the method is suitable for popularization and application. The model detection speed is high, complex intermediate steps are not needed, model transparency can be improved, and the method plays an important role in guiding model optimization and increasing credibility.

The invention discloses a dual-channel system for an inspection robot and a communication method, and ensures stable communication of the inspection robot in a complex and severe environment by adopting a dual-channel design. According to the technical scheme, redundancy guarantee is provided through two independent wireless paths. When one path breaks down or the signal quality is reduced, the system can be automatically switched to a redundant channel, the problems of signal interruption and data loss frequently occurring in a traditional communication system are avoided, and therefore the continuity of inspection tasks and reliable transmission of data are guaranteed. The dual-channel system provided by the invention not only solves the problems of unstable communication and signal interruption in the prior art, but also improves the reliability and efficiency of data transmission, ensures the stable operation of the inspection robot in different complex environments, and greatly enhances the intelligent level and safety of industrial inspection operation.

The invention belongs to the technical field of industrial detection and reverse engineering, and particularly discloses a conduit detection method based on scanning point clouds, which comprises the following steps: a dynamic self-calibration optical system acquires an initial point cloud of a measuring tube; segmenting the initial point cloud to form a complete tube; a linear section and a bent section are distinguished through the inclination angle change of the adjacent cylinder axes, bending points are aligned through a bending point ICP algorithm, and a matrix conversion relation is obtained; correcting the bending point based on the relation and solving process data; restoring conduit characteristic data; and finally, the reconstruction tube and the model tube are aligned through a bending point ICP algorithm, and the measurement deviation is determined. The method solves the problems of low tracking precision caused by environmental interference, influence of point cloud noise on alignment, difficulty in digitalization of process data, difficulty in feature restoration and the like, is suitable for high-precision detection of complex conduits, and provides a quantitative basis for quality control and process optimization of industrial production.

The invention discloses a distributed unmanned aerial vehicle cluster cooperative control method and system, and relates to the related technical field of unmanned aerial vehicle control, and the method comprises the steps: carrying out the inspection task planning of an unmanned aerial vehicle cluster and a first robot cluster, and obtaining a cooperative plan; unmanned aerial vehicle pilot-operated macroscopic inspection is carried out based on collaborative planning, and a target detection area is obtained; robot following type microscopic detection is carried out based on collaborative planning, and a target detection result is obtained; and performing fan diagnosis by combining the target detection area and the target detection result. The technical problems that in the prior art, the wind power plant inspection efficiency is low, the fan equipment diagnosis precision is insufficient, the collaboration of an unmanned aerial vehicle and a robot is poor, and the industrial control automation degree is low are solved. The technical effects of improving the industrial inspection automation level, optimizing the fan equipment diagnosis accuracy, enhancing the multi-equipment cooperative control stability and improving the overall efficiency of industrial operation and maintenance are achieved.

The invention relates to the technical field of intelligent inspection, in particular to an industrial inspection robot applied to ultrasonic detection and a control method, and the industrial inspection robot comprises an automatic guide vehicle which is used for carrying out navigation advancing according to a preset inspection route; the sensor integration module is installed on the automatic guide vehicle in a lifting mode through the lifting mechanism and used for collecting data of equipment at different heights; the sensor control unit is used for generating optimal working parameters of the sensor according to the historical data and the environmental data acquired by the sensor integrated module and controlling the sensor integrated module to perform ultrasonic data acquisition according to the optimal working parameters of the sensor; working parameters of the ultrasonic sensor can be dynamically adjusted according to environmental data, and the accuracy and reliability of detection are improved.