99results about How to "Realize intelligent detection" patented technology

The invention provides an LED product surface defect detection method, which comprises the following steps: carrying out defect feature extraction on a preprocessed LED surface image by adopting a multi-scale morphological algorithm, and respectively carrying out morphological opening operation and closing operation on surface recesses and fine scratches by setting structural elements with different sizes and shapes to obtain candidate regions of the recesses and the scratches; according to the geometric dimensions, morphological characteristics and spatial distribution detail attributes of the recesses and scratches, constructing defect feature vectors, and performing classification and severity evaluation on defect candidate areas through a support vector machine algorithm to obtain the types, positions and quantitative description parameters of defects; and updating the light source parameter database and the defect feature database according to a quality detection result of the LED product, analyzing an association rule between defects and light source parameters through a data mining algorithm, and optimizing a light source parameter configuration strategy and a defect detection model.

The invention relates to a pipeline detection robot defect identification method based on image processing, which comprises the following steps: carrying out continuous image acquisition on the inner wall of a pipeline through image acquisition equipment on a pipeline robot to obtain image data of the inner wall of the pipeline; performing image preprocessing on the pipeline inner wall image data; pipeline defects in the pipeline inner wall image data after image preprocessing are detected, and image segmentation is carried out on a defect area; and carrying out feature extraction on the defect area, carrying out defect classification, and outputting an identification classification result. According to the invention, based on algorithms of image processing, artificial intelligence and machine learning, defects of cracks, corrosion, leakage and the like in the pipeline can be automatically identified, the limitation of manual detection is solved, automatic and intelligent detection is realized, the technical effects are mainly embodied in the aspects of improving the detection precision and efficiency and the ability to adapt to different pipeline environments, and the method is suitable for popularization and application. High application and popularization values are realized.

The invention relates to the technical field of image processing, in particular to an intelligent detection method and system for shaft forgings. The method comprises the following steps: acquiring gray values, positions and gradient directions of all pixel points in a surface image of the shaft forging; clustering all pixel points in the surface image by using a condensed hierarchical clustering algorithm to obtain a plurality of regions; calculating the suspicious degree of each region; determining a scratch burr area in the surface image of the shaft forging to realize intelligent detection of the shaft forging; in each round of iteration process of the condensed hierarchical clustering algorithm, for two to-be-combined clusters in each round of iteration process, calculating the separation degree of the two clusters; calculating the feature similarity of the two clustering clusters; determining the number of clustering clusters of the current iteration; a plurality of regions of the surface image of the shaft forging are determined. By dynamically adjusting the number of clusters, the problem of wrong merging or premature iteration stopping of a traditional condensed hierarchical clustering algorithm under the fixed number of clusters is avoided.

The invention discloses a power equipment defect identification method, system and device and a storage medium, and belongs to the technical field of power equipment defect identification. The method comprises the following steps: acquiring and processing a target infrared light image and a target visible light image of target power equipment to obtain an infrared light sub-image set and a visible light sub-image set; obtaining a feature similarity matrix, obtaining a target element set with the maximum sum of similarity values in the feature similarity matrix, carrying out key point calculation and optimization processing to obtain a target key point set, and constructing a mapping matrix of the target key point set; registering and fusing the target infrared light image and the target visible light image to obtain a target detection image; inputting the target detection image into the trained defect detection model for detection, and outputting a defect detection result of the target detection image; by combining the information of the infrared light and visible light images, the state of the power equipment can be reflected more comprehensively, so that the defect detection precision is improved.

The invention relates to the technical field of building construction, and discloses a fabricated pavement slab prestress intelligent tensioning construction method which comprises the steps that an optical fiber sensing array is arranged on the surface of a pavement slab, strain data of different positions of the pavement slab are collected in real time through the optical fiber grating strain sensing technology, and according to the corresponding relation between strain and stress, the strain data of different positions of the pavement slab are obtained; calculating to obtain an actual prestress distribution condition; aiming at the detected prestressed defect position, carrying out visual representation on a defect region by adopting an image processing technology, drawing a defect contour through an edge extraction algorithm, calculating geometric parameters such as defect area and length, and quantitatively evaluating the defect severity; information such as prestress defect detection results, performance influence evaluation and safety risk analysis results is synthesized, an intelligent prestress defect detection report is generated, the prestress condition of the pavement slab is visually presented, and a basis is provided for subsequent quality control and maintenance decision making.

The invention discloses an intelligent detection system and method applied to a power adapter, and relates to the technical field of equipment intelligent detection, and the method comprises the steps: analyzing the equipment abnormity degree of the power adapter in a historical equipment detection record; obtaining an abnormal historical equipment detection record of the power adapter, obtaining feature operation data of the power adapter, and evaluating equipment operation parameters in the feature operation data and the equipment matching degree between the power adapter; acquiring a historical charging record and a historical charging priority setting record of the power adapter, and constructing a charging priority evaluation model of the power adapter based on the historical charging priority setting record; and charging the electronic equipment by using the power adapter, performing priority evaluation on the electronic equipment by using the charging priority evaluation model, performing equipment abnormal risk detection on the power adapter in combination with the operation data of the target equipment, and adjusting the charging strategy of the power adapter.

The invention relates to the technical field of intelligent robots, in particular to an intelligent palletizing robot safety management system and method based on AI vision. According to the method, the three-dimensional model is constructed by obtaining the multi-modal data of the stacking operation area; carrying out target detection and recognition by using a deep learning algorithm; performing dynamic safety area division on the operation area according to the target information; a moving path planning strategy is constructed, and the robot is controlled to operate safely; and meanwhile, the state of the stacked goods is detected through an AI visual model, and when abnormity is found, an alarm is automatically given, and safety measures are taken. The intelligent level and the safety performance of the palletizing robot can be improved, and wide application prospects are achieved.

The invention discloses a wound healing degree evaluation method and system based on multiple models, and relates to the technical field of medical treatment. According to the method, the wound edge, the wound color, the wound type and the outer layer dressing color of the patient are combined, the multiple neural network models are used for comprehensively detecting the wound, intelligent detection of the wound of the patient is achieved, medical staff are assisted in treatment management of the wound of the patient, the burden of the medical staff is relieved, and the infection risk of the wound of the patient is reduced.

The invention discloses an electric passenger car track detection method, which comprises the following steps of: starting initialization after a track detection host is electrified, entering a waiting state after completion, reading a tag ID, judging the tag ID, starting detection if the tag ID is started, and continuing to detect the tag ID if the tag ID is not started. The track detection host obtains the transverse / vertical acceleration of a vehicle through the triaxial accelerometer, obtains a line radio frequency tag signal through the radio frequency tag reader, and obtains and calculates track information through the inertial navigation device. And the synchronous controller outputs mileage information to the rail detection host after acquiring a signal of the speed sensor, and triggers the laser triangulation assembly to acquire steel rail profile data. The rail inspection host obtains the transverse / vertical acceleration, the track, the mileage and the steel rail profile information of the vehicle, and solves the geometric parameter data of the rail. The technical problems that an existing rail detection operation mode is low in operation efficiency, real-time online detection cannot be achieved, and detection line data cannot truly reflect line dynamic parameters in the running state of the electric passenger car can be solved.

The invention discloses a test method for a high-load heating tube for a mold, and belongs to the technical field of single-head electric heating tube detection. The method specifically comprises the following steps: S1, historical data acquisition: acquiring cold-state electrical performance data of single-head electric heating tubes with different specifications and under different working conditions; s2, historical data preprocessing: performing preprocessing including data cleaning, data normalization and data standardization on historical data to obtain a standardized data set; the test process design covers a full-period closed loop of cold state detection, hot state leakage current test, reliability test, secondary hot state leakage current test and secondary cold state detection, the electrical safety (leakage current control) under a high-load working condition is verified through the hot state test, and the reliability of an actual use scene is simulated through a rapid on-off test. Meanwhile, the appearance, temperature, gas release and other states of the heating pipe are monitored in real time, abnormal testing is terminated in time, safety risks are avoided, and it is ensured that the detection result can comprehensively reflect the actual use performance of the heating pipe.

The invention discloses a water intake monitoring method and device, an intelligent terminal and a storage medium, and the method comprises the steps: measuring the movement data of a water cup through an acceleration sensor, the movement data being the pose, movement speed and acceleration data of the water cup during the period from getting rid of the horizontal plane to returning to the horizontal plane; calculating a single water intake according to the mobile data; and generating a water intake report and a reminding message according to the single water intake. The method for detecting the water intake is high in accuracy, water intake management can be achieved only through the low-cost acceleration sensor, and the method has wide application prospects.

The present invention relates to the field of data processing technology, and specifically to a data-driven method and system for predicting wind turbine equipment faults. The present invention clusters collected real-time operation data through a DBSCAN algorithm, removes outlier data therein, obtains real operation data of wind turbine equipment, and effectively avoids the influence of outlier data. The real operation data of the wind turbine equipment is normalized through a maximum and minimum normalization method to reduce the amount of calculation for subsequent operations. A bidirectional search algorithm is used to search for wind turbine equipment corresponding to abnormal operation data of wind turbine equipment and generate abnormal wind turbine equipment text data through data identification, thereby improving the speed and efficiency of the search process. Wind turbine equipment fault prediction result data is constructed and wind turbine equipment fault warning operations are performed to realize real-time warning of wind turbine equipment.

The application discloses a contact line hard bending state detection method and device based on deep learning and a medium. The method comprises the following steps: acquiring a detection image containing a pantograph and a contact line which is shot in real time during train operation; locating a pantograph region from the detection image, and determining a contact line extraction region according to the pantograph region; extracting the contact line from the contact line extraction region to obtain a contact line image; inputting the extracted contact line image into a pre-trained hard bending state detection model to obtain a detection result output of whether the contact line exists hard bending, wherein the hard bending state detection model is obtained by training a deep learning model using an image data set, and each image data in the image data set contains the pantograph, the contact line and the labeling information of a hard bending region of the contact line with hard bending. The application has the advantages of simple implementation method, low cost, high detection precision and efficiency, strong flexibility and the like.

This invention belongs to the field of automotive assembly technology and discloses an automated production line and control method for double-sided machining of workpieces with dual spindles. It includes a flipping mechanism, a dual-spindle machining mechanism, a raw material conveying mechanism, a finished product conveying mechanism, a robot, and a workstation conveying mechanism. The machining devices of the dual-spindle machining mechanism are spaced apart on the chassis. The lifting platform of the flipping mechanism is located next to the chassis. A flipping gripper is fixedly mounted on the chassis and positioned above the lifting platform. The rotating mechanism of the flipping gripper drives the clamping mechanism to rotate horizontally by 180 degrees. Two spindle boxes are slidably connected to the upper part of the chassis. The spindle is located inside the spindle box, with its lower end extending out of the lower end of the spindle box and fixedly connected to a spherical positioning chuck. The robot is positioned between the raw material conveying mechanism, the finished product conveying mechanism, and the workstation conveying mechanism. This invention achieves automatic workpiece flipping, avoids positioning errors and clamping eccentricity, uses air blowing to clean foreign objects from the chuck, detects the airtightness of the positioning surface, prevents workpiece deformation, and extends the service life of the equipment.

The present invention discloses a method and system for intelligently detecting defects of chain cotter pins, the method comprising the following steps: obtaining an image of a cotter pin type chain joint to be detected; judging whether the cotter pin is contained in the image of the chain joint; if not, judging that there is a defect of missing chain cotter pin in the image to be detected; if contained, performing cotter pin key point detection based on the image of the region where the cotter pin is located to obtain multiple key point positions; according to the multiple key point positions, calculating the opening angle of the cotter pin tail to judge whether the cotter pin is normal. The present invention can quickly and accurately judge the state of the cotter pin.

The present invention provides a wear analysis method for a rigid contact network based on binocular vision. The method comprises: collecting contact line images of the rigid contact network on the top of the train and the top of the tunnel by two cameras; correcting the contact line image pair collected by the two cameras, and obtaining a disparity map of the corrected image pair by using a stereo matching algorithm; converting the disparity map into a depth map according to the principle of binocular vision stereo imaging, extracting the contact line part in the depth map, and performing three-dimensional reconstruction and visualization of the contact line part to obtain the wear characteristics and distribution of the rigid contact network. The present invention utilizes a three-dimensional map of the contact network surface and various types of wear disease curves to better describe the wear of the contact line surface and realize automated and intelligent detection of the rigid contact network.

The present invention provides a flexural and tensile reinforced coarse aggregate ultra-high performance concrete and a preparation method thereof, comprising the following components: 300-800 parts of cement, 100-300 parts of fly ash, 100-300 parts of ceramic powder, 200-300 parts of silica ash, 500-800 parts of sand, 300-600 parts of crushed stone, 10-25 parts of polycarboxylic acid water reducer, and 150-200 parts of water; and also comprising MXene-carbon fiber composite material and steel fiber. The MXene-carbon fiber composite material is 0.2% to 2.0% of the total volume of concrete; and the steel fiber accounts for 0.5% to 2.0% of the total volume of concrete. The loading of MXene makes the carbon fiber more hydrophilic and rough, reduces the attachment of bubbles on the fiber surface and the generation of voids, and improves the compactness of the concrete; at the same time, MXene increases the bonding force, toughness and impact resistance of the carbon fiber / epoxy resin composite material; in addition, the conductive material MXene and the carbon fiber both increase the conductivity of the concrete. The present invention utilizes the mixed doping of carbon fiber composite materials and steel fibers to reduce the overall density of the fiber part and the dead weight of concrete, thereby obtaining a concrete product with enhanced toughening and chemical corrosion resistance.

The present application relates to the technical field of laser welding, and provides a visual detection method and device based on laser welding and a readable storage medium, the method comprising: controlling a galvanometer welding head to weld according to a preset standard welding image to obtain a plurality of welding image samples; comparing and analyzing each welding image sample with the standard welding image to obtain welding data of the welding image sample, and labeling unqualified welding sections in the welding data to obtain labeled welding data; obtaining welding process parameters of the unqualified welding sections in the welding image sample, and associating the welding process parameters with the welding data of the welding image sample to obtain training data; constructing and training a visual detection model; inputting a collected welding image to be detected into the trained visual detection model, and the visual detection model judging whether the welding image to be detected has unqualified welding sections. The technical scheme provided in the present application embodiment can effectively improve the detection efficiency of visual detection welding effect.

The utility model discloses a detection device and a processing system. The detection device comprises a rack, a detection piece, a jacking piece, a discharging track and a control piece. The detection piece is arranged on the rack and is used for detecting a to-be-machined piece; the jacking piece is movably arranged relative to the rack, and the jacking piece and the detection piece are arranged in the height direction of the detection device. The jacking piece comprises a sliding guide face facing the detection piece, the sliding guide face is obliquely arranged towards the side away from the detection piece relative to the horizontal plane, and the horizontal plane is perpendicular to the height direction of the detection device. The discharging track is arranged on the rack, and the discharging track and the jacking piece are arranged in the inclination direction of the sliding guide face. The control part is connected with the detection part and used for controlling the jacking part to lift the to-be-machined part and slide the to-be-machined part into the discharging rail through the sliding guide face when the detection part detects that the to-be-machined part is unqualified. Therefore, the sorting efficiency of the detection device on defective products is improved, the structure is simple, the cost is reduced, and the production efficiency of the products is improved.

The present invention relates to the field of water content detection technology, and discloses a device and method for measuring the sensitive area of ​​a multi-section tubular water content sensor. The device for measuring the sensitive area of ​​a multi-section tubular water content sensor comprises: a measuring barrel, a lifting mechanism and a data collector; the interior of the measuring barrel is divided into a first space for placing the soil to be tested, a second space for placing a material with a fixed dielectric constant, and a third space for the soil water content sensor to move up and down; the first space is located in the second space, and the third space is located in the first space; the lifting mechanism is used to drive the soil water content sensor to move up and down, and the data collector is connected to the soil water content sensor signal. The device for measuring the sensitive area of ​​a multi-section tubular water content sensor directly observes the sensitive area of ​​the section tubular soil water content sensor through an experimental method, realizes the automatic and intelligent detection of the soil water content sensor, improves the detection work efficiency, and ensures the sensor is tested in a real soil sample environment.

The invention relates to the technical field of information retrieval processing, and discloses a method and system for fusion archiving of multimodal business data. The system comprises a data fusion archiving original parameter acquisition module, a data fusion processing module, and a data archiving processing module; autonomous retrieval of user-side data archiving repository object information is performed based on standard multimodal business fusion parameters, and accurate judgment is made as to whether the user-side data archiving repository object exists, thereby realizing intelligent detection of user-side data archiving repository object information; scientifically preset data archiving repository type constituent folder directory parameters corresponding to different data usage field types are combined with intelligent recognition algorithms and data usage field type text parameters to perform accurate retrieval of constituent folder directory parameters of archive repository types corresponding to multimodal business data, thereby realizing scientific analysis of data archiving repository constituent folder directory parameters in different data usage fields.

The present application relates to a kind of vehicle speed detection methods based on outer pole face image, belong to the field of automatic driving.The method includes: S1: periodically capture the image of the road in front of vehicle, obtain time-space distance image;S2: the curve cluster surface of the time-space distance image obtained is divided into several parts;S3: the curve corresponding to each part obtained;S4: by its analytic differential, the speed curve of each part is obtained;S5: the generation of full interval speed curve, according to the displacement of feature point, the speed of vehicle is calculated.The present application is based on the principle of visual kinematics, using the image obtained by vehicle-mounted camera and distance sensor, the speed of vehicle is calculated by observing the motion of feature point in image.Compared with the existing speed detection method based on physical sensor, the method is lower in cost, but the principle is simple, and the accuracy is also higher.The method can be used alone, can also be combined with the data of other sensors, improve detection accuracy and stability.

The invention provides an intelligent household electrical appliance control method, electronic equipment, a storage medium and a program product, and belongs to the technical field of intelligent household electrical appliance control. The intelligent household electrical appliance control method is applied to the intelligent household electrical appliance, and the intelligent household electrical appliance comprises a panel, a burner assembly, a first temperature sensor and a second temperature sensor. The method comprises the following steps: in response to the fact that the current temperature of the cooker is detected to be within a first temperature range, judging the current state of the cooker according to the temperature variation detected by the first temperature sensor and the second temperature sensor; in response to the fact that the current state of the cookware is the preset cooking state, the overflow state of the cookware is further judged, and the power of the burner assembly for heating the cookware is controlled according to the overflow state of the cookware. Therefore, the overflowing state can be accurately determined, the accuracy of judging the overflowing state is improved, and then the pot heating power of the burner assembly can be controlled according to the overflowing state of the pot.

The invention discloses a dynamic signal detection method based on a multi-channel communication receiving system, relates to the field of communication technology, and solves the problem that the current signal monitoring technology has insufficient detection performance and communication signal monitoring performance in a multi-user and high-load environment. The method is specifically as follows: acquiring base station configuration information of each communication base station in the multi-channel communication receiving system; performing communication detection on the communication base station, and evaluating the wireless channel quality of the communication base station according to the detection result of the communication detection; formulating a communication configuration plan of the communication base station according to the evaluation result of the wireless channel quality; performing real-time monitoring on the communication status of the communication base station, and obtaining real-time performance data of the communication base station through aggregation of the monitored data; performing comprehensive evaluation on the working status of the communication base station, and executing corresponding communication adjustment measures according to the evaluation result. The invention realizes intelligent detection of the communication situation in the multi-channel communication receiving system.

The invention relates to the technical field of construction site safety, and discloses a GAN-based construction site personnel unsafe behavior detection method and system, and the method comprises the following steps: 1, carrying out the literature analysis and summarization, and selecting an application scene with representative personnel unsafe behaviors; 2, collecting and constructing a construction site personnel behavior video data set containing normal and unsafe behaviors; 3, designing a generative adversarial network model framework of unsupervised learning; 4, training the proposed model on the self-built data set, evaluating the performance of the model, and comparing the model with a baseline model; 5, eliminating each pseudo-anomaly generation module through an ablation experiment, and evaluating the contribution of each pseudo-anomaly generation module to the model performance; the method is executed in the system. According to the invention, intelligent detection of unsafe behaviors of workers can be realized by using normal behavior data training, and safety management is carried out by using an informatization technology, so that the safety production level is improved.

The invention provides a steelmaking continuous casting digital twinning application intelligent configuration method and system, and the method comprises the steps: building a function logic through adjusting nodes and a node group according to a production plan, configuring the function parameters of the function logic, and recording the use information of the nodes; adding the function parameters and the use information into a data set, and training a BERT model through the data set; judging whether the prediction of the BERT model is accurate or not according to the information of the data set, and if so, executing the next step; if not, the last step is executed again; judging whether the function is realized; if the result is yes, the current log record and the function parameters are recorded in the corresponding json file, configuration is completed, and function logic is adjusted according to the configuration; if not, the first step is executed again. According to the method, verified functions and code logic are used, so that the possibility of bug occurrence is reduced, and efficient code assets which can be quickly retrieved and reused can be continuously accumulated.

The invention provides a building structure detection method based on WiFi, and belongs to the technical field of building detection. According to the method, existing WiFi signal transmitting and receiving equipment is arranged and installed in a building (including a house, a tunnel, an underground project and the like) or utilized, and propagation characteristic changes, such as changes of parameters such as signal strength, wavelength, frequency, phase and the like, of WiFi signals in the structure are monitored in real time. The characteristics of attenuation, reflection, multipath effect and the like of signals are utilized, an advanced signal processing algorithm is combined, damage, hidden defects and the like of the structure are analyzed, and the structural health state of the civil engineering building is obtained. Compared with a traditional sensor monitoring technology, the method has the advantages of being low in cost, free of invasion and easy to deploy, is suitable for a large-scale and multi-point distributed monitoring environment, remarkably improves the monitoring and detection efficiency of structural damage of buildings, underground engineering and other buildings and structures, and solves the problems that an existing method is high in cost, complex in deployment, poor in adaptability and the like.

The invention discloses a method for detecting the fastening state of intelligent equipment installed on amusement equipment, and the method comprises the steps: collecting sensor data of the intelligent equipment, comparing the sensor data with reference data or historical data, and judging the fastening state of the intelligent equipment. According to the scheme, the fastening state of the intelligent equipment is judged by monitoring the sensor data of the intelligent equipment, and an alarm can be sent, so that the operation safety of the equipment is improved, and manual intervention and misjudgment are reduced.

The invention provides an unmanned aerial vehicle attitude angle measurement method based on horizon line detection. The method comprises the following steps: defining horizon line key points; constructing an end-to-end horizon line key point detection neural network, wherein the end-to-end horizon line key point detection neural network comprises a backbone network, a transposed convolutional network and a self-attention module; predicting horizon line key point coordinates according to the end-to-end horizon line key point coordinate feature map; acquiring a horizon line according to the predicted horizon line key point coordinates; and the roll angle and the pitch angle of the unmanned aerial vehicle are obtained according to the imaging relation of the horizon line in the camera. The technical problem of low air alignment precision caused by difficulty in accurately acquiring the attitude of the micro inertial navigation system in an air flight scene in the prior art can be solved.

The invention discloses an acoustic detection system for non-disassembly of a railway wheel set bearing in a warehouse. The invention further discloses an acoustic detection method for the non-disassembly of the railway wheel set bearing in the warehouse. According to the invention, the wheel set operation sound is picked up; and a detection result is analyzed and output for a worker to judge whether the wheel set has defects or not (the wheel set is accelerated by means of a wheel set running-in machine, a proper distance is kept for the wheel set to be detected through a high-precision microphone sensor to collect sound signals, a wheel set bearing acoustic comparison oscillogram is generated, and the detector can judge whether bearing defects exist or not through the oscillogram). According to the invention, automation and intellectualization of the railway wheel set bearing maintenance process are realized, the operation safety is improved, the upgrading transformation process is accelerated, and the problems of misjudgment and missed judgment caused by strong subjectivity of existing manual detection are solved.