68results about How to "Realize high-precision detection" patented technology

The invention discloses a multi-modal data fusion-based high dam underwater defect measurement method, which comprises the following steps of: obtaining water flow velocity data, water pressure data and water quality turbidity data, performing preprocessing by adopting a self-adaptive median filtering algorithm, and constructing a hydrodynamic field feature model; then generating an initial trajectory based on the hydrodynamic field feature model and detection target area data, and performing trajectory optimization and stability evaluation in combination with dynamic constraint conditions; adjusting sonar parameters according to the turbidity influence and the trajectory parameters to perform data acquisition; carrying out space-time alignment through extended Kalman filtering, and carrying out feature fusion and optimization by adopting an attention mechanism; and finally, performing multi-scale consistency verification and accurate positioning mapping to obtain a final defect detection result. According to the invention, the problems of defect detection precision and reliability in a complex underwater environment are solved.

The invention discloses a coal mine shaft crack identification method in a non-illumination environment based on an optical fiber sensing grating, and belongs to the field of coal mine shaft disease detection. According to the method, distributed optical fibers form an optical fiber sensing grating, the optical fiber sensing grating is fixed on the surface of a well wall, an optical fiber demodulator is used for collecting strain along the optical fibers, a blank blind area of the optical fiber sensing grating is filled through an interpolation algorithm, and a strain field image data set of the well wall is obtained; and a CCD industrial camera is matched to shoot a well wall crack image sample at the optical fiber sensing grating, binarization processing is carried out, and a well wall crack image data set is established. And establishing a well wall crack identification model based on deep learning, introducing two parallel feature extraction branches, respectively receiving an optical fiber strain field image data set and a well wall crack image data set, and performing feature extraction and fusion of the two different types of data sets through training. Because the strain data is sensed through the optical fiber, the illumination environment is not needed, and after the model training is completed, the identification and early warning of the well wall crack in the illumination-free environment can be realized only by inputting the optical fiber strain data acquired in real time into the trained model.

The invention relates to a precision detection device for gear processing, which is suitable for detecting the dynamic performance and single tooth precision of a gear, and comprises a base, a rotating bracket, a fixed block, a motor, a reduction gearbox, a test gear, a to-be-tested gear, a main measuring rod, a plurality of measuring rods and the like. High-precision detection of gear dynamic balance, vibration parameters and the tooth outer end and the contour line of a single gear is achieved, the reduction gearbox reduces the rotating speed of the gear and strengthens torque control, and the gear thickness difference design is matched, so that the multiple measuring rods can slide along the tooth outer end of the gear, tooth surface deformation and the contact state are accurately detected, and the device is made of high-carbon low-alloy tool steel. The gear detection device has high strength and wear resistance, adapts to various gear specifications, can output detection data and analysis results in real time, is simple to operate, high in detection precision and high in applicability, and can be widely applied to the fields of gear manufacturing, quality detection and maintenance.

The invention discloses an AI-based PCBA circuit board defect detection system. The AI-based PCBA circuit board defect detection system comprises the following steps: S1, an image acquisition module, wherein the image acquisition module comprises a camera shooting assembly and a light source assembly; wherein the camera shooting assembly comprises a high-resolution camera, and the high-resolution camera is used for capturing an image of a PCBA circuit board to be detected; s2, an image processing module, wherein the image processing module comprises image preprocessing, image segmentation and image recognition; according to the invention, automatic defect detection is carried out on the PCBA circuit board by using an artificial intelligence technology, the system combines image processing and deep learning technologies to realize rapid and accurate detection of surface defects of the circuit board, and meanwhile, through an automatic and intelligent detection means, the detection precision, efficiency and consistency are greatly improved; and the system not only can meet the current production requirements, but also can be continuously optimized and expanded through data driving, and has remarkable competitive advantages.

The invention relates to the technical field of handbag material surface defect image detection, in particular to an environment-friendly handbag material surface defect image detection method and system, and the method comprises the following steps: obtaining image data of the surface of an environment-friendly handbag material; dividing the image data into a plurality of local areas; for each local area, extracting image features representing visual features of the local area; establishing a normal feature range of the local area, and continuously adjusting the normal feature range in combination with the environment information; comparing the current image feature with the continuously adjusted normal feature range to identify an abnormal region deviating from the continuously adjusted normal feature range; and according to the image feature mode of the abnormal region, natural textures and defects are distinguished, and defect information is output. According to the method, natural textures and real defects on the surface of the environment-friendly handbag material can be effectively distinguished, potential defects with low contrast and fuzzy edges can be identified, and the accuracy and reliability of detection are improved.

The invention provides a bionic fish and a monitoring base station thereof, and belongs to the field of deep sea detection. The problem that large disturbance is caused to an in-situ ecological system when a traditional underwater vehicle detects a seabed ecological system is solved. A bionic fish comprises a bionic shell which is a streamline cavity shell; the propelling assembly is arranged in the bionic shell and used for pushing the bionic shell to move; the angle adjusting assembly is arranged on the bionic shell and used for changing the moving angle of the bionic shell; the first pressure maintaining assembly is arranged on the bionic shell, and seawater flows into the first pressure maintaining assembly to change the internal pressure of the bionic shell; and the second pressure maintaining assembly is arranged on the bionic shell and is extruded by seawater to maintain the pressure of the bionic shell. The device is mainly used for deep-sea detection.

This invention relates to the field of industrial internet security technology, and discloses a method and system for evaluating industrial internet data security protection capabilities. The method includes acquiring network traffic logs and system response records; performing time-series correlation analysis and sequence causal relationship completion based on the logs and records to obtain a dynamic evolution sequence of attack behavior; constructing an attack path graph model and parsing the correlation structure based on the sequence to obtain an attack path correlation structure; identifying high-latency paths and calculating response time differences to obtain a set of high-latency paths; extracting time feature vectors and quantifying the similarity of protection effectiveness from this set to obtain a quantitative score for path protection effectiveness; combining the score and difference index to perform a comprehensive risk assessment of physical topology mapping and node cascade failure analysis to obtain the system's comprehensive protection weaknesses; and performing adaptive scenario reconstruction closed-loop verification based on the weaknesses. This method can achieve accurate evaluation and dynamic verification of system protection weaknesses in complex adversarial scenarios.

This invention discloses a multifunctional analyzer combining CL-LIBS-LA-ICP-MS, comprising a laser optical path module, an electronic excitation system, a spectral detection module, a sample chamber, and a mass spectrometry detection module. The laser optical path module includes a laser source, a beam adjustment system, a scanning galvanometer, and a coaxial microscopic observation system. The spectral detection module includes a light signal collection end, an echelle grating spectrometer, a gated camera, and a timing control module. Another aspect of this invention discloses an analytical method, comprising setting the environment to vacuum and performing CL imaging scanning; selecting a LIBS test area based on the CL imaging scan image and performing LIBS scanning; selecting a third test area based on the LIBS scan results, filling the sample chamber with carrier gas, and performing laser ablation mass spectrometry detection. This invention eliminates the positioning error caused by sample transfer between multiple instruments during combined techniques, provides more comprehensive information on the structure and composition of the sample, and further improves the detection efficiency of the entire method through two screening test ranges.

This application relates to a high-precision visual inspection system and method for product sides, belonging to the technical field of visual inspection. It includes a worktable, a light source, a vision camera, and reflectors. The worktable has an inspection area for placing the product. The light source illuminates the side of the product within the inspection area. The vision camera is positioned above the inspection area. Several reflectors are obliquely arranged on the worktable, each corresponding to a side of the product within the inspection area. The reflectors are located to the side of the corresponding side of the product. When the light source illuminates the side of the product, the light reflected from the side is reflected by the reflectors to the central area of ​​the vision camera. This application achieves high-precision inspection of all sides of a product under single-vision camera conditions, reducing equipment cost and size.

The invention provides a method and a device for determining the position of a single crystal rod ending lineation and electronic equipment. The method comprises the following steps: acquiring a lineation area image corresponding to the single crystal rod ending lineation; inputting the lineation area image into a pre-trained lineation identification model, extracting lineation local features, and determining a lineation pose corresponding to the ending lineation of the single crystal rod according to the lineation local features; determining a deviation value between the scribing pose and a preset target pose, and correcting the pose of the single crystal rod according to the deviation value until the scribing pose is matched with the preset target pose; and displaying an identification result corresponding to the ending lineation and the lineation pose to a user through a preset interactive interface. And high-precision detection of the scribing position of the single crystal rod can be realized.

The application discloses a radar voice interaction control method and system based on natural language processing, relates to the technical field of voice recognition, and solves the technical problems that it is difficult to combine radar echo signals with hyperspectral image data to detect target states, it is difficult to realize voice interaction control by using natural language processing, it is difficult to reasonably determine the priority of control instructions based on an environment comprehensive coefficient and an emergency degree value, it is difficult to establish a conflict detection mechanism to ensure the feasibility of instructions, and it is difficult to execute instructions under the conditions of multi-target game and dynamic priority change and monitor and evaluate deviation values to establish an error reporting mechanism. The application realizes target state detection by fusing radar echo signals and hyperspectral image data, realizes voice interaction control by using natural language processing, and dynamically generates hierarchical control signals in combination with an environment comprehensive coefficient and an emergency degree value.

The invention provides a power module, a motor controller of a vehicle and the vehicle. The power module comprises a power module and a detection mounting piece, the power module comprises a base body and a power chip installed on one side of the base body, and the detection installation part is connected to the side, provided with the power chip, of the base body. One side, facing the power chip, of the detection installation part is provided with a temperature detection part, and at least the part, corresponding to the temperature detection part, of the detection installation part has elasticity, so that the temperature detection part can elastically abut against the power chip to detect the temperature of the corresponding power chip, and the temperature detection precision can be improved.

The invention discloses a dexterous hand force tactile sensor structure, which belongs to the technical field of tactile perception of robot dexterous hands, and comprises a first force arm, a second force arm, a contact slide rail and a fiber grating sensor, distances between hinge points of the first force arm and the second force arm and same-direction ends of the first force arm and the second force arm are equal; one side of the first force arm and one side of the second force arm are slidably connected with the contact sliding rail and correspondingly connected with the two ends of the elastic element. The fiber grating sensor is connected with the other sides of the first force arm and the second force arm; according to the invention, the fiber grating is used as a core sensitive element, and the two ends of the fiber grating sensor are synchronously triggered by matching the two force arms which are arranged in a crossed manner with the contact slide rail, so that the problems of large size, poor durability, easy interference, poor precision and the like of a traditional electrical sensor in dexterous hand application are solved.

The utility model relates to the technical field of single crystal silicon rod detection, and provides a gantry type ground rail device for single crystal silicon rod detection, which comprises a ground rail mechanism, a gantry mechanism and a silicon rod rotating table, the ground rail mechanism comprises a base, a sliding rail and a sliding platform. Sliding rails are arranged on the two sides of the base respectively. A rack is arranged on the sliding rail; a sliding platform is arranged on each sliding rail; a servo motor is arranged on the sliding platform; gears are arranged at the output ends of the servo motors, and the gears are meshed with the corresponding racks; the gantry mechanism comprises a mounting plate and two gantry stand columns. The gantry stand columns are arranged on the corresponding sliding platforms, and a mounting plate is connected between the two gantry stand columns. The mounting plate is used for arranging a visual acquisition system; and the silicon rod rotating table is arranged on the base. According to the utility model, the measurement in a full-length range can be realized, and the stability of the visual acquisition system during automatic image data acquisition is ensured.

The application discloses a weak pressure detection method and system, which comprises a piezoelectric pressure sensor, a piezoresistive pressure sensor, a piezoelectric pressure sensor charge amplification filter circuit, a piezoresistive pressure sensor full-bridge amplification filter circuit, an analog switch circuit, an ADC conversion circuit and a processor; the piezoelectric pressure sensor charge amplification filter circuit comprises a charge amplification circuit, a follower and an amplification filter circuit; the piezoresistive pressure sensor full-bridge amplification filter circuit comprises a sensor full-bridge circuit and a differential amplification filter circuit; the input end of the analog switch circuit is electrically connected with the output end of the differential amplification filter circuit and the output end of the amplification filter circuit respectively, and the output end of the analog switch circuit is electrically connected with the input end of the processor. The application can adapt to signal frequency, simultaneously cover static and dynamic pressure detection, and realize high-precision weak signal extraction in a strong noise background.

The application is suitable for the technical field of gear defect detection, and provides a defect detection method and a detection system for PEEK gear production, which comprises the following steps: S1, after the workpiece is completed injection molding production, multi-modal data acquisition is performed on the workpiece; S2, defect analysis is performed on the workpiece according to appearance data and internal data; S3, the workpiece is sorted, when the workpiece is marked as a qualified workpiece, when the workpiece is marked as an unqualified workpiece, a triple screening mechanism is triggered to judge whether the workpiece needs to be repaired; S4, defect data of all the unqualified workpieces in the production process of the batch is recorded, and defect distribution data of the batch production is counted based on the defect type; and S5, a multi-objective optimization function is called to obtain production process parameters of the next batch production according to the updated prediction model M. The application guarantees the quality of the workpiece and improves the material utilization rate and production efficiency under the premise of reducing the waste of PEEK material.

The application discloses a fault detection method and device based on graph structure difference and electronic equipment, and relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring an observation data vector of a physical system to be monitored at time t, a linear mapping matrix and an upper state matrix vector, the observation data vector being determined by collecting sensing data of the physical system to be monitored in real time, and the linear mapping matrix and the upper state matrix vector being constructed based on a basic structure model, operating parameters and predictive input of the physical system to be monitored; constructing an optimization problem objective function at time t based on the observation data vector, the linear mapping matrix and the upper state matrix vector at time t; obtaining a sparse difference vector at time t by solving the optimization problem objective function at time t; comparing the sparse difference vector at time t with a threshold value to obtain a fault detection result, and sending a control instruction to a system controller. The method can improve the accuracy of fault diagnosis.

The invention provides an industrial defect detection method and device based on a deep learning chip, and a medium. The method comprises the following steps: splicing features of different to-be-detected industrial images to obtain a target splicing feature; obtaining a first pixel group and a second pixel group from the target splicing feature through a sliding window and pooling; obtaining a first attention score and a second attention score according to the first pixel group and the second pixel group, and setting a first parameter matrix and a second parameter matrix; determining a spatial relative coordinate and a logarithmic interval continuous position deviation between the second parameter matrix and the query, and further determining an attention weight of the first pixel group, an attention weight of the second pixel group and an attention value; carrying out industrial defect detection according to the attention value; in combination with different types of image features extracted in different modes and corresponding attention features, high-precision detection of small or hidden defects can be realized.

The present application relates to a kind of tunnel fire detection method, device and medium, wherein the method comprises the following steps: obtaining historical tunnel sensor dataset and constructing tunnel fire image dataset;Three-stage neural network architecture including trigger network, risk assessment network and complete detection network is constructed, trigger network carries out all-weather abnormal monitoring to the whole tunnel;Risk assessment network is activated when potential risk is detected by trigger network;Complete detection network is activated when high risk is determined by risk assessment network;The three-stage neural network is trained using historical tunnel sensor dataset and tunnel fire image dataset, and the trained fire detection model is obtained;Real-time acquisition tunnel video and sensor data, input trained fire detection model, and obtain tunnel fire detection recognition achievement.Compared with prior art, the present application solves the problems of low tunnel fire detection accuracy, serious waste of computing resources, insufficient real-time performance and high false detection rate.

The invention provides an HP-aZIF-Tar-Au sensor, a preparation method thereof and application of the HP-aZIF-Tar-Au sensor in detection of human sweat tyrosine, and belongs to the technical field of nano materials. The HP-aZIF-Tar-Au sensor provided by the invention comprises an AAO substrate and an HP-aZIF-Tar-Au composite material loaded on the surface of the AAO substrate, wherein the HP-aZIF-Tar-Au composite material is prepared from the following raw materials: HP-ZIF-67, HAuCl4 and L-tartaric acid. The preparation method of the HP-aZIF-Tar-Au sensor is simple and effective, the HP-aZIF-Tar-Au sensor can be used for ultrasensitive specific detection of tyrosine in human sweat, the process is stable, the sensitivity is high, and the selectivity is good.

The utility model belongs to the technical field of engineering detection, and particularly relates to a curtain wall natural vibration frequency detection device based on millimeter wave radar. At present, an appearance observation method and a vibration signal detection method are mainly adopted as detection methods for metal fittings and connecting structures on the back of a curtain wall. The two methods are limited by factors such as weather conditions and observation distance, are difficult to widely use in practical engineering, and cannot realize quantitative detection. The utility model provides a curtain wall natural vibration frequency detection device based on a millimeter wave radar, and particularly relates to a non-contact, remote, accurate, efficient and rapid curtain wall defect detection device. The detection device comprises an excitation unit, a detection unit and a signal processing unit, the excitation unit applies instant vibration force to the to-be-detected curtain wall through the knocking object; the detection unit comprises a millimeter-wave radar and a supporting structure below the millimeter-wave radar; the millimeter wave radar is used for transmitting electromagnetic wave signals to the curtain wall and receiving the electromagnetic wave signals reflected from the curtain wall.

The invention discloses a plastic pipe wall thickness testing method and device based on terahertz transmission, which relates to the field of terahertz transmission technology, including constructing a standard model; setting the frequency of a standard terahertz wave and the width of a beam, and constructing a standard setting array to obtain a plurality of setting arrays to be screened; a terahertz emission source respectively emits terahertz waves under a plurality of setting arrays to be screened, and obtains a plurality of thickness distribution diagrams with different degrees of precision; obtains signal-to-noise ratio influence data and measurement accuracy data when performing a plastic pipe wall thickness test; constructs a thickness distribution diagram resolution evaluation model based on a long-term and short-term neural network; and performs data analysis to obtain a screened setting array, and applies the screening setting array to the plastic pipe wall thickness test; the application solves the problem that the resolution setting of the thickness distribution diagram is not standardized, resulting in the inability to accurately capture subtle thickness changes while ensuring signal strength, thereby affecting the final test effect.

The invention discloses a roadway deformation detection method and system based on image processing, and relates to the technical field of coal mine roadway engineering. The method comprises the following specific steps: arranging angular points in a roadway; shooting angular points in the roadway to obtain a roadway image; performing corner detection on the roadway image through a Harris corner detection algorithm based on image recognition; carrying out superposition operation on the plurality of roadway images of which the angular points are detected according to different weights to obtain a superposed image; carrying out position coordinate solving on the identified angular points in the superposed image to obtain angular point coordinates; and comparing the position changes of the same angular point in the image at different moments to obtain the displacement of the angular point. According to the method, roadway deformation is detected through the Harris corner detection algorithm, high precision and stability are achieved, and the roadway deformation condition can be accurately detected in various complex mine environments.

This invention relates to a universal contour detection device and assembly method for assisted robotic optical processing, belonging to the field of advanced optical manufacturing technology, and solves the technical problem of the lack of an in-situ high-precision detection device in the optical processing of large-aperture mirrors by robotic arms in the prior art. The contour detection device includes: a workpiece turntable and a swing-arm air-floating turntable; a workpiece table is provided on the upper surface of the workpiece turntable; the workpiece to be measured is placed on the workpiece table; the upper end of the swing-arm air-floating turntable is connected to the end effector of the robotic arm via a quick-change detection fixture; the lower end of the swing-arm air-floating turntable is connected to the arm via an arm fixture; a probe is provided at one end of the arm, which is located near the upper surface of the workpiece to be measured. In the contour detection device of this invention, the end effector of the robotic arm remains stationary during the measurement of the optical element's surface shape; compared to the measurement mode of a moving probe at the end effector, the accuracy and stability of the robotic arm in the stationary state are better.

The invention discloses a multi-light-intensity scanning measurement method for nano-particle tracking analysis. According to the method, scanning measurement is carried out on the same sample according to a plurality of preset laser power gears in sequence, and particle scattering signals and motion trail data under different light intensities are obtained; processing the data under each light intensity to generate a concentration distribution curve, and evaluating a light intensity measurement result to determine the adaptive measurement light intensity of each particle size peak; and finally, aiming at the components with different particle size spans, carrying out data merging by adopting a'long-distance peak direct splicing 'strategy and a'neighbor peak weighted smooth fusion' strategy respectively. According to the invention, the problem of deviation caused by weak small particle signals and easy saturation of large particles during single light intensity measurement of a wide-distribution sample is solved.

The invention discloses a high-precision protein concentration detection method and device, and belongs to the technical field of protein sensing. The detection method is a novel two-parameter sensing theory capable of simultaneously detecting the protein concentration and the temperature, changes of the protein concentration and the temperature can be simultaneously calculated through the two-parameter sensing theory by measuring the refractive index sensitivity, the temperature sensitivity and the frequency shift of double resonance, and finally the protein concentration is calculated. The device is a double-BIC terahertz all-dielectric metasurface sensor, and a periodic unit is sequentially composed of a dielectric layer 1 and a dielectric layer 2 from top to bottom. The dielectric layer 1 is made of silicon; the dielectric layer 2 is composed of silicon dioxide. The invention provides a high-precision protein concentration detection method and a high-precision protein concentration detection device, which realize simultaneous detection of protein concentration and temperature, and can effectively eliminate interference of temperature change on protein concentration detection, thereby realizing high-precision detection of protein concentration. Wide application prospects are realized in the fields of clinical diagnosis and biological pharmacy.

The utility model provides a portable formula can manual regulation's track detection dolly relates to track detection technical field, including connecting rod one, the both ends fixed mounting of connecting rod one has the car body. The utility model discloses a 6x6 capacitor sensor matrix is installed in the car body bottom, can more accurately detect the damage condition of track surface and inside, cooperate the image information that OpenMV camera gathers, control terminal comprehensive analysis, has improved the accuracy of track damage identification greatly, effectively solved the problem of low detection accuracy of existing track detection car, in addition, through adopting hand -operated lifting wheel, the staff can manually adjust the distance between car body and track according to track height, make the dolly can nimblely adapt to the track detection demand of different height, at the same time, the flexible rotation of the OpenMV camera driven by the steering wheel can realize multi -angle detection and obstacle avoidance, greatly enhanced the flexibility of track detection dolly work in different track environment.

The utility model discloses a kind of nonwoven fabric weld detection equipment, it is related to automatic detection technical field, including conveying system, detection system, rejecting mechanism, stacking mechanism and man-machine interactive system, characterized by, the conveying system includes air suction belt, conveying belt support and base, air suction belt is set on conveying belt support and is fixed by base, adjustable conveying belt is further equipped on conveying belt support, the detection system includes shadowless lamp box and visual detection device, shadowless lamp box is installed above conveying system, visual detection device is located below shadowless lamp box and is connected with control system by output pivot, the rejecting mechanism includes flap and defective product box. A kind of nonwoven fabric weld detection equipment provided by the utility model, by the combination of shadowless lamp box and visual detection device, high-precision detection to nonwoven fabric weld is realized, and the reliability of detection is significantly improved.

The invention relates to a low-voltage transformer area power consumption data abnormal value detection method. The method comprises the following steps that 1, power consumption data of all users are collected through an intelligent electric meter installed in a low-voltage transformer area; 2, repairing the power consumption data collected in the step 1, and filling the vacancy value; 3, classifying the users in the low-voltage transformer area according to the power utilization characteristics of the users on the basis of the power utilization data repaired in the step 2; step 4, based on the normal data without the abnormal value, establishing an FPA-CNN-BiLSTM abnormal value detection model trained by the normal data; and step 5, through the FPA-CNN-BiLSTM abnormal value detection model trained in the step 4, predicting test set data containing an abnormal value, calculating a prediction error, and if the prediction error of a certain data point exceeds a threshold value, marking the data point as an abnormal value. According to the invention, the abnormal value detection precision of the low-voltage transformer area intelligent electric meter data can be improved.

The present application discloses a ceramic capacitor detection method and system based on visual nerves. By innovatively designing a transparent test cover and its internal component layout, efficient and comprehensive detection of surface gaps of multilayer ceramic capacitors is achieved. The system utilizes the principle of wedge-point interference fringes and a multi-angle backlight illumination strategy, which not only improves the detection accuracy, but also significantly improves the detection efficiency. Specifically, the specific angle setting of the transparent test cover and the capacitor to be tested, combined with the first detection component, ensures the effective capture of the top surface gap; and the ingenious configuration of the multi-zone backlight panel and the light-transmitting test board makes the gaps on the sides of the capacitor nowhere to hide. In addition, the image analysis capability of the processor further enhances the intelligent level of detection and reduces the need for manual intervention, thereby effectively reducing the product defect rate and improving the overall quality control capability of the production line, which is of great significance for meeting the needs of large-scale production.