330results about How to "Improve detection reliability" patented technology

The invention provides a semiconductor chip defect automatic detection method and system based on hyperspectral imaging, and relates to the technical field of chip detection, and the method comprises the steps: collecting a chip hyperspectral image, carrying out the image enhancement and illumination non-uniformity correction through an improved neuromorphic adaptive processing network and a dual-path diffusion model, and carrying out the detection of the hyperspectral image. Extracting multi-level features by using a dynamic mixed feature enhancement network based on neural architecture search, fusing the features through a cross-layer attention mechanism, generating an initial defect detection result by using a multi-branch expert decision-making module and an uncertainty dynamic integration strategy, constructing a space-time knowledge inference graph, and determining a defect detection result; and performing reliability evaluation and optimization on the initial defect detection result in combination with the form similarity, the spatial position relationship and the process parameter influence model to obtain a final defect detection result.

The invention provides a power distribution network traveling wave head fault calibration method. The method comprises the steps that initial fault voltage traveling waves of all measurement points in a power distribution network are collected; constructing a first phase-mode transformation matrix to perform phase-mode transformation on the initial fault voltage traveling wave, and selecting a fault voltage line-mode component of the initial fault voltage traveling wave; performing first characteristic mode decomposition on the fault voltage line mode component to obtain a square envelope spectrum Gini coefficient of the fault voltage line mode component; determining an optimal parameter combination corresponding to the Gini coefficient of the square envelope spectrum, performing second characteristic mode decomposition on the optimal parameter combination, calculating a kurtosis value of each initial fault traveling wave signal, and determining a wave head calibration mode component of the fault traveling wave signal; and carrying out differential operation on the wave head calibration mode component, introducing a differential energy operator, and determining the traveling wave head arrival time of the fault traveling wave signal. According to the technical scheme of the embodiment, the detection precision and reliability of the fault traveling wave head can be effectively improved, and the accuracy of power distribution network fault positioning is enhanced.

The invention discloses an imaging method and system based on cosmic ray muon transmission, which are applied to a control mechanism, and the control mechanism is electrically connected with at least one detection module. The imaging method based on cosmic ray muon transmission comprises the steps that the response state of at least one detection module is obtained, the detection module comprises a plurality of sensor units, and the sensor units are arranged in a string shape. And preprocessing the response state to obtain muon flux in unit area, unit time and unit solid angle. Based on the topographic data, the landform data and the muon flux, the density distribution in the muon ray irradiation range is reversely deduced through a preset algorithm. And performing visual imaging based on a density distribution result. According to the method, the working efficiency of muon geological imaging is improved, the system complexity is reduced, the adaptability in a narrow working environment is remarkably enhanced, and an efficient and convenient solution is provided for the fields of geological exploration, engineering detection and the like.

The invention discloses a textile fabric polyester fiber detection method based on spectral imaging and curve fitting, and relates to the technical field of hyperspectral imaging analysis, and the method comprises the steps: carrying out the characteristic decomposition and principal component analysis of a reflectivity function after baseline correction, and obtaining a characteristic curve; extracting feature information of the hyperspectral image, and fusing the feature information of the hyperspectral image with the feature curve to generate a multi-dimensional feature vector; and constructing a hyperspectral fiber content detection model, inputting the multi-dimensional feature vector and the feature parameter vector into the hyperspectral fiber content detection model, and generating the polyester fiber content. Not only are the problems of background noise and drift interference solved, but also the multi-dimensional characteristics of the hyperspectral data are fully mined. The content of the polyester fibers in the waste textile fabrics can be efficiently detected on line. Finally, according to the method, the detection precision and reliability are remarkably improved, and an efficient, environment-friendly and accurate technical means is provided for resource utilization of the waste textile fabrics.

The invention relates to a data calibration method suitable for an atmospheric pollutant sensor deployed in an embedded terminal. The method comprises the following steps: acquiring original response data of different sensors with different concentrations in different atmospheric environments after temperature compensation, and corresponding temperature, humidity, air pressure data and sampling time; collecting real concentration values of detection objects corresponding to different sensors at corresponding moments given by a national control station or a provincial control station; preprocessing the obtained data to obtain a final data set; a random forest algorithm is used as a teacher model, a gradient elevator algorithm is used as a student model, and hyper-parameters of the gradient elevator algorithm and the random forest algorithm are optimized by using a hedera helix algorithm; substituting a hyper-parameter optimal solution found by optimization into a corresponding algorithm, and retraining the teacher model and the student model by using hyper-parameter combinations; and deploying, updating and displaying the model in the embedded equipment. And the model size is reduced to realize the deployment of the embedded end on the premise of ensuring the accuracy.

The invention provides an adaptive CFAR detection method based on scene classification, and the method comprises the steps: carrying out the scene classification of an input SAR image through employing a K-means clustering algorithm, carrying out the target detection of a pure sea area obtained through classification through employing VI-CFAR, and carrying out the CFAR detection of a coastal area through employing a significance model. According to the method, the problems that a traditional CFAR algorithm is high in false alarm rate and unstable in detection performance in port and land complex backgrounds are effectively solved, detection parameters are optimized according to the complexity of different scene backgrounds, and therefore it is ensured that the CFAR algorithm can still maintain the constant false alarm rate in the sea clutter background, and the robustness of target detection is remarkably improved. The ocean area and the land area in the image are effectively distinguished, and the land area is shielded. According to the technical scheme, the problem that a traditional CFAR detection algorithm is interfered by a complex land background when detecting ships on the sea surface is solved.

The invention discloses a power equipment detection method and system, and relates to the technical field of power equipment detection, and the method comprises the steps: inputting a power equipment image into a trained enhanced target detection model; the trained enhanced target detection model comprises a backbone network, a neck network and a head network; performing multi-level feature extraction enhancement on the power equipment image through the backbone network to generate a low-level feature, a middle-level feature and a high-level feature; inputting the low-layer features, the middle-layer features and the high-layer features into a neck network for feature pyramid fusion, and outputting a plurality of fusion features; and respectively detecting each fusion feature by using a head network, adding elements one by one, and determining a detection result of the power equipment. The electrical equipment image is detected through the enhanced target detection model with an improved structure, so that the feature extraction capability is enhanced, the interaction of multi-scale information is realized, the accuracy and robustness of target detection are improved, and the detection reliability of the electrical equipment is improved.

The invention discloses a method and a device for detecting and positioning surface defects of a PCB (Printed Circuit Board). The method comprises the following steps: acquiring a reference image and a sample image of the PCB; processing the light and shade distribution of the sample image to obtain a sample image with uniform illumination; correcting a pixel difference between the sample image and the reference image to enable the reference image and the sample image to achieve pixel-level alignment; carrying out defect detection index calculation on the reference image and the sample image according to regions, and detecting and positioning defects. According to the method, the detection accuracy and efficiency are improved, the hardware cost and the calculation overhead are reduced, and deployment and implementation in the actual production and manufacturing process are easy; meanwhile, due to the high automation attribute, the detection reliability and the flexible adjustment capability, the method has wide application prospects and practical value.

The invention discloses a photovoltaic cell hot spot intelligent detection method fusing multi-modal data. The method comprises the following steps: S1, collecting multi-modal data of a photovoltaic cell; s2, carrying out preprocessing on the multi-modal data; s3, constructing a photovoltaic cell sample data set; s4, extracting features of the multi-modal data; s5, fusing the multi-modal features to generate a comprehensive feature vector; s6, training and optimizing a dynamic hot spot detection model based on the space-time convolutional network; s7, identifying a hot spot area in the photovoltaic cell by using the model; s8, positioning and outputting a hot spot detection result; and S9, optimizing the detection model in real time in combination with a dynamic adaptive algorithm. According to the method, the multi-modal data fusion, the space-time convolutional network and the dynamic adaptive optimization algorithm are introduced, so that the hot spots in the photovoltaic cell can be detected and positioned more accurately and quickly, the false detection rate in a complex environment is effectively reduced, and the operation efficiency and reliability of a photovoltaic cell system are remarkably improved.

A surface cleaning identification method for titanium alloy bar rolling relates to the technical field of image identification, and comprises the following steps: graying a global image of the surface of a titanium alloy bar to generate a grayscale image, and carrying out texture feature analysis on the grayscale image to obtain a global noise index of the grayscale image; based on the global noise index, obtaining a noise difference coefficient in a neighborhood range of each pixel point, and performing adaptive filtering on the grayscale image according to the noise difference coefficient to obtain a denoised grayscale image; performing gray change coefficient analysis on the denoised gray image, dividing the gray image into an abnormal sliding window or a normal sliding window, and performing local feature merging and erosion operation on each abnormal sliding window in the denoised gray image; and obtaining the stain type corresponding to the abnormal sliding window which completes local feature combination and erosion operation. According to the method, the efficiency and the accuracy of surface cleaning identification of titanium alloy bar rolling are remarkably improved.

The invention relates to a pipeline ultrasonic guided wave detection system which comprises a plurality of detection units distributed in the circumferential direction of a pipeline, a tightening assembly is hinged between the head detection unit and the tail detection unit, a connecting assembly is hinged between the other adjacent detection units, each detection unit comprises a frame and a probe detachably fixed in the frame, and two supports are arranged on the two sides of the frame respectively. A guide rod is arranged in the support, a roller is mounted on the guide rod through a wheel carrier, a first spring is arranged between the wheel carrier and the support, the probe comprises a shell, and a bottom-layer insulating spacer, an annular permanent magnet, an upper-layer insulating spacer and a disc-shaped electromagnetic coil which are fixed in the shell, a rectangular hole is formed in the bottom wall of the shell, and strip-shaped stop blocks are arranged on the two sides of the rectangular hole respectively; the bottom insulation spacer is connected with a coupling patch through a flexible deformable body, the disc-shaped electromagnetic coil is connected with an industrial interface extending out of the shell, the flexible deformable body is made of silica gel, and the coupling patch is made of ferrocobalt. The device has the advantages of being simple in structure, convenient to operate and control, high in reliability and good in adaptability.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly relates to an unmanned aerial vehicle insulator inspection system and method based on multi-light fusion. Comprising an unmanned aerial vehicle platform and a ground control station. The unmanned aerial vehicle platform is used for carrying inspection equipment and inspecting the insulator according to a preset path; comprising an unmanned aerial vehicle body, a multi-light fusion sensor module, a data processing module, a control module and a communication module, the multi-light fusion sensor module integrates a visible light sensor, an infrared sensor and an ultraviolet sensor and is used for acquiring visible light image information, infrared thermal image information and ultraviolet image information of the insulator, potential faults can be found in the early stage through the three-light fusion technology, and important data support is provided for preventive maintenance; through integration of data of different wavelengths, insulator fault diagnosis under the three-light fusion technology reduces the possibility of misjudgment of insulator faults, and high detection reliability and high efficiency can be maintained under various environmental conditions.

The invention discloses a saw blade tooth profile defect visual inspection system based on a deep learning model, and relates to the technical field of saw blade tooth profile defect detection.The saw blade tooth profile defect visual inspection system comprises a light source capable of providing a plurality of different specific wavelengths, and a first visual inspection device shoots a saw blade and generates a digital image; pixel information in the scanning image is subjected to edge detection, texture analysis and morphological operation, and then corresponding characteristic values are extracted; and the second visual detection device is used for receiving the light of different specific wavelengths reflected from the surface of the saw blade in a certain length range, and analyzing and generating reflection spectrum data. The method comprises the following steps: comparing a feature value extracted by a first visual detection device with a set threshold range to preliminarily identify an obvious tooth profile defect area, and taking a comparison result of the feature value and the set threshold as a starting condition for carrying out second accurate detection or not; and the secondary visual detection device performs more accurate detection on the area with the preliminarily detected potential defects, and deep defect features are identified.

The invention provides an insulating part defect identification method based on a synthetic aperture ultrasonic imaging technology, which belongs to the technical field of ultrasonic defect identification, and comprises the following steps: step 01, setting parameters of an ultrasonic transducer to enable the ultrasonic transducer to emit ultrasonic waves, and attaching the ultrasonic transducer to the surface of a detected insulating part; 02, connecting the ultrasonic sensor with an acquisition card, and acquiring real-time echo data by using acquisition software; step 03, importing the data into data processing software for data processing, and performing defect imaging by applying an imaging algorithm; and step 04, performing defect analysis and positioning on the formed ultrasonic image. According to the method, the accuracy, reliability and practicability of defect detection of the insulating part are remarkably improved, and more effective technical support is provided for quality control in the industrial field. Due to the automatic and intelligent characteristics of the system, the detection efficiency is greatly improved, the uncertainty caused by human factors is reduced, and the system has remarkable practical value and popularization significance.

The invention relates to the technical field of food detection, and discloses a food pesticide residue safety detection analysis method based on machine learning, which comprises the steps of food sample collection, extract pretreatment, two-dimensional liquid chromatography-high resolution mass spectrometry combined technology analysis, mass spectrum acquisition, mass spectrum numerical data conversion, data correction and machine learning model establishment. The method comprises the following steps: identifying and classifying pesticide residue components, extracting a pretreatment extract, analyzing a to-be-detected sample, acquiring a mass spectrum, converting the mass spectrum into to-be-detected mass spectrum numerical data, correcting the data, inputting a model, identifying and classifying pesticide residues, quantitatively analyzing, comparing food safety standards, judging conformity and generating a detection report. The invention aims to improve the accuracy, sensitivity and efficiency of pesticide residue detection. Meanwhile, through technical correction and optimization, the problems of environmental interference, data deviation and the like faced by an existing method are effectively solved.

The invention discloses a distributed fluorescence temperature measurement method based on 45-degree inclined gratings, and relates to the field of temperature monitoring under high-temperature conditions of engine turbine blades and the like. 45-degree inclined grating strings are designed and manufactured, the outer sides of the 45-degree inclined gratings are coated with fluorescent materials, and the fluorescent materials are made to emit light through ultraviolet pulse excitation light; the fluorescence returns to the optical fiber through the 45-degree inclined grating, the fluorescence lifetime is measured, temperature monitoring is achieved according to the relation between the fluorescence lifetime and the temperature, the detection position is calculated through the pulse light return time, and temperature distribution of different positions is obtained. According to the distributed fluorescence temperature measurement method based on the 45-degree inclined grating, the measurement precision and the signal processing capacity are improved through a time correlation single photon counting method, the grating can be inscribed on a sapphire optical fiber, and the method is suitable for temperature monitoring in the high-temperature field of engine turbine blades and the like; and meanwhile, the device has a multi-point temperature measurement effect with high precision and strong high-temperature adaptability, and is of great significance to high-temperature environment monitoring.

The invention relates to an impurity detection system for high-purity gas and a detection control method thereof, and the impurity detection system for the high-purity gas comprises a detection path selection module, a busbar sample introduction module, a chromatography pretreatment module and a moisture detection pretreatment module, the detection path selection module is connected with confluence path units contained in the busbar sample introduction module, the chromatography pretreatment module comprises a plurality of chromatography pretreatment units, the confluence path units are respectively connected with the corresponding chromatography pretreatment units, each confluence path unit is connected with the moisture detection pretreatment module, and the moisture detection pretreatment module is connected with the detection path selection module. The detection path selection module is used for acquiring storage information in a high-purity gas storage tank, and performing corresponding chromatographic analysis steps and / or moisture detection analysis steps on the high-purity gas according to the storage information; the opportunity and parameters of gas entering the detection equipment are automatically adjusted according to the storage states of different steel cylinders, so that the accuracy of a detection result is improved.

The invention relates to the technical field of filling detection, in particular to a fruit juice filling detection system and method.The fruit juice filling detection method comprises the steps that a container image is obtained through a filling module, a preset amount of fruit juice is filled according to the container image, an audio signal in the filling process is obtained, a conveying module conveys a container, and a vibration signal in the container conveying process is obtained; the cover sealing module performs cover sealing action on the container and obtains a stress value of the mechanical arm in the cover sealing process, the filling detection unit judges whether the container filling process is abnormal or not according to the combination of the audio signal and the vibration signal, and the cover sealing detection unit judges whether the container cover sealing process is abnormal or not according to the stress value. According to the invention, the filling process and the capping process are respectively detected, and various problems in the juice filling process are identified, so that the detection accuracy of juice filling is improved.

The invention discloses an improved YOLOv8-seg-based chip surface defect segmentation model and a training method and application thereof, a network of an improved YOLOv8-seg segmentation detection model is built, a Backbone module comprises a multi-layer ShuffleNetV2 network structure, a picture is subjected to multiple convolution processing, and three effective feature layers are extracted; the Neck module adopts a ConcatSDI network structure and an EMA attention mechanism network structure, the ConcatSDI network structure is used for carrying out multi-level feature fusion on an effective feature layer output by the Backbone module to obtain a feature map, and the EMA attention mechanism network structure is used for carrying out feature weighting on the feature map; the head module is a segmentation head and outputs final feature information based on an output result of the Neck module; and after the network structure of the improved YOLOv8-seg segmentation detection model is constructed, training, verification and testing are carried out. According to the method, the improved yov8-seg segmentation detection model is used for accurately segmenting and detecting the surface defects of the chip, and particularly, the detection precision can still be ensured when the method faces complex backgrounds and low-contrast defects.

The invention discloses a defect detection method and equipment based on an NV color center, and relates to the technical field of nondestructive testing. Comprising the following steps: preprocessing detection data obtained by magnetic flux leakage detection based on a diamond NV color center, and filtering out abnormal signals; filtering and denoising processing is carried out on the preprocessed data; and judging a defect signal according to the processed data and calculating the depth and width of the defect. According to the method, the reliability and the accuracy of detection data are ensured by establishing data preprocessing and filtering and denoising processes, the signal quality is improved, and interference is reduced; nV color center detection is carried out based on the magnetized leakage magnetic field, grading processing is carried out on detection data, and a corresponding data processing algorithm is adopted, so that the spatial resolution and sensitivity of detection are improved; an evaluation system is established through defect depth and width parameters, and the detection efficiency is improved.

The utility model discloses a brushless motor Hall plate detection device, which comprises a test board and a Hall plate arranged at the top of the test board, and is characterized in that the side edge of the test board is provided with a press-down driving mechanism, and the press-down driving mechanism is provided with a display control mechanism; the output end of the pressing driving mechanism is provided with a test piece, and the test piece can be in contact with the detection point under the driving of the pressing driving mechanism; the test piece can be driven by the pressing driving mechanism to do reciprocating motion, and the motion direction of the test piece points to the detection point; according to the utility model, through the arrangement of the downward pressing driving mechanism, the test piece can be driven to move downwards horizontally, so that the test contact of the test piece can be in stable contact with the detection point of the Hall plate, thereby replacing the operation of manually connecting the universal meter with the Hall plate by detection personnel, greatly reducing the labor intensity of the detection personnel, and improving the detection efficiency.

The invention discloses a vehicle-mounted camera visibility inversion method and device, a medium and electronic equipment, and the method comprises the steps: obtaining an image frame data set shot by a vehicle-mounted camera, carrying out the preprocessing, and constructing a visibility inversion data set; a visibility inversion model is constructed, and the visibility inversion model takes the visibility true value as output and is obtained by training and optimizing network weight; and inputting a picture shot by a vehicle-mounted camera in real time into the optimized visibility inversion model to obtain a current visibility true value. According to the method, the corresponding visibility can be accurately estimated, and high-precision visibility real-time inversion is realized.

The invention discloses a wafer clamping arm and a system, a method and a device for controlling the wafer clamping arm, the wafer clamping arm comprises a clamping arm, a sensor and peripheral support and a transmission structure, the sensor and peripheral support is connected with the clamping arm and comprises a receiving sensor support for holding and clamping the receiving end of a correlation sensor, and the transmission structure is connected with the sensor and peripheral support. The transmitting sensor support clamps the transmitting end of the correlation sensor, and the bottom of the receiving sensor support and the bottom of the transmitting sensor support are each of a large hydrophobic included angle structure; the transmission structure is configured to drive the clamping arm to lift the wafer to be processed out of the semiconductor wet process environment through a control instruction, and the control instruction is obtained under the condition that the correlation sensor determines that the clamping arm clamps the wafer to be processed. Therefore, when the wafer clamping arm can clamp the wafer to be lifted out of a wet grinding environment, the water drop path can be shunted and condensed, so that the water drops can be quickly separated and drop, and the detection reliability of the sensor is improved.

The invention provides a textile fabric anti-ultraviolet performance detection device, and relates to the technical field of fabric detection, the textile fabric anti-ultraviolet performance detection device comprises an unwinding mechanism, a first traversing mechanism, a second traversing mechanism and an adjusting traversing mechanism. The unwinding mechanism is used for unwinding and winding the to-be-detected cloth; cloth horizontally penetrates through the first transverse penetrating mechanism and the second transverse penetrating mechanism. The adjusting and penetrating mechanism has a first state and a second state which can be switched, so that the cloth can horizontally extend in the first state, and a downward arc-shaped recess and an upward arc-shaped bulge are formed in the second state. The linear ultraviolet sensor detection mechanism is used for detecting the ultraviolet transmittance of the cloth in the horizontal state; and the arc ultraviolet sensor detection mechanism is used for detecting the ultraviolet transmittance of the arc part of the cloth. The method can accurately detect the ultraviolet permeability of the fabric in different forms, is suitable for quality evaluation of anti-ultraviolet functional textiles, and reduces the quality defects after the textile fabric leaves a factory.

The invention discloses a nuclear-biochemical automatic monitoring system and a monitoring method, and relates to the technical field of chemical detection, the nuclear-biochemical automatic monitoring system comprises an environment probe combination, a data processing unit, an indication particle distribution device and an alarm unit; according to the invention, a dynamic balance model is constructed through the environment probe and the data processing unit, so that complex environment interference can be effectively dealt with, signals can be accurately distinguished, and the detection accuracy and reliability are improved; the indicating particle spreading device solves the problem of malicious disguise article detection by utilizing the interaction of materials; a low-cost environmental probe combination is adopted, so that the cost is reduced and the reliability is ensured; the data processing unit processes gas concentration data by combining a moving average method with a Kalman filtering algorithm, and performs classified counting on microorganisms through a deep learning image recognition technology; multiple technologies cooperate to construct a comprehensive detection mechanism, strategies can be flexibly adjusted, maintenance optimization and technology fusion are facilitated, and safety detection of industries such as postal service and the like is powerfully guaranteed.

The invention provides a fault detection system and a vehicle, the fault detection system comprises a plurality of conversion modules, a plurality of detection modules, a plurality of signal processing modules and a control module, and each conversion module can output a corresponding electric signal based on the connection state of a low-voltage detection connector; the plurality of detection modules receive the electric signals and output corresponding detection signals based on the electric signals; a plurality of signal processing modules receive the detection signals; and the control module receives the detection signals processed by the plurality of signal processing modules, and determines the on-off state of the high-voltage interlocking loop and the connection state of the plurality of low-voltage detection connectors based on the plurality of detection signals. According to the system, the connection state of the low-voltage detection connector can be converted into a high-low level detection signal which can be identified by the control module, so that the control module can accurately know the fault position and the on-off state of the high-voltage interlocking loop based on the detection signal, the detection and troubleshooting efficiency is improved, and the detection reliability is relatively high.

The invention discloses an ion exchange resin plate flatness detection tool, and relates to the technical field of flatness detection.The ion exchange resin plate flatness detection tool comprises a detection box, two supporting plates are symmetrically and fixedly connected to the side wall of the bottom end of the detection box, a first groove is formed in the inner wall of one end of the detection box, and a first electric sliding rail is fixedly connected to the inner wall of the first groove; a first sliding plate is slidably connected to the side wall of one end of the first electric sliding rail. The inclination angles of the first outer wall clamping plate, the second outer wall clamping plate and the inner wall clamping plate can be adjusted according to the inclination angles of the four side edges of the ion exchange resin plate, so that the inner wall and the outer wall of the ion exchange resin plate can be conveniently fixed; meanwhile, the direction of the laser detector can be adjusted according to the positions and inclination angles of the inner wall and the outer wall of the ion exchange resin plate with four side edges, it is ensured that the detection end of the laser detector can be perpendicular to the corresponding side wall of the ion exchange resin plate all the time, and accurate detection of the surface flatness of the ion exchange resin plate is facilitated.

The invention discloses a proportional electromagnet and an electromagnetic valve. The technical problems that an existing proportional electromagnet is large in size, high in assembly precision requirement and poor in armature position detection precision are mainly solved. The magnetic shell is provided with a cylindrical cavity along the central axis; the iron core is relatively fixed in the cylindrical cavity and is provided with an armature moving cavity along the central axis; the winding is wound between the magnetic shell and the iron core; the armature is movably embedded in the armature moving cavity; the core cap is arranged at one end of the iron core and is used for sealing the armature moving cavity; the magnetic sensor is embedded in the core cap and is used for detecting the position of the armature; and the thickness direction of the magnetic sensor is the same as the movement direction of the armature. The proportion electromagnet has the advantages of being small in size, wide in application range, high in armature action position control precision, easy to assemble, high in yield and the like.

The invention discloses a state detection method and device for a transformer bushing in a power grid and electronic equipment. The method comprises the steps that an optical image of a transformer bushing to be subjected to state detection in a power grid is acquired, and the optical image at least comprises a visible light image and an infrared image of the transformer bushing; feature extraction is carried out on the optical image, a target image feature descriptor of the optical image is obtained, and the target image descriptor is used for representing edge contour image features of the optical image; based on the target image feature descriptor, fusing the visible light image and the infrared image in the optical image to obtain a fused image; and analyzing the fused image to obtain a state detection result of the transformer bushing. According to the invention, the technical problem of low efficiency of state detection of the transformer bushing in the power grid in the prior art is solved.

The utility model discloses a resistance detection device, which is especially suitable for packaging a miniature resistor, the resistance detection device comprises a base, a circuit board and a jig, the base is provided with a fixing structure for fixing the circuit board, the circuit board is provided with a test point, the jig comprises a pedestal and a pressing plate, and the pressing plate is provided with a pressing groove. The base is stacked on the circuit board and fixed on the base, the base is provided with a bearing area used for placing a resistor sample piece, the base is further provided with a through hole corresponding to the test point position in a penetrating mode, the pressing plate is movably arranged on the base and used for pressing the resistor sample piece so that the detection contact arranged on the resistor sample piece can be in contact connection with the test point position of the circuit board through the through hole, and detection of the resistor sample piece is achieved. According to the design, the accuracy of testing is improved, the device is suitable for packaging the miniature resistor, the design of the pressing plate is more stable and durable than that of a traditional spring needle, the maintenance cost is reduced, the operation is simple and convenient, and the detection efficiency and reliability of packaging the miniature resistor are improved.