4182results about How to "Achieve positioning" patented technology

The invention discloses an SCL field safety accident analysis and early warning method and system, a medium and a program product, and the method comprises the steps: carrying out the distributed cleaning and feature extraction of multi-source heterogeneous monitoring data, and obtaining a multi-dimensional time sequence feature flow; constructing a dynamic association network of a security state; identifying a key influence factor chain, and establishing a semantic analysis model; constructing a safety threshold system; evaluating the multi-dimensional time sequence characteristic flow by using a safety threshold system; when the multi-dimensional time sequence characteristic flow triggers early warning, performing tracing analysis by using a semantic analysis model to obtain a root event triggering the early warning, and simulating the development trend of the root event based on a preset evolution model; determining a predicted risk range and a risk type according to the tracing cause analysis result and the accident development trend; and the corresponding accident early warning scheme is matched in the preset database according to the risk range and the risk type, and the accident early warning scheme is sent to the monitoring terminal, so that the accuracy of judgment and classified early warning of different types of safety accidents is improved.

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

The invention relates to the technical field of spatial positioning, and discloses a spatial positioning method based on multi-modal visual fusion. According to the method, mutual calibration among a laser radar, a short-focus camera and a long-focus camera is realized through a sensor joint calibration algorithm, a spliced image acquired by a bifocal lens of a # imgabs0 # angular color camera is processed, image data in an electric power operation scene shot by a joint calibrated camera system is read, the image is preprocessed, and the image data is acquired. Target detection is carried out on the preprocessed image through a deep learning method, identification and positioning of a target object are realized, a laser radar emits a laser beam to detect the target to obtain data, point cloud data are processed in combination with a visual program, and point cloud mapping, fitting reconstruction and position calculation are carried out on image information obtained by a # imgabs1 # angular color camera, so that the target object is identified and positioned. By means of the method, the precision of power operation complex scene space positioning is improved, and the position detection and recognition error is controlled to be 10 mm or below.

The invention provides a medical image segmentation method, which comprises the following steps: acquiring PET indirect development original image data, and preprocessing the image, including denoising, contrast enhancement and gray level normalization, to obtain a preprocessed PET image data set; designing segmentation middleware, integrating a plurality of segmentation algorithms including threshold segmentation, region growth and level set methods, and selecting proper segmentation algorithms for different types of lesion regions according to feature vector analysis results; pET image data with labels are generated through the simulation technology, a training data set is constructed in combination with real case data, the sample size is expanded through the data enhancement technology, and a large-scale data set used for algorithm training is generated; and the segmentation result of the U-Net is refined by using the segmentation middleware, the lesion area edge is focused on, and the lesion boundary is accurately positioned through an edge detection algorithm to obtain a fine segmentation result.

The invention discloses a dynamic and static target continuous positioning and tracking method and system based on millimeter waves. The method comprises the steps that dynamic point cloud data and static point cloud data reflected by a target are acquired through a millimeter wave radar sensor, the dynamic point cloud data reflect target motion state information, and the static point cloud data reflect vital sign information of the target in a static state; fusing the dynamic point cloud data and the static point cloud data to obtain fused point cloud data, and clustering the fused point cloud data by using a density-based clustering algorithm by taking a reflection point for distinguishing a target from an environment as a target to obtain a clustering result; and on the basis of the clustering result, executing continuous positioning and tracking on the plurality of targets to obtain a tracking result. The method can effectively extract the dynamic and static information of the target, achieves the continuous positioning and precise tracking of multiple targets, and is higher in practicality and application value.

The invention discloses a marketing data link abnormity diagnosis method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of multi-source data format difference, lack of real-time anomaly detection mechanism and low data management efficiency exist in anomaly diagnosis. The method comprises the steps that multi-source heterogeneous data of a marketing data link is collected in real time, and data cleaning, missing value filling and abnormal value processing are completed; extracting statistical characteristics of the link data, performing clustering analysis on the data, and generating a self-supervision label; constructing a support vector machine classification model, and detecting normal and abnormal data in the link; optimizing the anomaly detection model through a deep neural network; and realizing interpretability analysis and decision support of anomaly diagnosis by using a multi-classification result generated by the LSTM model and a self-supervised learning strategy. According to the invention, data link abnormity diagnosis is efficiently and intelligently realized, and data quality management, abnormity detection precision and service decision support capability are improved.

The invention discloses a multi-class defect detection method and device based on a variational auto-encoder and a denoising network, and the method comprises the steps: obtaining an industrial product surface defect image, carrying out the preprocessing, and constructing a training set and a verification set; the training process of the method is divided into two stages: in the first stage, a preprocessed training image is input into a variational auto-encoder for training, feature distribution of the image is learned, and features of a normal image are captured by reconstructing the image; in the second stage, a denoising network and a feature guiding module are used for conducting secondary reconstruction on image features in the feature space of the variational auto-encoder, and therefore the reconstruction capacity and the multi-class reconstruction capacity of the model are enhanced; in the reasoning stage, multi-scale features of an original image and a reconstructed image are extracted through a feature extractor, feature comparison is carried out, and an abnormal scoring graph is generated. According to the method, normal information is reserved while the reconstruction capability of the variational auto-encoder is enhanced, and defect missing detection caused by reconstruction failure is effectively reduced.

The invention discloses a cardiac interventional operation scene reconstruction method based on 3D ultrasonic imaging rendering. The method comprises the following steps: collecting a preoperative computed tomography or magnetic resonance imaging image as prior information; the real-time 3D ultrasonic image sequence is preprocessed; the method comprises the following steps of: performing voxelization on an image by combining prior information, converting image data into voxel representation in a three-dimensional space, and creating an initial 3D Gaussian function set; parameters of the initial Gaussian function are optimized through an iterative optimization algorithm, so that the difference between the reconstructed image and the actual image is minimized; in combination with interventional catheter shape constraint, adaptive density control of a 3D Gaussian function is alternately carried out; and rendering tiles one by one by adopting a Gaussian sputtering technology based on the tiles to realize real-time three-dimensional scene reconstruction and display. According to the method, prior information and interventional catheter shape constraints are combined, various structures in an operation scene are represented and simulated by using a 3D Gaussian function, and efficient rendering is realized through a tile-based sputtering technology.

The invention discloses a distribution network multi-parameter data fusion fault positioning method and system, and the method and system achieve the omnibearing monitoring of a power distribution network fault through the collection of multiple types of data, such as electrical signals, video images, meteorological information and environmental parameters. Compared with a traditional fault positioning method of a single information source, the system can reflect the fault condition more truly, and the positioning accuracy and reliability are remarkably improved. Machine learning or a deep learning model is utilized to analyze fusion data, fault types and positions are automatically identified, intelligent diagnosis is realized, manual intervention is reduced, the operation and maintenance cost is reduced, and the processing efficiency and accuracy are improved. The real-time data acquisition and transmission technology guarantees the system to monitor in real time and rapidly alarm potential faults. According to the method, historical data and an expert knowledge base are combined, fault causes are deeply analyzed, accurate information and maintenance suggestions are provided for maintenance personnel, operation and maintenance decisions are optimized, and the maintenance efficiency and quality are enhanced. The system provides an efficient and intelligent solution for distribution network fault positioning.

The invention relates to the technical field of pelletizers, and discloses a sodium alginate powder pelletizer which comprises a shell, a feeding mechanism, a pelletizing structure and a driving mechanism are arranged in the shell, the feeding mechanism feeds powder materials to the pelletizing structure, the pelletizing structure comprises a functional disc, a displacement rotary disc and a discharging mechanism, the displacement rotary disc is rotationally arranged between the functional disc and the discharging mechanism, and the driving mechanism drives the functional disc to rotate. A displacement table and an impact mechanism are arranged in the shell, the driving mechanism drives the displacement table and the impact mechanism to move up and down at the axis of the granulation structure, the displacement table drives the displacement turntable to rotate, and the impact mechanism impacts the powder into granules. Through cooperation of the functional disc, the displacement rotary disc and the abutting plate of the granulation structure, quantitative feeding, impact forming and rapid discharging are synchronously carried out, the granulation efficiency is improved, the impact forming function is achieved through the impact mechanism, and formed particles are more compact.

The invention provides a calibration method for an inertial measurement unit of an airborne photoelectric platform, and belongs to the technical field of precision optical machine installation and adjustment, and the method comprises the following steps: S1, detecting and adjusting the intersection degree of the position of an azimuth pitch axis system of the airborne platform; s2, calibration and precision detection are carried out on a rotating shaft of an azimuth pitch axis system of the airborne platform; s3, detecting and adjusting the perpendicularity of the azimuth pitch axis system of the airborne platform; s4, carrying out orthogonality detection and adjustment on the optical axis and the shaft system of the photoelectric platform; and S5, calibrating the coordinate system deviation of the inertial measurement unit. According to the method provided by the invention, through a series of optical machine adjustment steps, the shafting precision adjustment of the airborne platform and the deviation calibration of the inertial measurement unit are completed at the same time, the orthogonal coordinate system adjustment and positioning of the shafting and the optical axis of the airborne photoelectric platform are realized, and the deviation value of the inertial measurement unit and the orthogonal coordinate system is tested. And the information is fed back to a data processing subsystem to realize geometric space correlation calculation.

The invention relates to a multi-modal target identification and tracking prediction method based on a laser radar and a binocular camera, and belongs to the field of robot / unmanned system perception identification. Comprising the following steps: preprocessing a target two-dimensional image acquired by a binocular camera and target point cloud data acquired by a laser radar to obtain a preprocessed two-dimensional image and point cloud data; s2, performing two-dimensional target detection and tracking detection based on the preprocessed two-dimensional image to obtain a two-dimensional target detection and tracking result; s3, performing three-dimensional target detection based on the preprocessed point cloud data and the two-dimensional target detection and tracking result to obtain a three-dimensional detection result; and S4, based on the pixel coordinates in the two-dimensional target detection and tracking result and the pixel coordinates of the vertexes of the target point cloud boundary in the three-dimensional detection result, target fusion tracking prediction is carried out, and the target ID, the position, the category and the target point cloud boundary are obtained. And accurate identification and tracking of the target are realized.

The invention provides an industrial anomaly detection method and device based on a multi-modal joint memory pool, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: carrying out the multi-modal feature extraction of input RGB image data and point cloud data through a pre-training network; feature mapping and alignment operations are introduced to align point cloud features with RGB image features in space; two groups of intra-modal codec and two groups of cross-modal codec are introduced to perform intra-modal and cross-modal compression and reconstruction on the obtained RGB image features and point cloud features; training four groups of codecs by adopting a cosine similarity function; constructing a multi-modal feature memory pool; and adopting a nearest neighbor algorithm to calculate an abnormal score. According to the method, the RGB image and point cloud input are comprehensively utilized, abnormal areas of different scales and different forms can be accurately detected, and the universality and robustness are high.

The invention belongs to the technical field of humanoid robot control, and particularly relates to a humanoid double-arm robot valve tightening method and system, and the method comprises the steps: obtaining a valve image data set based on a binocular vision camera; labeling the valve image data set to generate a labeled data set; performing enhancement processing on the annotation data; recognizing and positioning the valve based on a preset binocular vision algorithm and YOLO, and recognizing the position, the frame and the center point of the valve; the two-arm robot is moved to the two sides of the valve, and the positions and poses of the tail ends of the two arms in other directions except the clamping direction are kept consistent; clamping control is carried out based on a self-adaptive variable admittance controller; the valve is driven to rotate through rotation of the joint at the tail end of the double-arm robot, so that the valve is tightened. According to the invention, rapid and accurate valve identification is realized, and the reliability and flexibility of the system in practical application are greatly enhanced.

The invention relates to the technical field of PCB etching, in particular to a PCB etching device and method, and the device comprises a box body which is used for containing an etching solution; the conveying mechanism is used for vertically conveying the PCBs; the spraying mechanism is used for spraying etching liquid to the two sides of the PCB; the dehumidification mechanism is used for erasing residual etching liquid on the PCB; the reciprocating mechanism is used for driving the spraying mechanism to do reciprocating motion; the conveying mechanism comprises two guide wheel devices which are symmetrical up and down, and one guide wheel device is connected with a driving device. According to the PCB etching device and etching method, the PCB can be conveyed in a vertical state by arranging the conveying mechanism, so that the contact time of the etching liquid and the two sides of the PCB is the same, the etching effects of the two sides of the PCB are more synchronous, the etching liquid can be sprayed to the two sides of the PCB at the same time through the spraying mechanism, the PCB can receive the etching liquid at the same time, and the etching efficiency of the PCB is improved. And the etching synchronism of the two sides of the PCB is ensured, so that the etching efficiency and quality are improved.

The invention discloses a Mura defect detection method and device of a TFT-LCD display screen and electronic equipment, and relates to the field of display screens. The method comprises the following steps: acquiring an original image of a TFT-LCD display screen to be detected; preprocessing the original image to obtain a target image, the preprocessing including image noise reduction and image enhancement; based on the target image, constructing a multi-scale feature map of the Mura defect, the multi-scale feature map comprising a local mean map and a local variance map under multiple scales: performing adaptive weight fusion on the local mean map and the local variance map to obtain a confidence map of the Mura defect; according to the confidence map, utilizing morphological operation to extract a suspected defect area; performing shape analysis and contrast analysis on the suspected defect area to obtain shape features and contrast features of the suspected defect area; and screening out a target Mura defect region from the suspected defect region based on the shape feature and the contrast feature. By implementing the technical scheme provided by the invention, the accuracy of Mura defect detection is improved.

The invention relates to the technical field of mechanical automation, and discloses a novel part material intelligent production line which comprises a feeding truss provided with 3D scanning equipment and used for recognizing and positioning part materials; the beveling machine is provided with an X-axis moving assembly, a Y-axis moving assembly and a Z-axis lifting assembly and is used for adjusting the position of a cutter so as to meet the machining requirements of shapes, outlines and thicknesses of different parts; the chip removal system comprises a spiral chip removal machine and a plate chain chip removal machine and is used for collecting and conveying chips generated in the machining process; the feeding and discharging truss comprises an intelligent classification module and is used for recognizing the machined parts and placing the machined parts to designated positions according to categories. And the central control system is used for integrally scheduling and monitoring the production process. By means of the 3D camera system with the depth sensing and image analysis functions, high-precision part material recognition and positioning can be achieved at the initial stage of the production process. The accurate initial treatment lays a foundation for subsequent processing links, and ensures the accuracy and repeatability of the whole processing flow.

The invention discloses a point switch part detection method and device based on a YOLOv8 model and a medium, and the method comprises the steps: marking a target part in a point switch part image, and generating a target image; the method comprises the following steps: replacing an original backbone network darknet53 of a YOLOv8 model with a Swin transfer network, and introducing a residual attention module into the Swin transfer network; the method comprises the following steps of: constructing an improved YOLOv8 model by combining a spatial pyramid pooling layer on the basis of a Swin transformer network and replacing an original loss function CIoU of the YOLOv8 model with a new loss function Focal-EIoU at the same time; and inputting the target image into the improved YOLOv8 model, and generating a prediction result. And accurate detection of the target part is realized based on the detection result output by the improved YOLOv8 model.

The invention discloses a gas cylinder storage leakage point positioning detection method and system, and the method comprises the steps: building a gas cylinder leakage model under the conditions of no wind and wind, simulating a leakage process through a gas turbulence diffusion model, and introducing a sensor array to detect a gas leakage point; an adaptive filtering algorithm AF is introduced to carry out noise removal on data detected by the sensor array; extracting time difference information hidden in a gas sensor signal by using continuous wavelet transform (CWT) and a modulus maximum value method, judging the position of an optimal leakage point, and obtaining positioning information of a certain time point; a window function is introduced into a Kalman filtering algorithm, multiple sets of positioning information at different time points are obtained through processing of the improved Kalman filtering algorithm, interference of measurement invalid signals not in the range of the window function is filtered out, and the optimal positioning point is estimated. Compared with the prior art, the sensor array is introduced to realize leakage detection and positioning, a window function of a related time point is introduced into Kalman filtering, and the detection accuracy is effectively improved.

The invention relates to a hazardous substance identification method fusing multi-source information collected by an unmanned aerial vehicle, and belongs to the technical field of hazardous substance detection. Optical detection data and magnetic detection data collected by the unmanned aerial vehicle for the first time are identified, invalid data are eliminated, and an optical detection position point and confidence and a magnetic detection position point and confidence are obtained; corresponding target points are obtained through matching on the basis that all distance combinations of the optical detection position points and the magnetic detection position points are smaller than a first distance threshold value, a batch of hazardous object target points are found out through a first preset threshold value, secondary detection within a narrowing range is carried out on the other target points, and the remaining hazardous object target points are found out; the problems that in the prior art, existing detection efficiency and reliability are not high, and analysis and processing of detection results are complex are solved.

The invention relates to the technical field of obstacle detection, in particular to a laser point cloud-based obstacle framing method in a converter station scene, which comprises the following steps of: based on a converter station environment, performing three-dimensional scanning through a laser radar, collecting point cloud data, performing noise filtering on the data, performing spatial alignment of the data, and obtaining processed point cloud data. According to the invention, three-dimensional scanning is carried out through the laser radar, the scanning angle and the resolution are adjusted, the coverage rate and the precision of the point cloud data are optimized, the point cloud data are mapped to the adjacent grid center, the data structure is simplified, the point cloud data are horizontally cut, the linear features of the electric wire are identified, and the obstacle identification process is optimized. Through combination of spatial statistical analysis and clustering analysis, the accuracy and the meticulous degree of obstacle recognition are improved, and the obstacles are framed by constructing a bounding box of each obstacle, so that three-dimensional space positioning and form recognition of the obstacles are realized.

The invention belongs to the technical field of transformer manufacturing, and discloses a shearing and stacking integrated lamination method for a large transformer iron core, which comprises the following steps: sequentially shearing and punching a roll material into silicon steel sheets according to the structure and specification of each layer of the iron core, sequentially conveying the silicon steel sheets formed by shearing and punching to a lamination station, and stacking the silicon steel sheets through translation, rotation and positioning operation in sequence. The invention further discloses a large transformer iron core shearing and stacking integrated lamination device which comprises a fixed door frame, at least two conveying assemblies located on the same straight line, a first lamination assembly, a rotation assembly, a second lamination assembly, a material stacking platform, a positioning assembly and a material pressing assembly. The stacking operation steps are simplified, manual operation links are reduced, the production efficiency and quality are improved, positioning of the positioning holes of the silicon steel sheets of different sheet types can be achieved, and the flexibility is good.

The invention, which relates to the technical field of power construction, discloses a live-line live-line operation parallel groove clamp installation tool and a use method thereof, and the tool comprises a support, a fastening assembly, a rotating assembly, a clamping assembly, a drainage wire pre-fixing assembly, a positioning assembly and a control assembly. A pin shaft eyebolt in the spring pin shaft mechanism is pulled open and released, so that a pin shaft enters a circumferential positioning hole of a flange, angle adjustment of an insulating rod and an installation tool is achieved, the operation requirements of different working angles are met, the pre-tightening mechanism is driven through the elastic pipe clamp and the steering engine, pre-fixing and releasing of a drainage wire are achieved, and the working efficiency is improved. Through the fixed end guide rail and the movable end guide rail, positioning and guiding of the transmission conductor are realized, the transmission conductor is accurately guided into the parallel groove clamp, and the reliability of installation operation is effectively ensured.

The invention belongs to the technical field of automobile part welding, and particularly relates to an automobile part machining-based welding system which comprises a welding table, a supporting plate is fixedly connected to the middle of the rear side of the upper end face of the welding table, two first sliding rods are slidably connected to the upper side of the supporting plate, and a fixing disc is fixedly connected to one end of a rotating frame. A plurality of clamping blocks are connected to the fixing disc in a sliding mode, and the welding table is provided with a follow-up type splash-proof slag knocking mechanism used for knocking welding slag at a welding seam into pieces. According to the following type splash-proof slag knocking mechanism, caked welding slag on the periphery of a welding seam can be knocked into pieces, follow-up personnel can conveniently clean the welding slag on the welding seam, the cleaning difficulty is reduced, in the striking process, the first sponge pads on the two sides are pressed on the outer wall of a pipeline through the pressing blocks, the welding slag can be intercepted by the first sponge pads when splashing, and the welding slag is prevented from splashing. Therefore, welding slag can be prevented from splashing to areas outside the welding seam, and unnecessary cleaning links are avoided.

The invention discloses a workpiece flaw detection method which comprises the following steps: acquiring point cloud data of a to-be-detected workpiece, and constructing a sample workpiece library at least comprising flaw sample workpieces according to the point cloud data of the to-be-detected workpiece; constructing and training a YOLO neural network model based on the sample workpiece library to obtain a flaw detection model; performing flaw detection on the to-be-detected workpiece by using the flaw detection model to obtain a flaw mark; according to the defect mark of the workpiece to be detected, PointNet + + feature extraction, registration processing and difference detection are carried out on the point cloud data of the workpiece to be detected, and a defect positioning result of the workpiece to be detected is obtained. According to the technical scheme provided by the invention, the workpiece is detected by constructing the YOLO neural network model, and the workpiece flaws are roughly positioned; then, a PointNet + + model, a point cloud registration algorithm and difference detection are utilized to carry out further workpiece flaw accurate positioning on the three-dimensional point cloud data of the workpiece; the workpiece is detected twice, so that tiny flaws of the workpiece can be accurately detected.

The invention provides a software system dependency defect tracking method and system based on a graph database, and relates to the technical field of software, and the method comprises the steps: parsing a source code into an abstract syntax tree, and extracting a dependency; constructing a dependency graph data model; establishing a defect mode feature library; performing pattern matching in the graph data model to identify potential defects; calculating a defect influence range and severity; and generating a defect tracking report, including defect priority ranking, complete call chain construction, defect detailed information recording and optimization suggestion generation. According to the method, the dependency relationship defect in a software system can be effectively identified and tracked, and the software quality is improved.

The invention relates to the technical field of material processing and transportation, in particular to a material positioning, processing and transporting device based on visual inspection, which comprises a machine base, a feeding assembly, a processing platform and a transporting assembly are mounted at the upper end of the machine base, a material body is conveyed on the feeding assembly, and the transporting assembly comprises a clamping mechanism and an adjusting mechanism. The clamping mechanism comprises a sliding frame, sliding blocks, connecting frames, limiting plates and clamping plates, the sliding frame is installed at the upper end of the machine base, the sliding blocks are slidably connected to the circumferential face of the sliding frame, the connecting frames are slidably connected into the sliding blocks through first guide rods, connecting rods are rotationally connected into the ends, away from the sliding blocks, of the connecting frames, and the limiting plates are installed at the rear ends of the connecting rods. According to the material conveying device, the servo motor is controlled to operate, the limiting plate is driven to rotate through the connecting rod, and then the effect that the front face and the back face of a material body can be adjusted on the basis of visual inspection in the material body conveying process is achieved.

The invention relates to the technical field of hydrogen storage bottle production, in particular to a preparation process of an aluminum alloy casting hollow pipe and an aluminum bar for a hydrogen storage bottle, and the device comprises a base, two positioning seats are fixedly mounted on the upper side of the base, the hollow pipe is placed between the two positioning seats, and an adjusting mechanism is arranged on the base. A fixing frame is fixedly installed on one side of the base, a rotating frame is rotationally connected to the fixing frame, an adjusting frame is arranged on the rotating frame in a sliding fit mode, an end face processing mechanism is rotationally connected to the adjusting frame, and side wall processing mechanisms are arranged at the other two ends of the adjusting frame. The cutting end of the hollow pipe can be trimmed, the cut edge is trimmed, flatness and smoothness of the cut edge are guaranteed, and the sealing performance and strength of the welding or connecting part of the hydrogen storage tank can be improved.

The invention relates to a direct current GIL / GIS surface charge and surface discharge on-line monitoring method, which is used for a direct current GIL / GIS, and comprises the following steps: S1, carrying out the corresponding division of the surface area of an insulator based on a number; s2, establishing a GIL / GIS model, and performing corresponding numbering and division; s3, the simulated induced voltage is calculated, and a contribution coefficient matrix is calculated based on the simulated induced voltage; s4, on the basis of the induction current, actual induction voltage is obtained through calculation in combination with the measurement resistance, and actual surface charge density is obtained through calculation; and S5, judging the position area of the creeping discharge point according to the amplitude of the pulse current on each segmented electrode. Compared with the prior art, the direct-current GIL / GIS basin-type insulator surface charge online measurement device has the advantages of realizing online measurement of the direct-current GIL / GIS basin-type insulator surface charge and the

The invention discloses a liquid cooling pipeline welding device with an automatic positioning function, and relates to the technical field of liquid cooling pipeline welding. Comprising a rotary clamping assembly, a base table, a positioning clamping assembly, a positioning detection device, a sliding base and a welding machine, the height difference of two sections of pipelines is converted into an electric signal capable of being monitored through a centering detection assembly, and a control system starts the rotary clamping assembly and the positioning clamping assembly according to the electric signal to conduct deviation rectification on the pipelines; therefore, the purpose of automatically positioning and rectifying the deviation of the pipeline is achieved; the oil stain detection box is used for converting the influence of oil stain on the friction force of the positioning detection wheel and the outer surface of the pipeline into electric signals capable of being monitored, and the purpose of detecting the oil stain on the outer surface of the pipeline is achieved by analyzing the interval time and size of the electric signals. The flatness detection assembly is used for converting the concave-convex condition of the section of the pipeline into an electric signal, whether the section is flat or not is judged according to the receiving state of the electric signal, and therefore the purpose of detecting the flatness of the section of the pipeline is achieved.