7321results about How to "Rapid positioning" patented technology

The invention discloses a knowledge question-answering system based on a large language model. The method comprises the steps that a power operation and maintenance knowledge base module obtains data from a data source for preprocessing, constructs a power operation and maintenance field knowledge graph, converts text content into semantic vectors and integrates the semantic vectors to form a power operation and maintenance knowledge base; the problem understanding module is used for identifying and analyzing problems and converting the user problems into semantic vectors; the retrieval and reordering module is used for performing semantic retrieval in the electric power operation and maintenance knowledge base, recalling knowledge information related to question semantics, and ordering retrieval results based on semantic relevance, knowledge confidence and information importance; and the answer generation module is used for integrating the knowledge information and the user question into input of a large language model, generating prompt information, and generating an answer in a natural language form according to the prompt information by adopting the large language model based on data fine adjustment in the field of power operation and maintenance. According to the scheme, the deep learning technology is used for understanding semantics of user questions, and accurate retrieval is carried out in the knowledge base.

The invention relates to the field of vehicle data analysis, in particular to an automobile data analysis method and system based on an intelligent diagnostic instrument. The method comprises the following steps: acquiring vehicle multi-modal monitoring data based on an intelligent diagnostic instrument; operation power change calculation is conducted on the vehicle multi-mode monitoring data, time sequence state feature mining is conducted, and vehicle time sequence state features are obtained; carrying out multi-frequency noise reduction optimization on the vehicle multi-modal monitoring data, and carrying out normalized periodic evolution mining so as to generate normalized vibration periodic evolution characteristics; potential feature association deep mining is carried out on the vehicle time sequence state features and the normalized vibration period evolution features, then association graph modeling is carried out, and a vehicle multi-modal feature knowledge graph is constructed; and abnormal parameter deviation calculation and fault positioning traceability are carried out on the real-time operation parameters of the vehicle based on the vehicle multi-modal feature knowledge graph, so that a potential abnormal fault positioning point is obtained. According to the invention, efficient and accurate vehicle fault risk prediction analysis is realized.

The invention relates to the technical field of data analysis, and discloses a business data analysis method, device and equipment based on big data and a storage medium, the method comprises the following steps: collecting and preprocessing original log data to obtain standardized log data, and performing adaptive multi-modal feature extraction to obtain comprehensive feature representation; inputting a heterogeneous graph neural network to carry out anomaly detection and multi-level root cause analysis to obtain a hierarchical anomaly event; executing task scheduling and resource allocation of reinforcement learning to obtain a task allocation scheme; performing deep learning enhanced root cause reasoning and knowledge graph construction on the task allocation scheme to obtain a system state diagram and a causal relationship; and generating a multi-dimensional task data analysis report. According to the method, comprehensive analysis, anomaly detection, root cause reasoning and intelligent scheduling of log data are realized. The reliability and performance of the system can be improved, and more intuitive and accurate decision support can be provided for operation and maintenance personnel.

The invention discloses a fault detection method and device for secondary equipment of a transformer substation. The method comprises the following steps: acquiring real-time operation parameters of the secondary equipment of the transformer substation, inputting the real-time operation parameters into a pre-trained graph neural network model to obtain equipment operation characteristics, and determining an equipment state vector according to a plurality of equipment operation characteristics in a continuous time window and a message passing mechanism; determining an equipment health degree score according to the equipment state vector and a knowledge graph, and triggering the intelligent inspection robot to execute a deep detection task under the condition that the health degree is lower than a preset health degree threshold value so as to collect target test data; determining a target fault type according to the target test data, the knowledge graph and the inference rule base, obtaining a fault processing scheme corresponding to a historical fault case, and generating corresponding maintenance guide information according to the fault processing scheme, thereby achieving the real-time detection, precise fault diagnosis and automatic maintenance of the secondary equipment of the transformer substation. And the operation safety and the maintenance efficiency of the power system are improved.

The invention belongs to the technical field of artificial intelligence, and relates to a power document intelligent question and answer method and system based on a large language model. The method comprises the steps of obtaining document data according to a preset period; preprocessing the document data to obtain an adaptive document; constructing a vector retrieval library through an embedded model according to the adaptive document; obtaining a user query question, inputting the user query question into a vector retrieval library, performing retrieval based on a vector similarity retrieval method and a keyword retrieval method to obtain related text blocks, and screening out the most related text block from the related text blocks through a two-stage rearrangement technology; a user queries the question and the most relevant text block and inputs the question and the most relevant text block into the large language model to obtain preliminary intelligent answer information; and correcting the preliminary intelligent answer information based on a preset self-provincial inversion mechanism to obtain final intelligent answer information. Multiple rounds of questions and answers and context understanding are supported based on a correction mechanism, context consistency can be kept in continuous query of complex technical questions, and the accuracy of multiple rounds of questions and responses is ensured.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention provides an esophageal squamous carcinoma detection method and system based on combination of multi-modal information fusion and artificial intelligence, and relates to the technical field of detection. And inputting the fusion feature map corresponding to the multi-modal image information into a pre-trained esophageal squamous carcinoma detection model to perform focus area detection and segmentation on the fusion feature map, and based on a detection result, performing benign and malignant classification and staging prediction on a detected focus by using an integrated learning method in combination with a classification algorithm to obtain the esophageal squamous carcinoma. Outputting a classification result and a visual heat map; converting the extracted focus area information into three-dimensional point cloud data by using an improved point cloud reconstruction algorithm; performing space division and noise reduction processing on the three-dimensional point cloud data by adopting a self-adaptive octree structure; and generating a smooth lesion three-dimensional model by using an improved Poisson surface reconstruction algorithm in combination with a curved surface subdivision technology.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention relates to the technical field of new energy power generation, and discloses an intelligent panoramic monitoring management system and method for a new energy box transformer substation, and the method comprises the steps: obtaining an original monitoring data set of a target box transformer substation, and carrying out the data cleaning; key features are extracted from the cleaned data, and a weighted feature set is generated; constructing a box transformer substation fault prediction model to obtain a fault risk assessment value; performing graded early warning on potential faults based on the evaluation values; constructing a box transformer fault diagnosis knowledge graph, and performing reasoning and tracing in combination with the knowledge graph during high-risk or medium-risk early warning; through multi-source data fusion and knowledge graph reasoning, comprehensive monitoring of the operation state of the box transformer substation and intelligent fault diagnosis are realized, and the operation reliability and maintenance efficiency of the new energy box transformer substation are improved.

The invention relates to the technical field of power grid fault repair, in particular to a power grid fault emergency rescue method. The method comprises the following steps: acquiring topological structure data, key equipment parameters and real-time operation state data of a power grid; constructing a power grid digital twin model according to the power grid topological structure data, the key equipment parameters and the real-time operation state data, and performing real-time state evaluation to obtain fault risk early warning data and real-time fault condition data; when the real-time fault condition data shows that a fault occurs, performing fault point positioning and fault type determination based on deep learning according to the fault risk early warning data and the real-time operation state data to obtain fault diagnosis data; and constructing a fault influence range model according to the fault diagnosis data and a preset FACTS and HVDC characteristic model. According to the method, deep learning, a graph theory and a multi-objective optimization technology are fused, and intelligent fault identification, influence range modeling and accurate emergency disposal scheme optimization are realized.

The invention discloses an electric power product operation fault detection system based on big data, and relates to the field of electric power fault detection systems, and the system comprises a data collection module which collects the multi-parameter operation data of various electric power products, including electrical and equipment states and environmental parameters, can dynamically adjust the collection frequency, and caches and encrypts the transmission data; the data preprocessing module is used for carrying out abnormal value correction, noise removal, normalization and integrity check; the feature extraction module is used for extracting key and time-frequency domain features by using multiple algorithms, selecting sensitive features according to a fault mode and identifying newly added features; and the fault detection model construction module constructs a model by combining machine learning and deep learning algorithms with ensemble learning and the like, and can update training. Through multi-module cooperation, features are effectively extracted to construct the model, accurate fault diagnosis and prediction are realized, visual display and knowledge support are provided, the intelligent level and efficiency of power product fault detection are improved, and stable operation of a power system is guaranteed.

The invention relates to a distributed energy management system for prolonging the cycle life of a lithium battery. The system comprises an acquisition unit used for acquiring battery state characteristic data of single batteries in a lithium battery pack; the identification unit is used for carrying out charge and discharge abnormal mode identification on the battery state characteristic data to obtain charge and discharge abnormal event data; the decomposition unit is used for performing hierarchical characteristic decomposition and multi-dimensional fusion based on the charging and discharging abnormal event data to obtain a cycle life attenuation characteristic matrix; and the equalization control unit is used for carrying out life equalization control based on the cycle life attenuation characteristic matrix to obtain a target charge and discharge coefficient. According to the invention, the local over-charge and over-discharge phenomena are effectively inhibited, the service life loss of the battery pack is reduced, and the energy balance adjustment of the multi-node control unit is realized.

The invention provides a natural language SQL (Structured Query Language) conversion method based on a data platform and a large language model, which relates to the technical field of data processing and comprises the following steps of: performing vectorization storage on metadata, blood relationship, indexes and historical query information, performing intention judgment and vector matching on a user query request, and determining a query condition based on a matching result. An association path is constructed according to data consanguinity, and an optimal SQL statement is generated by utilizing a historical query optimization table connection sequence and combining index matching. According to the method, intelligent conversion from the natural language to the efficient SQL can be realized, the query efficiency is improved, and the use threshold of a user is reduced.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention relates to the field of production fault analysis, in particular to a fault monitoring analysis method and system for lubricating oil production. The method comprises the following steps: acquiring a multi-dimensional working monitoring parameter and an omnibearing working image; performing abnormal outlier filtering processing on the multi-dimensional working monitoring parameters, and performing time sequence evolution simulation to generate time sequence state evolution data; performing continuous frame optical flow motion analysis on the omnibearing working image, performing dynamic parameter mapping based on time sequence state evolution data, and constructing a dynamic production twinborn model; obtaining a preset production standard demand log; carrying out dynamic process deviation detection based on a preset production standard demand log dynamic production twinborn model, and carrying out production fault analysis so as to obtain stage production fault data; performing potential fault traceability speculation on the staged production fault data to obtain an abnormal fault potential node; and constructing a potential fault mark twinborn model based on the abnormal fault potential nodes. According to the invention, efficient and accurate fault analysis of lubricating oil production is realized.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention discloses a power station photovoltaic field area automatic inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection, and the unmanned aerial vehicle comprises the following components: a meteorological self-adaptive intelligent inspection flight system, a power supply module, a rotor wing device and a communication module; the power supply module is used for providing power support for each component of the unmanned aerial vehicle; the communication module is used for realizing data interaction between the unmanned aerial vehicle and the ground control station and between the unmanned aerial vehicle and the photovoltaic field area monitoring equipment; by carrying the meteorological self-adaptive intelligent inspection flight integrated system, meteorological conditions of a photovoltaic field area can be perceived and analyzed in real time, and an inspection path and a flight attitude are dynamically optimized according to the meteorological conditions, so that the unmanned aerial vehicle can preferentially inspect an area where a problem is most likely to occur, and the inspection efficiency is remarkably improved; meanwhile, the unmanned aerial vehicle can keep stable flight under complex meteorological conditions such as strong wind, flight accidents are avoided, and the accuracy of inspection data is ensured.

The invention relates to a tree neighbor context-based big language model RAG optimization method, which comprises the following steps of: reading a directory structure and content of a document, generating a corresponding directory tree, converting the directory tree into a Markdown format containing a title and a level, and automatically extracting according to a document template if no title exists; segmenting the document into knowledge blocks according to the priority; after the knowledge blocks are recalled, the context is expanded by using a tree adjacent context method, and a new knowledge block list is formed; grouping and sorting the expanded knowledge blocks according to document identifiers, splicing reference data based on different fields and confidence coefficients, and generating a character string set from the original document; and splicing prompt words by using the constructed reference data and the new knowledge block list, and sending the spliced prompt words to the large model for processing. According to the scheme, the performance and practicability of the large model in the information retrieval and answer generation process are comprehensively enhanced by optimizing context information, emphasizing key content, adapting to various document formats, improving user experience and the like.

The invention discloses a collaborative operation system and method for a photovoltaic cleaning robot and a carrying robot, relates to the technical field of automatic control, and solves the defects that an existing collaborative operation system is unstable in communication and insufficient in emergency handling capacity. According to the invention, the environmental information of the photovoltaic power station is collected in real time through the environmental perception module, and the reliability of data transmission is ensured through an automatic retransmission request mechanism and error detection and correction codes based on 5G and LoRa communication protocols; communication parameters are adaptively adjusted through the communication self-adjusting module, so that the communication quality is ensured; the path planning module dynamically allocates cleaning tasks and plans an optimal path, the collaborative obstacle avoidance module dynamically adjusts the motion trails of the robots and avoids obstacles, the operation progress tracking module records the operation states of the robots in real time, and comprehensive operation progress information is provided through data processing and visual display. According to the invention, the communication stability, the autonomous decision-making capability and the operation efficiency of the system are obviously improved.

The invention discloses a battery production defect inspection method, system, medium and equipment, and relates to the technical field of battery defect inspection. The method comprises the following steps: acquiring a video stream corresponding to each battery production line, and extracting a key frame sequence corresponding to each video stream; performing fusion analysis on spatial features and time sequence features of the key frame sequences to obtain abnormal state features; performing multi-stage screening on each key frame sequence based on the abnormal state features to obtain a target key frame with defects; performing space-time mapping on the target key frame, and determining an associated station corresponding to the abnormal production line; and according to the process parameters of the associated stations, carrying out grading evaluation on the abnormal state features, and generating abnormal early warning information. By implementing the technical scheme provided by the invention, misjudgment or missed judgment of abnormal conditions on the battery production line can be effectively avoided, so that the production quality of battery products is improved.

The invention belongs to the technical field of safety monitoring of subway tunnels, and particularly relates to a subway tunnel water leakage monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-source sensor time sequence data in a subway tunnel, carrying out the feature extraction, and extracting feature parameters related to water leakage; carrying out fusion analysis on the characteristic parameters through a data fusion algorithm, and evaluating the position and severity of water leakage, including extracting time-space correlation characteristics of data by using a dynamic time warping method; after feature extraction is performed on the data set and the space-time correlation features by using a convolutional neural network and a recurrent neural network, feature fusion is performed; and performing anomaly detection and classification on the fused high-level features by using a neural network method to obtain the position and severity of the leakage water. The advanced data fusion technology and the feature extraction algorithm are adopted, various collected data can be analyzed in real time, abnormal conditions can be found in time, and early warning is carried out.

The invention discloses an intelligent inspection system and method for a photovoltaic module of a photovoltaic power generation system, and relates to the technical field of intelligent inspection. The method comprises the steps that a high-definition camera and an infrared imager carried by an unmanned aerial vehicle automatically inspect a photovoltaic panel, real-time operation data are acquired and transmitted to a preprocessing module, the preprocessing module optimizes an image through denoising, graying, contrast enhancement and geometric correction, a Unet algorithm is utilized to accurately segment strings and assemblies in the image, and the photovoltaic panel is obtained. And a defect identification module analyzes the processed image, calculates the average temperature, compares the average temperature with a threshold value, identifies hot spot defects, and finally outputs specific information, fault types and maintenance suggestions of a defective component by the system, and generates an inspection report. According to the technical scheme, a fault source can be quickly positioned, and cloud storage and an AI analysis tool are used for managing data and predicting potential maintenance requirements.

The invention relates to the technical field of fault detection, and discloses a remote electric energy meter fault detection and identification system and method, and the system comprises a data collection module, a data integration module, a data inspection module, a feature extraction module, a fault analysis module, and a fault alarm module. According to the method, multiple electric energy meter target data are collected and integrated into a corresponding data set, and a complete and ordered data basis is provided for analysis; abnormal values are recognized and replaced by calculating the average value and the standard deviation, the data quality is improved, and the accurate analysis result is guaranteed; key parameters covering multiple aspects are accurately extracted from a data set, and a powerful basis is provided for fault analysis; from metering, hardware and communication fault dimensions, according to comparison of extracted parameters and threshold values, fault types and positions are accurately judged in multiple directions; the fault alarm module remotely transmits an analysis result to a monitoring center, and generates an alarm message containing detailed information such as an electric energy meter number, a fault type, a parameter value, a threshold value and the like when a fault occurs, thereby facilitating timely maintenance by maintenance personnel.

The invention belongs to the technical field of front-end development, and particularly relates to a React framework-based tree component optimization method fusing virtualization rendering and increment rendering. Comprising the steps of S1, data preprocessing; S2, dynamic virtualization rendering: dynamically calculating a node index range in a visible area according to a real-time scrolling position of a user viewport, only rendering nodes in the range, and realizing seamless lazy loading of data through scrolling event monitoring; s3, intelligent incremental updating: identifying changed nodes through a state change comparison algorithm, and executing local re-rendering only for the changed nodes based on a component life cycle control mechanism of a React framework; and S4, lazy loading cooperative control. The method aims at solving the problems in the aspects of performance, memory management, user experience and the like when a tree component in a React ecological system processes large-scale data, and is widely applied to various Web application program development scenes needing to display and operate a large-scale tree data structure.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention discloses a fault monitoring method and system for a new energy photovoltaic power station power distribution network, and relates to the technical field of power distribution network fault monitoring. Analyzing and identifying abnormal areas based on the monitoring data of the edge monitoring point of each area, and updating the final monitoring level of each abnormal area; according to the fault detection sequence, the new energy photovoltaic power station power distribution network evaluation information of the monitoring points in all the abnormal areas and the difference information between the adjacent monitoring points are analyzed in sequence, the monitoring points in all the abnormal areas are checked, suspected monitoring points in all the abnormal areas are identified and marked, and a detection and maintenance path is generated. The problems of delayed detection, unreasonable resource allocation, low troubleshooting efficiency, insufficient intelligence, single maintenance strategy and insufficient data utilization in traditional new energy photovoltaic power station power distribution network fault monitoring are solved.

The invention relates to the technical field of short message channel testing, and discloses a short message channel testing configuration method, device and equipment and a storage medium, and the method comprises the following steps: firstly verifying user login information and distributing authority, then extracting and screening channel information from a database based on the authority, and presenting a result in a paging mode. A user can input channel configuration information, and the database is updated after verification. The system then generates an association configuration with the engine user based on the updated channel information. And establishing a TCP / IP Socket communication session and acquiring a session identifier according to the channel selected by the user and the associated configuration. And the system receives the test short message content input by the user, packages the test short message content and the session identifier into a sending request, and sends and records a result through the communication session. According to the method, the channel configuration information can be managed in a centralized manner, a visual user interface is provided, flexible query and screening functions are supported, and an automatic test process can be realized.

The invention provides an online car-hailing passenger position rapid positioning method based on voice interaction and visual perspective, and relates to the technical field of intelligent traffic, and the method comprises the following steps: arranging a sensor, a camera, a metal reflective curtain and a projector on a car; voice information of a driver is collected, and an intelligent voice wake-up model is designed; constructing a scenarized speech recognition model; detecting a pedestrian target for the panoramic image by using a sound-image multi-level cross-modal detection model, and obtaining appearance and posture feature detection information of the pedestrian target; a static feature matching method and a dynamic feature recognition model are designed, and passenger feature information matching is achieved; based on a matching result, determining position information of the passenger; acquiring a passenger direction taking the vehicle as a reference origin through position estimation in the panoramic image; according to the direction estimation result, a projector in the vehicle is controlled, the passenger information is projected on a curtain in the corresponding direction in the vehicle, and meanwhile voice information is used for giving a prompt.

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 utility model relates to the technical field of hydraulic pipe welding, and discloses a pipeline welding device with a positioning structure, which comprises a base, an annular frame is fixedly mounted on the upper surface of the base, a sliding block is slidably connected to the inner wall of the annular frame, a welding gun is fixedly mounted at the top of the sliding block, and the positioning structure is fixedly mounted on the welding gun. According to the pipeline welding device with the positioning structure, six sets of clamping jaws can synchronously rotate and get away from one another by pushing a lantern ring to be matched with a connecting rod, at the moment, a hydraulic pipe can be placed among the six sets of clamping jaws, and then the lantern ring is loosened to be matched with a spring and the connecting rod to enable the six sets of clamping jaws to synchronously rotate and get close to one another; and meanwhile, the hydraulic pipe can be clamped through cooperation with the rubber block, so that positioning of the hydraulic pipe is completed, through the clamping mode of the lantern ring, the connecting rod, the clamping jaw and the rubber block, the pipeline can be rapidly positioned, and the hydraulic pipe clamp can be suitable for hydraulic pipes with different pipe diameters and is more convenient to use.