2776 results about "Inspection method" patented technology

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

The invention discloses a concrete building outer wall detection method and system, computer equipment and a storage medium, and relates to the technical field of prefabricated building detection, and the method comprises the steps: obtaining multi-angle image data of a concrete building outer wall; preprocessing the image data, and extracting a target area of the outer wall of the concrete building; extracting feature information representing outer wall defects from the preprocessed image data; inputting the feature information into an artificial intelligence detection model, and judging whether an outer wall defect exists or not; and according to an output result of the detection model, determining distribution positions of the defects on the surface of the outer wall, and classifying the detected defects. According to the concrete building outer wall detection method provided by the invention, the unmanned aerial vehicle avoids low efficiency and safety risks of high-altitude operation in manual detection. Meanwhile, the convolutional neural network can automatically extract multi-level features from the image, various defects are accurately recognized, and the accuracy problem of traditional infrared detection under the change of environmental factors is solved.

The present invention provides an intelligent robot for power inspection and an inspection method thereof, comprising a quadruped robot, wherein a laser radar, a voiceprint sensor and an infrared sensor are provided at the front end of the quadruped robot; a pan-tilt base is provided at the middle of the quadruped robot, and a camera is provided on the pan-tilt base; a six-degree-of-freedom mechanical arm is also provided at the tail of the quadruped robot, and a mechanical claw is provided at the end of the mechanical arm. The robot flexibly plans tasks based on the layout of the substation, sets inspection cycles for different equipment according to their importance, and automatically generates accurate task plans, thereby avoiding the tediousness and omissions of manual planning and improving planning. The advanced path planning algorithm enables the robot to quickly plan the optimal path, integrates multiple positioning technologies to ensure accurate navigation, and effectively reduces travel time. Multiple inspection modes are carried out in parallel, non-stop inspections are dynamic screening, routine inspections are comprehensive inspections, and non-preset inspections are flexibly targeted at specific points, which greatly improves the coverage efficiency of the inspection and avoids detection blind spots.

The invention provides a multi-machine collaborative distribution network inspection method and system based on digital twinning, and belongs to the technical field of digital twinning. According to the technical scheme, an electric power distribution network framework and base station distribution are mapped to a GIS remote sensing map, a comprehensive digital twinning model is constructed, and an inspection area is delimited based on the comprehensive digital twinning model. By analyzing base station distribution and topographic features in the area, the comprehensive inspection difficulty is determined, and then the inspection area is divided into a plurality of task blocks. And selecting an optimal target nest from the deployed unmanned aerial vehicle nests by adopting a contract net mechanism to execute an inspection task, and generating an optimized inspection route according to the task block and the nest position. And finally, the unmanned aerial vehicle controlled by the target nest completes all inspection tasks according to the planned route. According to the scheme, intelligent scheduling of inspection resources and accurate distribution of tasks are realized, the inspection efficiency of the power distribution network is remarkably improved, the inspection cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power grid.

The invention belongs to the technical field of intelligent electric power inspection and multi-agent cooperative control, and provides an electric power multi-agent dynamic cooperative inspection method and system, and the technical scheme is that a path planning scheme is generated by using a reinforcement learning algorithm and a large language model, so that an agent can dynamically adjust an inspection path according to real-time data and task requirements, and the inspection efficiency is improved. Therefore, the minimization of the path and the maximization of the inspection efficiency are realized, and unnecessary travel and resource consumption are reduced. Meanwhile, a dynamic task redistribution mechanism in the system ensures that the intelligent agent can quickly respond and redistribute tasks under the conditions of equipment failure, environment change or task priority adjustment. And real-time communication and data synchronization among the intelligent agents ensure information sharing, so that the system still keeps task continuity and reliability in emergency.

The invention relates to the technical field of bridge detection, in particular to a bridge defect automatic identification and inspection method based on deep learning, which comprises the following steps: S1, multi-modal data acquisition: acquiring multi-modal data of the surface and internal structure of a bridge; s2, multi-modal data preprocessing: generating a space-time aligned enhanced data set; s3, feature fusion and extraction: generating a fusion feature spectrum; s4, adaptively optimizing the characteristic spectrum: adjusting the connection weight between network layers according to the real-time environment parameters; s5, defect positioning and type identification: positioning space coordinates of defects and synchronously outputting probability distribution of defect types; and S6, defect analysis and report generation: generating an inspection report including defect evolution trend analysis based on the result of defect positioning and type identification. According to the invention, the bridge inspection efficiency is improved, the labor cost is reduced, and the intelligent upgrading of the bridge management field is further promoted.

The invention relates to the technical field of data processing, and discloses a packaging equipment inspection method and system. The method comprises the steps that packaging process parameters are collected through multiple sensors, and a working condition data set is formed; extracting abnormal features by using spectral analysis, and generating an operation deviation index; quality and state data are compared to establish an association rule, and a fault precursor feature library is obtained; the station coordination is analyzed through time sequence comparison, and a component state evaluation table is formed; calculating the fault probability of the key component, and generating an inspection point sequence; and making an inspection scheme including parameters, standards and periods, and forming a preventive maintenance plan. According to the method, key parameters such as tension, temperature and speed in the packaging process are analyzed, the early characteristics of the abnormal operation state of the equipment are recognized, the routing inspection priority and the maintenance period are determined in combination with historical fault data and component state evaluation, and conversion from passive maintenance to active prevention is achieved.

The invention discloses an unmanned aerial vehicle multi-mode mine inspection method and system, a medium and a program product, and the method comprises the steps: dividing an inspection target into a short-range object and a long-range object based on a preset inspection target classification list; constructing a three-dimensional grid inspection path according to the position information of the short-range object and the remote object, and setting a plurality of standby inspection entrance points in a detection area of the short-range object; acquiring real-time environment parameters, wherein the real-time environment parameters comprise illumination intensity, atmospheric visibility, electromagnetic field intensity and wind speed and wind direction; calculating the reachability score of each standby inspection entrance point based on the influence degree score of the real-time environment parameters on the unmanned aerial vehicle, and determining the standby inspection entrance point with the highest reachability score as the current inspection entrance point; and generating an inspection path according to the current inspection entrance point, and controlling the unmanned aerial vehicle to inspect according to the inspection path. According to the method, the integrity of the inspection data is improved, the accuracy of the safety evaluation data obtained based on the inspection data is improved, and the safety of a mine is further improved.

The invention relates to the technical field of path planning, in particular to a multi-unmanned aerial vehicle oil depot storage tank inspection intelligent optimization method based on a layering strategy. The method comprises the following steps: constructing an equivalent terrain mathematical model according to oil depot storage tank characteristics, and establishing a multi-objective optimization objective function; performing global path planning inspection of multiple unmanned aerial vehicles by using an improved wolf pack optimization algorithm; abnormities are monitored through a carried sensor, and abnormal information is uploaded to a terminal; the terminal issues the target abnormal point to the single unmanned aerial vehicle; performing single-machine local path optimization by adopting an improved particle swarm optimization algorithm; and updating the state of the inspection task. According to the method, global path planning and local path optimization are effectively combined through a layered inspection strategy, the defects of a traditional method in inspection full coverage and local emergency response are overcome, the intelligent and efficient level of an unmanned aerial vehicle inspection system is improved, and the method is suitable for complex oil depot storage tank inspection tasks and emergency disposal scenes.

The invention provides a multi-mode cooperative sensing power station high-risk operation inspection method and system, and relates to the technical field of video recognition, and the method comprises the steps: activating an unmanned plane and a quadruped robot after a power station operation task is started; starting a video acquisition unit, and establishing a synchronous video stream; carrying out fusion alignment with the global reference coordinate system through an external synchronization signal; inputting the video sequence of the fused view angle into a multi-view angle action behavior recognition network, and establishing a dangerous behavior grade score; auditory data and olfactory data of the quadruped robot are obtained, and linkage abnormity is established; and polling abnormity is reported according to linkage abnormity and dangerous behavior grade scores. Through the method and the device, the technical problem of low inspection efficiency caused by difficulty in comprehensively identifying potential risks in a dynamic environment due to limitation of a single sensing mode is solved, and the inspection efficiency of a power station is improved by fusing multi-mode data, timely finding and processing the potential risks and improving the accuracy of video and audio identification.

The embodiment of the invention discloses a multi-nest remote take-off and landing unmanned aerial vehicle autonomous inspection method and system, and belongs to the technical field of unmanned aerial vehicles. According to the embodiment of the invention, the target inspection space area is identified, and the related vector and information are acquired, so that the inspection task planning can accurately meet the actual demand. A multi-nest distribution relation vector is input into a multi-agent depth deterministic strategy gradient algorithm, unmanned aerial vehicle path planning spatio-temporal data are discriminated and analyzed by means of a cooperative competition discrimination branch, complex relations such as nest layout and unmanned aerial vehicle paths can be fully considered, and resource allocation is optimized. According to the task indication information, a state vector is generated and a scheduling viewpoint is optimized, and then an autonomous cooperative inspection strategy is generated, so that the cooperative inspection efficiency of multiple unmanned aerial vehicles can be greatly improved, task conflicts are avoided, accurate matching of inspection task paths and task requirements is ensured, and a target area is effectively covered. The labor cost is reduced; and the accuracy and timeliness of routing inspection are improved.

The invention relates to the field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle automatic flight control method and system for underground cable trench inspection, and the system comprises an environment sensing subsystem and an underground tunnel navigation subsystem. The visual sensor module obtains image data, the multi-sensor joint calibration module processes laser radar and camera data, the multi-layer fusion algorithm module fuses the calibration data, the underground tunnel navigation subsystem solves the problem of navigation obstacle avoidance, map construction of a tunnel environment and accurate estimation of the pose of an unmanned aerial vehicle are achieved, and an inspection route is planned and optimized. All the modules are integrated to achieve efficient and safe inspection of the underground cable trench, the inspection safety and efficiency are remarkably improved, the defects of a traditional manual inspection method are overcome, wide application prospects are achieved, safe operation of the underground cable can be effectively guaranteed through implementation of the system, and powerful support is provided for stability of an electric power system.

The invention discloses a toxic and harmful substance leakage reinspection method, device and equipment based on an unmanned aerial vehicle, and a storage medium, relates to the technical field of Internet of Things and intelligent factories, and aims to solve the problem that the detection accuracy and efficiency of toxic and harmful substances are low. The method comprises the following steps: dividing a target area into a plurality of grids, and acquiring initial gas concentration data of each grid in real time through a fixed foundation sensing array; the grids with the initial gas concentration data larger than the preset concentration are determined as abnormal grids, and a reinspection flight path is dynamically generated according to the unmanned aerial vehicle parking position, the abnormal grid position and the environment parameters; the unmanned aerial vehicle is controlled to fly to the abnormal grid according to the reinspection flight path, and multi-dimensional data including visible light images, infrared thermal imaging data, gas component spectrums and space coordinate data of the abnormal grid are collected; and fusing the initial gas concentration data and the multi-dimensional data of the abnormal grid, constructing a three-dimensional dynamic concentration field model, and outputting leakage source position information and a diffusion trend.

The invention discloses a property security inspection method and system and a storage medium, and belongs to the technical field of property management. Through combination of intelligent inspection and manual inspection, the intelligent inspection adopts a multi-source data fusion and dynamic threshold determination mode to realize alarm of different risk levels of intelligent inspection abnormity; manual inspection is used for supplementing intelligent inspection and completing regular inspection tasks such as re-checking of intelligent inspection abnormity alarm and inspection of facilities and equipment without sensors; the dynamic threshold value model has the adaptive capacity of different scenes; through machine learning, the system can be continuously optimized, and multi-source data fusion is combined, so that the false alarm rate and the missing report rate of the system are effectively reduced, the accuracy and the dynamic adaptability of intelligent inspection are improved, and accurate alarm is realized; the cost problem caused by blind installation of the sensor can be avoided, and quantitative decision support is provided for property management; the management cost of the property can be reduced, and the intelligent management level and the working efficiency of the property can be improved.

The invention discloses a water conservancy inspection robot inspection method based on multi-modal data fusion, and relates to the field of intelligent inspection, and the method comprises the steps: obtaining the historical inspection information of a target dam; generating a water conservancy inspection strategy based on the historical inspection information and the dike data twinborn model; collecting multi-mode inspection information of the target dam; extracting multi-modal inspection features of the dam inspection area based on the multi-modal inspection information; and grade division of the multi-mode inspection features is completed, and dam abnormity grades are obtained. The accuracy of the inspection result can be effectively improved.

The invention relates to the technical field of Beidou positioning, and discloses a Beidou positioning inspection method and system. The method comprises the following steps: acquiring geographic information and equipment state parameters of an inspection area, and generating a structured task queue according to dimensions such as equipment types and fault levels; constructing a Beidou positioning dynamic model containing a position axis, a time axis and an environment factor axis; determining an initial inspection constraint condition according to the equipment type and the fault level, and performing path planning simulation to generate an initial inspection route; detecting a route anomaly, and adjusting an abnormal node by using a hybrid optimization algorithm to generate a stable route; model parameters are updated in combination with real-time environment data, and a final inspection instruction set containing information such as equipment moving time sequence is generated. The system comprises a data processing module, a model construction module, a constraint determination module, a path planning module, an anomaly detection module, an optimization adjustment module and the like. The inspection efficiency and precision are improved, the stability and reliability are enhanced, and the inspection requirements of complex environments can be effectively met.

The invention provides an intelligent inspection method and system for fan blades based on an unmanned aerial vehicle. The method comprises the steps that a visible light camera, an infrared thermal imager and a laser radar are integrated on the unmanned aerial vehicle; generating a flight route covering the whole surface based on the fan blade three-dimensional model; when the unmanned aerial vehicle is controlled to fly along the flight route, visible light images, infrared temperature data and laser point clouds are synchronously collected; the visible light image, the infrared temperature data and the laser point cloud are input into a defect analysis model, the defect type and grade are identified through conjoint analysis, a temperature abnormal area is positioned, and geometric deformation is quantified; a health assessment report is generated, the health assessment report comprises a defect positioning map and a graded maintenance decision matched with defect grades, multi-source data are collected through integration of a visible light camera, an infrared thermal imager and a laser radar, and then fusion analysis and recognition are carried out, so that compared with a manual inspection mode, the intelligent level of fan blade inspection is improved, and the inspection efficiency is improved. And the inspection efficiency of the fan blade is also improved.

The invention discloses a quality detection method for a safety valve spring, and relates to the technical field of spring quality detection, and the method comprises the steps: constructing an appearance detection module, and carrying out the surface defect detection and size verification of a target safety valve spring; performing performance test and performance grade evaluation on the target safety valve spring through a performance detection module; and combining a surface defect detection result, a size verification result and a performance evaluation result to carry out spring quality comprehensive evaluation, and extracting a defective finished product production number according to a quality judgment result to carry out correlation analysis and defective product traceability. The technical problem that an existing safety valve spring quality detection method is low in accuracy and reliability due to the complexity of an application environment and the diversity of performance requirements in the prior art is solved, and the purpose of detecting the quality of the safety valve spring through all-dimensional and multi-angle appearance quality detection and performance attribute detection is achieved. And the accuracy and reliability of quality detection of the safety valve spring are improved.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention discloses an unmanned aerial vehicle intelligent inspection method for particulate matter pollution prevention and control, and the method comprises the steps: S1, carrying out the aerial photographing to obtain a particulate matter pollution source image, and generating a pollution source image recognition model; s2, aerial photography is carried out to obtain a particulate matter pollution management and control facility image, and a pollution management and control facility image recognition model is generated; s3, collecting related data, and generating a particulate matter pollution source and particulate matter pollution management and control facility list and distribution map; s4, the terminal intelligent control platform plans an unmanned aerial vehicle inspection route; s5, when the particulate matter concentration data of the monitoring site reach an early warning value, starting inspection operation; s6, transmitting the data recorded by the inspection of the unmanned aerial vehicle to the terminal in real time; s7, the terminal identifies the pollution source type, the pollution degree and the pollution control facility state according to the transmitted data to form a pollution source patrol list; and S8, automatically sending the generated list to related personnel. The method is high in target identification precision and accurate in positioning, improves the inspection efficiency of particulate matter pollution prevention and control, and reduces the labor cost.

The invention provides an all-weather autonomous inspection method and system based on cluster task dynamic load balancing, and relates to the technical field of inspection monitoring, and the method comprises the steps: collecting the remaining electric quantity, the positioning position, the task queue length and the sensor health state of an unmanned aerial vehicle cluster in real time through an airborne terminal; fusing the meteorological data and the multi-source environment sensing data, and constructing a dynamic obstacle map and a meteorological influence model; dividing an inspection area into a plurality of sub-areas based on an electronic fence, dynamically adjusting the inspection priority of each sub-area, setting task weights of a water taking head and a raw water pipeline facility, and distributing inspection tasks according to the priorities; based on the remaining power of the unmanned aerial vehicle, the task priority, the current load and the meteorological data, a bipartite graph matching model of the unmanned aerial vehicle and the task is constructed, the matching weight is dynamically calculated, the optimal matching distribution of the unmanned aerial vehicle and the inspection task is completed, and the efficient inspection operation of the unmanned aerial vehicle cluster in the complex environment is realized.

The invention discloses a reservoir safety intelligent inspection method and system based on YOLO and VLM fusion, and the method comprises the following steps: S1, multi-source data collection and preprocessing: employing an unmanned plane and ground equipment to collect image / video data, and carrying out the noise reduction, enhancement and space-time alignment processing of the data; s2, improving YOLO target detection: optimizing a network structure and a training strategy; s3, link analysis after VLM: target / scene association judgment is realized by adopting the VLM; and S4, report generation. The invention provides a reservoir safety intelligent inspection method and system fusing YOLO and VLM. Cross-modal semantic understanding, zero sample reasoning and video global analysis capabilities of a visual language large model are utilized, the visual language large model is used as a post-processing tool of YOLO and is fused with the post-processing tool to work in parallel, full-process intelligentization of target detection-semantic analysis-report generation can be realized, the existing technical problems are effectively solved, and the visual language large model has a wide application prospect. And the comprehensiveness of the hydraulic engineering safety monitoring system is enhanced.

The invention provides an intelligent inspection method and system of a work order all-in-one machine for pressure-bearing special equipment, and relates to the technical field of equipment inspection.The intelligent inspection method comprises the steps that an inspection request sent by terminal equipment is received, and inspection standards and inspection work order information corresponding to the pressure-bearing special equipment to be inspected are searched for in a pre-stored equipment information database; according to the inspection work order information, inspection parameters corresponding to the to-be-inspected pressure-bearing special equipment are determined, and a prediction model used for predicting the remaining service life of the to-be-inspected pressure-bearing special equipment is constructed based on equipment historical inspection data and equipment use environment data; and using the prediction model to predict the remaining service life of the pressure-bearing special equipment to be detected, taking a prediction result as additional detection reference information, generating a dynamic optimized detection strategy, and repeatedly executing the detection process according to the optimized detection strategy until all detection processes required in detection work order information are completed. And comprehensively generating a test report and sending the test report to the terminal equipment.

The invention relates to the field of industrial automation and equipment maintenance, and discloses a boiler operation state real-time monitoring and regular safety inspection method, which comprises the following steps: acquiring key parameters of an industrial boiler operation state in real time through a plurality of sensors, including pressure, temperature, water level and combustion condition; and transmitting data collected by the sensor to a data processing platform, wherein the data processing platform integrates a big data analysis module, an artificial intelligence algorithm module and a historical data comparison module. Through a linkage mechanism of the data processing platform and the industrial boiler control system, operation parameters can be automatically adjusted; necessary safety measures are prompted to be taken through continuous on-line monitoring and alarming of boiler load and water quality. An artificial intelligence algorithm is introduced, dynamic maintenance cycle adjustment is realized, unnecessary maintenance is reduced, and the maintenance cost is reduced; the remote monitoring and control system supports real-time monitoring and remote control, and improves the flexibility of operation and maintenance and the fault response speed.

The invention provides an automatic inspection method and device and a readable storage medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: automatically logging in a plurality of devices through a pre-written automatic login script of terminal simulation software, and executing an inspection command; obtaining an original log output by the plurality of devices after executing the inspection command, and storing the original log in a local database; preprocessing the stored original log, and extracting a key index in the preprocessed original log by using a preset index extraction rule; and carrying out anomaly detection on the extracted key indexes to obtain the anomaly indexes and cross-device association anomaly results of the corresponding devices. According to the method, the device and the readable storage medium, the problems that the manual inspection efficiency is low, the log analysis intelligence degree is insufficient, intelligent assistance is lacked and potential safety hazards exist in data circulation due to the fact that an existing inspection technology mainly depends on manual operation or a basic script tool can be solved.

The invention relates to the technical field of information, and provides an intelligent power consumption behavior inspection method and device based on multi-source data and a storage medium, and the method comprises the steps: processing a mass power consumption data flow collected by a sensor and an intelligent electric meter in real time through employing a distributed calculation framework, completing the integration and standardization of the multi-source data, and generating a dynamically updated power consumption monitoring original data set; extracting a multi-dimensional feature vector from time series data and historical electricity utilization records containing the basic identifier, the time information, the real-time change feature and the abnormal mark, and outputting a classification result of the abnormal behavior; finally confirmed abnormal behavior data are extracted, a real-time monitoring instrument panel is generated through the visualization technology, and the abnormal behaviors of power utilization inspection of the power supply enterprise are dynamically displayed. According to the invention, multi-source heterogeneous data are effectively integrated, intelligent analysis and real-time monitoring of power consumption behaviors are realized, the accuracy and efficiency of power consumption inspection of power supply enterprises are improved, and powerful support is provided for safe and stable operation of a power system.

The invention provides a robot intelligent inspection method and system based on multi-source data fusion, and relates to the technical field of intelligent inspection, and the method comprises the steps: carrying out the data collection of an inspection region based on an inspection robot; adjusting the enhancement parameter of the equipment appearance image, highlighting the equipment operation sound in the inspection area sound, correcting the equipment temperature distribution, and compensating the area gas concentration; extracting defect features in the equipment appearance image, capturing sound features in equipment operation sound, extracting temperature features in equipment temperature distribution, and learning gas concentration features corresponding to regional gas concentration; and performing space-time fusion on each feature to generate a space-time fusion inspection feature, outputting an equipment fault diagnosis result and adjusting a current inspection path of the inspection robot, thereby cooperatively collecting multi-dimensional information of the equipment, optimizing data quality through adaptive processing, mining associated features through space-time fusion, and adjusting the inspection path in combination with intelligent decision. Fault diagnosis reliability is improved, and safe and stable operation of equipment is guaranteed.

The invention discloses a welding quality detection method and system, and relates to the technical field of welding quality detection.The method comprises the steps that a plurality of to-be-detected welding parts are determined, the temperature of a molten pool is continuously detected through a thermal imaging module in the welding process, and a molten pool temperature field distribution time sequence is generated. Based on the time series data, heat conduction analysis and cooling rate identification are performed to generate a heat conduction path and a cooling curve distribution. By analyzing the data, whether a heat accumulation or loss abnormal area exists or not is judged, and if the heat accumulation or loss abnormal area is abnormal, a quality detection signal is sent out. The technical problem that an existing welding quality detection method cannot accurately recognize welding defects caused by uneven heat conduction and abnormal cooling rate in the welding process in real time is solved, and the technical effect of improving the welding quality detection precision and timeliness is achieved.

The invention provides an unmanned aerial vehicle automatic inspection method for a transformer substation, and the method comprises the steps: transmitting shot image data, flight state data and equipment state data back to a nest in real time through a wireless communication link in the inspection process of an unmanned aerial vehicle, carrying out the primary processing of the received data through the nest, and carrying out the butt joint with a remote intelligent inspection system, uploading the data to a remote intelligent patrol system; the remote intelligent inspection system receives and displays the inspection data of the unmanned aerial vehicle in real time, analyzes and processes the image data by using an intelligent analysis algorithm, identifies equipment defects, fault hidden dangers, abnormal operation states and environmental abnormalities, and generates a preliminary analysis result; according to the intelligent analysis algorithm, deep learning target detection, time sequence analysis and other technologies are utilized to carry out deep processing on image data, and various equipment defects, fault hidden dangers and environment anomalies can be accurately recognized. The defect finding capability is greatly improved, measures are taken in time for repairing, equipment faults are avoided, and safe and stable operation of the transformer substation is guaranteed.

The invention belongs to the technical field of track detection devices, and particularly relates to a contact rail, induction plate and track static geometric parameter comprehensive inspection tester and an inspection method.The contact rail, induction plate and track static geometric parameter comprehensive inspection tester comprises a walking frame, and a track inspection assembly, a contact rail measurement assembly and an induction plate measurement assembly are arranged on the walking frame; static geometric parameters, contact rail parameters and induction plate parameters of the track can be measured at the same time, the walking frame is pushed on the track, three different types of geometric parameters are continuously measured in the pushing process, the number of on-track measuring personnel is reduced, the labor intensity is reduced, and the working efficiency is improved. The contact rail measurement assembly and the induction plate measurement assembly in the comprehensive inspection tester are detachably assembled and can be selectively used according to field conditions, and the use mode is flexible. The comprehensive inspection tester is provided with the lower contact type contact rail measurement assembly and the upper contact type contact rail measurement assembly, and can adapt to measurement of geometric parameters of contact rails in different power supply modes.