2177 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 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 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 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 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 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 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 relates to the technical field of industrial nondestructive testing, in particular to a paper drum defect full-inspection method based on machine vision, which comprises the following steps: step 1, constructing a curved surface self-adaptive optical environment: arranging annular low-angle strip-shaped light source arrays along the circumferential direction of a paper drum at equal intervals, and forming a dynamically adjusted set acute angle between the emergent direction of each light source and the surface normal of the paper drum, the acute angle is reduced along with the increase of the diameter of the paper barrel, and near-infrared coaxial diffused light sources are arranged at the two axial ends of the paper barrel; step 2, motion compensation three-dimensional reconstruction; 3, multi-modal defect hierarchical decision making: expanding the three-dimensional point cloud into a two-dimensional image along the circumferential direction, and constructing a dynamic reference grid with the grid cell size adaptively adjusted along with the pattern complexity on the two-dimensional image; cooperatively extracting geometric structure features, surface texture features and gluing seam features; and outputting defect classification through a three-level decision tree. By means of the method, efficient and accurate paper barrel full inspection can be achieved, and the automation level of a production line and the paper barrel quality control capacity are effectively improved.

The invention discloses an inspection method, system and device of an intelligent inspection robot and a medium, and belongs to the technical field of intelligent inspection, and the method comprises the steps that the intelligent inspection robot receives an inspection task instruction, and analyzes and obtains an inspection site, inspection time and inspection content; providing a navigation map; planning a plurality of polling paths to be selected, and calculating an optimal path through a dynamic planning algorithm; the intelligent inspection robot performs autonomous movement according to the optimal path, and performs data acquisition on an inspection object and an environment where an inspection point is located at a set point location; the intelligent inspection robot processes and analyzes the collected data, preliminarily recognizes potential abnormities, and transmits all the data to the cloud; generating alarm information and an inspection report; and the intelligent inspection robot executes automatic nest-returning charging operation according to the inspection period and the residual electric quantity state to guarantee continuous completion of the task. The task adaptability, the risk response efficiency and the continuous operation capability of the intelligent inspection robot in a complex environment are improved.

The invention relates to the field of deep application of an artificial intelligence technology, in particular to a mining area supervision method and system based on an intelligent agent and electronic equipment. The method comprises the following steps: in response to a received potential safety hazard analysis task for a mining area, calling an intelligent research and judgment agent to obtain mining area scene potential hazard information, and calling an intelligent text analysis agent to obtain mining area data potential hazard information; in response to the received law enforcement scheme compiling task, calling a law enforcement scheme compiling agent, and generating a law enforcement scheme corresponding to the mining area scene hidden danger information and the mining area data hidden danger information; in response to the received on-site law enforcement task for the mining area, calling a law enforcement assistant agent to generate supervision prompt information according to the law enforcement scheme, and obtaining an on-site law enforcement result corresponding to the supervision prompt information; and in response to the received law enforcement document generation task, calling the law enforcement document generation agent to generate an intelligent document corresponding to the on-site law enforcement result. By adopting the scheme, the intelligent level of supervision and supervision can be improved.

The invention relates to an automatic inspection method and system for intelligent fire fighting equipment of an LNG (Liquefied Natural Gas) filling station, in particular to the field of the intelligent fire fighting equipment of the LNG filling station, a dynamic drifting model is constructed by collecting environmental parameters in real time, and the interference of severe conditions on a sensor is accurately quantified; a time sequence prediction network is used for prospectively capturing a drift trend, and a digital twinning technology is combined to simulate an equipment state in an extreme scene; intelligently identifying an abnormal mode based on unsupervised clustering and dynamically optimizing a multi-source data fusion weight; and finally, through reinforcement learning of a closed-loop iteration drift model and a fusion strategy, the adaptability of the system is continuously improved, misjudgment and missed judgment caused by environmental interference are effectively eliminated in the whole process, the real-time performance and reliability of fire-fighting early warning are remarkably enhanced, the safety accident risk is greatly reduced, and all-weather, high-precision and self-evolution intelligent protection guarantee is provided for the LNG filling station.

The invention relates to the technical field of building structure health monitoring, and particularly discloses a building group safety inspection method based on external structure size displacement detection, and the method comprises the steps: obtaining three-dimensional laser scanning point cloud data of a target building group at a plurality of time points, adding timestamp information, and constructing a time sequence point cloud data set; initial registration is carried out by adopting a feature matching and motion recovery structure technology, and the registration precision and stability in a dynamic environment are improved in combination with a global optimization model fused with space-time consistency constraints; identifying and eliminating pseudo displacement point clusters through track consistency analysis, and extracting a static background structure; finally, key part deformation identification is carried out on the differential point cloud data, and a structure health state evaluation report is generated. According to the invention, full-process automatic processing from data acquisition, optimization registration to intelligent evaluation is realized, the dynamic interference influence is effectively inhibited, the precision and reliability of structural deformation monitoring are improved, and technical support is provided for long-term safety inspection of building groups.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The present disclosure provides an inspection device including a conveyance mechanism; a radiation source; a detector; a movement mechanism; and a determination means. The present disclosure also proposes using the device to inspect foreign matter in the sample.

The invention provides a safety patrol method, a patrol device and patrol equipment, and relates to the technical field of building safety, and the safety patrol method comprises the steps: responding to an operation instruction of an editable interface, configuring a multi-stage process template of building safety patrol, and generating a corresponding safety patrol task; the shooting device collects real-time patrol data of a construction site; identifying violation operation, equipment abnormity and environment risk in the real-time patrol data, and generating a corresponding risk level result; generating a visual alarm signal according to the risk level result, issuing the visual alarm signal to a mobile terminal, and sending the visual alarm signal to a monitoring center when the visual alarm signal is an emergency signal; monitoring response information of the mobile terminal, updating the state of the safety patrol task and recording an operation log of processing a risk level result by patrol personnel; if the mobile terminal does not respond within the preset time, the alarm information is sent to the mobile terminal again, the alarm information is sent to the monitoring center, and the patrol efficiency and the risk handling capacity can be improved.

The invention relates to the technical field of inspection unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle inspection method and system for a tobacco field, and the method comprises the steps: obtaining multi-source remote sensing image data of a preset tobacco planting region; detecting tobacco plants in the remote sensing image data, and performing spatial clustering on detection results to obtain a plurality of tobacco plant cluster targets; and generating an initial inspection path according to the position coordinate and the vegetation index of the tobacco plant cluster target. And in the flight process, the flight pose and environmental parameters of the target unmanned aerial vehicle and the deviation angle between the unmanned aerial vehicle and the current path section are collected in real time. And when the adjustment condition is satisfied, automatically generating an improved path, and adjusting the flight attitude and the camera angle of the target unmanned aerial vehicle. And identifying whether an abnormal condition exists in the image data acquired in the flight process. The tobacco planting scene-oriented inspection method integrating multi-source remote sensing perception, adaptive path planning and intelligent identification is realized.

The invention is applicable to the field of fluorescent penetrant detection, and provides a defect upgrading inspection method and system after fluorescent penetrant, and the method comprises the following steps: acquiring workpiece placement sensing information; based on the workpiece placement sensing information, generating and sending a workpiece surface cleanliness detection instruction and obtaining workpiece surface cleanliness information; on the basis of the workpiece surface cleanliness information, workpiece feature information is recognized and generated, and a workpiece targeted penetration instruction and a developer spraying instruction are generated and sent; generating a personalized ultraviolet light pulse instruction based on the workpiece feature information; workpiece defect images are collected and recognized, a workpiece visual report is generated and output, cleanliness pre-detection is set, misjudgment is effectively avoided, workpiece characteristics are adapted according to different workpiece working conditions, targeted penetration and intelligent spraying, ultraviolet light optimization imaging is carried out, the follow-up inspection image precision is improved, meanwhile, rapid judgment is carried out by means of a high-precision sensor, and the detection accuracy is improved. And erroneous judgment caused by visual fatigue or subjective negligence of workers is completely eradicated.

The invention discloses a thermal insulation performance detection method and system for a composite wallboard, and relates to the technical field of building material detection, and the method comprises the steps: dividing a composite wallboard sample into a plurality of grids, setting an inversion starting point for each grid according to initial data, starting an inversion algorithm to gradually adjust the heat conductivity coefficient of each grid, and generating a heat conductivity coefficient distribution diagram, calculating the average heat conductivity coefficient and the overall thermal resistance value of the sample; the test platform applies full-automatic thermal excitation and sound wave excitation to a sample, collects sound signals and thermal imaging data related to internal defects of the composite wallboard, calculates the optimal matching time point of the sound signals and the thermal imaging data, and extracts defect related characteristics in the sound signals and temperature anomaly characteristics in thermal imaging in a double-flow processing mode. Generating a comprehensive defect distribution diagram, and comparing the comprehensive defect distribution diagram with the heat conductivity coefficient distribution diagram and the average heat conductivity coefficient to obtain preliminary judgment; through acoustic-thermal multi-mode cooperative detection, the spatial distribution of tiny defects in a sample is accurately positioned, and the influence of the tiny defects on the heat-conducting property is judged.

The invention relates to an auxiliary inspection method, system and device for a box-type substation, in particular to the field of box-type substation monitoring, and the method achieves the efficient and accurate inspection of box-type substation equipment through the combination of a millimeter wave radar, an RGB-D camera, a thermal infrared imager, a vibration sensor, load current monitoring equipment and other sensors. By generating a three-dimensional topological graph and superposing sensor data, comprehensive state monitoring can be performed on equipment, electromagnetic intensity field and temperature field distribution of the equipment can be accurately predicted based on physical field distribution and a neural network model, in addition, by dynamically correcting a prediction result and calculating a fault risk index, the risk of equipment fault can be identified in real time, and the reliability of the equipment fault is improved. And the enhanced inspection instruction is triggered when the threshold value is exceeded, so that the stability and the safety of the equipment in the operation process are ensured, the intelligent degree and the fault early warning capability of inspection are effectively improved, and the maintenance efficiency of the transformer substation is optimized.

The embodiment of the invention discloses a mining equipment inspection method and device based on multi-modal data and a medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining a predetermined current inspection task, collecting real-time multi-modal data corresponding to each target mining equipment according to a preset inspection point in the current inspection task, and sending the real-time multi-modal data to the target mining equipment; and performing feature fusion on each piece of real-time multi-modal data, determining a real-time fusion feature corresponding to each piece of target mining equipment, performing anomaly detection and fault prediction on the target mining equipment based on the real-time fusion features, and determining a remote inspection result. The remote inspection result comprises equipment state data and fault probability data corresponding to each piece of target mining equipment; and according to the remote inspection result corresponding to each target mining device, optimizing the current inspection task, determining an optimized inspection task, and updating the current inspection task based on the optimized inspection task, the optimized inspection task including an inspection path and an inspection frequency.

The invention discloses a pumping and storage construction site unmanned aerial vehicle inspection method and system based on image recognition, and relates to the technical field of unmanned aerial vehicle inspection, and the method comprises the steps: delimiting a pumping and storage construction site inspection region, and setting unmanned aerial vehicle inspection parameters; an unmanned aerial vehicle inspection route is delimited, and the unmanned aerial vehicle collects images of the pumped storage power station construction site according to the route; route planning is conducted again according to the energy consumption condition of the unmanned aerial vehicle, and image analysis is conducted on construction site images collected by the unmanned aerial vehicle. According to the image recognition-based unmanned aerial vehicle inspection method for the pumping and storage construction site, multi-target optimization and dynamic path adjustment are adopted, so that the unmanned aerial vehicle can automatically adjust the inspection path according to the real-time environment and task requirements. Dynamic adjustment is carried out through the flight parameters, it is ensured that the unmanned aerial vehicle can automatically adjust the inspection path or return when the battery power is insufficient, task interruption caused by insufficient power is avoided, and the reliability and continuity of inspection are improved.