1923results about How to "Improve inspection efficiency" patented technology

The invention provides an oil depot tank field inspection robot task allocation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. Constructing a multi-target collaborative scheduling model, extracting features by adopting a space-time attention mechanism, establishing a task priority evaluation and path planning sub-model, and generating an optimal task allocation strategy; and an inspection task instruction is issued, the execution condition is monitored in real time, and when abnormity is detected, the emergency module is started for optimization adjustment. According to the invention, intelligent and collaborative routing inspection task distribution is realized, and the routing inspection efficiency and safety are improved.

The invention discloses a multi-modal analysis method, system and device for an industrial inspection scene and a medium. The method comprises the following steps: acquiring multi-modal data of an industrial inspection area in real time, wherein the multi-modal data comprises a video image and voice data; inputting the currently collected multi-modal data into a scene type identification model, and identifying a scene type; matching a corresponding multi-modal fusion mode based on the determined scene type; according to the matched multi-modal fusion mode, performing feature extraction on the video image by using a deep learning algorithm, performing feature extraction on the text converted from the voice data by using a natural language processing technology, and performing multi-modal fusion on the extracted features; performing joint reasoning on the multi-modal fusion features by using a deep learning algorithm, and identifying potential risks or equipment faults in the industrial inspection area; and generating an inspection report and decision based on the identified potential risk or equipment fault. According to the invention, intelligent analysis of the industrial inspection scene can be realized, and the inspection efficiency and accuracy are improved.

The invention discloses an electric power inspection system and method based on an unmanned aerial vehicle, which are used for solving the problem of how to realize autonomous path planning of the unmanned aerial vehicle in a complex three-dimensional environment so as to reduce redundant flight time and improve inspection efficiency. The electric power inspection system comprises a path planning unit, an obstacle avoidance positioning unit and a dynamic obstacle avoidance unit which are connected in sequence. Three-dimensional map data and real-time environment information of a to-be-inspected power line are obtained through a path planning unit, real-time path planning is carried out based on the three-dimensional map data and the real-time environment information, and an optimal flight path of the unmanned aerial vehicle is dynamically generated; obstacle avoidance positioning is carried out on the power equipment on the optimal flight path and the surrounding environment thereof through an obstacle avoidance positioning unit, and real-time positioning information of an obstacle is generated; and a dynamic obstacle avoidance unit constructs a dynamic obstacle map based on the real-time positioning information, and automatically plans an obstacle avoidance strategy according to the dynamic obstacle map, so that the unmanned aerial vehicle realizes dynamic obstacle avoidance based on the obstacle avoidance strategy.

The invention discloses an unmanned aerial vehicle disaster emergency inspection path intelligent planning system and method, and particularly relates to the technical field of unmanned aerial vehicles. A disaster area is divided into sub-disaster monitoring areas, disaster data of each area is acquired and analyzed in real time, and the area deterioration degree and the risk level are dynamically evaluated; an intelligent planning algorithm is combined to generate a preliminary inspection path, the energy consumption and coverage rate of the unmanned aerial vehicle are monitored in real time to assess the accuracy of path planning, the priority of the inspection path is further dynamically adjusted through fuzzy logic, the high efficiency, accuracy and safety of the inspection task of the unmanned aerial vehicle are ensured, and the risk of missing report and false report is reduced. Therefore, the decision quality of disaster emergency response and the rationality of resource allocation are improved.

The invention relates to the technical field of photovoltaic power station inspection, and provides a distributed photovoltaic power station unmanned aerial vehicle inspection method and system, and the method comprises the following steps: generating an inspection task list based on the multi-station distribution characteristics of a distributed photovoltaic power station; according to the inspection task list, performing scanning modeling on each photovoltaic sub-power station in sequence through a laser radar, and generating a photovoltaic power station three-dimensional map; dynamically planning an inspection path according to the characteristics of each photovoltaic sub-power station; according to the inspection path, performing multi-angle scanning on each photovoltaic module in different installation environments through a multi-source sensor, and obtaining photovoltaic module image data with position information in combination with the real-time position coordinates of the unmanned aerial vehicle; performing preprocessing, feature extraction and fault identification on the photovoltaic module image data by using a fault identification model, and establishing a fault feature library; and carrying out fusion analysis on the multi-source data, generating a fault detection report of each photovoltaic sub-power station based on the inspection task list, and formulating a corresponding maintenance scheme according to the fault detection report. According to the invention, multi-source data fusion analysis of the photovoltaic module image data, the fault feature library, the position coordinates and the three-dimensional map is realized, and the inspection efficiency and the fault detection accuracy of the distributed photovoltaic power station are improved.

The invention provides a community management method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of community management. The method comprises the steps of obtaining an initial inspection task of an unmanned aerial vehicle cluster and planning an inspection route, and controlling the unmanned aerial vehicle cluster to execute inspection operation to obtain multi-source inspection data. And constructing a community portrait based on the inspection data, identifying community management hidden dangers through a classification reasoning model, and generating an early warning event and a processing task. A processing task is decomposed into hierarchical sub-tasks by utilizing an ontology reasoning framework, a task execution main body is selected, and a task set is generated. And a multi-Agent reinforcement learning framework is adopted to carry out space-time collaborative arrangement on the task set to form an overall collaborative scheme, the overall collaborative scheme is converted into an interpretable scheduling decision instruction, and finally the unmanned aerial vehicle is controlled to execute the task. According to the technical scheme, the emergency response speed of security and protection and the capability of coping with complex conditions are improved.

The invention provides a multi-path security inspection robot intelligent scheduling method, system and device and a medium thereof, and the method comprises the following steps: installing a plurality of sensors in a target inspection area, and collecting the basic parameter information of inspection robots and the inspection area in real time; according to the invention, multi-dimensional data, including various information of the inspection robot and the inspection area, are acquired through various sensors, and through preprocessing and fusion algorithms, noise is removed and multi-source data are integrated, so that an accurate and comprehensive data basis is provided for subsequent decision making, and the problem of information fragmentation is solved; a plurality of factors such as landforms and obstacles are considered through a path planning algorithm, an optimal path is calculated, updating can be carried out according to real-time conditions, and rapid correction is carried out by using an incremental algorithm, so that the planning precision and flexibility are improved, and the inspection efficiency is improved; the robot and obstacle information is acquired in real time through the sensor, so that the route and the task are optimized according to the real-time information, and the inspection effect and the guarantee capability are improved.

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 unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention relates to the field of unmanned aerial vehicle route acquisition, in particular to an unmanned aerial vehicle dynamic inspection route acquisition method and system based on laser point cloud. The method comprises the steps of comprehensive processing of three-dimensional point cloud data, marking of equipment inspection point positions to generate waypoints, safe distance collision inspection of the waypoints, position adjustment of the waypoints, planning of inspection paths and the like, through comprehensive processing of the three-dimensional point cloud data, including denoising, classification and thinning, the quality of the point cloud data can be remarkably improved, and by means of the laser point cloud technology, the quality of the three-dimensional point cloud data is improved. The terrain and equipment characteristics of the to-be-inspected area can be accurately captured, so that the accuracy and reliability of inspection are improved. The steps of waypoint safe distance collision inspection and waypoint position adjustment further enhance the autonomous flight ability of the unmanned aerial vehicle, and ensure the safety and stability of the inspection process. When the routing inspection path is planned, an optimal safe path algorithm is adopted, the path between two waypoints is searched, a local route is generated, and finally the local route is connected to form a complete route, so that the high efficiency and integrity of routing inspection are ensured.

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 the technical field of space positioning and path planning, in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. The method comprises the following steps of performing accurate longitude and latitude height data acquisition based on a laser range finder to generate accurate longitude and latitude height data; obtaining tower equipment parameters; performing inspection point three-dimensional coordinate calculation according to the tower equipment parameters and the tower three-dimensional coordinate system to generate inspection point three-dimensional coordinate data; cruise point comprehensive constraint range data are obtained according to the unmanned aerial vehicle flight association data and the polling area terrain data, and cruise point comprehensive constraint range data are generated; performing cruise node three-dimensional coordinate design on the three-dimensional coordinate data of the inspection point according to the cruise point comprehensive constraint range data to generate cruise point three-dimensional coordinate data; and performing optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data. According to the invention, more accurate unmanned aerial vehicle tower space positioning and path planning are realized.

The invention discloses a small unmanned aerial vehicle narrow-distance space safety patrol method, relates to the technical field of space monitoring, and is used for solving the problem that a patrol path of an unmanned aerial vehicle in a transformer substation is not clearly generated. According to the substation equipment layout, inspection points are divided into safety points, dangerous points and transition points, an inspection path is optimized through a shortest path algorithm, flight safety and task efficiency and energy consumption minimization are ensured, inspection tasks are allocated to unmanned aerial vehicles according to priorities, emergency tasks are ensured to be processed preferentially, and then environment changes are monitored in real time, so that the safety of the substation equipment is ensured. According to the method, the unmanned aerial vehicle can rapidly and accurately arrive at the new dangerous point through the real-time monitoring of the unmanned aerial vehicle, new dangerous points and obstacles are timely identified, the flight path is dynamically adjusted through transition point optimization, it is ensured that the unmanned aerial vehicle can rapidly and accurately arrive at the new dangerous points, finally, high-precision positioning of the unmanned aerial vehicle in a narrow-distance space is ensured through combination of RTK and a vision-assisted positioning system, the flight state is dynamically corrected through a PID control algorithm, and it is ensured that the path is accurate and stable.

The invention discloses an air-ground collaborative dam termite trace detection method and device and electronic equipment. The method comprises the steps that an unmanned aerial vehicle is used for cruising and scanning the surface of a dam to obtain image information; constructing a three-dimensional map of the dam by using the image information; extracting termite activity trace characteristics on the image information; according to the image information, a detection and recognition model is constructed, and the detection and recognition model is that an efficient detector for a small target is introduced on the basis of yov8; deploying the detection and recognition model on a ground tracked robot, and performing initial detection and recognition on the termite activity trace characteristics by the unmanned aerial vehicle by using the detection and recognition model; the termite traces recognized through initial detection are determined as suspected features, and the position points of the suspected features are determined and calibrated on a map; and the ground tracked robot plans a one-way planning route map to a position point according to the position point calibrated on the three-dimensional map, then navigates to the position point with the suspected feature, and carries out detection confirmation, so that air-ground collaborative integrated dam termite earth surface exposure feature detection is comprehensively realized.

The invention relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and device, and the method comprises the steps: obtaining a three-dimensional routing inspection map of a target routing inspection region, the three-dimensional routing inspection map comprising routing inspection equipment and obstacles; constructing a target function based on the inspection coverage rate, the inspection energy consumption and the inspection efficiency of the unmanned aerial vehicle in the target inspection area; constructing constraint conditions based on the inspection speed and the inspection time of the unmanned aerial vehicle and the shooting limitation of the unmanned aerial vehicle; based on the objective function and the constraint condition, adopting a local optimization strategy to obtain an initial planning path; and optimizing the initial planning path by adopting a global optimization strategy to complete the path planning of the unmanned aerial vehicle inspection. Through the method, automatic, efficient and accurate route planning of the inspection unmanned aerial vehicle in the target inspection area is realized, blind areas of inspection equipment are reduced to the maximum extent, the integrity and reliability of inspection are enhanced, the inspection efficiency is improved, and the operation risk is reduced.

The invention discloses a spinning equipment real-time monitoring system based on an itinerant robot and stroboscopic control, particularly relates to the field of spinning equipment monitoring, is used for solving the problems of low monitoring precision and insufficient inspection efficiency, and is used for accurately evaluating the state of spinning equipment and generating a score based on historical operation data and real-time feedback. And then planning and dynamically adjusting an inspection path by adopting an ant colony optimization and reinforcement learning algorithm to ensure that high-risk equipment is preferentially monitored. Resource allocation is optimized and inspection efficiency is improved through accurate simulation of path time consumption and a coverage range; meanwhile, handshake connection is encrypted through multiple protocols, equipment operation parameters are extracted in real time, stroboscopic control is dynamically adjusted, and the definition of image collection and data safety are guaranteed; high-frame-rate high-definition camera shooting and self-adaptive stroboscopic control are combined, high-quality image data are collected and preprocessed, deep analysis is carried out by using a convolutional neural network and a multivariable correlation model, abnormal early warning is generated in real time, and an inspection path is optimized.

The invention relates to an offshore wind turbine generator data acquisition method and system, and the method comprises the steps: dividing a target offshore wind power plant into a plurality of inspection regions, and obtaining the structure parameters and topographic data of a wind turbine generator of each inspection region; according to the wind turbine generator structure parameters and topographic data, respectively constructing an unmanned aerial vehicle flight path of each inspection area, and presetting a surrounding radius and a vehicle position angle; based on the flight path, the surrounding radius and the camera position angle, controlling the unmanned aerial vehicle to carry out surrounding flight on the wind turbine generator in each inspection area, and collecting a wind turbine generator image and point cloud data in the surrounding flight process; and constructing a wind turbine generator three-dimensional model based on the wind turbine generator image and the point cloud data, constructing a three-dimensional map in combination with the topographic data, and fusing a flight path into the three-dimensional map to complete data acquisition of the target offshore wind plant. According to the invention, autonomous intelligent inspection of the offshore wind turbine generator can be realized.

The invention discloses a transformer substation patrol robot management method based on a company Internet of Things platform, and the method comprises the steps: collecting multi-modal data in a transformer substation, carrying out the fusion and analysis of the collected multi-modal data, and generating equipment state information based on a deep learning algorithm; according to the equipment state information and the routing inspection task priority, task allocation is performed on the routing inspection robot through a dynamic scheduling algorithm, according to a task allocation result, an optimal routing inspection path for the robot to execute tasks is formulated by using a path planning algorithm, and the path is adjusted through real-time data; constructing an equipment health management model, and predicting an equipment operation state based on the time sequence data and a machine learning algorithm; real-time monitoring is carried out through an Internet of Things platform, and data is stored in a centralized database. According to the invention, the inspection efficiency and accuracy of the transformer substation can be obviously improved, the potential risk caused by equipment failure or inspection omission is reduced, and the operation maintenance mode of the unattended transformer substation is optimized.

The invention discloses an unmanned intelligent inspection method and system for an intelligent power plant, and relates to the technical field of power plant inspection, and the method comprises the steps: carrying out the inspection partitioning according to the function partitioning and environment attribute information of a target power plant, and arranging an inspection equipment group and an edge processing node; receiving real-time environment monitoring data through each edge node, adaptively optimizing a conventional routing inspection scheme, and executing intelligent routing inspection to obtain a routing inspection data set; and obtaining a local inspection result through independent anomaly analysis, uploading the local inspection result to a master control center for correlation influence analysis, and correcting the local inspection result. The technical problem that in the prior art, along with the complexity trend of an intelligent power plant, a traditional inspection method cannot adapt to complex and changeable inspection requirements, so that the inspection efficiency and accuracy are low is solved, and real-time monitoring and quick response of the operation state of the power plant are achieved through unmanned intelligent inspection. And the inspection efficiency and the data accuracy are improved.

The invention discloses an intelligent street lamp intelligent supervision system and method based on cloud computing, and belongs to the technical field of intelligent street lamp supervision. The method comprises the following steps: acquiring position distribution of street lamps in a to-be-detected area and product specification information corresponding to each street lamp, detecting the street lamps in the to-be-detected area through a sensor, and generating street lamp detection feature information of each position in the to-be-detected area; analyzing the street lamp detection feature information after image recognition to obtain a corresponding street lamp quality evaluation report; according to the real-time street lamp quality evaluation report of the street lamp, generating a fault risk street lamp in combination with historical data; and calculating a fault risk reference value for the updated routing inspection route scheme, selecting the updated routing inspection route scheme with the minimum fault risk reference value as an optimal routing inspection scheme, and sending the optimal routing inspection scheme to a maintainer to which the optimal routing inspection scheme belongs. The influence on the original inspection route scheme is reduced, and the efficiency of inspection and maintenance of the street lamp is improved.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention discloses an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention discloses a planning method for a pumped storage power station unmanned aerial vehicle inspection complex task. The method comprises the following steps: based on historical inspection data of the pumped storage power station, identifying key indexes and modes in inspection through big data analysis so as to construct a multi-dimensional historical inspection data set; based on historical maintenance records and real-time monitoring data of pumped storage power station equipment, using a time sequence analysis and neural network model to construct an equipment fault prediction model; obtaining potential faults and maintenance requirements of the equipment by using the equipment fault prediction model; and constructing an intelligent path inspection model by adopting a path planning algorithm so as to formulate an intelligent path inspection plan for the unmanned aerial vehicle inspection complex task. The unmanned aerial vehicle is used for collecting real-time data of inspection, and the inspection task path is dynamically adjusted. Through the method, the efficiency, the accuracy and the adaptability of the unmanned aerial vehicle to the completion of the complicated inspection task are ensured, and a powerful technical support is provided for the safe operation of the pumped storage power station.

The invention belongs to the technical field of unmanned aerial vehicle nest control, and discloses an unmanned aerial vehicle nest remote control method and system, and the method comprises the steps: obtaining the remaining electric quantity and real-time position of each unmanned aerial vehicle in an inspection area; obtaining the state of each position of each unmanned aerial vehicle nest; constructing a charging scheduling function based on the residual electric quantity and the real-time position of each unmanned aerial vehicle, the state of each position of each unmanned aerial vehicle nest, the nest position and the inspection task; generating a dispatching instruction and a charging instruction of the unmanned aerial vehicle based on the charging dispatching function; the dispatching instruction is sent to the unmanned aerial vehicle needing to be charged, and the charging instruction is sent to the unmanned aerial vehicle nest with the parking space in the idle state; the unmanned aerial vehicle needing to be charged flies to the unmanned aerial vehicle nest with the parking space in the idle state based on the dispatching instruction, and the unmanned aerial vehicle nest charges the unmanned aerial vehicle entering the nest based on the charging instruction. The situation that the unmanned aerial vehicle queues for charging is avoided, and the inspection efficiency can be improved.

The invention provides a robot system based on self-adaptive cruise and remote operation and a control method thereof, and belongs to the field of robot inspection, the system comprises a six-axis mechanical arm, an intelligent inspection robot, an automatic inspection task module and a remote teleoperation control unit; the intelligent inspection robot carries a six-axis mechanical arm and an automatic inspection task module, the automatic inspection task module comprises a preset inspection task, the remote teleoperation control unit sends a control instruction of an operator to the intelligent inspection robot through wireless communication, and the control instruction comprises inspection path planning and operation tasks. The intelligent inspection robot performs an inspection task according to a preset inspection task of the automatic inspection task module or a control instruction sent by the remote operation control unit, and the six-axis mechanical arm is used for executing a target operation task. Through integration of advanced sensor technology, image processing technology, artificial intelligence algorithm and wireless communication technology, efficient, accurate and safe inspection of a complex environment is realized.

The invention is suitable for the technical field of public rental housing management, and provides a public rental housing inspection management system, which comprises a hardware equipment layer used for collecting environment data, equipment state data and inspection data of a public rental housing, and the hardware equipment layer comprises a plurality of sensor equipment, a wireless communication module and mobile inspection equipment; the data processing layer is used for receiving, storing and processing data collected by the hardware equipment layer, and the data processing layer comprises a data receiving module, a data processing module, a data storage module, a data analysis module and an early warning module; and the application service layer is used for providing user interaction and system management services according to the data acquired by the hardware equipment layer and the data processed by the data processing layer, and the application service layer comprises a housing resource management module, an inspection task distribution module, an inspection management module, an early warning management module, a contract management module and a housing maintenance management module. The problem of low inspection efficiency in the prior art is solved.

The invention discloses a gas leakage point leakage detection system and method based on an e-dog, and belongs to the technical field of gas leakage detection. Through the autonomous navigation capability of the mobile platform module, the system can automatically avoid obstacles in a complex environment and inspect along a predetermined path, optimizes the inspection path in real time after finding a gas leakage point, improves the inspection efficiency, guarantees the high-density inspection of a key area, and the rapid movement and precise positioning capability of an e-dog, and improves the inspection efficiency. The gas leakage detection is quicker and more efficient, the risk that personnel enter a dangerous environment is reduced, the system has the functions of autonomous navigation, obstacle avoidance, remote control and the like, the intelligent level of detection is improved, the system is suitable for detection requirements of various complex environments and different types of gases, and through cooperative work of the gas detection module and the data acquisition and processing module, the detection efficiency is improved. The gas concentration data can be accurately acquired, the position of the leakage point can be judged, and the leakage point can be quickly and accurately positioned based on the combination of the GPS and the gas concentration data.