9313 results about "Operation safety" patented technology

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention relates to the technical field of control and regulation systems, in particular to a hearth auxiliary regulation control system which comprises a data processing module, an analysis module, a control module, a regulation and control module, an optimization module and a safety early warning module. The data processing module collects temperature data of a hearth combustor area, a water cooling wall and a tail flue in real time through a distributed temperature sensor network, and a dynamic combustion field three-dimensional model is generated through Kalman filtering denoising, redundancy cross validation and a temperature compensation algorithm in combination with combustor flame position signals and an air door opening state. And the optimization module updates the strategy weight based on the combustion efficiency improvement rate, and reinforces learning to extend the adaptive boundary of the strategy library. And the safety early warning module calculates the matching coefficient of the pipe wall temperature and the allowable stress in real time, triggers a grading intervention strategy to reduce the overtemperature risk, forms full-process closed-loop control, and improves the thermal efficiency and the operation safety of the boiler.

The invention relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

The invention relates to the technical field of forklift risk management, in particular to a multi-scene-oriented forklift self-adaptive safety early warning decision-making method, which comprises the following steps of: through multi-scene dynamic modeling, constructing multi-dimensional scene feature vectors according to forklift operation environment spatial features, cargo physical attributes and driver behavior parameters; establishing a scene classification model by means of transfer learning; a multi-modal sensor data fusion technology is utilized, a heterogeneous sensor network is deployed to collect data, and data reliability is optimized through space-time alignment and confidence evaluation; a dynamic safety threshold value is generated through fuzzy logic and reinforcement learning, and a driver behavior correction factor is introduced for real-time adjustment; calculating a comprehensive risk index by using a dynamic weight distribution algorithm, and triggering graded early warning and response; a federated learning closed-loop optimization risk assessment model is adopted, robustness is verified in combination with digital twinning, and cooperative obstacle avoidance of multiple forklifts is achieved through inter-vehicle communication. According to the method, the operation safety and decision-making accuracy of the forklift in a complex and changeable scene are remarkably improved.

The invention relates to the technical field of smart energy management, in particular to a public energy consumption equipment operation and maintenance management system suitable for a smart park. The system comprises an equipment operation state sensing module, an energy consumption characteristic dynamic analysis module, an operation parameter optimization module, an equipment state balance regulation and control module, an equipment operation safety constraint verification module and an equipment cooperative operation execution module. According to the invention, through combination of time sequence analysis and load fluctuation trend extraction, dynamic monitoring and trend arrangement are realized, the accuracy and comprehensiveness of operation data are improved, the operation diagnosis capability is improved, the parameter adjustment flexibility is enhanced, multi-device cooperative regulation and control and load balancing are realized, energy consumption fluctuation is reduced, and the system efficiency is improved. And in combination with safety verification and offset risk analysis, potential risk areas are adjusted in real time, the operation reliability is enhanced, a global regulation and control scheme is dynamically generated, energy consumption waste is reduced, the management efficiency of public energy consumption equipment is comprehensively improved, and the efficient energy-saving target of the smart park is achieved.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

The invention relates to the technical field of intelligent management, in particular to an intelligent management method for production of a high-speed rotating transmission device, which comprises the following steps: extracting operation data, calculating deviation and marking abnormity, adjusting load generation configuration in combination with task information, judging a beat overlapping splitting priority recording path, and generating a regulation and control sequence by screening equipment. And extracting a temperature and pressure trend over-limit reset beat generation scheme. According to the method, a periodic alternation model is constructed by combining task numbers and running states to dynamically generate a load adjustment mode, so that the task adaptability is improved, a continuous scheduling channel is constructed by utilizing output time period difference judgment and beat conflict splitting, the task connection fluency is improved, and a resource configuration structure is optimized through equipment number de-weighting and conflict empowerment sorting; and the regulation and control efficiency is enhanced, real-time pre-judgment and cooling regulation are realized based on the temperature and pressure change trend, the operation safety guarantee is enhanced, and the accuracy, responsiveness and stability of intelligent management and control are integrally improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle intelligent inspection method for construction site operation safety, which comprises the following steps: step 1, deploying an unmanned aerial vehicle, personnel positioning equipment and a base station, and establishing a time synchronization network and a space coordinate conversion model through the base station; step 2, when the unmanned aerial vehicle executes an inspection task, detecting a shielding area in an image in real time based on a preset route, and generating a dynamic obstacle avoidance path according to the three-dimensional model to control the unmanned aerial vehicle to perform multi-view supplementary shooting; and step 3, carrying out space-time alignment on the multi-view supplementary shooting image and positioning data acquired by the personnel positioning equipment. According to the invention, the technical scheme of dynamic shielding perception and multi-view adaptive supplementary shooting is adopted, a high-precision space coordinate system constructed by a reference station and a real-time shielding detection mechanism are combined with dynamic obstacle avoidance path planning, and comprehensive coverage inspection of a high-altitude and hidden operation area is realized.

The invention discloses a safe trajectory tracking method and system for a ground differential drive robot, and particularly relates to the technical field of robot operation management. Comprehensive means of multi-source data fusion, dynamic safe distance setting, trajectory prediction uncertainty quantification and trajectory deviation cost optimization are introduced; the path planning capability of the robot in a complex dynamic environment is improved; the behavior characteristics and environment complexity of the dynamic obstacle are evaluated in real time, the safe distance is dynamically adjusted, and the dynamic obstacle area is accurately quantified in combination with the trajectory prediction model, so that the problems of low trajectory tracking precision and insufficient obstacle avoidance robustness in the background technology are solved; the path optimization is realized through the minimization strategy of the trajectory deviation cost function, the operation safety of the robot in diversified scenes is ensured, and meanwhile, the efficiency and stability of path planning are greatly improved.

The invention provides a metro driver abnormal behavior recognition method based on multi-modal data fusion. The method comprises the steps that metro driver abnormal behaviors are recognized based on multi-modal data fusion, track positioning and platform screen door sensor data are fused to detect the position of a train, and a station entering confirmation instruction is generated; according to the data of the seat pressure distribution sensor, driver off-seat inspection is judged; generating an outbound completion event in combination with a track circuit signal, an ATO instruction and the like; analyzing console touch pressure, voice instructions, hand actions and the like to verify operation compliance, and triggering an alarm when the operation is abnormal; aggregating results at edge computing nodes, encrypting and transmitting the aggregated results to a central scheduling system, dynamically mapping driver behaviors in a digital twin cockpit, and automatically triggering hierarchical alarms and associating data snapshots when the driver behaviors are abnormal; according to the invention, accurate recognition and real-time early warning of subway driver behaviors can be realized by means of multi-modal data fusion and intelligent analysis, and the subway operation safety and the emergency response efficiency are greatly improved.

The invention discloses a control method and device for a mounting and dismounting integrated robot of a sound barrier, and relates to the field of robot control. According to the method, a digital twinborn model and a dynamic environment model are constructed according to original sensor data, and a current environment risk value is determined; a mounting or dismounting sequence is generated based on a sound barrier topology rule, and a motion path and a mechanical arm motion track are generated according to the digital twin model, the dynamic environment model and the mounting or dismounting sequence; rehearsing the motion path and the motion track of the mechanical arm in the digital twin model, and when a rehearsing result meets a preset requirement, generating an initial instruction based on the motion path and the motion track of the mechanical arm; the current clamping force is determined through an impedance control algorithm, the current hoisting parameter is determined according to the load weight, the initial instruction is adjusted according to the current clamping force, the current hoisting parameter and the current environment risk value to obtain a control instruction, and the control instruction is executed. By implementing the technical scheme provided by the invention, the mounting and dismounting efficiency is improved, and the operation safety is enhanced.

The invention relates to the technical field of electrical monitoring, provides a general generator electrical monitoring system with a fault self-diagnosis function, and aims to deeply excavate potential correlation between electrical and mechanical parameters by judging a coherence coefficient and a phase difference between a current harmonic component and a bearing vibration frequency band and marking a fault coupling identifier by using a coupling mode library. Electrical and mechanical coupling faults can be accurately identified, and the identification capability of complex faults can be greatly improved; meanwhile, the diagnosis threshold is dynamically adjusted in combination with the load rate and the winding temperature, so that the system can better adapt to different operation conditions of the generator, and the diagnosis accuracy and reliability are improved; the fault causal chain is analyzed through the causal inference algorithm, and the fault source is positioned, so that compared with the existing fault tracing mode lacking systematicness, the fault generation reason and process can be analyzed more comprehensively and deeply, the fault source can be positioned quickly and accurately, operation and maintenance personnel can take targeted measures in time, and the fault tracing efficiency is improved. And the operation safety and reliability of the generator are improved.

The invention relates to the technical field of big data analysis, in particular to a contact defect detection system and method based on big data, and the system comprises a multi-dimensional data capture module, a time sequence analysis module, a trend evaluation and anomaly recognition module, a future state prediction module, and a fault classification and diagnosis module. According to the invention, by deeply capturing and screening contact operation state data, the accuracy of input data is ensured, abnormal interference of contact resistance is avoided, the data validity is improved, current impact fluctuation points are identified, the sensitivity to operation abrupt change is enhanced, the accuracy of time sequence analysis is ensured, long-term offset is tracked, the wear identification capability is improved, and the future state is accurately predicted. The method is advantaged in that maintenance plan precision is improved, contact pressure reduction trend is analyzed, fault classification is refined, defect identification accuracy is improved, data screening, trend tracking, offset characteristic calculation and fault classification are optimized, accurate detection is realized, and equipment reliability and operation safety are improved.

The invention belongs to the field of early warning and monitoring of power equipment, and particularly discloses an early warning system and method for inclination and collapse of a tower. The system comprises a sensor module, a data acquisition and preprocessing module, an edge calculation module, a data transmission and synchronization module, a central processing and intelligent analysis module, a visualization and early warning module, a power management module, a model training and optimization module and a system management and maintenance module. The system is high in data acquisition precision, strong in system robustness and high in early warning efficiency, can efficiently and accurately carry out real-time monitoring and inclination collapse early warning on the state of the tower of the transformer substation, and remarkably improves the operation safety and management efficiency of the transformer substation.

The invention discloses a main transformer equipment state evaluation and early warning method and system, and relates to the technical field of power equipment monitoring and fault diagnosis, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data; fusing the preprocessed multi-modal data to obtain a fused feature vector; obtaining a health index of the main transformer based on the fusion feature vector; based on the health index of the main transformer, evaluating the health state of the main transformer through quantum correlation analysis; analyzing the health trend of the main transformer through a time sequence based on the health state of the main transformer, and obtaining an early warning signal; and based on the early warning signal, main transformer faults are analyzed through historical working condition data of the main transformer, and a maintenance strategy is formulated. Through multi-modal data fusion and quantum correlation analysis, deeper health state information is provided, potential faults can be found in time, and the operation safety and reliability of equipment are improved.

The invention provides a reinforced diesel engine friction pair on-line wear detection device and a wear detection method. The reinforced diesel engine friction pair on-line wear detection device comprises a wear calculation terminal, an NI-PXIe8115 acquisition instrument, a sampling detection circulating oil path, a cylinder pressure sensor, a rotating speed sensor, a vibration sensor, an acoustic emission sensor, a six-in-one lubricating oil physical and chemical sensor, an abrasive particle sensor, an abrasive particle visual sensor and a CAN / USB / RS485 communication interface. The method has the beneficial effects that the load of a friction pair of the strengthened diesel engine is large, the process from symptoms to failure is extremely short, and serious fault events are difficult to control, the device can quickly warn an abnormal state after being implemented, gives a corresponding action instruction to a user, and provides a reliable identification means for strengthening research, development and operation safety of the diesel engine.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention belongs to the technical field of robot navigation, and discloses a robot path planning method based on a graph neural network, through causal perception dynamic graph construction and AST-GNN training, a causal relationship, such as pedestrian steering-path change, of obstacle movement is excavated, extreme scene features are learned in combination with adversarial training, and the path planning precision is improved. Therefore, when the robot is in an emergency scene (such as object falling and pedestrian sharp turning), the obstacle movement pre-judgment accuracy is improved, the path re-planning response speed is increased, the collision risk is greatly reduced, and the operation safety in a complex dynamic environment is ensured; a block chain alliance chain and an intelligent contract mechanism are adopted, path data credibility is guaranteed through ECC encryption, a PBFT algorithm rapidly reaches a consensus, an intelligent contract automatically allocates priorities, such as emergency task priority, and a path optimized through differential geometry is combined, so that the path conflict rate of multiple robots in dense scenes such as storage and venues is remarkably reduced.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention relates to the technical field of oil and gas equipment operation control, in particular to an oil and gas equipment operation parameter self-adaptive fine tuning method and system, and the method comprises the steps: constructing an operation parameter optimization library driven by reinforcement learning, defining an action space containing equipment types, working condition modes and key parameter combinations, predicting accuracy, response efficiency and safety compliance are optimized in combination with a multi-target reward function, an optimized instruction set is generated, safety perception mixing precision fine adjustment is carried out in parallel, calculation precision is configured in a layered mode, errors are monitored in real time, a high-precision mode is switched if the precision exceeds the limit, and a reservoir engineering equation mechanism constraint matrix is embedded to guide parameter updating. A generative adversarial network is utilized to synthesize fault data, a Darcy seepage causal engine is integrated to correct a loss function, the system comprises an action space construction unit, a precision dynamic scheduling unit, a mechanism constraint embedding unit and a data causal cooperation unit, and the method and the system realize data-mechanism fusion, balance precision and real-time performance, and improve equipment operation safety and adaptability.

The invention discloses a vacuum circuit breaker state online monitoring method and system based on digital twinning, and relates to the field of intelligent operation and maintenance of high-voltage power equipment. According to the method, various sensors are arranged at key parts of the vacuum circuit breaker, breaking current, shielding case potential and vibration signals are collected in real time, and a digital twinborn model is constructed by combining multi-source data preprocessing, three-dimensional modeling, behavior rule definition and fault simulation analysis; and on-line identification and trend prediction of electrical wear, vacuum degree deterioration and mechanical abnormity are realized. The system has the functions of real-time monitoring, three-dimensional visualization, model self-learning and intelligent early warning, can be widely applied to intelligent monitoring and state evaluation of circuit breaker equipment in a power system, and remarkably improves the operation safety and operation and maintenance efficiency of the equipment.

The invention discloses an electric actuating mechanism service life evaluation method and system based on dynamic load monitoring, and particularly relates to the technical field of service life evaluation. For a fretting wear accumulation effect caused by resonance of a load frequency spectrum and a mechanical gap and lubricating oil film fracture reconstruction lag caused by the fact that the load change rate exceeds a critical value, an output shaft torque signal is collected for time-frequency domain conversion, and a load change frequency spectrum and a load change rate are obtained; and in combination with oil film state monitoring, the lubrication state deterioration risk and the wear accumulation effect are evaluated, and the failure critical risk is determined. When the failure critical risk is lower than a safety threshold value, analyzing contact surface stress distribution through a finite element to evaluate the wear degree, predicting the residual life by combining the failure threshold value of the similar electric actuating mechanism in a historical database, and judging whether the electric actuating mechanism needs to be maintained or replaced in advance, so that the actual wear and failure trend of the electric actuating mechanism under multiple working conditions can be accurately reflected; and the scientificity of maintenance decision and the operation safety are improved.

The invention discloses a converter valve internal temperature monitoring and early warning method and system, and particularly relates to the technical field of converter valve temperature monitoring. Temperature sensors are arranged on a plurality of key heating units, local temperature data are collected in real time, a time sequence is built in combination with cooling water inlet and outlet temperature, a temperature trend prediction model is built based on temperature rise rate and temperature gradient change, local thermal runaway risks are accurately recognized, and a prediction result is compared with a grading threshold value, so that the local thermal runaway risk is determined. Multi-stage early warning signal output is realized, and a corresponding processing strategy is linked; under the extreme conditions that the local temperature is continuously and abnormally increased and the response of the cooling system is lagged, the system can trigger emergency shutdown protection in an override mode, device failure is avoided, the sensing capacity and response speed of local thermal faults are effectively improved, and the operation safety and the intelligent level of the converter valve system are remarkably improved.

The invention discloses an air flotation vacuum pump performance prediction system based on a specific calculation model, and relates to the technical field of mechanical engineering. Comprising a multi-parameter fluctuation sensing module, a weak feature sensitive extraction module, an abnormal coupling atlas construction module, an instability precursor identification module, a prediction model regulation and control module and a model adaptive optimization module, and carrying out fusion in a preset time window, constructing a multi-parameter synchronous fluctuation sensing model, and outputting a synchronous anomaly index. By introducing multi-parameter synchronous fluctuation perception, weak feature extraction and an abnormal coupling map, accurate identification and early warning of the early instability trend of the air floating vacuum pump are realized, and an intelligent system with real-time perception and closed-loop prediction capabilities is constructed by combining model regulation and control and a self-adaptive optimization mechanism, so that the operation safety and stability are improved.

The invention discloses an integrated intelligent welding material warehouse which comprises a storage area and an equipment area, the storage area is provided with multiple layers of goods shelves, and the equipment area is provided with a drying oven, a material taking mechanism, a transferring mechanism, a distribution bin mechanism, a discharging mechanism and a recycling mechanism. The material taking mechanism is used for stretching into the baking oven to take out the trays carrying the materials and placing the trays on the transfer mechanism platform, and then empty trays are taken back from the transfer mechanism platform and placed in the baking oven. The transfer mechanism is used for grabbing and moving the welding rods to a designated distribution bin for feeding; the distribution bin mechanism is used for ejecting the welding rods in the bin body out of the outlet and sliding the welding rods into a hopper of the discharging mechanism. The recycling mechanism is used for recycling the remaining welding rods into the recycling bin. The problem of manual distribution of welding rods in the whole process is solved, automation of the whole process of distribution of welding materials is achieved, welding rod management is improved, and construction quality and operation safety are guaranteed.

The invention relates to the technical field of semiconductor manufacturing, in particular to an automatic loading and unloading control system for semiconductor material plates. A clamping force dynamic control module; the path planning and obstacle avoidance module is used for dividing safety levels of loading and unloading areas on the basis of material plate stacking parameters, and generating an obstacle avoidance track by adopting an improved path search algorithm; optimizing an acceleration curve of the path according to the joint load data of the mechanical arm; the closed-loop verification module is used for carrying out deviation comparison on the actual pose of the tail end of the mechanical arm and the target coordinates, and if the deviation exceeds the limit, the visual positioning and morphology detection module is triggered to cooperatively reset to the path planning and obstacle avoidance module; the visual positioning and morphology detection module and the closed loop verification module are executed in sequence, and output parameters of the previous module serve as input parameters of the next module. Through close cooperation of the modules, efficient and accurate automatic loading and unloading control is achieved, the automation level is improved, and operation safety and accuracy are guaranteed.

The invention discloses an intelligent obstacle avoidance system and method for a low-altitude aircraft. Efficient obstacle avoidance is realized by fusing an improved attention mechanism and a binocular vision sensing multi-dimensional analysis model. In the system, a binocular vision perception unit collects double-path images and establishes motion association, a feature enhancement attention unit performs dynamic weighting according to image features, and a multi-dimensional analysis processing unit constructs a three-dimensional feature space to accurately recognize obstacles. The path planning decision-making unit combines the aircraft state and a safety threshold value, an optimal obstacle avoidance path is screened through heuristic search and cost evaluation, and the motion control instruction generation unit converts the path into a posture and speed control instruction to drive obstacle avoidance. According to the invention, the defects of the traditional technology in obstacle identification and path planning are overcome, dynamic or hidden obstacles can be accurately identified in a complex environment, the obstacle avoidance path is efficiently planned, and the operation safety and reliability of the low-altitude aircraft are improved.

The invention discloses grinding equipment with a surface self-adaption function for civil aircraft maintenance, and relates to the technical field of grinding equipment, the grinding equipment comprises a grinding mechanism, a self-adaption mechanism and a positioning mechanism, the grinding mechanism is fixedly connected with the self-adaption mechanism, and the self-adaption mechanism is fixedly connected with the positioning mechanism. The grinding mechanism is used for driving a grinding disc to work, and flexible grinding of the surfaces of different areas can be achieved. The self-adaptive mechanism is composed of a self-adaptive assembly, an expanding and contracting assembly and a rotating assembly, the grinding disc has the flexible adjusting capacity when making contact with the surface of a workpiece, the expanding and contracting assembly achieves expanding and contracting, the rotating assembly drives an expanding and contracting block to revolve, and therefore the adaptability of the grinding disc on the complex curved surface is guaranteed. And the positioning mechanism is used for positioning parts needing to be maintained, so that the purposes of adapting to complex curved surfaces, improving the polishing precision and efficiency, avoiding the problem of difficult attachment and ensuring the maintenance quality and the operation safety are achieved.