6156 results about "Operation safety" patented technology

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 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 a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention discloses an electricity meter terminal temperature anomaly identification method, system and device and a storage medium, and relates to the technical field of intelligent electricity meter monitoring and anomaly detection.The method comprises the steps that currents of a live line loop and a zero line loop of an intelligent electricity meter are sampled, and the deviation rate of the live line current relative to the zero line current is calculated; estimating the temperature of the terminal of the electric energy meter based on the deviation rate and by using the resistance characteristic metering error change trend of the manganese-copper shunt; and when the terminal temperature estimated value of the electric energy meter exceeds a preset threshold value, determining that the temperature of the live wire terminal is abnormal, and recording an alarm event. The method realizes real-time acquisition of current difference characteristics, provides a reliable data basis for subsequent terminal temperature estimation, realizes a method of indirectly reflecting terminal temperature rise through electrical characteristics, ensures that alarm can be triggered and a complete tracing chain can be formed when temperature abnormity occurs, and improves the accuracy of terminal temperature estimation. Therefore, the controllability of the operation safety of the electric energy meter and the completeness of post analysis are improved.

The present application relates to the technical field of safety management, in particular to a mine air compressor safe operation management method and system based on deep learning, comprising the following steps: collecting output pressure and input current, calculating slope change rate and direction switching frequency, clustering fluctuation amplitude and frequency by time period, generating working condition fluctuation label, identifying state mutation point and duration, calculating risk level label, analyzing load fluctuation coefficient, judging periodic fatigue early warning, combining synchronous rate to identify abnormal section, judging the need to switch state and generating instruction set. In the present application, by constructing the load risk level sequence on the basis of state duration and mutation point identification, the quantitative identification of multi-period load deviation is realized, the early warning period is marked by combining risk level and fatigue deviation ratio, and the abnormal load trend is judged according to the change direction of pressure and return gas flow, which strengthens the capture ability of abnormal operation section and improves the accuracy of operation safety risk identification and the adaptability of response strategy.

The invention provides a wall surface operation device with an obstacle crossing function, which comprises a device body and further comprises a plurality of obstacle crossing assemblies, each obstacle crossing assembly comprises a strip-shaped stroke piece and an obstacle crossing execution component, each obstacle crossing execution component comprises a fixing part and a driving assembly, and the driving assemblies are connected between the strip-shaped stroke pieces and the fixing parts and are connected with the device body. The control part is used for controlling the fixing part to get close to or away from the wall surface; the connecting assembly is used for connecting the obstacle crossing assembly and the device body, and the connecting assembly enables the device body to at least move relative to the corresponding obstacle crossing assembly in the length direction of the strip-shaped stroke piece; the control mechanism is used for controlling the corresponding obstacle crossing assemblies and the connecting assemblies to alternately act to achieve obstacle crossing operation, and the operation mechanism is connected to the device body and used for being in contact operation with the wall face in a detachable mode or being separated from the wall face by a preset distance. High-altitude operation safety and wall surface operation efficiency are both considered, and subsequent operation path planning and intelligent operation of an operation mechanism are facilitated.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention discloses a modularized intelligent tubular column conveying system based on digital twinning and self-adaptive control, and belongs to the technical field of digital twinning. The modularized intelligent tubular column conveying system is characterized in that a digital twinning model integrating a mechanical structure, electrical control and hydraulic power attributes is constructed; acquiring multi-source sensing data including an image sequence, a three-dimensional point cloud and vibration acceleration in real time, driving the digital twin model to perform synchronous simulation, and calculating a deviation value between the digital twin model and internal simulation data; on the basis of the deviation value, a control logic parameter adjusting instruction is generated through a predefined mapping rule, then updated control logic parameters are executed, and a complete pipe column conveying process is simulated to generate a predictive system state sequence; and finally, the safety-related state subsets are issued to a field controller to guide and execute preventive actions. According to the invention, deep fusion and closed-loop control of digital twinning and physical entities are realized, and the debugging efficiency, the environmental adaptability and the operation safety of a tubular column conveying system are effectively improved.

The invention belongs to the technical field of ship navigation and port automation, and particularly discloses a dock ship guiding path planning and dynamic obstacle avoidance system, which is characterized in that a data acquisition module acquires ship parameters, environmental data and obstacle information through a ship end sensing unit and a dock end monitoring unit and standardizes the ship parameters, the environmental data and the obstacle information; the initial path planning module generates an optimal initial ship guiding path based on an improved A * algorithm in combination with ship physical constraints, environmental constraints and operation rules; the dynamic obstacle avoidance module monitors the movement of an obstacle in real time, predicts the trajectory of a ship and the obstacle, and triggers local path re-planning and smoothing processing when a collision risk occurs; and the control output module converts the path into a control instruction, issues and receives ship feedback through two-way communication, and forms closed-loop control according to a deviation fine adjustment instruction or re-planning. The system realizes full-process automation of ship guiding, can dynamically adapt to environmental changes, improve path precision and operation safety, adapt to different docks and ship types, and ensure that ships are accurately and safely parked to target berths.

The invention discloses a quantum somatosensory-driven active protection method and system for thermal runaway of a charging pile, and relates to the technical field of charging pile protection. Comprising the following steps of S1, collecting quantum thermal runaway data in real time, performing data preprocessing, quantifying thermal risk sensitivity, and constructing a thermal runaway risk data set; s2, constructing a thermal runaway space-time matrix, constructing a thermal runaway risk principal component extraction model on the basis of the thermal runaway space-time matrix, and constructing a risk trend prediction model in combination with the thermal runaway space-time matrix and the thermal runaway risk principal component extraction model; s3, the thermal runaway risk is comprehensively judged, and early discovery and active protection of the thermal runaway risk are achieved; and S4, a safety threshold is judged, graded protection response of thermal runaway is carried out, and closed-loop optimization of the protection capability is realized. The problem that the existing traditional charging pile thermal runaway monitoring technology is difficult to timely and accurately discover, prevent and control the hidden danger of thermal runaway, and then the operation safety of the charging pile is affected is solved.

The invention discloses a hydroelectric generating set speed regulation control method considering excitation coordination. The method comprises the steps that 1, system state information is collected and preprocessed; step 2, on-line identification of excitation speed regulation coupling characteristics; 3, decomposing a multi-time-scale control target; step 4, designing a self-adaptive multivariable controller; 5, working condition self-adaptive parameter optimization; step 6, intelligently inhibiting the water attack effect; 7, multi-machine cooperative reactive power optimization control is carried out; 8, optimizing and executing a control strategy in real time; the technical problems that excitation speed regulation dynamic coupling, multi-time scale response mismatch, multi-machine cooperative control and the like in a hydroelectric generating set control system cannot be comprehensively solved in the prior art are solved, and the regulation response speed and the system operation safety are improved while the frequency and voltage stability is ensured.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention provides an automatic driving hazard scene detection method and system, electronic equipment and a vehicle, and relates to the technical field of automatic driving. According to the method, a multi-dimensional hazard scene detection rule system is established, so that accurate identification and response to key security events are realized. In the automatic driving operation process, the system continuously collects and compares a vehicle state signal with a preset triggering condition in real time, once any hazard scene condition is met, vehicle burying point data in a preset time window are collected and uploaded to a cloud end, and the vehicle burying point data are stored in the cloud end. Accident scene restoration, system risk assessment and fault root cause analysis are carried out based on the collected data, then a targeted safety repair scheme is generated, vehicle software is remotely upgraded in an OTA mode, closed-loop processing of safety problems and continuous optimization of the system are achieved, the safety, traceability and remote operation and maintenance capacity of the automatic driving system are effectively improved, and the safety and reliability of the automatic driving system are improved. And the running safety of the vehicle is ensured.

The invention discloses a firmware-level security protection system and method for a hard disk of industrial automation equipment, particularly relates to the technical field of storage security protection, and is used for solving the problem of abnormal tampering of firmware. According to the method, instruction calling, interface access and interrupt timestamps are collected in a sliding window, a behavior sequence set containing an instruction sequence, calling frequency and sensitive marks is constructed and compared with a reference sequence library, firmware-level judgment on operation abnormity, access border crossing and tampering risks is triggered based on a transfer relation and an offset index, and the operation safety is improved. The method comprises the following steps of: dividing firmware into a control domain, a data domain and a configuration domain, realizing minimum permission and dynamic isolation by relying on state and permission mapping and permission register and interface shielding, forming a flow from detection to isolation to closed loop recovery by combining protected segment integrity verification and local copy hot switching, writing an exception into a protected loop log area for threshold updating, and performing protection on the protected loop log area. And the tamper-proof performance, the operation safety and the maintainability of the firmware are integrally improved.

The invention discloses laser cladding repairing equipment for an exhaust valve rod of a marine main engine. The laser cladding repairing equipment comprises a rotating mechanism, a cleaning mechanism and a repairing mechanism. The rotating mechanism adopts a plurality of stepping motors to drive friction wheels, self-centering clamping and intermittent rotation of the exhaust valve rod are achieved through a hydraulic cylinder and a plane connecting rod mechanism, it is ensured that positioning is accurate, and the rotating mechanism adapts to workpieces with different diameters. The cleaning mechanism is arranged below the rotating mechanism, a centrifugal fan is used for simultaneously driving a grinding disc to rotate at a high speed to conduct surface grinding, scrap iron is discharged in time through the negative pressure suction function of the centrifugal fan, and cleaning and dust removal are integrated. And the anti-diffusion cover and the anti-diffusion plate are matched to form a local closed area, so that dust dissipation is effectively inhibited. The repairing mechanism adopts a mechanical arm to carry a laser cladding head, the curved surface of the valve rod can be tracked with high precision, and automatic cladding is completed. The equipment integrates automatic clamping, efficient cleaning, synchronous dust removal and intelligent repairing, and the automation level, the surface treatment quality and the operation safety of repairing of the exhaust valve rod are remarkably improved.

The invention relates to the technical field of transformer coil detection, and discloses a detection device for a transformer coil, which comprises a base frame, the driving mechanism is installed on the base frame and used for providing linear motion in the X direction, the Y direction and the Z direction; according to the scheme, by means of the detection mode that spiral scanning motion is combined with self-adaptive contact and laser ranging, the fatal defects that manual detection is low in efficiency, high in subjectivity and prone to making mistakes and missing detection are overcome, complete three-dimensional shape data of the surface of the coil can be rapidly and accurately obtained, and the detection accuracy is improved. According to the method, local fine defects which are difficult to discover manually can be detected, the reliability and the consistency of detection results are improved, objective and quantitative data support is provided for evaluating the winding quality, the mechanical strength and the long-term operation reliability of a transformer coil, and the manufacturing quality of a transformer and the operation safety of a power grid are guaranteed.

The invention provides an unmanned aerial vehicle route planning operation safety monitoring system and method based on virtual-real fusion, belongs to the technical field of unmanned aerial vehicle safety monitoring, and aims to solve the problems that the test training difficulty of an unmanned aerial vehicle is increased and the experiment cost of the unmanned aerial vehicle is high due to interference and terrain transformation in the task execution process of the unmanned aerial vehicle. The safety monitoring system comprises a ground distributed semi-physical virtual-real fusion simulation platform and an air experiment module. The ground distributed semi-physical virtual-real fusion simulation platform is used for constructing a virtual test scene of task execution of the unmanned aerial vehicle, optimizing a waypoint planning decision according to feedback information in a task execution process, and performing safety judgment and real-time obstacle avoidance on the current waypoint planning decision so as to optimize the waypoint planning decision; and the space-time experiment module is used for monitoring the flight state of the unmanned aerial vehicle and executing the optimized waypoint planning decision.

The invention provides a self-monitoring transformer, and belongs to the technical field of transformers. Wherein the piston is arranged on the first reference pipe, and the piston rod penetrates through the sealing plate. The filter plate is arranged in the first reference pipe. The piston rod is provided with a conveying flow channel, and the area between the piston and the filter plate is communicated with the conveying flow channel. A first one-way assembly is arranged in the conveying flow channel. A second one-way assembly is arranged at the end, away from the sealing plate, of the first reference pipe. The plug rod is driven by the driving assembly in a reciprocating mode. The visual assembly is arranged in the first reference tube. The processor is used for judging the health degree of the insulating oil in the transformer body according to the image data acquired by the visual assembly. The transformer insulating oil monitoring system can continuously and autonomously monitor the actual condition of the transformer insulating oil, avoids the defects of manual monitoring, and effectively improves the operation safety of the transformer.

The invention relates to the technical field of industrial vehicle intelligence, in particular to an intelligent flexible forklift system based on multi-mode sensing and self-adaptive path planning. The intelligent forklift comprises an intelligent forklift body, a vehicle-mounted integrated control system, a multi-mode sensing and navigation module, a self-adaptive decision-making and path planning module, a cooperative communication and task management module and a cloud scheduling and management platform, and the intelligent forklift body is provided with a pallet fork mechanism with a 3D camera and a force sensor; the vehicle-mounted integrated control system serves as a core to achieve multi-module data interaction and instruction output, the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion, the self-adaptive decision-making and path planning module completes path planning, obstacle avoidance and pre-verification, the cooperative communication and task management module achieves multi-vehicle cooperation and task disassembly, and the multi-mode sensing and navigation module generates an environment situation map through multi-source data fusion. And the cloud scheduling and management platform realizes global monitoring and management. According to the method, the adaptability to complex scenes and the operation safety are improved, and accurate forking and flexible path planning are realized.

The invention discloses a fault detection method suitable for a permanent magnet motor sewage pump, and particularly relates to the technical field of motor equipment operation fault detection.The fault detection method comprises the following steps that stage division is conducted on the operation process of the permanent magnet motor sewage pump, and staged dynamic baselines corresponding to all operation stages are established; extracting transient abrupt change of the operating parameters under the dynamic baseline constraint and forming a transient abnormal event sequence; the method comprises the following steps: judging the damage degree grade of a transient abnormal event, continuously accumulating risk integrals, outputting early warning information when a risk reaches a trigger condition, and generating an operation disposal instruction; according to the invention, by constructing a staged dynamic baseline and extracting a transient abnormal event, accurate identification of abnormal behaviors in different operation stages is realized; and in combination with a continuous accumulation mechanism of the damage degree grade and the risk integral, when the risk reaches a triggering condition, an early warning is output, and an operation disposal instruction is generated, so that the accuracy, timeliness and operation safety of fault detection of the permanent magnet motor sewage pump are improved.

The invention relates to the technical field of safety monitoring of a nuclear reactor, and particularly discloses a multi-region pressure overrun leakage monitoring method for a reactor pressure vessel. The method comprises the steps of multi-region pressure data acquisition, real-time pressure field model construction, dynamic pressure field baseline modeling, pressure overrun anomaly detection, leakage source accurate positioning and evaluation based on spatial gradient analysis, topological structure mapping and multi-sensor data fusion, and graded early warning and response. By the adoption of the technical scheme, real-time and fine monitoring of pressure distribution in the reactor pressure vessel can be achieved, the rapid and accurate judgment and positioning capacity of leakage events is improved, the operation safety and reliability of nuclear power facilities are enhanced, and the reliability of the nuclear power facilities is improved. The defects of independent monitoring, local accurate positioning and complex working condition adaptability of multi-region pressure over-limit leakage in the prior art are overcome.

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

The invention provides an intelligent diagnosis system and method for a wind turbine impeller-gearbox composite fault, and relates to the technical field of fault diagnosis, and the system comprises a multi-modal data collection module which is used for carrying out the deployment of a multi-source sensor based on coupling path analysis, and collecting the multi-modal data of the multi-source sensor; the tensor matrix construction module is used for constructing a tensor matrix according to the multi-modal data; the fault analysis module is used for constructing a feature-damage mapping model to perform fault analysis on the tensor matrix to obtain a fault diagnosis result; and the fault maintenance module is used for pushing the fault diagnosis result and carrying out fault maintenance according to the fault diagnosis result. According to the invention, the technical problem that the fault diagnosis accuracy and the operation safety and stability of the wind power equipment are affected due to the fact that tiny early faults of key parts of the wind turbine cannot be effectively identified in the prior art can be solved, and the fault diagnosis accuracy and the system response efficiency are improved; the service life of equipment is prolonged; and the maintenance cost is reduced.

The invention discloses a sealing structure, a negative pressure suction sheath and an endoscope assembly, and belongs to the technical field of medical instruments. The sealing structure is used for the negative pressure suction sheath and comprises a first cover body detachably connected with the near end of a sheath base of the negative pressure suction sheath, a first through hole is formed in the middle of the first cover body, and a first air hole is formed in the radial outer side of the first through hole; the second cover body is arranged on the outer side of the first cover body in a sleeving mode and detachably connected with a sheath base of the negative pressure suction sheath, a second through hole is formed in the middle of the second cover body, and a second air hole is formed in the radial outer side of the second through hole; the second cover body and the first cover body are in sliding fit, and the overlapping opening degree of the first air hole and the second air hole is adjusted through relative sliding; the second cover body axially limits the first cover body and provides support when the endoscope is inserted, so that the first cover body is prevented from collapsing. The sealing structure is used for the negative-pressure suction sheath, the branch switch is prevented from additionally occupying the operation space, and the operation safety is improved.

The invention discloses an urban operation and maintenance personnel state monitoring method and system, and belongs to the technical field of data fusion. Physiological signal data of operation and maintenance personnel is collected through wearable equipment, a multi-dimensional state feature vector is calculated, an operation and maintenance personnel state multi-dimensional feature matrix is constructed, a high-frequency abnormal mode is identified, and a fatigue evolution factor map is constructed; key fatigue features are extracted, and physiological fatigue state evolution weight vectors are generated; the method comprises the steps of projecting historical operation tracks and current position information of operation and maintenance personnel to a three-dimensional space-time task map in combination with the historical operation tracks and the current position information, generating a state evolution space-time path map, predicting state degradation probability distribution of future task nodes based on a depth map convolutional network, generating an operation and maintenance risk early-warning thermodynamic diagram, and determining the operation and maintenance risk early-warning thermodynamic diagram in combination with a task emergency degree and a personnel state level. The task allocation priority and the inspection route are automatically adjusted; according to the method, dynamic fatigue monitoring and task optimization scheduling can be realized, the operation safety is improved, the risk caused by fatigue of personnel is reduced, and the method has remarkable technical advantages.