12455 results about "Safe operation" patented technology

The invention relates to the technical field of robot control, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance control method based on machine vision, which comprises the following steps: acquiring 360-degree panoramic image data around a mechanical arm through a multi-camera system, and combining two-dimensional and three-dimensional information acquired by an RGB camera and a depth camera to construct an environment model. A pre-trained convolutional neural network model is used for identifying fixed and moving obstacles in an image, a Kalman filter is combined to predict the motion trail of the moving obstacle, an improved fast expansion random tree algorithm is used for path planning, it is ensured that the efficient and accurate obstacle avoidance capacity of the mechanical arm is kept in a dynamic environment, and the obstacle avoidance efficiency of the mechanical arm is improved. The motion state of the mechanical arm is controlled and monitored through real-time feedback, and deviation caused by inertia or errors is avoided. The dynamic adaptive capacity and the safe operation performance of the mechanical arm in the complex environment can be improved, and the mechanical arm is widely applied to robots in the fields of industry, medical treatment and the like.

The present application provides a method for pipeline defect detection using the magnetostrictive characteristics of ultrasonic guided waves, which relates to the technical field of pipeline defect detection, including: obtaining the geometric and material parameters of multi-layer heterogeneous pipelines, and establishing a mathematical model of the pipeline; according to the mathematical model, calculating the dispersion curve and modal characteristics of ultrasonic guided waves in the pipeline; selecting a suitable excitation frequency and guided wave mode, and optimizing the detection parameters of ultrasonic guided waves; presetting defects of different types and sizes in the pipeline, and analyzing the influence of defects on guided wave propagation characteristics through numerical simulation; building a multi-layer heterogeneous pipeline ultrasonic guided wave detection experimental system, collecting guided wave signals under different defect conditions, and extracting time-frequency domain characteristic parameters. The present invention can realize the effective detection and evaluation of internal defects of multi-layer heterogeneous pipelines at different temperatures, and provides an important technical means for ensuring the safe operation of pipelines.

A method and apparatus for determining the safety inertia of a power grid system, and a computer device. The method comprises: determining a first safety inertia for ensuring safe operation of a power grid system when a frequency change rate after disturbance occurs in the power grid system exceeds a first threshold (S302); determining a second safety inertia for ensuring the safe operation of the power grid system when the frequency after the disturbance occurs in the power grid system is less than a second threshold (S304); and determining a target safety inertia according to the first safety inertia and the second safety inertia, wherein the target safety inertia is a safety inertia lower limit value for ensuring the safe operation of the power grid system (S306).

The invention discloses a guaranteed power supply quality monitoring and optimization method and system, and relates to the technical field of guaranteed power supply quality monitoring, and the method comprises the steps: obtaining electrical and environmental parameters in real time, modeling a power grid topology into graph data, extracting space correlation and time sequence evolution characteristics, and dynamically adjusting the attention weight of the space and time sequence characteristics, generating a joint feature vector, adaptively setting a dynamic threshold value, comparing real-time parameters in multiple dimensions, marking abnormal nodes, reversely tracing an upstream path of the abnormal nodes, calculating a proportion of impedance attenuation and current-carrying capacity reduction by combining environmental parameters, judging whether a fault point is a fault point, and considering an environmental cumulative effect of the fault point and a load sudden change frequency. And establishing an insulation aging and residual current-carrying capacity model, quantifying a safe operation interval, and implementing a dynamic compensation strategy. Through real-time monitoring, dynamic analysis and self-adaptive compensation, the stability, safety and efficiency of power grid operation are remarkably improved, faults are effectively prevented, and the service life of equipment is prolonged.

The invention discloses an elevator control system based on artificial intelligence, which belongs to the technical field of elevator control, and comprises a plurality of layers of architectures such as data acquisition, transmission, processing, intelligent decision, execution control and the like. The data acquisition layer collects multi-source data through a car inside and outside and operation state sensor group; the data processing layer provides support for intelligent decision-making through preprocessing, analysis mining and model training optimization. An elevator dispatching module of the intelligent decision-making layer carries out peak dynamic zoning and flat peak energy-saving dispatching according to information such as passenger flow; the safety protection module monitors and processes hidden dangers in real time; the personalized service module provides customized services based on passenger behaviors and demands. And driving of the control layer, a door machine and a car environment control system are executed, and stable lifting of the car, safe operation of a door and intelligent environment adjustment are achieved correspondingly. Experiments show that the system can remarkably improve the elevator operation efficiency, safety and passenger satisfaction and reduce energy consumption, and has important application value.

A system and method are provided for placing security operations at selected enforcement points in a distributed security fabric. The enforcement points at which the security operations are placed can be endpoints, nodes, and / or network devices within the network. The security operations can be updated by monitoring data flows through the network to generate network data, and then determining, based on the network data, one or more changes to the security operations, based on the generated network data. Recommended changes can be obtained by applying the network data to a machine-learning model that indicates suspicious data packets (e.g., disseminates packets suspected of being malicious from normal traffic) and crafts new policies to deny the suspicious data packets. Performance of the network can also be improved by analyzing the security operations for redundancies and / or inefficiencies and modifying the security operations to mitigate them.

The invention discloses a vehicle-mounted boost inductance monitoring simulation test system and a test method thereof. The vehicle-mounted boost inductance monitoring simulation test system comprises a vehicle-mounted power supply simulation module, an accelerated life test module, an environment test box, a fault early warning module and a data analysis and diagnosis module. The actual stress state of the inductor is comprehensively simulated by constructing a power supply simulation model matched with the actual working condition of the electric vehicle and combining a multi-environmental-factor coupled accelerated life test; real-time monitoring and fault early warning of inductive performance degradation are realized by adopting an acoustic emission technology; an inductance fault diagnosis knowledge base and an intelligent reasoning system are constructed by applying big data analysis and machine learning technologies. According to the system and the method provided by the invention, the inductance reliability test efficiency can be remarkably improved, the early warning of inductance degradation is realized, the accuracy and the intelligent level of fault diagnosis are improved, a solid basic guarantee is provided for safe operation of a new energy electric vehicle, and the system and the method have wide application prospects and popularization values.

The invention provides a new energy photovoltaic dynamic inspection method and system based on artificial intelligence, and relates to the technical field of photovoltaic power station intelligent inspection. Inspection is triggered according to weather early warning, performance warning or timed tasks; initial path planning is carried out by combining terrain, weather and historical data, and the path is updated by dynamic obstacle avoidance through an RRT * algorithm; multi-modal data, including visible light images, infrared thermal imaging, EL detection data and positioning data, are acquired during inspection of the unmanned aerial vehicle; the unmanned aerial vehicle data and the ground sensor data are integrated to generate a unified fault feature matrix; positioning a defect area in real time by using a deep neural network, judging a defect type and dividing a fault level; and finally, the health degree of the photovoltaic system is scored according to the fault level, and the safe operation trend is analyzed. The multi-modal data real-time fusion and dynamic path planning are realized, the fault identification precision and the inspection efficiency are improved, the manual inspection cost and risk are reduced, and powerful support is provided for intelligent operation and maintenance of a photovoltaic system.

The invention discloses a signal processing and intelligent algorithm fused main helium fan residual life prediction method, and relates to the technical field of mechanical equipment state monitoring and maintenance management, and the method comprises the steps: collecting equipment operation data through multiple sensors, carrying out the feature fusion through principal component analysis, and achieving the fault mode recognition through a deep learning algorithm; and the residual life of the equipment is predicted by utilizing transfer learning and an XGBoost algorithm, and a diagnosis and prediction model is integrated into an online monitoring system, so that real-time perception and dynamic prediction of the health state of the equipment are realized, and decision support for maintenance and spare part management is provided. According to the method, the accuracy of fault diagnosis and life prediction of the primary helium fan is effectively improved, and a new thought is provided for safe operation and intelligent maintenance of equipment.

The invention discloses a thermal management control system and method for a power battery pack, and belongs to the technical field of electric automobiles, and the system comprises the following modules: a multi-parameter acquisition module which is used for acquiring parameter data of the battery pack in real time; the data preprocessing and fusion module is used for generating a high-quality and uniform-format data set; the dynamic prediction modeling module is used for realizing accurate temperature trend prediction; the intelligent decision module is used for generating an optimal heat management strategy; the thermal management execution module is used for realizing accurate temperature regulation and thermal management; the system monitoring and diagnosis module is used for ensuring safe operation of the system; a man-machine interaction module; the intelligent thermal management system is used for realizing man-machine collaborative intelligent thermal management. According to the thermal management control system and method for the power battery pack, the particle swarm optimization algorithm is utilized, the power battery thermal management system of a thermal management strategy is dynamically adjusted, more accurate temperature prediction and control are achieved, energy consumption is optimized, and the service life of the battery is prolonged.

The present disclosure relates to cyber-attack mitigation, information theoretic security in-motion, at-rest and storage intrusion mitigation with exclusive-path, content routing across an overlay network. Overlay network built upon Data Transport Controllers (DTC), SG nodes and SV nodes. System modules are deployed across various geo locations in a Wide Area Network, operating at the control of Universal Security Controller (USC). USC extracts system, security and storage activity telemetry data from Secure Vaults, Storage Gateways and Data Transport Controllers to orchestrate Autonomous Security Operations. SG nodes create SP fragments and store in SV nodes upon a data input operation. USC initiates an Exclusive-path content forwarding from the first SG node, across the DTC overlay through an exclusive list of DTC nodes and move it to second SG node. USC keeps the security control of SG nodes, DTC nodes and SV nodes and realize multi-vectored storage security at-rest and in-motion. SG nodes are connected to SV nodes which are micro-segmented, data vaults with restricted network reachability.

The invention provides an airport terminal video digital twinning method, device, equipment and medium, and the method comprises the steps: firstly obtaining data collected by a plurality of sensors, then constructing a high-precision airport terminal static three-dimensional model, and taking a world coordinate system as a reference, a mapping relation among a two-dimensional image coordinate system, an airport terminal static three-dimensional model coordinate system and a camera coordinate system is constructed, an airport terminal space initial layout is obtained, two-dimensional layout estimation is used as a key point, image textures are reconstructed, registration from the two-dimensional image textures to an airport terminal static three-dimensional model under a single camera is achieved, and the registration result is obtained. And finally, calculating a suture line in a fusion rendering process, and fusing a multi-camera two-dimensional image with the constructed static three-dimensional model to realize the three-dimensional panoramic digital twinning of the terminal building. Through the scheme of the invention, three-dimensional live-action visualization under the fusion of multiple cameras, structured data and the terminal indoor model in a large scene of the terminal is realized, comprehensive real-time monitoring is realized, and safe operation of an airport is guaranteed.

The invention discloses a rotating machine fault diagnosis method, which comprises the following steps of: acquiring vibration, temperature, acoustic emission and current signals at key parts of a rotating machine, and extracting characteristic parameters such as time domain and frequency domain after preprocessing such as filtering and noise reduction; and inputting the characteristic parameters into machine learning models such as a support vector machine, combining deep learning models such as a convolutional neural network and a long-short-term memory network, performing comparative analysis by using a digital twin model, and fusing diagnosis results to output fault types, positions, severity and maintenance suggestions. The method overcomes single diagnosis limitation, multi-source signal complementation, multi-model collaboration, accurate fault diagnosis and diagnosis reliability improvement, provides a scientific basis for equipment maintenance, and is of great significance for guaranteeing safe operation of rotating machinery, reducing maintenance cost and promoting industrial intelligent development.

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

The invention relates to the technical field of water gate maintenance, and discloses a water gate engineering safety risk assessment and early warning system which integrates five modules, namely a data analysis module, a real-time monitoring module, a state comparison module, a risk assessment module and an early warning notification module. The future safety state of the sluice project is predicted through a machine learning model, key safety parameters are monitored in real time, real-time data and predicted data are compared and analyzed, and a curve graph is drawn and visually displayed. And the risk assessment module calculates the difference degree of the two groups of curves and identifies risk points. And the early warning notification module comprehensively evaluates the safety state of the sluice project according to the risk point and the safety evaluation rule, and timely sends an evaluation result and early warning information to a manager so as to quickly take countermeasures. According to the system, the intelligent level of safety management of the water gate engineering is improved, early discovery and early warning of safety risks are realized, and safe operation of the water gate engineering is effectively guaranteed.

The invention discloses an air-ground cooperative monitoring and early warning method and system for a buried gas pipeline in a landslide hidden danger area, and the method comprises the steps: carrying out the ground monitoring through employing distributed optical fiber sensing, and enabling an optical fiber sensor to be embedded in the outer wall of the buried gas pipeline or to be laid with an accompanying optical fiber pipeline; collecting multiple physical quantity data of strain, temperature and vibration of the pipeline; in-air monitoring, a multi-sensor platform carried by an unmanned aerial vehicle is used for carrying out in-air inspection; the air monitoring data and the ground monitoring data are fused, and a landslide hidden danger area pipeline safety state evaluation model is constructed; performing real-time intelligent analysis on the fusion data by using an advanced mixed attention convolutional neural network, and identifying a potential catastrophe hidden danger area of the pipeline; and according to a risk assessment result, early warning grading is carried out, and a corresponding emergency plan is formulated. According to the method, real-time and accurate monitoring of the pipeline state can be realized, the landslide hidden danger can be found in time, accurate early warning can be realized, and safe operation of the buried gas pipeline in the landslide hidden danger area can be effectively guaranteed.

The invention provides a tunnel section identification and deformation detection method based on laser scanning point cloud data, and the method comprises the steps: directly processing the point cloud data in a three-dimensional space through polar coordinate conversion and local calculation weighted average modes to extract a central axis, so as to completely reserve the three-dimensional information of the point cloud data, and more accurately reflect the real spatial form of a tunnel. The problem that geometric feature information of the top and the bottom of the tunnel is lost due to projection is avoided. Tunnel section interception and section point cloud data determination are carried out on the basis, global and local deformation discrimination is carried out in combination with adaptive shape fitting, tunnel section deformation is accurately monitored and discriminated, possible tiny deformation of a tunnel in a local area can be sensitively captured, and even under the condition that overall deformation is not obvious, the tunnel section deformation can be accurately monitored and discriminated. Potential safety hazards can be found in advance, all-around and precise monitoring and judgment of tunnel section deformation are achieved, and powerful technical support is provided for long-term safe operation of a tunnel.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

A fluid management system for use in a tissue resection procedure includes a controller. An inflow pump is operated by the controller and configured to provide fluid inflow through a flow path to a site in patient's body. An outflow pump is operated by the controller and configured to provide fluid outflow through a flow path from the site in patient's body. A motor driven resecting device may be provided for resecting tissue at the site. The controller is configured to actuate an inflow pump and an outflow pump in response to various signals and various algorithms are provided to provide malfunction warnings and assure safe operation.

Methods, systems, and computer program products for robot safety. Multiple computer-implemented components are operatively interconnected to carry out operations for ensuring safe execution of robotic commands. Upon receiving a description (e.g., in text form or as an image) of an environment, and based on the description, forming a large language model (LLM) prompt that is provided to an artificial intelligence entity. After prompting the artificial intelligence entity, and upon receiving an LLM response that contains information about how to operate and / or constrain the robot, then guaranteeing safe operation of the robot by classifying aspects of the LLM response, labeling at least some parts of the LLM response, and then, based on the labeling, generating modified planning or control signals that ensure safe operation of the robot. Only the modified signals that ensure safe operation or other signals that are deemed to be safe are provided to the robot.

An icing detection method based on tension and tilt angle sensors. Two kinds of sensor data are comprehensively utilized, cross validation and mutual compensation are realized, and the precision of an icing monitoring result is improved; compared with a single sensor, data fusion of a multi-source heterogeneous sensor improves the robustness and the anti-interference ability of ice volume assessment, and improves the reliability of icing detection; an uncertainty analysis model for icing thickness is established, a confidence interval of a monitoring result is given, and more sufficient reference information is provided for related decisions; by setting a scientific and reasonable monitoring and early-warning mechanism, intelligent monitoring and early-warning of an icing condition are realized, and the intelligent level of the technology is improved; and by establishing an icing monitoring model, the icing condition of wires can be accurately predicted, and the early-warning and monitoring capabilities are improved to ensure the safe operation of a power grid. Also disclosed are an icing detection system based on tension and tilt angle sensors, a computer device and a computer-readable storage medium.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

The invention discloses a multi-sensor data fusion control method for an automatic assembly line, and relates to the technical field of data fusion, and the method comprises the following steps: when multi-source data fusion is carried out, firstly, data information is obtained in real time through a multi-source sensor, the obtained data information is preprocessed, and the accuracy and integrity of data are ensured; by analyzing data quality and fusion performance indexes and system robustness and environmental adaptability indexes in real time, the system can accurately detect the influence of electromagnetic interference and environmental fluctuation on data and dynamically evaluate the state. And an early warning and adjustment strategy is automatically triggered in case of abnormality, so that data fusion continuity is guaranteed, and downtime and human intervention are reduced. And when the abnormity exceeds the self-repairing capability of the system, the system can give an early warning, so that the efficient and safe operation of the production line is ensured, and the capability of dealing with sudden problems is improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a data-driven unmanned aerial vehicle flight control system real-time defect prediction system and method, and provides the following scheme that the system comprises a data acquisition module, a sensor verification module, a defect prediction module, a credibility analysis module and a flight control optimization module. And the sensor verification module identifies sensor data exceptions and distinguishes fault types through data consistency, time sequence exception detection and environment correlation analysis. And the defect prediction module analyzes the verified data by using machine learning and predicts potential faults. And the credibility analysis module verifies the prediction result and generates a credibility score. And the flight control optimization module selects an optimization control strategy according to the credibility score, adjusts flight parameters or executes safe operations such as return flight and landing. According to the method, the fault identification and prediction capability of the unmanned aerial vehicle in a complex environment is improved, and the flight safety and the system reliability are enhanced.

The invention discloses a tunnel state monitoring method based on structure and appearance data, and relates to the technical field of tunnel engineering health monitoring. By integrating the structure and the appearance data, the accuracy of tunnel health assessment and the timeliness of early warning are remarkably improved. According to the method, through deep mining of data features, construction of a comprehensive feature matrix and application of a multi-modal data fusion technology, the problem of information isolation is effectively solved, evaluation comprehensiveness is enhanced, a dynamic health evaluation model is combined with historical and real-time data, tunnel state changes are accurately predicted through time sequence analysis, and the evaluation accuracy is improved. In addition, through a risk linkage network and optimized state prediction, accuracy of risk management and data driving of maintenance decision are realized, and powerful technical support is provided for safe operation and management of the tunnel.

The invention relates to a pier damage early warning analysis method under the action of flood, and solves the technical problems of high disaster recognition cost, low bridge damage recognition precision, untimely early warning and alarming and the like based on monitoring equipment in the existing concrete bridge flood-disaster-resistant technology. According to the method, by means of an established plastic damage simulation model, expensive sensors do not need to be arranged on a bridge structure, only basic information of the bridge structure and flood information such as the flow speed, the water level and the scouring depth are obtained, and damage and failure analysis of the bridge pier structure under the action of different influence factors by the flood action can be studied; structural damage state qualitative analysis and modal integrity damage index quantitative analysis are combined to determine bridge pier structure damage grades and damage failure judgment criteria, and early warning values corresponding to different damage grades of a bridge pier are inverted, so that accurate damage identification and rapid grading early warning of the bridge pier structure under the flood effect are realized. The flood disaster resistance of the bridge structure is improved, safe operation of the bridge is guaranteed, and the service life of the bridge is prolonged.

The invention discloses a high-precision defect detection system for a track inspection robot, which relates to the technical field of track inspection and comprises a multi-mode visual acquisition module, a time sequence synchronization module, a data fusion processing module and a defect identification output module, by adopting a multi-modal visual fusion detection technology, various modal visual technologies of visible light vision, infrared vision and ultraviolet vision are combined, fusion processing is carried out on all modal image data, visual defects such as cracks and abrasion on the surface of a track can be clearly recognized through visible light vision, and the visual performance of the track is improved. The infrared vision can detect temperature abnormity of a track component to find out potential poor contact and other problems, the ultraviolet vision can be used for detecting corona discharge and other phenomena, track information is comprehensively obtained, the accuracy and reliability of defect recognition are effectively improved, defects which are difficult to find in a single mode can be detected, and the detection efficiency is improved. And powerful support is provided for safe operation and maintenance of the track.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention relates to the technical field of tunnel structure monitoring, in particular to a tunnel structure health monitoring intelligent analysis system which comprises a data acquisition module, a data transmission module, a data processing and analysis module, an alarm and decision support module and a user interaction module. The data acquisition module is used for acquiring tunnel structure data in real time, the data transmission module transmits the acquired data to the data processing and analysis module, the data processing and analysis module analyzes the data in real time through an isolated forest algorithm and identifies potential damage and abnormity, and the alarm and decision support module triggers multi-level alarms when detecting abnormity. The user interaction module displays the monitoring data, the analysis result and the alarm information in real time through the display screen and the control terminal, the accuracy and the real-time performance of tunnel structure health monitoring can be remarkably improved, an intelligent anomaly detection and early warning mechanism is provided, and safe operation of a tunnel and long-term stability of the structure are ensured.