1056 results about "Data reliability" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention relates to the technical field of soil remediation management, in particular to a method and system for soil remediation and digital management, and the method comprises the steps: data construction: fusing multi-source monitoring soil environment parameters with space-time background data such as a GIS layer and a meteorological time sequence to form a data set; state quantification: outputting a comprehensive state index for quantifying restoration urgency and complexity through the soil health assessment model; scheme generation: matching the restoration target library and the resource library, and generating a digital restoration scheme containing an expected state trajectory through an optimization algorithm; closed-loop control is carried out, feedback data comparison tracks are continuously collected, if the deviation exceeds a threshold value, the index is recalculated, the scheme is dynamically adjusted, and the system comprises a data construction module, a state quantification module, a scheme generation module and a closed-loop control module and executes the method. According to the method, the data reliability and evaluation scientificity are improved, the scheme adaptability is guaranteed, dynamic and controllable remediation is realized, and digital and efficient management of soil remediation is promoted.

The invention discloses a pressure injury risk early warning method and equipment, and relates to the technical field of medical monitoring instruments. The pressure sensor array layout can be self-adaptive to the body shape of a patient, a key osseous process part is covered, no monitoring dead angle is ensured, a temperature compensation mechanism is built in, environmental interference is reduced, the measurement precision is improved, an abnormal pressure threshold value can be dynamically set based on individual medical record data, data verification is performed before early warning is triggered, false alarm is avoided, and the array structure is light, thin and flexible. The mask is comfortable to fit skin, supports long-term wearing and is easy to disinfect and maintain; environment temperature and humidity parameters and pressure data are subjected to fusion analysis, the influence of a high-temperature and high-humidity environment on skin microcirculation can be recognized, the risk of pressure sores can be evaluated in an auxiliary mode, a calibration algorithm is built in a sensor, environment fluctuation interference is reduced, and data reliability is ensured; and the early warning unit comprises sound-light alarm equipment which supports a multi-stage alarm mode and is used for triggering an early warning signal and pushing information when the risk index exceeds a threshold value, so that the early warning timeliness is ensured and the false alarm risk is reduced.

The invention provides a geotechnical engineering slope deformation monitoring method and system, belongs to the field of geotechnical engineering monitoring, and is used for solving the problems of low monitoring precision and poor early warning reliability caused by spatial-temporal dislocation of multi-source data, static geological parameters, poor swivel scene adaptation and no closed-loop optimization in related technologies. According to the method, multi-source deformation data, geological parameters and rotation characteristics are collected, the type and progress of a rotation are identified, multi-resolution hierarchical fusion (dynamic weight adaptive data credibility and environmental interference) and dynamic inversion geological parameters are performed after space-time alignment to construct a prediction model, model parameters and an early warning threshold are dynamically scheduled in combination with the rotation characteristics, and the prediction result is obtained. The precision is improved through closed-loop feedback optimization iteration, the system provides hardware support for the method, and precise monitoring and efficient early warning of slope deformation are achieved.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention provides an elevator intelligent operation and maintenance management method and system based on a multi-modal neural network, and relates to the technical field of operation and maintenance management. The method comprises the steps that 1, multi-source time sequence monitoring data are collected, the multi-modal neural network is constructed, the multi-source time sequence monitoring data are input into the multi-modal neural network, and the multi-source time sequence monitoring data are input into the multi-modal neural network; obtaining a vibration feature vector, an acoustic feature vector and a visual feature vector; 2, inputting the vibration feature vector, the acoustic feature vector and the visual feature vector into a cross-modal feature fusion layer in a multi-modal neural network for fusion, and generating a multi-modal joint feature vector; through multi-modal feature fusion of multi-source time sequence monitoring data, multi-dimensional health state representation modeling and fusion weight dynamic adjustment driven by data reliability evaluation, the accuracy of elevator fault diagnosis and the reasonability of maintenance decision are effectively improved, and the improvement of elevator intelligent operation and maintenance precision and efficiency is achieved.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

The invention discloses a frozen soil roadbed hot melt settlement laser scanning measurement method and system, and relates to the technical field of measurement, the method comprises the following steps: obtaining gridding surface data, and obtaining an elevation data sequence of each grid point; acquiring an elevation change index of each grid point according to the elevation data sequence of each grid point; acquiring a plurality of grid points adjacent to the ith grid point and taking the plurality of grid points as a plurality of reference grid points of the ith grid point, acquiring each relative difference quantity of the ith grid point, and acquiring a correction proportion of the ith grid point according to the plurality of relative difference quantities of the ith grid point; and obtaining target elevation data of each grid point according to the correction proportion of each grid point and the measured elevation data at the current moment, and forming a measurement result including the gridding surface data according to the target elevation data of each grid point. The method has the advantages of identifying abnormal values, dynamically correcting measurement and improving data reliability.

The invention relates to the technical field of bridge health monitoring, and discloses a method for monitoring the bearing capacity state of a bridge latticed column structure. The method comprises the following steps: constructing an original monitoring field according to a sensor time sequence signal, and generating a state evolution sequence representing the overall dynamic evolution of the bridge latticed column through multi-dimensional space reconstruction, so as to capture the nonlinear characteristics of the structure behavior and provide a basis for anomaly detection. And positioning a potential abnormal region based on the deviation degree from the reference model, and extracting a time-varying characteristic spectrum of the potential abnormal region to drive the digital twin model to perform simulation so as to obtain a simulation response spectrum. The simulation response spectrum and the actual measurement state evolution sequence are subjected to space-time fusion, a mixed state field fusing real data and physical mechanism inference is generated, a monitoring blind area is made up, and the data reliability is improved. And by performing multi-scale decomposition on the mixed state field, the specific state mode of the bearing capacity of the bridge latticed column is identified, and more accurate and more complete evaluation of the bearing capacity abnormity is realized.

The invention relates to the technical field of distribution network unmanned aerial vehicle autonomous tour-inspection navigation, in particular to a distribution network unmanned aerial vehicle tour-inspection autonomous navigation method based on GPS information and visual information, which comprises the following steps: acquiring a GPS and smoothing projection coordinates, constructing a Thiessen region random index, optimizing a route and acquiring a flight image, evaluating a risk and adjusting exposure parameters, and generating a light environment value. According to the method, an environment model is constructed by fusing GPS information, Gaussian projection is adopted to eliminate earth curvature errors, moving average filtering is adopted to suppress signal noise, Thiessen polygon is utilized to quantify routing inspection point and obstacle distribution, a tornado optimization algorithm is combined to generate an optimal route, the shortest path without repetition and obstacle avoidance are realized, and obstacles are identified based on a YOLOv12 model. A risk avoiding strategy is made according to the risk value, a route is dynamically optimized, a light environment fingerprint database is constructed, illumination change is predicted in combination with LSTM, aperture shutter sensitivity and white balance are adjusted in a self-adaptive mode, image brightness and definition are optimized, and inspection efficiency and data reliability are improved.

The invention relates to the related technical field of intelligent scene labeling, in particular to an intelligent driving scene collaborative labeling method and system based on multi-modal fusion, and the method comprises the steps: constructing a dynamic feature fusion model, carrying out the cross-source correlation analysis of a multi-modal scene data set, setting organization structure elements, and setting a multi-dimensional scene labeling model. And configuring a potential danger interaction path and a scene risk index, and generating an annotation instruction set to carry out collaborative annotation operation. The technical problems that under interference of severe weather such as sensor noise, rain and fog and strong light and abnormal traffic behavior scenes, the labeling accuracy is insufficient, and complex scenes and multi-modal data requirements are difficult to adapt are solved, labeling noise data are introduced into an adversarial sample configuration assembly, a multi-dimensional scene labeling model is constructed in combination with organizational structure elements, and the multi-dimensional scene labeling accuracy is improved. The technical effects of improving the training data reliability, adapting to complex environment labeling requirements, configuring potential risk interaction paths and scene risk indexes, carrying out risk-oriented priority labeling and improving the risk scene labeling precision are achieved.

The invention relates to the technical field of fire safety, in particular to a fire simulation early warning system based on intelligent fire protection multi-source data fusion, which comprises a sensor health degree dynamic evaluation module, an intelligent sensor health degree dynamic evaluation module and a fire simulation early warning module, establishing a two-dimensional feature library of the state of the sensor and environmental interference; and based on the feature library data, a fuzzy comprehensive evaluation method is adopted. By filtering unreliable data of the sensor, distinguishing multi-source contradictory data, dynamically adjusting weights according to fire behavior stages, iteratively updating data credibility and optimizing parameters in a closed-loop mode, the problems of false alarm and missing alarm in the prior art are effectively solved, and the accuracy and practicability of the intelligent fire-fighting early warning system are improved.

The invention discloses a water body health assessment system based on big data, relates to the technical field of water body health assessment, and realizes time synchronization, space mapping and quality assessment and restoration of multi-source data, calculation of water quality semantic indexes, data fusion, health level output and sampling work order generation. According to the method, deep fusion and standardized processing of multi-source heterogeneous monitoring data are realized, data islands are broken through, through high-precision space-time alignment and intelligent quality control, data reliability is ensured, human activity events are creatively fused for causal analysis, result interpretation is enhanced, multi-scale dynamic evaluation and adaptive threshold determination are supported, accuracy is improved, and the method is suitable for popularization and application. A closed-loop mechanism of sampling verification is evaluated, the unmanned ship is used for actively checking a suspicious area, secondary judgment is triggered, the response speed and result reliability of emergencies are remarkably improved, the whole-process compliance recording and standardized interface guarantee process is traceable, results are easy to share, and key technical support is provided for fine management of a water body.

The invention relates to a positioning optimization method and system based on a vision-assisted positioning algorithm, and the method comprises the steps: obtaining the dynamics instruction data of an inspection unmanned plane, and synchronously collecting the micro-vibration data of a plane body and environment perception image data; based on the dynamics instruction data and the machine body micro-vibration data, establishing a prediction disturbance field through a physical-data hybrid driven disturbance prediction model; extracting an observation disturbance field based on the environment perception image data and the prediction disturbance field; performing structured comparison on the prediction disturbance field and the observation disturbance field, and calculating to obtain a prediction-observation consistency index; and taking the prediction-observation consistency index as real-time measurement of visual data reliability, dynamically adjusting the state information confidence coefficient of the visual assisted positioning algorithm according to the real-time measurement, and finally obtaining an optimized positioning result. Visual spoofing attacks are effectively resisted, and the positioning reliability of the inspection unmanned aerial vehicle in a high-security risk scene is guaranteed.

The invention relates to the technical field of power distribution. The invention discloses a power distribution cabinet wire fault detection and identification method, which is characterized in that a plurality of wires with different specifications are arranged in a power distribution cabinet, and the method comprises the following steps: obtaining real-time operation parameter data of the plurality of wires, and determining a fault wire based on a reference parameter set of normal work of a to-be-detected wire and a deviation characteristic value relation of real-time parameters; fault parameter data of the faulty electric wire are input into a multi-layer fault identification model, the model comprises a plurality of sub-identification units connected in sequence, each unit detects different types of faults, and finally a fault mode is obtained. According to the method, at least three parameters are acquired through distributed sensing, and data reliability is guaranteed through noise reduction and normalization processing. On the basis of a double-threshold criterion and dynamic weight calculation, faulty electric wires are accurately distinguished, and misjudgment and missed judgment are completely eradicated. The multi-layer model carries out progressive detection, an emergency fault is preferentially recognized, and the sub-recognition units are matched with an exclusive algorithm to improve the accuracy.

The invention relates to the technical field of multi-beam sounding calibration, and discloses a multi-beam sounding online real-time calibration method. The method comprises the following steps: acquiring original water depth data and corresponding sensor attitude data in real time; generating a data quality evaluation result containing a data credibility index and a data abnormal distribution feature based on the original water depth data; triggering a dynamic calibration mechanism containing a calibration period adaptive adjustment strategy and a calibration parameter optimization rule according to the data credibility index; optimizing the target calibration rule parameters in combination with the sensor attitude data to generate an optimized calibration parameter set, and correcting the original water depth data in real time by using the parameter set to generate primary calibration data; and performing topographic feature consistency check on the primary calibration data, if the primary calibration data does not pass the topographic feature consistency check, starting a re-calibration process containing a rule base updating mechanism and a parameter iteration mechanism, and analyzing a deviation trend based on historical calibration records to generate a system error prediction model.

The invention relates to the technical field of agricultural intellectualization, and discloses a fruit tree growth monitoring system based on multi-sensor fusion, which comprises a multi-mode sensor array, an integrated soil humidity sensor, a three-dimensional ultrasonic ranging sensor, a hyperspectral imaging sensor and a microenvironment meteorological station, and realizes multi-parameter synchronous acquisition; a nonlinear data calibration model adopts a layered architecture, and cross interference between sensors is eliminated; a dynamic weight distribution algorithm is built in the edge computing node, multi-source heterogeneous data are fused in real time, a fruit tree growth situation map is generated, and a low-power-consumption processor is adopted to support lightweight AI model deployment; the self-adaptive power supply module adopts a photovoltaic-temperature difference composite power generation structure to realize cable-free deployment; the wireless ad hoc network communication unit constructs an inter-tree relay network based on a LoRa protocol, supports low-power-consumption remote data return, and integrates a relay node health degree evaluation mechanism. The real-time performance of fruit tree growth monitoring, the data reliability and the situation analysis accuracy are improved.

The invention belongs to the technical field of medical data processing, and particularly provides a multi-mode heterogeneous medical data dynamic weighting intelligent disease analysis system. The system specifically comprises the following modules: a multi-modal heterogeneous data preprocessing module, a multi-modal data quality dynamic evaluation and quality control module, a cross-modal embedding space conversion network, a medical knowledge graph engine, a large model training architecture, a multi-modal decision fusion module, a disease analysis interpretability enhancement module and a Bayesian probability graph model integration module. And an edge cloud collaborative reasoning module. According to the invention, the accuracy and robustness of multi-modal disease analysis are improved, the adaptability quality control of different modal data is realized through the multi-dimensional dynamic quality evaluation module, and the reliability of basic data is improved in combination with intelligent quality control restoration.

The invention relates to the technical field of sensor networks and Internet of Things, in particular to a data center air quality intelligent early warning method and system based on a sensor network. The method comprises the following steps: constructing a self-organizing cooperative network by deploying multiple types of sensor nodes in a key area of a data center, and collecting and correcting multi-dimensional air data in real time; the data reliability is improved through inter-node dynamic weight fusion and local anomaly recognition; establishing an air parameter and machine room structure correlation model by utilizing space mapping and time sequence analysis, and identifying a micro-scale diffusion trend; a self-adaptive dynamic threshold mechanism is constructed, and threshold rolling optimization is realized in combination with historical statistics and real-time feedback; and generating a graded alarm strategy based on multi-stage early warning judgment and triggering conditions, and continuously self-optimizing early warning precision and response efficiency through closed-loop feedback. According to the invention, all-around, intelligent and high-reliability early warning and regulation and control of the air quality of the data center are realized.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention provides a synchronous positioning and mapping method for underwater pipeline detection, and belongs to the technical field of underwater robot navigation and positioning. In order to solve the problems of limited illumination, strong echo interference, high structural repeatability and the like in a seawater pipeline environment, the invention provides a factor graph optimization-based tight coupling pose estimation framework fusing a two-dimensional imaging sonar, a Doppler velocity meter DVL and an inertial measurement unit IMU. Through joint constraint and optimization of multi-source sensing information, the method can realize high-precision positioning and continuous mapping of the ROV in an environment with extremely low visibility and a complex pipeline structure, and effectively improves autonomy and data reliability of an underwater inspection process. The method is clear in structure, easy and convenient to implement, high in calculation efficiency, capable of stably running on a conventional ROS platform and suitable for various types of underwater pipeline detection and inspection tasks.

The invention discloses in-service gas cylinder ultrasonic detection equipment which comprises a rotary driving module, an axial linear module, a probe module, a main supporting module and an auxiliary supporting module, and the main supporting module and the auxiliary supporting module are installed on the periphery of a to-be-detected gas cylinder at intervals; the rotary driving module is arranged on the main supporting module; the first end of the axial linear module is connected with the output end of the rotary driving module so as to rotate circumferentially along with the rotary driving module, and the second end of the axial linear module is connected with the auxiliary supporting module through a driven part; the probe module is installed on the moving part of the axial linear module. According to the invention, automatic and full-coverage scanning of the ultrasonic probe on the surface of the in-service gas cylinder is realized, the problems of low manual detection efficiency and poor reliability are effectively solved, and the detection efficiency and the data reliability are remarkably improved.

The invention discloses an unmanned aerial vehicle multi-source data fusion unmanned aerial vehicle management method and system, and the method comprises the steps: obtaining original monitoring data from a plurality of unmanned aerial vehicle detection devices, carrying out the time-space standardization processing of the heterogeneous data, building a unified time-space reference, building an unmanned aerial vehicle mapping relation through target correlation matching, and carrying out the target correlation matching. And performing multi-source data fusion on the standardized monitoring data after association mapping, finally performing airspace supervision analysis based on the generated unmanned aerial vehicle comprehensive state data, and outputting an airspace supervision decision report including monitoring information, an authentication state and an early warning prompt. According to the invention, through cooperative sensing and fusion of multi-source data, the problems of sensing blind areas, insufficient data reliability and the like existing in single detection equipment are effectively solved, the all-time accurate supervision capability of cooperative and non-cooperative unmanned aerial vehicles is remarkably improved, and reliable technical support is provided for airspace safety management.

The invention relates to the technical field of airborne wind power measurement, and discloses an airborne wind measurement laser radar method and system. The method comprises the following steps: generating a scanning time sequence identifier and a path correction mark structure through scanning beat synchronization and stray light shielding processing; executing time-frequency demodulation and quality gating based on the back scattering signal to generate an observation wind speed initial value sequence; performing time alignment and motion compensation in combination with inertial navigation data, and generating a wind speed vector grid fragment through spatial interpolation and scanning geometric mapping; and finally, generating a configuration updating instruction structure through coordinate alignment and grid splicing. According to the invention, the precision and the anti-interference capability of wind field measurement in an airborne environment are effectively improved, and the data reliability and the system stability under a complex meteorological condition are remarkably enhanced through multi-source data fusion and a self-adaptive compensation mechanism.

The invention relates to the technical field of computer-aided planning, and discloses a territorial space planning decision-making method and system based on multi-dimensional influence assessment, and the method sequentially comprises the steps: multi-source data fusion and problem diagnosis, multi-dimensional influence quantitative assessment, scheme collaborative optimization, and scheme comprehensive screening and decision-making. The system corresponds to the method. According to the method, through multi-source data fusion processing, the data reliability is improved, and a solid foundation is provided for subsequent problem identification; according to a quantitative index and a GIS spatial analysis technology, key problem region identification is more targeted, and the defect of generalization adjustment of a traditional qualitative method is avoided; through traffic, environment and municipal dimension parallel evaluation, the efficiency bottleneck of serial evaluation is avoided, and the evaluation period is shortened; a Pareto candidate scheme set generated through a multi-objective evolutionary algorithm is combined with a comprehensive screening mechanism to ensure that a final scheme is balanced between performance requirements and constraint conditions, and the feasibility of planning landing is enhanced.

The invention relates to the technical field of power systems, and discloses an intelligent insulator monitoring and fault detection system for a high-voltage power grid, which comprises the following steps: S1, a multi-source sensor measurement module performs multi-source information acquisition and breaks through the limitation of a single parameter; s2, a multi-source information fusion processing module performs multi-source data fusion processing and constructs global state evaluation; according to the method, innovation and breakthrough are achieved through cooperative collection and data fusion processing of the multi-source sensor, multi-dimensional data such as the voltage, the electric field, vibration and the environment are obtained in real time, the filtering, fusion and global state evaluation technology is combined, and the real-time analysis of the multi-dimensional data is achieved. The data reliability and the sensitive identification capability for tiny faults and sudden fluctuations are effectively improved, and the spanning from single parameter judgment to multi-dimensional data collaborative decision is realized.

The invention discloses an ultra-wide thin-wall tunnel roof deformation monitoring device based on laser ranging, and relates to the technical field of laser measurement, the ultra-wide thin-wall tunnel roof deformation monitoring device comprises an industrial camera and a mounting seat, a laser is fixedly mounted on the mounting seat, a mounting bracket is mounted at the bottom of the mounting seat, and a protective shell is fixedly mounted on the mounting seat. A light beam collimator is fixedly installed on the laser device, and a beam splitting assembly is fixedly installed in the protective shell and used for equally dividing a light source emitted by the laser device into two laser beams. The tunnel section deformation monitoring method has the advantages that high-precision and high-efficiency deformation monitoring can be performed on the tunnel section, the problems of time desynchrony and optical distortion existing in a traditional method are effectively solved, and meanwhile, the adaptability and data reliability in a severe environment are remarkably improved; and reliable technical means and equipment support are provided for structural health assessment of the ultra-wide thin-wall tunnel top arch.

The invention discloses a road traffic blocking identification and accident site positioning method. The method comprises the following steps: acquiring expressway vehicle running data in real time through an ETC door frame; data preprocessing and data correction are carried out by comparing timestamps of adjacent records; extracting the identification information of the vehicles with the same journey in the front and rear gantries, and calculating the average running speed of the vehicles on each journey; drawing a vehicle space-time diagram of each portal section; and taking an intersection point of the spatial-temporal trajectories of the fast vehicle and the slow vehicle at the starting moment and the ending moment of the abnormal time difference as a blocking ending spatial-temporal position judgment point. No extra monitoring equipment needs to be deployed, the data reliability problem and the real-time problem of the jump door frame data are solved only based on the ETC door frame data, and second-level road blocking and accident site positioning are achieved.