2428results about "Electrical/magnetic solid deformation measurement" patented technology

The invention provides a slope deformation monitoring method and device based on multi-source data fusion, and relates to the technical field of dam slope safety monitoring. The method comprises the steps that a multi-source monitoring system composed of a global satellite signal receiver, a measuring robot and an interference synthetic aperture radar is arranged in a dam slope area; according to the collected slope deformation disturbance parameters, performing monitoring area selection division on a dam slope area; determining a data fusion weight corresponding to each data fusion observation area; collecting multi-source monitoring data in each data fusion observation area; and based on the determined data fusion weight, performing weighted fusion processing on the multi-source monitoring data in the selected area to generate a slope deformation monitoring result. According to the technical scheme, fusion of multi-source monitoring data is realized through region division, the reliability, comprehensiveness and environment adaptability of slope deformation monitoring data are improved, and the stability, accuracy and precision of a slope deformation monitoring result are improved.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a railway tunnel portal geological disaster deformation early warning system based on an SAR radar, and the system comprises a digital twinborn body construction module which constructs a digital twinborn body with initial parameters based on basic data; the SAR deformation monitoring module is used for acquiring SAR deformation observation data; the digital twinborn body dynamic optimization module is used for inverting and updating parameters by using an optimization algorithm based on the SAR data, and generating an optimized twinborn body; a risk prediction and key area identification module which deduces a disaster scene based on the optimized twinborn body, predicts the risk and identifies a key risk area; and the intelligent early warning module is used for generating early warning information based on the prediction risk and the key risk area. According to the method, the geomechanical digital twins are dynamically optimized by adopting the SAR data, accurate prediction, key area identification and intelligent grading early warning of the geological disaster of the railway tunnel portal are realized, and the initiative and accuracy of risk cognition and early warning are remarkably improved.

The invention, which relates to the industrial circuit board monitoring technology field, discloses an industrial circuit board operation abnormity monitoring system comprising a multi-sensor welding spot data acquisition module, a signal processing module, an element welding spot mechanical fatigue monitoring module, a welding spot fatigue fault grading early warning module, and an early warning and response module. Data fusion is carried out on the acoustic emission sensor, the strain gauge sensor and the ultrasonic sensor, the state of the welding spot is monitored synchronously from three dimensions of stress acoustic emission, mechanical deformation and internal defects, the strain gauge sensor captures tiny deformation around the welding spot, and the ultrasonic sensor detects internal potential defects. The acoustic emission sensor senses stress release signals, the three kinds of sensor data are subjected to deep fusion analysis through the D-S evidence theory, the problem of misjudgment caused by one-sided information is effectively avoided, stress waves during crack propagation are captured through the acoustic emission signals, and faults can be diagnosed in time in combination with deformation data of the strain gauge sensor.

The invention provides a building construction site safety monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of data processing.The method comprises the steps that a three-base-point dynamic topological structure is established in a target monitoring domain, a first datum point is arranged at the extension end of the axis vertical line of a slewing bearing of a tower crane, and a second datum point is arranged at the extension end of the axis vertical line of the slewing bearing of the tower crane; the second datum point is embedded in the radial section of a force bearing flange plate of the hydraulic jacking module, the third datum point is fixedly connected to the intersection joint of a deep foundation pit top beam and a supporting system, and a non-collinear vector set is constructed through three-point coordinate offset; and based on the non-collinear vector group, deformation compensation correction is performed on the dynamic calibration parameter set through a vector space topological transformation algorithm, a topological reconstruction data stream containing a displacement gradient tensor and strain energy density is generated, and a risk situation feature space is constructed based on the topological reconstruction data stream. According to the invention, the intelligent construction safety management is realized.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention discloses a self-adaptive InSAR-GNSS high-precision three-dimensional deformation resolving method with regularization introduced twice, and the method comprises the steps: obtaining InSAR deformation data and GNSS three-dimensional deformation data of a to-be-monitored region, and building an InSAR error correction model considering spatial correlation based on the InSAR deformation data and the GNSS three-dimensional deformation data; performing system error correction on the InSAR deformation data by using the corrected error correction model to obtain preliminary correction data; grouping the preliminary correction data according to data precision characteristics and determining an initial weight; iteratively calculating until unit weight variances of each group of observation data are approximately equal to obtain a final weight; and obtaining a high-precision earth surface three-dimensional deformation rate based on the final weight. According to the method, Tikhonov regularization is introduced twice, the weight is dynamically adjusted in combination with the IGGIII equivalent weight function, and various error influences are effectively reduced.

The invention discloses a tower foundation landslide monitoring method, system and device and a medium. The method comprises the steps that InSAR data and GNSS data are fused to conduct wide-area deformation monitoring, a quadratic fitting model is established to conduct error dynamic correction on InSAR observation values, and a potential landslide hidden danger area is recognized; the method comprises the following steps: performing multi-dimensional cooperative monitoring on a potential landslide hidden danger area through an unmanned aerial vehicle carrying a sensor, and fusing multi-source data by utilizing edge computing equipment to generate a landslide risk thermodynamic diagram; an active waveguide acoustic emission system is adopted to capture landslide deep deformation signals, and early warning is achieved through ringing counting and acoustic emissivity parameters. According to the invention, the InSAR and GNSS technologies are fused, and the Kalman filtering algorithm is combined, so that the precision of surface deformation monitoring is improved; the InSAR observation value is constrained and corrected through the GNSS observation value, so that the deformation monitoring precision is improved; more accurate data support is provided for landslide monitoring through the unmanned aerial vehicle carrying sensor equipment; powerful support is provided for early warning of landslide disasters through an active waveguide acoustic emission technology.

The invention relates to the field of bridge monitoring, and particularly discloses a crack monitoring system for a fixed end of a steel-concrete composite beam. The method is used for solving the problems that multi-source information fusion, hot spot stress on-line accurate calibration, fatigue life real-time accurate evaluation, S-N curve dynamic correction and remote visual early warning are difficult to realize in crack monitoring of a fixed end of an existing steel-concrete composite beam under complex load and environmental interference. Comprising a multi-sensor data acquisition module, an edge preprocessing module, a data fusion and damage identification module, a hot spot stress calibration module, an early warning decision module, a visual remote communication module and a digital twinborn calibration module. According to the invention, through integration of multi-sensor synchronous acquisition, edge intelligent preprocessing, multi-source data fusion, online hot spot stress calibration and digital twinborn calibration, Monte life prediction and early warning and hybrid energy management, high sensitivity, low power consumption and accurate early warning of steel-concrete composite beam fixed end crack monitoring are realized.

The invention provides a millimeter wave radar-based bridge deformation monitoring method and system, and the method comprises the steps: obtaining a transmitting signal and an echo signal of a radar, and carrying out the preprocessing of the signals, and obtaining an intermediate frequency signal; processing the intermediate frequency signal by adopting matched filtering and a zoomFFT algorithm to obtain target distance information; processing the intermediate frequency signal by adopting a MUSIC algorithm to obtain target angle information; and obtaining a target distance angle unit by combining the target distance information and the target angle information, and obtaining target deformation information by adopting phase difference and phase unwrapping. According to the invention, the interference of noise and background can be avoided, the deformation displacement of the target area can be accurately calculated, and the real-time monitoring and analysis of the deformation state of the bridge can be realized.

The invention provides a rock-soil body multi-field coupling intelligent monitoring system comprising a multi-field sensing device configured to monitor parameters of a deformation field, a seepage field, a temperature field and a stress field of a rock-soil body; the edge calculation and transmission device is configured to perform preprocessing and real-time transmission on the multi-field sensing data; and the intelligent decision-making device is configured to analyze the preprocessed data based on a multi-field coupling model and generate disaster early warning information. According to the rock-soil body multi-field coupling monitoring system and method provided by the invention, synchronous monitoring of a deformation field, a seepage field, a temperature field and a stress field is realized through the multi-field sensing device; real-time data processing is performed in combination with an edge calculation and transmission device, and a dynamic early warning mechanism is established by using an intelligent decision-making device, so that the technical problems of multi-field data space-time correlation failure, insufficient multi-source data fusion precision and poor early warning mechanism adaptability are effectively solved, and the geological disaster early warning method has the remarkable advantages of improving the geological disaster early warning accuracy and timeliness.

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

The invention provides a Beidou intelligent early warning analysis method and system applied to a drainage basin-level reservoir dam group, and the method comprises the steps: obtaining a dam body deformation time sequence record and a corresponding hydrological environment time sequence record which are collected by a monitoring system of the drainage basin-level reservoir dam group through a Beidou satellite positioning terminal; dam body deformation response mode self-encoding processing is carried out on the associated time sequence data set, a deformation response feature element set representing the dam body structure state is generated, the deformation response feature element set is input into a pre-constructed basin reservoir dam group topological relation graph, abnormal state propagation calculation is carried out through the state dependency relation between graph nodes, and the abnormal state propagation calculation is carried out. And obtaining a chain risk transmission path set, performing risk evolution trend deduction on dam nodes in each path based on the chain risk transmission path set, generating a risk evolution index matrix, and generating a threatened risk identification sequence for key dam nodes. According to the method, the structuralization and response operability of the early warning information can be improved, and the scientificity and practicability of early warning analysis of the basin-level reservoir dam group are comprehensively improved.

The invention relates to the technical field of dam safety monitoring, in particular to a method and system for improving dam GNSS deformation monitoring precision through base station and observation station combined network adjustment. The method comprises the following steps: arranging base stations and monitoring points to form a GNSS monitoring network; gNSS original observation data are collected in real time and preprocessed; constructing a joint network adjustment model, and taking base station coordinates as constraints and monitoring point coordinates as to-be-estimated parameters; performing baseline resolving based on the double-difference carrier phase observed quantity to obtain a baseline vector and a covariance matrix thereof; carrying out overall adjustment on the baseline vector by adopting a robust estimation method, and solving an optimal coordinate estimated value and precision information of the monitoring point; carrying out deformation analysis on the basis of the adjusted coordinate time sequence, and extracting tendency, periodicity and abnormal deformation; and outputting a deformation monitoring result, and carrying out visual display and early warning. According to the method, the precision and reliability of dam deformation monitoring are effectively improved through combination of network adjustment and robust estimation.

The invention relates to the technical field of synthetic aperture radar interferometry, in particular to an intelligent sea reclamation site deformation monitoring system and method based on InSAR, and the system comprises a data collection module which is used for collecting a multi-temporal satellite-borne SAR image of a sea reclamation area; the data processing module is used for executing sub-pixel matching, orbit error correction, atmospheric delay correction and residual optimization; the model construction module is used for constructing a segmented orbit error model, a tide-humidity coupling model and a dynamic regression model; the verification feedback module is used for verifying leveling precision and dynamically adjusting model parameters; according to the method, by fusing terrain mutability, tide-humidity coupling and soil rheological characteristics of a reclamation scene, accurate correction of multi-source error distribution of orbit errors, atmospheric delay and creep noise is realized, and the problem of insufficient adaptability of a traditional method in reclamation area deformation monitoring is solved; and millimeter-level reliable data support can be provided for cofferdam safety, hydraulic reclamation consolidation and port engineering.

The invention discloses a displacement auxiliary measuring mechanism and a reservoir dam monitoring and early warning system thereof, and belongs to the technical field of dam monitoring. The reservoir dam monitoring and early warning system comprises a data acquisition module, an attitude calibration module, a data fusion judgment module, a risk grading analysis module and a remote early warning trigger module. The distance measurement precision under the complex field condition is remarkably improved, the current mechanical direction and the reference direction are fused, a correction vector pointing to a target reflection point is generated, the direction drift error is effectively eliminated, and the distance measurement accuracy is improved by constructing a double-path model of main path fusion and auxiliary path comparison. According to the method, non-structural anomalies caused by sensor body interference or environmental disturbance are eliminated, data offset is corrected in real time, a stable trend curve is output, when a main path has a drift or error trend, the main path is automatically guided to converge to an auxiliary path, and the abnormal data recognition and restoration capability is enhanced.

The invention discloses an energy storage battery state monitoring analysis system and method based on flexible strain sensors, and relates to the technical field of energy storage equipment monitoring, the energy storage battery state monitoring analysis system based on the flexible strain sensors comprises a plurality of flexible strain sensors, a measurement module and an analysis module; the plurality of flexible strain sensors are correspondingly embedded among the plurality of battery cells; the flexible strain sensor is used for generating impedance change by sensing the pressure change of the side surface of the battery cell; the plurality of flexible strain sensors are connected with the measurement module; the measurement module is used for collecting impedance data of the plurality of flexible strain sensors; the analysis module is connected with the measurement module; and the analysis module is used for acquiring impedance data based on the plurality of flexible strain sensors for analysis and calculation to obtain an energy storage battery state monitoring analysis result. The hysteresis of energy storage battery state monitoring analysis is reduced, the accuracy of the monitoring result is improved, and the application range of energy storage battery state monitoring analysis is expanded.

The invention relates to an MT-InSAR self-adaptive fusion measurement method and device for long-term surface deformation of a coal mining subsidence area, and the method comprises the steps: obtaining surface deformation monitoring data in a plurality of modes, and carrying out the at least one of time-space alignment, noise removal and error correction of the surface deformation monitoring data, so as to obtain surface deformation data; calculating coherence indexes of the earth surface deformation data; generating a space adjustment coefficient by using a double-peak Gaussian function model constructed based on a coal mining subsidence area deformation rule; constructing an adaptive weighting method based on the coherence index and the spatial adjustment coefficient of the earth surface deformation data to calculate the weight of the multi-source InSAR data; and performing weighted fusion on the surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final surface deformation result. Therefore, the problems of insufficient monitoring precision, incomplete coverage, poor adaptability and the like due to the fact that a single InSAR technology is difficult to comprehensively reflect spatio-temporal evolution characteristics of long-term surface deformation of the wide-area coal mining subsidence area are solved.

The invention provides a detection device for internal pressure change of a battery cell, the battery cell comprises an explosion-proof valve or an explosion-proof sheet, and the explosion-proof valve or the explosion-proof sheet is arranged on a shell of the battery cell. When the internal pressure of the battery cell changes, the explosion-proof valve or the explosion-proof sheet deforms at first until the battery cell is broken due to overlarge internal pressure, and the detection device comprises an electrode sheet which is a multi-section type electrode sheet comprising a plurality of electrode sections, each electrode section is physically separated and is arranged above or on the side of the explosion-proof valve or the explosion-proof sheet, each electrode section and the explosion-proof valve or the explosion-proof sheet form an inductive capacitor, and deformation of different positions of the explosion-proof valve or the explosion-proof sheet is detected based on capacitance change; and the chip is a multi-channel chip, each channel is connected with each electrode section through a connecting line in a one-to-one correspondence manner, and the chip is also connected to an anti-explosion valve or an anti-explosion sheet through a connecting line. The invention also provides a battery core internal pressure early warning system.

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 discloses a respiration monitoring system based on a flexible strain sensor and a monitoring analysis method, and belongs to the technical field of sensors and respiration monitoring equipment. The respiration monitoring system comprises a flexible strain sensor, a connecting line, a data acquisition card and an upper computer; the flexible strain sensor is a band-aid type flexible strain sensor and can be directly adhered to the skin surface of a human body, and the deformation of muscles related to respiration is monitored and converted into the change of a resistance signal. And the flexible strain sensor is connected with the data acquisition card through a connecting line. And the data acquisition card is connected with the upper computer and is used for transmitting the acquired resistance signals to the upper computer. The quantitative breathing mode judgment standard is provided through dynamic measurement of the respiratory muscle group, and the defect that judgment is mainly conducted through naked eyes of doctors for a long time can be overcome; the relation between the respiratory muscle group deformation and the lung function parameters is associated, and the problem that the actual respiration condition of part of patients does not conform to the detection condition in conventional lung function detection can be solved.

The invention discloses a fault co-seismic sliding surface inversion method and system based on Beidou and artificial intelligence. The method comprises the steps of collecting Beidou data and seismic waveform data of a target area in real time, and performing preprocessing to extract co-seismic displacement and waveform features; fusing the Beidou deformation features and the seismic wave features to form a multi-modal feature vector; a fault prediction model is constructed based on a graph neural network, forward modeling and historical earthquake example data training are utilized, and multi-modal features are input to obtain preliminary fault sliding distribution prediction; rapid optimization under physical constraint is carried out through the elastic dislocation model, and a final fault sliding model conforming to the geophysical law is obtained; finally, uncertainty quantification is carried out, and a visual product of the dynamic process including sliding distribution, seismic moments and fault deformation is generated. According to the system, full-process automatic processing is achieved through cooperation of all the units, the inversion speed, precision and physical credibility are improved, and reliable support is provided for earthquake emergency response and disaster assessment.

The present disclosure provides a method and system for establishing a space-ground integrated real-time monitoring reference for dam deformation. The method includes: transmitting control instructions to a space-based Beidou system, a ground-based measurement robot system and a ground-based inverted plumb line system; obtaining a dynamic reference point position of a dam deformation area in real time through the space-based Beidou system; obtaining a spatial relative reference point position of the dam deformation area in real time through the ground-based measurement robot system; obtaining a static gravity reference point position of the dam deformation area in real time through the ground-based inverted plumb line system; and performing data fusion on the dynamic reference point position, the spatial relative reference point position, and the static gravity reference point position, and obtaining a monitoring reference value of the dam deformation area.

The invention relates to a wide-area coverage area high-precision deformation monitoring method based on an InSAR technology, and the method comprises the steps: obtaining the InSAR monitoring data of a plurality of data sources for a wide-area coverage area, carrying out the registration of the InSAR monitoring data, screening interference pairs according to the coherence, carrying out the interference pair combination of a single-view complex image set obtained through registration, and obtaining a single-view complex image set; performing time sequence analysis according to the interference pair combination result to generate a time sequence interference image; performing phase filtering and phase unwrapping on the time sequence interference image; for the time sequence interference image after phase unwrapping, baseline parameters are corrected based on a nonlinear least square method, and atmospheric delay errors are corrected based on a GACOS atmospheric delay model; according to the processed image, constructing a wide-area surface deformation rate map and a preset application scene surface deformation rate map, and analyzing the spatio-temporal evolution law of the wide-area surface deformation rate map and the preset application scene surface deformation rate map; and finally, visually displaying the wide-area surface deformation rate diagram, the preset application scene surface deformation rate diagram and the spatio-temporal evolution law analysis result.

A non-intrusive sensing system for monitoring an electrochemical device and a method of operating the non-intrusive sensing system can include multi-static ultrasonic sensors for detecting data indicative of a property of electrolytic media in an electrochemical device, an acoustic sensor for detecting and measuring a signature of electrodes associated with a health condition of the electrochemical device. A temperature sensor can be used to detect surface temperature data and correlate the surface temperature data with the signature identified and extracted by the acoustic sensor and the data indicative of the property of the electrolytic media. The data detected by the multi-static ultrasonic sensors, the signature detected by the acoustic sensor, and the surface temperature data identified can be subject to feature extraction and processing by a detection and prediction model to produce information pertaining to the safety, reliability and operating efficiency of the electrochemical device.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

The invention discloses a bridge deformation monitoring method based on RTK differential positioning and IMU fusion and a medium, which fully considers the requirements for displacement, angle and other data in the bridge deformation monitoring process, realizes multi-angle and multi-azimuth monitoring by using GNSS and IMU, and improves the accuracy of bridge monitoring. The RTK positioning data and the IMU measurement data are fused through the extended Kalman filtering algorithm, the IMU error can be compensated, the advantages of the two kinds of data can be fully mined, and data complementation and optimization are achieved. Under the condition that satellite signals are shielded or interfered, IMU data can assist in maintaining the stability of a monitoring system, and interruption of positioning information is avoided; when the IMU data fluctuates for a short time, the high-precision positioning result of the RTK can correct the fusion result to ensure that the output bridge deformation monitoring data is always accurate and continuous, so that the robustness and reliability of the whole monitoring system in a complex environment are improved.

A system for identifying a failure of the primary load path in an actuator. The actuator includes a screw shaft and a nut assembly moveable along the screw shaft. The nut assembly comprises a primary nut for transmitting load through the actuator along a primary load path, and a secondary nut for transmitting load through the actuator along a secondary load path. The nut assembly is configured such that, in normal operation, the secondary nut is not engaged with the screw shaft and load is transmitted through the actuator along the primary load path, and such that, upon failure of the primary load path, the secondary nut is configured to engage the screw shaft and transmit load through the actuator along the secondary load path. The system includes a strain gauge configured to be mounted to the actuator and to measure vibrations of the actuator.

The invention discloses a slope radar and GNSS three-dimensional deformation data combined observation fusion method and system. The method comprises the following steps: S1, acquiring slope radar data, performing discrete value elimination processing, acquiring GNSS three-dimensional deformation data, and performing discrete value elimination processing and spatial interpolation processing; s2, calculating displacement data of the slope radar data and converting the displacement data to a BLH coordinate system; s3, constructing a combined observation model of the slope radar data and the GNSS three-dimensional deformation data under the same BLH coordinate system; and S4, carrying out data fusion on the slope radar data and the GNSS three-dimensional deformation data according to the weight by taking the iteratively optimized weight of the combined observation model. According to the method, the combined observation model of the multi-source data is jointly constructed based on the slope radar observation equation and the GNSS observation equation, the weighted residual algorithm based on the least square method is introduced, the weighted residual sum is minimized, the high-precision three-dimensional deformation fusion result is generated, and reliable technical support is provided for subsequent slope deformation analysis.

The invention relates to the technical field of transformer substations, and particularly discloses a transformer substation channel cover plate data acquisition and intelligent analysis management system and method, and the method comprises the steps: collecting cover plate vibration data and strain data through an acceleration sensor and a resistive strain gauge; vibration direction change, vibration attenuation and cover plate deformation springback time are extracted from acceleration data, the maximum amplitude, the change rate and the strain gradient are extracted from strain data, and the stress deformation characteristics of the cover plate are comprehensively represented; based on the stress deformation and springback characteristics of the cover plate, a damage evaluation model is established by adopting a neural network model, and intelligent judgment of the damage degree of the cover plate is realized; predicting a damage change trend by using a neural network, and calculating a cover plate damage acceleration trend; and according to the damage evaluation result and the damage acceleration trend, calculating the remaining to-be-maintained time of the cover plate, and automatically triggering operation and maintenance early warning when the remaining to-be-maintained time is lower than an early warning threshold value. According to the method, intelligent monitoring, prediction and active operation and maintenance management of the health state of the cover plate can be realized.

The invention relates to a deformation monitoring method and device based on Beidou and inertial vision fusion, and the method comprises the steps: carrying out the data synchronization of a preset inertial measurement unit, an inertial camera and a Beidou positioning module, and building a unified time reference; the method comprises the following steps: imaging a preset target on a target facility through an inertial camera and extracting image point features at a first moment and a second moment on the basis of a unified time reference so as to obtain a first imaging picture and first attitude data as well as a second imaging picture and second attitude data; acquiring first position data of the inertial camera at a first moment and second position data of the inertial camera at a second moment, calculating relative displacement data of the inertial camera between the first moment and the second moment, and correcting to obtain corrected target image point displacement; and carrying out fusion calculation on the corrected target image point displacement and the real-time attitude data of the inertial camera to obtain actual settlement displacement data. The method and the device have the effect of improving the facility deformation monitoring accuracy.