259 results about "Adaptive tracking" patented technology

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention belongs to the technical field of side slope monitoring, and particularly discloses and provides a side slope deformation three-dimensional monitoring system based on fusion laser ranging, which comprises a deformation rule analysis module, a monitoring switching judgment module, an environment monitoring module, a side slope deformation analysis terminal and a deformation risk processing terminal. According to the invention, by identifying abnormal characteristics such as slope change overlimit, automatically capturing potential high-risk areas such as acceleration abrupt change areas and triggering monitoring setting dynamic updating, the problem of coverage blind areas of fixed monitoring areas is effectively solved, adaptive tracking monitoring of slope high-risk points is realized, and the method is suitable for large-scale popularization and application. Meanwhile, a multi-dimensional slope deformation risk assessment mechanism is constructed by performing fusion processing on a past environment tracking log and a three-dimensional coordinate sequence and setting a deformation risk compensation weight, the limitation of traditional single-point monitoring is broken through, and the slope deformation risk assessment mechanism is established by collecting the humidity of a slope area in real time and selecting laser wavelength. And the interference of environmental noise on three-dimensional coordinate data acquisition is reduced.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention relates to a digital lock-in amplifier based on a frequency adaptive technology and a weak signal acquisition system and method, and the system comprises a front-end signal processing module which is used for obtaining a weak signal to be detected; the frequency self-adaptive tracking module is used for performing fast Fourier transform on the frequency of the weak signal to be measured to obtain the position of the maximum spectral line so as to synthesize a frequency control word, further generate a local reference signal and simultaneously adjust the coefficients of a low-pass filter and a loop filter and the series adjustment mode of a carrier synchronization ring; the multi-rate narrow-band low-pass filter module is used for multiplying the digital signal by a local reference signal and then carrying out filtering and down-sampling on a high-frequency signal to obtain a plurality of mutually orthogonal difference frequency components; the vector operation module is used for carrying out vector operation on the plurality of mutually orthogonal difference frequency components to obtain the phase and the amplitude in the weak signal to be measured; and the man-machine interaction module is used for displaying and storing the phase and the amplitude in the weak signal to be detected.

The invention relates to the technical field of wind speed prediction, in particular to a wind speed prediction method and system based on entropy clustering, and the method comprises the following steps: carrying out the multi-scale decomposition of a wind speed sequence through a permutation entropy algorithm, generating subsequences, carrying out the sliding window probability distribution calculation of a plurality of subsequences, setting a dynamic classification threshold value based on a normalized entropy value, and carrying out the calculation of the sliding window probability distribution. And dividing the sub-sequence set into an entropy classification result set. According to the method, a classification threshold value is dynamically set through sliding window probability distribution, the sensitivity of fixed entropy division to data complexity change is overcome, nonlinear mapping between an original sequence and entropy components is established through GRU training, the feature extraction depth and low component error transmission are enhanced, high, medium and low entropy components are modeled through Bi-RNN, LSTM and SVR, feature extraction with different complexities is matched, and the accuracy of feature extraction is improved. Chaotic mapping optimization fusion weight, multi-target optimization balance component complementarity and dynamic error reference are combined with feedback to adjust a confidence interval, error distribution adaptive tracking is realized, and prediction reliability is enhanced.

The invention relates to the technical field of advertisement shooting, in particular to a holder self-adaptive tracking control method based on visual identification, which comprises the following steps of: acquiring a scene image digital signal in real time through a camera, then extracting the coordinate, the size and the motion trail of a detection target, and adopting an optical flow motion estimation algorithm in the extraction process, the optical flow motion estimation algorithm can calculate the motion information of the target by analyzing the motion of the pixel points in the image, and is combined with pure extraction of static information such as coordinates and sizes, so that the state of the detected target can be described more comprehensively and dynamically, the location of the target can be known, and the motion of the target can be accurately mastered; and then, according to the motion trail of the detection target, calculating and establishing a mapping relation between a pan-tilt angle and a camera visual field, and calculating the angle of the pan-tilt needing to be adjusted when the detection target shoots the advertisement and the detection target moves through the mapping relation between the pan-tilt angle and the camera visual field, so that the detection target can be always located at the central point coordinate of the image digital signal.

The invention relates to the technical field of underwater communication and positioning, in particular to a multi-mode underwater wireless communication system based on dynamic link calibration, which integrates underwater acoustic positioning guide, attitude adaptive tracking control, optical link enhancement and an error code correction mechanism. Comprising a central control core, an acoustic communication unit, an optical communication transmitting unit, an optical signal receiving unit, an optical control unit, a multi-axis attitude adjusting unit, a data fusion and verification processing unit and a high-speed modulation-demodulation and error code correction unit. And by using a double-shaft servo motor and automatic gain control, real-time target tracking and accurate alignment of optical signals are realized, FPGA and OFDM modulation are combined to optimize data throughput, and a data cross validation mechanism is introduced to correct error codes, so that the data integrity is improved.

The invention relates to the technical field of spraying track planning, and provides a track self-adaptive control method for a furniture spraying process. The method comprises the following steps: establishing a parameterized furniture geometric model; obtaining a spraying demand, performing spraying area unit division in combination with the parameterized model, and establishing a division result; extracting geometric features of a division result, and establishing a geometric partition label set; a main direction is extracted based on the label set, and a basic spraying track is established; according to requirements and a label set, track layering is carried out, a path is optimized, and a following spraying track is established; and feedback data are obtained in real time during spraying, self-adaptive following optimization is carried out according to the feedback data and the following track, and track self-adaptive control is completed. The technical problems of uneven spraying and unstable spraying quality caused by path deviation of the spraying gun in the spraying process are solved, and the technical effect that the spraying precision and the spraying process stability of the complex furniture surface are improved through main direction path planning and layered following control driven by geometrical characteristics is achieved.

The invention discloses a complex water body pollution detection method and system based on an adaptive cruise technology, and belongs to the technical field of environment monitoring. The method overcomes the inherent defect that an existing monitoring scheme based on a static preset path cannot adapt to a dynamic pollution environment by constructing a closed-loop feedback control architecture of real-time sensing, central decision making and dynamic execution. Generating initial global pollution distribution estimation and planning an initial path by the main aircraft; the subordinate vehicle sails along the path and estimates the local pollution concentration and the gradient direction in real time; and the main aircraft fuses all data to update global estimation, and dynamically generates an instruction for guiding the subordinate aircraft to move towards a pollution concentration gradient rising direction after identifying a pollution plume or a hot spot, thereby realizing active tracking and high-resolution surveying and mapping. According to the invention, the efficiency, precision and intelligent response capability of pollution monitoring are significantly improved, and accurate and adaptive tracking of a dynamic pollution field is realized.

The invention relates to the technical field of photoelectric pod target tracking, and discloses a multi-mode target automatic identification tracking method and system based on a photoelectric pod. A multi-modal data acquisition module of the system integrates visible light, infrared thermal imaging and laser ranging sensors based on a photoelectric pod, acquires target multi-modal sensing data and completes time-space synchronous calibration; the dynamic feature extraction module adopts a multi-scale convolutional neural network to process multi-modal data, generates a target multi-level feature map and performs significance region labeling; the adaptive tracking decision module deploys a recurrent neural network to predict a target motion trajectory according to a labeling result, and generates a tracking control instruction through an optimization algorithm; the execution control module drives the photoelectric pod holder mechanism and adjusts the orientation of the sensor to realize target tracking; the feedback optimization module monitors the tracking state in real time, calculates a tracking deviation index, and generates a strategy adjustment parameter to dynamically optimize the tracking strategy.

The invention provides a vehicle working condition virtual debugging simulation method based on multi-modal perception fusion, and relates to the technical field of digital twinning, and the method comprises the steps: achieving the precise fusion of multi-modal perception data through the synchronous collection of visual, radar and acoustic signals and the unification of a time reference and a coordinate system; constructing a vehicle dynamics state model based on mutual information weighting and recursive filtering, and obtaining dynamically consistent state vectors; combining space-time modeling and robust coding to generate driving characteristics capable of being physically explained, and inputting the driving characteristics into a simulation environment; in the digital twinborn model, parameter adaptive tracking is realized through online correction and joint recursive updating; and closed-loop feedback debugging is formed by using the health index and the fault probability, so that virtual and real consistent and dynamically adjustable vehicle working condition simulation and intelligent debugging are realized. According to the method, fusion of multi-mode sensing data can be realized, so that the simulation model dynamically tracks the working condition of the vehicle.

The invention belongs to the technical field of ship course control, and discloses an unmanned sailboat sail-rudder cooperative active-disturbance-rejection course control method based on ESO, and the method comprises the following steps: building a nonlinear course motion mathematical model; designing a self-adaptive tracking differentiator to smooth the expected course; the course, the yawing angular velocity, the sail aerodynamic moment and the comprehensive disturbance are observed in real time through ESO; based on a nonlinear state error feedback control law, in combination with the output of the tracking differentiator and the observed value of the ESO, generating a rudder torque control quantity, performing constraint processing on the rudder torque control quantity, and outputting a rudder angle instruction; and a dynamic optimal sail angle is decided according to the lift-drag ratio parameter, the apparent wind speed and the relative wind angle of the sail aerodynamic model corrected in real time, and a sail angle instruction is generated after the dynamic optimal sail angle is compensated and constrained by combining the course deviation. The adaptive capacity to the time-varying wind field and the model uncertainty is enhanced, and the anti-interference performance and the energy efficiency of the system are improved through dynamic optimization and compensation of the sail angle.

The invention provides a photovoltaic air conditioner and a control method and system thereof, and belongs to the technical field of photovoltaic air conditioners. The control method comprises the steps that the thermal comfort state in a building, the real-time operation power of the photovoltaic air conditioner and outdoor state parameters are monitored; calculating predicted power generation power and predicted photovoltaic air-conditioning power; calculating an absolute value of a difference value between the predicted power generation power and the predicted photovoltaic air-conditioning power, and judging whether the absolute value exceeds a preset source load power error threshold value or not; if the source load power error threshold value is exceeded, grading adjustment is carried out according to the size relation between the predicted power generation power and the predicted photovoltaic air conditioner power; and when the energy storage equipment is configured, the energy storage equipment is controlled to store or release energy. According to the method, the thermal comfort state, the photovoltaic power generation power and the air conditioner operation power are monitored in real time, graded adjustment or energy storage control is conducted on the basis of the prediction difference value, the problem of source load mismatching caused by photovoltaic output fluctuation is effectively solved with low cost, and self-adaptive tracking and efficient on-site consumption of photovoltaic fluctuation are achieved.

The invention discloses a multi-level pulse compensation type active EMI (Electro-Magnetic Interference) filter suitable for a multi-noise source switching power supply and a control method of the multi-level pulse compensation type active EMI filter. The invention provides a multi-level pulse compensation scheme based on switch behavior modeling, aiming at the problem of common-mode noise of adjacent rising edges and falling edges generated by a high-frequency switch in a multi-noise-source switching power supply. The time sequence delay and the edge slope of the compensation signal are calculated by analyzing the dv / dt characteristics in the switching-on and switching-off processes of the switching device, and accurate matching and offset with a noise source in phase and amplitude are achieved. Compensation control logic is realized by sharing a main control chip with a power circuit, and an additional controller and a high-precision sampling system are not needed; the self-adaptive tracking of the compensation amplitude is realized through the voltage following circuit; a multi-level compensation signal is output through the totem-pole circuit and injected into a common-mode path, and effective suppression of electromagnetic interference is achieved. The converter has the advantages of small size, low cost, simplicity and convenience in installation, high stability and the like, and is suitable for various multi-noise-source power electronic systems including a dual-active bridge converter, a phase-shifted full-bridge converter and an interleaving Buck converter.

The invention relates to the technical field of machine learning, and discloses a transformer operation state intelligent identification method based on machine learning, which is applied to a 110kV-500kV transformer and an edge-cloud collaborative architecture of an online monitoring system of the 110kV-500kV transformer. According to the method, multi-modal features are extracted based on multi-channel cleaning data, a modal enhancement TCN model fusing kernel feature mapping and a Koopman operator is introduced, and time-frequency coupling modeling and hidden space state representation are achieved. The model realizes online recursion and working condition adaptive tracking through a Sherman-Morrison incremental updating mechanism, outputs an operation state identification result and a risk score, realizes millisecond alarm linkage through an IEC 61850 GOOSE protocol, and provides high-precision and traceable technical support for intelligent monitoring and operation and maintenance of a transformer of a transformer substation.

The invention relates to the technical field of drilling, and provides a roofbolter feedforward-feedback model-free adaptive tracking control method and system based on rotation force. According to the method, a valve-controlled hydraulic propulsion control model for driving a hydraulic cylinder and a corresponding reversing valve in the working process of the hydraulic jumbolter is established, and a nonlinear autoregressive moving average model between the propulsion force in the working process of the hydraulic jumbolter and the output driving current of the reversing valve is obtained through the valve-controlled hydraulic propulsion control model; through a nonlinear autoregression moving average model of the hydraulic jumbolter, an output identification observer deployed with a data-driven adaptive control algorithm of the hydraulic jumbolter is established, and the output identification observer performs adaptive tracking control on the working state of the hydraulic jumbolter through the deployed data-driven adaptive control algorithm. The tracking control problem of the valve control electro-hydraulic servo unit under the complex working condition is effectively solved.

The invention discloses an industrial robot joint friction parameter compensation method based on machine vision. The three-dimensional pose information of the target feature points is extracted based on the image sequence, the tail end position and the pose change are accurately captured, the actual driving force is obtained, and a dynamic comparison mechanism between the theoretical driving force and the actual driving force is established, so that the state deviation is extracted, the load torque and the friction torque are separated out, and the driving precision is improved. Compared with a traditional control strategy depending on a fixed friction model coefficient, the dynamic friction compensation torque is obtained in a real-time image obtaining mode, non-linear friction interference changing along with time, temperature and abrasion can be tracked and compensated in a self-adaptive mode, and the method has the advantages that the dynamic friction compensation torque is obtained in a real-time image obtaining mode. The problems that an existing model lags behind and is large in compensation error are solved, and the friction estimation precision and the compensation capacity are effectively improved.

The invention discloses an unmanned ship event triggering adaptive tracking control method based on an improved optimization backstepping technology. The method comprises the following steps: S1, establishing a motion mathematical model of a three-degree-of-freedom unmanned ship considering external interference; s2, designing a fixed time disturbance observer based on the motion mathematical model, and estimating external marine environment disturbance without depending on velocity derivative information; s3, designing an event triggering mechanism based on the position error of the unmanned ship so as to measure a control input signal without depending on an additional sensor; and S4, adopting an improved optimal backstepping control strategy based on actor-commentator reinforcement learning, and directly estimating a cost function gradient by using a neural network so as to generate a final control law, thereby eliminating complex matrix differential operation required in a traditional method. The invention also discloses a system for realizing the method. According to the invention, the control precision, robustness and real-time performance of trajectory tracking of the unmanned ship in a complex marine environment are improved.

The invention discloses a power line real-time identification and spatial positioning method based on depth vision assistance, and relates to the technical field of power line real-time identification, and the method comprises the steps: constructing a lead identification and positioning frame with a strong generalization capability through deep fusion of a physical law and a depth vision technology; a vibration propagation model based on a wave equation can accurately capture signal interruption characteristics at a cross point, and a frequency domain fusion mechanism significantly improves the linear structure recovery capability of a backlight scene; dynamic vibration phases are coded into a continuous radiation density function by adopting an implicit neural radiation field technology, and the dependence of traditional point cloud reconstruction on dense sampling is broken through; and a dynamic deformable convolution kernel is introduced to realize self-adaptive tracking of the wire form through a physical deformation model, so that extreme working conditions such as icing can be effectively handled.

The invention discloses a photovoltaic support adaptive tracking control method and system, and relates to the technical field of adaptive control, and the method comprises the steps: collecting a sky image, illumination intensity, photovoltaic panel temperature and support attitude data through employing a plurality of sensing units; according to the method, real sunlight judgment is realized through multi-dimensional feature fusion, and pseudo peak values formed by adjacent-row reflected light, ground object reflected light and cloud shadow edge local strong light can be effectively identified by combining irradiation field illumination gradient analysis, photovoltaic panel temperature synchronism verification and cloud shadow edge position matching; when a suspicious pseudo peak value is detected, the accuracy of sunlight position judgment is further ensured through a dual mechanism of historical irradiation curve comparison and sun geometric position verification, the tracking deviation caused by false peak value misjudgment in a traditional single-point measurement or current feedback strategy is avoided, the photovoltaic support is enabled to be always aligned with the real optimal illumination direction, and the accuracy of sunlight position judgment is ensured. And local heating and aging risks of the assembly are reduced.

The invention relates to the technical field of photoelectric tube detection, in particular to a photoelectric tube high-voltage excitation type solar blind ultraviolet discharge detection method and system, and the method comprises the steps: obtaining original voltage sequence data collected by a high-speed transimpedance sampling circuit in real time; positioning feature points of each suspected pulse according to the original voltage sequence data, and extracting pulse waveform features; obtaining a physical confidence factor of each suspected pulse according to the falling edge attenuation characteristics; calculating effective discharge energy flow in unit time based on the physical confidence factor; calculating a discharge stability coefficient according to a coupling relationship between the time correlation entropy and the effective discharge energy flow; according to the method, a scheme from microscopic physical judgment, waveform form deconstruction and time law analysis to multi-dimensional trend prediction is constructed, so that the contradiction between the detection sensitivity and the quantification precision is solved; and adaptive tracking monitoring of the partial discharge process is realized.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a self-adaptive tracking unmanned aerial vehicle spectrum detection module which comprises a dynamic fingerprint database generation unit, a deep reinforcement learning decision unit, an intention prediction and compensation unit, an extended tracking control unit and a federal learning evolution unit. The dynamic fingerprint database accurately depicts the spatial-temporal characteristics of the signals; the deep reinforcement learning optimizes detection parameters in real time; the intention prediction unit actively compensates signal fading; the cross-modal data fusion improves the tracking robustness; therefore, the system has high-precision and high-reliability tracking capability in a complex environment.

The invention provides an intelligent tracking closed-loop control system for a side slope photovoltaic panel, and relates to the technical field of data processing. Acquiring illuminance data of the surface of the photovoltaic panel and biaxial angle data of the photovoltaic panel bracket in real time at a preset sampling frequency; wherein two reference identification points are fixedly arranged at the diagonal positions of the surface of the photovoltaic panel; establishing a plane coordinate system according to the position relation of the two reference identification points; dividing the surface of the photovoltaic panel into a plurality of area units based on a plane-coordinate system, and obtaining a data correction coefficient by analyzing the relative position distribution of each area unit; and performing standardization processing on the acquired illuminance data and the biaxial angle data according to the data correction coefficient to obtain optimized illuminance data and optimized biaxial angle data. According to the invention, high-precision self-adaptive tracking of the slope photovoltaic panel to the sun azimuth is realized.

The invention provides an adaptive tracking method for a complex scene. The adaptive tracking method comprises the following steps: respectively extracting template features and search features; similarity matching features are obtained through calculation, and feature fusion is carried out on the similarity matching features and the search features to obtain self-adaptive fusion features; respectively inputting the adaptive fusion features and the search features into a preset candidate region generation network to select a plurality of candidate regions; performing refined semantic matching calculation on the template features and each candidate region to obtain similarity features; respectively obtaining a matching semantic score and a coordinate frame of the similarity between each candidate region and the template in the matching semantic branch, and obtaining a foreground probability score and a coordinate frame of each candidate region as a foreground target in the foreground probability branch; calculating to obtain a punishment score of each candidate region; and performing weighted calculation on the penalty score, the matching semantic score and the foreground probability score of the same candidate region to obtain a final score of each candidate region, and selecting the candidate region with the highest score as a tracking target.

The invention discloses an industrial robot joint friction parameter compensation method based on machine vision. The three-dimensional pose information of the target feature points is extracted based on the image sequence, the tail end position and the pose change are accurately captured, the actual driving force is obtained, and a dynamic comparison mechanism between the theoretical driving force and the actual driving force is established, so that the state deviation is extracted, the load torque and the friction torque are separated out, and the driving precision is improved. Compared with a traditional control strategy depending on a fixed friction model coefficient, the dynamic friction compensation torque is obtained in a real-time image obtaining mode, non-linear friction interference changing along with time, temperature and abrasion can be tracked and compensated in a self-adaptive mode, and the method has the advantages that the dynamic friction compensation torque is obtained in a real-time image obtaining mode. The problems that an existing model lags behind and is large in compensation error are solved, and the friction estimation precision and the compensation capacity are effectively improved.

The invention relates to the technical field of remote monitoring, in particular to a boiler pressure data remote monitoring system which comprises a data feature extraction module, and the data feature extraction module analyzes time sequence data layer by layer based on boiler pressure data flow, extracts pressure fluctuation information and obtains short-term and long-term change parameters through step-by-step convolution operation. According to the invention, through analyzing time sequence data, extracting short-term and long-term change characteristics of boiler pressure and capturing details of pressure fluctuation, understanding of changes of different time dimensions is enhanced, and in combination with low-dimension processing and multi-term parameter optimization of hidden variables, expression precision of pressure characteristics is improved. According to the dynamic clustering method, through parameter distance judgment and sample information updating, self-adaptive tracking of real-time data changes is achieved, abnormal modes are effectively recognized, feature division is carried out, the time length and frequency analysis of the abnormal states are combined, alarm signals are more targeted, faults are helped to be prevented in time, and the safety and operation stability of the boiler are integrally enhanced.

The invention relates to the technical field of image processing, in particular to a numerical control milling machine workpiece three-dimensional modeling method and system. The method comprises the following steps: clamping a workpiece on a numerical control milling machine, controlling the workpiece to rotate at a constant speed, synchronously performing axial feeding, and collecting a correction spiral image sequence and a groove bottom image sequence; performing space-time registration on the corrected spiral image sequence and the groove bottom image sequence to extract workpiece modeling feature data; performing conversion from polar coordinates to rectangular coordinates based on the workpiece modeling feature data, resolving three-dimensional space coordinates and corresponding surface attribute values, and generating workpiece three-dimensional point cloud data; and inputting the three-dimensional point cloud data of the workpiece into three-dimensional modeling software, and carrying out gridding processing and texture rendering to obtain a three-dimensional digital model of the workpiece. According to the method, the workpiece is driven to execute self-adaptive tracking spiral motion, so that visual shielding caused by dense threads is avoided, and high-precision three-dimensional modeling of the small-helix-angle deep-groove workpiece is achieved.

The invention discloses an EMC interference source rapid positioning method, system and device, and relates to the technical field of electrical measurement, and the method comprises the steps: laying a monitoring sensor network based on an electromagnetic interference signal conduction path and a radiation path of a tested device; connecting a monitoring sensing network, and acquiring interference signal data of the tested equipment in real time; performing spectrum analysis and interference type judgment on the acquired interference signal data; and taking the data acquisition flag bit as a positioning engine, and respectively carrying out adaptive interference tracking analysis according to the interference type based on judgment to obtain an interference source positioning result. The technical problems that an existing electromagnetic interference source positioning method cannot position the interference source in real time when the equipment operates normally, the positioning efficiency is low, and the accuracy is poor are solved, and the technical effect that efficient and accurate interference source positioning is achieved in the normal operation state of the equipment by collecting interference signal data in real time to conduct self-adaptive tracking analysis is achieved.

The invention relates to the technical field of radiotherapy, and discloses a multi-modal image fusion guided chest tumor adaptive radiotherapy system. The system comprises an asynchronous bimodal sensing module, a spectrum preprocessing module, a parameter inversion module, a boundary construction module and a biomechanical calculation module. According to the method, the feedback voxel-level velocity field is utilized to execute Lagrange reverse warping on the high-energy projection with physical time delay, and motion artifacts are eliminated; and dynamically updating the Young modulus of the lung tissue mechanical model based on the inverted real-time electron density, and solving a full-field three-dimensional displacement field in combination with double boundary conditions of body surface flexibility and internal skeleton rigidity so as to reconstruct a tumor target region and generate a beam control instruction. According to the method, through closed-loop interaction of image physics and biomechanics, the problems of dual-energy imaging motion artifact interference and insufficient lung tissue fixed parameter modeling precision are solved, and accurate self-adaptive tracking of a tumor target region is realized.

The invention discloses an intelligent real-time monitoring system and an early warning method for a steel structure construction process, and particularly relates to the technical field of intelligent manufacturing equipment industry process monitoring and early warning. The method is used for solving the problem that an existing early warning method based on a static digital twinborn model is high in false alarm rate or reduced in sensitivity due to long-term evolution mismatch of a model and an entity structure. According to the method, sensor monitoring data and model theoretical data are compared in real time, an abnormal fluctuation mode is recognized, and whether the abnormal fluctuation mode is derived from construction stage conversion or not is judged; when the systematic deviation is confirmed, extracting and analyzing construction activity data associated with the systematic deviation to calculate causal association strength; when the association strength exceeds a threshold value, analyzing association characteristics of the characteristic parameters and the deviation at the current construction stage; and finally, dynamically adjusting the rigidity parameter distribution of the digital twin model according to the correlation characteristic, and performing accurate safety state evaluation and early warning by using the adjusted model, thereby realizing the self-adaptive tracking of the model on the real state of the structure and improving the early warning accuracy.