64 results about "P-wave" patented technology

The invention discloses a multi-wave chromatography and reflection combined detection method and device for a thin-covering-layer urban active fault and a medium. The method comprises the steps that longitudinal wave seismic data and transverse wave seismic data are collected; recording vertical component data based on the longitudinal wave seismic data for first arrival travel time pickup and tomography inversion to obtain a longitudinal wave velocity structure model Vp; extracting Rayleigh surface wave frequency dispersion through the vertical component data and carrying out multichannel surface wave analysis inversion to obtain a transverse wave velocity structure model Vs; calculating a longitudinal and transverse wave velocity ratio parameter structure model Vp / Vs, and calculating to obtain a Poisson's ratio parameter structure model v; obtaining a longitudinal wave (P wave) reflection profile and a transverse wave (S wave) reflection profile; identifying and summarizing into an interpretation fracture set Fc, and recording key elements; and by taking the interpretation fracture set Fc as a structural skeleton, completing unified registration and superposition display on Vp, Vs, Vp / Vs and v, a longitudinal wave reflection profile and a transverse wave reflection profile, and outputting a shallow underground structure comprehensive interpretation map of the thin covering layer region.

The disclosure provides a method and device for velocity tomography imaging of seabed shallow media, and electronic equipment. The method includes performing multichannel analyses of Guided-P and Scholte waves on seabed multi-component seismic gathers to determine measured multi-order dispersion curves of the two waves; based on a first seabed shallow media model and a theoretical dispersion equation of Guided-P wave, performing joint inversion of multi-order dispersion curves of Guided-P wave to iteratively update the P-wave velocity of seabed media; based on a second seabed shallow media model and a theoretical dispersion equation of Scholte wave, performing joint inversion of multi-order dispersion curves of Scholte wave to iteratively update the S-wave velocity of seabed media under the constraint of the P-wave velocity determined by the Guided-P wave dispersion inversion; and performing tomography imaging of seabed velocity structures with the inverted P-wave and S-wave velocities along a survey line.

According to the atrial fibrillation recognition method based on multi-feature fusion, multiple feature extraction task branches are constructed in the same deep learning model, the RR interval features, the P-wave features and the electrocardio waveform morphological features are extracted synchronously and parallelly, and the problems of feature loss and time sequence mismatching caused by multi-step processing are avoided; the model captures RR interval irregularity in a time domain, captures P wave missing and baseline f wave features in a morphological domain, forms a rhythm-morphological-frequency spectrum triple feature system, realizes more stable identification of atrial fibrillation, can effectively distinguish similar expressions of atrial fibrillation and ventricular premature, atrial premature, sinus arrhythmia and the like based on a coordination mechanism of appetite and morphological domain, and improves the accuracy of atrial fibrillation identification. And the identification rate of the atrial fibrillation signal in the electrocardiogram data can be effectively improved.

The invention discloses a multi-stage grouting reinforcement method for water-rich broken surrounding rock, which comprises the following steps: grading broken rock mass according to the breaking degree and the water content condition of disturbed rock mass and the result of a rock mass water content correction P wave test tested by a resistivity method, flexibly adjusting the type of grouting materials according to grading, and matching different grouting processes. While the grouting material is effectively utilized and the engineering budget is reduced, the reinforcing and plugging quality of multiple areas of the fractured rock mass can be guaranteed, the safe operation of the underground engineering is guaranteed, the reinforcing and plugging quality of the fractured rock mass is guaranteed, and the safe operation of the underground engineering is guaranteed. Rock masses with different crushing degrees can obtain the best reinforcing effect through the graded grouting strategy, the overall reinforcing strength is improved, and the anti-permeability performance and the reinforcing strength of a reinforcing body are greatly improved through application of the high polymer grouting material.

A method for performing an elastic full wave inversion (FWI) includes generating an initial P wave velocity model. The method also includes producing a subsurface seismic image or an image gather based upon the initial P wave velocity model. The method also includes estimating elastic properties based upon the subsurface seismic image or the image gather. The method also includes performing elastic full wave inversion (FWI) on the initial P wave velocity model and the elastic properties to produce updated elastic properties.

The invention provides a micro-seismic P-wave first arrival pickup method based on deep learning and an adaptive time window, and the method comprises the steps: (1) collecting and preprocessing a micro-seismic signal, making a data set, and dividing the data set into a training set and a test set; (2) building an MSC-NET deep learning neural network, taking a data set as input for training, and adjusting model parameters to achieve an optimal model effect; (3) performing a micro-seismic signal classification task by applying the trained deep learning model, and dividing a large number of micro-seismic signals into an effective available class and an ineffective unavailable class; and (4) finally, carrying out P-wave first arrival time pickup on the effective micro-seismic signals based on an energy ratio algorithm of an adaptive time window to obtain a relatively reliable pickup result. According to the method, the deep learning technology and the improved traditional signal processing technology are combined, the reliability and accuracy of the automatic pickup result of the micro-seismic signal P wave first arrival time are greatly improved, and meanwhile the method has the advantages of being high in robustness and applicability and the like.

The invention discloses a reservoir water fluctuation propagation direct numerical simulation method and device in gravity dam anti-seismic analysis, a medium and equipment. According to the method, fluctuation propagation in reservoir water under combined incidence of P waves and SV waves is considered, a reservoir water-foundation site free field is analyzed and calculated on the basis of the fluctuation theory and the potential function theory, then the site free field is converted into node counterforce on fluid and solid boundaries on the basis of a fluid and solid power artificial boundary substructure model, and the reservoir water-foundation site free field is obtained. Applying node counterforce on the reservoir water-foundation artificial boundary, and directly performing numerical simulation on reservoir water-foundation site free field response; based on a static-dynamic artificial boundary conversion method, considering the influence of the static effect of the gravity dam-reservoir water-foundation system on the dynamic effect of the system, and constructing a gravity dam seismic oscillation input model for directly simulating reservoir water fluctuation propagation; according to the method, the anti-seismic analysis precision of the gravity dam is improved, and the limitations of single-wave incidence during reservoir water fluctuation propagation simulation, failure to consider the static effect and the like are overcome.

The invention discloses a magnetocardiogram P-wave detection method based on wavelet analysis and a local noise self-adaptive threshold, which is characterized in that accurate and robust detection of a P wave is realized by searching on a characteristic scale which can highlight the P wave, a self-adaptive threshold confirmation mechanism based on local noise estimation is introduced, and a self-adaptive threshold confirmation mechanism is established for a time domain search window of each cardiac cycle. According to the method, the internal noise level is dynamically evaluated, and an estimated value capable of accurately reflecting the local noise condition of the current heart beat can be obtained by carrying out statistical analysis on wavelet coefficients in a characteristic scale in an area where P waves are most likely to appear.

The invention relates to the technical field of civil engineering, in particular to a method for calculating the seismic response of a pile group under the action of seismic P waves, a pile-soil coupling model is established based on a continuous elastic dynamics theory, and pile-pile dynamic interaction factors are calculated by decoupling displacement of a free field and a scattered field of a soil body and combining coordinate transformation, so that the seismic response of the pile group is calculated. The displacement response of a pile group system is solved according to the superposition principle, the influence of pile spacing and soil mass parameters on the anti-seismic performance of the pile group is quantitatively evaluated by adopting a method of combining frequency domain amplitude-frequency characteristics and time domain PGA attenuation analysis, and theoretical support is provided for pile foundation optimization design.

The application relates to the technical field of earthquake prediction, and particularly provides a lightweight on-site ground motion parameter prediction model construction method and an earthquake early warning system. The lightweight on-site ground motion parameter prediction model construction method comprises the following steps: training an initial prediction model according to acceleration waveform data when a P wave of an earthquake arrives, to obtain a pre-training prediction model; taking the pre-training prediction model as a teacher model, and transferring the knowledge of the teacher model to a student model through distillation; and constructing a lightweight on-site ground motion parameter prediction model based on the student model. The method can reduce the complexity and calculation amount of the student model by transferring the knowledge in the pre-training prediction model to the student model through distillation; and the lightweight on-site ground motion parameter prediction model can be constructed based on the student model, so that the demand of the model for hardware resources can be reduced while the on-site ground motion parameter prediction efficiency is ensured, and the deployable scene of the model is expanded.

The invention discloses an electrocardiogram ventricular escape real-time judgment method and system, relates to the technical field of electrocardiograms, solves the problem of lack of multi-dimensional analysis and dynamic characteristic verification of electrocardiogram waveforms, and aims at solving the problem that a time period in which waveform abnormity really exists is focused through continuous analysis of fluctuation points of a to-be-recognized section, so that the accuracy of the electrocardiogram ventricular escape real-time judgment is improved. A symmetric space vector range of a normal P wave is used as a reference, whether a to-be-analyzed waveform contains the P wave is visually judged through matching of the form and the position of a to-be-verified vector, missed judgment caused by low amplitude of the P wave or overlapping of the P wave in a T wave is avoided, and the accuracy of the judgment is improved. A complete analysis chain from electrocardiosignals to clinical diagnosis is constructed through a four-layer logic architecture of pacing point positioning, feature waveform matching, abnormal time capturing and escape pacing mechanism verification, high-precision recognition of ventricular escape pacing is achieved, and the accuracy of ventricular escape pacing recognition is improved through deep fusion of a physiological mechanism and an algorithm. And an interpretable and traceable technical path is provided for automatic electrocardio diagnosis.

The invention discloses a test device and method for simulating a coupling load of local explosion and four-way hysteresis on a component or a node. Comprising a vertical axial force loading assembly, a transverse pneumatic impact assembly, a double-spherical-hinge MTS actuator, a clamping assembly and a test component arranged in the vertical direction, and the clamping assembly comprises counter-force frames located on the two sides and the top of the test component correspondingly. A conventional axial load within the service period of a component can be accurately applied through the vertical axial force loading assembly, the double-spherical-hinge MTS actuator can alternately simulate P waves and Love surface waves after the P waves in an earthquake, and the transverse pneumatic impact assembly can apply a local explosion load at any time period under the action of a vertical axial force and a hysteretic load. According to the test device, the test working condition of coupling of local explosion and four-way hysteresis load on a test piece or a node can be truly simulated.

The application discloses a kind of based on transmission tower anti-seismic technology transmission tower anti-seismic device.The present application includes work platform and its upper surface swing component, the bottom end of the swing component is fixedly connected with bearing component, the top of the swing component is fixedly connected with pedestal;Including gravity center component, the gravity center component is located at the bottom of work platform and is fixedly connected with the lower surface of work platform;Including support component, including electric energy conversion module, calibration module and electromagnetic induction module.The present application is reduced by setting swing component, the degree of transmission tower gravity center deviation, improve the service life of transmission tower;By setting gravity center component, by reducing the gravity center of transmission tower when earthquake, to further control the degree of gravity center deviation of transmission tower;By setting support component, the impact caused by P wave to transmission tower in earthquake can be effectively absorbed and converted into the support force applied to transmission tower, so as to improve the anti-seismic strength of transmission tower.

A computer device for processing cardiac signals comprises: - a data storage (114) arranged to receive input data sets each comprising a plurality of P wave segments each associated with an electrocardiogram track and with an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window and having one or more activation sequence, - an input module comprising a plurality of classifiers, each associated with a respective atrial tachycardia class and arranged to receive an input data set, and to return a value indicating the probability that the input data set indicates an atrial tachycardia which belongs to the associated atrial tachycardia class, each classifier being further associated with a respective confusion matrix, said atrial tachycardia classes being arranged into a hierarchy made of layers each comprising one more node, each node of a given layer being connected to at least one other node in the hierarchy, only part in the layer immediately above and / or in the layer immediately below said given layer in said hierarchy, - a Bayesian engine comprising a prevalence matrix for pair of classifiers which atrial tachycardia classes are connected in said atrial tachycardia class hierarchy, arranged to receive a set of assessments comprising an atrial tachycardia class probability for each classifier, and to process said set of assessments according to the confusion matrices associated with the classifiers and according to said prevalence matrices in order to determine an updated set of assessments which maximizes the expression (I), where Z i are the updated set of assessments values, Y i are the set of assessments, YC i represents the states of the child nodes of the classifier associated with Y i in said atrial tachycardia class hierarchy, and N is the total number of classifiers, said device being arranged to receive an input data set, to process it with the input module, to derive therefrom a set of assessments provided to said Bayesian engine, and to return one or more atrial tachycardia information derived from said updated set of assessments.

The invention relates to a seismic source parameter prediction method based on physical constraint and array geometric adaptation, which comprises the following steps of: determining input, output and initial prediction models, sequentially obtaining station characteristics and predicted seismic source parameters, comparing the predicted seismic source parameters with a seismic source true value to obtain regression loss (if the seismic source true value does not exist, the regression loss is not recorded); obtaining phase loss according to the predicted seismic source parameter and the seismic phase label, and then obtaining total loss; carrying out a training process on the model according to the previous steps, and storing an optimal model; inputting related data into the optimal model, and obtaining predicted seismic source parameters; the phase loss only needs the arrival time data of the P wave and the S wave, and is the signal feature which is the most easily obtained signal feature, so that the application range is relatively wide; phase loss takes the physical law of seismic wave propagation as a hard constraint to be embedded into a model optimization process, so that the interpretability of a prediction result is improved; therefore, the method is wide in application range and high in interpretability when used for predicting the seismic source parameters.

The invention provides an arrhythmia classification method based on dynamic cardiac beat division and feature fusion, and the method comprises the steps: carrying out the preprocessing operation of an original electrocardiosignal, and obtaining a preprocessed signal; performing dynamic threshold screening on the preprocessed signal to obtain a P-wave peak; carrying out waveform sequence interception on the P wave peak through a cardiac beat window to obtain cardiac beat data; and performing feature fusion on the heart beat data through a convolutional neural network to obtain a classification result. According to the method, the P wave is positioned through the intelligent algorithm, and dynamic cardiac beat division is performed in combination with the RR interval change rate features and the neural network, so that the detection precision, the feature fusion effect and the classification accuracy are improved.

The embodiment of the invention provides a high-precision seismic elastic wave field forward modeling method, device and equipment and a storage medium, and the method comprises the steps: carrying out the discretization of a wave equation in an elastic medium through the combined application of an improved time high-order implicit staggered grid difference method, and obtaining two types of time-space domain frequency dispersion relationships related to a P wave and an S wave based on the plane wave arrangement and simplification; solving the two types of time-space domain dispersion relations of the P wave and the S wave by combining a least square method and a Taylor series expansion method to obtain high-order difference coefficients corresponding to the P wave and the S wave; based on a wave field separation technology, a P wave field component is calculated by using a high-order difference coefficient corresponding to a P wave, an S wave field component is calculated by using a high-order difference coefficient corresponding to an S wave, and the components are superposed to obtain a high-precision full-elastic wave field. According to the technical scheme, under the same parameter condition, better simulation precision can be generated, so that an effective wave field continuation scheme is provided for subsequent elastic wave migration imaging and waveform inversion.

The present application relates to a seismic phase picking method based on time-frequency analysis and deep learning, belonging to the field of seismology, a deep neural network comprising an empirical mode decomposition module, a residual shrinkage module, an LSTM layer, a transformer layer, an up-sampling layer and a convolution layer is established, a linear rectifier function is used as an activation function, an Adam adaptive optimization function is used for back propagation, a binary cross-entropy loss function is used as a loss function, and the average absolute error function, error standard deviation, precision, recall and F1 score are used as evaluation indexes to obtain a deep neural network model. When there are differences in the frequencies of noise, P waves and S waves, empirical mode decomposition can effectively separate the three, thereby helping the network to better identify P waves and S waves. Even for wideband noise, the noise will be decomposed into different intrinsic mode functions, thereby reducing the noise content in the intrinsic mode function where the P wave and S wave are located. This time-frequency analysis method can effectively improve the noise immunity of the network.

The application discloses a kind of waveform and P wave polarity combined inversion method and system of collapse earthquake, comprising the following steps: S1, the waveform data of target area collapse earthquake event is obtained, and P wave first arrival polarity observation value is extracted from waveform data;S2, waveform data is preprocessed;S3, based on the collapse seismic source physical model of the compound of preset force couple point source and single force point source, the joint inversion operator of waveform forward modeling and P wave polarity forward modeling is established;S4, construct joint inversion objective function;The beneficial effects of the application are as follows: the application is based on the seismic source model of the compound of force couple and single force, constructs waveform and P wave polarity joint inversion operator;Create joint objective function including waveform fitting term and polarity fitting term, minimize objective function by optimization algorithm, and obtain source mechanism solution and source time function by joint inversion;By constructing the seismic source model of the compound of force couple and single force, the complex physical process of collapse earthquake is more accurately described.

The invention discloses a ship sound vibration characteristic identification and positioning method based on a DAS system. The method comprises the following steps: S1, obtaining monitoring data based on the DAS system; s2, classifying data based on two modes of frequency domain features and differential distance, and extracting underwater acoustic signals and vibration signals; s3, performing feature extraction and beam forming on the underwater acoustic signal; s4, carrying out beam forming on the vibration signal, and outputting a final beam forming result through combined judgment with beam forming of the underwater acoustic signal; obtaining the time difference between the P wave and the S wave based on the speed difference between the P wave and the S wave; and S5, finally realizing target identification based on feature extraction of the underwater acoustic signal, realizing target orientation estimation based on a combined comparison result in the step S4, and determining the distance between the target and the optical cable array based on the time difference between the P wave and the S wave. The method effectively solves the technical problems that in the prior art, accurate positioning is difficult due to single dependence on underwater acoustic signals, and target recognition cannot be achieved only according to vibration signals.

The invention provides a pile foundation seismic response analysis method and system considering a transverse inertial effect, a storage medium and application of the pile foundation seismic response analysis method and system. Aiming at the problem that a pile-soil coupling vibration model in the prior art neglects the fractional order viscoelasticity characteristic of unsaturated soil and the transverse inertia effect of an uncoupled pile body, the invention provides a large-diameter end-bearing pile seismic response analysis model based on a fractional order viscoelasticity constitutive structure. A fractional derivative is introduced to describe the stress relaxation characteristic of an unsaturated soil skeleton, a Rayleigh-Love beam theory is combined to characterize the transverse deformation effect of a pile body, and a pile-soil coupling vibration control equation under the action of earthquake P waves is constructed. Laplacian transformation, potential function decomposition and operator decomposition methods are adopted to realize equation dimensionality reduction solution, a pile-soil interface continuous condition and a boundary constraint condition are synthesized, a theoretical analytical solution of vibration response in a frequency domain is obtained, and a theoretical basis is provided for aseismic design of a pile foundation in an unsaturated land area.

The application provides an ice cover granular snow layer density estimation method and system based on a vehicle-mounted noise source, relates to the technical field of physical exploration and ice cover detection, and the method comprises the following steps: according to the exact start and end time period of each vehicle-mounted noise event, a corresponding multi-channel vehicle-mounted noise event data is obtained by intercepting from an original continuous noise record; the mutual coherence processing and linear superposition of the intercepted multi-channel vehicle-mounted noise event data are carried out to obtain a virtual shot record with improved signal-to-noise ratio; P wave and surface wave in the record are jointly inverted to obtain a P wave and S wave velocity structure, and on the basis, a double-parameter granular snow layer density formula fitting is carried out. The application is suitable for realizing the quantitative estimation of the regional scale ice cover granular snow layer density structure by using passive source seismic signals, and provides parameter support for ice cover engineering activity site evaluation and environmental evolution research.

The invention discloses a microseismic event positioning method based on microseismic signals, based on a microseismic acquisition device, the microseismic acquisition device comprises a plurality of three-component microseismic detectors arranged in a long drill hole at equal intervals, and the three-component microseismic detectors are used for acquiring the microseismic signals generated by excitation of a microseismic event in the deep position of a coal seam in the long drill hole. Comprising the following steps: S1, acquiring a microseismic signal by using a microseismic acquisition device; p waves and S waves of microseismic signals are simultaneously collected through a microseismic collection device, and an absolute distance to a seismic source is directly provided for each receiver by using the time difference between the P waves and the S waves, so that the positioning process is converted into a spherical surface intersection problem with each receiver as a sphere center and the calculated distance as a radius. According to the method, systematic errors caused by uncertainty of the earthquake generating moment are eliminated, and a large amount of redundant data is provided for cross validation, so that the precision and reliability of a positioning result are greatly improved. And the accuracy and comprehensiveness of signal acquisition are improved.

A quasi-real-time imaging method for an underground medium structure under the action of fluid in a reservoir area comprises the following steps: continuously acquiring continuous seismic waveform data through an earthquake monitoring station network arranged in the reservoir area, automatically identifying P-wave and S-wave seismic phases by adopting an artificial intelligence technology, and generating a standardized data set when the P-wave and S-wave seismic phases are picked up; on the basis, ray distribution parameters are calculated through ray distribution quantitative modeling; ray distribution parameters are judged in real time by establishing a multi-dimensional data quality control standard, Q value inversion is automatically triggered when the ray distribution parameters reach the standard, and data are intelligently supplemented until requirements are met when the ray distribution parameters do not reach the standard; calculating Q value distribution by adopting a full-automatic inversion algorithm; and finally, generating and outputting an imaging result of the underground medium structure of the reservoir area based on the Q value distribution, and forming a continuous imaging cycle by periodically and repeatedly executing the process. According to the design, the blank of dynamic imaging of a medium structure is filled, the monitoring timeliness is greatly improved, and meanwhile, the reliability and precision of an imaging result are guaranteed through quantitative quality control and full-process automation.

The application provides a strong earthquake magnitude sparse classification method for early warning, specifically by constructing a training dictionary D, performing discrete Fourier transform on the P wave signal of the recorded shallow source strong earthquake to obtain a training sample, and constructing the training dictionary D; obtaining a to-be-classified vector y from the P wave signal of the to-be-classified shallow source strong earthquake; performing dimension reduction on the training dictionary D and the to-be-classified vector y, inputting the to-be-classified signal y, normalizing each column vector of the training dictionary D and the to-be-classified signal y, and solving a minimum L1 norm problem; the low-rank matrix recovery method can also be used to perform dimension reduction and dictionary optimization on the constructed training dictionary D, and through a block optimization calculation process, a sparse solution calculation residual minimum value is finally solved to determine the category of the to-be-classified vector y; the training dictionary can be optimized through the category i range and hierarchical sparse classification model according to the earthquake magnitude in the training dictionary D.

The invention provides a mine Ordovician limestone water pressure and top interface crack detection method based on a micro-seismic technology. The method comprises the following steps: collecting actual micro-seismic data; de-noising processing is carried out on the collected actual micro-seismic data by using a wavelet threshold de-noising algorithm; a P-S wave combined positioning algorithm based on P wave and S wave first arrival is adopted to position the denoised micro-seismic event; seismic source parameter calculation and seismic source mechanism inversion are carried out based on the positioned micro-seismic event waveform and the initial polarity; ground stress inversion work is carried out, and the change condition of Ordovician limestone water pressure is deduced; calculating advanced attributes representing stratum stress or fracture characteristics on the basis of micro-seismic event source parameters, determining an advanced attribute threshold range causing Ordovician limestone water damage, and judging possible crack conditions; the Ordovician limestone top crack condition and Ordovician limestone water pressure are comprehensively analyzed, and whether the risk that Ordovician limestone water protrudes along the crack exists or not is judged. The method can monitor the water pressure change of the Ordovician limestone in real time, accurately detect the water flowing fracture of the Ordovician limestone top interface, and provide a scientific basis for mine water disaster prevention and control.

The invention discloses a shallow water OBN time difference correction method based on background noise body waves. The correction method comprises the steps of collecting data and performing data preprocessing; mutual correlation of adjacent node pairs is calculated; carrying out relative drift measurement by taking a P-wave EGF as a scale; robust quality control and exception elimination; performing multi-component joint weighting and whole-network inversion; recording correction and iterative convergence; and verifying the effect and outputting the result. Compared with a single component, the multi-component combined solution has the advantages that the cross-correlation coherence is obviously improved, and millisecond jump can be positioned and eliminated.

The invention relates to a seismic data processing method and device, equipment, a storage medium and a program product, and relates to the technical field of geophysical exploration. The method comprises the following steps: acquiring seismic profile data of a target area; obtaining logging data corresponding to one or more drilling wells in the target area; determining well seismic calibration results corresponding to the one or more drilling wells according to data of the positions of the corresponding drilling wells in the seismic profile data and the logging data; the well-seismic calibration result is used for indicating amplitude data corresponding to the longitudinal wave and the converted shear wave in the synthesis channel and the seismic channel respectively; reconstructing the seismic profile data according to the well seismic calibration results corresponding to the one or more drilling wells; and obtaining a multi-wave fusion data volume of the target area according to the reconstructed seismic section data. When the fusion data volume of the scheme is used for carrying out subsequent depiction, the data of the longitudinal waves and the converted transverse waves do not need to be independently depicted and artificially fused.

The invention provides a chest wall port catheter head end position real-time monitoring method and system. The chest wall port catheter head end position real-time monitoring method comprises the steps that a reference X-ray image containing a chest wall port catheter head end and a corresponding reference electrocardiosignal are obtained; when a space included angle formed by the X-ray perspective equipment and the reference shooting angle reaches a preset auxiliary shooting angle, obtaining an auxiliary X-ray image and a corresponding auxiliary electrocardiosignal, and constructing an initial three-dimensional coordinate of the head end of the chest wall port catheter in a predetermined three-dimensional coordinate system based on the reference X-ray image and the auxiliary X-ray image; identifying the positioning association relationship between the electrocardiosignal characteristics and the three-dimensional position of the head end of the chest wall port catheter according to the corresponding P-wave characteristics and the initial three-dimensional coordinates in the reference electrocardiosignal and the auxiliary electrocardiosignal; and recognizing the real-time three-dimensional position of the head end of the chest wall port catheter based on the P-wave features corresponding to the real-time electrocardiosignals and the positioning incidence relation. According to the technical scheme, accurate positioning of the three-dimensional coordinates of the head end of the chest wall port catheter can be achieved under the influence of breathing movement and body position changes of the target patient.

There is provided a configuration that includes: at least one transfer mechanism configured to transfer a substrate and at least one processing mechanism configured to process the substrate; an earthquake detector configured to detect an earthquake; and a controller configured to control the at least one transfer mechanism and the at least one processing mechanism according to a detection result of the earthquake detector, wherein the controller is configured to be capable of performing a stopping operation of the at least one transfer mechanism according to a P wave (initial tremor wave) and an S wave (principal fluctuation wave).