134 results about "Waveform matching" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention provides a transient recording fault intelligent judgment method combining waveform matching and cloud edge cooperation, and relates to the technical field of fault diagnosis. Comprising the following steps: collecting global topological information and fault judgment reference parameters of a master station synchronous power system, and outputting initial transient recording data; carrying out preprocessing and feature extraction on the initial transient recording data, realizing edge side layered fault detection by adopting an improved waveform matching algorithm, and outputting multi-layer fault information; constructing a global fault judgment model, updating the global fault judgment model through the hierarchical model, and optimizing the fault judgment model; multi-level fault information is input into the optimized fault judgment model for accurate fault judgment, and graded response measures are taken for real-time judgment results with different confidence degrees; according to the invention, through deep fusion and system-level optimization of multiple technical streams, a transient recording fault intelligent judgment system integrating real-time performance and accuracy is created.

The invention discloses a high-precision mileage alignment method and device for multi-source detection data of a high-speed railway, which can be used in the field of track engineering detection, and the method comprises the following steps: correcting vehicle body acceleration data, axle box acceleration data and wheel-rail force data through interpolation; calculating the speed and mileage of the train at any moment in the running process according to the longitudinal acceleration; calculating the mileage deviation when the train passes through the curve road section by adopting linear interpolation to obtain the running mileage deviation; according to the waveform matching result and the detection interval, calculating the time delay amount among the multiple pieces of multi-source detection data, and correcting the train speed; and correcting the train speed at any moment according to the running mileage deviation, determining real-time running mileage data of the train, and carrying out mileage alignment on the mileage at any moment in the running process of the train. According to the method, the track disease detection accuracy and timeliness can be improved, and the high-speed railway mileage alignment accuracy is improved.

The invention, which relates to the technical field of leakage current detection, discloses a leakage current waveform matching method based on combination of multi-machine learning and a weighted voting mechanism, comprising the following steps: S1, preprocessing current data; s2, feature extraction of the current data; s3, establishing and training a multi-machine learning model; and S4, performing leakage current waveform matching of a multi-machine learning model in combination with a weighted voting mechanism. According to the leakage current waveform matching method combining multi-machine learning with the weighted voting mechanism, the classification accuracy and robustness of the model are effectively improved through the multi-model weighted voting mechanism in the accurate matching and classification identification of the leakage current waveform; the method has the specific effects that multiple features are extracted from three dimensions of a time domain, a frequency domain and a time-frequency domain, and the features of the leakage current waveform are comprehensively and deeply described, so that the classification model can more fully identify the features of different types of leakage currents, and the identification accuracy of the model is greatly improved.

The invention relates to a wireless power transmission system parameter identification method based on a time domain differential model, and belongs to the technical field of wireless power transmission system control. The method comprises the following steps: establishing a parameterized time domain dynamic physical model; collecting system dynamic response data; constructing an optimization target based on a time domain residual error; and performing optimal parameter estimation based on iterative optimization. According to the method, a time domain dynamic physical model of a wireless power transmission system is constructed, and unknown parameters are solved through an iterative optimization algorithm with a minimum waveform matching error as a target. According to the algorithm, in an iteration process, a physical model is called to carry out forward differential equation solution, a time domain residual error between a model solution current and a real measurement current is used to drive a parameter estimation value to converge, and finally high-precision and high-robustness wireless power transmission system parameter identification under any working frequency is realized. The problem that in the prior art, due to static stiffness and sensitivity to waveform distortion of a theoretical model, the identification precision is insufficient is effectively solved.

The invention discloses a safety maintenance method suitable for a vehicle-mounted intelligent cabin, and relates to the technical field of intelligent cabin safety maintenance, and the method comprises the following steps: extracting an original echo waveform sequence of a current period and a previous period in the same space region based on original echo data collected in a continuous work period of a vehicle-mounted millimeter wave radar system; according to the method, through cross-period echo waveform matching and multi-dimensional feature fusion, a historical signal interference trend is accurately quantified, an echo superposition index is dynamically output, an identification strategy for an extremely-low-speed target is adjusted, and reconfirmation is carried out only when actual space displacement of the target occurs. The method effectively reduces the misrecognition and false alarm rate of a radar system to a static or quasi-static target, avoids the false triggering of ACC, FCW, AEB and other functions, remarkably improves the stability and safety of a vehicle-mounted sensing system in a complex traffic environment, and has good engineering practicability and popularization value.

The invention discloses an abnormal waveform matching method for electric energy meter voltage abnormity identification, and relates to the technical field of waveform matching, and the method comprises the steps: collecting a historical abnormal waveform, extracting a feature vector, and constructing an abnormal waveform dictionary through a dynamic dictionary learning algorithm; performing coverage analysis on historical abnormal waveforms, and screening core features to establish a simplified feature set; collecting a to-be-recognized voltage waveform in real time, extracting a feature vector to be matched with the simplified feature set, and primarily judging whether dominant anomaly exists or not; if yes, candidate template types are screened through the characteristic deviation ratio, and abnormal types are judged; if the dominant anomaly does not exist, a normal waveform feature baseline model is constructed, deviation analysis is carried out to judge whether the implicit anomaly exists or not, and if the implicit anomaly exists, the abnormal waveform dictionary is iteratively optimized in combination with the to-be-recognized waveform features; the method can effectively identify the voltage abnormity of the electric energy meter, balance the identification precision and the calculation efficiency, and adapt to a novel abnormal mode and equipment gradual change fault characteristics.

The invention provides an electricity stealing detection method based on line loss, non-intrusive monitoring and dynamic time warping, and the method comprises the following steps: analyzing the electricity consumption of a user in a power distribution area with an abnormal daily line loss rate, and generating the line loss evidence of a suspected user; comparing the load data of the suspected user obtained based on the non-intrusive load monitoring with the power consumption behavior of the suspected user, and generating a contradictory evidence that the power consumption behavior has contradictions; based on the current waveform library of the electricity stealing behaviors, analyzing the similarity between the user current waveform of the suspected user and an electricity stealing waveform template recorded in the current waveform library, and generating waveform matching evidence; and based on the line loss evidence, the contradiction evidence and the waveform matching evidence, weighting and fusing to obtain the comprehensive score of the suspected user, and screening the target electricity stealing user. The electricity larceny detection method based on line loss, non-intrusive monitoring and dynamic time warping has the advantages of high efficiency, high precision, low cost and strong interference capability, and can effectively identify electricity larceny users.

The invention provides a semiconductor detector particle pulse waveform data generation and analysis platform based on a digital twinning technology, and the platform comprises an automatic full-link multi-working-condition semiconductor detector particle pulse waveform simulation system which is used for controlling GEANT4, Weightfield2 and PyLTSPICE software, and outputting simulated energy data and differential current data; the dual-channel pulse waveform automatic acquisition system is used for acquiring and storing energy data and current data actually measured by the accelerator by using a physical entity and performing multi-channel display; and the digital twinborn and physical twinborn data comparison and analysis system is used for performing waveform matching degree analysis by utilizing the actually measured waveform of the accelerator and the simulation waveform under the same working condition and the same circuit parameter. The method has the advantage that mass pulse waveform data under different working conditions can be efficiently and automatically generated.

The invention discloses a laser radar multi-target ranging method and system based on adaptive waveform matching, and relates to the technical field of laser ranging, and the technical scheme is characterized in that the adaptive waveform matching mechanism proposed by the invention dynamically adjusts convolution kernel sampling points through a deformable convolutional network (DCN), and the adaptive waveform matching is realized; various nonlinear waveform distortions caused by electric field interference and complex target reflection characteristics can be actively adapted, and a customized matching template is generated for each input waveform. Equivalently, the system is endowed with the capability of eliminating counterfeit and keeping true in a strong electromagnetic interference environment, so that the robustness to special working conditions of ultra-high voltage hot-line work is fundamentally improved.

The invention relates to the technical field of seismic data processing, in particular to a seismic waveform inversion method and system based on deep learning image registration, and the method comprises the steps: carrying out the least square migration based on small offset observation data and a background velocity field, and constructing a reflection coefficient model; performing Born forward modeling according to the reflection coefficient and the background velocity field to obtain a simulated seismic record image; acquiring a seismic record deformation field by adopting deep learning image registration based on the simulated seismic record image and the seismic observation image; calculating a normalized deformation field, and judging whether the normalized deformation field meets a preset condition or not; if yes, terminating the iteration process; otherwise, executing the next step; and according to the seismic record deformation field and the accompanying seismic source, updating the background velocity field through a conjugate gradient algorithm, and repeating the steps. According to the method, high efficiency and high precision of deep learning are utilized, and the problem that a cycle jump problem which is prone to occurring in waveform matching falls into a local minimum value is avoided.

The invention relates to the technical field of hemodialysis pathway monitoring, and particularly discloses a hemodialysis pathway stenosis early warning system and method based on voiceprint recognition and compression waveform matching. The system comprises an integrated acoustic-pressure control attachment module, a signal acquisition and control unit, a feature extraction module, an individual template library and a risk judgment module. By collecting blood flow pulsation sound and turbulence voiceprint in real time and combining rebound sound response induced by controllable external pressure, various dynamic parameters are extracted and compared with an individual acoustic template of a patient, and the risk level of passage stenosis is judged. The system has the advantages of being non-invasive, wearable, active in induction and individualized in recognition, and is suitable for early warning of passage stenosis of clinical dialysis patients.

A waveform matching-based flexible direct-current transmission line single-ended protection method comprises the steps of establishing a traveling wave expression general formula, performing dual distinguishing on fault types, and generating a matching waveform based on the distinguished fault types. Constructing a plurality of fault type criteria based on the matching waveform; and collecting the traveling wave of the line-mode voltage in a fault state, and judging the type of a real-time fault through a fault type criterion. And the system notifies the protection device to perform corresponding protection action based on the live fault type. According to the method, the accurate matching waveform is constructed based on double distinguishing, and the auxiliary criterion is constructed by using the waveform characteristics of the matching waveform, so that the protection can accurately identify different fault types, no dead zone exists in the protection, and the protection reliability is high; in addition, all criteria of the protection method are judged based on the characteristic quantity of the waveform, and the method is easy to implement and has good engineering application value.

The invention discloses a maneuvering target long-time coherent accumulation method, system and device and a medium, and relates to the technical field of radar signal processing, and the method comprises the steps: enabling a plane array to achieve the complete airspace coverage in the azimuth angle range dimension through the introduction of FDA; according to the planar array, three-dimensional information of a target is utilized, so that the target can obtain information with higher dimensionality than that of coherent FDA. Then, an azimuth angle-pitch angle-distance three-dimensional matching filter is designed, and rapid waveform matching is realized. The method aims at the influence of a maneuvering target phase-coherent accumulation process. And finally, compensating range migration and Doppler frequency migration in a high-altitude maneuvering target detection process by adopting a second-order Keysone transform-LVD algorithm to realize coherent accumulation of the target. The invention not only provides a novel radar array combining PA and FDA, but also is applied to the field of target detection, especially for complex detection conditions such as high-altitude maneuvering targets.

The invention discloses a distribution network traveling wave fault identification method based on symplectic geometric mode decomposition. The method comprises the following steps: step 1, sampling a distribution network traveling wave fault current signal; step 2, carrying out two-end symmetric point continuation on the initial signal by adopting a self-adaptive waveform matching endpoint continuation method; 3, using a symplectic geometric mode decomposition method to decompose a distribution network traveling wave initial current signal; step 4, effective components of the continuation components obtained in the step 3 are determined by calculating Pearson's correlation coefficients between construction components, and similar initial components in the effective components are reconstructed by constructing a Pearson's correlation coefficient matrix, so that final symplectic geometric components are obtained; and 5, demodulating the optimized symplectic geometric component by using a frequency weighted energy operator to obtain a time-energy spectrum diagram of the symplectic geometric component, and finally identifying a fault. In combination with ASGMD-FWEO, the method shows excellent performance in the aspects of adaptive preferential selection, interference resistance, noise resistance and the like.

The invention provides an underwater weak target integrated detection and positioning method using a deep sea distributed MIMO sonar, the distributed MIMO sonar is used to suppress channel fluctuation and target intensity fluctuation, and at the same time, the signal waveform of each detection node and the two-way propagation characteristic of a seabed ejection channel are combined, so that the underwater weak target integrated detection and positioning method based on the deep sea distributed MIMO sonar can be realized. A joint matching filter for simultaneously realizing signal waveform matching and channel structure matching is designed, a multipath cumulative gain is obtained on the basis of obtaining a traditional space-time processing gain, the signal-to-mix ratio and the signal-to-noise ratio are synchronously improved, and finally, a peak value fused and output by each node of the distributed MIMO sonar is used as a test statistic, so that the test accuracy is improved. And meanwhile, the peak value position is used as a target positioning result, so that integrated detection and positioning of the weak target in the deep-sea shadow area are realized. Compared with a traditional target detection method using multipath arrival feature matching, the method provided by the invention makes full use of the seabed ejection channel gain, and has better target detection and positioning effects under the conditions of low signal-to-mix ratio and low signal-to-noise ratio.

The invention relates to a hydroelectric generating set fault prediction method based on an EMD and LSTM fusion model, and the method comprises the steps: collecting a hydroelectric generating set vibration signal data sequence, and carrying out the set preprocessing of the data sequence, and obtaining a data set D1; performing empirical mode decomposition on the data set D1 by using a waveform matching continuation method of parameter traversal optimization to obtain a plurality of corresponding decomposition components; performing high and low frequency signal combination on the plurality of intrinsic mode components obtained by decomposition based on an extreme point division method to generate high and low frequency component reconstruction components; and respectively predicting the high-frequency and low-frequency reconstruction components by using a fusion model based on EMD and LSTM, and carrying out equal weight summation and superposition on prediction results to obtain a final fault prediction result of the hydroelectric generating set. According to the method, the EMD and LSTM fusion model is constructed, the improved empirical mode decomposition is utilized, the potential unit operation state information of the hydroelectric generating set vibration signals is deeply mined, the endpoint effect problem existing in the traditional empirical mode decomposition is inhibited, and the data decomposition quality is improved.

The invention provides a self-adaptive carrying and dense parking planning method, system and device and a medium, and belongs to the technical field of intelligent logistics control. The method comprises the steps of detecting a vehicle contour in real time, and obtaining a vehicle width value and an attitude angle; based on the clamping jaw material section inertia moment and the moment safety threshold value, the safety distance is calculated; a quaternion PID controller is used for synchronously adjusting the rotating speed of a steering motor and the stroke of a hydraulic cylinder, and the minimum safety distance is calculated; a parking path is generated by adopting an improved path planning algorithm, and multipath interference is eliminated through waveform matching; and storing the parking space state based on the block chain technology, verifying whether the vehicle is parked in the preset parking space after parking is completed, and updating the global map through the smart contract after verification is passed. Through the modes of dynamic perception, adaptive control, multi-target optimization and credible interaction, the operation precision and stability of the AGV in a carrying scene are realized.

The present application relates to oil and gas geophysical exploration technology field, more particularly, it relates to a kind of P-S wave data matching method based on wavelet compression, it includes the following steps: S1: PS wave seismic data is converted to compress to PP time domain;Extract seismic PP wavelet, PS wavelet, PP wavelet spectrum and PS wavelet spectrum;S2: introduce frequency variable scale conversion factor, utilize Fourier transform method to carry out wavelet compression to PS wavelet;S3: obtain filter according to PS wavelet and new PS wavelet;S4: utilize filter to carry out filter processing to PS wave seismic data after time domain conversion;S5: carry out waveform matching.The present application is based on the pre-matching in time domain, introduce frequency variable scale conversion factor, utilize the Fourier scale conversion method of frequency variable to carry out compression to PS wavelet.Then filter is designed using regularization method to carry out filter processing to original PS wave seismic record, can improve the similarity of PP, PS wave both seismic record waveform, improve the precision of final matching.

The invention discloses a facial stimulation waveform generation method and device based on visual feedback, and the method comprises the steps: collecting a neuroelectric signal of the motion of a healthy side surface of a patient in real time through an implanted neural electrode, decoding the neuroelectric signal, and obtaining an electrode facial motion parameter; synchronizing, acquiring a facial movement image of the healthy side of the patient in real time by using an image acquisition device, and identifying the facial movement image to obtain image facial movement parameters; iteratively optimizing the decoding process of the electroneurographic signal based on the feedback of the image facial motion parameters, and outputting the facial motion mode of the unhealthy side; based on the face movement mode of the unhealthy side, a matched face stimulation waveform is generated and used for stimulating the affected side. According to the method, the decoding process of the electroneurographic signals is iteratively optimized based on the feedback of the image facial movement parameters, so that the finally obtained facial movement mode tends to be real, the generated facial stimulation waveform matching is better, the stimulation on a patient is more effective, and the curative effect is better.

The invention relates to the technical field of track characteristic data analysis, and discloses a track mileage calibration method and system based on track geometric dynamic and static waveform matching, the system comprises a track geometric waveform parameter processing module, a track mileage calibration analysis module and a track mileage calibration execution module; based on numerical analysis science, track static and dynamic waveform sampling frequency consistency is detected, and meanwhile, an interpolation algorithm is combined to carry out track geometric static waveform sampling frequency intelligent adjustment, so that track geometric waveform sampling frequency autonomous identification and correction in track geometric dynamic waveform mileage calibration are realized; track mileage calibration algorithm type fine matching is carried out on the basis of the static and dynamic waveform combination information of the track and the static and dynamic waveform combination information of the standard track corresponding to different track mileage calibration algorithms based on big data storage in combination with an artificial intelligence bionic algorithm, and the precision of track geometric dynamic waveform mileage calibration is improved.

The specification discloses a multi-antenna array LCMV anti-interference method based on adaptive noise protection, relates to the technical field of anti-interference, and comprises the following steps: performing LCMV anti-interference algorithm processing, weighting and communication waveform matching processing on a received signal to obtain a first peak amplitude, a second peak amplitude and a second signal-to-interference-and-noise ratio; determining whether to increase noise power based on the first peak amplitude and the second peak amplitude; if it is determined to increase the noise power, target noise is added; performing LCMV anti-interference algorithm processing and communication waveform matching processing on the noise-added signal to obtain a new second signal-to-interference-and-noise ratio and a corresponding weight; and determining whether the noise power of the noise-added signal reaches an upper limit; if yes, the weight corresponding to the maximum value in the second signal-to-interference-and-noise ratios is taken as an optimal weight, and adaptive anti-interference is completed, so as to solve the problem that, in the prior art, a signal suppression interference method for an unknown signal wave direction reduces the output power of a communication signal while suppressing an interference signal, thereby resulting in limited anti-interference capability.

This invention discloses a high-repetition-rate interpolation amplification system, relating to the field of high-power laser technology. It employs a three-stage architecture of time-domain separation, parallel amplification, and polarization beam combining. The core idea is to distribute the thermal load and suppress the accumulation of nonlinear effects by interleaving pulses in the time domain, and then synergistically improve the overall performance of the system through incoherent polarization beam combining. Compared with traditional coherent combining schemes, this invention completely eliminates the dependence on precise phase control, extremely high synchronization accuracy, and strict waveform matching, thus providing a highly promising technical path for synchronously achieving high repetition rate, high single-pulse energy, and high average power laser output.

A device includes a machine-learning audio encoder and a waveform-matching audio encoder. The device includes a controller configured to cause a segment of audio data to be input to the machine-learning audio encoder, to the waveform-matching audio encoder, or to both, based on a classification associated with the segment.

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 relates to the technical field of data analysis, in particular to a fault signal phase synchronization method and device based on a data fusion algorithm. The method comprises the following steps: preprocessing a collected bearing operation data signal; the preprocessed data signals are fused, and reliable phase offset estimation of each operation period and position is calculated; the fused data signals are processed through waveform matching and a phase correction algorithm, phase deviation caused by driving speed and load changes is eliminated, and data signals after phase synchronization are obtained; and based on the data signals after phase synchronization, bearing fault features are extracted, and a phase consistency coefficient is calculated. According to the technical scheme, the identifiability of fault signal characteristics is improved through multi-sensor data fusion and an advanced phase synchronization algorithm, and an important technical means is provided for fault diagnosis of rotating machinery.

The invention relates to an ultrasonic wave flight time measuring method and device, electronic equipment and a readable medium, and the method comprises the steps: applying a driving signal to a first transducer, enabling the first transducer to emit ultrasonic waves, and starting a timer to collect a transmitted waveform sequence; calculating the minimum flight time as a coarse value based on the distance between the transducers and the resultant velocity of the sound velocity and the wind speed; after the time is over, collecting a receiving waveform sequence of which the count value and the amplitude are within a preset range; transmitting and receiving waveforms are analyzed through waveform matching, a time offset value is obtained, and a coarse value is corrected to obtain first absolute flight time; switching a transmitting-receiving transducer, and similarly obtaining second absolute flight time; and matching the two received waveforms, calculating the offset time for correcting the difference between the two minimum flight times, and finally obtaining the high-precision relative flight time. The problem that the measurement precision and the operation efficiency cannot be considered when the ultrasonic flight time is measured is solved.

The present invention discloses a fracture weakness parameter inversion method, comprising the following steps: S1: acquiring 3CVSP data of a target study area, and processing to obtain an upgoing S1 wave and an upgoing S2 wave; S2: obtaining the P wave velocity and the S wave velocity of the overlying stratum above the target layer according to the first arrival waves of the P wave and the S wave in the logging data and the 3CVSP data; S3: calculating and obtaining the travel time of the target layer at different detectors; S4: picking up the phase axis one closest to the target layer in the upgoing S1 wave according to the travel time, and obtaining the observed PS1 propagation time; S5: using the upgoing S1 wave, extracting the phase axis two of the target layer in the upgoing S2 wave through waveform matching, and obtaining the observed PS2 propagation time according to the travel time corresponding to the phase axis two; S6: establishing an objective function based on the observed PS1 propagation time and the observed PS2 propagation time, and inverting and obtaining the fracture weakness parameter according to the objective function. The present invention can accurately invert the fracture weakness parameter using the propagation time of the PS1 wave and the PS2 wave.

According to the complex closed space global low-frequency noise reduction method based on the active micro-perforated plate sound absorber, an active control technology is introduced into a small-size MPPA, the low-frequency acoustic reactance of a cavity of the MPPA is reduced by offsetting the sound field of the cavity of the MPPA, and the sound energy dissipation performance of the MPP is improved by breaking the waveform matching of the sound fields on the two sides of the MPP; the noise of the whole complex closed space is reduced in an active sound absorption mode, and meanwhile the problem that the noise reduction performance of some medium-high frequency resonance frequency points is weak due to coupling of a closed space sound field and an MPPA cavity sound field can be solved. The invested active control measure is low in cost, and the low-frequency noise reduction performance of the complex closed space is remarkable, so that the method has important engineering application value.

The invention discloses a microseism Gaussian particle swarm differential evolution positioning method and device. The method comprises the following steps: carrying out parameter initialization setting based on a particle swarm; aiming at any particle in the particle swarm, calculating to obtain theoretical travel time from the particle to each detector; performing waveform superposition on the particles, and obtaining fitness values of the particles according to the waveform superposition and theoretical travel time of the particles to each detector; according to the individual optimal position of the particle and the global optimal position in the population, updating the speed and the position of the particle by using a first random number and a second random number generated by a preset Gaussian probability density function, and executing variation, crossover and selection operations in differential evolution by taking the global optimal particle determined after updating processing as a base vector; judging whether a preset stop condition is met or not; if not, waveform matching superposition, particle updating and differential evolution are repeated until a preset stop condition is met; and determining the estimated position of the seismic source according to the position of the global optimal particle.