3673 results about "Sound wave" patented technology

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

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

The invention provides an oil and gas equipment leakage acoustic characteristic intelligent identification early warning method and system, and relates to the technical field of oil and gas equipment safety monitoring, and the method comprises the steps: arranging a sonic sensor array to collect signals, calculating a wavefront propagation path, optimizing the wavefront curvature, carrying out the multi-scale decomposition of a sonic signal, and extracting time-frequency characteristics; and reconstructing a sound field by using an acoustic propagation function to determine a leakage point space coordinate. The method can accurately identify the acoustic characteristics of oil and gas equipment leakage, improves the leakage point positioning precision, achieves the early warning of leakage, and guarantees the safe operation of oil and gas equipment.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The technology described herein is directed towards one or more modular metasurfaces arranged with unit cells for sound absorption, in which the unit cells are based on Helmholtz resonators that are deployed proximate to a server, without substantially obstructing airflow from the server fan(s). Each sound absorbing unit-cells is designed and constructed based on a specific resonance frequency, and includes a neck portion and air chamber dimensioned to resonate at the desired resonance frequency and thereby inverse phase cancel corresponding narrowband frequencies of incoming sound waves. The metasurfaces can be arranged in a modular frame structure of surrounding unit cells, positioned behind the server's fan(s), with an opening in the frame structure to facilitate airflow therethrough, without obstructing or substantially obstructing the airflow to the surrounding environment.

The technology described herein is directed towards modular metasurfaces arranged with unit cells for narrowband, wideband and / or multiband sound absorption, in which the unit cells are based on Helmholtz resonators that are deployed in a server rack, proximate to a server, without substantially obstructing airflow from the server fan(s). Each sound absorbing unit-cells is designed and constructed based on a specific resonance frequency, and includes a neck portion and air chamber dimensioned to resonate at the desired resonance frequency and thereby inverse phase cancel corresponding narrowband frequencies of incoming sound waves. Differently-dimensioned unit cells can be built into a single metasurface module, thereby facilitating wideband and / or multiband sound absorption with that metasurface. The modular metasurfaces can be attached to the sidewalls of a server rack, or positioned in otherwise empty slots above and below a server, optionally coupled to blanking panels for covering the openings of those slots.

The invention provides a machine tool machining parameter adjustment method and system, relates to the technical field of machine tool machining parameter adjustment, and aims to infer local acoustic impedance of a workpiece material in real time by emitting sound waves and receiving echo signals on the workpiece material in front of cutting of a cutter and extracting acoustic features. And when the local acoustic impedance is continuously higher than a preset normal reference value and exceeds a preset threshold value, determining that a local abnormal region exists, and generating an early warning signal. And before the tool makes contact with the local abnormal area, the machining parameters are adjusted in advance according to the early warning signal. And real-time feedback signals in the cutting process are continuously monitored, and the machining parameters are further adjusted based on the real-time feedback signals and the early warning signals. The problems that in the prior art, due to uneven local hardness of a workpiece material, a tool is unexpected to be abraded, and machining size deviation is accumulated can be effectively solved.

The invention relates to the technical field of transformer detection, and discloses a nondestructive testing optimization method and system for an oil-immersed power transformer, and the method comprises the steps: building a three-dimensional dielectric response coordinate system, and generating a preliminary defect positioning map; aging-dominated and damp-dominated defects are detected and identified through spiral frequency sweep excitation; establishing a temperature gradient excitation scheme based on the defect type to generate a defect degree quantitative evaluation index; designing a sound wave modulation excitation scheme to generate a high-resolution defect characteristic spectrum; constructing a multi-mode intelligent sensor network, and combining a defect development trend prediction model to realize defect evolution prediction and generate a graded early warning signal; according to the method, the whole-process accurate detection of the insulation defect of the transformer from positioning, classification and quantitative evaluation to evolution prediction is realized, and a reliable basis is provided for operation and maintenance.

The invention discloses an optical cable fitting fatigue damage detection method and system, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, constructing a wind field and structure coupling observation baseline, obtaining a full-time-domain vibration response signal of an optical cable fitting in a non-uniform wind field environment, generating a phase consistency distribution map, and obtaining a phase consistency distribution map; establishing a corresponding relation between the multi-path reflection source group and the time correlation sequence as a traceable reference for phase analysis; and S2, based on the phase consistency distribution map, performing causal beam demixing processing, performing arrival time difference densification calculation and curvature spectrum separation analysis on each sound wave propagation path, and extracting crack propagation pointing data. According to the method, through wind field-structure coupling observation, path unmixing, phase regression and error checking, multi-path propagation recognition and correction are achieved, crack propagation topology is reconstructed, polarization rotation and a phase suppression mechanism are combined, and direction recognition stability and detection adaptivity are improved.

The invention discloses a smart bracelet earthquake early warning service system and method, and the method comprises the steps: collecting user physiological parameters and earthquake early warning signals, building a coupling relation between earthquake intensity parameters and user physiological features, and forming an individualized disaster bearing reference; performing gravity sensing detection in combination with the geographic position of the user and the anti-seismic grade of the building, analyzing the spatial relationship between the micro-inclination angle of the building and the position of the user, and generating gravity field risk features; analyzing a group movement mode through relative position changes among multiple bracelets, extracting movement coordination characteristics and direction coordination coefficients, and forming a displacement coordination early warning sequence; a disaster emergency coefficient is generated based on user motion coordination efficiency and protection response behaviors, a sound wave positioning network of a multi-device linkage trigger area is constructed, collaborative alarm and personalized evacuation guidance are realized, differentiated earthquake early warning services can be provided according to individual physiological features and environmental conditions, and the pertinence and effectiveness of early warning are improved.

The invention relates to the technical field of sewage detection, provides an intelligent detection method for suspended particles in coking sewage based on image deep learning, and aims to solve the problems of detection lag and low precision in the prior art. According to the technical scheme, the method comprises the following steps: synchronously acquiring sound wave and image data through a high-frequency ultrasonic transducer and an industrial camera, and extracting acoustic parameters and optical characteristic parameters; using a dual-channel convolutional neural network to fuse features, and combining a cross-modal attention mechanism to perform dynamic weighting; and outputting a particle concentration classification result and a particle size distribution prediction value through a multi-task learning model, and performing online learning to compensate environmental interference factors. The device is mainly used for realizing real-time and high-precision suspended particle monitoring and automatic process adjustment of coking sewage treatment.

The invention provides a fly ash composite material goaf filling body interface quality intelligent evaluation method, and belongs to the technical field of mining engineering and artificial intelligence detection crossing. The method comprises the steps that firstly, filling body interface quality characteristic data are collected and comprise interface sound wave signals, stress strain, coal ash composite material physical parameters and environment working condition data; secondly, constructing a multi-physical field data completion model, performing unsupervised learning on the acquired sound wave, stress, temperature and moisture content data, and generating completion data of global spatial distribution; secondly, constructing a multi-field fusion interface quality index prediction model, and inputting multi-source data into the model to obtain an interface quality index; and finally, combining the quality index to realize interface defect mode classification and grade evaluation, and generating a targeted maintenance strategy. The invention provides an intelligent evaluation method which fuses multi-source data and gives consideration to real-time performance and comprehensiveness, so as to solve the industrial pain points of interface quality evaluation lag, low precision, large destructiveness and the like.

The technology described herein is directed towards a metasurface arranged with unit cells for broadband and / or multiband sound absorption, in which the unit cells are based on the principles of Helmholtz resonators. Deeply subwavelength sound absorbing unit cells are designed and constructed based on desired resonance frequencies. Each unit cell includes a neck portion and air chamber dimensioned to resonate at a desired resonance frequency and thereby inverse phase cancel corresponding frequencies of incoming sound waves. Differently designed and arranged subgroups of unit cells are part of the metasurface, which can be positioned proximate to a noise source. As practical examples, the metasurface or multiple metasurfaces can be placed near or wrapped around a computer server or rack of servers to absorb fan noise. The metasurface components (including the unit cells) can be printed by a 3D printer to result in a thin, light-weight, and cost effective noise absorbing metasurface.

The utility model discloses a mute floor which comprises a surface treatment layer, a protection layer, a decoration layer, a first carrier, a first buffer layer and a second carrier. The surface treatment layer is arranged on one side of the protection layer, the decoration layer is arranged on the other side of the protection layer, the first carrier is arranged between the decoration layer and the first buffer layer, the first buffer layer is arranged between the first carrier and the second carrier, and the first buffer layer is of a porous structure and / or a net structure. A buffer groove is formed in at least one side face of the first buffer layer and extends to the two ends of the side face. By arranging the floor with the first buffer layer with the porous and / or honeycomb structure, noise is reflected and refracted for multiple times in the porous and / or honeycomb structure, so that sound is attenuated, a sound wave propagation path is prolonged, and the effect of more effectively reducing noise propagation is achieved.

The invention relates to the technical field of on-line monitoring, in particular to a flue gas cooler leakage monitoring system and method.The method comprises the steps that a multi-source real-time sensing layer module continuously collects tube plate weld joint area temperature data, cooling water flow characteristics and flue gas sulfur-containing gas concentration, and the sampling period is 250 milliseconds; the intelligent feature fusion module performs noise reduction processing and dynamic weight fusion on the multi-source data and outputs a leakage probability vector; the dual-system collaborative early warning module maps the probability vectors into confidence levels, a 65% confidence threshold triggers an acoustic verification system to start, an acoustic emission sensor array captures fractured sound wave features and then compares the fractured sound wave features with a voiceprint library through a convolutional neural network, and an early warning signal is output when the fractured sound wave features exceed a 0.85 similarity threshold; and the dynamic knowledge base updates the case characteristics online according to the early warning result. According to the system, second-level identification and continuously optimized closed-loop monitoring at the initial stage of leakage germination are realized, and the problem of response delay caused by intermittent detection in the prior art is solved.

The invention discloses an on-line qualitative detection method for partial discharge of power equipment, which comprises a detection system and detection steps, and also comprises the following steps: the detection system comprises a high-frequency partial discharge detector for detecting a high-frequency current transformer; the ultrahigh frequency detection system is used for detecting electromagnetic wave radiation which is generated by partial discharge and has extremely short duration time; the ultrasonic detection system is used for detecting sound waves and ultrasonic signals generated along with energy release when partial discharge occurs; a transient ground voltage method detection system is used for live-line detection of equipment such as a medium-voltage switch cabinet. According to the online qualitative detection method for the partial discharge of the power equipment, different detected equipment and different detection methods are adopted to perform online detection on the partial discharge of the equipment, so that the early partial discharge phenomenon of the power equipment can be found in time, the long-term discharge monitoring data of the power equipment can be stored, and the overall detection effect is improved.

The invention provides a data processing method and system based on intelligent construction, and relates to the technical field of intelligent construction.The method comprises the steps that viscosity, a temperature peak value, temperature and humidity and time delay distortion data in the solidification stage are collected in real time in the concrete pouring construction process, and a material stress relaxation model is constructed to calculate three-dimensional stress distribution; meanwhile, mapping sound wave time delay distortion data into a spatial topological graph, and extracting propagation anomaly features by using a space-time graph convolutional network; and generating a crack risk distribution diagram by fusing stress distribution and abnormal characteristics, and finally, accurately positioning a potential defect area according to risk gradient change. The early warning capability of concrete construction quality defects is improved.

------ Acoustic Enclosure and Method for Leveling an Acoustic Enclosure The present invention relates to an acoustic enclosure (20) comprising a loudspeaker (21) mounted flush on a front face of the enclosure (20), characterized in that at least one Helmholtz resonator (26) is formed on the front face of the enclosure (20) between each lateral edge of the front face and the loudspeaker (21), the at least one Helmholtz resonator (26) being dimensioned to have a band gap to block the propagation, in the plane of the front face of the enclosure (20), of acoustic waves in the useful frequency band of the loudspeaker (21). Figure to be published with the abstract: Figure 6a

The invention provides a concrete crack automatic identification and classification method based on machine learning, and belongs to the technical field of wind tunnels, and the method comprises the steps: collecting a visible light image and a thermal image of a concrete structure surface, and extracting an initial crack contour; based on a plant growth simulation algorithm, the growth direction and speed are determined from a seed point according to local gradient intensity and texture feature directivity; a concrete crack intelligent analysis model is adopted to fuse crack path temperature and stress distribution information, attention weight is dynamically adjusted according to crack characteristics to output an accurate classification result and a severity degree evaluation value, and sound wave propagation speed data is combined to accurately estimate crack depth so as to realize accurate classification of fine and coarse cracks. An evaluation system based on a comprehensive risk coefficient is established to scientifically divide low, medium and high risk states, and the technical problem of inaccurate classification caused by insufficient concrete crack detection precision is solved.

The invention relates to the field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention discloses an automatic monitoring method and device for pipe explosion of a heating surface of a boiler, and relates to the technical field of monitoring of pipe explosion of the heating surface of the boiler. The monitoring method comprises a multi-source signal acquisition step, a data preprocessing and feature extraction step, a double-model parallel prediction step, a dynamic weighted fusion step, a consistency check and feedback calibration step and a risk response output step. According to the CNN-LSTM model sound wave frequency domain feature-temperature field gradient change spatio-temporal multi-source data joint identification, the limitation that a traditional model only pays attention to a single dimension is solved, feature distribution Attention and a physical guidance fusion mechanism solve the false alarm problem of a pure data driving model through a user-defined physical rule constraint function, and the accuracy and the reliability of the model are improved. A dynamic confidence SNR weighting mechanism enables the tube burst identification accuracy of the model under a strong noise working condition to be improved, and physical-data dual-drive consistency check realizes long-term maintenance of high positioning precision.

The invention relates to the technical field of crack monitoring, in particular to a concrete structure crack full-life-cycle monitoring method and system, and the method comprises the following steps: arranging sound wave points to obtain a stress change to generate an initial signal, extracting an image feature recognition extension path, analyzing a size acceleration to generate an extension identifier, and screening an active cycle at intersection time to generate an evolution fragment. And classifying crack behaviors according to displacement and width trends to generate an annotation set. According to the method, stress evolution is reflected based on interval transit time, a crack expansion trend is extracted in combination with an image feature point track, an active state is identified by superimposing crack size speed increase synchronism, a high-frequency change fragment is positioned through multi-index time intersection, and crack behavior types are divided according to a displacement direction and a width increase trend. Continuous tracking and classified marking of the whole process from crack starting to evolution are achieved, the response capacity to abnormal crack behaviors is enhanced, and the precision and timeliness of structural safety state recognition are improved.

The invention discloses a multi-medium three-dimensional flaw detection system based on ultrasonic pulse echoes. The multi-medium three-dimensional flaw detection system comprises a synchronous data acquisition module, a temperature compensation module, a multi-physical field simulation optimization module, a signal processing module and a three-dimensional imaging and output module, the synchronous data acquisition module scans an object, records coordinates and synchronously transmits and receives ultrasonic echoes; the temperature compensation module calculates real-time sound velocity according to the water temperature, the real-time sound velocity is used by the multi-physics field simulation module, and the multi-physics field simulation module simulates sound wave propagation and generates a defect-free theoretical waveform as a reference by combining intrinsic parameters of an object and sound wave propagation parameters; the signal processing module carries out processing and feature extraction on actually measured echoes, and compares the actually measured echoes with a reference to identify defect signals; and the three-dimensional imaging module associates the signals with the coordinates, a three-dimensional model is generated by adopting slice reconstruction and a compressed sensing algorithm, and defects are visualized, so that the precision, the efficiency and the environmental adaptability of three-dimensional flaw detection can be improved.

The invention discloses a gas pipeline leakage source positioning method, and relates to the field of gas leakage detection. According to the method, acceleration signals and sound wave signals are collected through sensors arranged at detection points, peak value extraction is carried out in respective corresponding frequency domain complex spectrums through data preprocessing, and main frequency signals and frequency multiplication signals are obtained; distinguishing real leakage and interference signals according to the stable frequency integral multiple relation characteristic and time sequence consistency of the two signals; the phase difference is calculated by extracting the phase of the dominant frequency signal and the phase of the corresponding frequency multiplication signal, and the position of the leakage source relative to the detection point is calculated by combining the path sound velocity. The method can capture small distance differences, effectively improves the precision and robustness of leakage positioning, and is suitable for online leakage monitoring and diagnosis of gas or liquid medium pipelines.

The invention discloses a bionic joint humanoid robot navigation positioning method, device and equipment, and the method comprises the steps: monitoring a landing shock wave signal and reflection echo data of a robot, carrying out the time domain expansion of the shock wave signal to form a pulse characteristic spectrum, extracting multipath propagation components, and carrying out the time difference correlation to generate an environment response value. Screening a direct wave component by using an environmental response value, positioning a sound wave propagation trajectory, and detecting impedance mutation to construct an acoustic matrix. A strong reflection zone is identified through node analysis, a sound pressure gradient field is extracted to determine an obstacle boundary, and a landmark feature bit is determined in combination with an environmental response value. And adjusting a dynamic stride frequency interval based on a landmark feature bit, forming an adaptive detection sequence, and performing sparse processing to construct path configuration. The mechanical vibration component of the shock wave signal is used for piezoelectric conversion to generate recycled electric quantity, the recycled electric quantity is matched with path configuration power to determine a sensing period, intermittent sensing is executed to obtain detection data to construct a detection convergence field, and finally an accurate positioning window is determined and converted into position coordinates.

The invention discloses a farmland environment intelligent monitoring system and method based on multi-modal sensor fusion, and relates to the technical field of farmland environment monitoring, and the method comprises the steps: obtaining first integrated data in a farmland, and carrying out the preprocessing of the first integrated data; constructing a sound wave attenuation model based on the preprocessed first comprehensive data, and inverting the air humidity and the vegetation density through the sound wave attenuation model; conflict detection and resolution processing are carried out based on an inversion result; and constructing a three-dimensional microclimate field based on a conflict resolution result, and carrying out verification and iterative optimization by fusing a crop physiological model. According to the method, farmland space-time heterogeneity is adapted by means of a dynamic space-time weight coefficient, so that a three-dimensional microclimate field covering horizontal and vertical dimensions is constructed, and a'monitoring-verification-optimization 'closed loop is formed through crop physiological model association and iterative optimization; the problems of limitation of a single sensor, low inversion precision, poor data continuity, extensive monitoring and lack of decision association in the prior art are effectively broken through.

The invention discloses a leakage monitoring method of a sealing connection structure for hydrogen conveying, and relates to the field of gas conveying and storage monitoring, and the method comprises the steps: S1, arranging an annular negative pressure sampling cavity to collect multi-modal data, and calculating the angle section suspiciousness; s2, applying thermal pulse and sweep frequency sound wave excitation, and collecting a response signal to generate a thermoacoustic leakage score; s3, environment compensation and multi-sensor fusion are carried out to obtain an angle section hydrogen concentration time sequence; s4, obtaining an inversion flux based on the comprehensive response and sparse-smooth inversion to realize leakage positioning; and S5, calculating a risk score and performing graded alarming according to a threshold value. Multi-point detection units are arranged in the circumferential direction of the sealing connection structure, signal fusion is adopted, full-coverage leakage monitoring and high-precision positioning of the sealing structure are achieved, emergency measures are automatically triggered in combination with risk grading, and the discovery timeliness and safety are improved.

The invention provides a laser plasma sound barrier and magneto-rheological antiknock array collaborative protection method. The method comprises the following steps: firstly, constructing a collaborative protection structure consisting of a laser plasma sound barrier layer and a magneto-rheological antiknock array layer; a shock wave leading signal is detected in real time and input into a preset multi-dimensional feature model, the model dynamically generates a protection strategy, and a sound barrier instruction and an anti-explosion instruction are output respectively. The laser plasma sound barrier layer outputs specific reverse sound waves according to a sound barrier instruction and attenuates shock waves through destructive interference; meanwhile, the magneto-rheological anti-explosion array layer instantaneously solidifies a magnetic field according to an anti-explosion instruction, and shock wave energy is efficiently absorbed. According to the laser plasma sound barrier and magneto-rheological anti-explosion array cooperative protection method provided by the invention, the cooperative protection structure and the preset multi-dimensional feature model are constructed, so that the cooperative effect of the sound barrier and the anti-explosion array is realized, the response speed to shock waves is increased, the energy processing efficiency is improved, and the adaptability and reliability of protection are enhanced.

The invention discloses a series ultrahigh frequency sensing GIS three-dimensional positioning and monitoring system and method, and belongs to the technical field of high-voltage equipment state monitoring. The system comprises a sensing layer, a processing layer, a transmission layer and an application layer, wherein the sensing layer synchronously collects ultrahigh-frequency electromagnetic signals, sound wave vibration signals and SF6 gas state parameters through a plurality of serial multi-physical-quantity sensing nodes; the processing layer eliminates interference based on signal homology verification, calls an intelligent response model to dynamically compensate propagation path errors in combination with a GIS three-dimensional structure model and real-time environment data, outputs accurate three-dimensional coordinates of the partial discharge source by adopting a progressive time difference of arrival positioning algorithm, and identifies fault types and risk levels by fusing multi-dimensional features; the transmission layer encrypts and uploads the diagnosis report; and the application layer maps defect positions in the three-dimensional visual model and automatically generates an operation and maintenance work order according to risk levels. According to the invention, high-precision positioning, multi-source fusion diagnosis and closed-loop operation and maintenance are realized, and the accuracy and intelligent level of GIS equipment monitoring are remarkably improved.