30 results about "Spectral centroid" patented technology

The invention relates to the field of electrical variable measurement, in particular to an insulating layer compactness detection method for a mineral cable, which comprises the following steps: firstly, acquiring a time domain reflection signal of the cable, and carrying out denoising pretreatment by utilizing synchronous average and wavelet transform; and then, carrying out spectrum analysis on the discrete signal segments, calculating a spectrum centroid offset rate to determine high-frequency energy attenuation, and constructing a dynamic compensation gain coefficient according to the high-frequency energy attenuation. And then, improving a traditional layer stripping algorithm by using the gain coefficient, eliminating calculation deviation caused by transmission loss, and accurately reconstructing characteristic impedance distribution along the cable. And finally, by comparing the deviation between the reconstructed impedance and the nominal impedance, positioning and judging the abnormities of the compactness of the insulating layer of the cable and the eccentricity defect of the cable core are realized.

The invention discloses a slight overdischarge state identification method and equipment based on an ultrasonic phased array technology, and belongs to the technical field of lithium batteries. The method comprises the following steps: establishing a lithium battery ultrasonic phased array detection system, and collecting full-matrix ultrasonic data in charge and discharge cycles; carrying out superposition synthesis on the full-matrix ultrasonic data, and extracting frequency spectrum centroid characteristics of a synthetic wave to reflect frequency center deviation of an ultrasonic signal so as to realize characterization of a slight overdischarge behavior; in combination with spatial position information of phased array elements, mapping the spectrum centroid features to a physical space, constructing a spatially resolved spectrum centroid map, and realizing collaborative representation of spatial domain and frequency domain information; and performing Gabor multi-scale texture enhancement on the frequency spectrum centroid diagram, extracting spatial features representing local frequency anomaly, and inputting the spatial features into a KNN identification model, thereby realizing identification of the slight overdischarge state of the lithium battery. According to the invention, rapid and nondestructive detection of early-stage electric abuse damage of the battery can be realized.

The invention provides a sound wave optical cable positioning method and system, and relates to the technical field of deep learning, and the method comprises the steps: uniformly arranging vibration sensors on a sensing optical cable, demodulating a back scattering light signal to obtain a differential phase signal, and constructing a self-correlation loss function. Preprocessing sound source data, extracting an audio frequency semantic feature vector, and calculating a dot product of a frequency spectrum centroid and frequency spectrum flatness to obtain a frequency spectrum loss function; and a phase change loss function is obtained by combining the ratio and the refractive index of the strained optical fiber and the non-strained optical fiber. And weighting the three loss functions to form a fusion loss function. And constructing a sound wave positioning model, and training the model to be optimal based on the fusion loss function. And finally, segmenting and enhancing audio data, and inputting the audio data into the trained model to obtain a positioning result. According to the method, the positioning model is trained by fusing multiple different types of loss functions, so that the positioning precision of sound waves in the optical cable is improved.

The invention relates to a method and a device for identifying a void defect of a steel plate concrete structure. The method comprises the following steps: acquiring a to-be-identified audio signal of the steel plate concrete structure acquired through excitation conditions; extracting feature information on each frame of the preprocessed audio signal, wherein the feature information at least comprises a Mel-frequency cepstrum coefficient, root mean square energy and two items of a spectral centroid statistical mean value and a standard deviation; the extracted frame feature information is input into a void defect recognition model, void confidence of each frame is obtained, and the void defect recognition model is obtained through training by utilizing a marked audio sample training set based on a support vector machine; in response to the fact that the void confidence coefficient corresponding to each frame of audio is higher than a confidence threshold value, judging that the corresponding frame is a void frame; and taking a judgment result of the majority frames as a final void defect identification result of the audio signal to be identified. The problems that a steel plate concrete structure is low in small-size void defect sensitivity and is greatly interfered by the thickness of a steel plate and an internal complex structure are solved.

The invention relates to the technical field of information security, and discloses a multi-point recording interference method and system, and the method comprises the steps: obtaining environment information, and determining a basic interference volume according to the environment information; acquiring a sound source-node distance vector and sound field uniformity, and converting the sound source-node distance vector and the sound field uniformity into a spatial distribution coefficient; acquiring a voice initial instantaneous slope and an interference-voice cross-correlation function peak value, and converting the slope and the peak value into a dynamic response coefficient; receiving a target voice, obtaining the spectral characteristics and the voice spectrum centroid of the target voice, and converting the spectral characteristics and the voice spectrum centroid into a spectral efficiency coefficient; reading the basic interference volume, the spatial distribution coefficient, the dynamic response coefficient and the spectral efficiency coefficient, and generating an adjustment instruction; according to the invention, through collaborative analysis of the environment reference coefficient, the spatial distribution coefficient, the dynamic response coefficient and the spectrum efficiency coefficient, multi-dimensional adaptive matching of the interference volume and the environment state, the spatial layout, the voice dynamic characteristic and the spectrum characteristic is realized, and the flexibility is extremely high.

The application discloses a kind of intelligent digital media content sentiment analysis method and system, content includes multi-source data acquisition, multi-level feature extraction, cross-modal fusion sentiment analysis modeling and intelligent analysis.The application relates to the technical field of media content sentiment analysis, specifically refers to a kind of intelligent digital media content sentiment analysis method and system, the scheme is in text passage calculation sentiment embedding offset and dynamically updates multi-center vector, in visual passage through space aggregation convolution activation measures emotional intensity, in audio passage using spectral centroid third central moment intensity is measured;At the same time, the key word intensity index and the modification mode coefficient are combined to realize the enhancement of middle-level semantics, and the global correlation of multi-modal nodes is constructed through the hypergraph structure, the context gate dynamic updating hyperedge weight is innovatively introduced, the graph attention aggregation feature is used, the time sequence self-attention and graph convolution are combined for spatiotemporal collaborative coding, which improves the accuracy, robustness and real-time adaptive ability of sentiment recognition.

The application discloses an intelligent noise monitoring system, comprising: a distributed sampling probe array installed on the bottom of a vehicle, a carriage and a skirt plate; a damping protection unit; a data acquisition and preprocessing unit; a vehicle-mounted multi-source information fusion module; an intelligent analysis and processing unit containing feature extraction, deep learning classification, anomaly detection and comprehensive evaluation modules; a remote operation and maintenance and self-calibration unit; a data storage and communication unit, which collects multi-channel noise and synchronizes the vehicle state; extracts MFCC, short-time energy, spectral centroid and formant features; inputs the feature matrix into a CRNN model to identify noise event types; calculates an anomaly score using an isolation forest and LOF fusion model; and judges the anomaly in combination with the vehicle state, classification probability and anomaly score. The application solves the problems that the existing system cannot adapt to the subway environment, has low recognition accuracy and poor operation and maintenance efficiency, and realizes precise monitoring and intelligent operation and maintenance.

The application discloses a bridge wind-induced vibration adaptive early warning method and system, the method acquires bridge acceleration time history data and synchronous wind speed data, and divides and analyzes samples according to a preset time interval; the acceleration time history is pretreated, and the response is decomposed into multiple intrinsic mode functions by using variational mode decomposition; the instantaneous frequency, instantaneous energy and instantaneous frequency entropy of each mode are calculated, the dominant mode of wind-induced vibration is screened according to the instantaneous frequency entropy and central frequency, and the wind-induced vibration time history is reconstructed; characteristics such as root mean square, peak factor, spectral centroid and the like are extracted from the wind-induced vibration time history, and an average wind speed is fused to construct a comprehensive early warning index; a peak threshold extreme value model is used to dynamically update the early warning threshold, and a Bayesian online change point detection is combined to identify risk mutations, and finally, graded early warning information is output. The application can inhibit the influence of vehicle vibration and other operation interference on wind-induced vibration risk identification, and improve the early warning capability of wind-induced risks such as vortex-induced vibration and buffeting.

This invention relates to a method for identifying weathered rock strata interfaces and adaptively controlling excavation parameters based on vibration spectrum analysis, belonging to the field of intelligent excavator construction technology. It addresses the problems of relying on manual experience for soil-rock interface identification during weathered rock strata trench excavation, low efficiency of mechanical switching, and slope vibration risks. The method collects vibration acceleration signals using a triaxial accelerometer, extracts frequency domain feature vectors including the dominant frequency amplitude, high-frequency band energy proportion, spectral centroid, and kurtosis value, and inputs these vectors into a multi-class support vector machine model to output category labels. When the category label changes from "cohesive silt" to "strongly weathered gneiss," the input current to the proportional solenoid valve of the excavator's hydraulic main pump is reduced, and a breaker hammer warning signal is activated. When the category label is "moderately weathered gneiss with interbedded boulders" and the high-frequency band energy proportion and kurtosis value exceed the safe range, the pilot oil pressure of the breaker hammer is limited. This method enables real-time identification of the soil-rock interface and adaptive control of excavation parameters.

The invention discloses a heading machine cutting head fault diagnosis method based on a frequency gating tree topology network. The specific implementation process is as follows: channel rearrangement is guided by using the spectrum centroid of a channel, accurate feature decoupling is performed on the channel by using anisotropic convolution in combination with the energy distribution characteristics of high and low frequency signals, and semantic dynamic generation of a sample specific weight is captured in real time through a multi-branch convolution gating mechanism. The tree topology structure brings multi-view key features such as accurate coordinates, a long-range structure and global context to the trunk network, and in order to make full use of the key features, a tree structure-oriented distillation strategy is provided. And mapping the injected multi-view key features and the output features of the backbone network to the same semantic metric space by using a parameter sharing classification mechanism, and obtaining a semantic soft mask by means of confidence transformation. Experiments show that by means of an intelligent feature extraction mechanism, the workload of fault diagnosis can be effectively reduced, and meanwhile the diagnosis efficiency is remarkably improved.

This invention relates to the field of industrial automation control technology, specifically disclosing a fault diagnosis method and system for control valves. The method includes: acquiring operating data of the control valve, including pressure data, temperature data, and vibration acoustic data; extracting features from the vibration acoustic data, obtaining features including root mean square value, peak value, peak factor, spectral centroid, energy ratio of a specific frequency band, and main direction of the sound source; wherein, based on a dynamic adaptive beamforming algorithm, spatial filtering and anti-interference mechanisms are introduced to calculate the main direction of the sound source; inputting a fault diagnosis model to calculate the fault type and fault probability; when a fault occurs, adjusting the valve according to the operating data. This invention can accurately identify various control valve faults, and the classification accuracy of cavitation faults reaches over 92%, making it particularly suitable for cavitation fault identification of electro-hydraulic linkage control valves under harsh operating conditions such as high pressure, large diameter, large pressure difference, and high precision regulation.

The invention discloses a pump unit cavitation fault diagnosis method based on a multi-modal physical information neural network, and the method comprises the steps: calculating vibration logarithm total energy and a frequency spectrum centroid based on vibration signal data, calculating a current standard deviation based on current signal data, constructing and training a teacher model composed of three parallel independent sub-networks, and carrying out the fault diagnosis of a pump unit cavitation fault. A student model is constructed and trained, the student model comprises a shared vibration encoder, a shared current encoder, a feature fusion layer, a fault classification head and a physical regression head, the weighted sum of classification loss, physical consistency loss and physical evolution trend constraint loss is adopted as a total loss function, and the student model is trained in a knowledge distillation mode. And forcing the model to learn a characteristic manifold conforming to a cavitation physical evolution rule. According to the method, the physical mechanism is embedded into the neural network through the PINN architecture, and the problems that a pure data driving model lacks physical interpretability and is weak in generalization ability are solved.

The invention relates to the technical field of digital audio processing, and discloses a music generation method, device and equipment based on cat call characteristics and a storage medium, and the method comprises the steps: extracting cat call sound, calculating a fundamental frequency track, a harmonic-to-noise ratio mark, formant characteristics, transient intensity and a frequency spectrum centroid in the cat call sound, forming a multi-dimensional characteristic vector, and obtaining a multi-dimensional characteristic vector; the problem that the cat call core acoustic information cannot be accurately captured by applying a human singing feature extraction method in an existing scheme is solved. Re-sampling the multi-dimensional feature vector to a beat resolution, and generating a control parameter for beat alignment, thereby solving the problem that the features are difficult to match with music beats due to the non-periodic characteristics of cat call; and mapping the control parameters into rhythm grids, chord labels and timbre vectors, and inputting the rhythm grids, chord labels and timbre vectors to a control interface of the music generation model to output music segments synchronized with the cat call features, and compared with the prior art, the problem that the cat call features cannot be effectively integrated into music generation due to the lack of an adaptive interface in an existing model is solved.

Provided is an optical fiber sensor that is capable of highly accurately detecting an abnormality in a structure from vibration information. This optical fiber sensor includes an optical fiber that is laid in the vicinity of a structure, a light source for introducing pulsed light of a specific period into the optical fiber, and an optical sensor for detecting return light that has been obtained as a result of the introduction of the pulsed light into the optical fiber. The structure is determined to have an abnormality if the spectral centroid of vibration information exceeds a threshold.

The application discloses a pump unit cavitation fault diagnosis method based on a multi-modal physical information neural network, comprising the following steps: calculating vibration logarithmic total energy and spectrum centroid based on vibration signal data, calculating current standard deviation based on current signal data, constructing and training a teacher model composed of three parallel independent sub-networks, constructing and training a student model, wherein the student model comprises a shared vibration encoder, a shared current encoder, a feature fusion layer, a fault classification head and a physical regression head, adopting a weighted sum of a classification loss, a physical consistency loss and a physical evolution trend constraint loss as a total loss function, training the student model through a knowledge distillation mode, and forcing the model to learn a feature manifold conforming to a cavitation physical evolution law. The application embeds a physical mechanism into a neural network through a PINN architecture, and solves the problems of lack of physical interpretability and weak generalization ability of a pure data-driven model.

This invention discloses a product design strategy generation method based on environmental feature extraction, belonging to the field of intelligent product design technology. The steps are as follows: constructing a multimodal dataset containing ambient sound, scene images, and odor data; extracting root mean square energy, zero-crossing rate, spectral centroid, and spectral flux features from the ambient sound; extracting explicit visual features from the scene images and implicit style features through an improved Swin-Transformer model; extracting intensity, pleasantness, and familiarity features from the odor data; performing semantic mapping through Mamdani fuzzy inference to generate a design guidance parameter vector, including sensory stimulation, morphological simplicity, emotional affinity, technological feel, interaction rhythm, stability, sense of security, and environmental integration; and generating a product design strategy based on this vector. This invention automatically transforms environmental perception into a product design strategy, improving the objectivity and efficiency of design decisions.

The application discloses a cross-domain migration method and device for intelligent diagnosis, and belongs to the technical field of signal processing and intelligent diagnosis. The cumulative energy frequency and the spectrum centroid distribution of a vibration signal are calculated, a unified sampling frequency is selected based on time-frequency feature difference, and feature set unification and standardization preprocessing are carried out; a subspace alignment algorithm with maximum mean difference constraint is adopted to map source domain and target domain features to a shared low-dimensional subspace, so that the distribution difference is reduced; a multi-task learning model with fusion credibility decision is further constructed, and through dynamic weight distribution and credibility weighted fusion, the final fault diagnosis of the target domain sample is realized. The application improves the accuracy and adaptability of fault diagnosis under cross-condition and cross-equipment conditions.

The application discloses a speech structure sensitive area constraint enhancement method and system for a coal mine scene, and the method comprises the following steps: obtaining an original amplitude spectrum and a high-order statistical quantity feature vector corresponding to a noisy speech signal, and performing splicing processing on the two to obtain a composite tensor; time dimension features and frequency dimension features are extracted based on the composite tensor, a preliminary enhanced amplitude spectrum is further obtained, and a joint feature tensor comprising a spectral centroid change rate, spectral flatness, an inter-frame correlation coefficient and a medium frequency energy proportion is constructed; a sensitive probability graph is constructed based on the joint feature tensor, which is isomorphic with the original amplitude spectrum; a suppression penalty term is constructed according to the sensitive probability graph, the preliminary enhanced amplitude spectrum and a pure speech signal amplitude spectrum; and a preset speech deep learning model is optimized and trained by using the suppression penalty term. The application can consider both the suppression of non-stationary transient noise and the preservation of the sensitive area of the speech structure, and solves the problem of the decline of speech quality and communication intelligibility under extreme working conditions.

The application relates to the field of device life prediction, and provides a contact wear intelligent evaluation method, system, device, product and medium, which comprises the following steps: measuring contact closing vibration data through a vibration sensor and measuring contact closing temperature data through a temperature sensor; performing self-calibration vibration data collection and frequency response function identification to obtain a frequency response function, and obtaining interface coupling characteristic parameters according to the frequency response function; compensating the contact closing vibration data, correcting the spectral centroid, and obtaining compensated vibration parameters; performing short-time Fourier transform and modulus conversion to obtain a two-dimensional sound spectrogram, constructing a convolutional neural network, inputting the two-dimensional sound spectrogram into the convolutional neural network, and obtaining a first wear amount; performing band-pass filtering to obtain high-frequency vibration energy, obtaining a second wear amount according to the high-frequency vibration energy, performing weighted summation to obtain a contact wear amount, evaluating contact residual life through the contact wear amount, and completing evaluation of the contact wear.

The invention discloses an intelligent digital media content sentiment analysis method and system. The intelligent digital media content sentiment analysis method comprises the steps of multi-source data collection, multistage feature extraction, cross-modal fusion sentiment analysis modeling and intelligent analysis. The invention relates to the technical field of media content sentiment analysis, in particular to an intelligent digital media content sentiment analysis method and system.According to the scheme, sentiment embedding offset is calculated in a text path, a multi-center vector is dynamically updated, sentiment intensity is measured through spatial aggregation convolution activation in a visual path, and sentiment analysis is achieved. Measuring the intense degree in an audio channel by utilizing a third-order central moment of a frequency spectrum centroid; meanwhile, middle-layer semantic enhancement is achieved in combination with a keyword intensity index and a modification mode coefficient, multi-modal node global association is constructed through a hypergraph structure, context gating is innovatively introduced to dynamically update hyperedge weights, graph attention aggregation features are adopted, time sequence self-attention and graph convolution are combined to conduct space-time collaborative coding, and the time sequence self-attention and graph convolution are combined to achieve time-space collaborative coding. And the accuracy, robustness and real-time adaptive capability of emotion recognition are improved.

This invention relates to the field of physiological signal analysis technology, specifically to an automatic analysis method and system for electroskin activity (ESA) signals based on multi-domain features. The method includes the following steps: acquiring ESA signals and correcting abnormal fluctuations within a fixed time window to obtain stable and continuous time-series data; filtering and downsampling the corrected signals to characterize the time-domain stability; analyzing signal change trends using moving averages and extracting low-frequency evolution and differential changes to form a multi-component joint characterization; iteratively calculating the multi-component power distribution and spectral centroid features from the frequency domain; statistically judging the frequency domain consistency of the multi-components; and fusing time-domain stability information to form a multi-domain feature analysis result. In this invention, robust statistical correction of abnormal fluctuations and characterization of signal stability ensures that the ESA time series remains consistent under noise drift. The fusion of low-frequency and differential changes constructs a multi-component characterization, and combined with frequency domain consistency analysis, achieves time-frequency verification, improving the stability and comparability of the results.

To easily detect a sign of abnormality occurrence in an object system when measurement data from the object system largely fluctuate even in a normal time or when the measurement data are sampled by a high sampling frequency.SOLUTION: A frequency spectrum is acquired by performing Fourier transform or the like on measurement data (step 101), a portion belonging to a frequency band is extracted from the frequency spectrum for each frequency band on the basis of one or more predetermined frequency bands to obtain a partial spectrum (step 102), a coordinate value of a centroid of the partial spectrum is obtained and accumulated as a data vector (step 103), and an abnormality degree (for example, a Mahalanobis distance) of a data vector corresponding to a short processing period is calculated for each processing unit period on the basis of the accumulated data vector (step 105).SELECTED DRAWING: Figure 2

The invention relates to the technical field of circuit testing, in particular to an aging test method for a timekeeping circuit of an electric energy meter based on multi-dimensional operation data. The specific implementation process comprises the following steps: acquiring electric energy meter time keeping circuit data, and reconstructing the data into a frequency domain texture energy spectrum diagram; spectrum noise particle size distribution characteristics are extracted, quartz crystal oscillation items are introduced to calibrate spectrum gravity center micro-drift, and circuit characteristic fingerprints are formed; obtaining aging texture ghosts through a synchronous harmonic stripping mechanism, and inputting the aging texture ghosts into a stress flux integration model to quantify a geometric erosion rate; and performing time sequence extrapolation to simulate an evolution trajectory according to the geometric erosion rate, and calculating and outputting a performance erosion index and a residual life prediction result. According to the invention, by using a quartz crystal oscillation item and a synchronous harmonic stripping mechanism, reversible temperature drift is effectively decoupled, environmental transient interference is separated, the reliability of evolution trajectory simulation under complex and real working conditions is ensured, and the accuracy of prospective early warning and residual life evaluation of the time keeping circuit of the electric energy meter is improved.

The invention relates to an insulating layer state evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an electrical quantity signal of a to-be-evaluated wire; based on a Hilbert-Huang transform method, determining features corresponding to the electrical quantity signals; the features comprise a peak value, a root mean square, a spectral centroid and a frequency variance; inputting the features into a pre-trained evaluation model, and determining the state of the insulating layer of the to-be-evaluated wire; the evaluation model is obtained by training a neural network model according to a training sample, and the training sample comprises training features and the insulating layer state of the wire corresponding to the training features. By using the insulating layer state evaluation method provided by the invention, the efficiency and accuracy of detecting the state of the insulating layer of the wire can be improved.

The application discloses a device working state recognition method based on a voiceprint recognition model and relates to the technical field of industrial device operation state recognition. The device working state recognition method based on the voiceprint recognition model collects running audio waveform data of a target device, extracts acoustic representation data containing parameters such as short-time energy, spectral centroid, spectral flux, MFCC and zero-crossing rate, inputs the acoustic representation data into a pre-trained voiceprint recognition model to extract a voiceprint feature representation vector, and determines a current running state category through a state classification model. The application divides an audio signal into frames, extracts acoustic features such as short-time energy, spectral centroid, spectral flux, MFCC and zero-crossing rate, models interframe evolution relationship in combination with a bidirectional neural network, introduces an attention mechanism to highlight key frame segments, effectively enhances the recognition capability for working conditions such as state boundary ambiguity or unclear transition, and improves the time sequence analysis and state discrimination precision.

This invention provides a scene-aware intelligent audio equalizer adaptive adjustment system, including an audio acquisition and preprocessing module, a feature extraction module, a scene classification module, and a dynamic equalizer configuration module. The audio acquisition and preprocessing module uses a microphone to sample and acquire raw audio data in real time, obtaining preprocessed data. The feature extraction module performs frequency domain transformation on the preprocessed data and calculates acoustic feature values, including Mel-frequency cepstral coefficients (MFCC), spectral centroid, and zero-crossing rate. The scene classification module inputs the acoustic feature values ​​into a lightweight decision tree model to achieve scene recognition and output scene labels. The dynamic equalizer configuration module enables seamless adjustment of audio effects. This system can improve the environmental adaptability, real-time performance, and power efficiency of audio processing, and can reduce computational overhead and energy consumption.

A method, system, storage medium, and program product for controlling the rhythmic flashing of an LED light strip are disclosed. The method involves: calculating the relative entropy values ​​between high-frequency, mid-frequency, and low-frequency bands; establishing an energy flow diagram and determining the dominant frequency band based on the topological structure of the energy flow in the diagram; constructing a phase tracking function and classifying the beat markers into hierarchical levels; establishing a recursive segmentation window, calculating the spectral centroid trajectory, and extracting the inflection point sequence; establishing the correspondence between polar coordinate angles and LED light strip color parameters, and the correspondence between polar radius and brightness parameters; designing the spatial propagation mode of the LED light strip based on the topological structure of the energy flow diagram, ensuring that the luminous efficacy along the propagation direction of the LED light strip aligns with the energy flow direction between frequency bands, and adjusting the propagation speed according to the energy flow intensity; and generating a driving control sequence for the LED light strip based on a gradient function. The method also captures the emotional progression and tension changes in music and accurately controls the rhythmic flashing of the LED light strip based on these changes.

The application discloses a kind of gas extraction drilling hole gas leakage rapid detection device and method based on acoustic spectrum analysis, belong to coal mine safety and gas control technical field.For the problems of low detection efficiency, difficult positioning and difficult to prevent explosion handheld of existing drilling hole gas leakage, lightweight acoustic rapid screening scheme is proposed.The device includes explosion-proof handheld shell and directional double microphone, three-stage cascade signal conditioning circuit and intelligent signal processing and main control component, the leakage turbulent noise is captured by directional microphone, after amplification, filtering and gain control, the dual judgment of sound pressure level increment and spectral centroid offset is executed by intelligent signal processing and main control component to identify leakage, and the incident direction angle of sound source is calculated based on cross-correlation time delay estimation, and the leakage intensity level is divided according to sound pressure level increment.The application can realize single-handed, non-insertion type rapid screening, leakage point direction indication and intensity evaluation in seconds in high background noise environment in coal mine, improve inspection efficiency, and provide basis for maintenance decision.