393 results about "Sound intensity" patented technology

The invention discloses a tunnel fire air curtain smoke prevention control method and system, and relates to the technical field of intelligent fire fighting, and the method comprises the steps: building a digital twinborn prediction model, and generating a fire source position coordinate and a smoke diffusion trend prediction map through multi-physics field coupling simulation; analyzing the stability requirement of the air curtain jet flow through a Brapare-Prandtt boundary layer analysis algorithm, and generating a jet flow enhancement requirement parameter table in combination with temperature gradient correction; optimizing an acoustic vortex main frequency band by adopting a Slodhaar number, and generating an acoustic vortex control parameter set in combination with a matched sound pressure level and a configured resonator phase difference; and converting the acoustic vortex control parameter set into an execution instruction, monitoring the jet velocity, the vortex street stability and the smoke retardation effect in real time, and dynamically correcting the parameters of the digital twinborn prediction model. According to the method, physical quantity outline matching of the blocking intensity and the sound pressure level is achieved through the sound intensity-momentum equivalent method, and the optimal phase configuration is determined in combination with POD modal analysis.

The invention provides a sound standardization evaluation system for multi-dimensional acoustic parameter and emotional scene dynamic analysis, and aims to realize structured and objective evaluation of voice performance. The system comprises an audio acquisition module, an audio processing module, a standard evaluation module and an integration processing module. The audio processing module performs noise reduction, pre-emphasis, sampling rate adjustment, framing and slicing operation on the original voice data; the standard evaluation module executes character and pronunciation accuracy analysis, acoustic basic skill feature evaluation or emotion and scene adaptation degree analysis based on multi-modal fusion according to an evaluation type set by a user; parameters including fundamental frequency, formant, sound intensity, fundamental frequency perturbation, amplitude perturbation and the like are extracted in evaluation, and classification prediction is completed in combination with a machine learning model; and finally, the integration processing module generates an evaluation report including a scoring result, question prompts and personalized training suggestions, and provides closed-loop feedback to support sound training and expression optimization.

The invention discloses a vehicle-mounted voice identification and positioning intelligent early warning system and method, and relates to the technical field of vehicle-mounted intelligent early warning. The system comprises a sound detector module which collects an audio signal of a surrounding environment in real time, and a sound recognition module which analyzes the audio signal through a deep learning model and recognizes a sound source type. And the calibration module performs space-time calibration to ensure data synchronization and alignment of the sensors. The positioning module combines time difference of arrival (TDOA) and a sound intensity attenuation model, and optimizes sound source positioning through particle swarm optimization (PSO) and extended Kalman filter (EKF) technologies. The risk assessment module assesses the collision risk based on the sound source position, the shielding state and the object type, gives an alarm to a driver in time through the early warning module, and automatically triggers a deceleration instruction. And the self-learning module dynamically adjusts the early warning sensitivity and the noise compensation coefficient through a federated learning framework updating model. The environmental adaptability of the vehicle-mounted early warning system is improved, and the vehicle-mounted early warning system has remarkable safety improvement and wide application prospects.

The invention belongs to the technical field of underwater small target object analysis and recognition methods, and particularly relates to a sound signal feature optimization method for underwater small target object analysis and recognition. Comprising the following steps: 1, acquiring and processing original data; 2, eliminating interference line spectrums, and intercepting and splicing by adopting a time window to obtain a time recombination sound signal; using range transformation to optimize signal amplitude characteristics; processing the sound signal by using an FIR digital high-pass filter; obtaining an optimized signal by using a homomorphic filter; 3, extracting structural features, energy features, frequency features and sound intensity features of the optimized digital signals; step 4, sound signal feature fusion: carrying out underwater target sound signal feature fusion processing based on a canonical correlation analysis method; the invention provides a method for extracting effective sound signal data of an underwater target under the background of optimizing a complex marine environment, enhancing signal effectiveness during long-distance detection, and constructing effective sound signal features capable of being used for classification and recognition of the underwater target at the same time.

The invention relates to the technical field of model construction, in particular to a construction method and system of an electrolysis energy consumption model and a storage medium. The method comprises the following steps: collecting electrolysis process parameters by using a multi-mode sensor system, wherein the electrolysis process parameters comprise a current value, a voltage value, an electrolyte temperature and electrolysis time; constructing an electrolysis energy consumption initial model based on the electrolysis process parameters, and inputting the electrolysis process parameters into the electrolysis energy consumption initial model to output a unit product energy consumption value and a current-voltage efficiency curve; electrolytic bubble acoustic monitoring is conducted on the side wall of the electrolytic cell to obtain bubble breakage acoustic wave signals; and analyzing the sound intensity and the rupture frequency of the bubble rupture sound wave signal. According to the method, through the multi-modal sensor, multi-dimensional signal analysis and dynamic parameter correction, the defects of single data and static state of a traditional electrolysis energy consumption model are broken through, and high-precision dynamic modeling and optimization of energy consumption in the electrolysis process are achieved.

The invention provides an acoustic coalescence elimination method for hydrogen bubbles in molten aluminum, and belongs to the technical field of molten aluminum bubble elimination. According to the method, sound field collaborative focusing is achieved by arranging a phase control array transducer set in a molten aluminum smelting furnace, primary gathered sound waves and efficient coalescence sound waves are applied in stages according to bubble size evolution in a double-frequency scanning mode, and the hydrogen bubbles in the molten aluminum are eliminated. A target frequency is dynamically adjusted in combination with a bubble resonance frequency optimization model to match the inherent frequency of a bubble, a sound-bubble coupling dynamic tracking system is used for monitoring bubble evolution triggering stage switching in real time, and a sound field focus point dynamically scans and covers a three-dimensional space area through phase delay control. A delay detection method is adopted to eliminate cavitation bubble interference and accurately measure the hydrogen content, sound intensity and frequency parameters are optimized according to a sound field parameter self-adaptive regulation and control model, and the technical problem that hydrogen bubbles in a molten aluminum deep area are difficult to be effectively driven by sound waves and cannot be fully coalesced and eliminated is solved.

The invention discloses an aircraft noise source positioning method comprising the following steps: sound field signals are collected, and the sound field signals comprise sound pressure signals and three-dimensional particle vibration velocity signals; preprocessing the sound pressure signal and the three-dimensional particle vibration velocity signal to obtain frequency spectrum data; inputting the frequency spectrum data into a trained noise recognition model, and judging a noise event; when the noise event is judged to be aircraft noise, noise source positioning is carried out according to corresponding spectrum data; according to the invention, noise event identification can be carried out firstly, so that the influence of the sound source arrival direction and other noise sources is eliminated, and errors are avoided; and the sound pressure and the three-dimensional particle vibration velocity are collected for preprocessing, effective sound intensity signals are obtained for positioning, and the positioning precision is improved.

The invention discloses a new medicine application of a local injection acoustic response stent in enhancing ultrasound histotripsy. The acoustic response scaffold (ARS) comprises a scaffold material, fluorocarbon gas or liquid fluorocarbon particles with liquid-vapor phase change capacity, and microbubble cloud in the scaffold generates a strong cavitation ablation treatment effect in a target area under the action of low-frequency and low-sound-pressure ultrasound. Fluorocarbon gas or liquid fluorocarbon particles contained in the ARS form microbubble cloud and ideal cavitation nuclei under low-frequency low-sound-pressure ultrasonic excitation, inertia cavitation resonance is achieved under the ultrasonic action, and then cavitation mechanical damage with local tissue controllable strength is generated. The low-intensity ultrasonic histotripsy has the characteristics of low sound pressure, low sound intensity, high targeting property, small tissue damage outside a target region, simplicity in instrument manufacturing and the like, and the targeting property and the safety of treatment are improved. The method does not need very accurate ultrasonic focusing, reduces the operation difficulty, and has application prospects in multiple medical fields such as prostatic hyperplasia, tumor ablation and tumor immunotherapy.

The invention relates to the technical field of voice processing, in particular to a sound source direction-finding positioning method and system for a complex environment, and the method comprises the steps: serializing the sound intensity of each microphone, and calculating a maximum receiving interval according to the distance between adjacent microphones, the sound transmission speed and the sampling frequency; the method comprises the following steps: determining a short-time audio similarity through moving a sound source signal sequence, extracting a sound source synchronization sequence and a sound source same-frequency sequence, and further evaluating a tone quality deviation degree; optionally selecting a reference microphone, and comparing the reference microphone with a sound source synchronization sequence of other microphones to obtain a time delay data length and a sound source similar sequence; based on the arrangement values of the same position elements of the sound source similar sequence, a sound intensity distribution sequence is obtained, and the sound intensity interference degree of each microphone is obtained; and determining a fixed beam direction and calculating the distance between the reference microphone and the sound source. The invention aims to improve the accuracy of sound source positioning.

The invention discloses a demisting water-saving natural ventilation wet cooling tower and a working method, the cooling tower comprises a cooling tower body, a filler, a water distribution system, a dehydrator and a plurality of sound wave generators are arranged in the cooling tower body, and the sound wave generators are mounted at the upper part and the nearby area of a water distribution tank maintenance channel in the cooling tower; the sound wave generators are distributed in a high-humidity air flow area, a high-water-drop-content area, a medium-humidity air flow area and a medium-water-drop-content area. The sound wave generator adjusts the frequency and the sound intensity to the maximum extent according to the water drop characteristics, the environmental conditions and the water collecting efficiency; a certain proportion of water is recycled according to different working conditions through the sound wave water collecting technology, waste of water resources is effectively reduced, and the water saving effect is more prominent when the unit operates at low load; and meanwhile, when the water is recovered, the generation of fog plumes around the cooling tower is reduced, the surrounding environment is improved, and the potential influence of the fog plumes on peripheral equipment and facilities is reduced.

The invention discloses a GIS equipment defect three-dimensional visual positioning method based on acoustic imaging, and relates to the related technical field of GIS equipment, and the method comprises the steps: constructing a sound intensity detection dual system; sound source signals of the GIS equipment are synchronously acquired, and near-field acoustic signals and far-field acoustic signals are output; performing frequency band separation through a self-adaptive band-pass filter, and identifying near acoustic abnormal features and far acoustic abnormal features of the separated frequency band; carrying out spatial positioning fusion and outputting a positioning point; and importing a GIS equipment three-dimensional model according to the sound source fusion positioning point, and outputting a GIS equipment visual positioning result. The technical problems that in the prior art, the GIS equipment defect detection range is limited, the positioning precision is insufficient, the defect space position is difficult to visually present, the equipment defect positioning is not rapid and accurate enough, and the equipment operation and maintenance efficiency and reliability are affected are solved. The technical effects of quickly and accurately positioning the equipment defects and effectively improving the operation and maintenance efficiency and reliability of the equipment are achieved.

The invention discloses an acoustic response light-emitting composite nanomaterial as well as a preparation method and application thereof, and belongs to the technical field of light-emitting composite nanomaterials. The acoustic response luminescent composite nano material provided by the invention comprises hollow mesoporous silica nano particles and europium-doped barium titanate nano particles loaded on the surfaces of the hollow mesoporous silica nano particles and in mesopores; the chemical formula of the europium-doped barium titanate nanoparticles is BaTiO3: Eu < 3 + >; the mass ratio of the hollow mesoporous silica nanoparticles to the europium-doped barium titanate nanoparticles is 1: (0.8-1.5). The sound response luminescent composite nano material provided by the invention can realize detection of sound intensity, and has good temperature stability, and meanwhile, the preparation method is simple and low in cost.

The invention provides a moving target Doppler compensation coherent detection method and system based on a single-vector hydrophone, and the method comprises the steps: S1, collecting a receiving signal of the single-vector hydrophone, and carrying out the segmentation of the receiving signal, and obtaining segmented data; s2, estimating a scale factor of the received data of the current segment according to the segmented data; s3, compensating the corresponding segmented data according to the scale factor to obtain a compensated signal; and S4, performing coherent accumulation processing on the compensated signal to obtain a sound intensity detection result, namely signal-to-noise ratio gain. The scale factor is a Doppler coefficient. Based on the improvement of a conventional sound pressure Doppler compensation method, in view of the advantages of the vector hydrophone, a new moving target Doppler compensation coherent detection method based on the single vector hydrophone is provided, and the detection performance of the single vector hydrophone on the moving target is improved.

The invention discloses a vehicle door control method and device, electronic equipment and a vehicle, and belongs to the technical field of vehicles. The method comprises the steps that when the vehicle senses an external knocking action, the environment sound intensity is detected; the response feasibility of the knocking signal is determined according to the environment sound intensity to control the vehicle door to be opened. According to the method, a user can unlock and open the vehicle door only through simple knocking without traditional key or key operation, the method is particularly suitable for the situation that a heavy object is held by hand or the key cannot be easily taken out, whether the knocking signal is responded or not is determined according to the environment sound intensity, false triggering can be reduced, high accuracy of knocking to open the door in the complex environment is achieved, and the user experience is improved. And the trouble that the user can take effect and is invalid when knocking to open the vehicle door is reduced.

The invention discloses a woodworking machine tool noise sound power level measurement method based on a sound intensity method, and belongs to the technical field of machine tool noise measurement, and the method specifically comprises the steps: determining a housing surface covered by a tool rotation plane projection as a main sound radiation region, and determining a main bearing pedestal installation surface as an infrasound radiation region; sound pressure, vibration acceleration signals and dynamic air pressure signals of a main sound radiation area are collected; resampling a dynamic air pressure signal rotation angle domain, extracting a reference pneumatic pulsating waveform synchronous with tool tooth sweeping, and establishing an airflow induced sound pressure model according to the reference pneumatic pulsating waveform and a phase relation; calculating a surface normal vibration speed according to the vibration acceleration, and establishing a structure radiation sound pressure model in combination with a sound wave equation; decomposing the sound pressure signal to obtain two types of component estimation values, and performing parameter optimization to obtain net sound intensity of a main sound radiation area; extracting vibration frequency band energy of an infrasound radiation area, calculating equivalent sound intensity by combining a sound radiation efficiency model, and outputting a total sound power level by space integration; the invention provides an effective method for accurately measuring and controlling the noise of the woodworking machine tool.

The invention discloses a video editing method based on multi-modal large model collaboration, and belongs to the technical field of video processing. The method specifically comprises the following steps: S1, video preprocessing and intelligent blocking: preprocessing an original video, cutting the video into small blocks, ensuring that the end of each small block is a complete statement, outputting a timestamp and an audio text segment of each block, and storing the timestamp and the audio text segment in the form of structured data; and S2, multi-modal scene separation and verification: inputting each block, namely an audio text segment and a timestamp thereof into a large language model, initially dividing a scene according to a rule, then constructing a spurious chart, inputting a large visual model to verify whether a commodity is switched, and finally outputting scene time, a title and a brief introduction. Through a combination strategy of audio feature extraction, semantic integrity judgment and quantitative blocking rules, the problems of statement truncation and timestamp confusion which are easy to occur in traditional video blocking are solved. Specifically, sound wave intensity, voice pause features and a statement boundary identified by a language large model are combined.

The invention relates to a personalized timbre migration and synthesis method based on a virtual singer, and the method comprises the steps: obtaining a reference singing audio of a target virtual singer, and extracting a timbre identity benchmark feature used for representing the timbre identity stability through timbre coding; obtaining personalized timbre migration demand information for the virtual singer, wherein the demand information comprises a to-be-migrated timbre attribute and a corresponding target change amplitude or change direction; calculating a compatibility score according to the timbre identity reference feature and a migration demand, generating a timbre migration risk indication value, and comparing the risk indication value with a preset threshold value or a preset threshold value interval to determine that the migration is high-risk migration or low-risk migration or conservative-risk migration; according to the method, the tone identity benchmark features in the reference singing audio are extracted, and the tone identity invariant set with stable transpitch and sounding intensity is further constructed, so that the core tone identity of the virtual singer is accurately described.

The invention relates to a method for testing a participant's ability to accurately detect speech under background noise, comprising: (a) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to a participant's first ear at a fixed or varying signal-to-noise ratio (SNR) in a quiet or noisy environment, (b) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to the second ear of the participant at a fixed or random signal-to-noise ratio (SNR) in a quiet or noisy environment, where the numbers are presented in the native language of the participant or the fluent language of the participant, (c) recording the numbers or sequence of numbers correctly recognized by the participant, and (c) storing the recorded numbers or sequence of numbers at the second ear of the participant. And (d) calculating a score as a function of the percentage of the number or sequence of numbers correctly replicated by the subject, or as a function of the sound intensity difference between the speech and noise required to achieve the correct recognition of 50% sequence of numbers. The invention also relates to a device and a method for measuring the digital recognition capability related to hearing loss, and to a computer-implemented method for evaluating the speech recognition capability of a participant.

The invention discloses a passive target speed estimation algorithm based on a sparse Bayesian learning cross spectrum, and belongs to the technical field of array signal processing. The method comprises the following steps: constructing a target cross-correlation field model, carrying out cross-spectrum on sound pressure and a delayed sound pressure channel to obtain cross-spectrum sound intensity flow, constructing a dictionary matrix according to target cross-spectrum frequency point features, establishing a joint probability density distribution function by virtue of a sparse Bayesian learning model, and finally estimating signal power according to maximum logarithm Bayesian evidence. And carrying out spectrum peak search on the signal power estimation value to realize speed estimation of the target. According to the method, the cross-spectrum sound intensity flow is used as input, all cross-spectrum frequency points are compensated, alignment of cross-spectrum results of different frequency points is achieved, the background noise suppression effect is achieved, and multi-frequency-point processing results are fused. Meanwhile, the method has good speed estimation precision, can maintain good performance under the condition of low signal-to-noise ratio, and has high engineering application value.

A directional sound pickup method and a device implement targeted sound pickup for one or more targets without affecting video recording. An electronic device may identify a directional sound pickup target based on selection of a target object (or a region) in a viewfinder frame so that during audio recording, a sound signal from an identified target is picked up in a targeted manner, for example, a sound intensity of the directional sound pickup target is enhanced and / or a sound intensity of another target is weakened, or a sound intensity of another target is excluded.

The invention relates to the technical field of intelligent voice, in particular to a pet emotion recognition method and system based on sound analysis, and the method comprises the following steps: extracting a dominant frequency value, a frequency spectrum gravity center and an energy concentration degree through audio collection equipment, and respectively mapping the dominant frequency value, the frequency spectrum gravity center and the energy concentration degree into three-dimensional coordinate axes to generate time sequence emotion state points; and calculating a sound intensity change rate by utilizing Gaussian kernel density estimation, judging an emotion activation source through a tension response function to generate a response graph, executing gradient calculation and non-maximum suppression to generate a label intensity graph, and generating a composite emotion expression result by combining a path turning angle and the graph. According to the method, emotional coordinates are mapped through a main frequency value, a frequency spectrum gravity center and an energy concentration degree, sound intensity changes are estimated through Gaussian kernel density, disturbance and space are correlated through a tension response function, a focused emotional interval is suppressed through a non-maximum value, path turning is analyzed through dynamic time warping, emotional evolution is described through a label map, and judgment continuity and consistency are improved through multi-dimensional feature fusion.

The invention relates to the technical field of image communication, in particular to an intelligent camera with active defense and a defense method thereof, and the intelligent camera comprises a data acquisition module, an extraction module, a judgment module, a determination module, a screening module and a defense module. According to the invention, through fusion of a multi-parameter analysis technology and combination of the target position, the attitude angle and the height of a real-time image and dynamic changes of sound intensity, accurate identification of an intrusion target is realized, and the judgment module adopts a multi-level logic of combination of boundary judgment and attitude angle change, so that the accuracy of abnormal target judgment is effectively improved; the screening module reduces erroneous judgment through comprehensive screening of sound intensity and position; the defense module calculates a launching track based on the real-time position and height of the target, fixed-point active defense is achieved through a water bomb, passivity of a traditional voice alarm mode is avoided, and the problem that the response speed is not timely due to single fingerprint recognition and vibration monitoring data is effectively solved.

The present invention relates to the technical field of rail transit, in particular to a testing apparatus and a testing method. The testing apparatus comprises an upper sound insulation chamber (1), a lower sound insulation chamber (2), a sample piece under test (3), a vibration exciter (5), a force sensor (6), a sound intensity measuring apparatus (7) and a vibration sensor (8). The upper sound insulation chamber (1) is communicated with the lower sound insulation chamber (2) via an opening, said sample piece (3) being sealedly provided at the opening. The vibration exciter (5) is provided in the lower sound insulation chamber (2), and acts on said sample piece (3) by means of the force sensor (6). The force sensor (6) is used for acquiring the value F of an excitation force applied to said sample piece (3). The sound intensity measuring apparatus (7) is provided in the upper sound insulation chamber (1) and is used for acquiring a radiation sound power L of said sample piece (3). The vibration sensor (8) is arranged on said sample piece (3) and is used for acquiring a vibration value A of said sample piece (3). Thus, the vibration frequency response V=A / F, and the sound radiation S=L / F. The testing apparatus and the testing method can solve the problem that vibration and radiation noises of existing train chasses cannot be predicted.

The invention relates to the technical field of volume adjustment, and particularly discloses an intelligent volume adjustment method and system based on an MEMS earphone and a storage medium, and the method comprises the following steps: obtaining the acoustic characteristics of a sound signal in a time window, and obtaining the actual sound intensity based on the acoustic characteristics; matching the actual sound intensity with historical volume preference data of the user, and determining a target output volume of the currently played content; acquiring a driving voltage amplitude, and realizing digital gain control on original PCM audio sampling data; and sending the audio data subjected to digital gain control into a high-speed digital-to-analog converter, outputting a corresponding analog voltage signal, and controlling an MEMS acoustic unit to carry out audio output according to the target output volume based on the analog voltage signal. According to the invention, the convenience of volume adjustment of the MEMS earphone is improved.

The invention discloses a method for detecting and analyzing the defect or damage condition of a full-water pipeline, and the method comprises the steps: presetting a sound wave reflection time threshold value of each medium interface when an underwater robot is located in the middle of the pipeline according to the size of the full-water pipeline, and carrying out the sound wave detection through the underwater robot, the collected reflection sound intensity and reflection time data are transmitted to the data processing module, the data processing module carries out pre-classification, whether the collected reflection time is compared with a sound wave reflection time threshold value or not is judged according to the classification condition, a medium algorithm is matched, and the medium algorithm obtains the distance of the reflection time and the reflection sound intensity; and judging whether the reflection interface accords with a medium interface corresponding to the medium algorithm, if so, uploading and adjusting the underwater robot to keep the middle position according to the distance data, if not, uploading and reporting an error, and finally, receiving data three-dimensional modeling by a modeling analysis module, and generating a three-dimensional detection diagram. The problem that the identification effect of the deformation or damage condition of the pipeline covered by the sediment is poor is solved.

The invention relates to an audio-based slag splashing state monitoring method and system, and the method comprises the steps: collecting real-time data needed by model calculation in a slag splashing process after the starting of the slag splashing of a converter; analyzing and processing the collected real-time data; setting a slag rising threshold value, observing and learning sound intensity time sequence characteristics during slag rising, establishing a slag rising point automatic detection model, and determining a slag rising point; establishing a slag splashing early warning point detection model, and determining an early warning point; and establishing a slag splashing endpoint detection model, determining a slag splashing endpoint, and realizing automatic forecasting of the slag splashing endpoint. According to the method, the audio signal in the slag splashing process of the converter is monitored in real time, the audio time sequence characteristics are extracted, and the detection of the slag starting point and the accurate forecasting of the slag splashing end point are realized in combination with a machine learning algorithm and an end point prediction technology. According to the method, the production efficiency in the slag splashing process of the converter is improved, the manual intervention cost is reduced, the negative influence on the service life of the converter due to overlong slag splashing time is avoided, and a powerful technical support is provided for continuous progress of the steelmaking industry.

The invention relates to the technical field of data processing, in particular to a human-computer interaction spoken language evaluation method and system based on speech recognition, and the method comprises the steps: obtaining a phoneme sequence of a speech signal of a target examinee, calculating the initial segmentation probability of a target moment in a time window of a target phoneme, and obtaining a speech signal of the target examinee; taking a time window between the target moment and the ending moment of the target phoneme as a first time period, taking a time window between the starting moment of the next phoneme and any moment of the next phoneme as a second time period, and calculating the similarity overlapping degree of the sound intensity of the first time period and the sound intensity of the second time period, and calculating a final segmentation probability of the target moment based on the initial segmentation probability and the similarity overlapping degree, and screening segmentation points to segment the phoneme sequence by using the final segmentation probability, so as to obtain a matching score of the segmented phoneme sequence and a pre-stored standard phoneme sequence based on a GOP algorithm. According to the invention, the accuracy of the obtained matching score can be improved, so that the accuracy of the spoken language evaluation result is ensured.

The invention discloses an underwater structure low-frequency radiation sound power forecasting method based on blind source separation, and the method comprises the following steps: (1), setting a housing sound source in a shallow sea environment, and obtaining discrete sound pressure; (2) obtaining an estimation signal of the source signal through a blind source separation algorithm; (3) windowing the estimated signal space data of the source signal in the z direction to obtain the sound pressure propagating outwards after windowing; (4) the sound pressure propagating outwards is converted into a wave number domain through discrete space Fourier transform, and a holographic surface sound pressure cylindrical wave spectrum is obtained; (5) filtering evanescent waves in a wavenumber domain by using a wavenumber domain filter to obtain propagation waves on a holographic surface; (6) transmitting the filtered sound pressure to a far field in a spherical shape through a near-field acoustical holography technology by means of stable-phase estimation; (7) deducing the sound intensity by using the far-field sound pressure so as to obtain the radiation sound power; according to the invention, the measurement workload is reduced; measurement is carried out on a holographic surface close to a sound source, the influence of boundary reflection is small, and a calculation result is accurate.

The invention relates to the technical field of artificial intelligence, and discloses a reinforcement learning detrapping method and system for complex path planning of a swimming pool robot, and the method comprises the steps: extracting deep fusion features in an underwater holographic image, analyzing the multi-dimensional feature semantics of the deep fusion features, and determining an environment obstacle in an environment where the swimming pool robot is located; calculating an obstacle sensing distance corresponding to the environmental obstacle; based on the sound wave signal frequency, calculating a medium energy absorption coefficient corresponding to the sonar device in the underwater environment, calculating sound energy transmission loss of the sonar device in water, calculating a radiation sound intensity level corresponding to the sonar device, and calculating obstacle strength corresponding to an environmental obstacle; and the echo signal energy intensity corresponding to the environmental obstacle is calculated, a corresponding escape-cleaning planning path of the swimming pool robot about the environmental obstacle is formulated, motion control processing of the swimming pool robot is executed, and a control result is obtained. According to the invention, the planning accuracy of the swimming pool robot in a complex path can be improved.

The invention relates to the technical field of intelligent medical monitoring and health data analysis, and particularly discloses a digestive tract health data analysis method and system. Differential frequency and sound intensity data generated by intestinal tract movement are collected in real time through a multi-source sensor array deployed on the body surface of the abdomen; the method comprises the following steps: extracting an intestinal motion rhythm stability characteristic value by combining empirical mode decomposition and Hilbert transform, calculating a borborygmus intensity variation characteristic value by adopting a normalization and Haar wavelet decomposition method, constructing an intestinal function state characteristic vector, and inputting the intestinal function state characteristic vector into a health assessment model trained on the basis of a random forest algorithm to obtain a health assessment result. According to the system and the method, individual digestive tract health scores and health level indexes are output, multi-type abnormal early warning signals are triggered, a closed-loop management system from data acquisition, intelligent analysis to risk early warning is constructed, and the intelligent level of early recognition and health management of digestive tract diseases is improved.