41 results about "Industrial noise" patented technology

The invention relates to the technical field of data processing, and discloses an automatic noise monitoring system and method based on multi-sensor data fusion. According to the system, a calibration module carries out time synchronization processing on noise data collected by multiple sensors; the extraction module adopts a singular value decomposition algorithm to extract frequency domain and time domain features; the separation module analyzes and separates traffic, construction and industrial noise sources through attention independent components; the construction module generates noise space propagation characteristics in combination with geographic information data; the classification module identifies the type of a noise source through a space-time convolutional network, locates coordinates and allocates responsibility weight. The technical problem that the existing noise monitoring technology cannot realize multi-source noise intelligent identification and pollution source accurate traceability is solved.

The invention discloses an acousto-optic combined detection and fusion positioning method and device for underground gas leakage, and belongs to the technical field of industrial safety monitoring. In order to solve the problems of high false alarm rate, insufficient positioning precision, poor dynamic environment adaptability and the like of a single sensor in the prior art, the invention provides a multi-modal signal space-time collaborative fusion technical scheme which specifically comprises an acousto-optic heterogeneous sensor, an integrated MEMS ultrasonic array and a tunable laser spectrum module. Microsecond-level synchronous acquisition of acousto-optic signals is realized through a GPS taming clock, and a cross-correlation time delay compensation model is designed to eliminate space-time mismatch errors. And designing a three-stage dynamic fusion framework: improving LMS adaptive filtering to suppress industrial noise, establishing a light intensity attenuation model in combination with dust environment concentration, carrying out multi-modal feature extraction of acousto-optic signals, carrying out signal preliminary fusion, and judging a gas leakage condition. And constructing an acousto-optic Bayesian joint probability model, and calculating the leakage posterior probability. And optimizing positioning parameters based on a double-Kalman filter group, predicting sound wave time delay by forward filtering, and correcting a light diffusion coefficient by backward filtering to realize three-dimensional coordinate solution. Temperature, humidity and dust concentration parameters are fused in real time, a sound velocity calculation model and a light intensity attenuation factor are corrected, and the robustness of complex working conditions is improved. According to the invention, the advantages of acousto-optic signals are combined, errors of various complex environmental factors are further fused and compensated, the overall error is greatly reduced, and the method has the characteristic of dynamic adaptation, and is very suitable for gas leakage detection and positioning in complex environments such as underground environments and the like.

The invention relates to the technical field of audio signal processing, and discloses an industrial noise identification method and system, and the method comprises the steps: obtaining an audio signal, and carrying out the time-frequency decomposition, and obtaining preliminary decomposition data; classifying the coupling features of the preliminary decomposition data to obtain a coupling region boundary value; if the boundary value of the coupling region exceeds a preset boundary threshold value, performing filtering processing to obtain separated noise spectrum data; calculating an overlapping proportion of the separated noise spectrum data and a preset target sound; if the overlapping proportion causes extraction deviation, extracting dynamic change characteristics of the separated noise spectrum data and fusing the dynamic change characteristics into a spectrum extraction process to obtain an accurate noise spectrum; calculating an intensity value according to the accurate noise spectrum, and determining an intensity evaluation result; and comparing the intensity evaluation result with a preset pollution threshold, calculating a noise pollution degree and outputting a noise identification result. The method is suitable for industrial environment noise monitoring, and can accurately extract the noise spectrum.

The invention belongs to the technical field of nuclear equipment monitoring, and aims to solve the problems of spatial resolution reduction caused by a multi-source acoustic signal aliasing effect in a complex industrial environment and feature extraction difficulty caused by industrial noise and fault sound source spectrum overlapping. According to the method, spatial decoupling is carried out, sparse Bayesian learning and minimum variance undistorted response beam forming joint optimization is carried out, a sound source orientation is preliminarily estimated, synchronous extrusion wavelet transform and NMF joint time-frequency analysis is carried out, and a spatial anomaly acoustic diagnosis result is obtained. Sound source transient characteristics and energy evolution laws are extracted through time-frequency conjoint analysis, a multi-dimensional characteristic space is constructed, the number of fault types is determined for fault type classification, and three-dimensional coordinate mapping and error feedback are displayed; the system comprises an airspace decoupling module, a time-frequency decomposition module, a feature fusion module and a dynamic imaging module. According to the invention, the sound source positioning precision can be realized, and the resolution limitation of traditional beam forming is broken through.

The invention discloses a device for exciting ion sound waves for noise reduction, and belongs to the technical field of vibration reduction and noise reduction. The device comprises a cathode structure, an anode structure, a noise source, a square pipeline and a high-voltage power supply device, the frequency, phase and amplitude characteristics of the ion sound wave are directly modulated through the preset combination of applied voltage, electrode gap and discharge position parameters; the cathode structure and the anode structure are composed of three kinds of electrodes, namely a cathode of a needle electrode structure, an anode of a metal wire structure and a perforated plate-shaped grounding electrode. The high-voltage power supply device is used for applying negative high voltage to the needle electrode and grounding the perforated plate, or applying positive high voltage to the metal wire electrode and grounding the cathode so as to generate corona discharge; and ion sound waves generated by corona discharge and industrial noise emitted by the noise source are subjected to sound wave superposition in the square pipeline so as to realize sound pressure counteracting. Ion sound waves are directly generated through corona discharge and are superposed and counteracted with noise sound waves, and passive noise reduction is achieved.

This invention discloses a low-frequency noise reduction and sound insulation device for industrial applications, belonging to the field of noise control technology. The device includes an outer soundproof cover, an inner composite sound-absorbing structure, and an active noise control system. The outer soundproof cover is constructed of high-density board. The inner composite sound-absorbing structure is located on the inner wall of the outer soundproof cover and includes an acoustic metamaterial layer and a micro-perforated plate layer, which are spaced apart to form a first resonant cavity. The active noise control system includes: an error microphone embedded in the inner wall of the outer soundproof cover; a controller electrically connected to the error microphone; and a secondary sound source electrically connected to the controller and installed on the inner wall of the outer soundproof cover. This invention achieves broadband passive sound absorption through the composite structure of acoustic metamaterial and micro-perforated plate, providing a high signal-to-noise ratio environment for the active control system. Furthermore, the active control system based on the FxLMS algorithm precisely cancels residual low-frequency line spectra, achieving efficient and stable low-frequency noise reduction.

The application relates to the technical field of industrial earmuffs, and provides an industrial noise reduction earmuff with working condition self-adaptive adjustment, which comprises a supporting block arranged on a safety cap shell, an adjusting unit arranged on the outer surface of the supporting block and used for adjusting the sealing effect of the earmuff and the human body, an earmuff shell fixedly connected to the bottom of the adjusting unit, a decibel detector fixedly connected to the outer side of the earmuff shell, a detection ring connected to the decibel detector through an electric wire, sound-absorbing cotton placed in the interlayer of the earmuff shell, and an air inflation air bag fixedly connected to the inner side of the earmuff shell; a first adjusting unit and a second adjusting unit arranged on the inner wall of the earmuff shell; the sound-absorbing cotton can absorb part of the noise from the outside, and the detection ring can detect the size of the external noise so as to drive the adjusting unit, the first adjusting unit and the second adjusting unit to work, and the noise reduction effect is self-adjusted according to the actual working condition.

The invention is applicable to the technical field of interphones, and provides a noise reduction interphone with adaptive environment induction, which comprises an interphone shell, and a microphone, a loudspeaker and an antenna are arranged on the interphone shell. A main control module, an environment sensing module, a noise reduction processing module, an audio processing module, a radio frequency module, a storage module and a power management module are arranged in an inner cavity of the interphone shell. According to the noise reduction interphone with the self-adaptive environment sensing function, through the interphone composed of the interphone shell, the main control module, the environment sensing module, the noise reduction processing module, the audio processing module, the radio frequency module, the storage module, the power management module, the microphone, the loudspeaker and the antenna, cooperative work of environment sensing and intelligent noise reduction can be carried out; noise features of different scenes such as factories and cities can be automatically recognized, the optimal noise reduction effect is achieved, voice can still be kept clear and loud in the 85dB industrial noise environment, and meanwhile power consumption is lower than that of a traditional interphone.

This invention discloses a composite sound insulation and vibration damping board, its preparation method, and its application, belonging to the field of building and industrial noise control materials. The board comprises, from top to bottom, an upper sound insulation layer, a middle matrix layer, and a lower vibration damping layer, which are bonded together using a water-based adhesive; the upper sound insulation layer has a density of 30-60 kg / m³. 3 The polyurethane foam board has an intermediate matrix layer composed of cement-based cloth, fiber cloth, non-woven fabric, plastic film, or aluminized film, and a lower shock-absorbing layer with a density of 15-25 kg / m³. 3 EPP foam board. This board combines the sound insulation and heat preservation properties of polyurethane, the sound insulation enhancement of the middle matrix layer, and the shock absorption and cushioning properties of EPP through a three-layer composite material. It has good sound insulation, shock absorption and heat preservation performance, and is suitable for building and industrial applications with requirements for comprehensive performance.

The invention relates to the technical field of industrial noise control and monitoring, and discloses a large air compression station noise analysis and control system, which comprises a sensor array, a data acquisition module, a spectrum analysis module and the like, and is characterized in that the sensor array acquires original signals in an air compression station and in a factory boundary, and transmits the original signals to the data acquisition module for preprocessing and packaging a data packet; the spectrum analysis module receives the data packet, extracts data characteristics through FFT and 1 / 3 octave analysis, and transmits the data characteristics to the central intelligent control module; the central intelligent control module generates a control strategy in combination with the noise feature database, sends an instruction to the related execution module, and sends state data to the man-machine interaction early warning module; and each execution and interaction module receives the instruction and the data as required, and completes corresponding display and adjustment operations. Noise reduction and heat dissipation are achieved by integrating the functions of noise analysis, noise elimination, sound insulation, vibration reduction and intelligent ventilation, the treatment strategy can be dynamically adjusted along with the load of the air compressor, and collaborative optimization of environment noise control and equipment operation safety is achieved.

The invention discloses a sound quality automatic detection method and system, a product and a medium, and relates to the technical field of sound testing. The method comprises the following steps: acquiring multiple paths of audio signals of a to-be-tested sound box in an industrial noise environment by using a phase-synchronized microphone array; pure audio signals are separated from the multiple paths of audio signals through multi-stage noise reduction processing combining spatial filtering and acoustic feature comparison; quantizing and calculating acoustic quality indexes such as frequency response and distortion based on the pure audio signal to obtain an acoustic quality quantization result; and finally, comparing the quantification result with a preset standard, and judging whether the tone quality is qualified or not. According to the invention, a framework of first purification and second analysis is adopted, pure signals of the sound equipment are effectively separated through multi-stage noise reduction, the problem of pollution of industrial noise to collected signals is solved from the source, misjudgment caused by signal misalignment is avoided, and thus the accuracy of automatic detection of the sound quality of the sound equipment is improved.

The present invention relates to the technical field of pipeline detection, and specifically refers to a non-destructive pipeline risk level detection method based on audio processing, including: if the initial noise pattern of the audio frame is the residential noise pattern, and the proportion of the industrial noise feature band in this audio frame is greater than a preset multiple of the proportion of the residential noise feature band, then the target noise pattern of this audio frame is the industrial noise pattern; if the initial noise pattern of the audio frame is the industrial noise pattern, and the proportion of the residential noise feature band in this audio frame is greater than a preset multiple of the proportion of the industrial noise feature band, then the target noise pattern of this audio frame is the residential noise pattern; select a filter corresponding to the frequency of the target noise pattern of each audio frame for denoising, obtain each target audio frame, then perform frame combination and feature processing to obtain the feature set of the target audio signal, input it into a trained classifier, and obtain the risk level classification result of the pipeline to be detected. The present invention improves the accuracy of the pipeline risk level classification result.

The utility model relates to a signal acquisition device for an audio slagging system, and belongs to the technical field of inspection equipment. According to the technical scheme, a sound collecting pipe (1) is connected with a cooling water tank (2), and the sound collecting pipe (1) is embedded in the middle section of the converter cooling flue; the cooling water tank (2) is fixed on the outer wall of the middle section of the cooling flue; the protection device is arranged outside the cooling water tank (2); the water inlet regulating valve (8), the water return regulating valve (9) and the relief valve (10) are arranged on the cooling water tank (2); and the sound collecting tube (1), the industrial noise signal amplifier (11) and the audio slagging instrument (12) are in signal connection in sequence. According to the utility model, the influence of environment high temperature on the temperature of the cooling liquid in the cooling water tank is effectively reduced, the cooling effect is optimized, the interference of external noise in a converter workshop on the noise in the converter can be effectively reduced, the accuracy of signals acquired by an audio slagging system is high, the thickness of foamed slag in the converter is truly reflected, and the quality of molten steel is improved.

The invention relates to an industrial noise source positioning and separating method and device, equipment and a storage medium, and the method comprises the steps: collecting a multi-channel audio signal, and carrying out the preprocessing and local feature extraction of the multi-channel audio signal, and obtaining a local feature vector packet; carrying out sound source localization based on the local feature vector packet by adopting a hybrid localization strategy to generate space position information of a sound source, and calculating localization confidence; sound source separation is carried out on the multi-channel audio signals, and separated audio signals and sound source separation quality confidence coefficients of all independent sound sources are obtained; collaborative management is carried out on the output of each agent through the arbitration agent, and confidence fusion and conflict judgment are carried out; and when the confidence coefficient is lower than a first preset threshold value, triggering an artificial verification process or scheduling a mobile detection unit to carry out auxiliary measurement so as to determine target sound source positioning and separation data, and storing the target sound source positioning and separation data in a vector database. The method has the advantages of high-precision positioning and reliable separation of the industrial noise source, and can adapt to the change of a complex acoustic environment.

This invention belongs to the field of industrial noise control technology and discloses an active noise control method for vibration noise in precast beam factories. The method includes the following steps: S1, signal preprocessing and frequency band focusing; S2, secondary path identification and modeling; S3, calculation of the filtered reference signal; S4, initialization of control parameters; S5, calculation of the desired signal for the main path; S6, main loop processing. The beneficial effects of this invention are as follows: 1. An adaptive divergence detection mechanism based on error signal energy monitoring is introduced into the traditional SAS-FxLMS algorithm. When the error energy exceeds a preset multiple of the original energy, the algorithm is determined to be diverging, and a recovery mechanism is automatically triggered. 2. When the norm exceeds a preset threshold, the weight vector is scaled proportionally to reduce its norm to within the threshold range, ensuring the numerical stability of the algorithm. 3. The transfer function models of the main path and secondary paths are optimized according to the characteristics of the target frequency band center frequency (150Hz); the algorithm parameters are dynamically adjusted according to the peak factor, RMS value, and other characteristic parameters of the input signal.

The invention provides a bearing fault lightweight diagnosis method for an industrial noise environment, and the method comprises the steps: collecting a vibration signal of a bearing, and dividing the vibration signal into a plurality of vibration signal samples through a sliding window; the lightweight bearing fault diagnosis network comprises an input layer, a feature extraction layer and a classification layer which are connected in sequence, and the feature extraction layer comprises at least one enhanced phantom module; training a lightweight bearing fault diagnosis network by using the vibration signal samples; and inputting a to-be-detected vibration signal acquired in real time into the trained diagnosis model for online reasoning, and outputting a fault diagnosis state of the bearing. For an industrial strong noise scene, a multi-scale anti-noise network is further constructed based on a lightweight bearing fault diagnosis network, and model noise robustness is enhanced through multi-scale feature extraction and anti-noise training strategy optimization. According to the invention, double challenges of model lightweight and noise robustness in edge device deployment are solved, and high-precision and high-efficiency fault diagnosis is realized.

This invention belongs to the field of industrial noise control and relates to a method and system for echo cancellation and noise reduction in steel plant workshop environments. The method includes the following steps: (1) real-time acquisition of sound signals from on-site monitoring points in the steel plant workshop environment during operation; the on-site monitoring points include various noise sources and various target sources; (2) time alignment of the original sound signal and the workshop echo signal from the same on-site monitoring point to obtain an aligned signal; (3) filtering the aligned signal obtained in step (2) to suppress superimposed noise and echo signals in the channel; (4) training a neural network mask, using the mask frequency domain to process the sound signal to suppress residual noise signals, and outputting a clean sound signal. The method provided by this invention effectively solves the limitations of traditional sound noise reduction technology in complex industrial environments.

The invention discloses an industrial noise intelligent monitoring system based on a dynamic environment adaptive algorithm, which comprises a mounting bracket, and is characterized in that the mounting bracket is provided with a first monitoring device, a second monitoring device, a third monitoring device, a fourth monitoring device, a video acquisition device and a communication device; the communication device is used for receiving data information of the first monitoring device, the second monitoring device, the third monitoring device, the fourth monitoring device and the video acquisition device and wirelessly transmitting the data information to the cloud platform, the cloud platform analyzes the data information after receiving the data information, and the cloud platform wirelessly transmits an analysis result to the communication device; the communication device sends the received analysis result to the edge calculation module; the edge calculation module is used for carrying out deep analysis on the received analysis result and sending a deep analysis result to a display screen for displaying; and the display screen is used for displaying the depth analysis result sent by the edge calculation module. Through multi-dimensional technical innovation, high-precision operation of noise monitoring under extreme climate conditions is realized.

The application relates to the technical field of audio signal processing, and discloses an industrial noise identification method and system.The method comprises the following steps: acquiring an audio signal and performing time-frequency decomposition to obtain preliminary decomposition data; classifying coupling features of the preliminary decomposition data to obtain a coupling region boundary value; if the coupling region boundary value exceeds a preset boundary threshold value, performing filtering processing to obtain separated noise spectrum data; calculating an overlap ratio of the separated noise spectrum data and a preset target sound; if the overlap ratio leads to extraction deviation, extracting dynamic change characteristics of the separated noise spectrum data and integrating the dynamic change characteristics into a spectrum extraction process to obtain an accurate noise spectrum; calculating an intensity value according to the accurate noise spectrum to determine an intensity evaluation result; comparing the intensity evaluation result with a preset pollution threshold value, calculating a noise pollution degree, and outputting a noise identification result.The method is suitable for industrial environment noise monitoring and can realize accurate extraction of a noise spectrum.

This application relates to a method and apparatus for constructing an equipment noise detection model based on industrial noise analysis technology, belonging to the technical field of equipment noise detection. The method includes: acquiring sound sampling data in response to a request; processing the sound sampling data to obtain sound reconstruction data; acquiring first volume information and first density information of the sound reconstruction data in each frequency band over any time period; and constructing an equipment noise detection model based on the first volume information and the first density information. This application has the effect of detecting abnormal sound frequency bands and the volume and density distribution relationships existing in each frequency band.

The invention discloses a harmonic reducer small sample in-situ fault diagnosis method. The method comprises the steps of S1, data acquisition and preprocessing; s2, a feature extractor is constructed, a fault diagnosis model is constructed in combination with a label smoothing loss function with a streaming regular term, and the feature extractor comprises an adaptive Laplace wavelet decomposition module, a dual-path convolution module, a feature fusion module and a multi-scale attention mechanism module; s3, training a fault diagnosis model; s4, performing fault diagnosis; according to the method, the application condition in industrial practice is fully considered, high-precision fault diagnosis can be achieved under the conditions that the total sample data size is extremely limited and industrial noise influences exist, the actual production requirement is met, meanwhile, the actual application potential of extreme optimization of resource utilization and technical innovation is achieved, and the method is suitable for popularization and application. And the method has certain industrial application value.

The invention discloses handheld industrial noise and vibration integrated detection equipment, and relates to the field of industrial equipment noise and vibration, the handheld industrial noise and vibration integrated detection equipment comprises a mounting box, a mounting pipe is fixedly connected in the mounting box, a transmission column is slidably connected in the mounting pipe, and one end of the transmission column is fixedly connected with a mounting frame; according to the handheld industrial noise and vibration integrated detection equipment, a user only needs to press air bags on the two sides of a mounting box, air enters a mounting pipe to push a transmission column, then a mounting frame and a noise and vibration probe are driven to move into industrial equipment, the extension length can be flexibly adjusted according to the detection position, use is greatly facilitated, and the practicability is high. And meanwhile, vibration and noise in the industrial equipment are integrally overhauled. And meanwhile, the equipment also has a comprehensive buffer protection function. When the mounting box falls, the air bag can buffer the front and rear sides of the floor; and meanwhile, bottom buffering is achieved by compressing gas in the mounting pipe and a second spring, and therefore the mounting box is comprehensively protected.

The utility model relates to the technical field of noise isolation earmuffs, and discloses an adjustable industrial noise isolation earmuff which comprises a safety helmet, the left side and the right side of the safety helmet are both connected with mounting plates through connecting mechanisms, the far sides of the two mounting plates are both fixedly connected with head hoops, the head hoops are connected with adjusting rods through adjusting mechanisms, and the adjusting rods are fixedly connected with the safety helmet. The bottom end of the adjusting rod is rotatably connected with a connecting frame, the bottom end of the connecting frame is provided with a shell, the connecting mechanism comprises a mounting rod, the interior of the mounting rod is slidably connected with an extrusion rod, the exterior of the extrusion rod is fixedly connected with a limiting disc, and the exterior of the extrusion rod is sleeved with a strong spring. Threaded blocks are rotationally connected to the sides, far away from each other, of the multiple extrusion rods. According to the safety helmet, the safety risk that the head of a worker is hit by a falling object due to the fact that the safety helmet is not stably worn or taken down is greatly reduced, and the overall safety of the working environment is improved.

The application discloses an easy-to-clean industrial noise reduction earmuff and belongs to the technical field of earmuffs. The earmuff comprises an earmuff main body and an ear pad arranged on the earmuff main body, and the ear pad is composed of a flexible base material and a fabric layer wrapped on the outer surface of the flexible base material. The fabric layer is prepared through a specific process: the fabric is immersed in a treatment liquid, and then cross-linked and dried to obtain the fabric layer. The treatment liquid comprises 5-10% of polyacrylic acid, 3-8% of nanocellulose, 0.3-0.7% of a cross-linking agent, 0.1-0.5% of an additive and the balance of deionized water. The industrial noise reduction earmuff prepared by the application has the characteristics of easy cleaning, can effectively block the penetration of sweat and significantly improves the sanitary safety, wearing comfort and service life of the earmuff.

The invention discloses an industrial noise reduction earmuff easy to clean, and belongs to the technical field of earmuffs. The earmuff comprises an earmuff main body and an ear pad arranged on the earmuff main body, and the ear pad is composed of a flexible base material and a fabric layer wrapping the outer surface of the flexible base material. Wherein the fabric layer is prepared by a specific process comprising the following steps: soaking a fabric in a treating fluid, and carrying out crosslinking and drying to obtain the fabric layer. The treating fluid comprises the following components in percentage by weight: 5%-10% of polyacrylic acid, 3%-8% of nano cellulose, 0.3%-0.7% of a cross-linking agent, 0.1%-0.5% of an auxiliary agent and the balance of deionized water. According to the industrial noise-reducing earmuff prepared by the invention, the fabric layer has the characteristic of being easy to clean, and meanwhile, sweat permeation can be effectively blocked, so that the sanitary safety and the wearing comfort of the earmuff are remarkably improved, and the service life of the earmuff is remarkably prolonged.

The invention relates to an integrated silencing grille door, and belongs to the technical field of industrial noise control and ventilation equipment. The grille door comprises a door frame assembly, a grille assembly, a composite sound absorption assembly and an intelligent control assembly. The door frame assembly is connected with a wall through a nitrile rubber damping rubber mat, and a modular interface is reserved to be matched with a special-shaped door opening. According to the grating assembly, grating blades are driven to rotate through a rope-rope wheel transmission system, and opening adjustment is achieved. The composite sound absorption assembly comprises a metal cover, a metal framework and three sound insulation layers (rock wool, a damping sound insulation plate and hydrophobic glass fiber cotton), and the noise path is prolonged through honeycomb-shaped protrusions. The intelligent control assembly monitors in real time through the noise sensor, the microcontroller controls the motor to adjust the blade angle according to the noise frequency band, medium-high frequency noise is mainly used for horizontal wide-angle ventilation of the blade, and low-frequency noise is mainly used for the blade in a 45-degree silent mode. According to the invention, 100-4000 Hz full-band noise reduction is realized, and the ventilation efficiency, vibration isolation and intelligent regulation and control functions are realized.

This utility model discloses a shuttle lifting mechanism, comprising: a frame, a set of support frames vertically mounted on the frame, a first gear rotatably mounted above the support frames, a second gear rotatably mounted on the frame, a toothed belt connecting the first gear and the second gear, and a first drive assembly for driving the second gear to rotate. By combining reliability and quiet performance, the self-healing toothed assembly extends the life of the toothed belt to more than four times that of traditional toothed belts, significantly reducing replacement costs. The quiet component eliminates industrial noise pollution while maintaining high torque transmission, meeting stringent requirements for the working environment. Compared to existing chain or ordinary toothed belt solutions, it completely avoids the risk of wire rope breakage and the hidden danger of gear disengagement.

The invention discloses a man-machine interaction welding method and system based on multi-modal fusion and natural language, and the method comprises the steps: synchronously collecting voice, gesture and environment signals in real time, and preprocessing the obtained multi-modal data; based on the preprocessed multi-modal data, deep fusion of the multi-modal data is carried out, and the intention of an operator is accurately identified and confirmed; when the user intention is determined, continuously optimizing an adaptive speech recognition model, performing deep analysis on the user intention in a natural language form, and extracting task logic and parameters; based on the analyzed intention logic and parameters, an executable macro instruction sequence is generated, and the welding robot is driven to complete a task; in the task execution process, the task execution condition is continuously monitored to provide safety feedback, subsequent optimization is performed, and it is guaranteed that the task is safely and efficiently completed. The method provided by the invention can adapt to industrial noise and operator accent, solve the problem of single modal ambiguity, understand complex task instructions, and realize low delay and data security.

The invention discloses an equipment surface defect detection method based on a deep learning target detection technology, relates to the technical field of computer vision and industrial detection, and provides a solution integrating dynamic multi-scale feature fusion. A local-global feature collaborative enhancement mechanism is constructed through an innovatively designed MSECAAttention module, so that the analysis capability of the model on cross-scale defect features is remarkably improved; an ODConv module is adopted to realize space-channel-convolution kernel-sample four-dimensional dynamic parameter adjustment, the static limitation of traditional convolution is broken through, and geometrical characteristics of irregular defects such as cracks and oxidation spots are accurately captured; in combination with a Sigmoid gating mechanism and a multi-scale feature screening strategy of an SA-FEM module, the feature significance of the tiny defects is enhanced while industrial noise interference is suppressed. According to the method, high-precision real-time detection of steel surface defects is realized through the synergistic effect of multi-level feature optimization and dynamic parameter adjustment.

The invention discloses a noise reduction optimization system for a desulfurization fan room based on sound field simulation. The noise reduction optimization system comprises a data acquisition module, a sound field simulation module, a noise analysis module, a noise reduction scheme optimization module and a scheme output and execution module. By integrating a sound field simulation technology and a modular optimization system, accurate prediction and active optimization of the noise of the desulfurization fan room are realized, and the system can perform high-precision sound field simulation and identify a main noise source and a propagation path on the basis of actual equipment parameters and a machine room structure, so that a targeted noise reduction scheme is generated; according to the method, the blindness of a traditional method is effectively avoided, the noise reduction effect is remarkably improved, the treatment cost is reduced, the long-term effectiveness of measures is guaranteed through real-time monitoring and dynamic optimization functions, meanwhile, the equipment operation efficiency and the machine room safety are both considered, and a scientific and intelligent solution is provided for industrial noise treatment.